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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="Uhl, C"> <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" 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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/2410.01956">arXiv:2410.01956</a> <span> [<a href="https://arxiv.org/pdf/2410.01956">pdf</a>, <a href="https://arxiv.org/format/2410.01956">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Learning-Based Autonomous Navigation, Benchmark Environments and Simulation Framework for Endovascular Interventions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Karstensen%2C+L">Lennart Karstensen</a>, <a href="/search/cs?searchtype=author&query=Robertshaw%2C+H">Harry Robertshaw</a>, <a href="/search/cs?searchtype=author&query=Hatzl%2C+J">Johannes Hatzl</a>, <a href="/search/cs?searchtype=author&query=Jackson%2C+B">Benjamin Jackson</a>, <a href="/search/cs?searchtype=author&query=Langej%C3%BCrgen%2C+J">Jens Langej眉rgen</a>, <a href="/search/cs?searchtype=author&query=Breininger%2C+K">Katharina Breininger</a>, <a href="/search/cs?searchtype=author&query=Uhl%2C+C">Christian Uhl</a>, <a href="/search/cs?searchtype=author&query=Sadati%2C+S+M+H">S. M. Hadi Sadati</a>, <a href="/search/cs?searchtype=author&query=Booth%2C+T">Thomas Booth</a>, <a href="/search/cs?searchtype=author&query=Bergeles%2C+C">Christos Bergeles</a>, <a href="/search/cs?searchtype=author&query=Mathis-Ullrich%2C+F">Franziska Mathis-Ullrich</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="2410.01956v1-abstract-short" style="display: inline;"> Endovascular interventions are a life-saving treatment for many diseases, yet suffer from drawbacks such as radiation exposure and potential scarcity of proficient physicians. Robotic assistance during these interventions could be a promising support towards these problems. Research focusing on autonomous endovascular interventions utilizing artificial intelligence-based methodologies is gaining p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.01956v1-abstract-full').style.display = 'inline'; document.getElementById('2410.01956v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.01956v1-abstract-full" style="display: none;"> Endovascular interventions are a life-saving treatment for many diseases, yet suffer from drawbacks such as radiation exposure and potential scarcity of proficient physicians. Robotic assistance during these interventions could be a promising support towards these problems. Research focusing on autonomous endovascular interventions utilizing artificial intelligence-based methodologies is gaining popularity. However, variability in assessment environments hinders the ability to compare and contrast the efficacy of different approaches, primarily due to each study employing a unique evaluation framework. In this study, we present deep reinforcement learning-based autonomous endovascular device navigation on three distinct digital benchmark interventions: BasicWireNav, ArchVariety, and DualDeviceNav. The benchmark interventions were implemented with our modular simulation framework stEVE (simulated EndoVascular Environment). Autonomous controllers were trained solely in simulation and evaluated in simulation and on physical test benches with camera and fluoroscopy feedback. Autonomous control for BasicWireNav and ArchVariety reached high success rates and was successfully transferred from the simulated training environment to the physical test benches, while autonomous control for DualDeviceNav reached a moderate success rate. The experiments demonstrate the feasibility of stEVE and its potential for transferring controllers trained in simulation to real-world scenarios. Nevertheless, they also reveal areas that offer opportunities for future research. This study demonstrates the transferability of autonomous controllers from simulation to the real world in endovascular navigation and lowers the entry barriers and increases the comparability of research on endovascular assistance systems by providing open-source training scripts, benchmarks and the stEVE framework. