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arXiv 2021年

作者： Ni, Wanli Liu, Yuanwei Yang, Zhaohui Tian, Hui Shen, Xuemin State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

With the aim of integrating over-the-air federated learning (AirFL) and non-orthogonal multiple access (NOMA) into an on-demand universal framework, this paper proposes a reconfigurable intelligent surface (RIS)-aided hybrid network by leveraging the RIS to flexibly adjust the decoding order of heterogeneous data. A new metric of computation rate is defined to measure the performance of AirFL users. Upon this, the objective of this work is to maximize the achievable hybrid rate by jointly optimizing the transmit power, controlling the receive scalar, and designing the reflection coefficients. Since the concurrent transmissions of all computation and communication signals are aided by the discrete phase-shifting elements at the RIS, the formulated problem (P0) is a challenging mixed-integer programming problem. To tackle this intractable issue, we decompose the original problem (P0) into a non-convex problem (P1) and a combinatorial problem (P2), which are characterized by the continuous and discrete variables, respectively. For the transceiver design problem (P1), the power allocation subproblem is first solved by difference-of-convex programming, and then the receive control subproblem is addressed by successive convex approximation, where the closed-form expressions of simplified cases are derived to obtain deep insights. For the reflection design problem (P2), a relaxation-then-quantization method is adopted to find a suboptimal solution for striking a trade-off between complexity and performance. Afterwards, an alternating optimization algorithm is developed to solve the non-linear non-convex problem (P0) iteratively. Finally, simulation results reveal that i) the proposed RIS-aided hybrid network can support on-demand communication and computation efficiently, ii) the system performance can be improved by properly selecting the location of the RIS, and iii) the designed algorithms are also applicable to conventional networks with only AirFL or NOMA users. Copyr

关键词： Iterative methods

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Spreading code identification of legal drones in IoT environment 8

Spreading code identification of legal drones in IoT environ...

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8th International Conference on Emerging Security Technologies, EST 2019

作者： Ali Alheeti, Khattab M. Al-Ani, Muzhir Shaban McDonald-Maier, Klaus Department of Computer Networking Systems College of Computer Science and Information Technology University of Anbar Iraq University of Human Development College of Science and Technology Department of Information Technology Sulaymaniyah Iraq School of Computer Sciences and Electronic Engineering University of Essex Colchester United Kingdom

ISBN: (纸本)9781728155463

The widespread use of drones has become very common today with large-scale civil and military applications. In the next few coming years, the outlook is expected that the number of drones will reach millions. So, these need to be well organised and managed in order to achieve the benefits of IoT with this accelerated environment. Drones or Unmanned Aerial Vehicles (UAVs) must achieved a level of communications to authenticate a legal working. The proposed approach concentrated on preparing each drone with identification key based on the combination of its international sim number with the date of the first action and the local country code. This approach is called Drone IDentification (DID) that generate a unique code for each drone via spreading technique. In this case any drone not apply this regulation is considered as unauthenticated drone and does not allowed to fly. This approach is very important to establish drone regulation via IoT. © 2019 IEEE.

关键词： Drones

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Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies

arXiv

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arXiv 2021年

作者： Shoeibi, Afshin Ghassemi, Navid Khodatars, Marjane Moridian, Parisa Alizadehsani, Roohallah Zare, Assef Khosravi, Abbas Subasi, Abdulhamit Rajendra Acharya, U. Manuel Gorriz, J. K. N. Toosi University of Technology Tehran Iran Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Deakin University VIC3217 Australia Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Institute of Biomedicine Faculty of Medicine University of Turku Turku20520 Finland Department of Computer Science College of Engineering Effat University Jeddah21478 Saudi Arabia Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore599489 Singapore the Department of Biomedical Engineering School of Science and Technology SUSS University Singapore599491 Singapore Department of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Department of Signal Theory Networking and Communications Universidad de Granada Spain Department of Psychiatry University of Cambridge United Kingdom

Epileptic seizures are one of the most crucial neurological disorders, and their early diagnosis will help the clinicians to provide accurate treatment for the patients. The electroencephalogram (EEG) signals are widely used for epileptic seizures detection, which provides specialists with substantial information about the functioning of the brain. In this paper, a novel diagnostic procedure using fuzzy theory and deep learning techniques is introduced. The proposed method is evaluated on the Bonn University dataset with six classification combinations and also on the Freiburg dataset. The tunable-Q wavelet transform (TQWT) is employed to decompose the EEG signals into different sub-bands. In the feature extraction step, 13 different fuzzy entropies are calculated from different sub-bands of TQWT, and their computational complexities are calculated to help researchers choose the best set for various tasks. In the following, an autoencoder (AE) with six layers is employed for dimensionality reduction. Finally, the standard adaptive neuro-fuzzy inference system (ANFIS), and also its variants with grasshopper optimization algorithm (ANFIS-GOA), particle swarm optimization (ANFIS-PSO), and breeding swarm optimization (ANFIS-BS) methods are used for classification. Using our proposed method, ANFIS-BS method has obtained an accuracy of 99.74 © 2021, CC BY-NC-ND.

