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Accurate, Secure, and Efficient Semi-Constrained Navigation Over Encrypted City Maps

IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2642-2658页

作者： Li, Meng Chen, Yifei Gao, Jianbo Wu, Jingyu Zhang, Zijian He, Jialing Zhu, Liehuang Conti, Mauro Lin, Xiaodong Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei230002 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230002 China Hefei University of Technology Anhui Province Key Laboratory of Industry Safety and Emergency Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei230002 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100811 China University of Science and Technology of China School of Computer Science and Technology Hefei230026 China Beijing Institute of Technology Southeast Institute of Information Technology Fujian Putian351100 China Chongqing University College of Computer Science Chongqing400044 China University of Padua Department of Mathematics HIT Center Padua35131 Italy Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft2628 CD Netherlands University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Navigation services enable users to find the shortest path from a starting point S to a destination D, reducing time, gas, and traffic congestion. Still, navigation users risk the exposure of their sensitive location data. Our motivation arises from how users can accurately, securely, and efficiently navigate from S to Dwhile passing through k unordered stops, i.e., midway locations with a non-fixed visiting order. In this work, we formally define Semi-Constrained Navigation (SCN) and present a novel scheme Hermestoachieveaccurate,secure, andefficient SCN. Specifically, weproposeadivide-and-conquerapproachtostrikeagoodbalance betweenaccuracyandefficiency. It recursively depth-first-searches the whole area (a navigation tree) and invokes five carefully-crafted strategies stop-by-stop to compute three subpaths in three se quential subareas. We construct a path-distance oracle to encrypt the road graph and securely implement the strategies by using homomorphic encryption and garble circuits. We formally prove the security in the random oracle model and analyze the search complexity to be less than O(k2). We experiment over a real-world city map and compare with six baselines. Results show that path search with k = 4 among N = 1000 intersections requires 5.58 seconds with a 3.2% distance deviation rate and an 82.5% path similarity. © 2004-2012 IEEE.

关键词： Traffic congestion

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Dual-Path Spectrogram Refinement Network for Robust Speaker Verification

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Journal of Shanghai Jiaotong University (science) 2025年 1-9页

作者： Wang, Zonghui Fang, Zhihua He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

The accuracy and reliability of automatic speaker verification (ASV) face significant challenges in noisy environments. In recent years, joint training of speech enhancement front-end and ASV back-end has been widely applied to improve the robustness of ASV systems. Traditional joint training directly uses the enhanced speech features as the input of the back-end. However, the diversity of noise types and noise intensities will excessively suppress the enhanced features, resulting in speech distortion or residual noise. To alleviate this problem, we propose a dual-path spectrogram refinement network that enables the enhanced features to learn supplementary information from the separated noise features. In addition, we incorporate coordinate attention into the overall joint architecture to capture more comprehensive frequency and temporal information of the speaker from different spatial positions. We conduct extensive experiments on the VoxCeleb1 test set, the out-of-domain noise test set, and the VOiCES corpus. The experimental results demonstrate that our proposed method significantly improves the accuracy and robustness of speaker verification systems in both clean and noisy environments. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Spectrographs

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Anomalous Sound Detection Using Time-Frequency Feature and Mixbatch

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Journal of Shanghai Jiaotong University (science) 2025年 1-8页

作者： Huang, Shun Zhang, Yunxiang Fang, Zhihua Tang, Minrui Xu, Ruifeng He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

The sound emitted by machines under abnormal working conditions exhibits various frequency patterns. Currently, the most advanced anomalous sound detection (ASD) approach is to apply a multi-head self-attention mechanism to the Log-Mel spectrogram for automatic frequency pattern analysis. However, the Log-Mel spectrogram may filter out high-frequency components of abnormal sounds;thus the use of self-attention mechanisms on Log-Mel spectrogram seems to have certain limitations. In this paper, we construct a simple convolutional neural network to extract comprehensive frequency features from raw audio to complement spectral-temporal information fusion. The parameters of this neural network are continuously updated during the training process to extract better frequency features for downstream classification neural networks in ASD. Additionally, a method for data augmentation in the batch dimension was developed to help the classification model learn both types of comprehensive features simultaneously. The proposed method achieved an AUC of 94.13% and a pAUC of 89.09% on the DCASE 2020 Challenge Task 2 dataset. Even when using only the features proposed in this paper, an AUC of 84.62% was achieved. (Figure presented.). © Shanghai Jiao Tong University 2025.

