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International Conference on Systems and Control (ICSC)

作者： M. Dahmani N. Bettayeb Laboratory of Speech Communication and Signal Processing of University of Science and Technology Houari Boumediene Electrical Engineering Faculty Faculté des News Technologies of information and Communications University Kasdi Merbah

ISBN: (数字)9798331531812

ISBN: (纸本)9798331531829

In this work, the information obtained from plotting the Recurrence Plot (RP) diagram and Recurrence Quantification Analysis (RQA), referring to the glottal signal waveform, is explored for the diagnosis and assessment of human voice disorders. For that, the glottal signal was initially obtained from the vocal signal through the use of an inverse filtering algorithm : Iterative Adaptive Inverse Filtering (IAIF). RP tracing and RQA were applied to the glottal signal to evaluate the dynamic process and vibration of Vocal Folds (VFs). the normal or healthy voice is considered as a reference. Visual inspection of the RPs and parametric analysis reveals a valuable information about the state of vocal folds under pathologies.

关键词： Vibrations Pathology Visualization Hospitals Speech enhancement Inspection Iterative algorithms Human voice Medical diagnostic imaging Principal component analysis

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A demonstration of vector symbolic architecture as an effective integrated technology for AI at the network edge 2

A demonstration of vector symbolic architecture as an effect...

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Artificial Intelligence for Security and Defence Applications II 2024

作者： Bent, Graham Davies, Cai Vilamala, Marc Roig Li, Yuhua Preece, Alun Di Caterina, Gaetano Sola, Alex Vicente Kirkland, Paul Pearson, Gavin Tutcher, Benomy Neurosynapse Ltd. United Kingdom Security Crime and Intelligence Institute Cardiff University United Kingdom Neuromorphic Sensor Signal Processing Lab. EEE Dept. University of Strathclyde United Kingdom Defence Science and Technology Laboratory Porton Down United Kingdom Frazer-Nash Consultancy Ltd. Bristol United Kingdom

ISBN: (纸本)9781510681200

Vector Symbolic Architecture (VSA), a.k.a. Hyperdimensional Computing has transformative potential for advancing cognitive processing capabilities at the network edge. This paper presents a technology integration experiment, demonstrating how the VSA paradigm offers robust solutions for generation-after-next AI deployment at the network edge. Specifically, we show how VSA effectively models and integrates the cognitive processes required to perform intelligence, surveillance, and reconnaissance (ISR). The experiment integrates functions across the observe, orientate, decide and act (OODA) loop, including the processing of sensed data via both a neuromorphic event-based camera and a standard CMOS frame-rate camera;declarative knowledge-based reasoning in a semantic vector space;action planning using VSA cognitive maps;access to procedural knowledge via large language models (LLMs);and efficient communication between agents via highly-compact binary vector representations. In contrast to previous ‘point solutions’ showing the effectiveness of VSA for individual OODA tasks, this work takes a ‘whole system’ approach, demonstrating the power of VSA as a uniform integration technology. © 2024 SPIE.

关键词： Photons

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Blockchain-Assisted Revocable Cross-Domain Authentication for Vehicular Ad-Hoc Networks

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Li, Ru Cui, Jie Zhang, Jing Wei, Lu Zhong, Hong He, Debiao Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230039 China Anhui University Anhui Engineering Laboratory of IoT Security Technologies Hefei230039 China Wuhan University School of Cyber Science and Engineering 430072 China

With the rapid development of vehicular ad-hoc networks (VANETs) and the increasing diversification of user demands, interactions between different management domains have become more frequent. Identity authentication is an effective way to establish cross-domain trust and secure communication. However, the existing cross-domain authentication schemes of VANETs are limited to the same management or authentication technology for each domain and rely on centralized cross-domain identity management. Even distributed management solutions encounter latency sensitivity, security and privacy challenges. To address these challenges, we propose a blockchain-assisted revocable cross-domain authentication scheme for VANETs. The proposed scheme can establish trust between domain entities by deploying different authentication methods and using distributed management to avoid single-point failures. In addition, the scheme can revoke the identity of malicious vehicles by updating the group public key, thereby ensuring the security and privacy of cross-domain Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. This design avoids the additional impacts of blockchain technology constraints on the high mobility and real-time requirements of VANETs. Security analysis and performance evaluation show that our scheme can resist more attacks and has better security than other related schemes while also achieving a better balance between communication and computational cost. © 2025 IEEE.

