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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Ji, F"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" type="radio" value="hide"> <label for="abstracts-1">Hide abstracts</label></li></ul> </div> <div class="box field is-grouped is-grouped-multiline level-item"> <div class="control"> <span class="select is-small"> <select id="size" name="size"><option value="25">25</option><option selected value="50">50</option><option value="100">100</option><option value="200">200</option></select> </span> <label for="size">results per page</label>. </div> <div class="control"> <label for="order">Sort results by</label> <span class="select is-small"> <select id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.00416">arXiv:2411.00416</a> <span> [<a href="https://arxiv.org/pdf/2411.00416">pdf</a>, <a href="https://arxiv.org/ps/2411.00416">ps</a>, <a href="https://arxiv.org/format/2411.00416">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Edge centrality and the total variation of graph distributional signals </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.00416v1-abstract-short" style="display: inline;"> This short note is a supplement to [1], in which the total variation of graph distributional signals is introduced and studied. We introduce a different formulation of total variation and relate it to the notion of edge centrality. The relation provides a different perspective of total variation and may facilitate its computation. </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.00416v1-abstract-full" style="display: none;"> This short note is a supplement to [1], in which the total variation of graph distributional signals is introduced and studied. We introduce a different formulation of total variation and relate it to the notion of edge centrality. The relation provides a different perspective of total variation and may facilitate its computation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.00416v1-abstract-full').style.display = 'none'; document.getElementById('2411.00416v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.04229">arXiv:2409.04229</a> <span> [<a href="https://arxiv.org/pdf/2409.04229">pdf</a>, <a href="https://arxiv.org/format/2409.04229">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Generalized Graph Signal Reconstruction via the Uncertainty Principle </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhao%2C+Y">Yanan Zhao</a>, <a href="/search/eess?searchtype=author&query=Jian%2C+X">Xingchao Jian</a>, <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Tay%2C+W+P">Wee Peng Tay</a>, <a href="/search/eess?searchtype=author&query=Ortega%2C+A">Antonio Ortega</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.04229v1-abstract-short" style="display: inline;"> We introduce a novel uncertainty principle for generalized graph signals that extends classical time-frequency and graph uncertainty principles into a unified framework. By defining joint vertex-time and spectral-frequency spreads, we quantify signal localization across these domains, revealing a trade-off between them. This framework allows us to identify a class of signals with maximal energy co… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.04229v1-abstract-full').style.display = 'inline'; document.getElementById('2409.04229v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.04229v1-abstract-full" style="display: none;"> We introduce a novel uncertainty principle for generalized graph signals that extends classical time-frequency and graph uncertainty principles into a unified framework. By defining joint vertex-time and spectral-frequency spreads, we quantify signal localization across these domains, revealing a trade-off between them. This framework allows us to identify a class of signals with maximal energy concentration in both domains, forming the fundamental atoms for a new joint vertex-time dictionary. This dictionary enhances signal reconstruction under practical constraints, such as incomplete or intermittent data, commonly encountered in sensor and social networks. Numerical experiments on real-world datasets demonstrate the effectiveness of the proposed approach, showing improved reconstruction accuracy and noise robustness compared to existing methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.04229v1-abstract-full').style.display = 'none'; document.getElementById('2409.04229v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.17274">arXiv:2408.17274</a> <span> [<a href="https://arxiv.org/pdf/2408.17274">pdf</a>, <a href="https://arxiv.org/ps/2408.17274">ps</a>, <a href="https://arxiv.org/format/2408.17274">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> The Transferability of Downsamped Sparse Graph Convolutional Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Shu%2C+Q">Qinji Shu</a>, <a href="/search/eess?searchtype=author&query=Sheng%2C+H">Hang Sheng</a>, <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+H">Hui Feng</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+B">Bo Hu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.17274v2-abstract-short" style="display: inline;"> To accelerate the training of graph convolutional networks (GCNs) on real-world large-scale sparse graphs, downsampling methods are commonly employed as a preprocessing step. However, the effects of graph sparsity and topological structure on the transferability of downsampling methods have not been rigorously analyzed or theoretically guaranteed, particularly when the topological structure is aff… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.17274v2-abstract-full').style.display = 'inline'; document.getElementById('2408.17274v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.17274v2-abstract-full" style="display: none;"> To accelerate the training of graph convolutional networks (GCNs) on real-world large-scale sparse graphs, downsampling methods are commonly employed as a preprocessing step. However, the effects of graph sparsity and topological structure on the transferability of downsampling methods have not been rigorously analyzed or theoretically guaranteed, particularly when the topological structure is affected by graph sparsity. In this paper, we introduce a novel downsampling method based on a sparse random graph model and derive an expected upper bound for the transfer error. Our findings show that smaller original graph sizes, higher expected average degrees, and increased sampling rates contribute to reducing this upper bound. Experimental results validate the theoretical predictions. By incorporating both sparsity and topological similarity into the model, this study establishes an upper bound on the transfer error for downsampling in the training of large-scale sparse graphs and provides insight into the influence of topological structure on transfer performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.17274v2-abstract-full').style.display = 'none'; document.getElementById('2408.17274v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.03142">arXiv:2408.03142</a> <span> [<a href="https://arxiv.org/pdf/2408.03142">pdf</a>, <a href="https://arxiv.org/format/2408.03142">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> A Graph Signal Processing Perspective of Network Multiple Hypothesis Testing with False Discovery Rate Control </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Jian%2C+X">Xingchao Jian</a>, <a href="/search/eess?searchtype=author&query=G%C3%B6lz%2C+M">Martin G枚lz</a>, <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Tay%2C+W+P">Wee Peng Tay</a>, <a href="/search/eess?searchtype=author&query=Zoubir%2C+A+M">Abdelhak M. Zoubir</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.03142v2-abstract-short" style="display: inline;"> We consider a multiple hypothesis testing problem in a sensor network over the joint spatio-temporal domain. The sensor network is modeled as a graph, with each vertex representing a sensor and a signal over time associated with each vertex. We assume a hypothesis test and an associated $p$-value for every sample point in the joint spatio-temporal domain. Our goal is to determine which points have… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.03142v2-abstract-full').style.display = 'inline'; document.getElementById('2408.03142v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.03142v2-abstract-full" style="display: none;"> We consider a multiple hypothesis testing problem in a sensor network over the joint spatio-temporal domain. The sensor network is modeled as a graph, with each vertex representing a sensor and a signal over time associated with each vertex. We assume a hypothesis test and an associated $p$-value for every sample point in the joint spatio-temporal domain. Our goal is to determine which points have true alternative. By parameterizing the unknown $p$-value distribution under the alternative and the prior probabilities of hypotheses being null with a bandlimited generalized graph signal, we can obtain consistent estimates for them. Consequently, we also obtain an estimate of the local false discovery rates (lfdr). We prove that by using a step-up procedure on the estimated lfdr, we can achieve asymptotic false discovery rate control at a pre-determined level. Numerical experiments validate the effectiveness of our approach compared to existing methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.03142v2-abstract-full').style.display = 'none'; document.getElementById('2408.03142v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.11081">arXiv:2403.11081</a> <span> [<a href="https://arxiv.org/pdf/2403.11081">pdf</a>, <a href="https://arxiv.org/format/2403.11081">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Enhanced Index Modulation Aided Non-Orthogonal Multiple Access via Constellation Rotation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Huang%2C+R">Ronglan Huang</a>, <a href="/search/eess?searchtype=author&query=ji%2C+F">Fei ji</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+Z">Zeng Hu</a>, <a href="/search/eess?searchtype=author&query=Wan%2C+D">Dehuan Wan</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+P">Pengcheng Xu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+Y">Yun Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.11081v1-abstract-short" style="display: inline;"> Non-orthogonal multiple access (NOMA) has been widely nominated as an emerging spectral efficiency (SE) multiple access technique for the next generation of wireless communication network. To meet the growing demands in massive connectivity and huge data in transmission, a novel index modulation aided NOMA with the rotation of signal constellation of low power users (IM-NOMA-RC) is developed to th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.11081v1-abstract-full').style.display = 'inline'; document.getElementById('2403.11081v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.11081v1-abstract-full" style="display: none;"> Non-orthogonal multiple access (NOMA) has been widely nominated as an emerging spectral efficiency (SE) multiple access technique for the next generation of wireless communication network. To meet the growing demands in massive connectivity and huge data in transmission, a novel index modulation aided NOMA with the rotation of signal constellation of low power users (IM-NOMA-RC) is developed to the downlink transmission. In the proposed IM-NOMA-RC system, the users are classified into far-user group and near-user group according to their channel conditions, where the rotation constellation based IM operation is performed only on the users who belong to the near-user group that are allocated lower power compared with the far ones to transmit extra information. In the proposed IM-NOMA-RC, all the subcarriers are activated to transmit information to multiple users to achieve higher SE. With the aid of the multiple dimension modulation in IM-NOMA-RC, more users can be supported over an orthogonal resource block. Then, both maximum likelihood (ML) detector and successive interference cancellation (SIC) detector are studied for all the user. Numerical simulation results of the proposed IM-NOMARC scheme are investigate for the ML detector and the SIC detector for each users, which shows that proposed scheme can outperform conventional NOMA. