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Systems Dispatch in Distribution Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Hou%2C+S">Shengren Hou</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a>, <a href="/search/eess?searchtype=author&query=Vergara%2C+P+P">Pedro P. Vergara</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="2411.00995v1-abstract-short" style="display: inline;"> The integration of distributed energy resources (DER) has escalated the challenge of voltage magnitude regulation in distribution networks. Traditional model-based approaches, which rely on complex sequential mathematical formulations, struggle to meet real-time operational demands. Deep reinforcement learning (DRL) offers a promising alternative by enabling offline training with distribution netw… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.00995v1-abstract-full').style.display = 'inline'; document.getElementById('2411.00995v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.00995v1-abstract-full" style="display: none;"> The integration of distributed energy resources (DER) has escalated the challenge of voltage magnitude regulation in distribution networks. Traditional model-based approaches, which rely on complex sequential mathematical formulations, struggle to meet real-time operational demands. Deep reinforcement learning (DRL) offers a promising alternative by enabling offline training with distribution network simulators, followed by real-time execution. However, DRL algorithms tend to converge to local optima due to limited exploration efficiency. Additionally, DRL algorithms can not enforce voltage magnitude constraints, leading to potential operational violations when implemented in the distribution network operation. This study addresses these challenges by proposing a novel safe imitation reinforcement learning (IRL) framework that combines IRL and a designed safety layer, aiming to optimize the operation of Energy Storage Systems (ESSs) in active distribution networks. The proposed safe IRL framework comprises two phases: offline training and online execution. During the offline phase, optimal state-action pairs are collected using an NLP solver, guiding the IRL policy iteration. In the online phase, the trained IRL policy's decisions are adjusted by the safety layer to maintain safety and constraint compliance. Simulation results demonstrate the efficacy of Safe IRL in balancing operational efficiency and safety, eliminating voltage violations, and maintaining low operation cost errors across various network sizes, while meeting real-time execution requirements. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.00995v1-abstract-full').style.display = 'none'; document.getElementById('2411.00995v1-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/2410.05151">arXiv:2410.05151</a> <span> [<a href="https://arxiv.org/pdf/2410.05151">pdf</a>, <a href="https://arxiv.org/format/2410.05151">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> Editing Music with Melody and Text: Using ControlNet for Diffusion Transformer </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Hou%2C+S">Siyuan Hou</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+S">Shansong Liu</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+R">Ruibin Yuan</a>, <a href="/search/eess?searchtype=author&query=Xue%2C+W">Wei Xue</a>, <a href="/search/eess?searchtype=author&query=Shan%2C+Y">Ying Shan</a>, <a href="/search/eess?searchtype=author&query=Zhao%2C+M">Mangsuo Zhao</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+C">Chao Zhang</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="2410.05151v1-abstract-short" style="display: inline;"> Despite the significant progress in controllable music generation and editing, challenges remain in the quality and length of generated music due to the use of Mel-spectrogram representations and UNet-based model structures. To address these limitations, we propose a novel approach using a Diffusion Transformer (DiT) augmented with an additional control branch using ControlNet. This allows for lon… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.05151v1-abstract-full').style.display = 'inline'; document.getElementById('2410.05151v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.05151v1-abstract-full" style="display: none;"> Despite the significant progress in controllable music generation and editing, challenges remain in the quality and length of generated music due to the use of Mel-spectrogram representations and UNet-based model structures. To address these limitations, we propose a novel approach using a Diffusion Transformer (DiT) augmented with an additional control branch using ControlNet. This allows for long-form and variable-length music generation and editing controlled by text and melody prompts. For more precise and fine-grained melody control, we introduce a novel top-$k$ constant-Q Transform representation as the melody prompt, reducing ambiguity compared to previous representations (e.g., chroma), particularly for music with multiple tracks or a wide range of pitch values. To effectively balance the control signals from text and melody prompts, we adopt a curriculum learning strategy that progressively masks the melody prompt, resulting in a more stable training process. Experiments have been performed on text-to-music generation and music-style transfer tasks using open-source instrumental recording data. The results demonstrate that by extending StableAudio, a pre-trained text-controlled DiT model, our approach enables superior melody-controlled editing while retaining good text-to-music generation performance. These results outperform a strong MusicGen baseline in terms of both text-based generation and melody preservation for editing. Audio examples can be found at https://stable-audio-control.github.io/web/. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.05151v1-abstract-full').style.display = 'none'; document.getElementById('2410.05151v1-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 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 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">5 pages, 1 figure</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.03685">arXiv:2408.03685</a> <span> [<a href="https://arxiv.org/pdf/2408.03685">pdf</a>, <a href="https://arxiv.org/ps/2408.03685">ps</a>, <a href="https://arxiv.org/format/2408.03685">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="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> RL-ADN: A High-Performance Deep Reinforcement Learning Environment for Optimal Energy Storage Systems Dispatch in Active Distribution Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Hou%2C+S">Shengren Hou</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+S">Shuyi Gao</a>, <a href="/search/eess?searchtype=author&query=Xia%2C+W">Weijie Xia</a>, <a href="/search/eess?searchtype=author&query=Duque%2C+E+M+S">Edgar Mauricio Salazar Duque</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a>, <a href="/search/eess?searchtype=author&query=Vergara%2C+P+P">Pedro P. Vergara</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="2408.03685v2-abstract-short" style="display: inline;"> Deep Reinforcement Learning (DRL) presents a promising avenue for optimizing Energy Storage Systems (ESSs) dispatch in distribution networks. This paper introduces RL-ADN, an innovative open-source library specifically designed for solving the optimal ESSs dispatch in active distribution networks. RL-ADN offers unparalleled flexibility in modeling distribution networks, and ESSs, accommodating a w… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.03685v2-abstract-full').style.display = 'inline'; document.getElementById('2408.03685v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.03685v2-abstract-full" style="display: none;"> Deep Reinforcement Learning (DRL) presents a promising avenue for optimizing Energy Storage Systems (ESSs) dispatch in distribution networks. This paper introduces RL-ADN, an innovative open-source library specifically designed for solving the optimal ESSs dispatch in active distribution networks. RL-ADN offers unparalleled flexibility in modeling distribution networks, and ESSs, accommodating a wide range of research goals. A standout feature of RL-ADN is its data augmentation module, based on Gaussian Mixture Model and Copula (GMC) functions, which elevates the performance ceiling of DRL agents. Additionally, RL-ADN incorporates the Laurent power flow solver, significantly reducing the computational burden of power flow calculations during training without sacrificing accuracy. The effectiveness of RL-ADN is demonstrated using in different sizes of distribution networks, showing marked performance improvements in the adaptability of DRL algorithms for ESS dispatch tasks. This enhancement is particularly beneficial from the increased diversity of training scenarios. Furthermore, RL-ADN achieves a tenfold increase in computational efficiency during training, making it highly suitable for large-scale network applications. The library sets a new benchmark in DRL-based ESSs dispatch in distribution networks and it is poised to advance DRL applications in distribution network operations significantly. RL-ADN is available at: https://github.com/ShengrenHou/RL-ADN and https://github.com/distributionnetworksTUDelft/RL-ADN. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.03685v2-abstract-full').style.display = 'none'; document.getElementById('2408.03685v2-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 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 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/2310.04992">arXiv:2310.04992</a> <span> [<a href="https://arxiv.org/pdf/2310.04992">pdf</a>, <a href="https://arxiv.org/format/2310.04992">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"> VisionFM: a Multi-Modal Multi-Task Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Qiu%2C+J">Jianing Qiu</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+J">Jian Wu</a>, <a href="/search/eess?searchtype=author&query=Wei%2C+H">Hao Wei</a>, <a href="/search/eess?searchtype=author&query=Shi%2C+P">Peilun Shi</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+M">Minqing Zhang</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+Y">Yunyun Sun</a>, <a href="/search/eess?searchtype=author&query=Li%2C+L">Lin Li</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+H">Hanruo