关键词： Spectrographs

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Resource Allocation for Fairness Enhancement in Multi-Cell Vehicular VLC System With Optical IRS: A Cooperative Transmission Approach

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IEEE Internet of Things Journal 2025年第11期12卷 16931-16946页

作者： An, Nan Yang, Fang Liu, Chang Cheng, Ling Song, Jian Han, Zhu Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Center of National Railway Intelligent Transportation System Engineering and Technology Beijing100081 China China Academy of Railway Sciences Corporation Limited Signal and Communication Research Institute Beijing100081 China University of the Witwatersrand School of Electrical and Information Engineering Johannesburg2050 South Africa Tsinghua University Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Beijing100084 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China 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

In multi-cell downlink vehicular visible light communication (VLC) systems, vehicles at a cell edge experience lower achievable data rates compared with those at a cell center, primarily due to the weaker channel gain and inter-cell interference, leading to unfairness among vehicles. In this paper, a cooperative transmission approach is proposed for a vehicular VLC system with optical intelligent reflecting surface (OIRS) to enhance the max-min fairness of the system by leveraging the additional OIRS-reflected channels and the interference-mitigating capabilities of cooperative transmission. To this end, the system model is established, followed by the formulation of a resource allocation problem aiming at enhancing max-min fairness, in which the minimum achievable data rate among vehicles is optimized. Then, an effective resource algorithm is proposed, transforming the original problem into an equivalent form and subsequently decomposing it into three subproblems, focusing on OIRS assignment, subchannel allocation, and power adjustment, respectively. By employing a block coordinate descent algorithm, the three subproblems are solved iteratively until convergence. In addition, simulation results confirm the improvement in max-min fairness brought by OIRS and the proposed cooperative transmission approach. Moreover, compared with various baselines, the proposed resource allocation algorithm significantly enhances in the max-min fairness for the vehicular VLC system, crucial for this application. © 2014 IEEE.

关键词： Resource allocation

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Fast Demodulation of OFDR-Based Distributed Sensing Based on Enhanced Buneman Frequency Estimation

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Journal of Lightwave technology 2025年第9期43卷 4283-4289页

作者： Zhang, Zhaopeng Liu, Bo Liu, Xiao Li, Caiyun Alsalman, Osamah Zhu, Chen Zhejiang Laboratory Research Center for Novel Computing Sensing and Intelligent Processing Hangzhou311121 China King Saud University Department of Electrical Engineering College of Engineering Riyadh11421 Saudi Arabia Missouri University of Science and Technology Department of Electrical and Computer Engineering RollaMO65409 United States

Aiming at realizing high-efficiency distributed strain sensing through optical frequency domain reflectometry (OFDR), this paper introduces a fast demodulation algorithm to determine strain-induced spectral shifts from coarse Rayleigh backscattering (RBS) cross-correlation spectra. The proposed approach employs an enhanced Buneman frequency estimation (BFE) algorithm, enabling direct spectral shift analysis across coarse signals. By applying this algorithm, the need for dense interpolation in the conventional cross-correlation demodulation process—typically required for a finer spectral sampling interval but at the cost of demodulation efficiency—can be eliminated. Both theoretical analysis and experimental investigation reveal the equivalence of the BFE and conventional algorithms in terms of underlying physical principles and resultant demodulation noise distribution, establishing a strong foundation for adopting the BFE algorithm to improve demodulation efficiency without sacrificing performance metrics. Demonstration experiments using identical hardware configurations show that the proposed BFE method enhances demodulation efficiency by over 17 times without sacrificing other performance metrics, confirming its effectiveness and practicality. This advancement holds promise for improving the practical application of OFDR-based distributed sensing systems. © 1983-2012 IEEE.

关键词： Frequency domain analysis

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Integrating Spectro-Temporal Cross Aggregation and Multi-Scale Dynamic Learning for Audio Deepfake Detection

Integrating Spectro-Temporal Cross Aggregation and Multi-Sca...

