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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Ma, Zibo Zhang, Bo Zhang, Zheng Wang, Wendong Mi, Yue Huang, Haiwen Wu, Jingyun Beijing University of Posts and Telecommunications Beijing100876 China Beijing University of Posts and Telecommunications School of Computer Science National Pilot Software Engineering School State Key Laboratory of Networking and Switching Technology China Beijing University of Posts and Telecommunications School of Modern Post China Peking University First Hospital Beijing100034 China Peking University First Hospital Department of Urology China Peking University First Hospital Department of Radiology China

ISBN: (纸本)9781665480451

High-performance deep learning models require large amounts of data with high quality annotations for model training, while the labeling work usually takes a lot of time for the experts. Meanwhile, the inter-observer variability al-ways exist between annotations from different experts and the distribution shift between the data acquired from different medical institutions. To address these challenges, we propose an end-to-end domain adaptive collaborative learning network for multi-institutional prostate MRI segmentation. Specifically, we introduce an unpaired image translation module to match the image domains between different institutions, which can alleviate the heterogeneity between 1.5T and 3T prostate MR images during model training. Moreover, we design a self-taught strategy to transfer domain-aware knowledge to jointly learn generic and unique representations. Furthermore, we evaluate our approach in scenarios with limited or without annotations, experimental results show that our approach has better adaptation performance than traditional supervised learning approaches, and has the potential to extend to unsupervised domain adaptation scenario. We also evaluate our approach with prostate MRI segmentation benchmark datasets, experimental results show that our approach outperforms several state-of-the-art methods. © 2022 IEEE.

关键词： Deep learning

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The Age of Generative AI and AI-Generated Everything

arXiv

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arXiv 2023年

作者： Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Zhang, Ping Cui, Shuguang Shen, Xuemin Mao, Shiwen Han, Zhu Jamalipour, Abbas Vincent Poor, H. Kim, Dong In School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative AI (GAI) has emerged as a significant advancement in artificial intelligence, renowned for its language and image generation capabilities. This paper presents "AIGenerated Everything" (AIGX), a concept that extends GAI beyond mere content creation to real-time adaptation and control across diverse technological domains. In networking, AIGX collaborates closely with physical, data link, network, and application layers to enhance real-time network management that responds to various system and service settings as well as application and user requirements. Networks, in return, serve as crucial components in further AIGX capability optimization through the AIGX lifecycle, i.e., data collection, distributed pre-training, and rapid decision-making, thereby establishing a mutually enhancing interplay. Moreover, we offer an in-depth case study focused on power allocation to illustrate the interdependence between AIGX and networking systems. Through this exploration, the article analyzes the significant role of GAI for networking, clarifies the ways networks augment AIGX functionalities, and underscores the virtuous interactive cycle they form. This article paves the way for subsequent future research aimed at fully unlocking the potential of GAI and networks. © 2023, CC BY.

关键词： Decision making

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Optimizing QoS in Internet of Multimedia Things using Cross-Layer Design and Artificial Bee Colony

Optimizing QoS in Internet of Multimedia Things using Cross-...

引用

Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Ahmed A. Khalifa Shreyas J Gururaj H L Udayaprasad P K Srinidhi N N Department of Networking and Security College of Information Technology University of Fujairah Fujairah UAE Switching and Traffic Department National Telecommunication Institute Cairo Egypt Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal Karnataka India Department of Artificial Intelligence & Machine Learning Global Academy of Technology Bengaluru India Department of Computer Science & Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal Karnataka India

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

Futuristic demands of multimedia applications involve comprehensive sharing of dynamic data attributes within the Multimedia Items (IoMT) Internet sense. Exploring the existing research void in this area reveals need of building an integrated routing strategy for IoMT that can cope with multiple network characteristics, physical artifacts, and with cost-effective networks and distribution modules satisfy connectivity requirements. This paper suggest a layer cross architect-layerure optimization for IoMT applications with a emphasis on connectivity efficiency factors such as rising latency, energy limitations, and optimizing low-cost energy usage. The proposed work models and integrates a swarm intelligence paradigm which involve intelligent computing of Artificial Bee Colony (ABC) maintaining effective link creation and selects the best path from source to target IoT nodes. The proposed research achieve the strongest routing usefulness for multimedia data exchange over IoT. To ensure this, the output of the proposed device is validated with an comprehensive simulation test. Results tested using MATLAB simulation method indicate an improvement of 8% in energy usage, 10% increase in throughput, 8% decrease in end to end delay and 10% increase in packet delivery ratio.

关键词： Cross layer design Energy consumption Routing Throughput Artificial bee colony algorithm Delays Multimedia communication

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Accelerate Inference of CNN Models on CPU via Column Combining Based on Simulated Annealing

Accelerate Inference of CNN Models on CPU via Column Combini...

