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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/2501.03416">arXiv:2501.03416</a> <span> [<a href="https://arxiv.org/pdf/2501.03416">pdf</a>, <a href="https://arxiv.org/format/2501.03416">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> <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"> TinySense: A Lighter Weight and More Power-efficient Avionics System for Flying Insect-scale Robots </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yu%2C+Z">Zhitao Yu</a>, <a href="/search/eess?searchtype=author&query=Tran%2C+J">Joshua Tran</a>, <a href="/search/eess?searchtype=author&query=Li%2C+C">Claire Li</a>, <a href="/search/eess?searchtype=author&query=Weber%2C+A">Aaron Weber</a>, <a href="/search/eess?searchtype=author&query=Talwekar%2C+Y+P">Yash P. Talwekar</a>, <a href="/search/eess?searchtype=author&query=Fuller%2C+S">Sawyer Fuller</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="2501.03416v1-abstract-short" style="display: inline;"> In this paper, we investigate the prospects and challenges of sensor suites in achieving autonomous control for flying insect robots (FIRs) weighing less than a gram. FIRs, owing to their minuscule weight and size, offer unparalleled advantages in terms of material cost and scalability. However, their size introduces considerable control challenges, notably high-speed dynamics, restricted power, a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.03416v1-abstract-full').style.display = 'inline'; document.getElementById('2501.03416v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.03416v1-abstract-full" style="display: none;"> In this paper, we investigate the prospects and challenges of sensor suites in achieving autonomous control for flying insect robots (FIRs) weighing less than a gram. FIRs, owing to their minuscule weight and size, offer unparalleled advantages in terms of material cost and scalability. However, their size introduces considerable control challenges, notably high-speed dynamics, restricted power, and limited payload capacity. While there have been notable advancements in developing lightweight sensors, often drawing inspiration from biological systems, no sub-gram aircraft has been able to attain sustained hover without relying on feedback from external sensing such as a motion capture system. The lightest vehicle capable of sustained hover -- the first level of "sensor autonomy" -- is the much larger 28 g Crazyflie. Previous work reported a reduction in size of that vehicle's avionics suite to 187 mg and 21 mW. Here, we report a further reduction in mass and power to only 78.4 mg and 15 mW. We replaced the laser rangefinder with a lighter and more efficient pressure sensor, and built a smaller optic flow sensor around a global-shutter imaging chip. A Kalman Filter (KF) fuses these measurements to estimate the state variables that are needed to control hover: pitch angle, translational velocity, and altitude. Our system achieved performance comparable to that of the Crazyflie's estimator while in flight, with root mean squared errors of 1.573 degrees, 0.186 m/s, and 0.139 m, respectively, relative to motion capture. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.03416v1-abstract-full').style.display = 'none'; document.getElementById('2501.03416v1-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 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </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">Submitted to a robotics conference</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.00217">arXiv:2407.00217</a> <span> [<a href="https://arxiv.org/pdf/2407.00217">pdf</a>, <a href="https://arxiv.org/ps/2407.00217">ps</a>, <a href="https://arxiv.org/format/2407.00217">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> <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"> A flexured-gimbal 3-axis force-torque sensor reveals minimal cross-axis coupling in an insect-sized flapping-wing robot </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Weber%2C+A">Aaron Weber</a>, <a href="/search/eess?searchtype=author&query=Dhingra%2C+D">Daksh Dhingra</a>, <a href="/search/eess?searchtype=author&query=Fuller%2C+S+B">Sawyer B. Fuller</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="2407.00217v1-abstract-short" style="display: inline;"> The mechanical complexity of flapping wings, their unsteady aerodynamic flow, and challenge of making measurements at the scale of a sub-gram flapping-wing flying insect robot (FIR) make its behavior hard to predict. Knowing the precise mapping from voltage input to torque output, however, can be used to improve their mechanical and flight controller design. To address this challenge, we created a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.00217v1-abstract-full').style.display = 'inline'; document.getElementById('2407.00217v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.00217v1-abstract-full" style="display: none;"> The mechanical complexity of flapping wings, their unsteady aerodynamic flow, and challenge of making measurements at the scale of a sub-gram flapping-wing flying insect robot (FIR) make its behavior hard to predict. Knowing the precise mapping from voltage input to torque output, however, can be used to improve their mechanical and flight controller design. To address this challenge, we created a sensitive force-torque sensor based on a flexured gimbal that only requires a standard motion capture system or accelerometer for readout. Our device precisely and accurately measures pitch and roll torques simultaneously, as well as thrust, on a tethered flapping-wing FIR in response to changing voltage input signals. With it, we were able to measure cross-axis coupling of both torque and thrust input commands on a 180 mg FIR, the UW Robofly. We validated these measurements using free-flight experiments. Our results showed that roll and pitch have maximum cross-axis coupling errors of 8.58% and 17.24%, respectively, relative to the range of torque that is possible. Similarly, varying the pitch and roll commands resulted in up to a 5.78% deviation from the commanded thrust, across the entire commanded torque range. Our system, the first to measure two torque axes simultaneously, shows that torque commands have a negligible cross-axis coupling on both torque and thrust. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.00217v1-abstract-full').style.display = 'none'; document.getElementById('2407.00217v1-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 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 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">This work has been submitted to the IEEE for possible publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.09327">arXiv:2406.09327</a> <span> [<a href="https://arxiv.org/pdf/2406.09327">pdf</a>, <a href="https://arxiv.org/format/2406.09327">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> </div> </div> <p class="title is-5 mathjax"> Towards AI Lesion Tracking in PET/CT Imaging: A Siamese-based CNN Pipeline applied on PSMA PET/CT Scans </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Hein%2C+S+P">Stefan P. Hein</a>, <a href="/search/eess?searchtype=author&query=Schultheiss%2C+M">Manuel Schultheiss</a>, <a href="/search/eess?searchtype=author&query=Gafita%2C+A">Andrei Gafita</a>, <a href="/search/eess?searchtype=author&query=Zaum%2C+R">Raphael Zaum</a>, <a href="/search/eess?searchtype=author&query=Yagubbayli%2C+F">Farid Yagubbayli</a>, <a href="/search/eess?searchtype=author&query=Tauber%2C+R">Robert Tauber</a>, <a href="/search/eess?searchtype=author&query=Rauscher%2C+I">Isabel Rauscher</a>, <a href="/search/eess?searchtype=author&query=Eiber%2C+M">Matthias Eiber</a>, <a href="/search/eess?searchtype=author&query=Pfeiffer%2C+F">Franz Pfeiffer</a>, <a href="/search/eess?searchtype=author&query=Weber%2C+W+A">Wolfgang A. Weber</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="2406.09327v3-abstract-short" style="display: inline;"> Assessing tumor response to systemic therapies is one of the main applications of PET/CT. Routinely, only a small subset of index lesions out of multiple lesions is analyzed. However, this operator dependent selection may bias the results due to possible significant inter-metastatic heterogeneity of response to therapy. Automated, AI based approaches for lesion tracking hold promise in enabling th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.09327v3-abstract-full').style.display = 'inline'; document.getElementById('2406.09327v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.09327v3-abstract-full" style="display: none;"> Assessing tumor response to systemic therapies is one of the main applications of PET/CT. Routinely, only a small subset of index lesions out of multiple lesions is analyzed. However, this operator dependent selection may bias the results due to possible significant inter-metastatic heterogeneity of response to therapy. Automated, AI based approaches for lesion tracking hold promise in enabling the analysis of many more lesions and thus providing a better assessment of tumor response. This work introduces a Siamese CNN approach for lesion tracking between PET/CT scans. Our approach is applied on the laborious task of tracking a high number of bone lesions in full-body baseline and follow-up [68Ga]Ga- or [18F]F-PSMA PET/CT scans after two cycles of [177Lu]Lu-PSMA therapy of metastatic castration resistant prostate cancer patients. Data preparation includes lesion segmentation and affine registration. Our algorithm extracts suitable lesion patches and forwards them into a Siamese CNN trained to classify the lesion patch pairs as corresponding or non-corresponding lesions. Experiments have been performed with different input patch types and a Siamese network in 2D and 3D. The CNN model successfully learned to classify lesion assignments, reaching a lesion tracking accuracy of 83 % in its best configuration with an AUC = 0.91. For remaining lesions the pipeline accomplished a re-identification rate of 89 %. We proved that a CNN may facilitate the tracking of multiple lesions in PSMA PET/CT scans. Future clinical studies are necessary if this improves the prediction of the outcome of therapies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.09327v3-abstract-full').style.display = 'none'; document.getElementById('2406.09327v3-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 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 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">25 pages, 9 figures, 3 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.07137">arXiv:2403.07137</a> <span> [<a href="https://arxiv.org/pdf/2403.07137">pdf</a>, <a href="https://arxiv.org/format/2403.07137">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> <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"> Exploring Cluster Analysis in Nelore Cattle Visual Score Attribution </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Bezerra%2C+A+d+O">Alexandre de Oliveira Bezerra</a>, <a href="/search/eess?searchtype=author&query=Mateus%2C+R+G">Rodrigo Goncalves Mateus</a>, <a href="/search/eess?searchtype=author&query=Weber%2C+V+A+d+M">Vanessa Ap. de Moraes Weber</a>, <a href="/search/eess?searchtype=author&query=Weber%2C+F+d+L">Fabricio de Lima Weber</a>, <a href="/search/eess?searchtype=author&query=de+Arruda%2C+Y+A">Yasmin Alves de Arruda</a>, <a href="/search/eess?searchtype=author&query=Gomes%2C+R+d+C">Rodrigo da Costa Gomes</a>, <a href="/search/eess?searchtype=author&query=Higa%2C+G+T+H">Gabriel Toshio Hirokawa Higa</a>, <a href="/search/eess?searchtype=author&query=Pistori%2C+H">Hemerson Pistori</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="2403.07137v1-abstract-short" style="display: inline;"> Assessing the biotype of cattle through human visual inspection is a very common and important practice in precision cattle breeding. This paper presents the results of a correlation analysis between scores produced by humans for Nelore cattle and a variety of measurements that can be derived from images or other instruments. It also presents a study using the k-means algorithm to generate new way… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.07137v1-abstract-full').style.display = 'inline'; document.getElementById('2403.07137v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.07137v1-abstract-full" style="display: none;"> Assessing the biotype of cattle through human visual inspection is a very common and important practice in precision cattle breeding. This paper presents the results of a correlation analysis between scores produced by humans for Nelore cattle and a variety of measurements that can be derived from images or other instruments. It also presents a study using the k-means algorithm to generate new ways of clustering a batch of cattle using the measurements that most correlate with the animal's body weight and visual scores. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.07137v1-abstract-full').style.display = 'none'; document.getElementById('2403.07137v1-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 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/2310.15402">arXiv:2310.15402</a> <span> [<a href="https://arxiv.org/pdf/2310.15402">pdf</a>, <a href="https://arxiv.org/format/2310.15402">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"> Towards contrast-agnostic soft segmentation of the spinal cord </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=B%C3%A9dard%2C+S">Sandrine B茅dard</a>, <a href="/search/eess?searchtype=author&query=Karthik%2C+E+N">Enamundram Naga Karthik</a>, <a href="/search/eess?searchtype=author&query=Tsagkas%2C+C">Charidimos Tsagkas</a>, <a href="/search/eess?searchtype=author&query=Pravat%C3%A0%2C+E">Emanuele Pravat脿</a>, <a href="/search/eess?searchtype=author&query=Granziera%2C+C">Cristina Granziera</a>, <a href="/search/eess?searchtype=author&query=Smith%2C+A">Andrew Smith</a>, <a href="/search/eess?searchtype=author&query=Weber%2C+K+A">Kenneth Arnold Weber II</a>, <a href="/search/eess?searchtype=author&query=Cohen-Adad%2C+J">Julien Cohen-Adad</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="2310.15402v2-abstract-short" style="display: inline;"> Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and monitoring of cord compression or neurodegenerative diseases such as multiple sclerosis. While several semi and automatic methods exist, one key limitation remains: the segmentation depends on the MRI contrast, resulting in different CSA across contrasts. This… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.15402v2-abstract-full').style.display = 'inline'; document.getElementById('2310.15402v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.15402v2-abstract-full" style="display: none;"> Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and monitoring of cord compression or neurodegenerative diseases such as multiple sclerosis. While several semi and automatic methods exist, one key limitation remains: the segmentation depends on the MRI contrast, resulting in different CSA across contrasts. This is partly due to the varying appearance of the boundary between the spinal cord and the cerebrospinal fluid that depends on the sequence and acquisition parameters. This contrast-sensitive CSA adds variability in multi-center studies where protocols can vary, reducing the sensitivity to detect subtle atrophies. Moreover, existing methods enhance the CSA variability by training one model per contrast, while also producing binary masks that do not account for partial volume effects. In this work, we present a deep learning-based method that produces soft segmentations of the spinal cord. Using the Spine Generic Public Database of healthy participants ($\text{n}=267$; $\text{contrasts}=6$), we first generated participant-wise soft ground truth (GT) by averaging the binary segmentations across all 6 contrasts. These soft GT, along with aggressive data augmentation and a regression-based loss function, were used to train a U-Net model for spinal cord segmentation. We evaluated our model against state-of-the-art methods and performed ablation studies involving different loss functions and domain generalization methods. Our results show that using the soft segmentations along with a regression loss function reduces CSA variability ($p < 0.05$, Wilcoxon signed-rank test). The proposed spinal cord segmentation model generalizes better than the state-of-the-art methods amongst unseen datasets, vendors, contrasts, and pathologies (compression, lesions), while accounting for partial volume effects. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.15402v2-abstract-full').style.display = 'none'; document.getElementById('2310.15402v2-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 July, 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> <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">Revision Submitted to Medical Image Analysis</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.09011">arXiv:2305.09011</a> <span> [<a href="https://arxiv.org/pdf/2305.09011">pdf</a>, <a href="https://arxiv.org/format/2305.09011">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"> The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn) </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Li%2C+H+B">Hongwei Bran Li</a>, <a href="/search/eess?searchtype=author&query=Conte%2C+G+M">Gian Marco Conte</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+Q">Qingqiao Hu</a>, <a href="/search/eess?searchtype=author&query=Anwar%2C+S+M">Syed Muhammad Anwar</a>, <a href="/search/eess?searchtype=author&query=Kofler%2C+F">Florian