Liu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+H">Hongyi Liu</a>, <a href="/search/eess?searchtype=author&query=Hou%2C+S">Simeng Hou</a>, <a href="/search/eess?searchtype=author&query=Zhao%2C+Y">Yuyang Zhao</a>, <a href="/search/eess?searchtype=author&query=Shi%2C+X">Xuehui Shi</a>, <a href="/search/eess?searchtype=author&query=Xian%2C+J">Junfang Xian</a>, <a href="/search/eess?searchtype=author&query=Qu%2C+X">Xiaoxia Qu</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+S">Sirui Zhu</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lijie Pan</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+X">Xiaoniao Chen</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+X">Xiaojia Zhang</a>, <a href="/search/eess?searchtype=author&query=Jiang%2C+S">Shuai Jiang</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+K">Kebing Wang</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+C">Chenlong Yang</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+M">Mingqiang Chen</a>, <a href="/search/eess?searchtype=author&query=Fan%2C+S">Sujie Fan</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+J">Jianhua Hu</a>, <a href="/search/eess?searchtype=author&query=Lv%2C+A">Aiguo Lv</a> , et al. (17 additional authors not shown) </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="2310.04992v1-abstract-short" style="display: inline;"> We present VisionFM, a foundation model pre-trained with 3.4 million ophthalmic images from 560,457 individuals, covering a broad range of ophthalmic diseases, modalities, imaging devices, and demography. After pre-training, VisionFM provides a foundation to foster multiple ophthalmic artificial intelligence (AI) applications, such as disease screening and diagnosis, disease prognosis, subclassifi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.04992v1-abstract-full').style.display = 'inline'; document.getElementById('2310.04992v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.04992v1-abstract-full" style="display: none;"> We present VisionFM, a foundation model pre-trained with 3.4 million ophthalmic images from 560,457 individuals, covering a broad range of ophthalmic diseases, modalities, imaging devices, and demography. After pre-training, VisionFM provides a foundation to foster multiple ophthalmic artificial intelligence (AI) applications, such as disease screening and diagnosis, disease prognosis, subclassification of disease phenotype, and systemic biomarker and disease prediction, with each application enhanced with expert-level intelligence and accuracy. The generalist intelligence of VisionFM outperformed ophthalmologists with basic and intermediate levels in jointly diagnosing 12 common ophthalmic diseases. Evaluated on a new large-scale ophthalmic disease diagnosis benchmark database, as well as a new large-scale segmentation and detection benchmark database, VisionFM outperformed strong baseline deep neural networks. The ophthalmic image representations learned by VisionFM exhibited noteworthy explainability, and demonstrated strong generalizability to new ophthalmic modalities, disease spectrum, and imaging devices. As a foundation model, VisionFM has a large capacity to learn from diverse ophthalmic imaging data and disparate datasets. To be commensurate with this capacity, in addition to the real data used for pre-training, we also generated and leveraged synthetic ophthalmic imaging data. Experimental results revealed that synthetic data that passed visual Turing tests, can also enhance the representation learning capability of VisionFM, leading to substantial performance gains on downstream ophthalmic AI tasks. Beyond the ophthalmic AI applications developed, validated, and demonstrated in this work, substantial further applications can be achieved in an efficient and cost-effective manner using VisionFM as the foundation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.04992v1-abstract-full').style.display = 'none'; document.getElementById('2310.04992v1-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 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/2307.14304">arXiv:2307.14304</a> <span> [<a href="https://arxiv.org/pdf/2307.14304">pdf</a>, <a href="https://arxiv.org/ps/2307.14304">ps</a>, <a href="https://arxiv.org/format/2307.14304">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> A Constraint Enforcement Deep Reinforcement Learning Framework for Optimal Energy Storage Systems Dispatch </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Hou%2C+S">Shengren Hou</a>, <a href="/search/eess?searchtype=author&query=Duque%2C+E+M+S">Edgar Mauricio Salazar Duque</a>, <a href="/search/eess?searchtype=author&query=Palensky%2C+P">Peter Palensky</a>, <a href="/search/eess?searchtype=author&query=Vergara%2C+P+P">Pedro P. Vergara</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="2307.14304v1-abstract-short" style="display: inline;"> The optimal dispatch of energy storage systems (ESSs) presents formidable challenges due to the uncertainty introduced by fluctuations in dynamic prices, demand consumption, and renewable-based energy generation. By exploiting the generalization capabilities of deep neural networks (DNNs), deep reinforcement learning (DRL) algorithms can learn good-quality control models that adaptively respond to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.14304v1-abstract-full').style.display = 'inline'; document.getElementById('2307.14304v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.14304v1-abstract-full" style="display: none;"> The optimal dispatch of energy storage systems (ESSs) presents formidable challenges due to the uncertainty introduced by fluctuations in dynamic prices, demand consumption, and renewable-based energy generation. By exploiting the generalization capabilities of deep neural networks (DNNs), deep reinforcement learning (DRL) algorithms can learn good-quality control models that adaptively respond to distribution networks' stochastic nature. However, current DRL algorithms lack the capabilities to enforce operational constraints strictly, often even providing unfeasible control actions. To address this issue, we propose a DRL framework that effectively handles continuous action spaces while strictly enforcing the environments and action space operational constraints during online operation. Firstly, the proposed framework trains an action-value function modeled using DNNs. Subsequently, this action-value function is formulated as a mixed-integer programming (MIP) formulation enabling the consideration of the environment's operational constraints. Comprehensive numerical simulations show the superior performance of the proposed MIP-DRL framework, effectively enforcing all constraints while delivering high-quality dispatch decisions when compared with state-of-the-art DRL algorithms and the optimal solution obtained with a perfect forecast of the stochastic variables. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.14304v1-abstract-full').style.display = 'none'; document.getElementById('2307.14304v1-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 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 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">This paper has been submitted to a publication in a journal. This corresponds to the submitted version. After acceptance, it may be removed depending on the journal's requirements for copyright</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.13085">arXiv:2304.13085</a> <span> [<a href="https://arxiv.org/pdf/2304.13085">pdf</a>, <a href="https://arxiv.org/format/2304.13085">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> AI-Synthesized Voice Detection Using Neural Vocoder Artifacts </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Sun%2C+C">Chengzhe Sun</a>, <a href="/search/eess?searchtype=author&query=Jia%2C+S">Shan Jia</a>, <a href="/search/eess?searchtype=author&query=Hou%2C+S">Shuwei Hou</a>, <a href="/search/eess?searchtype=author&query=Lyu%2C+S">Siwei Lyu</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="2304.13085v2-abstract-short" style="display: inline;"> Advancements in AI-synthesized human voices have created a growing threat of impersonation and disinformation, making it crucial to develop methods to detect synthetic human voices. This study proposes a new approach to identifying synthetic human voices by detecting artifacts of vocoders in audio signals. Most DeepFake audio synthesis models use a neural vocoder, a neural network that generates w… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.13085v2-abstract-full').style.display = 'inline'; document.getElementById('2304.13085v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.13085v2-abstract-full" style="display: none;"> Advancements in AI-synthesized human voices have created a growing threat of impersonation and disinformation, making it crucial to develop methods to detect synthetic human voices. This study proposes a new approach to identifying synthetic human voices by detecting artifacts of vocoders in audio signals. Most DeepFake audio synthesis models use a neural vocoder, a neural network that generates waveforms from temporal-frequency representations like mel-spectrograms. By identifying neural vocoder processing in audio, we can determine if a sample is synthesized. To detect synthetic human voices, we introduce a multi-task learning framework for a binary-class RawNet2 model that shares the feature extractor with a vocoder identification module. By treating vocoder identification as a pretext task, we constrain the feature extractor to focus on vocoder artifacts and provide discriminative features for the final binary classifier. Our experiments show that the improved RawNet2 model based on vocoder identification achieves high classification performance on the binary task overall. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.13085v2-abstract-full').style.display = 'none'; document.getElementById('2304.13085v2-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> 27 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 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">Paper accepted in CVPRW 2023. Codes and data can be found at https://github.com/csun22/Synthetic-Voice-Detection-Vocoder-Artifacts. arXiv admin note: substantial text overlap with arXiv:2302.09198</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.11763">arXiv:2303.11763</a> <span> [<a href="https://arxiv.org/pdf/2303.11763">pdf</a>, <a href="https://arxiv.org/format/2303.11763">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"> Reconfigurable Intelligent Surface Aided Hybrid Beamforming: Optimal Placement and Beamforming Design </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Saqib%2C+N+U">Najam Us Saqib</a>, <a href="/search/eess?searchtype=author&query=Hou%2C+S">Shumei Hou</a>, <a href="/search/eess?searchtype=author&query=Chae%2C+S+H">Sung Ho Chae</a>, <a href="/search/eess?searchtype=author&query=Jeon%2C+S">Sang-Woon Jeon</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="2303.11763v1-abstract-short" style="display: inline;"> We consider reconfigurable intelligent surface (RIS) aided sixth-generation (6G) terahertz (THz) communications for indoor environment in which a base station (BS) wishes to send independent messages to its serving users with the help of multiple RISs. For indoor environment, various obstacles such as pillars, walls, and other objects can result in no line-of-sight signal path between the BS and a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.11763v1-abstract-full').style.display = 'inline'; document.getElementById('2303.11763v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.11763v1-abstract-full" style="display: none;"> We consider reconfigurable intelligent surface (RIS) aided sixth-generation (6G) terahertz (THz) communications for indoor environment in which a base station (BS) wishes to send independent messages to its serving users with the help of multiple RISs. For indoor environment, various obstacles such as pillars, walls, and other objects can result in no line-of-sight signal path between the BS and a user, which can significantly degrade performance. To overcome such limitation of indoor THz communication, we firstly optimize the placement of RISs to maximize the coverage area. Under the optimized RIS placement, we propose 3D hybrid beamforming at the BS and phase adjustment at RISs, which are jointly performed at the BS and RISs via codebook-based 3D beam scanning with low complexity. Numerical simulations demonstrate that the proposed scheme significantly improves the average sum rate compared to the cases of no RIS and randomly deployed RISs. It is further shown that the proposed codebook-based 3D beam scanning efficiently aligns analog beams between BS--user links or BS--RIS--user links and, as a consequence, achieves the average sum rate close to that of coherent beam alignment requiring global channel state information. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.11763v1-abstract-full').style.display = 'none'; document.getElementById('2303.11763v1-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 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 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">This manuscript contains 18 pages and 9 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/2302.09198">arXiv:2302.09198</a> <span> [<a href="https://arxiv.org/pdf/2302.09198">pdf</a>, <a href="https://arxiv.org/format/2302.09198">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Exposing AI-Synthesized Human Voices Using Neural Vocoder Artifacts </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Sun%2C+C">Chengzhe Sun</a>, <a href="/search/eess?searchtype=author&query=Jia%2C+S">Shan Jia</a>, <a href="/search/eess?searchtype=author&query=Hou%2C+S">Shuwei Hou</a>, <a href="/search/eess?searchtype=author&query=AlBadawy%2C+E">Ehab AlBadawy</a>, <a href="/search/eess?searchtype=author&query=Lyu%2C+S">Siwei Lyu</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="2302.09198v2-abstract-short" style="display: inline;"> The advancements of AI-synthesized human voices have introduced a growing threat of impersonation and disinformation. It is therefore of practical importance to developdetection methods for synthetic human voices. This work proposes a new approach to detect synthetic human voices based on identifying artifacts of neural vocoders in audio signals. A neural vocoder is a specially designed neural net… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.09198v2-abstract-full').style.display = 'inline'; document.getElementById('2302.09198v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.09198v2-abstract-full" style="display: none;"> The advancements of AI-synthesized human voices have introduced a growing threat of impersonation and disinformation. It is therefore of practical importance to developdetection methods for synthetic human voices. This work proposes a new approach to detect synthetic human voices based on identifying artifacts of neural vocoders in audio signals. A neural vocoder is a specially designed neural network that synthesizes waveforms from temporal-frequency representations, e.g., mel-spectrograms. The neural vocoder is a core component in most DeepFake audio synthesis models. Hence the identification of neural vocoder processing implies that an audio sample may have been synthesized. To take advantage of the vocoder artifacts for synthetic human voice detection, we introduce a multi-task learning framework for a binary-class RawNet2 model that shares the front-end feature extractor with a vocoder identification module. We treat the vocoder identification as a pretext task to constrain the front-end feature extractor to focus on vocoder artifacts and provide discriminative features for the final binary classifier. Our experiments show that the improved RawNet2 model based on vocoder identification achieves an overall high classification performance on the binary task. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.09198v2-abstract-full').style.display = 'none'; document.getElementById('2302.09198v2-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> 27 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 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">Dataset and codes will be available at https://github.com/csun22/LibriVoc-Dataset</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2209.03777">arXiv:2209.03777</a> <span> [<a href="https://arxiv.org/pdf/2209.03777">pdf</a>, <a href="https://arxiv.org/format/2209.03777">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 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/LWC.2022.3204353">10.1109/LWC.2022.3204353 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Joint Optimization of STAR-RIS Assisted UAV Communication Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+Q">Qin Zhang</a>, <a href="/search/eess?searchtype=author&query=Zhao%2C+Y">Yang Zhao</a>, <a href="/search/eess?searchtype=author&query=Li%2C+H">Hai Li</a>, <a href="/search/eess?searchtype=author&query=Hou%2C+S">Shujuan Hou</a>, <a href="/search/eess?searchtype=author&query=Song%2C+Z">Zhengyu Song</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="2209.03777v1-abstract-short" style="display: inline;"> In this letter, we study the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted unmanned aerial vehicle (UAV) communications. Our goal is to maximize the sum rate of all users by jointly optimizing the STAR-RIS's beamforming vectors, the UAV's trajectory and power allocation. We decompose the formulated non-convex problem into three subproblems and so… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.03777v1-abstract-full').style.display = 'inline'; document.getElementById('2209.03777v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2209.03777v1-abstract-full" style="display: none;"> In this letter, we study the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted unmanned aerial vehicle (UAV) communications. Our goal is to maximize the sum rate of all users by jointly optimizing the STAR-RIS's beamforming vectors, the UAV's trajectory and power allocation. We decompose the formulated non-convex problem into three subproblems and solve them alternately to obtain the solution. Simulations show that: 1) the STAR-RIS achieves a higher sum rate than traditional RIS; 2) to exploit the benefits of STAR-RIS, the UAV's trajectory is closer to STAR-RIS than that of RIS; 3) the energy splitting for reflection and transmission highly depends on the real-time trajectory of UAV. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.03777v1-abstract-full').style.display = 'none'; document.getElementById('2209.03777v1-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, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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, 4 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> IEEE Wireless Communications Letters, 2022 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2107.04856">arXiv:2107.04856</a> <span> [<a href="https://arxiv.org/pdf/2107.04856">pdf</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"> Graphene-based Distributed 3D Sensing Electrodes for Mapping Spatiotemporal Auricular Physiological Signals </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Huang%2C+Q">Q. Huang</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+C">C. Wu</a>, <a href="/search/eess?searchtype=author&query=Hou%2C+S">S. Hou</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+H">H. Sun</a>, <a href="/search/eess?searchtype=author&query=Yao%2C+K">K. Yao</a>, <a href="/search/eess?searchtype=author&query=Law%2C+J">J. Law</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+M">M. Yang</a>, <a href="/search/eess?searchtype=author&query=Vellaisamy%2C+A+L+R">A. L. R. Vellaisamy</a>, <a href="/search/eess?searchtype=author&query=Yu%2C+X">X. Yu</a>, <a href="/search/eess?searchtype=author&query=Chan%2C+H+Y">H. Y. Chan</a>, <a href="/search/eess?searchtype=author&query=Lao%2C+L">L. Lao</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+Y">Y. Sun</a>, <a href="/search/eess?searchtype=author&query=Li%2C+W+J">W. J. Li</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="2107.04856v1-abstract-short" style="display: inline;"> Underneath the ear skin there are richly branching vascular and neural networks that ultimately connecting to our heart and brain. Hence, the three-dimensional (3D) mapping of auricular electrophysiological signals could provide a new perspective for biomedical studies such as diagnosis of cardiovascular diseases and neurological disorders. However, it is still extremely challenging for current se… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.04856v1-abstract-full').style.display = 