引用

International Symposium on Computing and networking (CANDAR)

作者： Chien-Hung Lin Ding-Yong Hong Pangfeng Liu Jan-Jan Wu Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan The Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan The Graduate School of Advanced Technology National Taiwan University Taipei Taiwan

Convolutional Neural Networks (CNNs) have been successful in various computer vision tasks. However, the size of state-of-the-art CNN models tends to be tremendous, which results in very long inference times and high memory usage. Model compression technology such as unstructured pruning can prune a significant proportion of parameters without affecting accuracy, but efficient utilization of sparsity remains a challenge. Column combining compress unstructurally-pruned CNN models by combining multiple sparse columns in a convolutional filter matrix into a single dense column. In addition, pruning all but the largest magnitude weight in each row of the combined column further compresses the matrix effectively. However, previous work did not address the details of partitioning sparse columns to minimize the negative impact of additional pruning on the performance of the model. In this work, we first prove that the column partition problem is an NP-Complete problem. Next, we propose a column combining scheme based on simulated annealing and global unstructured pruning to minimize the adverse effects of additional pruning on model performance. We implement the acceleration of column-combined CNN models using the TVM AI compiler without special hardware support. The proposed scheme achieves more efficient model compression, leading to a 0.65% improvement in accuracy and a $1.24\times$ faster inference time on VGG19 under 88% sparsity with the TinyImageNet dataset.

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Node Localization Method in Wireless Sensor Networks Using Combined Crow Search and Weighted Centroid Method

SSRN

引用

SSRN 2023年

作者： Sankaranarayanan, Suresh Rajaram, V. Srividhya, S. Lorenz, Pascal Rodrigues, Joel J.P.C. Department of Computer Science College of Computer Science and Information Technology King Faisal University Al Hofuf Saudi Arabia Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India University of Haute Alsace Colmar France Qingdao266555 China Instituto de Telecomunicações Covilhã6201-001 Portugal

Localization of nodes is critical for acquiring access to diverse nodes that would provide services in remote places. Single-anchor localization techniques suffer from a co-linearity problem, resulting in poor performance. The reliable multiple anchor selection method can be computationally intensive and requires a lot of processing power and time to identify suitable anchor nodes. There has been work where researchers have employed nature-inspired algorithms over conventional optimization method towards localization of unknown nodes in WSN. These methods have challenges like longer time and lot of processing power to solve. In addition, these methods are sensitive to parameter selection impacting the ***, we in this research work have employed the nature-inspired crow search algorithm for selecting the suitable anchors from the population thereby reducing errors in identifying the location of unknown nodes. In addition, the weighted centroid method is proposed for the identification of the exact location of an unknown node. This makes the weighted centroid method and the crow search algorithm a more trustworthy and efficient way for node localization in wireless sensor networks. The Crow Search-based Weighted Centroid Localization (CS-WCL) has outperformed WCL and DV-Hop with the use of the best anchor node in terms of Localization Error, Total Data packets, and Energy Consumption. This resulted in CSWCL outperforming other nature-inspired algorithms for node localization. © 2023, The Authors. All rights reserved.

关键词： Energy utilization

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Efficient Dual Batch Size Deep Learning for Distributed Parameter Server Systems

Efficient Dual Batch Size Deep Learning for Distributed Para...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Kuan-Wei Lu Pangfeng Liu Ding-Yong Hong Jan-Jan Wu Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Academia Sinica Institute of Information Science Taipei Taiwan

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

Distributed machine learning is essential for applying deep learning models with many data and parameters. Current researches on distributed machine learning focus on using more hardware devices powerful computing units for fast training. Consequently, the model training prefers a larger batch size to accelerate the training speed. However, the large batch training often suffers from poor accuracy due to poor generalization ability. Researchers have come up with many sophisticated methods to address this accuracy issue due to large batch sizes. These methods usually have complex mechanisms, thus making training more difficult. In addition, powerful training hardware for large batch sizes is expensive, and not all researchers can afford it. We propose a dual batch size learning scheme to address the batch size issue. We use the maximum batch size of our hardware for maximum training efficiency we can afford. In addition, we introduce a smaller batch size during the training to improve the model generalization ability. Using two different batch sizes in the same training simultaneously will reduce the testing loss and obtain a good generalization ability, with only a slight increase in the training time. We implement our dual batch size learning scheme and conduct experiments. By increasing 5% of the training time, we can reduce the loss from 1.429 to 1.246 in some cases. In addition, by appropriately adjusting the percentage of large and small batch sizes, we can increase the accuracy by 2.8% in some cases. With the additional 10% increase in training time, we can reduce the loss from 1.429 to 1.193. And after moderately adjusting the number of large batches and small batches used by GPUs, the accuracy can increase by 2.9%. Using two different batch sizes in the same training introduces two complications. First, the data processing speeds for two different batch sizes are different, so we must assign the data proportionally to maximize the overall processing speed. I

关键词： Training Deep learning Computational modeling Neural networks Predictive models Data models Hardware