关键词： Vehicular ad hoc networks

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Design of a Wideband SIW Slot Antenna with Enhanced Gain

Design of a Wideband SIW Slot Antenna with Enhanced Gain

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2023 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2023

作者： Wang, Min Li, Xuan Mo, Dongsheng Chen, Zhengchuan Chongqing University of Posts and Telecommunications Postdoctoral Research Center of Chongqing Key Laboratory of Optoelectronic Information Sensing and Transmission Technology Chongqing400065 China Guilin University of Electronic Technology Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory Guilin541004 China University of Electronic Science and Technology of China Chengdu611731 China Chongqing University School of Microelectronics and Communication Engineering Chongqing400044 China

ISBN: (纸本)9798350336740

This article demonstrates a wideband SIW slot antenna with gain-enhancing capability. The upper layer of SIW is composed of a 50 Ω microstrip line and radiation slots. The radiation slots are composed of a multiple branch slot and a couple of oblique slots. The multiple branch slot can excite triple resonant modes for extending impedance matching bandwidth and gain, while the oblique slots are utilized to match the microwave line so that further enhancing the impedance matching bandwidth. Simulated results of the SIW slot antenna indicate that the relative bandwidth is up to 16.7%. The gain at 9.0 GHz reaches 9.3 dBi, while the 1-dB gain bandwidth reaches 25.5%. © 2023 IEEE.

关键词： Slot antennas

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Hierarchical Metadata Information Constrained Self-Supervised Learning for Anomalous Sound Detection under Domain Shift

Hierarchical Metadata Information Constrained Self-Supervise...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Haiyan Lan Qiaoxi Zhu Jian Guan Yuming Wei Wenwu Wang Group of Intelligent Signal Processing College of Computer Science and Technology Harbin Engineering University China National Engineering Laboratory for Modeling and Emulation in E-Government Harbin Engineering University China Centre for Audio Acoustics and Vibration University of Technology Sydney Australia Centre for Vision Speech and Signal Processing University of Surrey UK

Self-supervised learning methods have achieved promising performance for anomalous sound detection (ASD) under domain shift by incorporating the metadata of domain shift types and machine sound attributes in feature learning. However, the relation between domain shifts and machine sound attributes has yet to be fully utilised despite their potential benefits for characterising domain shifts. This paper presents a hierarchical metadata information constrained self-supervised ASD method, where the hierarchical relation between domain shift types (section IDs) and attributes is constructed and used as constraints to improve feature representation. In addition, we propose an attribute-group-centre based method for calculating the anomaly score under the domain shift condition. Experiments show improved audio feature learning over the state-of-the-art methods in DCASE 2022 challenge Task 2.

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A Two-Stage Iteratively Reweighted Least Squares Estimator for Robust Elliptic Localization in the Presence of Impulsive Noise

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Zhao, Xiaoyu Lu, Zukun Li, Jun Huang, Long Yu, Yi Ou, Gang The College of Electronic Science and Technology National University of Defense Technology Changsha410073 China The Key Laboratory of Satellite Navigation Technology Changsha410073 China The National Key Laboratory of Radar Signal Processing Xidian University Xi’an710071 China The Hunan Xinghe Electronics Co.LTD Changsha410073 China

As a range-based localization approach, elliptic localization based on bistatic ranges (BRs) has wide range of applications in multistatic systems. This paper addresses the problem of robust elliptic localization using the minimum `pnorm criterion in scenarios affected by impulsive α-stable noise. To solve the `p-norm formulation, we develop an asymptotically efficient two-stage iteratively reweighted least squares (TSIRLS) estimator via incorporating pseudolinear estimation and multistage weighted least squares estimation into the framework of iteratively reweighted least squares (IRLS) methods. The estimator consists of two cascaded `p-norm minimization estimators. To obtain a closed-form solution at each iteration, we transform nonlinear BR measurement equations into pseudolinear forms through introducing auxiliary variables, thus resulting in an iteratively reweighted pseudolinear least squares (IRPLS) estimator in the first stage. The dependencies of unknown parameters and the estimate error derived from the IRPLS are utilized in the second stage to enhance the performance of target location estimate. The analytical derivation demonstrates that, under the assumptions of a sufficiently large number of measurements and small noise, the final position estimate obtained from the TSIRLS achieves the theoretical covariance of the general `pnorm minimization estimation. Extensive numerical simulations highlight the advantages of TSIRLS over existing least squares and robust estimators in terms of positioning performance and runtime. The TSIRLS is also observed to generate approximately unbiased estimates with mean square errors that closely approach the Cramér-Rao lower bound. © 2025 IEEE.

关键词： Impulse noise

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Model-Free Non-Singular Fast Terminal Sliding Mode Controller Using Extended Sliding Mode Observer for Series Elastic Actuator-Based Robotic Arm

Model-Free Non-Singular Fast Terminal Sliding Mode Controlle...

