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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Sami Briouza Hassène Gritli Nahla Khraief Safya Belghith Dilbag Singh Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Le Belvédère Tunis Tunisia Higher Institute of Information and Communication Technologies University of Carthage Borj Cedria Tunis Tunisia RISC Lab (LR16ES07) National Engineering School of Tunis University of Tunis El Manar Le Belvédère Tunis Tunisia School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju South Korea

ISBN: (纸本)9781665490597

Electromyography (EMG) classification has been an important step to achieve the rehabilitation goal for lower/upper limbs and hands using robotic devices. To perform this step effectively, many researchers have adopted machine learning and deep learning algorithms. In this study, a hybrid CNN-SVM architecture was developed for the classification of surface EMG (sEMG) signals. The CNN part of the proposed architecture is used to extract relevant features from the data and the SVM part would use the extracted features for the classification task. This can be helpful as it will reduce human input and make results more consistent. For this work, we use the Ninapro DB2’s dataset, which contains 3 different Exercises B, C, and D. Thus, we obtained the following accuracy results: an accuracy of 78.56% for Exercise B, an accuracy of 72.84% for Exercise C, and an accuracy of 88.24% for Exercise D.

关键词： Support vector machines Performance evaluation Biological system modeling Feature extraction Electromyography Classification algorithms Data mining

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Effects of antagonistic network-targeted tDCS on brain co-activation patterns depends on the networks’ electric field: a simultaneous tDCS-fMRI study

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NeuroImage 2025年 316卷 121318页

作者： Hechun Li Hongru Shi Sisi Jiang Changyue Hou Haonan Pei Hanxi Wu María Luisa Bringas Vega Gang Yao Dezhong Yao Cheng Luo The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation Center for Information in Medicine School of Life Science and Technology University of Electronic Science and Technology of China Chengdu PR China Cuban Neuroscience Center La Habana Cuba China-Cuba Belt and Road Joint Laboratory on Neurotechnology and Brain-Apparatus Communication University of Electronic Science and Technology of China Chengdu PR China Research Unit of NeuroInformation Chinese Academy of Medical Sciences 2019RU035 Chengdu PR China

Background Brain networks should be ideal targets for non-invasive brain stimulation, as network dysfunction is a common feature of various neuropsychiatric disorders. Understanding the mechanisms of network-targeted stimulation is essential for advancing its clinical applications. Material and method The current study utilized simultaneous network-targeted transcranial direct current stimulation(tDCS) and functional magnetic resonance imaging (fMRI) to investigate the effects of tDCS targeting antagonistic networks on brain dynamics. A total of 143 healthy participants were recruited and assigned to receive central executive network (CEN)-targeted tDCS (C-targeted group), default mode network (DMN)-targeted tDCS (D-targeted group), or sham tDCS (sham group). fMRI data with three sections (pre-stimulation, during-stimulation, post-stimulation) were collected across all subjects. Individual electric field (EF) strength was simulated using individual head model. Six recurring brain patterns (co-activation patterns, CAPs) were identified. The temporal indices of these CAPs (occurrence, fraction time, persistence time) and their transition probabilities were calculated. This study first examined the effects of C-targeted / D-targeted / sham tDCS on temporal indices and further explored the contribution of brain networks’ EF strength on the altered temporal indices. Results C-targeted tDCS significantly increased the temporal indices of CAPs dominated by DMN and the transition probabilities from other CAPs to DMN-dominated CAPs during stimulation. Meanwhile, the decreased temporal indices of CAP dominated by CEN, and its transition probabilities to these CAPs were also found during C-targeted tDCS. In contrast, the d-targeted tDCS had only a slight effect on brain dynamics, while sham tDCS showed no significant impact. Further fusion analyses revealed that the EF strength in the salience network made a large contribution to the temporal indices of CAPs during stimulation

关键词： Network-targeted tDCS Co-activation pattern fMRI Electric field Brain dynamic

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Mapping ultrafast timing jitter in dispersion-managed 89 GHz frequency microcombs via self-heterodyne linear interferometry

arXiv

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arXiv 2021年

作者： Wang, Wenting Liu, Hao Yang, Jinghui Vinod, Abhinav Kumar Lim, Jinkang Jang, Yoon-Soo Zhou, Heng Yu, Mingbin Lo, Patrick Guo-Qiang Kwong, Dim-Lee DeVore, Peter Chou, Jason Wong, Chee Wei Fang Lu Mesoscopic Optics and Quantum Electronics Laboratory University of California Los AngelesCA90095 United States Key Lab of Optical Fiber Sensing and Communication Networks University of Electronic Science and Technology of China Chengdu611731 China Institute of Microelectronics A*STAR Singapore117865 Singapore State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Shanghai Industrial Technology Research Institute Shanghai China Advanced Micro Foundry Singapore117685 Singapore Lawrence Livermore National Laboratory LivermoreCA94550 United States

