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Graph Regularized L_p Smooth Non-negative Matrix Factorization for Data Representation

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IEEE/CAA Journal of Automatica Sinica 2019年第2期6卷 584-595页

作者： Chengcai Leng Hai Zhang Guorong Cai Irene Cheng Anup Basu the School of Mathematics Northwest University School of Mathematics and Information Sciences Nanchang Hangkong University Nanchang 330063 China the Department of Computing Science University of Alberta the Faculty of Information Technology & State Key Laboratory of Quality Research in Chinese Medicines Macao University of Science and Technology the College of Computer Engineering Jimei University the Department of Computing Science University of Alberta

This paper proposes a Graph regularized Lpsmooth non-negative matrix factorization(GSNMF) method by incorporating graph regularization and L_p smoothing constraint, which considers the intrinsic geometric information of a data set and produces smooth and stable solutions. The main contributions are as follows: first, graph regularization is added into NMF to discover the hidden semantics and simultaneously respect the intrinsic geometric structure information of a data set. Second,the Lpsmoothing constraint is incorporated into NMF to combine the merits of isotropic(L_2-norm) and anisotropic(L_1-norm)diffusion smoothing, and produces a smooth and more accurate solution to the optimization problem. Finally, the update rules and proof of convergence of GSNMF are given. Experiments on several data sets show that the proposed method outperforms related state-of-the-art methods.

关键词： Data clustering dimensionality reduction graph regularization Lp smooth non-negative matrix factorization(SNMF)

Unity makes strength: Coalition Formation-based Group-buying for Timely UAV Data Collection

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Unity makes strength: Coalition Formation-based Group-buying...

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Nan Qi Yeting Huang Wen Sun Shi Jin Theodoros A. Tsiftsis Qihui Wu Xiang Su National Mobile Communications Research Laboratory Southeast University Nanjing China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China School of Intelligent Systems Science & Engineering Jinan University Zhuhai China Department of Computer Science Norwegian University of Science and Technology Gjøvik Norway Center for Ubiquitous Computing University of Oulu Oulu Finland

ISBN: (纸本)9781665435413

With their high mobility, unmanned aerial vehicles (UAVs) become appealing data collectors in hard-to-reach wide-area distributed sensor networks. Different from existing works focusing on the perspective of UAVs for service order optimization and UAV utility maximization, we consider the utilities of both sensors and UAVs, and innovatively model the competition among sensors (buyers) for the service of UAVs (sellers) as an auction game. A “unity makes strength” strategy is exploited. That is, to strengthen the bidding competitiveness, a group-buying coalition auction method that encourages sensors to form coalitions to bid for UAV service is proposed. Besides, we propose a parallel variable neighborhood ascent search algorithm, we can quickly determine the approximately optimal group-buying coalition structure. Numerical results show that the proposed method outperforms the joint trajectory design-task scheduling (TDTS) UAV-to-community method and the single coalition formation game (CFG) method.

关键词： Wireless sensor networks Games Data collection Autonomous aerial vehicles Search problems information age Trajectory

Wireless Large AI Model: Shaping the AI-Native Future of 6G and Beyond

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arXiv 2025年

作者： Zhu, Fenghao Wang, Xinquan Li, Xinyi Zhang, Maojun Chen, Yixuan Huang, Chongwen Yang, Zhaohui Chen, Xiaoming Zhang, Zhaoyang Jin, Richeng Huang, Yongming Feng, Wei Yang, Tingting Bai, Baoming Gao, Feifei Yang, Kun Liu, Yuanwen Muhaidat, Sami Yuen, Chau Huang, Kaibin Wong, Kai-Kit Niyato, Dusit Debbah, Mérouane College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Automation Southeast University Nanjing210096 China Tsinghua University Beijing100084 China Pengcheng Laboratory Shenzhen518066 China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China State Key Laboratory of Novel Software Technology Nanjing University Nanjing210008 China Suzhou215163 China The University of Hong Kong Hong Kong KU 6G Research Center Computer and Communication Engineering Khalifa University Abu Dhabi127788 United Arab Emirates School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates

The emergence of sixth-generation and beyond communication systems is expected to fundamentally transform digital experiences through introducing unparalleled levels of intelligence, efficiency, and connectivity. A promising technology poised to enable this revolutionary vision is the wireless large AI model (WLAM), characterized by its exceptional capabilities in data processing, inference, and decision-making. In light of these remarkable capabilities, this paper provides a comprehensive survey of WLAM, elucidating its fundamental principles, diverse applications, critical challenges, and future research opportunities. We begin by introducing the background of WLAM and analyzing the key synergies with wireless networks, emphasizing the mutual benefits. Subsequently, we explore the foundational characteristics of WLAM, delving into their unique relevance in wireless environments. Then, the role of WLAM in optimizing wireless communication systems across various use cases and the reciprocal benefits are systematically investigated. Furthermore, we discuss the integration of WLAM with emerging technologies, highlighting their potential to enable transformative capabilities and breakthroughs in wireless communication. Finally, we thoroughly examine the high-level challenges hindering the practical implementation of WLAM and discuss pivotal future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Sorting

Deep Networks for Medical Images Classification, a Comparative Study

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Deep Networks for Medical Images Classification, a Comparati...

Artificial Intelligence & Green Energy (ICAIGE), IEEE International Conference on

作者： Hussain K. Ibrahim Nizar Rokbani Ali Wali Adel M. Alimi National school of electronics and telecommunications (ENET'COM) of Sfax University Sfax Tunisia High Institute of Applied Sciences Technology of Sousse University of Sousse Tunisia Research Groups in Intelligent Machines (REGIM Laboratory) National Engineering School of Sfax (ENIS)University of Sfax Tunisia Information Technology Wasit University Wasit Iraq Department of Biomedical Technology College of Applied Medical Sciences in Al-Kharj Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabi Higher Institute of Computer Science and Multimedia of Sfax University of Sfax Tunisia Research Groups in Intelligent Machines (REGIM Laboratory) National Engineering School of Sfax (ENIS) Sfax University Sfax Tunisia Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa

ISBN: (数字)9798350389838

ISBN: (纸本)9798350389845

Machine learning (ML) and deep learning (DL) have had a significant impact on healthcare, particularly in the field of medical picture categorization, in recent years. These technologies provide a multitude of advantages that enhance the quality of patient care and increase the effectiveness of healthcare services. The primary rationale for employing (ML) and (DL) in medical picture categorization lies in their remarkable capacity to identify illnesses at an early stage. Automated classification systems has the ability to rapidly analyses and interpret pictures, surpassing the speed of human capabilities. This is particularly vital for effectively handling extensive datasets and becomes critical in emergency scenarios where prompt diagnosis is of utmost importance. The objective of this investigation is to categorize medical images into four distinct categories: normal, COVID-19, lung opacity, and viral pneumonia. In this paper three deep learning models were implemented: the classical CNN, AlexNet and GoogleNet. The comparative analysis showed that AlexNet achieved an accuracy of 96.74%, a processing time of 225 seconds, and a loss of 0.1192. The GoogleNet architecture recorded an accuracy of 93.98%, a processing time of 233 seconds, and a loss of 0.1945 while the classical CNN achieved an accuracy of 94.56%, a processing time of 315 seconds, and a loss of 0.1298. Results showed that the AlexNet has a little advantage in regrads to GoogleNet and classical CNN which report very close performances'.

关键词： Deep learning COVID-19 Green energy Accuracy Pneumonia Lungs Medical services Artificial neural networks Medical diagnostic imaging Image classification

Cross-Domain Dual-Functional OFDM Waveform Design for Accurate Sensing/Positioning

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arXiv 2023年

作者： Zhang, Fan Mao, Tianqi Liu, Ruiqi Han, Zhu Chen, Sheng Wang, Zhaocheng Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing100084 China Tsinghua Shenzhen International Graduate School Shenzhen518055 China The State Key Laboratory of CNS/ATM Beijing Institute of Technology Beijing100081 China The MIIT Key Laboratory of Complex-field Intelligent Sensing Beijing Institute of Technology Beijing100081 China The Wireless and Computing Research Institute ZTE Corporation Beijing100029 China The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Orthogonal frequency division multiplexing (OFDM) has been widely recognized as the representative waveform for 5G wireless networks, which can directly support sensing/positioning with existing infrastructure. To guarantee superior sensing/positioning accuracy while supporting high-speed communications simultaneously, the dual functions tend to be assigned with different resource elements (REs) due to their diverse design requirements. This motivates optimization of resource allocation/waveform design across time, frequency, power and delay-Doppler domains. Therefore, this article proposes two cross-domain waveform optimization strategies for effective convergence of OFDM-based communications and sensing/positioning, following communication- and sensing-centric criteria, respectively. For the communication-centric design, to maximize the achievable data rate, a fraction of REs are optimally allocated for communications according to prior knowledge of the communication channel. The remaining REs are then employed for sensing/positioning, where the sidelobe level and peak-to-average power ratio are suppressed by optimizing its power-frequency and phase-frequency characteristics for sensing performance improvement. For the sensing-centric design, a 'locally' perfect auto-correlation property is ensured for accurate sensing and positioning by adjusting the unit cells of the ambiguity function within its region of interest (RoI). Afterwards, the irrelevant cells beyond RoI, which can readily determine the sensing power allocation, are optimized with the communication power allocation to enhance the achievable data rate. Numerical results demonstrate the superiority of the proposed waveform designs. Copyright © 2023, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