'inline'; document.getElementById('2107.04856v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2107.04856v1-abstract-full" style="display: none;"> Underneath the ear skin there are richly branching vascular and neural networks that ultimately connecting to our heart and brain. Hence, the three-dimensional (3D) mapping of auricular electrophysiological signals could provide a new perspective for biomedical studies such as diagnosis of cardiovascular diseases and neurological disorders. However, it is still extremely challenging for current sensing techniques to cover the entire ultra-curved auricle. Here, we report a graphene-based ear-conformable sensing device with embedded and distributed 3D electrodes which enable full-auricle physiological monitoring. The sensing device, which incorporates programable 3D electrode thread array and personalized auricular mold, has 3D-conformable sensing interfaces with curved auricular skin, and was developed using one-step multi-material 3D-printing process. As a proof-of-concept, spatiotemporal auricular electrical skin resistance (AESR) mapping was demonstrated. For the first time, 3D AESR contours were generated and human subject-specific AESR distributions among a population were observed. From the data of 17 volunteers, the auricular region-specific AESR changes after cycling exercise were observed in 98% of the tests and were validated via machine learning techniques. Correlations of AESR with heart rate and blood pressure were also studied using statistical analysis. This 3D electronic platform and AESR-based new biometrical findings show promising biomedical applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.04856v1-abstract-full').style.display = 'none'; document.getElementById('2107.04856v1-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 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 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">23 pages, 6 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/2012.14142">arXiv:2012.14142</a> <span> [<a href="https://arxiv.org/pdf/2012.14142">pdf</a>, <a href="https://arxiv.org/format/2012.14142">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"> Perception Consistency Ultrasound Image Super-resolution via Self-supervised CycleGAN </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liu%2C+H">Heng Liu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+J">Jianyong Liu</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+T">Tao Tao</a>, <a href="/search/eess?searchtype=author&query=Hou%2C+S">Shudong Hou</a>, <a href="/search/eess?searchtype=author&query=Han%2C+J">Jungong Han</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="2012.14142v1-abstract-short" style="display: inline;"> Due to the limitations of sensors, the transmission medium and the intrinsic properties of ultrasound, the quality of ultrasound imaging is always not ideal, especially its low spatial resolution. To remedy this situation, deep learning networks have been recently developed for ultrasound image super-resolution (SR) because of the powerful approximation capability. However, most current supervised… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.14142v1-abstract-full').style.display = 'inline'; document.getElementById('2012.14142v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2012.14142v1-abstract-full" style="display: none;"> Due to the limitations of sensors, the transmission medium and the intrinsic properties of ultrasound, the quality of ultrasound imaging is always not ideal, especially its low spatial resolution. To remedy this situation, deep learning networks have been recently developed for ultrasound image super-resolution (SR) because of the powerful approximation capability. However, most current supervised SR methods are not suitable for ultrasound medical images because the medical image samples are always rare, and usually, there are no low-resolution (LR) and high-resolution (HR) training pairs in reality. In this work, based on self-supervision and cycle generative adversarial network (CycleGAN), we propose a new perception consistency ultrasound image super-resolution (SR) method, which only requires the LR ultrasound data and can ensure the re-degenerated image of the generated SR one to be consistent with the original LR image, and vice versa. We first generate the HR fathers and the LR sons of the test ultrasound LR image through image enhancement, and then make full use of the cycle loss of LR-SR-LR and HR-LR-SR and the adversarial characteristics of the discriminator to promote the generator to produce better perceptually consistent SR results. The evaluation of PSNR/IFC/SSIM, inference efficiency and visual effects under the benchmark CCA-US and CCA-US datasets illustrate our proposed approach is effective and superior to other state-of-the-art methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.14142v1-abstract-full').style.display = 'none'; document.getElementById('2012.14142v1-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, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2020. </p> </li> </ol> <div class="is-hidden-tablet"> <!-- feedback for mobile only --> <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" 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