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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Fabunmi, Temitayo Emmanuel Olukayode Ojo, Adedayo Gbadamosi, Saheed Lekan Ekweozor, Chinaza Ashigye, Joseph Chidera, Christogonus Afe Babalola University Ado-Ekiti College of Engineering Department of Electrical/Electronics and Computer Engineering Ekiti State Ado-Ekiti Nigeria Bowen University Department of Electrical and Electronic Engineering Osun State Iwo Nigeria

ISBN: (纸本)9798331542559

This paper introduces a new approach to predicting heating loads in homes using the adaptive neuro- fuzzy inference system (ANFIS). Predicting heating loads accurately is crucial for managing energy usage and ensuring that indoor temperatures are comfortable for occupants. Traditional models often struggle with the complexity and fluctuating nature of heating needs in different homes. To tackle this, this article proposes the an ANFIS framework, which blends the flexibility of fuzzy inference logic (FIS) with the learning abilities of artificial neural networks (ANN) for solving heating load prediction tasks. The data set used includes building parameters such as wall area, relative compactness, and surface area of the residential building. The performance results observed from the proposed model showed good predictive accuracy and generalization ability. This technique therefore stands as a viable approach for modeling and predicting heating loads in residential buildings. © 2024 IEEE.

关键词： Apartment houses

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Intrusion Detection System for Big Data Analytics in IoT Environment

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computer Systems Science & engineering 2022年第10期43卷 381-396页

作者： M.Anuradha G.Mani T.Shanthi N.R.Nagarajan P.Suresh C.Bharatiraja Department of Computer Science and Engineering St.Joseph’s College of EngineeringChennai600119India Department of Computer Science and Engineering University College of Engineering ArniThatchur632326India Department of Electronics&Communication Engineering Kings College of EngineeringPudukkottai613303India Department of Electronics&Communication Engineering K.Ramakrishnan College of EngineeringTiruchirapalli621112India Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Electrical and Electronics Engineering SRM Institute of Science and TechnologyChennai603203India

In the digital area,Internet of Things(IoT)and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal *** IoT networks are popular and widely employed in real world applications,security in IoT networks remains a challenging *** intrusion detection systems(IDS)cannot be employed in IoT networks owing to the limitations in resources and ***,this paper concentrates on the design of intelligent metaheuristic optimization based feature selection with deep learning(IMFSDL)based classification model,called IMFSDL-IDS for IoT *** proposed IMFSDL-IDS model involves data collection as the primary process utilizing the IoT devices and is preprocessed in two stages:data transformation and data *** manage big data,Hadoop ecosystem is ***,the IMFSDL-IDS model includes a hill climbing with moth flame optimization(HCMFO)for feature subset selection to reduce the complexity and increase the overall detection ***,the beetle antenna search(BAS)with variational autoencoder(VAE),called BAS-VAE technique is applied for the detection of intrusions in the feature reduced *** BAS algorithm is integrated into the VAE to properly tune the parameters involved in it and thereby raises the classification *** validate the intrusion detection performance of the IMFSDL-IDS system,a set of experimentations were carried out on the standard IDS dataset and the results are investigated under distinct *** resultant experimental values pointed out the betterment of the IMFSDL-IDS model over the compared models with the maximum accuracy 95.25%and 97.39%on the applied NSL-KDD and UNSW-NB15 dataset correspondingly.

关键词： Big data cybersecurity IoT networks intrusion detection deep learning metaheuristics intelligent systems

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Privacy, Security, Trust and Applications in Internet of Things 6

Privacy, Security, Trust and Applications in Internet of Thi...

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6th IEEE International Conference on Advanced Systems and Emergent Technologies, IC_ASET 2023

作者： Yaklaf, Sliman K.A.A. Elmezughi, Abdurrezagh S. Naas, Salah Mohamed H. Ekreem, Nasser Bashir Azzaytuna University Faculty of Engineering Electrical and Electronics Engineering Department Tarhuna Libya Azzaytuna University Faculty of Engineering Computer Engineering Department Tarhuna Libya

ISBN: (纸本)9798350321029

Internet of Things (IoT) is a network to connect anything with the Internet. While its purpose is to provide efficient and effective solutions, security of the network devices is a challenging issue. Therefore, security and privacy has emerged as a significant challenge for the IoT. Within the available Internet infrastructure, IoT devices collecting data through wireless technology. Now Days, IoT is research area with many devices are connected through internet. IoT concept brings a new challenge concerning privacy, trust, security, and reliability. To build smart systems, IoT technology has become important technology for people use. In this paper, IoT technologies, applications and characteristics will be briefly discussed. This paper gives a complete image of IoT security issues and its applications. The platform is required to secure exchange between consumers of their information and IoT devices. © 2023 IEEE.