Laser frequency microcombs provide equidistant coherent frequency markers over a broad spectrum, enabling new frontiers in chip-scale frequency metrology, laser spectroscopy, dense optical communications, precision distance metrology and astronomy. Here we demonstrate thermally stabilized frequency microcomb formation in dispersion-managed microresonators at the different mode-locking states featured with the negligible center frequency shift and broad frequency bandwidth. Subsequently, femtosecond timing jitter in the microcombs are characterized, supported by precision metrology on the timing phase, relative intensity noise and instantaneous linewidth. We contrast the fundamental noise for a range of 89 GHz microcomb states, from soliton crystals to multiple solitons and single-soliton regimes, determined by pump-resonance detuning. For the single-soliton state, we report a close-to-shot-noise-limited relative intensity noise of -153.2 dB/Hz and a quantum-noise-limited timing jitter power spectral density of 0.4 as2/Hz, at 100 kHz offset frequency. This is enabled by a self-heterodyne linear interferometer with 94.2 zs/Hz1/2 timing resolution, 50.6 mHz/Hz1/2 RF frequency resolution, and 6.7 µV/Hz frequency discrimination sensitivity. We achieve an integrated timing jitter at 1.7 ± 0.07 fs, integrated from 10 kHz to 1 MHz. Measuring and understanding the fundamental noise parameters in these high-clock-rate frequency microcombs are essential to advance soliton physics and precision microwave-optical clockwork. Copyright © 2021, The Authors. All rights reserved.

关键词： Shot noise

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Wireless communication Signal Strength Prediction Method Based on the K-nearest Neighbor Algorithm 5th

Wireless Communication Signal Strength Prediction Method Bas...

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5th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2019

作者： Chen, Zhao Xiong, Ning Wang, Yujue Ding, Yong Xiang, Hengkui Tang, Chenjun Liu, Lingang Zou, Xiuqing Luo, Decun Key Lab on Cognitive Radio and Information Processing School of Information and Communication Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9789811501173

Existing interference protection systems lack automatic evaluation methods to provide scientific, objective and accurate assessment results. To address this issue, this paper develops a layout scheme by geometrically modeling the actual scene, so that the hand-held full-band spectrum analyzer would be able to collect signal field strength values for indoor complex scenes. An improved prediction algorithm based on the K-nearest neighbor non-parametric kernel regression was proposed to predict the signal field strengths for the whole plane before and after being shield. Then the highest accuracy set of data could be picked out by comparison. The experimental results show that the improved prediction algorithm based on the K-nearest neighbor non-parametric kernel regression can scientifically and objectively predict the indoor complex scenes’ signal strength and evaluate the interference protection with high accuracy. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Spectrum analyzers

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Deep Reinforcement Learning for User Association in Heterogeneous Networks with Dual Connectivity

Deep Reinforcement Learning for User Association in Heteroge...

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IEEE Conference on Wireless communications and Networking

作者： Mengjie Yi Yan Zhang Xijun Wang Chao Xu Xiao Ma State Key Lab of Integrated Service Networks Information Science Institue Xidian University Xi’an Shaanxi China School of Electronics and Information Technology Sun Yat-sen University Guangzhou China Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences 865 Changning Road Shanghai China School of Information Engineering Northwest A&F University Yangling Shaanxi China Shaanxi Normal University Xi’an P. R. China

The dual connectivity is emerging as a promising solution to boost capacity in heterogeneous networks. However, it is challenging to obtain an optimal user association in heterogeneous networks with dual connectivity, due to its non-convex and combinatorial nature. In this paper, we propose a user association scheme based on deep reinforcement learning to maximize the overall network utility, which takes both throughput and user fairness into account, in the downlink of a heterogeneous network. Particularly, each user associates with the macro base station (BS) and a micro BS. We apply a deep Q-network (DQN) to obtain the nearly optimal policy to associate the users and micro BSs. Simulation results demonstrate that DQN-based user association performs better compared to the conventional user association schemes in heterogeneous networks with dual connectivity, and it behaves good scalability when the environment changes.

关键词： Base stations Simulation Scalability Conferences Reinforcement learning Markov processes Throughput

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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Resource Mobility Aware Hybrid Task Planning in Space information Networks

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Journal of communications and information Networks 2019年第4期4卷 107-116页

作者： Runzi Liu Yiting Zhu Yan Zhang Weihua Wu Di Zhou Kai Chi School of Information and Control Engineering Xi’an University of Architecture and TechnologyXi’anShaanxi 710055China The State Key Lab of ISN Xidian UniversityXi’anShaanxi 710071China Science and Technology on Communication Networks Laboratory CETC the 54th research instituteShijiazhuangHebei 050081China Electric Information Network Laboratory CETC the 20th research instituteXi’anShaanxi 710068China

In this paper,a resource mobility aware two-stage hybrid task planning algorithm is proposed to reduce the resource conflict between emergency tasks and the common tasks,so as to improve the overall performance of space information ***,in the common task planning stage,a resource fragment avoidance task planning algorithm is proposed,which reduces the contention between emergency tasks and the planned common tasks in the next stage by avoiding the generation of resource *** emergency tasks,we design a metric to quantify the revenue of the candidate resource combination of emergency tasks,which considers both the priority of the tasks and the impact on the planned common *** on this,a resource mobility aware emergency task planning algorithm is proposed,which strikes a good balance between improving the sum priority and avoiding disturbing the planned common ***,simulation results show that the proposed algorithm is superior to the existing algorithms in both the sum task priority and the task completion rate.