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.11081v1-abstract-full').style.display = 'none'; document.getElementById('2403.11081v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.10493">arXiv:2402.10493</a> <span> [<a href="https://arxiv.org/pdf/2402.10493">pdf</a>, <a href="https://arxiv.org/ps/2402.10493">ps</a>, <a href="https://arxiv.org/format/2402.10493">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Online Signed Sampling of Bandlimited Graph Signals </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liu%2C+W">Wenwei Liu</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+H">Hui Feng</a>, <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+B">Bo Hu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.10493v2-abstract-short" style="display: inline;"> The theory of sampling and recovery of bandlimited graph signals has been extensively studied. However, in many cases, the observation of a signal is quite coarse. For example, users only provide simple comments such as "like" or "dislike" for a product on an e-commerce platform. This is a particular scenario where only the sign information of a graph signal can be measured. In this paper, we are… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.10493v2-abstract-full').style.display = 'inline'; document.getElementById('2402.10493v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.10493v2-abstract-full" style="display: none;"> The theory of sampling and recovery of bandlimited graph signals has been extensively studied. However, in many cases, the observation of a signal is quite coarse. For example, users only provide simple comments such as "like" or "dislike" for a product on an e-commerce platform. This is a particular scenario where only the sign information of a graph signal can be measured. In this paper, we are interested in how to sample based on sign information in an online manner, by which the direction of the original graph signal can be estimated. The online signed sampling problem of a graph signal can be formulated as a Markov decision process in a finite horizon. Unfortunately, it is intractable for large size graphs. We propose a low-complexity greedy signed sampling algorithm (GSS) as well as a stopping criterion. Meanwhile, we prove that the objective function is adaptive monotonic and adaptive submodular, so that the performance is close enough to the global optimum with a lower bound. Finally, we demonstrate the effectiveness of the GSS algorithm by both synthesis and realworld data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.10493v2-abstract-full').style.display = 'none'; document.getElementById('2402.10493v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.16871">arXiv:2401.16871</a> <span> [<a href="https://arxiv.org/pdf/2401.16871">pdf</a>, <a href="https://arxiv.org/ps/2401.16871">ps</a>, <a href="https://arxiv.org/format/2401.16871">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Node Flux-Linkage Synchronizing Control of Power Systems with 100% Wind Power Generation Based on Capacitor Voltage Balancing Scheme </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liu%2C+Y">Yang Liu</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+Y">Yanshan Chen</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+Y">Yuexi Yang</a>, <a href="/search/eess?searchtype=author&query=Pei%2C+X">Xiangyu Pei</a>, <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.16871v1-abstract-short" style="display: inline;"> This paper proposes a node flux-linkage synchronizing control method (NFSCM) for power systems with 100% wind power generation based on a capacitor voltage balancing scheme (CVBS). Different from the conventional grid-forming controllers, NFSCM is designed to regulate inverters as virtual flux-linkage sources. Auto-synchronization of flux-linkage vectors is achieved through the CVBS-based NFSCM. T… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.16871v1-abstract-full').style.display = 'inline'; document.getElementById('2401.16871v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.16871v1-abstract-full" style="display: none;"> This paper proposes a node flux-linkage synchronizing control method (NFSCM) for power systems with 100% wind power generation based on a capacitor voltage balancing scheme (CVBS). Different from the conventional grid-forming controllers, NFSCM is designed to regulate inverters as virtual flux-linkage sources. Auto-synchronization of flux-linkage vectors is achieved through the CVBS-based NFSCM. The mismatch among the angular frequencies of flux-linkage vectors is eliminated by regulating the tracking errors of DC-link voltages, which establishes a negative feedback between the output frequency and active power of the inverter. NFSCM is adaptive to weak and strong grids. It avoids the excitation inrush currents in the step-up transformer of wind power generators. It also eliminates the DC components of the three-phase currents, and avoids low-frequency oscillations in active power. In order to limit the short-circuit current of inverters, a logic-based bang-bang funnel control (LBFC) is designed to control the switches of inverter bridges when over-current is detected. LBFC is able to restrict various fault currents within an acceptable range within the shortest time. LBFC and NFSCM are designed to operate in a switched manner according to a state-dependent switching strategy. Time-domain simulations were conducted on a 100% wind power generation test system, and the performance of NFSCM and LBFC were investigated. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.16871v1-abstract-full').style.display = 'none'; document.getElementById('2401.16871v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages, 9 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 34-11 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.04358">arXiv:2401.04358</a> <span> [<a href="https://arxiv.org/pdf/2401.04358">pdf</a>, <a href="https://arxiv.org/ps/2401.04358">ps</a>, <a href="https://arxiv.org/format/2401.04358">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Message-Passing Receiver for OCDM over Multi-Lag Multi-Doppler Channels </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liu%2C+Y">Yun Liu</a>, <a href="/search/eess?searchtype=author&query=Ji%2C+F">Fei Ji</a>, <a href="/search/eess?searchtype=author&query=Wen%2C+M">Miaowen Wen</a>, <a href="/search/eess?searchtype=author&query=Qing%2C+H">Hua Qing</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.04358v1-abstract-short" style="display: inline;"> As a new candidate waveform for the next generation wireless communications, orthogonal chirp division multiplexing (OCDM) has attracted growing attention for its ability to achieve full diversity in uncoded transmission, and its robustness to narrow-band interference or impulsive noise. Under high mobility channels with multiple lags and multiple Doppler-shifts (MLMD), the signal suffers doubly s… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.04358v1-abstract-full').style.display = 'inline'; document.getElementById('2401.04358v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.04358v1-abstract-full" style="display: none;"> As a new candidate waveform for the next generation wireless communications, orthogonal chirp division multiplexing (OCDM) has attracted growing attention for its ability to achieve full diversity in uncoded transmission, and its robustness to narrow-band interference or impulsive noise. Under high mobility channels with multiple lags and multiple Doppler-shifts (MLMD), the signal suffers doubly selective (DS) fadings in time and frequency domain, and data symbols modulated on orthogonal chirps are interfered by each other. To address the problem of symbol detection of OCDM over MLMD channel, under the assumption that path attenuation factors, delays, and Doppler shifts of the channel are available, we first derive the closed-form channel matrix in Fresnel domain, and then propose a low-complexity method to approximate it as a sparse matrix. Based on the approximated Fresnel-domain channel, we propose a message passing (MP) based detector to estimate the transmit symbols iteratively. Finally, under two MLMD channels (an underspread channel for terrestrial vehicular communication, and an overspread channel for narrow-band underwater acoustic communications), Monte Carlo simulation results and analysis are provided to validate its advantages as a promising detector for OCDM. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.04358v1-abstract-full').style.display = 'none'; document.getElementById('2401.04358v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">15 pages, 10 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> B.4.1 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.00133">arXiv:2401.00133</a> <span> [<a href="https://arxiv.org/pdf/2401.00133">pdf</a>, <a href="https://arxiv.org/format/2401.00133">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Lossless digraph signal processing via polar decomposition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.00133v1-abstract-short" style="display: inline;"> In this paper, we present a signal processing framework for directed graphs. Unlike undirected graphs, a graph shift operator such as the adjacency matrix associated with a directed graph usually does not admit an orthogonal eigenbasis. This makes it challenging to define the Fourier transform. Our methodology leverages the polar decomposition to define two distinct eigendecompositions, each assoc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.00133v1-abstract-full').style.display = 'inline'; document.getElementById('2401.00133v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.00133v1-abstract-full" style="display: none;"> In this paper, we present a signal processing framework for directed graphs. Unlike undirected graphs, a graph shift operator such as the adjacency matrix associated with a directed graph usually does not admit an orthogonal eigenbasis. This makes it challenging to define the Fourier transform. Our methodology leverages the polar decomposition to define two distinct eigendecompositions, each associated with different matrices derived from this decomposition. We propose to extend the frequency domain and introduce a Fourier transform that jointly encodes the spectral response of a signal for the two eigenbases from the polar decomposition. This allows us to define convolution following a standard routine. Our approach has two features: it is lossless as the shift operator can be fully recovered from factors of the polar decomposition. Moreover, it subsumes the traditional graph signal processing if the graph is directed. We present numerical results to show how the framework can be applied. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.00133v1-abstract-full').style.display = 'none'; document.getElementById('2401.00133v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.08124">arXiv:2312.08124</a> <span> [<a href="https://arxiv.org/pdf/2312.08124">pdf</a>, <a href="https://arxiv.org/format/2312.08124">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Modeling Sparse Graph Sequences and Signals Using Generalized Graphons </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Jian%2C+X">Xingchao Jian</a>, <a href="/search/eess?searchtype=author&query=Tay%2C+W+P">Wee Peng Tay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.08124v3-abstract-short" style="display: inline;"> Graphons are limit objects of sequences of graphs and are used to analyze the behavior of large graphs. Recently, graphon signal processing has been developed to study signal processing on large graphs. A major limitation of this approach is that any sparse sequence of graphs inevitably converges to the zero graphon, rendering the resulting signal processing theory trivial and inadequate for spars… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.08124v3-abstract-full').style.display = 'inline'; document.getElementById('2312.08124v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.08124v3-abstract-full" style="display: none;"> Graphons are limit objects of sequences of graphs and are used to analyze the behavior of large graphs. Recently, graphon signal processing has been developed to study signal processing on large graphs. A major limitation of this approach is that any sparse sequence of graphs inevitably converges to the zero graphon, rendering the resulting signal processing theory trivial and inadequate for sparse graph sequences. To overcome this limitation, we propose a new signal processing framework that leverages the concept of generalized graphons and introduces the stretched cut distance as a measure to compare these graphons. Our framework focuses on the sampling of graph sequences from generalized graphons and explores the convergence properties of associated operators, spectra, and signals. Our signal processing framework provides a comprehensive approach to analyzing and processing signals on graph sequences, even if they are sparse. Finally, we discuss the practical implications of our theory for real-world large networks through numerical experiments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.08124v3-abstract-full').style.display = 'none'; document.getElementById('2312.08124v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.03737">arXiv:2311.03737</a> <span> [<a href="https://arxiv.org/pdf/2311.03737">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Lagrangian Modelling and Motion Stability of Synchronous Generator Power Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+L">Lu Gao</a>, <a href="/search/eess?searchtype=author&query=Lin%2C+C">Chang Lin</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+Y">Yang Liu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.03737v2-abstract-short" style="display: inline;"> This paper proposes to analyze the motion stability of synchro-nous generator power systems using a Lagrangian model derived in the configuration space of generalized position and speed. In the first place, a Lagrangian model of synchronous generators is derived based on Lagrangian mechanics. The generalized potential energy of inductors