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4th International Conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Sun, Kangjian Huo, Ju Jiang, Xiaona Gao, Yichao Cai, Chen School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin China China FAW Corporation Limited Changchun China College of Design and Engineering National University of Singapore Singapore Signal and Communication Research Institute China Academy of Railway Sciences Beijing China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Low-light scenes are common in fields such as autonomous driving, which tests the robustness of intelligent systems. This paper tests the performance of object detection in low-light scenes based on the YOLOv8n model. In order to solve the constraint of limited computing power of embedded devices, the YOLOv8n model is adjusted through model pruning and knowledge distillation. The complexity of the model is reduced through these lightweight operations. While ensuring the detection performance as much as possible, the multiply-accumulate operations (MACs) and parameters of the model decreased by about 40% and 27% respectively. The proposed model is evaluated on the Exclusively Dark (ExDARK) dataset and achieves a mean average precision (mAP) value of 0.672. In addition, the proposed model is migrated to the embedded platform NVIDIA Jetson Xavier NX. The deployment process is optimized by multi-process scheduling and multi-thread scheduling. Experiments are also conducted on real data, and the results show that the proposed model can provide a feasible solution for vision-based nighttime autonomous driving. © 2025 SPIE.

关键词： Object detection Deep learning Image processing

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Flexible Inverse design of Microwave Filter Customized on Demand With Wavelet Transform Deep Learning

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IEEE Transactions on Computer-Aided design of Integrated Circuits and systems 2025年第3期44卷 806-817页

作者： Xu, Kuiwen Wang, Jianguo Cai, Jialin Ma, Xuetiao Lv, Qinyi Chen, Shichang Liu, Jie Liu, Jun Hangzhou Dianzi University Innovation Center for Electronic Design Automation Technology The Key Laboratory of RF Circuits and Systems Ministry of Education Hangzhou310018 China Chang'an Campus Northwestern Polytechnical University School of Electronic Information Xi'an710060 China University of Electronic Science and Technology of China School of Electronic Science and Engineering Chengdu610054 China Information Science Academy China Electronics Technology Group Corporation Beijing100846 China

Artificial intelligence (AI) techniques are increasingly being used for the inverse design of microwave devices. However, several challenges, including intensive computation costs for training samples, high-dimensional data, nonuniformity, and low-quality samples in the design space, can negatively impact the final modeling performance. To alleviate these issues, a high-quality sampling inverse design scheme incorporating wavelet transform deep learning (HQS-WTDL) is proposed to achieve customized, automated microwave filter design. In the forward simulation-based sampling, particle swarm optimization (PSO) is used to tentatively select rule-defined samples. Multilabel synthetic minority over-sampling technique (MLSMOTE) is then applied to enlarge the training samples and improve their uniformity in the design space. An inverse modeling approach using neural networks to map the nonlinear relationship between a given set of S-parameters and required filter structural parameters is presented. Given that the dimension of the S-parameters is much higher than that of the structure parameters, the corresponding neural network used in this approach is deep and complex, with multiple layers and neurons. To reduce the number of input variables, the S-parameters are subjected to wavelet transformation, allowing for more efficient representation by the neural network. The proposed method is validated using a band-pass microstrip hairpin filter as an example. Results demonstrate that the proposed approach achieves better modeling effectiveness and inverse design efficiency than conventional methods. In addition, the proposed method allows for fast customization of device parameters, such as center frequencies and bandwidths with good prediction accuracy. © 1982-2012 IEEE.

关键词： Inverse problems

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Fast Nonlinear Reconstruction for Radiation Patterns with Phaseless Measurement in Reverberation Chamber

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IEEE Antennas and Wireless Propagation Letters 2025年

作者： Wang, Mengsheng Xu, Kuiwen Peng, Fangyun Xu, Ning Chen, Xiaoming Hangzhou Dianzi University Innovation Center for Electronic Design Automation Technology Key Laboratory of RF Circuits and Systems of Ministry of Education Hangzhou310018 China Xi'an Jiaotong University School of Information and Communication Engineering Xi'an710049 China

This paper explores the potential of a nonlinear optimization algorithm, known as the Levenberg-Marquardt (LM) algorithm, to expedite the calculation of spherical wave coefficients. Reconstruction of antenna radiation patterns (AUT) within a reverberation chamber (RC) could be accomplished through the utilization of spherical wave expansion (SWE) with the measurement of power correlation coefficients. However, solving the SWE of electrically large AUT is complex and time-consuming. The matrix decomposition approach is used to convert high-dimensional matrices into multiple low-dimensional ones. Additionally, it analytically solves the Jacobian matrix within the LM, achieving a time savings of more than fivefold. The efficacy and precision of this method are substantiated through numerical calculations, simulations, and measurements. © 2002-2011 IEEE.