Efficient thermal dissipation in wafer-scale heterogeneous integration of single-crystalline𝛽β-Ga_(2)O_(3)thin film on SiC

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Fundamental Research 2021年第6期1卷 691-696页

作者： Wenhui Xu Tiangui You Yibo Wang Zhenghao Shen Kang Liu Lianghui Zhang Huarui Sun Ruijie Qian Zhenghua An Fengwen Mu Tadatomo Suga Genquan Han Xin Ou Yue Hao Xi Wang State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China The State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology School of MicroelectronicsXidian UniversityXi’an 710071China School of Science and Ministry of Industry and Information Technology Key Laboratory of Micro-Nano Optoelectronic Information System Harbin Institute of TechnologyShenzhen 518055China Department of Physics State Key Laboratory of Surface PhysicsInstitute of Nanoelectronic Devices and Quantum ComputingFudan UniversityShanghai 200433China High-Frequency High-Voltage Device and Integrated Circuits R&D Center Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029China Collaborative Research Center Meisei UniversityHinoJapan Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China

The semiconductor,β-Ga_(2)O_(3)is attractive for applications in high power electronic devices with low conduction loss due to its ultra-wide bandgap(∼4.9 eV)and large Baliga’s figure of ***,the thermal conductivity of𝛽β-Ga_(2)O_(3)is much lower than that of other wide/ultra-wide bandgap semiconductors,such as SiC and GaN,which results in the deterioration of𝛽β-Ga_(2)O_(3)-based device performance and reliability due to *** overcome this problem,a scalable thermal management strategy was proposed by heterogeneously integrating wafer-scale single-crystalline𝛽β-Ga_(2)O_(3)thin films on a highly thermally conductive SiC *** of the transferred𝛽β-Ga_(2)O_(3)thin film indicated a uniform thickness to within±2.01%,a smooth surface with a roughness of 0.2 nm,and good crystalline quality with an X-ray rocking curves(XRC)full width at half maximum of 80 *** thermoreflectance measurements were employed to investigate the thermal *** thermal performance of the fabricated𝛽β-Ga_(2)O_(3)/SiC heterostructure was effectively improved in comparison with that of the𝛽β-Ga_(2)O_(3)bulk wafer,and the effective thermal boundary resistance could be further reduced to 7.5 m 2 K/GW by a post-annealing *** barrier diodes(SBDs)were fabricated on both a𝛽β-Ga_(2)O_(3)/SiC heterostructured material and a𝛽β-Ga_(2)O_(3)bulk *** thermal imaging revealed the temperature increase of the SBDs on𝛽β-Ga_(2)O_(3)/SiC to be one quarter that on the𝛽β-Ga_(2)O_(3)bulk wafer with the same applied power,which suggests that the combination of the𝛽-Ga_(2)O_(3)thin film and SiC substrate with high thermal conductivity promotes heat dissipation in𝛽β-Ga_(2)O_(3)-based devices.

关键词： Thermal management Heterogeneous integration Wafer scale𝛽β-Ga_(2)O_(3)on SiC Ion-cutting technique Schottky barrier diodes(SBDs) Transient thermoreflectance(TTR) measurements

Global-and-Local Collaborative Learning for Co-Salient Object Detection

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arXiv 2022年

作者： Cong, Runmin Yang, Ning Li, Chongyi Fu, Huazhu Zhao, Yao Huang, Qingming Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The Department of Computer Science City University of Hong Kong Hong Kong The School of Computer Science and Engineering Nanyang Technological University Singapore A*STAR Singapore The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

