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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Lu, William Makur, Anuran Purdue University Department of Computer Science West LafayetteIN47907 United States Purdue University School of Electrical and Computer Engineering West LafayetteIN47907 United States

ISBN: (纸本)9781665475549

Propelled by recent advances in the study of point-to-point permutation channels, which stem from communication networks and biological communications applications, we analyze the permutation binary adder multiple-access channel (PAMAC) in this work. The PAMAC network model consists of d senders communicating with a single receiver through a standard binary adder multiple-access channel followed by a random permutation block that shuffles the output codeword of the multiple-access channel. We formally define an appropriate notion of permutation sum-capacity Cpsum for this model, and then establish that Cpsum = d/2. To derive an achievability bound we construct d randomized encoders where input codewords are i.i.d. samples from Bernoulli distributions with carefully chosen parameters. These parameters can be perceived as roots of the probability generating function of the distribution over the output alphabet after the PAMAC's addition operation. Our achievability proof crucially uses eigenvalue perturbation results to provide quantitative estimates on the stability of the roots, which allows us to analyze decoding performance. This argument also yields an inner bound on the permutation capacity region of the PAMAC model. Finally, we also obtain a converse bound on Cpsum matching our achievability result. © 2023 IEEE.

关键词： Adders

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Forecasting Groundwater Depletion in Quetta Sub-Basin using Remote Sensing Data: Deep Learning Approach 17

Forecasting Groundwater Depletion in Quetta Sub-Basin using ...

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17th International Conference on Open Source Systems and Technologies, ICOSST 2023

作者： Khan, Pamir Umer, Rahila Fatima Muhammad, Jan Rauf, Zahid Ghafoor, Hifsa Computer Science Baluchistan University of IT Engineering and Management Science Quetta Quetta87300 Pakistan Balochistan University of IT Engineering & Management Sciences Quetta Department of Computer Science Quetta87300 Pakistan Balochistan University of IT Engineering & Management Sciences Quetta Department of Computer Engineering Quetta87300 Pakistan Balochistan University of IT Engineering & Management Sciences Quetta Department of Electrical Engineering Quetta87300 Pakistan

ISBN: (纸本)9798350381320

Groundwater resource is the largest freshwater storage resource, and it is also the largest storage for most human usage via domestic, industrial, and agricultural water supply and has maintained global food and water security. Therefore, it's important to sustain the supply and demand to balance the huge population. Pakistan is one of the largest groundwater consumers in the region of South Asia, which extracts roughly 60 billion m3, and Pakistan's groundwater table is dropping a meter per year. Groundwater has been over-exploited for drinking and agriculture. Balochistan is the country's largest province by size but the smallest by population;the situation is worse than in other regions of the country because groundwater level (GWL) is declining at a pace of 2 to 3 m per year. The primary goal of this research is to anticipate GWL changes and depletion, as well as to evaluate various machine learning techniques in predicting the best deep learning models for forecasting GWL changes utilizing GRACE and GLDAS data in the Quetta District of Balochistan. Deep learning models are used to predict aquifers' water-balancing capacities. The proposed approach would use data modeling with groundwater levels and meteorological factors as training parameters. By combining advanced groundwater modeling techniques and various datasets, the study aims to provide insights and implications for the entire research area. The ultimate goal is to develop effective management measures and ensure a sustainable supply of groundwater to support the needs of the large population in Balochistan, Pakistan, and the region of South Asia as a whole. © 2023 IEEE.

关键词： Aquifers

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Dark Forest Algorithm:A Novel Metaheuristic Algorithm for Global Optimization Problems

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computers, Materials & Continua 2023年第5期75卷 2775-2803页

作者： Dongyang Li Shiyu Du Yiming Zhang Meiting Zhao Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo315000China Engineering Laboratory of Advanced Energy Materials Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo315000China School of Material and Chemical Engineering Ningbo UniversityNingbo315000China

Metaheuristic algorithms,as effective methods for solving optimization problems,have recently attracted considerable attention in science and engineering *** are popular and have broad applications owing to their high efficiency and low *** algorithms are generally based on the behaviors observed in nature,physical sciences,or *** study proposes a novel metaheuristic algorithm called dark forest algorithm(DFA),which can yield improved optimization results for global optimization *** DFA,the population is divided into four groups:highest civilization,advanced civilization,normal civilization,and low *** civilization has a unique way of *** verify DFA’s capability,the performance of DFA on 35 well-known benchmark functions is compared with that of six other metaheuristic algorithms,including artificial bee colony algorithm,firefly algorithm,grey wolf optimizer,harmony search algorithm,grasshopper optimization algorithm,and whale optimization *** results show that DFA provides solutions with improved efficiency for problems with low dimensions and outperforms most other algorithms when solving high dimensional *** applied to five engineering projects to demonstrate its *** results show that the performance of DFA is competitive to that of current well-known metaheuristic ***,potential upgrading routes for DFA are proposed as possible future developments.

