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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Jena, Rohit Sethi, Deeksha Chaudhari, Pratik Gee, James C. Computer and Information Science United States Electrical and Systems Engineering United States Radiology United States Penn Image Computing and Science Laboratory United States

Classical optimization and learning-based methods are the two reigning paradigms in deformable image registration. While optimization-based methods boast generalizability across modalities and robust performance, learning-based methods promise peak performance, incorporating weak supervision and amortized optimization. However, the exact conditions for either paradigm to perform well over the other are shrouded and not explicitly outlined in the existing literature. In this paper, we make an explicit correspondence between the mutual information of the distribution of per-pixel intensity and labels, and the performance of classical registration methods. This strong correlation hints to the fact that architectural designs in learning-based methods is unlikely to affect this correlation, and therefore, the performance of learning-based methods. This hypothesis is thoroughly validated with state-of-the-art classical and learning-based methods. However, learning-based methods with weak supervision can perform high-fidelity intensity and label registration, which is not possible with classical methods. Next, we show that this high-fidelity feature learning does not translate to invariance to domain shift, and learning-based methods are sensitive to such changes in the data distribution. We reassess and recalibrate performance expectations from classical and DLIR methods under access to label supervision, training time, and its generalization capabilities under minor domain shifts. © 2024 Neural information processing systems foundation. All rights reserved.

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Bridging the gap between theory and practice in local energy sharing - Energy communities in Croatia

Bridging the gap between theory and practice in local energy...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Antić, Mirna Gržanić Capuder, Tomislav Medved, Tomi Faculty of Electrical Engineering and Computing University of Zagreb Department of Energy and Power Systems Croatia Faculty of Electrical Engineering University of Ljubljana Laboratory of Energy Policy Slovenia

ISBN: (纸本)9789531842976

The European directives focus on unlocking the potential of final customers in the clean energy transition. With the recent trend of rising electricity and petrol prices, coupled with reduced investment costs in low-carbon technologies, the installation of photovoltaic solar panels for end customers has become more profitable compared to previous years, leading to a shorter payback period. Additionally, to enhance citizen engagement in the integration of renewable energy sources, various forms of local energy-sharing models are discussed in the literature. This paper elaborates on the regulatory framework and policies at the European level concerning renewable energy communities, citizen energy communities, and collective self-consumption, with a particular focus on Croatia's perspective. It highlights the steps needed to accelerate the realization of energy communities in practice. Under current Croatian regulations, the profit from selling surplus community energy to the supplier cannot be directly remunerated to community members. The results indicate that community members could achieve 20% lower electricity costs annually if the surplus energy were directly remunerated. To avoid losing potential profits from excess energy, the paper models an investment in a community-owned battery storage system, aiming to increase community self-consumption and minimize the amount of energy sold to suppliers. It is shown that with a static sharing scheme, individual community members' costs can decrease by 11% to 16%, and by up to 20% with a dynamic scheme compared to cases where profit from excess energy cannot be directly remunerated to community members. © 2024 IEEE.

关键词： Clean energy

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Analysis of LiDAR-Camera Fusion for Marine Situational Awareness with Emphasis on Cluster Selection in Camera Frustum 15

Analysis of LiDAR-Camera Fusion for Marine Situational Aware...

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15th IFAC Conference on Control Applications in Marine systems, Robotics and Vehicles, CAMS 2024

作者： Obradović, Juraj Fabijanić, Matej Batoš, Matko Mišković, Nikola Faculty of Electrical Engineering and Computing University of Zagreb Department LABUST Laboratory for Underwater Systems and Technologies Unska 3 Zagreb Croatia

Providing accurate information about all objects in the environment surrounding an autonomous vehicle is essential for the development of a maritime navigation system used on board autonomous and unmanned vessels. In this paper, we introduce a method that integrates LiDAR and camera data for precise 3D object detection and tracking. The approach utilizes LiDAR for acquiring object position information and ensures accurate object classification through data from the camera. The proposed architecture is presented with a detailed explanation of all algorithms used. Several scenarios have been created in a simulated environment to compare three approaches for the selection of target object in the camera frustum (the Nearest Center, Gaussian Likelihood, and Intersection-Over-Union). Finally, the full algorithm setup is tested in a simulator environment with multiple vessels and a background coast. © 2024 Elsevier B.V.. All rights reserved.

关键词： Marine navigation

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Speaker Extraction with Verification of Present and Absent Target Speakers

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Journal of Shanghai Jiaotong University (Science) 2025年 1-6页

作者： Zhang, Ke Borsdorf, Marvin Liu, Tianchi Wang, Shuai Wei, Yangjie Li, Haizhou Key Laboratory of Intelligent Computing in Medical Image Northeastern University Shenyang110819 China Shenzhen Guangdong518000 China Machine Listening Lab University of Bremen Bremen28359 Germany Department of Electrical and Computer Engineering National University of Singapore Singapore119077 Singapore

Target speaker extraction (TSE) models are expected to extract the target speech from a cocktail party mixture signal. When only trained with present target speaker samples (PT), these models output noise in the absence of the target speaker (AT). One may enhance the TSE quality by providing the information about the PT and AT. However, the detection of the target speaker is not perfect. In this paper, we propose a new model, TSEV, which performs target speaker extraction and speaker verification simultaneously. The TSEV model outputs an extracted speech and generates two speaker embeddings per inference to detect the target speaker. By sharing the speaker encoder and low-level modules, the speaker verification task can be performed in low signal-to-noise ratio scenarios. We train the TSEV model on multi-talker PT and AT conditions with fully overlapped speech. Experiments verify the superiority of jointly performing two tasks in the proposed model. The TSEV model achieves better verification performance without degrading the extraction performance compared with the baseline. © Shanghai Jiao Tong University 2025.