The goal of co-salient object detection (CoSOD) is to discover salient objects that commonly appear in a query group containing two or more relevant images. Therefore, how to effectively extract inter-image correspondence is crucial for the CoSOD task. In this paper, we propose a global-and-local collaborative learning architecture, which includes a global correspondence modeling (GCM) and a local correspondence modeling (LCM) to capture comprehensive inter-image corresponding relationship among different images from the global and local perspectives. Firstly, we treat different images as different time slices and use 3D convolution to integrate all intra features intuitively, which can more fully extract the global group semantics. Secondly, we design a pairwise correlation transformation (PCT) to explore similarity correspondence between pairwise images and combine the multiple local pairwise correspondences to generate the local inter-image relationship. Thirdly, the inter-image relationships of the GCM and LCM are integrated through a global-and-local correspondence aggregation (GLA) module to explore more comprehensive inter-image collaboration cues. Finally, the intra- and inter-features are adaptively integrated by an intra-and-inter weighting fusion (AEWF) module to learn co-saliency features and predict the co-saliency map. The proposed GLNet is evaluated on three prevailing CoSOD benchmark datasets, demonstrating that our model trained on a small dataset (about 3k images) still outperforms eleven state-of-the-art competitors trained on some large datasets (about 8k̴200k images). © 2022, CC BY-NC-SA.

关键词： Convolution

Cascade of the delocalization transition in a non-Hermitian interpolating Aubry-André-Fibonacci chain

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Physical Review B 2021年第1期104卷 014202-014202页

作者： Liang-Jun Zhai Guang-Yao Huang Shuai Yin The School of Mathematics and Physics Jiangsu University of Technology Changzhou 213001 China Department of Physics Nanjing University Nanjing 210093 China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha 410073 China School of Physics Sun Yat-Sen University Guangzhou 510275 China

In this paper, the interplay of the non-Herimiticity and the cascade of delocalization transition in a quasiperiodic chain are studied. The study is applied in the non-Hermitian interpolating Aubry-André-Fibonacci (IAAF) model, which combines the non-Hermitian Aubry-André (AA) model and the non-Hermitian Fibonacci model through a varying parameter, and the non-Hermiticity in this model is introduced by nonreciprocal hopping. In the non-Hermitian AA limit, the system undergoes a delocalization transition by tuning the potential strength. At the critical point, the spatial distribution of the critical state shows a self-similar structure with the relative distance between the peaks being the Fibonacci sequence, and the finite-size scaling of the inverse participation ratios (IPRs) of the critical ground state with lattice size L shows that IPRg∝L−0.1189. In the non-Hermitian Fibonacci limit, we find that the system is always in the extended phase. Along the continuous deformation from the non-Hermitian AA model into the non-Hermitian Fibonacci model in the IAAF model, the cascade of the delocalization transition is found, but only a few plateaux appear. Moreover, the self-similar structure of spatial distribution for the critical modes along the cascade transition is also found. In addition, we find that the delocalization transition and the real-complex transition for the excited states happen at almost the same parameter. Our results show that the non-Hermiticity provides an additional knob to control the cascade of the delocalization transition besides the on-site potential.

关键词： Anderson localization Critical phenomena Localization Phase transitions Quantum phase transitions

Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach

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arXiv 2023年

作者： Shi, Zhaoyuan Lu, Huabing Xie, Xianzhong Yang, Helin Huang, Chongwen Cai, Jun Ding, Zhiguo The Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology Dalian116024 China Chongqing Key Lab of Computer networks and Communication Technology Chongqing University of Posts and Telecommunications Chongqing China The Department of Information and Communication Engineering School of Informatics Xiamen University Xiamen361005 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States The School of Electrical and Electronic Engineering The University of Manchester Manchester United Kingdom

An active reconfigurable intelligent surface (RIS)-aided multi-user downlink communication system is investigated, where non-orthogonal multiple access (NOMA) is employed to improve spectral efficiency, and the active RIS is powered by energy harvesting (EH). The problem of joint control of the RIS’s amplification matrix and phase shift matrix is formulated to maximize the communication success ratio with considering the quality of service (QoS) requirements of users, dynamic communication state, and dynamic available energy of RIS. To tackle this non-convex problem, a cascaded deep learning algorithm namely long short-term memory-deep deterministic policy gradient (LSTM-DDPG) is designed. First, an advanced LSTM based algorithm is developed to predict users’ dynamic communication state. Then, based on the prediction results, a DDPG based algorithm is proposed to joint control the amplification matrix and phase shift matrix of the RIS. Finally, simulation results verify the accuracy of the prediction of the proposed LSTM algorithm, and demonstrate that the LSTM-DDPG algorithm has a significant advantage over other benchmark algorithms in terms of communication success ratio performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy harvesting