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arXiv 2024年

作者： Zhao, Can Chen, Bin Cai, Jiaqi Liang, Zhiwei Lei, Yi Xiong, Junjie Ma, Lin Hu, Daohui Sun, Lin Shen, Gangxiang School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Electronic and Information Engineering Soochow University Suzhou China State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Shanghai China

A 16-dimensional Voronoi constellation concatenated with multilevel coding is experimentally demonstrated over a 50 km four-core fiber transmission system. The proposed scheme reduces the required launch power by 6 dB and provides a 17 dB larger operating range than 16QAM with BICM at the outer HD-FEC BER threshold. © 2024, CC BY-NC-SA.

关键词： Digital television

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PolarBEVU: Multi-Camera 3D Object Detection in Polar Bird's-Eye View via Unprojection

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IEEE Transactions on Circuits and systems for Video technology 2025年

作者： Hou, Minghui Lyu, Chuanhao Wang, Gang Ma, Baorui Xu, Rongtao Hu, Jue Fan, Xiaopeng Jilin University College of Computer Science and Technology Changchun China Tsinghua University School of Software Beijing China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China Harbin Institute of Technology National Key Laboratory of Science and Technology on Advanced Composites in Special Environments Harbin150080 China Harbin Institute of Technology Centre for Composite Materials and Structures Harbin150001 China Harbin Institute of Technology Faculty of Computing Harbin150001 China

3D object detection from a Bird's Eye View (BEV) has emerged as a novel perception paradigm for autonomous driving scenarios. While most current 3D object detection methods still rely on the conventional Cartesian coordinates, they fail to align with the non-aligned coordinate system inherent in image geometry. The Polar coordinates, on the other hand, better fit with the geometric shape corresponding to the perception of cameras. However, transforming between coordinate systems introduces distortions in the perception information, resulting in issues such as "Weak Adaptability to Heatmap Distribution"and "Offset in the Center Point of the Bounding Box."To address these challenges, this paper proposes a cutting-edge 3D object detection model named PolarBEVU, which leverages the bird's-eye view under the Polar coordinates along with multi-camera unprojection. The model introduces an innovative "Deformable Uniform Heatmap Distribution"method that adjusts heatmap computations based on box shapes, generating high-quality heatmaps and effectively resolving the issue of "Weak Adaptability to Heatmap Distribution."Moreover, the model incorporates the concept of "Dynamic High-risk Regression Region"to enhance the accuracy and robustness of the center point regression at the bounding box, thus mitigating the issue of "Offset in the Center Point of the Bounding Box."In extensive experiments on the nuScenes dataset, PolarBEVU achieves impressive results with 49.9% mAP and 57.4% NDS on the test set, surpassing other comparative approaches and reaching the state-of-the-art (SOTA) performance among methods utilizing Polar coordinates. This clearly demonstrates the efficacy and superiority of PolarBEVU. In addition, the model is successfully deployed on Nvidia Jetson AGX Orin, showcasing real-time inference speeds of 31.42ms. These findings affirm PolarBEVU's potential for practical applications. Code is available at https: //***/JLUrob/PolarBEVU. © 2025 IEEE.

关键词： Object tracking

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Graph Signal Interpolation and Extrapolation Using Reproducing Kernel Hilbert Space Over Manifold of Gaussian Mixture

SSRN

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SSRN 2022年

作者： Zach, Itay Dvorkind, Tsvi G. Talmon, Ronen Viterbi Faculty of Electrical and Computer Engineering Technion - Israel Institute of Technology Haifa3200003 Israel Rafael Advanced Defense Systems Haifa31021 Israel

Signals with an underlying irregular geometric structure are prevalent in modern applications and are often well-represented using graphs. The field of graph signal processing has emerged to accommodate such signals' analysis, processing, and interpolation. In this paper, we address the latter, where signal samples are given only on a subset of graph nodes, and the goal is to estimate the graph signal on the remaining nodes. In addition, we consider a related extrapolation task in which new graph nodes that were not part of the original graph are added, and the goal is to estimate the graph signal on those nodes as well. We present a new approach for both the interpolation and extrapolation tasks, which is based on modeling the graph nodes as samples from a continuous manifold with a Gaussian mixture distribution and the graph signal as samples of a continuous function in a reproducing kernel Hilbert space. This model allows us to propose an interpolation and extrapolation algorithm that utilizes the closed-form expressions of the Gaussian kernel eigenfunctions. We test our algorithm on synthetic and real-world signals and compare it to existing methods. We demonstrate superior or on-par accuracy results achieved in significantly shorter run times. © 2022, The Authors. All rights reserved.