Kofler</a>, <a href="/search/eess?searchtype=author&query=Ezhov%2C+I">Ivan Ezhov</a>, <a href="/search/eess?searchtype=author&query=van+Leemput%2C+K">Koen van Leemput</a>, <a href="/search/eess?searchtype=author&query=Piraud%2C+M">Marie Piraud</a>, <a href="/search/eess?searchtype=author&query=Diaz%2C+M">Maria Diaz</a>, <a href="/search/eess?searchtype=author&query=Cole%2C+B">Byrone Cole</a>, <a href="/search/eess?searchtype=author&query=Calabrese%2C+E">Evan Calabrese</a>, <a href="/search/eess?searchtype=author&query=Rudie%2C+J">Jeff Rudie</a>, <a href="/search/eess?searchtype=author&query=Meissen%2C+F">Felix Meissen</a>, <a href="/search/eess?searchtype=author&query=Adewole%2C+M">Maruf Adewole</a>, <a href="/search/eess?searchtype=author&query=Janas%2C+A">Anastasia Janas</a>, <a href="/search/eess?searchtype=author&query=Kazerooni%2C+A+F">Anahita Fathi Kazerooni</a>, <a href="/search/eess?searchtype=author&query=LaBella%2C+D">Dominic LaBella</a>, <a href="/search/eess?searchtype=author&query=Moawad%2C+A+W">Ahmed W. Moawad</a>, <a href="/search/eess?searchtype=author&query=Farahani%2C+K">Keyvan Farahani</a>, <a href="/search/eess?searchtype=author&query=Eddy%2C+J">James Eddy</a>, <a href="/search/eess?searchtype=author&query=Bergquist%2C+T">Timothy Bergquist</a>, <a href="/search/eess?searchtype=author&query=Chung%2C+V">Verena Chung</a>, <a href="/search/eess?searchtype=author&query=Shinohara%2C+R+T">Russell Takeshi Shinohara</a>, <a href="/search/eess?searchtype=author&query=Dako%2C+F">Farouk Dako</a>, <a href="/search/eess?searchtype=author&query=Wiggins%2C+W">Walter Wiggins</a> , et al. (44 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="2305.09011v6-abstract-short" style="display: inline;"> Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time const… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.09011v6-abstract-full').style.display = 'inline'; document.getElementById('2305.09011v6-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.09011v6-abstract-full" style="display: none;"> Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.09011v6-abstract-full').style.display = 'none'; document.getElementById('2305.09011v6-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> 24 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 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">Technical report of BraSyn</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.14085">arXiv:2205.14085</a> <span> [<a href="https://arxiv.org/pdf/2205.14085">pdf</a>, <a href="https://arxiv.org/format/2205.14085">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</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"> Vehicle mission guidance by symbolic optimal control </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Weber%2C+A">Alexander Weber</a>, <a href="/search/eess?searchtype=author&query=Fiege%2C+F">Florian Fiege</a>, <a href="/search/eess?searchtype=author&query=Knoll%2C+A">Alexander Knoll</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="2205.14085v1-abstract-short" style="display: inline;"> Symbolic optimal control is a powerful method to synthesize algorithmically correct-by-design state-feedback controllers for nonlinear plants. Its solutions are (near-)optimal with respect to a given cost function. In this note, it is demonstrated how symbolic optimal control can be used to calculate controllers for an optimized routing guidance of vehicle systems in continuous state space. In fac… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.14085v1-abstract-full').style.display = 'inline'; document.getElementById('2205.14085v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.14085v1-abstract-full" style="display: none;"> Symbolic optimal control is a powerful method to synthesize algorithmically correct-by-design state-feedback controllers for nonlinear plants. Its solutions are (near-)optimal with respect to a given cost function. In this note, it is demonstrated how symbolic optimal control can be used to calculate controllers for an optimized routing guidance of vehicle systems in continuous state space. In fact, the capacitated vehicle routing problem and a variant of travelling salesman problem are investigated. The latter problem has a relevant application in case of loss of vehicles during mission. A goods delivery scenario and a reconnaissance mission, involving bicycle and aircraft dynamics respectively, are provided as examples. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.14085v1-abstract-full').style.display = 'none'; document.getElementById('2205.14085v1-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 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 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">7 pages, 5 figures. To be published in: Proc. European Control Conference (ECC), 2022</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 49M25 (Primary) 49N35; 93C10; 93C30; 93C55; 93C57; 90C27 (Secondary) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2103.00260">arXiv:2103.00260</a> <span> [<a href="https://arxiv.org/pdf/2103.00260">pdf</a>, <a href="https://arxiv.org/ps/2103.00260">ps</a>, <a href="https://arxiv.org/format/2103.00260">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</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"> On the Solution of the Travelling Salesman Problem for Nonlinear Salesman Dynamics using Symbolic Optimal Control </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Weber%2C+A">Alexander Weber</a>, <a href="/search/eess?searchtype=author&query=Knoll%2C+A">Alexander Knoll</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="2103.00260v1-abstract-short" style="display: inline;"> This paper proposes an algorithmic method to heuristically solve the famous Travelling Salesman Problem (TSP) when the salesman's path evolves in continuous state space and discrete time but with