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作者： Ghasemi, Akbar Motahari, Abolfazl Seyed Khandani, Amir Keyvan Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Computer Engineering Sharif University of Technology Tehran11155-8639 Iran Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

We consider the K-user Multiple Input Multiple Output (MIMO) Gaussian interference channel with M antennas at each transmitter and N antennas at each receiver. It is assumed that channel coefficients are constant real numbers and are available at all transmitters and at all receivers. The main objective of this paper is to characterize the number of Degrees of Freedom (DoF) of this channel. Using the real interference alignment technique introduced in Motahari et al., 2014, we show that MN M+N K degrees of freedom can be achieved for almost all channel realizations. Also, a new upper-bound on the DoF of this channel is provided. This upper-bound coincides with our achievable DoF for K K u M+N gcd (M,N), where (M,N) denotes the greatest common divisor of M and N. This gives an exact characterization of DoF for M× N MIMO Gaussian interference channel in the case of K K u. Since there is no cooperation between transmit (or receive) antennas of each user in our transmission scheme, this result shows that the DoF benefit of joint processing in collocated antennas vanishes when the number of users is greater than a certain threshold. © 1963-2012 IEEE.

关键词： Transmitters

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Zero-day Android botnet detection using neural networks

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Neural Computing and Applications 2025年第17期37卷 10795-10805页

作者： Seraj, Saeed Pimenidis, Elias Trovati, Marcello Polatidis, Nikolaos Department of Computer Science Royal Holloway University EghamTW20 0EX United Kingdom School of Computing and Creative Technologies University of the West of England BristolBS16 1QY United Kingdom Department of Computer Science Edge Hill University OrmskirkL39 4QP United Kingdom School of Architecture Technology and Engineering University of Brighton BrightonBN2 4GJ United Kingdom

Android devices have evolved to offer a diverse array of services, spanning applications related to banking, business, health, and entertainment. The widespread adoption of Android devices, coupled with the open-source architecture of the Android operating system, has rendered them a prime target for malicious actors. Among the most perilous threats are Android botnets, which enable malicious actors, often referred to as botmasters, to exert remote control for the execution of destructive attacks. Android botnets have huge potential to be an emerging threat to mobile device security. In this paper, we focus on detecting evolving Android botnets and introduce a new dataset of 3458 apps, represented by 455 permission-based features. We propose an improved multilayer perceptron neural network for zero-day botnet detection. Our methodology, in this way, achieves an accuracy of 98.5%, thus outperforming traditional classifiers. It has a lot of functionality and is based on the neural network approach, making it able to identify slight botnet behaviours in order to improve Android security. © The Author(s) 2024.

关键词： Bot (Internet)

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Methodology for Classifying Objects in High-Resolution Optical Images Using Deep Learning Techniques 7th

Methodology for Classifying Objects in High-Resolution Optic...

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7th International Conference on Microelectronics, Electromagnetics and Telecommunication, ICMEET 2022

作者： Lalitha Kumari, P. Das, Santanu Kannadasan, B. Sampathila, Niranjana Saravanakumar, C. Anand, Rohit Gupta, Ankur Department of Computer Science and Engineering Malla Reddy Institute of Technology Andhra Pradesh Secunderabad Maisammaguda India Department of Biotechnology Seshadripuram First Grade College Yelahanka New Town Bangalore India Department of Civil Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Tamil Nadu Chennai India Department of Biomedical Engineering Manipal Institute of Technology Karnataka Manipal India Department of Electronics and Communication Engineering SRM Valliammai Engineering College Tamilnadu Kattankulathur India DSEU G.B.Pant Okhla-1 Campus New Delhi India Department of Computer Science and Engineering Vaish College of Engineering Haryana Rohtak India