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Optimizing AoI in RIS-aided Wireless Powered Backscatter-Active Communication Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Hu, Huimin Zhang, Ruichen Ding, Zhiguo Jiang, Fan Jiang, Ruihong Niyato, Dusit Xi'an University of Posts and Telecommunications School of Communications and Information Engineering Xi'an710121 China Nanyang Technological University College of Computing and Data Science Singapore Singapore Khalifa University Department of Computer and Information Engineering Abu Dhabi United Arab Emirates Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

In this paper, we investigate the average age of information (AoI) in wireless powered backscatter-active communication networks (WPBACN) assisted by a reconfigurable intelligent surface (RIS). To minimize the average AoI, we jointly optimize the phase shift of the RIS, the backscatter-active communication time allocation factor, and the transmitted power of IoT devices. Since the optimization problem is composed of coupled variables, the optimization variables are implicit in the objective function and the problem is non-convex, which makes it challenging to solve. To address this challenge, we decompose the problem into an RIS phase shift optimization subproblem and a joint power and time allocation subproblem. Simulation results indicate that the proposed scheme has a significant performance advantage in reducing the average AoI compared to the scheme using random RIS phase shifts and the scheme without RIS and supports the challenging quality of service required by real-time information update applications. © 1967-2012 IEEE.

关键词： age of information Reconfigurable intelligent surface WPBACN

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Cramér-Rao Bound Minimization for IRS-Enabled Multiuser Integrated Sensing and Communications

arXiv

引用

arXiv 2023年

作者： Song, Xianxin Qin, Xiaoqi Xu, Jie Zhang, Rui Shenzhen518172 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

This paper investigates an intelligent reflecting surface (IRS) enabled multiuser integrated sensing and communications (ISAC) system, which consists of one multi-antenna base station (BS), one IRS, multiple single-antenna communication users (CUs), and one target at the non-line-of-sight (NLoS) region of the BS. The IRS is deployed to not only assist the communication from the BS to the CUs, but also enable the BS’s NLoS target sensing based on the echo signals from the BS-IRS-target-IRS-BS link. We consider two types of targets, namely the extended and point targets, for which the BS aims to estimate the complete target response matrix and the target direction-of-arrival (DoA) with respect to the IRS, respectively. To provide full degrees of freedom for sensing, we consider that the BS sends dedicated sensing signals in addition to the communication signals. Accordingly, we model two types of CU receivers, namely Type-I and Type-II CU receivers, which do not have and have the capability of canceling the interference from the sensing signals, respectively. Under each setup, we jointly optimize the transmit beamforming at the BS and the reflective beamforming at the IRS to minimize the Cramér-Rao bound (CRB) for target estimation, subject to the minimum signal-to-interference-plus-noise ratio (SINR) constraints at the CUs and the maximum transmit power constraint at the BS. We present efficient algorithms to solve the highly non-convex SINR-constrained CRB minimization problems, by using the Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

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Adaptive self-interference cancellation for full-duplex wireless communication systems

arXiv

引用

arXiv 2021年

作者： Balti, Elyes Evans, Brian L. The Wireless Networking and Communications Group Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78712 United States

In this letter, we consider single-cell, single-user systems wherein uplink and downlink user equipment communicate with a full-duplex relay. Due to the near-far problem, the self-interference (SI) can be 100-1000x the received signal power. In this context, we consider the adaptive Least Mean Squares (LMS) algorithm to estimate the SI channel and then subtract the SI from the desired received signal before the analog-to-digital converter (ADC). We measure the robustness of this technique in terms of bit error rate (BER) and spectral efficiency. Copyright © 2021, The Authors. All rights reserved.

关键词： Analog to digital conversion

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TXOP sharing with coordinated spatial reuse in multi-AP cooperative IEEE 802.11be WLANs

arXiv

引用

arXiv 2021年

作者： Nunez, David Wilhelmi, F. Avallone, S. Smith, M. Bellalta, B. Wireless Networking Group Universitat Pompeu Spain Department of Computer Engineering University of Napoli Federico II Italy CISCO Systems

IEEE 802.11be networks (aka Wi-Fi 7) will have to cope with new bandwidth-hungry and low-latency services such as eXtended Reality and multi-party cloud gaming. With this goal in mind, transmit opportunity (TXOP) sharing between coordinated access points (APs) may contribute to alleviating inter-AP contention, hence increasing the overall network throughput. This paper evaluates two coordinated TXOP sharing strategies: coordinated time division multiple access (c-TDMA) and coordinated-TDMA with spatial reuse (c-TDMA/SR). We show that, while c-TDMA alone does not result in any significant improvement in terms of the WLAN throughput, it lays the groundwork to implement coordinated SR (c-SR) techniques. To evaluate the performance of c-TDMA/SR, we propose a fair scheduler able to select the best subset of parallel transmissions in WLAN deployments, as well as the appropriate power levels to be used by APs and stations (STAs), leading to maximum performance. The results obtained for c-TDMA/SR show significant throughput gains compared with c-TDMA, with values higher than 140% in 90% of the considered scenarios. Copyright © 2021, The Authors. All rights reserved.

关键词： Wireless local area networks (WLAN)

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