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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Zhao, Jinghao Liu, Qiuming Tan, Bin School of Software engineering Jiangxi University of Science and Technology Nanchang China Nanchang Key laboratory of Virtual Digital Factory and Cultural Communications Nanchang China Jiangxi Provincial Key Laboratory of Electronic Data Control and Forensics China School of Electronics and Information Engineering Jinggangshan University Ji’an China

ISBN: (纸本)9781510689251

The rapid advancements in autonomous driving technology necessitate the extensive deployment of automotive radars operating within the 77-81 GHz millimeter-wave band in the forthcoming years. In contrast to earlier Frequency Modulated Continuous Wave (FMCW) radars, Phase-Modulated Continuous Wave (PMCW) radars exhibit notable improvements in processing speed and flexibility, offering superior range and velocity resolution. These enhancements are critical for the precise detection and interpretation of dynamic and complex traffic scenarios. This paper initially presents the simulation and testing of algorithms for range and speed measurement using single-input single-output (SISO) PMCW millimeter-wave radar. Building upon these results, further simulations incorporating multi-input multi-output (MIMO) systems utilizing Hadamard codes are conducted to augment PMCW radar performance. The final phase of this study involves implementing the relevant signal processing algorithms on a custom-developed digital signal processor (DSP) named SWIFT. Experimental findings demonstrate that PMCW radar significantly mitigates multipath effects and clutter, maintaining high performance in complex environments. Furthermore, the algorithms executed on the DSP meet the anticipated performance standards. The proposed methodology not only validates the theoretical framework but also establishes a foundation for future hardware implementation. © 2025 SPIE.

关键词： Automotive radar

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A spectrum-enhanced attention model for semantic segmentation of remote sensing images

A spectrum-enhanced attention model for semantic segmentatio...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Xin Xu, Feng Tao, Feifei Tong, Yao Lyu, Xin Zhong, Jianyi Kaup, André College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology Ministry of Water Resources Hohai University Nanjing China School of Computer Engineering Jiangsu Ocean University Lianyungang China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (纸本)9798350368741

Semantic segmentation of remote sensing images (RSIs) is essential for applications such as environmental monitoring, urban planning, and disaster management. Convolutional Neural Networks (CNNs) and their variants struggle to capture comprehensive spectral context for learning discriminative representations. In this paper, we propose a Spectrum-Enhanced Network (SPENet) that leverages the Frequency Transformer Block (FTB) to capture rich spectral context. FTB integrates Spectrum-Enhanced Attention (SEA) with Multi-Head Frequency Self-Attention (MH-FSA), incorporating more informative contextual cues. Specifically, SEA aggregates spectral statistics through covariance matrix normalization before applying channel-wise attention. By projecting feature maps onto the frequency domain, MH-FSA provides the network with a broader context, extending beyond the low-frequency focus of standard self-attention mechanisms. Extensive experiments on the ISPRS Potsdam and LoveDA datasets show that SPENet significantly outperforms state-of-the-art methods. Besides, the proposed SEA module notably rises average F1-score/overall accuracy/mean insert over union wiht more than 2.5/2.6%/2.3%, as demonstrated by ablation study. © 2025 IEEE.

关键词： frequency transformer remote sensing images Semantic segmentation spectrum enhancement

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Antenna Pattern Design in Frequency Selective Channels

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IEEE Wireless communications Letters 2025年

作者： Zhang, Cuicui Wang, Shuo Chen, Xin Zhang, Ming Wang, Kewei Zheng, Tong-Xing Zhang, Anxue Xi’an Jiaotong University School of Information and Communications Engineering Shanxi Xi’an710049 China Autonavi Software Co. Ltd Beijing100102 China Purple Mountain Laboratories Nanjing211111 China National Key Laboratory of Multi-domain Data Collaborative Processing and Control Xi’an710068 China

Antenna configuration plays a critical role in improving channel capacity through optimized antenna spacing, radiation patterns, and coupling. While previous works have explored antenna pattern design for flat fading channels, the optimization of patterns for frequency selective channels, remains under-explored. This paper addresses the antenna pattern design aimed at maximizing capacity in frequency selective channels. We propose a novel discrete representation model for antenna patterns. Based on that, the capacity maximization problem is formulated and solved using a combination of alternating optimization, convex optimization, auxiliary variable method, and the bisection method to achieve antenna pattern design. Numerical simulations validate the effectiveness of the proposed approach, demonstrating significant improvements in system capacity. © 2012 IEEE.

关键词： Directional patterns (antenna)

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Privacy-Aware Edge Computation Offloading With Federated Learning in Healthcare Consumer Electronics System

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IEEE Transactions on Consumer Electronics 2025年

作者： Fei, Yiming Fang, Hao Yan, Zheng Qi, Lianyong Bilal, Muhammad Li, Yang Xu, Xiaolong Zhou, Xiaokang Nanjing University of Information Science and Technology School of Software Nanjing210044 China NanJing University of Information Science and Technology Reading Academy Nanjing210044 China Xidian University School of Cyber Engineering Shaanxi Xi'an710026 China College of computer science and technology Qingdao China Lancaster University School of Computing and Communications United Kingdom Shihezi University College of Mechanical and Electrical Engineering Shihezi832003 China Kansai University Faculty of Business and Data Science Japan

The continuous iteration of consumer electronics has significantly promoted the development of medical devices, which has enabled the collection of large amounts of heterogeneous medical data. These data are offloaded from local devices to cloud servers for processing using traditional methods, which results in high transmission latency and the risk of privacy leakage. Additionally, researchers have employed federated learning to protect data privacy, but this approach can lead to a straggler effect due to the limited computational and communication resources of terminal devices. To address these issues, a computation offloading framework is designed to optimize task and resource allocation, addressing multi-optimization problems and mitigating the straggler effect in federated learning. Moreover, a novel computation offloading method within a federated learning framework assisted by edge computing, named DRWB, is proposed. Specifically, we develop a deep reinforcement learning-based approach to transfer lagging training tasks to idle edge servers, enhancing data processing speed, minimizing transmission delays, and protecting user privacy. Extensive experimental results demonstrate that the DRWB method outperforms baseline methods, showcasing superior performance in handling heterogeneous medical data tasks. © 1975-2011 IEEE.