ISBN: (纸本)9789811988646

The classification of objects that are present in the images or in the videos is being developed progressively to obtain good results because of the use of convolutional networks. In this work, we have used the convolutional networks for the detection of objects that are present in high-resolution satellite images. Tests were carried out on ships that are on the high seas and in the ports. This classification is useful for monitoring the coasts, as well as for analyzing the dynamics of the ships which can be applied in the search of ships. To cover this task of classifying ships in the spectral images, the use of high-resolution satellite images of coastal areas and with a large number of ships is used in order to build a set of images, containing images of the ships. In order to be used for training setting and testing of the convolutional network, a very particular configuration of the convolutional network caused by the particularity of high-resolution satellite images is presented. The methodology developed indicating the procedures performed is also presented in which a set of images containing 300 was built images of ships that are in the sea or are anchored in the ports. The results obtained in the classification using the convolutional networks are acceptable to be able to be used in different applications. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Ships

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Improved High Step-Up Cockcroft-Walton Magnetic Coupling Inverter

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IEEE Transactions on Circuits and Systems II: Express Briefs 2023年第6期70卷 1981-1985页

作者： Esmaeili, Soroush Khan, Ashraf Ali Jamil, Mohsin Khan, Usman Ali Ahmed, Hafiz Furqan Ahmed, Shehab Memorial University of Newfoundland Department of Electrical and Computer Engineering St. John'sNLA1B 3X7 Canada Yonsei University Department of Electrical and Electronics Engineering Seoul03722 Korea Republic of National Sun Yat-sen University Department of Electrical Engineering Kaohsiung804 Taiwan King Abdullah University of Science and Technology Cemse Division Thuwal23955 Saudi Arabia

An improved dual winding magnetically coupled-inductors inverter based on a Cockcroft-Walton multiplier voltage cell is proposed in this brief. Compared with the conventional generalized Cockcroft-Walton multiplier voltage Z-source inverters, the proposed inverter attains higher voltage gain by decreasing the transformer's turns number. As a result, lower cost, higher efficiency, larger voltage boost factor, and higher density can be realized in the proposed inverter. Furthermore, unlike conventional magnetically coupled impedance source inverters, the proposed topology offers a larger voltage boost factor for small shoot-through duty ratios with a smoother slope, which enhances the practical controllability of the inverter. Hence, a higher modulation ratio can be applied, which results in a better quality of the inverter's waveforms. A clamping circuit is used to reduce the leakage inductance effects of the coupled inductors. As a result, the huge voltage spikes across the inverter bridge are eliminated. In addition, the proposed inverter draws a smooth and continuous input current from the input dc source, which is suitable for renewable energy sources. Detailed, theoretical analysis, simulation and experimental results are provided. © 2004-2012 IEEE.

关键词： Inductance

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A Comparison Study of FC-RDVM and RIWM Router Placement Methods for WMNs: Performance Evaluation Results by WMN-PSOHC Simulation System Considering Chi-Square Distribution and Different Instances 1

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37th International Conference on Advanced Information Networking and Applications, AINA 2023

作者： Sakamoto, Shinji Barolli, Admir Liu, Yi Kulla, Elis Barolli, Leonard Takizawa, Makoto Department of Information and Computer Science Kanazawa Institute of Technology 7-1 Ohgigaoka Ishikawa Nonoichi921-8501 Japan Department of Information Technology Aleksander Moisiu University of Durres L.1 Rruga e Currilave Durres Albania Department of Computer Science National Institute of Technology Oita College 1666 Maki Oita870-0152 Japan Department of System Management Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Higashi-Ku Fukuoka811-0295 Japan Department of Information and Communication Engineering Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Higashi-Ku Fukuoka811-0295 Japan Department of Advanced Sciences Faculty of Science and Engineering Hosei University Tokyo Kajino-Machi Koganei-Shi184-8584 Japan

ISBN: (数字)9783031290565

ISBN: (纸本)9783031290558

In this work, we deal with the node placement problem in Wireless Mesh Networks (WMNs). We present a hybrid intelligent simulation system called WMN-PSOHC, which combines Particle Swarm Optimization (PSO) and Hill Climbing (HC). We implement in WMN-PSOHC system the Fast Convergence Rational Decrement of Vmax Method (FC-RDVM) and Random Inertia Weight Method (RIWM) router replacement methods. By using WMN-PSOHC system, we carry out simulations the access the performance of these two methods considering Chi-Square distribution of mesh clients and two different instances. The simulation results show that FC-RDVM performs better then RIWM in the considered scenario. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Particle swarm optimization (PSO)