关键词： Wavelet transforms

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An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

arXiv

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arXiv 2021年

作者： Shoeibi, Afshin Moridian, Parisa Khodatars, Marjane Ghassemi, Navid Jafari, Mahboobeh Alizadehsani, Roohallah Kong, Yinan Gorriz, Juan Manuel Ramírez, Javier Khosravi, Abbas Nahavandi, Saeid Acharya, U. Rajendra Data Science and Computational Intelligence Institute University of Granada Spain Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Computer Engineering department Ferdowsi University of Mashhad Mashhad Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran Deakin University VIC3217 Australia School of Engineering Macquarie University Sydney2109 Australia Department of Signal Theory Networking and Communications Universidad de Granada Spain Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these, neuroimaging modalities have received considerable attention from specialist physicians. One method to facilitate the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS) based on deep learning (DL) and neuroimaging modalities. This paper has studied a comprehensive overview of DL methods employed for epileptic seizures detection and prediction using neuroimaging modalities. First, DL-based CADS for epileptic seizures detection and prediction using neuroimaging modalities are discussed. Also, descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic seizures detection and prediction have been included. Then, research on rehabilitation tools has been presented, which contains brain-computer interface (BCI), cloud computing, internet of things (IoT), hardware implementation of DL techniques on field-programmable gate array (FPGA), etc. In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction. The challenges in epileptic seizures detection and prediction using neuroimaging modalities and DL models have been described. In addition, possible directions for future works in this field, specifically for solving challenges in datasets, DL, rehabilitation, and hardware models, have been proposed. The final section is dedicated to the conclusion which summarizes the significant findings of the paper. © 2021, CC BY-NC-ND.

关键词： Forecasting

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Retraction Note to: A systematic literature review: the role of assistive technology in supporting elderly social interaction with their online community

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Journal of Ambient Intelligence and Humanized Computing 2022年第1期14卷 233-233页

作者： Embarak, Farhat Ismail, Nor Azman Othman, Salem School of Computing Faculty of Engineering University Teknologi Malaysia (UTM) Johor Bahru Malaysia Department of Computer Science Faculty of Computing and Information Technology University of Ajdabiya Ajdabiya Libya Department of Computer Science and Networking Wentworth Institute of Technology Boston USA

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A deep-learning-based MAC for integrating channel access, rate adaptation, and channel switch

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Digital Communications and Networks 2024年

作者： Jiantao Xin Wei Xu Bin Cao Taotao Wang Shengli Zhang College of Electronic and Information Engineering Shenzhen University Shenzhen 518060 China Computer Science Department New York University 251 Mercer Street New York NY 10012 USA The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing 100876 China

With increasing density and heterogeneity in unlicensed wireless networks, traditional MAC protocols, such as Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) in Wi-Fi networks, are experiencing performance degradation. This is manifested in increased collisions and extended backoff times, leading to diminished spectrum efficiency and protocol coordination. Addressing these issues, this paper proposes a deep-learning-based MAC paradigm, dubbed DL-MAC, which leverages spectrum data readily available from energy detection modules in wireless devices to achieve the MAC functionalities of channel access, rate adaptation, and channel switch. First, we utilize DL-MAC to realize a joint design of channel access and rate adaptation. Subsequently, we integrate the capability of channel switching into DL-MAC, enhancing its functionality from single-channel to multi-channel operations. Specifically, the DL-MAC protocol incorporates a Deep Neural Network (DNN) for channel selection and a Recurrent Neural Network (RNN) for the joint design of channel access and rate adaptation. We conducted real-world data collection within the 2.4 GHz frequency band to validate the effectiveness of DL-MAC. Experimental results demonstrate that DL-MAC exhibits significantly superior performance compared to traditional algorithms in both single and multi-channel environments, and also outperforms single-function designs. Additionally, the performance of DL-MAC remains robust, unaffected by channel switch overheads within the evaluation range.

关键词： Deep learning Channel access Rate adaptation Channel switch

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DCM: A Dense-Attention Context Module For Semantic Segmentation

DCM: A Dense-Attention Context Module For Semantic Segmentat...

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IEEE International Conference on Image Processing

作者： Shenghua Li Quan Zhou Jia Liu Jie Wang Yawen Fan Xiaofu Wu Longin Jan Latecki National Engineering Research Center of Communications and Networking Nanjing University of Posts & Telecommunications P.R. China Department of Computer and Information Sciences Temple University Philadelphia USA

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

For image semantic segmentation, a fully convolutional network is usually employed as the encoder to abstract visual features of the input image. A meticulously designed decoder is used to decoding the final feature map of the backbone. The output resolution of backbones which are designed for image classification task is too low to match segmentation task. Most existing methods for obtaining the final high-resolution feature map can not fully utilize the information of different layers of the backbone. To adequately extract the information of a single layer, the multi-scale context information of different layers, and the global information of backbone, we present a new attention-augmented module named Dense-attention Context Module (DCM), which is used to connect the common backbones and the other decoding heads. The experiments show the promising results of our method on Cityscapes dataset.