and generalized kinetic energy of capacitors are defined. Th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.03737v2-abstract-full').style.display = 'inline'; document.getElementById('2311.03737v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.03737v2-abstract-full" style="display: none;"> This paper proposes to analyze the motion stability of synchro-nous generator power systems using a Lagrangian model derived in the configuration space of generalized position and speed. In the first place, a Lagrangian model of synchronous generators is derived based on Lagrangian mechanics. The generalized potential energy of inductors and generalized kinetic energy of capacitors are defined. The mechanical and electrical dynamics can be modelled in a unified manner through constructing a Lagrangian function. Taking the first benchmark model of subsynchronous oscillation as an example, a Lagragian model is construct-ed and numerical solution of the model is obtained to validate the accuracy and effectiveness of the model. Compared with the traditional EMTP model in PSCAD, the obtained Lagrangian model is able to accurately describe the electromagnetic transient process of the system. Moreover, the Lagrangian model is analytical, which enables to analyze the motion stability of the system using Lyapunov motion stability theory. The Lagrangian model can not only be used to discuss the power angle stability, but also used for analyzing the stability of node voltages and frequency. It provides the feasibility for studying the unified stability of power systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.03737v2-abstract-full').style.display = 'none'; document.getElementById('2311.03737v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">20 pages, 11 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> published by CSEE Journal of Power and Energy Systems in 2024 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.14683">arXiv:2310.14683</a> <span> [<a href="https://arxiv.org/pdf/2310.14683">pdf</a>, <a href="https://arxiv.org/ps/2310.14683">ps</a>, <a href="https://arxiv.org/format/2310.14683">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> A sampling construction of graphon 1-norm convergence </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Jian%2C+X">Xingchao Jian</a>, <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Tay%2C+W+P">Wee Peng Tay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.14683v2-abstract-short" style="display: inline;"> In the short note, we describe a sampling construction that yields a sequence of graphons converging to a prescribed limit graphon in 1-norm. This convergence is stronger than the convergence in the cut norm, usually used to study graphon sequences. The note also contains errata of the previous version of the note. </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.14683v2-abstract-full" style="display: none;"> In the short note, we describe a sampling construction that yields a sequence of graphons converging to a prescribed limit graphon in 1-norm. This convergence is stronger than the convergence in the cut norm, usually used to study graphon sequences. The note also contains errata of the previous version of the note. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.14683v2-abstract-full').style.display = 'none'; document.getElementById('2310.14683v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 23 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.07141">arXiv:2310.07141</a> <span> [<a href="https://arxiv.org/pdf/2310.07141">pdf</a>, <a href="https://arxiv.org/ps/2310.07141">ps</a>, <a href="https://arxiv.org/format/2310.07141">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Time and Frequency Offset Estimation and Intercarrier Interference Cancellation for AFDM Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Tang%2C+Y">Yuankun Tang</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+A">Anjie Zhang</a>, <a href="/search/eess?searchtype=author&query=Wen%2C+M">Miaowen Wen</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+Y">Yu Huang</a>, <a href="/search/eess?searchtype=author&query=Ji%2C+F">Fei Ji</a>, <a href="/search/eess?searchtype=author&query=Wen%2C+J">Jinming Wen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.07141v3-abstract-short" style="display: inline;"> Affine frequency division multiplexing (AFDM) is an emerging multicarrier waveform that offers a potential solution for achieving reliable communications over time-varying channels. This paper proposes two maximum-likelihood (ML) estimators of symbol time offset and carrier frequency offset for AFDM systems. One is called joint ML estimator, which evaluates the arrival time and carrier frequency o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.07141v3-abstract-full').style.display = 'inline'; document.getElementById('2310.07141v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.07141v3-abstract-full" style="display: none;"> Affine frequency division multiplexing (AFDM) is an emerging multicarrier waveform that offers a potential solution for achieving reliable communications over time-varying channels. This paper proposes two maximum-likelihood (ML) estimators of symbol time offset and carrier frequency offset for AFDM systems. One is called joint ML estimator, which evaluates the arrival time and carrier frequency offset by comparing the correlations of samples. Moreover, we propose the other so-called stepwise ML estimator to reduce the complexity. Both proposed estimators exploit the redundant information contained within the chirp-periodic prefix inherent in AFDM symbols, thus dispensing with any additional pilots. To further mitigate the intercarrier interference resulting from the residual frequency offset, we design a mirror-mapping-based scheme for AFDM systems. Numerical results verify the effectiveness of the proposed time and carrier frequency offset estimation criteria and the mirror-mapping-based modulation for AFDM systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.07141v3-abstract-full').style.display = 'none'; document.getElementById('2310.07141v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">accepted by IEEE Wireless Communications and Networking Conference (WCNC) 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.07169">arXiv:2309.07169</a> <span> [<a href="https://arxiv.org/pdf/2309.07169">pdf</a>, <a href="https://arxiv.org/format/2309.07169">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Spectral Convergence of Complexon Shift Operators </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+P">Purui Zhang</a>, <a href="/search/eess?searchtype=author&query=Jian%2C+X">Xingchao Jian</a>, <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Tay%2C+W+P">Wee Peng Tay</a>, <a href="/search/eess?searchtype=author&query=Wen%2C+B">Bihan Wen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.07169v4-abstract-short" style="display: inline;"> Topological Signal Processing (TSP) utilizes simplicial complexes to model structures with higher order than vertices and edges. In this paper, we study the transferability of TSP via a generalized higher-order version of graphon, known as complexon. We recall the notion of a complexon as the limit of a simplicial complex sequence [1]. Inspired by the graphon shift operator and message-passing neu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.07169v4-abstract-full').style.display = 'inline'; document.getElementById('2309.07169v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.07169v4-abstract-full" style="display: none;"> Topological Signal Processing (TSP) utilizes simplicial complexes to model structures with higher order than vertices and edges. In this paper, we study the transferability of TSP via a generalized higher-order version of graphon, known as complexon. We recall the notion of a complexon as the limit of a simplicial complex sequence [1]. Inspired by the graphon shift operator and message-passing neural network, we construct a marginal complexon and complexon shift operator (CSO) according to components of all possible dimensions from the complexon. We investigate the CSO's eigenvalues and eigenvectors and relate them to a new family of weighted adjacency matrices. We prove that when a simplicial complex signal sequence converges to a complexon signal, the eigenvalues, eigenspaces, and Fourier transform of the corresponding CSOs converge to that of the limit complexon signal. This conclusion is further verified by two numerical experiments. These results hint at learning transferability on large simplicial complexes or simplicial complex sequences, which generalize the graphon signal processing framework. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.07169v4-abstract-full').style.display = 'none'; document.getElementById('2309.07169v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages, 2 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.05260">arXiv:2309.05260</a> <span> [<a href="https://arxiv.org/pdf/2309.05260">pdf</a>, <a href="https://arxiv.org/format/2309.05260">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Generalized Graphon Process: Convergence of Graph Frequencies in Stretched Cut Distance </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Jian%2C+X">Xingchao Jian</a>, <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Tay%2C+W+P">Wee Peng Tay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.05260v1-abstract-short" style="display: inline;"> Graphons have traditionally served as limit objects for dense graph sequences, with the cut distance serving as the metric for convergence. However, sparse graph sequences converge to the trivial graphon under the conventional definition of cut distance, which make this framework inadequate for many practical applications. In this paper, we utilize the concepts of generalized graphons and stretche… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.05260v1-abstract-full').style.display = 'inline'; document.getElementById('2309.05260v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.05260v1-abstract-full" style="display: none;"> Graphons have traditionally served as limit objects for dense graph sequences, with the cut distance serving as the metric for convergence. However, sparse graph sequences converge to the trivial graphon under the conventional definition of cut distance, which make this framework inadequate for many practical applications. In this paper, we utilize the concepts of generalized graphons and stretched cut distance to describe the convergence of sparse graph sequences. Specifically, we consider a random graph process generated from a generalized graphon. This random graph process converges to the generalized graphon in stretched cut distance. We use this random graph process to model the growing sparse graph, and prove the convergence of the adjacency matrices' eigenvalues. We supplement our findings with experimental validation. Our results indicate the possibility of transfer learning between sparse graphs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.05260v1-abstract-full').style.display = 'none'; document.getElementById('2309.05260v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.07926">arXiv:2307.07926</a> <span> [<a href="https://arxiv.org/pdf/2307.07926">pdf</a>, <a href="https://arxiv.org/format/2307.07926">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> The faces of Convolution: from the Fourier theory to algebraic signal processing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Tay%2C+W+P">Wee Peng Tay</a>, <a href="/search/eess?searchtype=author&query=Ortega%2C+A">Antonio Ortega</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2307.07926v1-abstract-short" style="display: inline;"> In this expository article, we provide a self-contained overview of the notion of convolution embedded in different theories: from the classical Fourier theory to the theory of algebraic signal processing. We discuss their relations and differences. Toward the end, we provide an opinion on whether there is a consistent approach to convolution that unifies seemingly different approaches by differen… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.07926v1-abstract-full').style.display = 'inline'; document.getElementById('2307.07926v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.07926v1-abstract-full" style="display: none;"> In this expository article, we provide a self-contained overview of the notion of convolution embedded in different theories: from the classical Fourier theory to the theory of algebraic signal processing. We discuss their relations and differences. Toward the end, we provide an opinion on whether there is a consistent approach to convolution that unifies seemingly different approaches by different theories. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.07926v1-abstract-full').style.display = 'none'; document.getElementById('2307.07926v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.12042">arXiv:2306.12042</a> <span> [<a href="https://arxiv.org/pdf/2306.12042">pdf</a>, <a href="https://arxiv.org/ps/2306.12042">ps</a>, <a href="https://arxiv.org/format/2306.12042">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Data Structures and Algorithms">cs.DS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Block-Wise Index Modulation and Receiver Design for High-Mobility OTFS Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Qian%2C+M">Mi Qian</a>, <a href="/search/eess?searchtype=author&query=Ji%2C+F">Fei Ji</a>, <a href="/search/eess?searchtype=author&query=Ge%2C+Y">Yao Ge</a>, <a href="/search/eess?searchtype=author&query=Wen%2C+M">Miaowen Wen</a>, <a href="/search/eess?searchtype=author&query=Cheng%2C+X">Xiang Cheng</a>, <a href="/search/eess?searchtype=author&query=Poor%2C+H+V">H. Vincent Poor</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.12042v1-abstract-short" style="display: inline;"> As a promising technique for high-mobility wireless communications, orthogonal time frequency space (OTFS) has been proved to enjoy excellent advantages with respect to traditional orthogonal frequency division multiplexing (OFDM). Although multiple studies have considered index modulation (IM) based OTFS (IM-OTFS) schemes to further improve system performance, a challenging and open problem is th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.12042v1-abstract-full').style.display = 'inline'; document.getElementById('2306.12042v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.12042v1-abstract-full" style="display: none;"> As a promising technique for high-mobility wireless communications, orthogonal time frequency space (OTFS) has been proved to enjoy excellent advantages with respect to traditional orthogonal frequency division multiplexing (OFDM). Although multiple studies have considered index modulation (IM) based OTFS (IM-OTFS) schemes to further improve system performance, a challenging and open problem is the development of effective IM schemes and efficient receivers for practical OTFS systems that must operate in the presence of channel delays and Doppler shifts. In this paper, we propose two novel block-wise IM schemes for OTFS systems, named delay-IM with OTFS (DeIM-OTFS) and Doppler-IM with OTFS (DoIM-OTFS), where a block of delay/Doppler resource bins are activated simultaneously. Based on a maximum likelihood (ML) detector, we analyze upper bounds on the average bit error rates for the proposed DeIM-OTFS and DoIM-OTFS schemes, and verify their performance advantages over the existing IM-OTFS systems. We also develop a multi-layer joint symbol and activation pattern detection (MLJSAPD) algorithm and a customized message passing detection (CMPD) algorithm for our proposed DeIMOTFS and DoIM-OTFS systems with low complexity. Simulation results demonstrate that our proposed MLJSAPD and CMPD algorithms can achieve desired performance with robustness to the imperfect channel state information (CSI). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.12042v1-abstract-full').style.display = 'none'; document.getElementById('2306.12042v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">arXiv admin note: text overlap with arXiv:2210.13454</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.06899">arXiv:2305.06899</a> <span> [<a href="https://arxiv.org/pdf/2305.06899">pdf</a>, <a href="https://arxiv.org/format/2305.06899">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Generalized signals on simplicial complexes </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Jian%2C+X">Xingchao Jian</a>, <a href="/search/eess?searchtype=author&query=Tay%2C+W+P">Wee Peng Tay</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+M">Maosheng Yang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2305.06899v2-abstract-short" style="display: inline;"> Topological signal processing (TSP) over simplicial complexes typically assumes observations associated with the simplicial complexes are real scalars. In this paper, we develop TSP theories for the case where observations belong to general abelian groups, including function spaces that are commonly used to represent time-varying signals. Our approach generalizes the Hodge decomposition and allows… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.06899v2-abstract-full').style.display = 'inline'; document.getElementById('2305.06899v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.06899v2-abstract-full" style="display: none;"> Topological signal processing (TSP) over simplicial complexes typically assumes observations associated with the simplicial complexes are real scalars. In this paper, we develop TSP theories for the case where observations belong to general abelian groups, including function spaces that are commonly used to represent time-varying signals. Our approach generalizes the Hodge decomposition and allows for signal processing tasks to be performed on these more complex observations. We propose a unified and flexible framework for TSP that expands its applicability to a wider range of signal processing applications. Numerical results demonstrate the effectiveness of this approach and provide a foundation for future research in this area. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.06899v2-abstract-full').style.display = 'none'; document.getElementById('2305.06899v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.00139">arXiv:2305.00139</a> <span> [<a href="https://arxiv.org/pdf/2305.00139">pdf</a>, <a href="https://arxiv.org/format/2305.00139">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Lee%2C+S+H">See Hian Lee</a>, <a href="/search/eess?searchtype=author&query=Meng%2C+H">Hanyang Meng</a>, <a href="/search/eess?searchtype=author&query=Zhao%2C+K">Kai Zhao</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+J">Jielong Yang</a>, <a href="/search/eess?searchtype=author&query=Tay%2C+W+P">Wee Peng Tay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2305.00139v1-abstract-short" style="display: inline;"> In node classification using graph neural networks (GNNs), a typical model generates logits for different class labels at each node. A softmax layer often outputs a label prediction based on the largest logit. We demonstrate that it is possible to infer hidden graph structural information from the dataset using these logits. We introduce the key notion of label non-uniformity, which is derived fro… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.00139v1-abstract-full').style.display = 'inline'; document.getElementById('2305.00139v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.00139v1-abstract-full" style="display: none;"> In node classification using graph neural networks (GNNs), a typical model generates logits for different class labels at each node. A softmax layer often outputs a label prediction based on the largest logit. We demonstrate that it is possible to infer hidden graph structural information from the dataset using these logits. We introduce the key notion of label non-uniformity, which is derived from the Wasserstein distance between the softmax distribution of the logits and the uniform distribution. We demonstrate that nodes with small label non-uniformity are harder to classify correctly. We theoretically analyze how the label non-uniformity varies across the graph, which provides insights into boosting the model performance: increasing training samples with high non-uniformity or dropping edges to reduce the maximal cut size of the node set of small non-uniformity. These mechanisms can be easily added to a base GNN model. Experimental results demonstrate that our approach improves the performance of many benchmark base models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.00139v1-abstract-full').style.display = 'none'; document.getElementById('2305.00139v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.03507">arXiv:2304.03507</a> <span> [<a href="https://arxiv.org/pdf/2304.03507">pdf</a>, <a href="https://arxiv.org/format/2304.03507">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Distributional Signals for Node Classification in Graph Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Lee%2C+S+H">See Hian Lee</a>, <a href="/search/eess?searchtype=author&query=Zhao%2C+K">Kai Zhao</a>, <a href="/search/eess?searchtype=author&query=Tay%2C+W+P">Wee Peng Tay</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+J">Jielong Yang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2304.03507v1-abstract-short" style="display: inline;"> In graph neural networks (GNNs), both node features and labels are examples of graph signals, a key notion in graph signal processing (GSP). While it is common in GSP to impose signal smoothness constraints in learning and estimation tasks, it is unclear how this can be done for discrete node labels. We bridge this gap by introducing the concept of distributional graph signals. In our framework, w… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.03507v1-abstract-full').style.display = 'inline'; document.getElementById('2304.03507v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.03507v1-abstract-full" style="display: none;"> In graph neural networks (GNNs), both node features and labels are examples of graph signals, a key notion in graph signal processing (GSP). While it is common in GSP to impose signal smoothness constraints in learning and estimation tasks, it is unclear how this can be done for discrete node labels. We bridge this gap by introducing the concept of distributional graph signals. In our framework, we work with the distributions of node labels instead of their values and propose notions of smoothness and non-uniformity of such distributional graph signals. We then propose a general regularization method for GNNs that allows us to encode distributional smoothness and non-uniformity of the model output in semi-supervised node classification tasks. Numerical experiments demonstrate that our method can significantly improve the performance of most base GNN models in different problem settings. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.03507v1-abstract-full').style.display = 'none'; document.getElementById('2304.03507v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.12421">arXiv:2302.12421</a> <span> [<a href="https://arxiv.org/pdf/2302.12421">pdf</a>, <a href="https://arxiv.org/format/2302.12421">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Probability">math.PR</span> </div> </div> <p class="title is-5 mathjax"> Graph signal processing with categorical perspective </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Jian%2C+X">Xingchao Jian</a>, <a href="/search/eess?searchtype=author&query=Tay%2C+W+P">Wee Peng Tay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2302.12421v1-abstract-short" style="display: inline;"> In this paper, we propose a framework for graph signal processing using category theory. The aim is to generalize a few recent works on probabilistic approaches to graph signal processing, which handle signal and graph uncertainties. </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.12421v1-abstract-full" style="display: none;"> In this paper, we propose a framework for graph signal processing using category theory. The aim is to generalize a few recent works on probabilistic approaches to graph signal processing, which handle signal and graph uncertainties. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.12421v1-abstract-full').style.display = 'none'; document.getElementById('2302.12421v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.11104">arXiv:2302.11104</a> <span> [<a href="https://arxiv.org/pdf/2302.11104">pdf</a>, <a href="https://arxiv.org/format/2302.11104">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> On distributional graph signals </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Jian%2C+X">Xingchao Jian</a>, <a href="/search/eess?searchtype=author&query=Tay%2C+W+P">Wee Peng Tay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2302.11104v1-abstract-short" style="display: inline;"> Graph signal processing (GSP) studies graph-structured data, where the central concept is the vector space of graph signals. To study a vector space, we have many useful tools up our sleeves. However, uncertainty is omnipresent in practice, and using a vector to model a real signal can be erroneous in some situations. In this paper, we want to use the Wasserstein space as a replacement for the vec… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.11104v1-abstract-full').style.display = 'inline'; document.getElementById('2302.11104v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.11104v1-abstract-full" style="display: none;"> Graph signal processing (GSP) studies graph-structured data, where the central concept is the vector space of graph signals. To study a vector space, we have many useful tools up our sleeves. However, uncertainty is omnipresent in practice, and using a vector to model a real signal can be erroneous in some situations. In this paper, we want to use the Wasserstein space as a replacement for the vector space of graph signals, to account for signal stochasticity. The Wasserstein is strictly more general in which the classical graph signal space embeds isometrically. An element in the Wasserstein space is called a distributional graph signal. On the other hand, signal processing for a probability space of graphs has been proposed in the literature. In this work, we propose a unified framework that also encompasses existing theories regarding graph uncertainty. We