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Brain Tumor Segmentation from 3D MRI Scans Using U-Net

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SN computer Science 2023年第4期4卷 386页

作者： Montaha, Sidratul Azam, Sami Rakibul Haque Rafid, A.K.M. Hasan, Md. Zahid Karim, Asif Department of Computer Science and Engineering Daffodil International University 102/1 Sukrabad Mirpur Rd Dhaka 1207 Bangladesh Faculty of Science and Technology Charles Darwin University Ellengowan Drive Casuarina 0909 NT Australia

A fully automated system based on three-dimensional (3D) magnetic resonance imaging (MRI) scans for brain tumor segmentation could be a diagnostic aid to clinical specialists, as manual segmentation is challenging, arduous, tedious and error prone. Employing 3D convolutions requires large computational cost and memory capacity. This study proposes a fully automated approach using 2D U-net architecture on BraTS2020 dataset to extract tumor regions from healthy tissue. All the MRI sequences are experimented with the model to determine for which sequence optimal performance is achieved. After normalization and rescaling, using optimizer Adam with learning rate 0.001 on T1 MRI sequence, we get an accuracy of 99.41% and dice similarity coefficient (DSC) of 93%, demonstrating the effectiveness of our approach. The model is further trained with different hyper-parameters to assess the robustness and performance consistency. © 2023, The Author(s).

关键词： 3D MRI Brain tumor segmentation BraTS dataset U-Net

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Wireless Federated Learning With Hybrid Local and Centralized Training: A Latency Minimization Design

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IEEE Journal on Selected Topics in Signal Processing 2023年第1期17卷 248-263页

作者： Huang, Ning Dai, Minghui Wu, Yuan Quek, Tony Q. S. Shen, Xuemin State Key Lab of Internet of Things for Smart City China University of Macau Department of Computer and Information Science China University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science China Zhuhai Um Science and Technology Research Institute Zhuhai519031 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore National Cheng Kung University Taiwan University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Wireless federated learning (FL) is a collaborative machine learning (ML) framework in which wireless client-devices independently train their ML models and send the locally trained models to the FL server for aggregation. In this paper, we consider the coexistence of privacy-sensitive client-devices and privacy-insensitive yet computing-resource constrained client-devices, and propose an FL framework with a hybrid centralized training and local training. Specifically, the privacy-sensitive client-devices perform local ML model training and send their local models to the FL server. Each privacy-insensitive client-device can have two options, i.e., (i) conducting a local training and then sending its local model to the FL server, and (ii) directly sending its local data to the FL server for the centralized training. The FL server, after collecting the data from the privacy-insensitive client-devices (which choose to upload the local data), conducts a centralized training with the received datasets. The global model is then generated by aggregating (i) the local models uploaded by the client-devices and (ii) the model trained by the FL server centrally. Focusing on this hybrid FL framework, we firstly analyze its convergence feature with respect to the client-devices' selections of local training or centralized training. We then formulate a joint optimization of client-devices' selections of the local training or centralized training, the FL training configuration (i.e., the number of the local iterations and the number of the global iterations), and the bandwidth allocations to the client-devices, with the objective of minimizing the overall latency for reaching the FL convergence. Despite the non-convexity of the joint optimization problem, we identify its layered structure and propose an efficient algorithm to solve it. Numerical results demonstrate the advantage of our proposed FL framework with the hybrid local and centralized training as well as our proposed alg

关键词： Information management

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Milestones of Wireless Communication Networks and technology Prospect of Next Generation(6G)

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computers, Materials & Continua 2022年第6期71卷 4803-4818页

作者： Mohammed H.Alsharif Md.Sanwar Hossain Abu Jahid Muhammad Asghar Khan Bong Jun Choi Samih M.Mostafa Department of Electrical Engineering College of Electronics and Information EngineeringSejong University209 Neungdong-roGwangjin-guSeoul05006Korea Department of Electrical and Electronic Engineering Bangladesh University of Business and TechnologyDhaka1216Bangladesh Department of Electrical&Computer Engineering University of OttawaOttawaON K1N 6N5Canada Department of Electrical Engineering Hamdard University KarachiIslamabad CampusIslamabad44000Pakistan School of Computer Science and Engineering&School of Electronic Engineering Soongsil UniversitySeoulKorea Faculty of Computers and Information South Valley UniversityQena83523Egypt Academy of Scientific Research and Technology Cairo11516Egypt