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.01956v1-abstract-full').style.display = 'none'; document.getElementById('2410.01956v1-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> 2 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2008.12132">arXiv:2008.12132</a> <span> [<a href="https://arxiv.org/pdf/2008.12132">pdf</a>, <a href="https://arxiv.org/format/2008.12132">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="General Economics">econ.GN</span> </div> </div> <p class="title is-5 mathjax"> How Much Ad Viewability is Enough? The Effect of Display Ad Viewability on Advertising Effectiveness </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Uhl%2C+C">Christina Uhl</a>, <a href="/search/cs?searchtype=author&query=Nabout%2C+N+A">Nadia Abou Nabout</a>, <a href="/search/cs?searchtype=author&query=Miller%2C+K">Klaus Miller</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="2008.12132v1-abstract-short" style="display: inline;"> A large share of all online display advertisements (ads) are never seen by a human. For instance, an ad could appear below the page fold, where a user never scrolls. Yet, an ad is essentially ineffective if it is not at least somewhat viewable. Ad viewability - which refers to the pixel percentage-in-view and the exposure duration of an online display ad - has recently garnered great interest amon… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.12132v1-abstract-full').style.display = 'inline'; document.getElementById('2008.12132v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2008.12132v1-abstract-full" style="display: none;"> A large share of all online display advertisements (ads) are never seen by a human. For instance, an ad could appear below the page fold, where a user never scrolls. Yet, an ad is essentially ineffective if it is not at least somewhat viewable. Ad viewability - which refers to the pixel percentage-in-view and the exposure duration of an online display ad - has recently garnered great interest among digital advertisers and publishers. However, we know very little about the impact of ad viewability on advertising effectiveness. We work to close this gap by analyzing a large-scale observational data set with more than 350,000 ad impressions similar to the data sets that are typically available to digital advertisers and publishers. This analysis reveals that longer exposure durations (>10 seconds) and 100% visible pixels do not appear to be optimal in generating view-throughs. The highest view-through rates seem to be generated with relatively lower pixel/second-combinations of 50%/1, 50%/5, 75%/1, and 75%/5. However, this analysis does not account for user behavior that may be correlated with or even drive ad viewability and may therefore result in endogeneity issues. Consequently, we manipulated ad viewability in a randomized online experiment for a major European news website, finding the highest ad recognition rates among relatively higher pixel/second-combinations of 75%/10, 100%/5 and 100%/10. Everything below 75\% or 5 seconds performs worse. Yet, we find that it may be sufficient to have either a long exposure duration or high pixel percentage-in-view to reach high advertising effectiveness. Our results provide guidance to advertisers enabling them to establish target viewability rates more appropriately and to publishers who wish to differentiate their viewability products. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.12132v1-abstract-full').style.display = 'none'; document.getElementById('2008.12132v1-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 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2006.05914">arXiv:2006.05914</a> <span> [<a href="https://arxiv.org/pdf/2006.05914">pdf</a>, <a href="https://arxiv.org/format/2006.05914">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> Mind the GAP: Security & Privacy Risks of Contact Tracing Apps </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Baumg%C3%A4rtner%2C+L">Lars Baumg盲rtner</a>, <a href="/search/cs?searchtype=author&query=Dmitrienko%2C+A">Alexandra Dmitrienko</a>, <a href="/search/cs?searchtype=author&query=Freisleben%2C+B">Bernd Freisleben</a>, <a href="/search/cs?searchtype=author&query=Gruler%2C+A">Alexander Gruler</a>, <a href="/search/cs?searchtype=author&query=H%C3%B6chst%2C+J">Jonas H枚chst</a>, <a href="/search/cs?searchtype=author&query=K%C3%BChlberg%2C+J">Joshua K眉hlberg</a>, <a href="/search/cs?searchtype=author&query=Mezini%2C+M">Mira Mezini</a>, <a href="/search/cs?searchtype=author&query=Mitev%2C+R">Richard Mitev</a>, <a href="/search/cs?searchtype=author&query=Miettinen%2C+M">Markus Miettinen</a>, <a href="/search/cs?searchtype=author&query=Muhamedagic%2C+A">Anel Muhamedagic</a>, <a href="/search/cs?searchtype=author&query=Nguyen%2C+T+D">Thien Duc Nguyen</a>, <a href="/search/cs?searchtype=author&query=Penning%2C+A">Alvar Penning</a>, <a href="/search/cs?searchtype=author&query=Pustelnik%2C+D+F">Dermot Frederik Pustelnik</a>, <a href="/search/cs?searchtype=author&query=Roos%2C+F">Filipp Roos</a>, <a href="/search/cs?searchtype=author&query=Sadeghi%2C+A">Ahmad-Reza Sadeghi</a>, <a href="/search/cs?searchtype=author&query=Schwarz%2C+M">Michael Schwarz</a>, <a href="/search/cs?searchtype=author&query=Uhl%2C+C">Christian Uhl</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="2006.05914v2-abstract-short" style="display: inline;"> Google and Apple have jointly provided an API for exposure notification in order to implement decentralized