关键词： Reverberation

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Classifier architectures for single chamber arrhythmia recognition

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APPLIED INTELLIGENCE 1996年第3期6卷 215-224页

作者： Jabri, M Tinker, E Systems Engineering and Design Automation Laboratory Sydney University Electrical Engineering Australia

In this paper we study heart arrhythmia classification for single chamber implantable cardio-verter defibrillators. Our research shows that performance of conventional classification methods using only simple heart rate timing based features can be improved with the inclusion of morphology analysis on samples of the right ventricular apex lead. While morphology classification is typically patient dependent, and computationally expensive, we show that the performance of a patient independent classifier which uses a multi-layer perceptron for morphology recognition and heart beat timing decision tree is superior to that of a timing only classifier, while remaining economical (silicon area and power dissipation) from an implementation perspective. We also show that performance can be significantly improved in the patient dependent case.

关键词： implantable cardioverter defibrillator intracardiac electrogram pattern recognition neural network

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A framework for real-time traffic risk prediction incorporating cost-sensitive learning and dynamic thresholds

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Accident Analysis and Prevention 2025年 218卷 108087页

作者： Wu, Dan Xing, Lu Li, Ye Wong, Yiik Diew Lee, Jaeyoung Jay Dong, Changyin School of Traffic and Transportation Engineering Central South University Hunan Changsha410075 China School of Civil and Environmental Engineering Nanyang Technological University 639798 Singapore School of Automation Central South University Changsha China Department of Automation Tsinghua University Beijing China Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-infrastructure Systems Changsha University of Science &Technology China Department of Civil Environmental & Construction Engineering University of Central Florida OrlandoFL32816 United States School of Aeronautics Northwestern Polytechnical University Xi'an710072 China National Key Laboratory of Aircraft Configuration Design Xi'an710072 China

In recent years, researchers have explored an innovative approach that leverages real vehicle trajectory data to simultaneously derive traffic state and risk level for real-time risk prediction, which is crucial for traffic safety. However, existing studies largely overlook the costs associated with incorrect predictions and the varying consequences of different misclassifications, which undermines the reliability of the obtained prediction results. To address these gaps, this study refined traffic risk classification into four levels (i.e., no, low, medium, and high risks) and incorporated misclassification costs into the prediction process through cost-sensitive learning (CSL). Furthermore, considering that multi-class prediction tasks often face performance degradation and increased risk level granularity worsens class imbalance, further amplifying this degradation, this study introduced dynamic thresholds (DTs) to improve model performance. The aforementioned cost coefficients and thresholds were pinpointed using a genetic algorithm (GA). Furthermore, the employed data, comprising variables related to traffic state and associated risk data, were sourced from the HighD dataset. Subsequently, CSL-DTs-based models were built by integrating CSL and DTs with four distinct baseline machine/deep learning models, and the prediction performance (e.g., precision) and computation time of these models were compared. Results show that, compared to the corresponding baseline models, the proposed models perform better for multi-class prediction tasks. Additionally, the computation time of the CSL-DTs-based models is found to be acceptable for real-time prediction purposes. Finally, to ensure the reliability of the results obtained through the GA optimization (e.g., avoiding local optima), convergence curves were plotted, confirming the robustness of the optimization process. A robustness analysis also demonstrates that the models are highly stable under slight perturbations of

关键词： Deep learning

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A HYBRID ANALOG AND DIGITAL VLSI NEURAL-NETWORK FOR INTRACARDIAC MORPHOLOGY CLASSIFICATION

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IEEE JOURNAL OF SOLID-STATE CIRCUITS 1995年第5期30卷 542-550页

作者： COGGINS, R JABRI, M FLOWER, B PICKARD, S Systems Engineering and Design Automation Laboratory Department of Electrical Engineering University of Sydney Sydney Australia

Current Implantable Cardioverter Defibrillators (ICD's) use timing based decision trees for cardiac arrhythmia classification, Timing alone does not distinguish all rhythms for all patients. Hence, more computationally intensive morphology analysis is required for complete diagnosis. An analog VLSI neural network has been designed and tested to perform cardiac morphology classification tasks. Analog techniques were chosen to meet the strict power and area requirements of the implantable system while incurring the design difficulties of noise, drift and offsets inherent in analog approaches. The robustness of the neural network architecture however, to a large extent, overcomes these inherent shortcomings of the analog approach. The network is a 10:6:3 multilayer perceptron with on chip digital weight storage. The chip also includes a bucket brigade input to feed the Intracardiac Electrogram (ICEG) to the network and a Winner Take All circuit for converting classifications to a binary representation. The training system trained the network in loop and included a commercial implantable defibrillator in the signal processing path. The system has successfully distinguished two arrhythmia classes on a morphological basis for seven different patients with an average of 95% true positive and 97% true negative detections for the dangerous rhythm. The chip was implemented in 1.2 mu m CMOS and consumes less than 200 nW maximum average power from a 3 V supply in an area of 2.2 x 2.2 mm(2).