Since around 1980,a new generation of wireless technology has arisen approximately every 10 ***-generation(1G)and secondgeneration(2G)began with voice and eventually introduced more and more data in third-generation(3G)and became highly popular in the fourthgeneration(4G).To increase the data rate along with low latency and mass connectivity the fifth-generation(5G)networks are being installed from ***,the 5G technology will not be able to fulfill the data demand at the end of this ***,it is expected that 6G communication networks will rise,providing better services through the implementation of new enabling technologies and allowing users to connect everywhere.6G technology would not be confined to cellular communications networks,but would also comply with non-terrestrial communication system requirements,such as satellite *** ultimate objectives of this work are to address the major challenges of the evolution of cellular communication networks and to discourse the recent growth of the industry based on the key scopes of application and *** main areas of research topics are summarized into(i)major 6G wireless networkmilestones;(ii)key performance indicators;(iii)future new applications;and(iv)potential fields of research,challenges,and open issues.

关键词： Wireless networks beyond 5G 6G communications terahertz frequency terahertz communications holographic calls

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Generic Recommendation System for Business Process Modeling 6th

Generic Recommendation System for Business Process Modeling

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Shreya, J.L. Saini, Anu Kumari, Sunita Jain, Astha Department of Computer Science and Engineering Dr. SPM International Institute of Information Technology Naya Raipur India Department of Computer Science and Engineering G. B. Pant DSEU Okhla-1 Campus DSEU New Delhi India Tata Consultancy Services Limited Uttar Pradesh Noida India

ISBN: (纸本)9789819930098

Process modeling helps in the comprehension and reorganization of corporate activity. Manual process modeling takes longer time and is liable for errors. We propose to build and provide a supporting system for people who manage the creation and application of business process models for their organizations. Any person intending to build and get recommendations for business process models can utilize the proposed recommender system, depending upon the kind of organization the person is working for. The core functions of the recommender system are to recommend fragments of a business process model as well as in-built process models with the benefit of creating, storing, and utilizing them for multiple purposes. The recommendations are based on a number of factors such as the number of people who prefer a particular model along with the models’ ratings. Here, we use the basic principle of collaborative filtering. This recommendation system provides a user-friendly interface, with a lucid structure of query interface to help users surf easily. The recommendation system also avoids the risk of misusing users’ personal details since it does not use cookies. Users can freely utilize this recommendation system even if they are novice. This extended work takes into account all of the specifications of a business process model for a range of industries, such as education, software, food, medical, automotive, banking, and so on, thus developing a generic system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recommender systems

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Neural Network-Based Obstacle and Pothole Avoiding Robot 4th

Neural Network-Based Obstacle and Pothole Avoiding Robot

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4th International Conference on Trends in Cognitive Computation engineering, TCCE 2022

作者： Al Arafat, Md. Mahedi Hossain, Mohammad Shahadat Hossain, Delowar Andersson, Karl Department of Computer Science and Engineering University of Chittagong Chittagong4331 Bangladesh Cumming School of Medicine University of Calgary CalgaryABT2N 1N4 Canada Department of Computer Science Electrical and Space Engineering Lulea University of Technology SkellefteåSE-931 87 Sweden

ISBN: (纸本)9789811994821

The main challenge of any mobile robot is to detect and avoid obstacles and potholes. This paper presents the development and implementation of a novel mobile robot. An Arduino Uno is used as the processing unit of the robot. A Sharp distance measurement sensor and Ultrasonic sensors are used for taking inputs from the environment. The robot trains a neural network based on a feedforward backpropagation algorithm to detect and avoid obstacles and potholes. For that purpose, we have used a truth table. Our experimental results show that our developed system can ideally detect and avoid obstacles and potholes and navigate environments. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mobile robots

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