develop signal processing tools to study the new notion of distributional graph signals. We also demonstrate how the theory can be applied by using real datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.11104v1-abstract-full').style.display = 'none'; document.getElementById('2302.11104v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.13454">arXiv:2210.13454</a> <span> [<a href="https://arxiv.org/pdf/2210.13454">pdf</a>, <a href="https://arxiv.org/ps/2210.13454">ps</a>, <a href="https://arxiv.org/format/2210.13454">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> A Novel Block-Wise Index Modulation Scheme for High-Mobility OTFS Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Qian%2C+M">Mi Qian</a>, <a href="/search/eess?searchtype=author&query=Ge%2C+Y">Yao Ge</a>, <a href="/search/eess?searchtype=author&query=Wen%2C+M">Miaowen Wen</a>, <a href="/search/eess?searchtype=author&query=Ji%2C+F">Fei Ji</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2210.13454v1-abstract-short" style="display: inline;"> As a promising technique for high-mobility wireless communications, orthogonal time frequency space (OTFS) has been proved to enjoy excellent advantages with respect to traditional orthogonal frequency division multiplexing (OFDM). However, a challenging problem is to design efficient systems to further improve the performance. In this paper, we propose a novel block-wise index modulation (IM) sch… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.13454v1-abstract-full').style.display = 'inline'; document.getElementById('2210.13454v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.13454v1-abstract-full" style="display: none;"> As a promising technique for high-mobility wireless communications, orthogonal time frequency space (OTFS) has been proved to enjoy excellent advantages with respect to traditional orthogonal frequency division multiplexing (OFDM). However, a challenging problem is to design efficient systems to further improve the performance. In this paper, we propose a novel block-wise index modulation (IM) scheme for OTFS systems, named Doppler-IM with OTFS (DoIM-OTFS), where a block of Doppler resource bins are activated simultaneously. For practical implementation, we develop a low complexity customized message passing (CMP) algorithm for our proposed DoIM-OTFS scheme. Simulation results demonstrate our proposed DoIM-OTFS system outperforms traditional OTFS system without IM. The proposed CMP algorithm can achieve desired performance and robustness to the imperfect channel state information (CSI). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.13454v1-abstract-full').style.display = 'none'; document.getElementById('2210.13454v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.04504">arXiv:2210.04504</a> <span> [<a href="https://arxiv.org/pdf/2210.04504">pdf</a>, <a href="https://arxiv.org/format/2210.04504">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Sampling of Correlated Bandlimited Continuous Signals by Joint Time-vertex Graph Fourier Transform </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ni%2C+Z">Zhongyi Ni</a>, <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Sheng%2C+H">Hang Sheng</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+H">Hui Feng</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+B">Bo Hu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2210.04504v1-abstract-short" style="display: inline;"> When sampling multiple signals, the correlation between the signals can be exploited to reduce the overall number of samples. In this paper, we study the sampling theory of multiple correlated signals, using correlation to sample them at the lowest sampling rate. Based on the correlation between signal sources, we model multiple continuous-time signals as continuous time-vertex graph signals. The… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.04504v1-abstract-full').style.display = 'inline'; document.getElementById('2210.04504v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.04504v1-abstract-full" style="display: none;"> When sampling multiple signals, the correlation between the signals can be exploited to reduce the overall number of samples. In this paper, we study the sampling theory of multiple correlated signals, using correlation to sample them at the lowest sampling rate. Based on the correlation between signal sources, we model multiple continuous-time signals as continuous time-vertex graph signals. The graph signals are projected onto orthogonal bases to remove spatial correlation and reduce dimensions by graph Fourier transform. When the bandwidths of the original signals and the reduced dimension signals are given, we prove the minimum sampling rate required for recovery of the original signals, and propose a feasible sampling scheme. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.04504v1-abstract-full').style.display = 'none'; document.getElementById('2210.04504v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2209.13909">arXiv:2209.13909</a> <span> [<a href="https://arxiv.org/pdf/2209.13909">pdf</a>, <a href="https://arxiv.org/format/2209.13909">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> On semi shift invariant graph filters </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Lee%2C+S+H">See Hian Lee</a>, <a href="/search/eess?searchtype=author&query=Tay%2C+W+P">Wee Peng Tay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2209.13909v1-abstract-short" style="display: inline;"> In graph signal processing, one of the most important subjects is the study of filters, i.e., linear transformations that capture relations between graph signals. One of the most important families of filters is the space of shift invariant filters, defined as transformations commute with a preferred graph shift operator. Shift invariant filters have a wide range of applications in graph signal pr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.13909v1-abstract-full').style.display = 'inline'; document.getElementById('2209.13909v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2209.13909v1-abstract-full" style="display: none;"> In graph signal processing, one of the most important subjects is the study of filters, i.e., linear transformations that capture relations between graph signals. One of the most important families of filters is the space of shift invariant filters, defined as transformations commute with a preferred graph shift operator. Shift invariant filters have a wide range of applications in graph signal processing and graph neural networks. A shift invariant filter can be interpreted geometrically as an information aggregation procedure (from local neighborhood), and can be computed easily using matrix multiplication. However, there are still drawbacks to using solely shift invariant filters in applications, such as being restrictively homogeneous. In this paper, we generalize shift invariant filters by introducing and studying semi shift invariant filters. We give an application of semi shift invariant filters with a new signal processing framework, the subgraph signal processing. Moreover, we also demonstrate how semi shift invariant filters can be used in graph neural networks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.13909v1-abstract-full').style.display = 'none'; document.getElementById('2209.13909v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2207.09565">arXiv:2207.09565</a> <span> [<a href="https://arxiv.org/pdf/2207.09565">pdf</a>, <a href="https://arxiv.org/ps/2207.09565">ps</a>, <a href="https://arxiv.org/format/2207.09565">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Low Complexity First: Duration-Centric ISI Mitigation in Molecular Communication via Diffusion </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Chen%2C+X">Xuan Chen</a>, <a href="/search/eess?searchtype=author&query=Ji%2C+F">Fei Ji</a>, <a href="/search/eess?searchtype=author&query=Wen%2C+M">Miaowen Wen</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+Y">Yu Huang</a>, <a href="/search/eess?searchtype=author&query=Tang%2C+Y">Yuankun Tang</a>, <a href="/search/eess?searchtype=author&query=Eckford%2C+A+W">Andrew W. Eckford</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2207.09565v1-abstract-short" style="display: inline;"> In this paper, we propose a novel inter-symbol interference (ISI) mitigation scheme for molecular communication via diffusion (MCvD) systems with the optimal detection interval. Its rationale is to exploit the discarded duration (i.e., the symbol duration outside this optimal interval) to relieve ISI in the target system. Following this idea, we formulate an objective function to quantify the impa… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.09565v1-abstract-full').style.display = 'inline'; document.getElementById('2207.09565v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2207.09565v1-abstract-full" style="display: none;"> In this paper, we propose a novel inter-symbol interference (ISI) mitigation scheme for molecular communication via diffusion (MCvD) systems with the optimal detection interval. Its rationale is to exploit the discarded duration (i.e., the symbol duration outside this optimal interval) to relieve ISI in the target system. Following this idea, we formulate an objective function to quantify the impact of the discarded time on bit error rate (BER) performance. Besides, an optimally reusable interval within the discarded duration is derived in closed form, which applies to both the absorbing and passive receivers. Finally, numerical results validate our analysis and show that for the considered MCvD system, significant BER improvements can be achieved by using the derived reusable duration. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.09565v1-abstract-full').style.display = 'none'; document.getElementById('2207.09565v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, 5 figures. arXiv admin note: text overlap with arXiv:2204.08636</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2207.04478">arXiv:2207.04478</a> <span> [<a href="https://arxiv.org/pdf/2207.04478">pdf</a>, <a href="https://arxiv.org/format/2207.04478">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TVT.2022.3230143">10.1109/TVT.2022.3230143 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Model-Driven Based Deep Unfolding Equalizer for Underwater Acoustic OFDM Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhao%2C+H">Hao Zhao</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+C">Cui Yang</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+Y">Yalu Xu</a>, <a href="/search/eess?searchtype=author&query=Ji%2C+F">Fei Ji</a>, <a href="/search/eess?searchtype=author&query=Wen%2C+M">Miaowen Wen</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+Y">Yankun Chen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2207.04478v2-abstract-short" style="display: inline;"> It is challenging to design an equalizer for the complex time-frequency doubly-selective channel. In this paper, we employ the deep unfolding approach to establish an equalizer for the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system, namely UDNet. Each layer of UDNet is designed according to the classical minimum mean square error (MMSE) equalizer. Moreover, we c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.04478v2-abstract-full').style.display = 'inline'; document.getElementById('2207.04478v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2207.04478v2-abstract-full" style="display: none;"> It is challenging to design an equalizer for the complex time-frequency doubly-selective channel. In this paper, we employ the deep unfolding approach to establish an equalizer for the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system, namely UDNet. Each layer of UDNet is designed according to the classical minimum mean square error (MMSE) equalizer. Moreover, we consider the QPSK equalization as a four-classification task and adopt minimum Kullback-Leibler (KL) to achieve a smaller symbol error rate (SER) with the one-hot coding instead of the MMSE criterion. In addition, we introduce a sliding structure based on the banded approximation of the channel matrix to reduce the network size and aid UDNet to perform well for different-length signals without changing the network structure. Furthermore, we apply the measured at-sea doubly-selective UWA channel and offshore background noise to evaluate the proposed equalizer. Experimental results show that the proposed UDNet performs better with low computational complexity. Concretely, the SER of UDNet is nearly an order of magnitude lower than that of MMSE. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.04478v2-abstract-full').style.display = 'none'; document.getElementById('2207.04478v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2206.04498">arXiv:2206.04498</a> <span> [<a href="https://arxiv.org/pdf/2206.04498">pdf</a>, <a href="https://arxiv.org/format/2206.04498">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Abstract message passing and distributed graph signal processing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Lu%2C+Y">Yiqi Lu</a>, <a href="/search/eess?searchtype=author&query=Tay%2C+W+P">Wee Peng Tay</a>, <a href="/search/eess?searchtype=author&query=Chong%2C+E">Edwin Chong</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2206.04498v1-abstract-short" style="display: inline;"> Graph signal processing is a framework to handle graph structured data. The fundamental concept is graph shift operator, giving rise to the graph Fourier transform. While the graph Fourier transform is a centralized procedure, distributed graph signal processing algorithms are needed to address challenges such as scalability and privacy. In this paper, we develop a theory of distributed graph sign… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.04498v1-abstract-full').style.display = 'inline'; document.getElementById('2206.04498v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.04498v1-abstract-full" style="display: none;"> Graph signal processing is a framework to handle graph structured data. The fundamental concept is graph shift operator, giving rise to the graph Fourier transform. While the graph Fourier transform is a centralized procedure, distributed graph signal processing algorithms are needed to address challenges such as scalability and privacy. In this paper, we develop a theory of distributed graph signal processing based on the classical notion of message passing. However, we generalize the definition of a message to permit more abstract mathematical objects. The framework provides an alternative point of view that avoids the iterative nature of existing approaches to distributed graph signal processing. Moreover, our framework facilitates investigating theoretical questions such as solubility of distributed problems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.04498v1-abstract-full').style.display = 'none'; document.getElementById('2206.04498v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2204.08636">arXiv:2204.08636</a> <span> [<a href="https://arxiv.org/pdf/2204.08636">pdf</a>, <a href="https://arxiv.org/ps/2204.08636">ps</a>, <a href="https://arxiv.org/format/2204.08636">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Detection Interval for Diffusion Molecular Communication: How Long is Enough? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Chen%2C+X">Xuan Chen</a>, <a href="/search/eess?searchtype=author&query=Wen%2C+M">Miaowen Wen</a>, <a href="/search/eess?searchtype=author&query=Ji%2C+F">Fei Ji</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+Y">Yu Huang</a>, <a href="/search/eess?searchtype=author&query=Tang%2C+Y">Yuankun Tang</a>, <a href="/search/eess?searchtype=author&query=Eckford%2C+A+W">Andrew W. Eckford</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2204.08636v1-abstract-short" style="display: inline;"> Molecular communication has a key role to play in future medical applications, including detecting, analyzing, and addressing infectious disease outbreaks. Overcoming inter-symbol interference (ISI) is one of the key challenges in the design of molecular communication systems. In this paper, we propose to optimize the detection interval to minimize the impact of ISI while ensuring the accurate det… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.08636v1-abstract-full').style.display = 'inline'; document.getElementById('2204.08636v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2204.08636v1-abstract-full" style="display: none;"> Molecular communication has a key role to play in future medical applications, including detecting, analyzing, and addressing infectious disease outbreaks. Overcoming inter-symbol interference (ISI) is one of the key challenges in the design of molecular communication systems. In this paper, we propose to optimize the detection interval to minimize the impact of ISI while ensuring the accurate detection of the transmitted information symbol, which is suitable for the absorbing and passive receivers. For tractability, based on the signal-to-interference difference (SID) and signal-to-interference-and-noise amplitude ratio (SINAR), we propose a modified-SINAR (mSINAR) to measure the bit error rate (BER) performance for the molecular communication system with a variable detection interval. Besides, we derive the optimal detection interval in closed form. Using simulation results, we show that the BER performance of our proposed mSINAR scheme is superior to the competing schemes, and achieves similar performance to optimal intervals found by the exhaustive search. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.08636v1-abstract-full').style.display = 'none'; document.getElementById('2204.08636v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2203.00832">arXiv:2203.00832</a> <span> [<a href="https://arxiv.org/pdf/2203.00832">pdf</a>, <a href="https://arxiv.org/format/2203.00832">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> To further understand graph signals </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Tay%2C+W+P">Wee Peng Tay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2203.00832v2-abstract-short" style="display: inline;"> Graph signal processing (GSP) is a framework to analyze and process graph-structured data. Many research works focus on developing tools such as Graph Fourier transforms (GFT), filters, and neural network models to handle graph signals. Such approaches have successfully taken care of ``signal processing'' in many circumstances. In this paper, we want to put emphasis on ``graph signals'' themselves… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.00832v2-abstract-full').style.display = 'inline'; document.getElementById('2203.00832v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.00832v2-abstract-full" style="display: none;"> Graph signal processing (GSP) is a framework to analyze and process graph-structured data. Many research works focus on developing tools such as Graph Fourier transforms (GFT), filters, and neural network models to handle graph signals. Such approaches have successfully taken care of ``signal processing'' in many circumstances. In this paper, we want to put emphasis on ``graph signals'' themselves. Although there are characterizations of graph signals using the notion of bandwidth derived from GFT, we want to argue here that graph signals may contain hidden geometric information of the network, independent of (graph) Fourier theories. We shall provide a framework to understand such information, and demonstrate how new knowledge on ``graph signals'' can help with ``signal processing''. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.00832v2-abstract-full').style.display = 'none'; document.getElementById('2203.00832v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2109.12576">arXiv:2109.12576</a> <span> [<a href="https://arxiv.org/pdf/2109.12576">pdf</a>, <a href="https://arxiv.org/ps/2109.12576">ps</a>, <a href="https://arxiv.org/format/2109.12576">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Recovery of Graph Signals from Sign Measurements </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liu%2C+W">Wenwei Liu</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+H">Hui Feng</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+K">Kaixuan Wang</a>, <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+B">Bo Hu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2109.12576v1-abstract-short" style="display: inline;"> Sampling and interpolation have been extensively studied, in order to reconstruct or estimate the entire graph signal from the signal values on a subset of vertexes, of which most achievements are about continuous signals. While in a lot of signal processing tasks, signals are not fully observed, and only the signs of signals are available, for example a rating system may only provide several simp… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.12576v1-abstract-full').style.display = 'inline'; document.getElementById('2109.12576v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2109.12576v1-abstract-full" style="display: none;"> Sampling and interpolation have been extensively studied, in order to reconstruct or estimate the entire graph signal from the signal values on a subset of vertexes, of which most achievements are about continuous signals. While in a lot of signal processing tasks, signals are not fully observed, and only the signs of signals are available, for example a rating system may only provide several simple options. In this paper, the reconstruction of band-limited graph signals based on sign sampling is discussed and a greedy sampling strategy is proposed. The simulation experiments are presented, and the greedy sampling algorithm is compared with random sampling algorithm, which verify the validity of the proposed approach. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.12576v1-abstract-full').style.display = 'none'; document.getElementById('2109.12576v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2108.11695">arXiv:2108.11695</a> <span> [<a href="https://arxiv.org/pdf/2108.11695">pdf</a>, <a href="https://arxiv.org/format/2108.11695">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> PAENet: A Progressive Attention-Enhanced Network for 3D to 2D Retinal Vessel Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wu%2C+Z">Zhuojie Wu</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Z">Zijian Wang</a>, <a href="/search/eess?searchtype=author&query=Zou%2C+W">Wenxuan Zou</a>, <a href="/search/eess?searchtype=author&query=Ji%2C+F">Fan Ji</a>, <a href="/search/eess?searchtype=author&query=Dang%2C+H">Hao Dang</a>, <a href="/search/eess?searchtype=author&query=Zhou%2C+W">Wanting Zhou</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+M">Muyi Sun</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2108.11695v5-abstract-short" style="display: inline;"> 3D to 2D retinal vessel segmentation is a challenging problem in Optical Coherence Tomography Angiography (OCTA) images. Accurate retinal vessel segmentation is important for the diagnosis and prevention of ophthalmic diseases. However, making full use of the 3D data of OCTA volumes is a vital factor for obtaining satisfactory segmentation results. In this paper, we propose a Progressive Attention… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.11695v5-abstract-full').style.display = 'inline'; document.getElementById('2108.11695v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2108.11695v5-abstract-full" style="display: none;"> 3D to 2D retinal vessel segmentation is a challenging problem in Optical Coherence Tomography Angiography (OCTA) images. Accurate retinal vessel segmentation is important for the diagnosis and prevention of ophthalmic diseases. However, making full use of the 3D data of OCTA volumes is a vital factor for obtaining satisfactory segmentation results. In this paper, we propose a Progressive Attention-Enhanced Network (PAENet) based on attention mechanisms to extract rich feature representation. Specifically, the framework consists of two main parts, the three-dimensional feature learning path and the two-dimensional segmentation path. In the three-dimensional feature learning path, we design a novel Adaptive Pooling Module (APM) and propose a new Quadruple Attention Module (QAM). The APM captures dependencies along the projection direction of volumes and learns a series of pooling coefficients for feature fusion, which efficiently reduces feature dimension. In addition, the QAM reweights the features by capturing four-group cross-dimension dependencies, which makes maximum use of 4D feature tensors. In the two-dimensional segmentation path, to acquire more detailed information, we propose a Feature Fusion Module (FFM) to inject 3D information into the 2D path. Meanwhile, we adopt the Polarized Self-Attention (PSA) block to model the semantic interdependencies in spatial and channel dimensions respectively. Experimentally, our extensive experiments on the OCTA-500 dataset show that our proposed algorithm achieves state-of-the-art performance compared with previous methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.11695v5-abstract-full').style.display = 'none'; document.getElementById('2108.11695v5-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by BIBM 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2108.09192">arXiv:2108.09192</a> <span> [<a href="https://arxiv.org/pdf/2108.09192">pdf</a>, <a href="https://arxiv.org/format/2108.09192">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Graph Signal Processing over a Probability Space of Shift Operators </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Tay%2C+W+P">Wee Peng Tay</a>, <a href="/search/eess?searchtype=author&query=Ortega%2C+A">Antonio Ortega</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2108.09192v3-abstract-short" style="display: inline;"> Graph signal processing (GSP) uses a shift operator to define a Fourier basis for the set of graph signals. The shift operator is often chosen to capture the graph topology. However, in many applications, the graph topology may be unknown a priori, its structure uncertain, or generated randomly from a predefined set for each observation. Each graph topology gives rise to a different shift operator… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.09192v3-abstract-full').style.display = 'inline'; document.getElementById('2108.09192v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2108.09192v3-abstract-full" style="display: none;"> Graph signal processing (GSP) uses a shift operator to define a Fourier basis for the set of graph signals. The shift operator is often chosen to capture the graph topology. However, in many applications, the graph topology may be unknown a priori, its structure uncertain, or generated randomly from a predefined set for each observation. Each graph topology gives rise to a different shift operator. In this paper, we develop a GSP framework over a probability space of shift operators. We develop the corresponding notions of Fourier transform, MFC filters, and band-pass filters, which subsumes classical GSP theory as the special case where the probability space consists of a single shift operator. We show that an MFC filter under this framework is the expectation of random convolution filters in classical GSP, while the notion of bandlimitedness requires additional wiggle room from being simply a fixed point of a band-pass filter. We develop a mechanism that facilitates mapping from one space of shift operators to another, which allows our framework to be applied to a rich set of scenarios. We demonstrate how the theory can be applied by using both synthetic and real datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.09192v3-abstract-full').style.display = 'none'; document.getElementById('2108.09192v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2105.13296">arXiv:2105.13296</a> <span> [<a href="https://arxiv.org/pdf/2105.13296">pdf</a>, <a href="https://arxiv.org/format/2105.13296">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/JSTSP.2022.3144020">10.1109/JSTSP.2022.3144020 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Federated Meta Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things Underwater Acoustic Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhao%2C+H">Hao Zhao</a>, <a href="/search/eess?searchtype=author&query=Ji%2C+F">Fei Ji</a>, <a href="/search/eess?searchtype=author&query=Guan%2C+Q">Quansheng Guan</a>, <a href="/search/eess?searchtype=author&query=Li%2C+Q">Qiang Li</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+S">Shuai Wang</a>, <a href="/search/eess?searchtype=author&query=Dong%2C+H">Hefeng Dong</a>, <a href="/search/eess?searchtype=author&query=Wen%2C+M">Miaowen Wen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2105.13296v1-abstract-short" style="display: inline;"> Sixth-generation wireless communication (6G) will be an integrated architecture of "space, air, ground and sea". One of the most difficult part of this architecture is the underwater information acquisition which need to transmitt information cross the interface between water and air.In this senario, ocean of things (OoT) will play an important role, because it can serve as a hub connecting Intern… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2105.13296v1-abstract-full').style.display = 'inline'; document.getElementById('2105.13296v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2105.13296v1-abstract-full" style="display: none;"> Sixth-generation wireless communication (6G) will be an integrated architecture of "space, air, ground and sea". One of the most difficult part of this architecture is the underwater information acquisition which need to transmitt information cross the interface between water and air.In this senario, ocean of things (OoT) will play an important role, because it can serve as a hub connecting Internet of things (IoT) and Internet of underwater things (IoUT). OoT device not only can collect data through underwater methods, but also can utilize radio frequence over the air. For underwater communications, underwater acoustic communications (UWA COMMs) is the most effective way for OoT devices to exchange information, but it is always tormented by doppler shift and synchronization errors. In this paper, in order to overcome UWA tough conditions, a deep neural networks based receiver for underwater acoustic chirp communication, called C-DNN, is proposed. Moreover, to improve the performance of DL-model and solve the problem of model generalization, we also proposed a novel federated meta learning (FML) enhanced acoustic radio cooperative (ARC) framework, dubbed ARC/FML, to do transfer. Particularly, tractable expressions are derived for the convergence rate of FML in a wireless setting, accounting for effects from both scheduling ratio, local epoch and the data amount on a single node.From our analysis and simulation results, it is shown that, the proposed C-DNN can provide a better BER performance and lower complexity than classical matched filter (MF) in underwater acoustic communications scenario. The ARC/FML framework has good convergence under a variety of channels than federated learning (FL). In summary, the proposed ARC/FML for OoT is a promising scheme for information exchange across water and air. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2105.13296v1-abstract-full').style.display = 'none'; document.getElementById('2105.13296v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 May, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2101.01369">arXiv:2101.01369</a> <span> [<a href="https://arxiv.org/pdf/2101.01369">pdf</a>, <a href="https://arxiv.org/ps/2101.01369">ps</a>, <a href="https://arxiv.org/format/2101.01369">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> A Splitting-Detection Joint-Decision Receiver for Ultrasonic Intra-Body Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wang%2C+Q">Qianqian Wang</a>, <a href="/search/eess?searchtype=author&query=Guan%2C+Q">Quansheng Guan</a>, <a href="/search/eess?searchtype=author&query=Cheng%2C+J">Julian Cheng</a>, <a href="/search/eess?searchtype=author&query=Ji%2C+F">Fei Ji</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2101.01369v1-abstract-short" style="display: inline;"> Ultrasonic intra-body communication (IBC) is a promising enabling technology for future healthcare applications, due to low attenuation and medical safety of ultrasonic waves for the human body. A splitting receiver, referred to as the splitting-detection separate-decision (SDSD) receiver, is introduced for ultrasonic pulse-based IBCs, and SDSD can significantly improve bit-error rate (BER) perfor… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.01369v1-abstract-full').style.display = 'inline'; document.getElementById('2101.01369v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2101.01369v1-abstract-full" style="display: none;"> Ultrasonic intra-body communication (IBC) is a promising enabling technology for future healthcare applications, due to low attenuation and medical safety of ultrasonic waves for the human body. A splitting receiver, referred to as the splitting-detection separate-decision (SDSD) receiver, is introduced for ultrasonic pulse-based IBCs, and SDSD can significantly improve bit-error rate (BER) performance over the traditional coherent-detection (CD) and energy detection (ED) receivers. To overcome the high complexity and improve the BER performance of SDSD, a splitting-detection joint-decision (SDJD) receiver is proposed. The core idea of SDJD is to split the received signal into two steams that can be separately processed by CD and ED, and then summed up as joint decision variables to achieve diversity combining. The theoretical channel capacity and BER of the SDSD and SDJD are derived for M-ary pulse position modulation (M-PPM) and PPM with spreading codes. The derivation takes into account the channel noise, intra-body channel fading, and channel estimation error. Simulation results verify the theoretical analysis and show that both SDSD and SDJD can achieve higher channel capacity and lower BER than the CD and ED receivers with perfect channel estimation, while SDJD can achieve the lowest BER with imperfect channel estimation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.01369v1-abstract-full').style.display = 'none'; document.getElementById('2101.01369v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 January, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">29 pages, 10 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2012.06296">arXiv:2012.06296</a> <span> [<a href="https://arxiv.org/pdf/2012.06296">pdf</a>, <a href="https://arxiv.org/format/2012.06296">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Signal processing with a distribution of graph operators </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Tay%2C+W+P">Wee Peng Tay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2012.06296v1-abstract-short" style="display: inline;"> In this paper, we develop a signal processing framework of a network without explicit knowledge of the network topology. Instead, we make use of knowledge on the distribution of operators on the network. This makes the framework flexible and useful when accurate knowledge of graph topology is unavailable. Moreover, the usual graph signal processing is a special case of our framework by using the d… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.06296v1-abstract-full').style.display = 'inline'; document.getElementById('2012.06296v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2012.06296v1-abstract-full" style="display: none;"> In this paper, we develop a signal processing framework of a network without explicit knowledge of the network topology. Instead, we make use of knowledge on the distribution of operators on the network. This makes the framework flexible and useful when accurate knowledge of graph topology is unavailable. Moreover, the usual graph signal processing is a special case of our framework by using the delta distribution. The main elements of the theory include Fourier transform, theory of filtering and sampling. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.06296v1-abstract-full').style.display = 'none'; document.getElementById('2012.06296v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 December, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2010.09952">arXiv:2010.09952</a> <span> [<a href="https://arxiv.org/pdf/2010.09952">pdf</a>, <a href="https://arxiv.org/format/2010.09952">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Sampling Theory of Bandlimited Continuous-Time Graph Signals </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+H">Hui Feng</a>, <a href="/search/eess?searchtype=author&query=Sheng%2C+H">Hang Sheng</a>, <a href="/search/eess?searchtype=author&query=Tay%2C+W+P">Wee Peng Tay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2010.09952v2-abstract-short" style="display: inline;"> A continuous-time graph signal can be viewed as a time series of graph signals. It generalizes both the classical continuous-time signal and ordinary graph signal. Therefore, such a signal can be considered as a function on two domains: the graph domain and the time domain. In this paper, we consider the sampling theory of bandlimited continuous-time graph signals. To formulate the sampling proble… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.09952v2-abstract-full').style.display = 'inline'; document.getElementById('2010.09952v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2010.09952v2-abstract-full" style="display: none;"> A continuous-time graph signal can be viewed as a time series of graph signals. It generalizes both the classical continuous-time signal and ordinary graph signal. Therefore, such a signal can be considered as a function on two domains: the graph domain and the time domain. In this paper, we consider the sampling theory of bandlimited continuous-time graph signals. To formulate the sampling problem, we need to consider the interaction between the graph and time domains. We describe an explicit procedure to determine a discrete sampling set for perfect signal recovery. Moreover, in analogous to the Nyquist-Shannon sampling theorem, we give an explicit formula for the minimal sample rate. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.09952v2-abstract-full').style.display = 'none'; document.getElementById('2010.09952v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2005.04851">arXiv:2005.04851</a> <span> [<a href="https://arxiv.org/pdf/2005.04851">pdf</a>, <a href="https://arxiv.org/format/2005.04851">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Subgraph Signal Processing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Tay%2C+W+P">Wee Peng Tay</a>, <a href="/search/eess?searchtype=author&query=Kahn%2C+G">Giacomo Kahn</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2005.04851v3-abstract-short" style="display: inline;"> Graph signal processing, like the graph Fourier transform, requires the full graph signal at every vertex of the graph. However, in practice, only signals at a subset of vertices may be available. We propose a subgraph signal processing framework that relates a graph shift operator or filter on a subgraph with a filter on the ambient graph through an operator loss. It allows us to define shift ope… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.04851v3-abstract-full').style.display = 'inline'; document.getElementById('2005.04851v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2005.04851v3-abstract-full" style="display: none;"> Graph signal processing, like the graph Fourier transform, requires the full graph signal at every vertex of the graph. However, in practice, only signals at a subset of vertices may be available. We propose a subgraph signal processing framework that relates a graph shift operator or filter on a subgraph with a filter on the ambient graph through an operator loss. It allows us to define shift operators for the subgraph signal, which has a meaningful interpretation and relation to mixtures of shift invariant filters restricted to different subgraphs of the ambient graph (which we call semi shift invariant). This leads to a notion of frequency domain for the subgraph signal consistent in some sense with that of the full graph signal. We apply the subgraph signal processing machinery to several applications and demonstrate the utility of this framework in cases where only partial graph signals are observed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.04851v3-abstract-full').style.display = 'none'; document.getElementById('2005.04851v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2004.02392">arXiv:2004.02392</a> <span> [<a href="https://arxiv.org/pdf/2004.02392">pdf</a>, <a href="https://arxiv.org/format/2004.02392">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Signal processing on simplicial complexes </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Kahn%2C+G">Giacomo Kahn</a>, <a href="/search/eess?searchtype=author&query=Tay%2C+W+P">Wee Peng Tay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2004.02392v2-abstract-short" style="display: inline;"> Theoretical development and applications of graph signal processing (GSP) have attracted much attention. In classical GSP, the underlying structures are restricted in terms of dimensionality. A graph is a combinatorial object that models binary relations, and it does not directly model complex n-ary relations. One possible high dimensional generalization of graphs are simplicial complexes. They ar… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.02392v2-abstract-full').style.display = 'inline'; document.getElementById('2004.02392v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2004.02392v2-abstract-full" style="display: none;"> Theoretical development and applications of graph signal processing (GSP) have attracted much attention. In classical GSP, the underlying structures are restricted in terms of dimensionality. A graph is a combinatorial object that models binary relations, and it does not directly model complex n-ary relations. One possible high dimensional generalization of graphs are simplicial complexes. They are a step between the constrained case of graphs and the general case of hypergraphs. In this paper, we develop a signal processing framework on simplicial complexes, such that we recover the traditional GSP theory when restricted to signals on graphs. It is worth mentioning that the framework works much more generally, though the focus of the paper is on simplicial complexes. We demonstrate how to perform signal processing with the framework using numerical examples. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.02392v2-abstract-full').style.display = 'none'; document.getElementById('2004.02392v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1904.11655">arXiv:1904.11655</a> <span> [<a href="https://arxiv.org/pdf/1904.11655">pdf</a>, <a href="https://arxiv.org/format/1904.11655">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TSP.2019.2952055">10.1109/TSP.2019.2952055 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A Hilbert Space Theory of Generalized Graph Signal Processing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Tay%2C+W+P">Wee Peng Tay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1904.11655v2-abstract-short" style="display: inline;"> Graph signal processing (GSP) has become an important tool in many areas such as image processing, networking learning and analysis of social network data. In this paper, we propose a broader framework that not only encompasses traditional GSP as a special case, but also includes a hybrid framework of graph and classical signal processing over a continuous domain. Our framework relies extensively… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.11655v2-abstract-full').style.display = 'inline'; document.getElementById('1904.11655v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1904.11655v2-abstract-full" style="display: none;"> Graph signal processing (GSP) has become an important tool in many areas such as image processing, networking learning and analysis of social network data. In this paper, we propose a broader framework that not only encompasses traditional GSP as a special case, but also includes a hybrid framework of graph and classical signal processing over a continuous domain. Our framework relies extensively on concepts and tools from functional analysis to generalize traditional GSP to graph signals in a separable Hilbert space with infinite dimensions. We develop a concept analogous to Fourier transform for generalized GSP and the theory of filtering and sampling such signals. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.11655v2-abstract-full').style.display = 'none'; document.getElementById('1904.11655v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 September, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 April, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1903.03741">arXiv:1903.03741</a> <span> [<a href="https://arxiv.org/pdf/1903.03741">pdf</a>, <a href="https://arxiv.org/format/1903.03741">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Folded Graph Signals: Sensing with Unlimited Dynamic Range </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Pratibha"> Pratibha</a>, <a href="/search/eess?searchtype=author&query=Tay%2C+W+P">Wee Peng Tay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1903.03741v2-abstract-short" style="display: inline;"> Self-reset analog-to-digital converters (ADCs) are used to sample high dynamic range signals resulting in modulo-operation based folded signal samples. We consider the case where each vertex of a graph (e.g., sensors in a network) is equipped with a self-reset ADC and senses a time series. Graph sampling allows the graph time series to be represented by the signals at a subset of sampled vertices… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1903.03741v2-abstract-full').style.display = 'inline'; document.getElementById('1903.03741v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1903.03741v2-abstract-full" style="display: none;"> Self-reset analog-to-digital converters (ADCs) are used to sample high dynamic range signals resulting in modulo-operation based folded signal samples. We consider the case where each vertex of a graph (e.g., sensors in a network) is equipped with a self-reset ADC and senses a time series. Graph sampling allows the graph time series to be represented by the signals at a subset of sampled vertices and time instances. We investigate the problem of recovering bandlimited continuous-time graph signals from folded signal samples. We derive sufficient conditions to achieve successful recovery of the graph signal from the folded signal samples, which can be achieved via integer programming. To resolve the scalability issue of integer programming, we propose a sparse optimization recovery method for graph signals satisfying certain technical conditions. Such an approach requires a novel graph sampling scheme that selects vertices with small signal variation. The proposed algorithm exploits the inherent relationship among the graph vertices in both the vertex and time domains to recover the graph signal. Simulations and experiments on images validate the feasibility of our proposed approach. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1903.03741v2-abstract-full').style.display = 'none'; document.getElementById('1903.03741v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 January, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 March, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1902.09173">arXiv:1902.09173</a> <span> [<a href="https://arxiv.org/pdf/1902.09173">pdf</a>, <a href="https://arxiv.org/format/1902.09173">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> GFCN: A New Graph Convolutional Network Based on Parallel Flows </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+J">Jielong Yang</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Q">Qiang Zhang</a>, <a href="/search/eess?searchtype=author&query=Tay%2C+W+P">Wee Peng Tay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1902.09173v4-abstract-short" style="display: inline;"> In view of the huge success of convolution neural networks (CNN) for image classification and object recognition, there have been attempts to generalize the method to general graph-structured data. One major direction is based on spectral graph theory and graph signal processing. In this paper, we study the problem from a completely different perspective, by introducing parallel flow decomposition… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1902.09173v4-abstract-full').style.display = 'inline'; document.getElementById('1902.09173v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1902.09173v4-abstract-full" style="display: none;"> In view of the huge success of convolution neural networks (CNN) for image classification and object recognition, there have been attempts to generalize the method to general graph-structured data. One major direction is based on spectral graph theory and graph signal processing. In this paper, we study the problem from a completely different perspective, by introducing parallel flow decomposition of graphs. The essential idea is to decompose a graph into families of non-intersecting one dimensional (1D) paths, after which, we may apply a 1D CNN along each family of paths. We demonstrate that the our method, which we call GraphFlow, is able to transfer CNN architectures to general graphs. To show the effectiveness of our approach, we test our method on the classical MNIST dataset, synthetic datasets on network information propagation and a news article classification dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1902.09173v4-abstract-full').style.display = 'none'; document.getElementById('1902.09173v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 February, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1810.05333">arXiv:1810.05333</a> <span> [<a href="https://arxiv.org/pdf/1810.05333">pdf</a>, <a href="https://arxiv.org/format/1810.05333">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TSP.2019.2908133">10.1109/TSP.2019.2908133 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> On the Properties of Gromov Matrices and their Applications in Network Inference </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ji%2C+F">Feng Ji</a>, <a href="/search/eess?searchtype=author&query=Tang%2C+W">Wenchang Tang</a>, <a href="/search/eess?searchtype=author&query=Tay%2C+W+P">Wee Peng Tay</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1810.05333v3-abstract-short" style="display: inline;"> The spanning tree heuristic is a commonly adopted procedure in network inference and estimation. It allows one to generalize an inference method developed for trees, which is usually based on a statistically rigorous approach, to a heuristic procedure for general graphs by (usually randomly) choosing a spanning tree in the graph to apply the approach developed for trees. However, there are an intr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1810.05333v3-abstract-full').style.display = 'inline'; document.getElementById('1810.05333v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1810.05333v3-abstract-full" style="display: none;"> The spanning tree heuristic is a commonly adopted procedure in network inference and estimation. It allows one to generalize an inference method developed for trees, which is usually based on a statistically rigorous approach, to a heuristic procedure for general graphs by (usually randomly) choosing a spanning tree in the graph to apply the approach developed for trees. However, there are an intractable number of spanning trees in a dense graph. In this paper, we represent a weighted tree with a matrix, which we call a Gromov matrix. We propose a method that constructs a family of Gromov matrices using convex combinations, which can be used for inference and estimation instead of a randomly selected spanning tree. This procedure increases the size of the candidate set and hence enhances the performance of the classical spanning tree heuristic. On the other hand, our new scheme is based on simple algebraic constructions using matrices, and hence is still computationally tractable. We discuss some applications on network inference and estimation to demonstrate the usefulness of the proposed method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1810.05333v3-abstract-full').style.display = 'none'; document.getElementById('1810.05333v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 March, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 October, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2018. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> 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