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

作者： Hao, Yunqi Xu, Minqiang Chen, Yihao Liu, Yanyan He, Liang Fang, Lei Liu, Lin School of Computer Science and Technology Xinjiang University Urumqi China Hefei iFly Digital Technology Co. Ltd. Hefei China University of Science and Technology of China Hefei China Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (纸本)9798350368741

Audio deepfake refers to the technology of synthesizing speech using deep learning or large model algorithms. Compared to human voice, synthetic deepfake speech exhibits artifacts at global and local levels, which can be leveraged by audio deepfake detection (ADD) to distinguish real and fake speech. In this paper, we designed the spectro-temporal cross aggregation (STCA) module and the local multi-scale dynamic convolution (LMDC) module to extract global and local artifacts for detecting forged information, respectively. The STCA module utilizes a dual-branch structure with cross-attention, extracting global temporal and frequency features through the branches and aggregating mutual artifacts via cross-attention. The LMDC module uses multi-scale dynamic convolution for grouped features, to extract local information. Experimental results on multiple test sets demonstrate the effectiveness of our method. Specifically, we achieved an EER of 1.87% on the ASVspoof2021 DF evaluation set, surpassing the current state-of-the-art system by a relative 14.6%. © 2025 IEEE.

关键词： Audio deepfake detection Global-local modeling Multi-scale feature XLS-R

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Morphable 3D architectures enabled by shear-guided approach

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Materials Today 2025年 86卷 28-41页

作者： Li, Kan Liu, Zhi Hu, Xiaonan Xu, Zijian Zhou, Yunlei Chen, Lin Huang, Yonggang Bo, Renheng Zhang, Yihui AML Department of Engineering Mechanics State Key Laboratory of Flexible Electronics Technology Tsinghua University Beijing100084 China State Key Laboratory of Digital Manufacture Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China State Key Laboratory for Mechanical Behavior of Materials School of Materials Science and Engineering Xi'an Jiaotong University Shaanxi Xi'an710000 China Departments of Mechanical Engineering Civil and Environmental Engineering Materials Science and Engineering and Center for Bio-Integrated Electronics Northwestern University EvanstonIL60208 United States

3D structures and electronic devices assembled through mechanically guided approaches have found important applications in a wide spectrum of emerging fields, including healthcare, biomedical engineering, wearable devices, robotics, among others. To broaden the obtainable functional 3D architectures via mechanical pathways, in this work, a shear-guided assembly approach is demonstrated. A series of structures in forms of ribbon, membrane and hybrid configurations with previously inaccessible geometrical features, such as reversible local flips and centrosymmetric charity are showcased. Finite element analysis (FEA) is performed, suggesting that such shear-induced structures are capable of reversible post-assembly shape transformation among different modes. Given the above, a facile design, consisting of two ribbon-like 3D units (dual-3D-unit design), is introduced and carefully calibrated for shear sensing. Notably, such design is capable of translating the hard-to-measure shear deformation into easily-accessed vertical displacements while insensitive to finite tensile loadings. A multifunctional arrayed electronic interface is fabricated using such dual-3D-unit design, suggesting promising potentials in human–machine interactions. © 2025 Elsevier Ltd

关键词： Shear deformation

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AtheroRisk: An Integrated computer Software System for Stroke Risk Stratification Utilizing Carotid Plaque Analysis in Ultrasound Videos

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SN computer science 2025年第2期6.0卷 1-16页

作者： Gemenaris, Michalis Liapi, Georgia D. Markides, Christos Constantinou, Kyriacos Loizou, Christos P. Neophytou, Michalis Kardoulas, Dimitrios Pattichis, Marios S. Griffin, Maura Nicolaides, Andrew Pattichis, Constantinos S. Kyriacou, Efthyvoulos Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Department of Computer Science and Engineering Frederick University Nicosia Cyprus Department of Computer Science University of Cyprus Nicosia Cyprus American Medical Center Nicosia Cyprus Vascular Surgery Clinic Attikon General University Hospital Athens Greece Department of Electrical and Comp. Engineering Univ. of New Mexico Albuquerque NM United States Vascular Screening and Diagnostic Center Nicosia Cyprus

In this study we propose and evaluate AtheroRisk, a standalone integrated computer software system for the analysis of carotid B-mode ultrasound (U/S) images and videos. Our goal was to provide a tool to help physicians stratify stroke risk. The presented system is based on outcomes from different research studies, as well as European guidelines for the evaluation and treatment of carotid artery disease. The goal is to enable reproducible analysis of atherosclerotic plaques in real time. AtheroRisk brings together analysis of U/S image and/or video of the carotid arteries facilitating anonymization, standardization, noise filtering and segmentation of plaques;followed by image and motion feature extraction to derive plaque-composition and stability. All the above steps are combined to determine the annual stroke risk rate and the 5-year stroke-free survival rate. The system is powered by an SQLite local database, in which the end user can save and manage data extracted from processing and analysis in an anonymized way. The first version of AtheroRisk was developed following an incremental software development process. The developed system was verified using 54 U/S videos (27 Asymptomatic, AS;27 Symptomatic, SY cases) while the clinician’s satisfaction and comments were collected through a questionnaire-based validation process. The results show that the integrated system proposed in this study can be successfully used for the automated image and video analysis of the CCA plaque in ultrasound videos. © The Author(s) 2025.