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Automatics and Informatics (ICAI), International Conference

作者： Hongfei Sun Haoping Wang Yang Tian Sino-French International Joint Laboratory of Automatic Control and Signal Processing (LaFCAS) School of Automation Nanjing University of Science and Technology Nanjing China

This paper proposes an adaptive admittance controller and a model-free non-singular fast terminal sliding mode controller based on an extended sliding mode observer for hybrid force and position control of a robotic arm driven by series elastic actuators (SEA) with external disturbances and model uncertainty. According to the ultra-local model method, simplifying the robotic arm driven by SEA dynamic model and all unknown terms which include the uncertainty of the model and unknown disturbance are integrated into one term called lumped total disturbance. In order to estimate the integrated unknown disturbance terms, an extended sliding mode observer (ESMO) is constructed. For better position control performance, a model-free non-singular fast terminal sliding mode controller was constructed by combining ultra-local model with non-singular fast terminal sliding mode control. In the outer loop, in order to accurately control the contact torque generated by interaction with the external environment in the hybrid force and position control of the robotic arm with changes in external environmental stiffness, an adaptive admittance controller is designed. To validate the effectiveness of the control strategy and the optimization effect on control performance, co-simulations of the SEA driven robotic arm are realized by using SolidWorks and MATLAB. Compare the co-simulation results with TDE-iPD to show the high accuracy and fast convergence characteristics of this control method.

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Model-Free Based Nonsingular Fast Terminal Sliding Mode Control for Gough-Stewart Platform

Model-Free Based Nonsingular Fast Terminal Sliding Mode Cont...

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Chinese Control Conference (CCC)

作者： Yunhua He Haoping Wang Yang Tian Xingyu Zhou Sino-French International Joint Laboratory of Automatic Control and Signal Processing (LaFCAS) School of Automation Nanjing University of Science and Technology Nanjing China

In this paper, a model-free based nonsingular fast terminal sliding mode control (MFNFTSMC) approach is presented to track the position and pose of an uncertain Gough-Stewart platform (GSP). Firstly, the kinematic and dynamic modeling of the GSP system is constructed. Then, a MFNFTSMC method is presented. The model uncertainty is compensated using a time-delay estimation (TDE) approach, and the error resulting from the delay estimate is then compensated using a Super-Twisting algorithm (STA). Thirdly, by integrating STA dynamics and NFTSMC dynamics, a model-free strategy based on the ultra-local model is provided to diminish the demand of acquiring the precise model of the GSP. Finally, to verify the performance and effectiveness of the proposed MFNFTSMC method, the virtual prototype of Gough-Stewart platform is built in Solidworks, co-simulation is carried out with Matlab/Simulink on a Gough-Stewart platform in comparison with the NFTSMC method. Key Words: Gough-Stewart platform, Model-free control, Time-delay estimation, Terminal sliding mode control

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Meta-feature-based Concept Evolution Detection on Feature Streams

Meta-feature-based Concept Evolution Detection on Feature St...

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6th International Conference on Intelligent Computing and signal processing (ICSP)

作者： Yufeng Guo Peng Zhou Yanping Zhang Xin Jiang School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei China

Data in the real world is often not static but generated and processed in streams, such as real-time adjustment of device setting parameters and real-time GPS positioning data. Feature streams means the number of samples is fixed, and their features are generated and arrive individually over time. A significant challenge of learning from online streaming data is a phenomenon known as concept evolution, that the concept of the data may change over time. In the streaming feature scenario, we define meta-features as univariate statistics describing data distribution and use meta-features to capture the data distribution and statistical properties of concepts. Therefore, an efficient Meta-Feature-based Concept Evolution Detection framework on Feature Streams (MF-CED-FS) is proposed, which consists of a sliding window, meta-feature vector similarity discrimination, and a concept detection method based on a weighted bipartite graph. Extensive experiments on real-world high-dimensional datasets verify the effectiveness of MF-CED-FS.

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Research on Spatial Angle Super-Resolution Algorithm Based on Adaptive Layered Sparse

Research on Spatial Angle Super-Resolution Algorithm Based o...

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Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Min Xue Yuexin Gao Zhixin Wu National Key Laboratory of Radar Signal Processing Xidian University Xi'an China School of Aerospace Science and Technology Xidian University Xi'an China

Compressed Sensing (CS) theory is based on the sparsity of signals and involves compressively sampling high-dimensional data to obtain a small number of linear observations that contain the complete information of the signals. By solving an optimization problem, the original signals can be recovered from these compressed linear observations. This paper combines CS theory with the sparse characteristics of targets in the spatial domain and proposes an adaptive layered sparse spatial angle super-resolution algorithm. This algorithm converts the target spatial angle super-resolution into the problem of using orthogonal basis to reconstruct sparse signals. Then, the target spatial angle super-resolution is carried out by using single snapshot data through optimization solution, and the target spatial angle domain is continuously reduced through layered orthogonal matching pursuit (LOMP) solution to complete the target spatial angle reconstruction. The effectiveness and robustness of the algorithm are verified by using the proposed algorithm to process the measured data.

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