关键词： Metaheuristic algorithm global optimization

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Smart Home Automation with IoT Sensor Systems for Energy Conservation using Zigbee and Wifi 5

Smart Home Automation with IoT Sensor Systems for Energy Con...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Jagannathan, Srinivasan Deepa, V. Sakthi Karthi Durai, B. Rajan, M. Sundar Celin, J. Jeya A. Dhamodaran, Sasikala Madanapalle Institute of Technology & Science Department of Computer Applications Madanapalle India Tiruppur Kumaran College for Women Pg and Research Department of Computer Science Tamil Nadu Tiruppur India Kalasalingam Academy of Research and Education Computer Science and Engineering India Arbaminch Institute of Technology Arbaminch University Faculty of Electrical and Computer Engineering Ethiopia Kalasalingam Academy of Research and Education Department of Information Technology Viruthunagar Krishnankoil626126 India Rajasthan Jaipur India

ISBN: (纸本)9798350379945

This study presents a comprehensive study on smart home automation systems utilizing Internet of Things (IoT) sensor technology to achieve efficient energy conservation. The proposed system integrates ZigBee and WiFi communication protocols for effective and reliable connectivity across various smart devices within a home. Through the deployment of interconnected sensors and intelligent controllers, the system allows real-time monitoring and management of household energy usage, optimizing energy consumption and reducing waste. The combination of ZigBee for low-power, long-range sensor networks and WiFi for high-speed data transmission enables seamless interaction between smart appliances, lighting, heating, and cooling systems. Proposesd approach offers a scalable, cost-effective solution for enhancing home automation with an emphasis on energy conservation and sustainability. Also discussed the implementation, challenges, and future potential of IoT-based smart home systems for improved energy efficiency. © 2024 IEEE.

关键词： Smart homes

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Active Power Curtailment and Reactive Power Control in PV-Rich Low-Voltage Distribution Network 20

Active Power Curtailment and Reactive Power Control in PV-Ri...

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20th International Conference on the European Energy Market, EEM 2024

作者： Dubravac, Marina Topić, Danijel Fekete, Krešimir Šimić, Zvonimir Prenc, Rene Rojnić, Michele Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia University of Rijeka Faculty of Engineering Rijeka Croatia

ISBN: (纸本)9798350381740

High PV systems integration faces distribution networks (DN) with technical issues. However, PVs are inverter- based sources and with active power curtailment (APC) and reactive power control (RPC) can provide ancillary services for distribution system operators (DSOs) and participate as problem/solution principle. This paper proposes an optimal power flow (OPF)-based model for improving voltages and reducing losses by using PV inverter APC and RPC strategies. The proposed optimization model is presented as nonlinear programming (NLP). For solving this optimization problem without approximations, a co-simulation of OpenDSS power system analysis software and PyGMO package with embedded computational intelligence (CI) optimization methods is used. Several case studies are carried out over the IEEE European low-voltage test feeder (LVTF). Regardless of which strategy is applied, the obtained results show that the proposed model improves technical conditions in PV-rich DN and illustrate PV potential in providing ancillary services to DSO. © 2024 IEEE.

关键词： Power distribution networks

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DAEMD: Deep Autoencoder based Malware Detection from Android Network Flows 26

DAEMD: Deep Autoencoder based Malware Detection from Android...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Hasan, Nasimul Rahman, Shams Ishtiaque Shakil Ahamed Shohag, Md International Islamic University Department of Computer Science and Engineering Chittagong Bangladesh State University of New York at Stony Brook United States University of Windsor Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350359015

Android OS is an enticing target for attacks due to its popularity. Malware attacks are prevalent and growing. Further, the attack pattern is changing rapidly to avoid intrusion detection. Thus, effective malware detection that can adapt to rapid structure and behaviour changes is in demand. We provide a two-layer mobile malware detection method in this research. Deep learning represents the feature set into a latent feature space in the first layer, while the second layer is a straightforward multi-layer perceptron classifier. Elastic Weight Consolidation was added to the neural network classifier to enable continuous malware learning. We ran trials to evaluate performance. We compared our model's accuracy to other machine learning models. We used cutting-edge methods to build our framework. Performance comparison with the state-of-the-art approaches shows the efficacy of the proposed framework. It can also learn new threats while maintaining detection performance. The testing findings show that our framework can detect intrusion with 98.8% Precision and 98.7% Recall. Additionally, the continuous learning system can accurately learn malware. © 2023 IEEE.