关键词： Carotid plaque analysis Motion analysis Plaque features Stroke risk stratification Ultrasound video

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UltraSegNet:A Hybrid Deep Learning Framework for Enhanced Breast Cancer Segmentation and Classification on Ultrasound Images

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computers, Materials & Continua 2025年第5期83卷 3303-3333页

作者： Suhaila Abuowaida Hamza Abu Owida Deema Mohammed Alsekait Nawaf Alshdaifat Diaa Salama Abd Elminaam Mohammad Alshinwan Department of Computer Science Faculty of Prince Al-Hussein Bin Abdallah II for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Medical Engineering Department Faculty of EngineeringAl-Ahliyya Amman UniversityAmman19328Jordan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Faculty of Computers Science Misr International UniversityCairo11800Egypt Jadara Research Center Jadara UniversityIrbid21110Jordan

Segmenting a breast ultrasound image is still challenging due to the presence of speckle noise,dependency on the operator,and the variation of image *** paper presents the UltraSegNet architecture that addresses these challenges through three key technical innovations:This work adds three things:(1)a changed ResNet-50 backbone with sequential 3×3 convolutions to keep fine anatomical details that are needed for finding lesion boundaries;(2)a computationally efficient regional attention mechanism that works on high-resolution features without using a transformer’s extra memory;and(3)an adaptive feature fusion strategy that changes local and global featuresbasedonhowthe image isbeing *** evaluation on two distinct datasets demonstrates UltraSegNet’s superior performance:On the BUSI dataset,it obtains a precision of 0.915,a recall of 0.908,and an F1 score of *** the UDAIT dataset,it achieves robust performance across the board,with a precision of 0.901 and recall of ***,these improvements are achieved at clinically feasible computation times,taking 235 ms per image on standard GPU ***,UltraSegNet does amazingly well on difficult small lesions(less than 10 mm),achieving a detection accuracy of *** is a huge improvement over traditional methods that have a hard time with small-scale features,as standard models can only achieve 0.63–0.71 *** improvement in small lesion detection is particularly crucial for early-stage breast cancer *** from this work demonstrate that UltraSegNet can be practically deployable in clinical workflows to improve breast cancer screening accuracy.

关键词： Breast cancer ultrasound image segmentation classification deep learning

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A Systematic Flexible-Window based Scheduling Framework for Time-Sensitive Networking

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IEEE Internet of Things Journal 2025年第11期12卷 15636-15649页

作者： Sun, Wenjing Zou, Yuan Guan, Nan Zhang, Xudong Liu, Jiahui Farzaneh, Morteza Hashemi Beijing Institute of Technology National Engineering Laboratory for Electric Vehicles School of Mechanical Engineering and Collaborative Innovation Center of Electric Vehicles in Beijing Beijing100081 China City University of Hong Kong Department of Computer Science Hong Kong RealtimeIT Germany

Time-Sensitive Networking (TSN) is increasingly applied in automotive and industrial internet fields due to its low latency and deterministic communication. Gate control list (GCL) is foundational for deploying TSN. Currently, most scheduling research focuses on frame-to-window based scheduling. This scheduling approach typically generates a specific window for each frame, leading to a proliferation of GCL in large networks, which increases the complexity of implementing TSN. To simplify deployment and enhance scheduling reliability, this paper introduces a systematic flexible-window based scheduling framework. Utilizing a gapless GCL design approach, it optimizes flow's worst-case end-to-end (e2e) delays through window length design, with delays obtained through network calculus analysis. A generic solving framework based on metaheuristic algorithms is established to address this optimization problem. The scheduling framework also features a load-balanced turn prohibition routing strategy to balance link loads and avoid cyclic dependencies, alongside a K-means priority clustering method based on routing overlap to reduce the number of priorities. Simulation validation in a high-level autonomous driving vehicle's in-vehicle network shows that the proposed method can decrease GCL numbers by nearly 90% against frame-to-window scheduling. In common industrial internet scenario, it significantly reduces worst-case e2e delays and enhances scheduling success rates compared to the analogous scheduling method. Large-scale complex network scenario further demonstrates its scalability. © 2025 IEEE.

关键词： K-means clustering

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