contract tracing apps using Bluetooth Low Energy, the so-called "Google/Apple Proposal", which we abbreviate by "GAP". We demonstrate that in real-world scenarios the current GAP design is vulnerable to (i) profiling and possibly de-anonymizing infected persons, and (ii) relay-based wormhole… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.05914v2-abstract-full').style.display = 'inline'; document.getElementById('2006.05914v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2006.05914v2-abstract-full" style="display: none;"> Google and Apple have jointly provided an API for exposure notification in order to implement decentralized contract tracing apps using Bluetooth Low Energy, the so-called "Google/Apple Proposal", which we abbreviate by "GAP". We demonstrate that in real-world scenarios the current GAP design is vulnerable to (i) profiling and possibly de-anonymizing infected persons, and (ii) relay-based wormhole attacks that basically can generate fake contacts with the potential of affecting the accuracy of an app-based contact tracing system. For both types of attack, we have built tools that can easily be used on mobile phones or Raspberry Pis (e.g., Bluetooth sniffers). The goal of our work is to perform a reality check towards possibly providing empirical real-world evidence for these two privacy and security risks. We hope that our findings provide valuable input for developing secure and privacy-preserving digital contact tracing systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.05914v2-abstract-full').style.display = 'none'; document.getElementById('2006.05914v2-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 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 June, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1902.01777">arXiv:1902.01777</a> <span> [<a href="https://arxiv.org/pdf/1902.01777">pdf</a>, <a href="https://arxiv.org/format/1902.01777">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chaotic Dynamics">nlin.CD</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/ICASSP.2019.8682601">10.1109/ICASSP.2019.8682601 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Dynamical Component Analysis (DyCA) and its application on epileptic EEG </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Korn%2C+K">Katharina Korn</a>, <a href="/search/cs?searchtype=author&query=Seifert%2C+B">Bastian Seifert</a>, <a href="/search/cs?searchtype=author&query=Uhl%2C+C">Christian Uhl</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="1902.01777v1-abstract-short" style="display: inline;"> Dynamical Component Analysis (DyCA) is a recently-proposed method to detect projection vectors to reduce the dimensionality of multi-variate deterministic datasets. It is based on the solution of a generalized eigenvalue problem and therefore straight forward to implement. DyCA is introduced and applied to EEG data of epileptic seizures. The obtained eigenvectors are used to project the signal and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1902.01777v1-abstract-full').style.display = 'inline'; document.getElementById('1902.01777v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1902.01777v1-abstract-full" style="display: none;"> Dynamical Component Analysis (DyCA) is a recently-proposed method to detect projection vectors to reduce the dimensionality of multi-variate deterministic datasets. It is based on the solution of a generalized eigenvalue problem and therefore straight forward to implement. DyCA is introduced and applied to EEG data of epileptic seizures. The obtained eigenvectors are used to project the signal and the corresponding trajectories in phase space are compared with PCA and ICA-projections. The eigenvalues of DyCA are utilized for seizure detection and the obtained results in terms of specificity, false discovery rate and miss rate are compared to other seizure detection algorithms. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1902.01777v1-abstract-full').style.display = 'none'; document.getElementById('1902.01777v1-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, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2019. </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, accepted for IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2019</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1807.10629">arXiv:1807.10629</a> <span> [<a href="https://arxiv.org/pdf/1807.10629">pdf</a>, <a href="https://arxiv.org/format/1807.10629">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chaotic Dynamics">nlin.CD</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/MLSP.2018.8517024">10.1109/MLSP.2018.8517024 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Dynamical Component Analysis (DyCA): Dimensionality Reduction For High-Dimensional Deterministic Time-Series </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Seifert%2C+B">Bastian Seifert</a>, <a href="/search/cs?searchtype=author&query=Korn%2C+K">Katharina Korn</a>, <a href="/search/cs?searchtype=author&query=Hartmann%2C+S">Steffen Hartmann</a>, <a href="/search/cs?searchtype=author&query=Uhl%2C+C">Christian Uhl</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="1807.10629v2-abstract-short" style="display: inline;"> Multivariate signal processing is often based on dimensionality reduction techniques. We propose a new method, Dynamical Component Analysis (DyCA), leading to a classification of the underlying dynamics and - for a certain type of dynamics - to a signal subspace representing the dynamics of the data. In this paper the algorithm is derived leading to a generalized eigenvalue problem of correlation… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1807.10629v2-abstract-full').style.display = 'inline'; document.getElementById('1807.10629v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1807.10629v2-abstract-full" style="display: none;"> Multivariate signal processing is often based on dimensionality reduction techniques. We propose a new method, Dynamical Component Analysis (DyCA), leading to a classification of the underlying dynamics and - for a certain type of dynamics - to a signal subspace representing the dynamics of the data. In this paper the algorithm is derived leading to a generalized eigenvalue problem of correlation matrices. The application of the DyCA on high-dimensional chaotic signals is presented both for simulated data as well as real EEG data of epileptic seizures. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1807.10629v2-abstract-full').style.display = 'none'; document.getElementById('1807.10629v2-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 March, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 July, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2018. </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">Published in Proc. 2018 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING; 7 figures; Corrected formula (16)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1807.10058">arXiv:1807.10058</a> <span> [<a href="https://arxiv.org/pdf/1807.10058">pdf</a>, <a href="https://arxiv.org/format/1807.10058">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Representation Theory">math.RT</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/CONTROLO.2018.8514300">10.1109/CONTROLO.2018.8514300 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Fast cosine transform for FCC lattices </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Seifert%2C+B">Bastian Seifert</a>, <a href="/search/cs?searchtype=author&query=H%C3%BCper%2C+K">Knut H眉per</a>, <a href="/search/cs?searchtype=author&query=Uhl%2C+C">Christian Uhl</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="1807.10058v1-abstract-short" style="display: inline;"> Voxel representation and processing is an important issue in a broad spectrum of applications. E.g., 3D imaging in biomedical engineering applications, video game development and volumetric displays are often based on data representation by voxels. By replacing the standard sampling lattice with a face-centered lattice one can obtain the same sampling density with less sampling points and reduce a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1807.10058v1-abstract-full').style.display = 'inline'; document.getElementById('1807.10058v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1807.10058v1-abstract-full" style="display: none;"> Voxel representation and processing is an important issue in a broad spectrum of applications. E.g., 3D imaging in biomedical engineering applications, video game development and volumetric displays are often based on data representation by voxels. By replacing the standard sampling lattice with a face-centered lattice one can obtain the same sampling density with less sampling points and reduce aliasing error, as well. We introduce an analog of the discrete cosine transform for the facecentered lattice relying on multivariate Chebyshev polynomials. A fast algorithm for this transform is deduced based on algebraic signal processing theory and the rich geometry of the special unitary Lie group of degree four. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1807.10058v1-abstract-full').style.display = 'none'; document.getElementById('1807.10058v1-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, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2018. </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">Presented at 13th APCA International Conference on Automatic Control and Soft Computing (CONTROLO 2018); 9 figures</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a 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