otherwise arbitrary (nonlinear) dynamics. The presented method is based on the framework of Symbolic Control. In this way, our method returns a provably correct state-feedback controller for the underlyin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.00260v1-abstract-full').style.display = 'inline'; document.getElementById('2103.00260v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2103.00260v1-abstract-full" style="display: none;"> This paper proposes an algorithmic method to heuristically solve the famous Travelling Salesman Problem (TSP) when the salesman's path evolves in continuous state space and discrete time but with otherwise arbitrary (nonlinear) dynamics. The presented method is based on the framework of Symbolic Control. In this way, our method returns a provably correct state-feedback controller for the underlying coverage specification, which is the TSP leaving out the requirement for optimality on the route. In addition, we utilize the Lin-Kernighan-Helsgaun TSP solver to heuristically optimize the cost for the overall taken route. Two examples, an urban parcel delivery task and a UAV reconnaissance mission, greatly illustrate the powerfulness of the proposed heuristic. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.00260v1-abstract-full').style.display = 'none'; document.getElementById('2103.00260v1-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 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 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">7 pages, 6 figures. To be published in: Proc. European Control Conference (ECC), 2021</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 49M25 (Primary) 49N35; 93C10; 93C30; 93C55; 93C57; 90C27 (Secondary) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2006.03862">arXiv:2006.03862</a> <span> [<a href="https://arxiv.org/pdf/2006.03862">pdf</a>, <a href="https://arxiv.org/ps/2006.03862">ps</a>, <a href="https://arxiv.org/format/2006.03862">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</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/CDC42340.2020.9304048">10.1109/CDC42340.2020.9304048 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Approximately Optimal Controllers for Quantitative Two-Phase Reach-Avoid Problems on Nonlinear Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Weber%2C+A">Alexander Weber</a>, <a href="/search/eess?searchtype=author&query=Knoll%2C+A">Alexander Knoll</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="2006.03862v1-abstract-short" style="display: inline;"> The present work deals with quantitative two-phase reach-avoid problems on nonlinear control systems. This class of optimal control problem requires the plant's state to visit two (rather than one) target sets in succession while minimizing a prescribed cost functional. As we illustrate, the naive approach, which subdivides the problem into the two evident classical reach-avoid tasks, usually does… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.03862v1-abstract-full').style.display = 'inline'; document.getElementById('2006.03862v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2006.03862v1-abstract-full" style="display: none;"> The present work deals with quantitative two-phase reach-avoid problems on nonlinear control systems. This class of optimal control problem requires the plant's state to visit two (rather than one) target sets in succession while minimizing a prescribed cost functional. As we illustrate, the naive approach, which subdivides the problem into the two evident classical reach-avoid tasks, usually does not result in an optimal solution. In contrast, we prove that an optimal controller is obtained by consecutively solving two special quantitative reach-avoid problems. In addition, we present a fully-automated method based on Symbolic Optimal Control to practically synthesize for the considered problem class approximately optimal controllers for sampled-data nonlinear plants. Experimental results on parcel delivery and on an aircraft routing mission confirm the practicality of our method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.03862v1-abstract-full').style.display = 'none'; document.getElementById('2006.03862v1-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 June, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2020. </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">14 pages, 7 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 49M25 (Primary) 49N35; 93C10; 93C30; 93C55; 93C57 (Secondary) </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 2020 59th IEEE Conference on Decision and Control (CDC) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2001.06231">arXiv:2001.06231</a> <span> [<a href="https://arxiv.org/pdf/2001.06231">pdf</a>, <a href="https://arxiv.org/ps/2001.06231">ps</a>, <a href="https://arxiv.org/format/2001.06231">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</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"> A generalized Bellman-Ford Algorithm for Application in Symbolic Optimal Control </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Weber%2C+A">Alexander Weber</a>, <a href="/search/eess?searchtype=author&query=Kreuzer%2C+M">Marcus Kreuzer</a>, <a href="/search/eess?searchtype=author&query=Knoll%2C+A">Alexander Knoll</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="2001.06231v1-abstract-short" style="display: inline;"> Symbolic controller synthesis is a fully-automated and correct-by-design synthesis scheme whose limitations are its immense memory and runtime requirements. A current trend to compensate for this downside is to develop techniques for parallel execution of the scheme both in mathematical foundation and in software implementation. In this paper we present a generalized Bellman-Ford algorithm to be u… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.06231v1-abstract-full').style.display = 