关键词： Continual learning Malware detection Network Security Network Traffic

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Design,simulation,and testing of a tunable MEMS multi-threshold inertial switch

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Microsystems & Nanoengineering 2024年第2期10卷 37-46页

作者： Qiu Xu Rodrigo T.Rocha Yousef Algoos Eric Feron Mohammad I.Younis Faculty of Electronic Information Engineering Huaiyin Institute of TechnologyHuai’an223003PR China Computer Electrical and Mathematical Science and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal23955-6900Saudi Arabia Piezoelectric Microsystem Technologies Silicon Austria LabsVillach9524Austria Physical Sciences and Engineering Division King Abdullah University of Science and TechnologyThuwal23955Saudi Arabia Mechanical Engineering Department State University of New YorkBinghamtonNY13902USA

This paper presents a tunable multi-threshold micro-electromechanical inertial switch with adjustable threshold *** demonstrated device combines the advantages of accelerometers in providing quantitative acceleration measurements and g-threshold switches in saving power when in the inactive state upon experiencing acceleration below the *** designed proof-of-concept device with two thresholds consists of a cantilever microbeam and two stationary electrodes placed at different positions in the sensing *** adjustable threshold capability and the effect of the shock duration on the threshold acceleration are analytically investigated using a nonlinear beam *** are shown for the relationships among the applied bias voltage,the duration of shock impact,and the tunable *** fabricated prototypes are tested using a shock-table *** analytical results agree with the experimental *** designed device concept is very promising for the classification of the shock and impact loads in transportation and healthcare applications.

关键词： tunable acceleration testing

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A Fuzzy Approach Towards the Measurement of Subject-Specific Cognitive Load during Mental Task Using Hemodynamic Response 4

A Fuzzy Approach Towards the Measurement of Subject-Specific...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Karmakar, Subashis Pal, Tandra Koley, Chiranjib National Institute of Technology Durgapur Department of Computer Science and Engineering West Bengal India National Institute of Technology Durgapur Department of Electrical Engineering West Bengal India

ISBN: (纸本)9798350387063

Every individual exhibits a different cognitive load for a particular task of the same level of difficulty. Measuring the cognitive load of an individual can help to develop an effective learning environment, design personalized learning materials and diagnose specific learning impairments. The primary aim of this study is to measure the subject-specific utilization of cognitive load during the mental arithmetic task using functional near-infrared spectroscopy (fNIRS) signals. For this purpose, we have collected fNIRS signals from fourteen healthy subjects while performing mental arithmetic tasks of three difficulty levels: low, medium and high. Statistical features are extracted from the collected fNIRS signal. After that, a fuzzy classifier has been developed where the fuzzy membership functions for each of the input features are generated using fuzzy c-means clustering. The experimental results show that the subjects undergoing the experiment utilize different levels of cognitive loads for a given task with the same level of difficulty. The proposed model achieves 89.92 % classification accuracy. © 2024 IEEE.

关键词： Near infrared spectroscopy

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Gesture Recognition Techniques 15

Gesture Recognition Techniques

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15th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Sharma, Varun Kolivand, Hoshang Asadianfam, Shiva Al-Jumeily, Dhiya Jayabalan, Manoj Liverpool John Moores University Department Computer Science Liverpool United Kingdom School of Computer Science and Maths Liverpool John Moores University Liverpool United Kingdom Qom University of Technology Faculty of Electrical and Computer Engineering Qom Iran Islamic Azad University Qom Department of Computer Engineering Branch Qom Iran

ISBN: (纸本)9798350335149

Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. It is a subdiscipline of computer vision. In this paper, we describe some of Gesture recognition techniques such as Vision based gesture recognition and Graph based gesture recognition. Also, we explore these techniques with previous studies. © 2023 IEEE.

关键词： computer vision

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An Improved Archimedes Optimization Algorithm (IAOA)

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第3期8卷 693-709页

作者： Wang, Yan-Jiao Chen, Ming-Chi Ku, Chin Soon School of Electrical Engineering Northeast Electric Power University 169 Changchun Road Jilin132012 China Department of Computer Science Universiti Tunku Abdul Rahman Kampar31900 Malaysia

The Archimedes optimization algorithm (AOA) is a new meta-heuristic inspired by Archimedes’ principle in physics. However, when solving complex optimization problems, there are some defects, such as slow convergence and easy falling into local optimum. In order to further improve the convergence performance of AOA, an improved Archimedes optimization algorithm (IAOA) is proposed in this paper. This paper mainly improves the learning factor and the acceleration updating formula of individual attributes in the exploration stage to further maintain the diversity of the population and improve the early convergence speed of the algorithm. At the same time, this paper proposes a mechanism to update the individual position according to the probability and introduces an adjustment factor in the development stage to improve the development ability of the algorithm. In addition, the simplex method mechanism is used to correct the poor individuals and improve the convergence speed and accuracy of the algorithm. The experimental results of the CEC2013 test set show that IAOA has significant advan-tages in terms of convergence speed, accuracy, and stability compared with the original AOA algorithm and the four most excellent meta-heuristics algorithms so far. © 2023 ISSN 2414-8105 (Online).

关键词： Heuristic algorithms

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