关键词： Speech recognition

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Evaluation Techniques for PolSAR Speckle Filtering

Evaluation Techniques for PolSAR Speckle Filtering

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2023 International Conference on Earth Observation and Geo-Spatial Information, ICEOGI 2023

作者： Souhila, Boutarfa Youcef, Smara Chaimaa, Youcef Radia, Ballah Laboratory of Innovative Technologies Department of Electrical Engineering and Industrial Computing Bordj El Kiffan Algiers16087 Algeria Laboratory of Image Processing and Radiation Faculty of Electrical Engineering Department of Telecommunications Bab Ezzouar Algiers16111 Algeria

ISBN: (纸本)9798350396720

Speckle noise is the biggest drawback that affects polarimetric SAR (Synthetic Aperture Radar) images. Due to its multiplicative nature, it degrades their quality, complicates their interpretation and reduces the information accuracy. Its effect is reduced by applying polarimetric synthetic aperture radar (PolSAR) filtering methods. In the literature, there are several methods of PolSAR filtering, it is not obvious to choose the most effective filter, and among the techniques for evaluating the performance of these filters, it is difficult to find the best analysis method that leads to the best filtering quality without information loss. In this paper, we present a comparative study of evaluation methods for polarimetric SAR filters considering three criteria: smoothing of homogeneous areas, edge preservation and conservation of polarimetric information. In this work, we propose a more simplified approach to evaluate a polarimetric SAR filter. Each criterion includes study techniques that will help to identify the analysis method that will facilitate the selection of the best filter. © 2023 IEEE.

关键词： Speckle

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ETAG: An Energy-Neutral Ear Tag for Real-Time Body Temperature Monitoring of Dairy Cattle 23

ETAG: An Energy-Neutral Ear Tag for Real-Time Body Temperatu...

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29th Annual International Conference on Mobile computing and Networking, MobiCom 2023

作者： Vu, Hien Chung, Hanwook Choi, Christopher Kim, Younghyun Department of Electrical and Computer Engineering College of Engineering Wisconsin Embedded Systems and Computing Laboratory University of Wisconsin-Madison MadisonWI60032179 United States

ISBN: (纸本)9781450399906

Heat stress, caused by a warming climate and the increasingly high milk-producing dairy cattle, is one of the major threats to the well-being of dairy cattle as well as the economic, environmental, and social sustainability of dairy farming around the world. Timely identification of cows under heat stress is crucial to improving animal welfare, preventing milk production losses, and preserving water and energy for *** paper presents a smart ear tag, named eTag, and an associated system that can read a passive microchip temperature sensor subcutaneously injected into the animal with minimal discomfort. It features a lightweight design using a single coil shared for microchip scanning and wireless charging. eTag is autonomously recharged by a wireless charger over the head during daily milking sessions, enabling perpetual operation without battery replacement after deployment. The real-world performance of the proposed system was examined intensively in a three-week deployment on seven lactating Holstein cows. We demonstrate that eTag can reliably collect accurate body temperature in real time while maintaining a positive energy flow. The deployment of eTag will enable the timely detection of heat stress and facilitate precision control of barn cooling systems. © 2023 ACM.

关键词： Energy harvesting

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Deep Learning in Medical image Registration: Magic or Mirage?

arXiv

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arXiv 2024年

作者： Jena, Rohit Sethi, Deeksha Chaudhari, Pratik Gee, James C. Computer and Information Science Electrical and Systems Engineering Radiology Penn Image Computing and Science Laboratory United States

Classical optimization and learning-based methods are the two reigning paradigms in deformable image registration. While optimization-based methods boast generalizability across modalities and robust performance, learning-based methods promise peak performance, incorporating weak supervision and amortized optimization. However, the exact conditions for either paradigm to perform well over the other are shrouded and not explicitly outlined in the existing literature. In this paper, we make an explicit correspondence between the mutual information of the distribution of per-pixel intensity and labels, and the performance of classical registration methods. This strong correlation hints to the fact that architectural designs in learning-based methods is unlikely to affect this correlation, and therefore, the performance of learning-based methods. This hypothesis is thoroughly validated with state-of-the-art classical and learning-based methods. However, learning-based methods with weak supervision can perform high-fidelity intensity and label registration, which is not possible with classical methods. Next, we show that this high-fidelity feature learning does not translate to invariance to domain shift, and learning-based methods are sensitive to such changes in the data distribution. Finally, we propose a general recipe to choose the best paradigm for a given registration problem, based on these observations. © 2024, CC BY.

关键词： Contrastive Learning

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Guest Editorial Enabling Technologies and systems for Industry 5.0: From Foundation Models to Foundation Intelligence

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IEEE Transactions on systems, Man, and Cybernetics: systems 2024年第11期54卷 6496-6499页

作者： Tang, Ying Tian, Yonglin Lin, Yilun Lv, Chen Fanti, Maria Pia Rowan University Department of Electrical and Computer Engineering GlassboroNJ08028 United States Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Shanghai AI Laboratory Urban Computing Laboratory Shanghai200232 China Nanyang Technological University School of Mechanical and Aerospace Engineering Jurong West639798 Singapore Polytechnic University of Bari Department of Electrical and Information Engineering Bari70126 Italy

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Deep learning in medical image registration: magic or mirage? 24

Deep learning in medical image registration: magic or mirage...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Rohit Jena Deeksha Sethi Pratik Chaudhari James C. Gee Computer and Information Science and Penn Image Computing and Science Laboratory Computer and Information Science Computer and Information Science and Electrical and Systems Engineering Computer and Information Science and Radiology and Penn Image Computing and Science Laboratory

ISBN: (纸本)9798331314385

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Edge-Driven Industrial computing Power Networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

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