'inline'; document.getElementById('2001.06231v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2001.06231v1-abstract-full" style="display: none;"> Symbolic controller synthesis is a fully-automated and correct-by-design synthesis scheme whose limitations are its immense memory and runtime requirements. A current trend to compensate for this downside is to develop techniques for parallel execution of the scheme both in mathematical foundation and in software implementation. In this paper we present a generalized Bellman-Ford algorithm to be used in the so-called symbolic optimal control, which is an extension of the aforementioned synthesis scheme. Compared to the widely used Dijkstra algorithm our algorithm has two advantages. It allows for cost functions taking arbitrary (e.g. negative) values and for parallel execution with the ability for trading processing speed for memory consumption. We motivate the usefulness of negative cost values on a scenario of aerial firefighting with unmanned aerial vehicles. In addition, this four-dimensional numerical example, which is rich in detail, demonstrates the great performance of our algorithm. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.06231v1-abstract-full').style.display = 'none'; document.getElementById('2001.06231v1-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 January, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2020. </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 version has been accepted for publication in Proc. European Control Conference (ECC), 2020</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 93B40 (Primary) 93B52; 93C10; 93C30; 93C55; 93C57 (Secondary) </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Proc. European Control Conference (ECC), pp. 2007-2014, 2020 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1906.04994">arXiv:1906.04994</a> <span> [<a href="https://arxiv.org/pdf/1906.04994">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 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/GLOCOMW.2018.8644331">10.1109/GLOCOMW.2018.8644331 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Energy Efficient Massive MIMO Array Configurations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Halbauer%2C+H">Hardy Halbauer</a>, <a href="/search/eess?searchtype=author&query=Weber%2C+A">Andreas Weber</a>, <a href="/search/eess?searchtype=author&query=Wiegner%2C+D">Dirk Wiegner</a>, <a href="/search/eess?searchtype=author&query=Wild%2C+T">Thorsten Wild</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="1906.04994v1-abstract-short" style="display: inline;"> The high spectral efficiency of massive MIMO (Multiple Input Multiple Output) is mainly achieved through the exploitation of spatial multiplexing, i.e. by using a high number of MIMO layers that are applied simultaneously to many users. The power consumption of a massive MIMO base station is determined by the hardware driving a high number of antenna ports and elements. This paper focuses on pract… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1906.04994v1-abstract-full').style.display = 'inline'; document.getElementById('1906.04994v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1906.04994v1-abstract-full" style="display: none;"> The high spectral efficiency of massive MIMO (Multiple Input Multiple Output) is mainly achieved through the exploitation of spatial multiplexing, i.e. by using a high number of MIMO layers that are applied simultaneously to many users. The power consumption of a massive MIMO base station is determined by the hardware driving a high number of antenna ports and elements. This paper focuses on practical deployment situations with varying user load. During hours with low number of users a certain significant part of hardware power consumption would remain with conventional massive MIMO processing, while the full potential of spectral efficiency cannot be exploited due to the low number of users, resulting in low power efficiency and cost. We investigate the impact of different hybrid array architectures on spectral efficiency, average user throughput and power consumption and show how to design a massive MIMO system with significantly improved energy efficiency for a given target scenario, while maintaining a targeted service quality. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1906.04994v1-abstract-full').style.display = 'none'; document.getElementById('1906.04994v1-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> 12 June, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1904.11828">arXiv:1904.11828</a> <span> [<a href="https://arxiv.org/pdf/1904.11828">pdf</a>, <a href="https://arxiv.org/format/1904.11828">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Dynamical Systems">math.DS</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/10.1103/PhysRevX.9.041046">10.1103/PhysRevX.9.041046 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Structural Invertibility and Optimal Sensor Node Placement for Error and Input Reconstruction in Dynamic Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Kahl%2C+D">Dominik Kahl</a>, <a href="/search/eess?searchtype=author&query=Wendland%2C+P">Philipp Wendland</a>, <a href="/search/eess?searchtype=author&query=Neidhardt%2C+M">Matthias Neidhardt</a>, <a href="/search/eess?searchtype=author&query=Weber%2C+A">Andreas Weber</a>, <a href="/search/eess?searchtype=author&query=Kschischo%2C+M">Maik Kschischo</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="1904.11828v5-abstract-short" style="display: inline;"> Despite recent progress in our understanding of complex dynamic networks, it remains challenging to devisesufficiently accurate models to observe, control or predict the state of real systems in biology, economics or other fields. A largely overlooked fact is that these systems are typically open and receive unknown inputs from their