关键词： Extrapolation

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Deep Neural Network Enhanced Chip-on-board Hybrid-Packaged Photonic-Electrical Silicon Transceiver for High-speed Optical Interconnects

Deep Neural Network Enhanced Chip-on-board Hybrid-Packaged P...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Qin, Jun Yang, Qipeng Han, Changhao Zhou, Yan Zhang, Yunhao Wu, Yichen Sun, Yu Lu, Junde Liu, Siming Li, Yueqin Sun, Jian Min, Miao Hu, Weiwei He, Zhixue Wang, Lei Yu, Shaohua Shu, Haowen Wang, Xingjun Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing100192 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Peking University Yangtze Delta Institute of Optoelectronics Nantong226010 China Peng Cheng Laboratory Shenzhen518055 China State Key Laboratory of Information Photonics and Optical Communications BUPT Beijing100876 China

We explore implementing a multilevel deep neural network to enhance the performance of a 4-channel photonic-electrical hybrid-packaged silicon transceiver. Stable transmission and reception of 150 Gbps/λ PAM4 signals... 详细信息

关键词： Optical interconnects

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Deep Neural Network Enhanced Chip-on-board Hybrid-Packaged Photonic-Electrical Silicon Transceiver for High-speed Optical Interconnects

Deep Neural Network Enhanced Chip-on-board Hybrid-Packaged P...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Optical interconnects

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Machine Learning for Automated Bladder Event Classification from Single-Channel Vesical Pressure Recordings

Machine Learning for Automated Bladder Event Classification ...

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2022 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2022

作者： Abbaraju, V. Lewis, K. Majerus, S.J.A. Case Western Reserve University Department of Electrical Computer and Systems Engineering ClevelandOH United States Advanced Platform Technology Center Louis Stokes Cleveland Veterans Affairs Medical Center ClevelandOH United States Glickman Urological and Kidney Institute Cleveland Clinic ClevelandOH United States

ISBN: (纸本)9781665470292

Analyzing urodynamic study (UDS) tracings can be prone to error in the presence of artifacts and subjective due to lack of standardization in clinical UDS interpretation. As such, the diagnosis of patients undergoing UDS would greatly benefit from a standardized, automated method to assist clinicians in interpreting UDS tracings. In this work, we evaluated a machine learning framework for automatically classifying bladder events from single-channel vesical pressure recordings (PVES) (N=60) into 4 possible classes: abdominal event (i.e., cough or Valsalva), voiding contraction, detrusor overactivity (DO) and no event. Wavelet multiresolution analysis of PVES was used to extract time-frequency localized wavelet coefficient vectors which were segmented into 0.8 second segments with 55 statistical features per segment. Feature selection was subsequently applied for three classifier architectures: a k-nearest classifier (KNN), an artificial neural network classifier (ANN) and a support vector machine classifier (SVM). Each classifier was trained and evaluated using five-fold cross validation, from which we derived the sensitivity, specificity, F1 score and AUC for all four classes and the overall classification accuracy for each classifier. The KNN, ANN and SVM classifiers labeled 7,861 0.8 second PVES segments with 91.5%, 90.8% and 82.4% accuracy, respectively. We have thus proposed the first framework for automated multi-event bladder classification using single-channel UDS data. © 2022 IEEE.

关键词： Support vector machines

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Onboard Rooftop Solar Charging for Green Transportation in tourism sector

Onboard Rooftop Solar Charging for Green Transportation in t...

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Renewable Energy and Hydrogen Technologies (GlobConHT), IEEE IAS Global Conference on

作者： Moumita Pramanik Hiranmay Samanta Sudipta Basu Pal Konika Das Bhattacharya Chandan Kumar Chanda Hiranmay Saha Department of Electrical Engineering(EE) Indian Institute of Engineering Science and Technology (IIEST) Shibpur Howrah India Green Energy and Sensor Systems(SAMGESS) School of Advanced Materials Indian Institute of Engineering Science and Technology(IIEST) Shibpur Howrah India Department of Computer Science Technology and Computer Science Information Technology Engineering University of Engineering and Management(UEM) Kolkata Kolkata India

The need for energy conservation and environmental protection have triggered the development and deployment of electric vehicles. Light electric vehicles and fossil fuel based light vehicles are popular means of transport for short distances in not only remote rural areas but also in places of tourist attractions. For Islands in the tropical belt like the Sunderbans in India and other pristine sea beaches in South East Asia where nature is luxuriantly present, green transportation is proposed in this work. The objective is to develop environment friendly transport for these natural havens and islands that have ample renewable resources especially in the form of solar power. In this work, a model of a light electric vehicle is developed that utilizes the solar energy during the daytime for charging its batteries. Battery charge controller inbuilt with boost based MPPT (Maximum Power Point Tracking) of solar irradiation have especially been designed to meet the requirements. The efficiency of the designed MPPT is studied. As accumulation of dust and sand reduces the solar module efficiency, an automated self-cleaning mechanism has also been designed that automatically cleans the solar panel regularly. The life cycle cost and the reduction in carbon emissions coupled with the Cost of Capital Investment and operating expenses of the developed model of green transportation far outweighs the similar values obtained from other modes of remote transportation.