关键词： Electronic health record

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A spectrum-enhanced attention model for semantic segmentation of remote sensing images

A spectrum-enhanced attention model for semantic segmentatio...

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

作者： Xin Li Feng Xu Feifei Tao Yao Tong Xin Lyu Jianyi Zhong André Kaup College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Computer Engineering Jiangsu Ocean University Lianyungang China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China Chair of Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Semantic segmentation Frequency-domain analysis Urban planning Semantics Speech enhancement Signal processing Transformers Sensors Remote sensing Standards

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Probabilistic logic under uncertainty

Probabilistic logic under uncertainty

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13th Computing: The Australasian Theory Symposium, CATS2007

作者： Jøsang, Audun School of Software Engineering and Data Communications QUT Brisbane Australia

ISBN: (纸本)1920682465

Probabilistic logic combines the capability of binary logic to express the structure of argument models with the capacity of probabilities to express degrees of truth of those arguments. The limitation of traditional probabilistic logic is that it is unable to express uncertainty about the probability values themselves. This paper provides a brief overview subjective logic which is a probabilistic logic that explicitly takes uncertainty about probability values into account. More specifically, we describe equivalent representations of uncertain probabilities, and their interpretations. Subjective logic is directly compatible with binary logic, probability calculus and classical probabilistic logic. The advantage of using subjective logic is that real world situations can be more realistically modelled, and that conclusions more correctly refiect the ignorance and uncertainties about the input arguments. © 2007, Australian Computer Society, Inc.

关键词： Probabilistic logics

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An improvement to collaborative filtering for recommender systems

An improvement to collaborative filtering for recommender sy...

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International Conference on Computational Intelligence for Modelling, Control and Automation/International Conference on Intelligent Agents Web Technologies and International Commerce

作者： Weng, Li-Tung Xu, Yue Li, Yuefeng Nayak, Richi School of Software Engineering and Data Communications Queensland University of Technology QLD 4001 Australia

ISBN: (纸本)0769525040

Collaborative filtering recommenders utilize a database of user preferences to make personal product suggestions, and have achieved widespread successes in various e-commerce applications nowadays. Inverse User Frequency is one of most well known approaches to improve the accuracy of the standard collaborative filtering recommender[1]. In this paper, we propose a Statistical Attribute Distance method that uses the similarity in statistics of users' ratings to calculate the user correlation instead of using the statistics of users that rate for similar items. Form our experiment results we suggest the Statistical Attribute Distance outperforms Inverse User Frequency in recommendation accuracy and scalability.

关键词： Digital filters

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Hardware in-the-loop training of analogue neural network chip

Hardware in-the-loop training of analogue neural network chi...

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3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

作者： Zhang, Liang Sitte, Joaquin School of Software Engineering and Data Communications Queensland University of Technology Brisbane Australia

ISBN: (纸本)3540344829

In this paper we present the results of neural network hardware in-the-loop training for an analogue Local Cluster Neural Network (LCNN) chip. We use a Probabilistic Random Weight Change (PRWC) algorithm that is a combination of the random weight change and simulated annealing algorithms. We applied the PRWC algorithm to in-the-loop training for multi-dimensional function approximations and for predictions. We discuss the training strategy and the experiment results. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Neural networks

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G2DGA: An Adaptive Framework for Internetbased Distributed Genetic Algorithms 05

G2DGA: An Adaptive Framework for Internetbased Distributed G...

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GECCO 2005 - Genetic and Evolutionary Computation Conference

作者： Berntsson, Johan School of Software Engineering and Data Communications Queensland University of Technology QLD4001 Australia

ISBN: (纸本)1595930108

The Internet is different from traditional parallel computing environments, and Distributed Genetic Algorithms (DGAs) for the Internet need to be designed to address these differences. This paper presents a framework for Internet island model DGAs that uses adaptation methods to maintain efficiency and robustness in a volatile and dynamic run-time environment. The applicability of the methods is demonstrated on benchmark tests, and a real-world optimization problem in VLSI design. © 2005 ACM.

关键词： Genetic algorithms

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Mining for useful association rules using the ATMS

Mining for useful association rules using the ATMS

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International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, IAWTIC 2005

作者： Yue, Xu Yuefeng, Li School of Software Engineering and Data Communications Queensland University of Technology Brisbane QLD 4001 Australia

ISBN: (纸本)0769525040

Association rule mining has made many achievements in the area of knowledge discovery in databases. Recent years, the quality of the extracted association rules has drawn more and more attention from researchers in data mining community. One big concern is with the size of the extracted rule set. Very often tens of thousands of association rules are extracted among which many are redundant thus useless. In this paper, we first analyze the redundancy problem in association rules and then propose a novel ATMS-based method for extracting non-redundant association rules. © 2005 IEEE.

关键词： data mining

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