environment. A further fundamental obstacle are structural model… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.11828v5-abstract-full').style.display = 'inline'; document.getElementById('1904.11828v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1904.11828v5-abstract-full" style="display: none;"> Despite recent progress in our understanding of complex dynamic networks, it remains challenging to devisesufficiently accurate models to observe, control or predict the state of real systems in biology, economics or other fields. A largely overlooked fact is that these systems are typically open and receive unknown inputs from their environment. A further fundamental obstacle are structural model errors caused by insufficient or inaccurate knowledge about the quantitative interactions in the real system. Here, we show that unknown inputs to open systems and model errors can be treated under the common framework of invertibility, which is a requirement for reconstructing these disturbances from output measurements. By exploiting the fact that invertibility can be decided from the influence graph of the system, we analyse the relationship between structural network properties and invertibility under different realistic scenarios. We show that sparsely connected scale free networks are the most difficult to invert. We introduce a new sensor node placement algorithm to select a minimum set of measurement positions in the network required for invertibility. This algorithm facilitates optimal experimental design for the reconstruction of inputs or model errors from output measurements. Our results have both fundamental and practical implications for nonlinear systems analysis, modelling and design. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.11828v5-abstract-full').style.display = 'none'; document.getElementById('1904.11828v5-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 October, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 April, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 93C15 </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Phys. Rev. X 9, 041046 (2019) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1711.01637">arXiv:1711.01637</a> <span> [<a href="https://arxiv.org/pdf/1711.01637">pdf</a>, <a href="https://arxiv.org/ps/1711.01637">ps</a>, <a href="https://arxiv.org/format/1711.01637">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Logic in Computer Science">cs.LO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</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/TAC.2016.2642794">10.1109/TAC.2016.2642794 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Optimized State Space Grids for Abstractions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Weber%2C+A">Alexander Weber</a>, <a href="/search/eess?searchtype=author&query=Rungger%2C+M">Matthias Rungger</a>, <a href="/search/eess?searchtype=author&query=Reissig%2C+G">Gunther Reissig</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="1711.01637v1-abstract-short" style="display: inline;"> The practical impact of abstraction-based controller synthesis methods is currently limited by the immense computational effort for obtaining abstractions. In this note we focus on a recently proposed method to compute abstractions whose state space is a cover of the state space of the plant by congruent hyper-intervals. The problem of how to choose the size of the hyper-intervals so as to obtain… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.01637v1-abstract-full').style.display = 'inline'; document.getElementById('1711.01637v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1711.01637v1-abstract-full" style="display: none;"> The practical impact of abstraction-based controller synthesis methods is currently limited by the immense computational effort for obtaining abstractions. In this note we focus on a recently proposed method to compute abstractions whose state space is a cover of the state space of the plant by congruent hyper-intervals. The problem of how to choose the size of the hyper-intervals so as to obtain computable and useful abstractions is unsolved. This note provides a twofold contribution towards a solution. Firstly, we present a functional to predict the computational effort for the abstraction to be computed. Secondly, we propose a method for choosing the aspect ratio of the hyper-intervals when their volume is fixed. More precisely, we propose to choose the aspect ratio so as to minimize a predicted number of transitions of the abstraction to be computed, in order to reduce the computational effort. To this end, we derive a functional to predict the number of transitions in dependence of the aspect ratio. The functional is to be minimized subject to suitable constraints. We characterize the unique solvability of the respective optimization problem and prove that it transforms, under appropriate assumptions, into an equivalent convex problem with strictly convex objective. The latter problem can then be globally solved using standard numerical methods. We demonstrate our approach on an example. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.01637v1-abstract-full').style.display = 'none'; document.getElementById('1711.01637v1-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 November, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2017. </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 is the accepted version of a paper published in IEEE Trans. Automat. Control</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> Primary; 93B51; Secondary; 93B52; 93C10; 93C30; 93C55; 93C57; 93C65 </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> IEEE Trans. Automat. Control, vol. 62, no. 11, pp. 5816-5821, 2017 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1503.03715">arXiv:1503.03715</a> <span> [<a href="https://arxiv.org/pdf/1503.03715">pdf</a>, <a href="https://arxiv.org/ps/1503.03715">ps</a>, <a href="https://arxiv.org/format/1503.03715">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Logic in Computer Science">cs.LO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</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/TAC.2016.2593947">10.1109/TAC.2016.2593947 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Feedback Refinement Relations for the Synthesis of Symbolic Controllers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Reissig%2C+G">Gunther Reissig</a>, <a href="/search/eess?searchtype=author&query=Weber%2C+A">Alexander Weber</a>, <a href="/search/eess?searchtype=author&query=Rungger%2C+M">Matthias Rungger</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="1503.03715v3-abstract-short" style="display: inline;"> We present an abstraction and refinement methodology for the automated controller synthesis to enforce general predefined specifications. The designed controllers require quantized (or symbolic) state information only and can be interfaced with the system via a static quantizer. Both features are particularly important with regard to any practical implementation of the designed controllers and, as… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1503.03715v3-abstract-full').style.display = 'inline'; document.getElementById('1503.03715v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1503.03715v3-abstract-full" style="display: none;"> We present an abstraction and refinement methodology for the automated controller synthesis to enforce general predefined specifications. The designed controllers require quantized (or symbolic) state information only and can be interfaced with the system via a static quantizer. Both features are particularly important with regard to any practical implementation of the designed controllers and, as we prove, are characterized by the existence of a feedback refinement relation between plant and abstraction. Feedback refinement relations are a novel concept introduced in this paper. Our work builds on a general notion of system with set-valued dynamics and possibly non-deterministic quantizers to permit the synthesis of controllers that robustly, and provably, enforce the specification in the presence of various types of uncertainties and disturbances. We identify a class of abstractions that is canonical in a well-defined sense, and provide a method to efficiently compute canonical abstractions. We demonstrate the practicality of our approach on two examples. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1503.03715v3-abstract-full').style.display = 'none'; document.getElementById('1503.03715v3-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 January, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 March, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2015. </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 work has been accepted for publication in the IEEE Trans. Automatic Control. v3: Definition VIII.2 corrected; plus minor modifications; accepted version</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> Primary 93B51; Secondary; 93B52; 93C10; 93C30; 93C55; 93C57; 93C65 </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> IEEE Trans. Automat. Control 62, no 4, pp 1781-1796, 2017 </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"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg> <a href="https://info.arxiv.org/help/contact.html"> Contact</a> </li> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>subscribe to arXiv mailings</title><desc>Click here to subscribe</desc><path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"/></svg> <a href="https://info.arxiv.org/help/subscribe"> Subscribe</a> </li> </ul> </div> </div> </div>   <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/help/license/index.html">Copyright</a></li> <li><a href="https://info.arxiv.org/help/policies/privacy_policy.html">Privacy Policy</a></li> </ul> </div> <div class="column sorry-app-links"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/help/web_accessibility.html">Web Accessibility Assistance</a></li> <li> <p class="help"> <a class="a11y-main-link" href="https://status.arxiv.org" target="_blank">arXiv Operational Status <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 256 512" class="icon filter-dark_grey" role="presentation"><path d="M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z"/></svg></a><br> Get status notifications via <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/email/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg>email</a> or <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/slack/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512" class="icon filter-black" role="presentation"><path d="M94.12 315.1c0 25.9-21.16 47.06-47.06 47.06S0 341 0 315.1c0-25.9 21.16-47.06 47.06-47.06h47.06v47.06zm23.72 0c0-25.9 21.16-47.06 47.06-47.06s47.06 21.16 47.06 47.06v117.84c0 25.9-21.16 47.06-47.06 47.06s-47.06-21.16-47.06-47.06V315.1zm47.06-188.98c-25.9 0-47.06-21.16-47.06-47.06S139 32 164.9 32s47.06 21.16 47.06 47.06v47.06H164.9zm0 23.72c25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06H47.06C21.16 243.96 0 222.8 0 196.9s21.16-47.06 47.06-47.06H164.9zm188.98 47.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06h-47.06V196.9zm-23.72 0c0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06V79.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06V196.9zM283.1 385.88c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06v-47.06h47.06zm0-23.72c-25.9 0-47.06-21.16-47.06-47.06 0-25.9 21.16-47.06 47.06-47.06h117.84c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06H283.1z"/></svg>slack</a> </p> </li> </ul> </div> </div> </div>  </div> </footer> <script src="https://static.arxiv.org/static/base/1.0.0a5/js/member_acknowledgement.js"></script> </body> </html>

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