关键词： Maximum power point trackers Renewable energy sources Radiation effects Costs Solar energy Fossil fuels Green transportation

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Self-Propelled Capsule Endoscope and Tracking Control of Its Wireless Power Supply Coil

Self-Propelled Capsule Endoscope and Tracking Control of Its...

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Robotics, Engineering, Science, and technology (RESTCON), International Conference on

作者： Takahiro Ito Kohei Fujita Kenji Miyauchi Sunao Murakami Hiroshi Ohtake Chihiro Katsuta Kaoru Karasawa Akane Tanaka Hiroshi Matsuda Toshihiro Kimura Hiromu Kutsumi Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Division of Animal Life Science Tokyo University of Agriculture and Technology Fuchu-shi Japan Savior Inc Yokohama-Sh Japan Center for Clinical Research and Advanced Medicine Establishment Shiga University of Medical Science Otsu-Shi Japan

We have developed the self-propelled capsule endoscope. Although it is the same size as a conventional capsule endoscope, with a diameter of 11 mm and a length of 31 mm, it can travel inside the gastrointestinal tract. A running mechanism that uses vibrations allows it to run without any protrusions on the outer shell. Therefore, there is no risk of harming the inside of the body. This is a noteworthy feature of this capsule. Experiments have confirmed that this capsule can travel in the intestines of living dogs. Because the power supply to this capsule needs to continue stably for over an hour, we developed a wireless power supply method to the internal capsule. Conventional wireless power supply with a fixed power transmission coil had the problem that power supply would be interrupted if the capsule moved. Therefore, we investigated stabilizing the power supply by moving the power transmission coil in X-Y to follow the capsule in response to increases and decreases in the power supply. We were able to confirm the feasibility through experiments. This technology can be expected to perform endoscopic examinations with less burden on both patients and doctors, and can also be applied to power supply to medical devices inside the body.

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Meta Structure Search for Link Weight Prediction in Heterogeneous Graphs

Meta Structure Search for Link Weight Prediction in Heteroge...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiaoou Zhang Yang Gao Yang Liu Yujia Zhu Peng Zhang Chuan Zhou Qingyun Liu Hongyang Chen Chinese Academy of Sciences Institute of Information Engineering School of Cyber Security University of Chinese Academy of Sciences College of Computer Science and Technology Zhejiang University Chinese Academy of Sciences Academy of Mathematics and Systems Science Cyberspace Institute of Advanced Technology Guangzhou University Zhejiang Lab Research Center for Graph Computing

Recently link weight prediction has attracted an increasing research interest due to its merits in quantifying the strength between nodes within a graph. Nonetheless, current link weight prediction methods focus solely on graph topology, disregarding node feature information embedded in graphs. In real-world applications, we often collect heterogeneous graph data where multiple types of nodes linked by multiple types of edges are available for analysis, and it is essential and challenging to quantify the proximity of different types of nodes. To solve this challenge, we present a new model for Heterogeneous Graph Link Weight Prediction (HLWP for short). In HLWP, message passing in heterogeneous graph neural networks is described as a meta structure, which can be effectively designed by Differentiable Neural Architecture Search (DARTS) algorithms. Thus, HLWP can enhance the message passing in heterogeneous graphs by DARTS. In addition, HLWP employs a perturbation-based algorithm to enhance stability and precision. Through empirical experiments conducted on three real-world datasets, we demonstrate that HLWP achieves accurate predictions of link weights. Our results highlight the superiority of HLWP over existing methods for link weight prediction and baseline GNN models in terms of accurately predicting link weights within heterogeneous graphs.

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Guest Editorial: Special issue on computational methods and artificial intelligence applications in low-carbon energy systems

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IET Renewable Power Generation 2024年第3期18卷 303-305页

作者： Wang, Yishen Zhou, Fei Guerrero, Josep M. Baker, Kyri Chen, Yize Wang, Hao Xu, Bolun Xu, Qianwen Zhu, Hong Agwan, Utkarsha State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China Department of Energy Technology Aalborg University Aalborg Denmark Department of Civil Environmental and Architectural Engineering University of Colorado-Boulder BoulderCO United States Guangzhou China Department of Data Science and AI Monash University ClaytonVIC Australia Department of Earth and Environmental Engineering Columbia University New YorkNY United States Electric Power and Energy Systems Division KTH Royal Institute of Technology Stockholm Sweden State Grid Jiangsu Electric Power Co. Ltd. Nanjing China Department of Electrical Engineering and Computer Sciences University of California - Berkeley BerkeleyCA United States

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