关键词： Convolution Feature extraction Semantics Data mining Image segmentation Kernel Decoding

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Leveraging Named Data networking for Industrial Automation: Opportunities and Challenges

Leveraging Named Data Networking for Industrial Automation: ...

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IEEE International Conference on Communications Workshops, ICC

作者： Athreya H. Nagaraj Mohit P. Tahiliani Deepaknath Tandur Hariram Satheesh Department of Computer Science and Engineering Wireless Information Networking Group (WiNG) National Institute of Technology Karnataka Mangalore India ABB India Corporate Research Center Bangalore India

ISBN: (数字)9781728174402

ISBN: (纸本)9781728174419

Information Centric networking (ICN) has emerged as a promising solution to address the data dissemination and scalability concerns with the traditional TCP/IP Internet architecture. Named Data networking (NDN) is a prominent ICN architecture that supports in-network caching, easy-to-deploy multicast communication and in-built security mechanisms, thus facilitating efficient, secure and timely delivery of content to the end users. These attributes make NDN a promising solution to meet the requirements of IoT applications. Consequently, NDN based IoT deployments for health care to smart home systems, smart buildings and smart cities are on the rise. Nonetheless, the feasibility of using NDN for building next generation Industrial Automation and Control Systems (IACS) has not been studied. Industrial automation requires an efficient and scalable networking infrastructure that facilitates data sharing to drive operational improvements and develop business intelligence. Ethernet based networking solutions are becoming increasingly popular in IACS to provide seamless integration of industrial networks with the Internet. In this paper, we explore the possibilities to leverage the benefits of NDN to provide an efficient, flexible and scalable networking solution for IACS. We study the impact of making this radical change to industrial networks, highlighting the opportunities and challenges for future research and development in this direction.

关键词： TCPIP Automation Internet computer architecture Protocols Industries

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QoE-Driven transmission-aware cache placement and cooperative beamforming design in Cloud-RANs

QoE-Driven transmission-aware cache placement and cooperativ...

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作者： Sun, Ruijin Wang, Ying Cheng, Nan Lyu, Ling Zhang, Shan Zhou, Haibo Shen, Xuemin State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Telecommunications Engineering State Key Laboratory of Integrated Services Network Xidian University Xi'an710071 China School of Information Science and Technology Dalian Maritime University Dalian116026 China School of Computer Science and Technology Beihang University Beijing100191 China School of Electronic Science and Engineering Nanjing University Nanjing210093 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Pre-caching popular videos at base stations (BSs) is a cost-effective way to significantly alleviate the backhaul pressure. With the video caching, the cache placement and the transmission strategy are intertwined with each other and jointly affect the system performance. Furthermore, the cache placement is updated in a much longer timescale than the transmission strategy. In this paper, the long-term transmission-aware cache placement and the short-term transmission strategy are designed to enhance the quality of experience (QoE) for the video streaming in cloud radio access networks (cloud-RANs). Specifically, consider a cache-enabled cloud-RAN, video contents are cached at BSs, and user requests are cooperatively satisfied by multiple BSs via the cooperative beamforming. To improve the weighted sum of users' QoE, the long-term transmission-aware caching problem in the caching stage and the short-term transmission problem in the delivery stage are respectively formulated, taking into account the backhaul capacity constraint, the transmission power constraint, and the storage size constraint. For the caching problem, the sample average approach is first used to approximate the long-term average QoE value. Then, cache placement strategies are devised in both centralized and distributed manner. For the transmission problem, the full-cooperative beamforming scheme is studied with the optimized cache placement, and an iterative algorithm is proposed. Simulation results show that our proposed transmission-aware cache placement and transmission strategies can achieve higher QoE performance than other cache placement and transmission strategies. © 1967-2012 IEEE.

关键词： Iterative methods

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High-Resolution Synchronous Digital Ballistocardiography Setup

High-Resolution Synchronous Digital Ballistocardiography Set...

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2019 Computing in Cardiology, CinC 2019

作者： Jähne-Raden, Nico Kulau, Ulf Gütschleg, Henrike Clausen, Thiemo Jura, Tobias Sigg, Stephan Wolf, Lars Peter L. Reichertz Institute for Medical Informatics Germany TU Braunschweig Institute of Computer Engineering Germany Aalto University Department of Communications and Networking Finland TU Braunschweig Institute of Operating Systems and Computer Networks Germany

ISBN: (纸本)9781728169361

Ballistocardiography (BCG) describes the measurement and analysis of ballistic forces that are evoked by the heart's movement and propagated to the surface of the body. Although there are several research groups focusing on BCG, the understanding on general BCG signals is still limited, as existing studies only consider a small number of subjects and insufficient measurement *** paper presents first results of our ongoing study to collect BCG ground-truth data. We present a custom hardware implementation for both, high data quality and simultaneous ECG reference measurement. In a large-scale study we currently collected more than 10 hours of BCG data from 34 subjects including BCG in resting position as well as under physical stress. In addition, related biometric data are collected to enable comprehensive investigations. We plan to provide access to the data for interested researchers. © 2019 Creative Commons.

关键词： Electrocardiograms

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