关键词： Multilayer perceptrons Taxonomy Rhythms neural net architecture Cardiac Arrhythmias Very large scale integration Implantable Defibrillators Neural network Decision Trees Bucket brigades

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A LOW-POWER VLSI ARRHYTHMIA CLASSIFIER

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1995年第6期6卷 1435-1445页

作者： LEONG, PHW JABRI, MA Systems Engineering and Design Automation Laboratory SEDAL Department of Electrical Engineering University of Sydney Australia

The design, implementation, and operation of a low-power multilayer perceptron chip (Kakadu) in the framework of a cardiac arrhythmia classification system is presented in this paper, This classifier, called MATIC, makes timing decisions using a decision tree, and a neural network is used to identify heartbeats with abnormal morphologies, This classifier was designed to be suitable for use in implantable devices and a VLSI (very large scale integration) neural-network chip (Kakadu) was designed so that the computationally expensive neural-network algorithm can be implemented with low power consumption, Kakadu implements a (10, 6, 4) perceptron and has a typical power consumption of tens of microwatts, When used with the arrhythmia classification system, the chip can operate with-an average power consumption of less than 25 nW.

关键词： Very large scale integration Energy consumption Neural networks Voltage Morphology Classification algorithms Multilayer perceptrons Medical treatment Heart rate Australia

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Wireless Hallucination in Generative AI-enabled Communications: Concepts, Issues, and Solutions

arXiv

引用

arXiv 2025年

作者： Wang, Xudong Wang, Jiacheng Feng, Lei Niyato, Dusit Zhang, Ruichen Kang, Jiawen Xiong, Zehui Du, Hongyang Mao, Shiwen State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China College of Computing and Data Science Nanyang Technological University Singapore School of Automation Guangdong University of Technology Guangzhou510006 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong Department of Electrical and Computer Engineering Auburn University Auburn United States

Generative AI (GenAI) is driving the intelligence of wireless communications. Due to data limitations, random generation, and dynamic environments, GenAI may generate channel information or optimization strategies that violate physical laws or deviate from actual real-world requirements. We refer to this phenomenon as wireless hallucination, which results in invalid channel information, spectrum wastage, and low communication reliability but remains underexplored. To address this gap, this article provides a comprehensive concept of wireless hallucinations in GenAI-driven communications, focusing on hallucination mitigation. Specifically, we first introduce the fundamental, analyze its causes based on the GenAI workflow, and propose mitigation solutions at the data, model, and post-generation levels. Then, we systematically examines representative hallucination scenarios in GenAI-enabled communications and their corresponding solutions. Finally, we propose a novel integrated mitigation solution for GenAI-based channel estimation. At the data level, we establish a channel estimation hallucination dataset and employ generative adversarial networks (GANs)-based data augmentation. Additionally, we incorporate attention mechanisms and large language models (LLMs) to enhance both training and inference performance. Experimental results demonstrate that the proposed hybrid solutions reduce the normalized mean square error (NMSE) by 0.19, effectively reducing wireless hallucinations. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A LOW-POWER NETWORK FOR ONLINE DIAGNOSIS OF HEART-PATIENTS

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IEEE MICRO 1995年第3期15卷 18-25页

作者： COGGINS, R JABRI, M FLOWER, B PICKARD, S Systems Engineering and Design Automation Laboratory (SEDAL) University of Sydney University of Sydney SEDAL University of Sydney

Implantable cardioverter defibrillators detect and treat dangerous cardiac arrhythmias. Current ICDs, however, cannot distinguish between some potentially fatal arrhythmias and benign conditions. Our system classifies intracardiac electrograms to detect such arrhythmias and uses analog techniques to meet the strict power and area requirements of implantable systems. A robust neural network architecture reduces the impact of noise, drift, and offsets inherent in analog approaches.

关键词： Neural Networks Low Power design Analog Circuits Medical systems

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Embedding Reliability Verification Constraints into Generation Expansion Planning

arXiv

引用

arXiv 2025年

作者： Liu, Peng Cheng, Lian Omell, Benjamin P. Burgard, Anthony P. School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China The Institute for Design of Advanced Energy Systems National Energy Technology Laboratory Pittsburgh United States

Generation planning approaches face challenges in managing the incompatible mathematical structures between stochastic production simulations for reliability assessment and optimization models for generation planning, which hinders the integration of reliability constraints. This study proposes an approach to embedding reliability verification constraints into generation expansion planning by leveraging a weighted oblique decision tree (WODT) technique. For each planning year, a generation mix dataset, labeled with reliability assessment simulations, is generated. An WODT model is trained using this dataset. Reliability-feasible regions are extracted via depth-first search technique and formulated as disjunctive constraints. These constraints are then transformed into mixed-integer linear form using a convex hull modeling technique and embedded into a unit commitment-integrated generation expansion planning model. The proposed approach is validated through a long-term generation planning case study for the Electric Reliability Council of Texas (ERCOT) region, demonstrating its effectiveness in achieving reliable and optimal planning solutions. © 2025, CC BY-NC-SA.

关键词： Stochastic models

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