关键词： space information network task planning resource mobility

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The Phase Sensitivities for Different Phase-Shift Configurations in an SU(1,1) Interferometer

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communications in Theoretical Physics 2019年第12期71卷 1435-1442页

作者： Fan Wang Wei Zhong Lan Zhou Yu-Bo Sheng Institute of Quantum Information and Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China National Laboratory of Solid State Microstructures Nanjing UniversityNanjing 210093China School of Science Nanjing University of Posts and TelecommunicationsNanjing 210003China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and TelecommunicationsMinistry of EducationNanjing 210003China

We theoretically study the phase sensitivities of two different phase-shift configurations in an SU(1,1)interferometer with coherent■squeezed vacuum *** to quantum Cramér-Rao theorem,we analytically obtain the ultimate phase sensitivities for two types of phase shift accumulating in one-and *** with the case of one-arm phase shift,the model with phase shift encoding in both arms may provide a better sensitivity when the strength of squeezed vacuum state is large ***,we discuss the achievable sensitivities with the homodyne measurement by invoking of error-propagation *** addition,we study the effect of internal and outernal photon losses on the phase sensitivity of the SU(1,1)interferometer and find that the unbalanced interferometer is helpful to improve precision even with high external losses.

关键词： SU(1,1)interferometer quantum Fisher information one-and two-arm phase shifts Homodyne measurement

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Multiple residual dense networks for reconfigurable intelligent surfaces cascaded channel estimation

arXiv

引用

arXiv 2021年

作者： Jin, Yu Zhang, Jiayi Huang, Chongwen Yang, Liang Xiao, Huahua Ai, Bo Wang, Zhiqin The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China Zhejiang Provincial Key Lab of Information Processing Communication and Networking Zhejiang University Hangzhou310007 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China ZTE Corporation State Key Laboratory of Mobile Network Mobile Multimedia Technology Shenzhen518057 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518055 China China Academy of Information and Communications Technology Beijing100191 China

Reconfigurable intelligent surface (RIS) constitutes an essential and promising paradigm that relies programmable wireless environment and provides capability for space-intensive communications, due to the use of low-cost massive reflecting elements over the entire surfaces of man-made structures. However, accurate channel estimation is a fundamental technical prerequisite to achieve the huge performance gains from RIS. By leveraging the low rank structure of RIS channels, three practical residual neural networks, named convolutional blind denoising network, convolutional denoising generative adversarial networks and multiple residual dense network, are proposed to obtain accurate channel state information, which can reflect the impact of different methods on the estimation performance. Simulation results reveal the evolution direction of these three methods and reveal their superior performance compared with existing benchmark schemes. Copyright © 2021, The Authors. All rights reserved.

关键词： Channel estimation

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Deep Reinforcement Learning for Fresh Data Collection in UAV-assisted IoT Networks

Deep Reinforcement Learning for Fresh Data Collection in UAV...

引用

IEEE Conference on Computer communications Workshops, INFOCOM Wksps

作者： Mengjie Yi Xijun Wang Juan Liu Yan Zhang Bo Bai Science and Technology on Communication Network Laboratory Shijiazhuang Hebei China Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences 865 Changning Road Shanghai China School of Electrical Engineering and Computer Science Ningbo University Zhejiang China Theory Lab (FKA Future Network Theory Lab) 2012 Labs Huawei Technologies Co. Ltd. Hong Kong

ISBN: (数字)9781728186955

ISBN: (纸本)9781728186962

Due to the flexibility and low operational cost, dispatching unmanned aerial vehicles (UAVs) to collect information from distributed sensors is expected to be a promising solution in Internet of Things (IoT), especially for time-critical applications. How to maintain the information freshness is a challenging issue. In this paper, we investigate the fresh data collection problem in UAV-assisted IoT networks. Particularly, the UAV flies towards the sensors to collect status update packets within a given duration while maintaining a non-negative residual energy. We formulate a Markov Decision Process (MDP) to find the optimal flight trajectory of the UAV and transmission scheduling of the sensors that minimizes the weighted sum of the age of information (AoI). A UAV-assisted data collection algorithm based on deep reinforcement learning (DRL) is further proposed to overcome the curse of dimensionality. Extensive simulation results demonstrate that the proposed DRL-based algorithm can significantly reduce the weighted sum of the AoI compared to other baseline algorithms.

关键词： Sensors Trajectory Data collection Unmanned aerial vehicles Internet of Things Wireless sensor networks Energy consumption

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