关键词： Internet of things

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Color-image encryption scheme based on channel fusion and spherical diffraction

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Chinese Physics B 2022年第3期31卷 276-284页

作者： Jun Wang Yuan-Xi Zhang Fan Wang Ren-Jie Ni Yu-Heng Hu Sichuan University School of Electronics and Information EngineeringChengdu 610065China Department of Electrical and Computer Engineering University of Wisconsin-MadisonMadisonWIS3706USA

A secure encryption scheme for color images based on channel fusion and spherical diffraction is proposed in this paper. In the proposed encryption scheme, a channel fusion technology based on the discrete wavelet transformation is used to transform color images into single-channel grayscale images, firstly. In the process of transformation, the hyperchaotic system is used to permutate and diffuse the information of red–green–blue(RGB) channels to reduce the correlation of channels. Then the fused image is encrypted by spherical diffraction transform. Finally, the complex-valued diffraction result is decomposed into two real parts by the improved equal module decomposition, which are the ciphertext and the private key. Compared with the traditional color image encryption schemes that encrypt RGB channels separately, the proposed scheme is highly secure and robust.

关键词： channel fusion hyper-chaotic system asymmetric encryption spherical diffraction

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Remote Intelligent System for Monitoring and Control of Water Distribution Network Using Remote I/O Module for Smart City

Remote Intelligent System for Monitoring and Control of Wate...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Raj, C. Mohan Babu, W. Rajan Gomathi, V. Bharath, S. Jagatap, Govind Vishwanath Kumar, S. Siva Sri Eshwar College of Engineering Department of Electrical and Electronics Engineering Coimbatore India Dhanalakshmi Srinivasan College of Engineering Department of Electrical and Electronics Engineering Coimbatore India Sns College of Technology Department of Electrical and Electronics Engineering Coimbatore India Bharati Vidyapeeth College of Engineering Department of Mechanical Engineering Navi Mumbai India Nehru Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665474511

The need for drinking water is very essential in today's world. The government has been adopting a variety of measures to save drinking water and spend it economically. It is everyone's duty to save water and use it sparingly. Water is wasted due to breakage of the water pipe, excessive use of water, measurement in a faulty instrument and so on. The leakage of water from the pipeline and the usage of water is intelligently detected and measured by PLC - Remote I/O Module based system. Each house or utility is installed with intelligent flow meters and the inputs or readings are collected from the individual meters by water distribution board via the iOT. Faults and other abnormal conditions are identified instantaneously, and the health of the distribution system is monitored by SCADA. © 2023 IEEE.

关键词： Potable water

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Development of a Telematic Control Unit for Capturing Vital Vehicle Data without Using Company Fitted Telematic Ports

Development of a Telematic Control Unit for Capturing Vital ...

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2024 International Conference on Science, Technology, engineering and Management, ICSTEM 2024

作者： Sri Dhananjayan, K. Maheshwaran, M. Kayalvizhi, P. Lakshmanan, P. Pushpalatha, N. Senthooriya, O.S. Department of Electrical and Electronics Engineering Sri Eshwar College of Engineering Coimbatore641 202 India Department of Mechatronics Engineering Nehru Institute of Engineering and Technology Thirumalayampalayam Tamilnadu Coimbatore641105 India Department of Computer science Engineering M.Kumarasamy College of Engineering Tamilnadu India Department of Electrical and Electronics Engineering Narasaraopeta Engineering College Guntur District Andhra Pradesh Narasaraopet India

ISBN: (纸本)9798350376913

Many companies use vehicle telemetry data to learn more about driver behaviour, fleet performance, and maintenance requirements. But occasionally, a vehicle might not have a telemetry data port, or the port might not be available because of restrictions put in place by the vehicleowner. The task at hand involves creating a Telematic Control Unit (TCU) that is capable of gathering crucial vehicle information without depending on the telemetry data port installed by the corporation. For fleet management purposes, the TCU should offer a dependable and strong means of gathering data from the car's numerous sensors and systems, guaranteeing real-time tracking and analysis *** solution elements involves the, Data Capture: Creating a TCU that can communicate with the sensors and systems of the vehicle in order to gather essential data, speed, fuel consumption, engine performance, payload, and diagnostic *** should be possible for the TCU to reliably and accurately retrieve data from any vehicle, regardless of the brand or model. Wireless Communication: Set up a wireless communication system to transfer the TCU's collected data to a cloud platform or central server. A strong and secure wireless protocol that guarantees data integrity and secrecy should be supported by the solution. Real-time Monitoring and Analysis: Provide fleet managers with the ability to monitor and analyze vehicle data in real-time by creating an intuitive dashboard or *** order to help businesses make wise decisions and maximize their operations, the dashboard should offer information into vehicle performance, payload, fuel efficiency, driver behaviour, and maintenance requirements. Integration and Compatibility: Make sure the TCU solution is easily linked with third-party applications that are often used in the business or with the fleet management systems that are currently in place. APIs or other integration features should be included in the solution to facilitate smooth

关键词： Telematics

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A DeNoising FPN with Transformer R-CNN for Tiny Object Detection

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IEEE Transactions on Geoscience and Remote Sensing 2024年 62卷 1-15页

作者： Liu, Hou-I Tseng, Yu-Wen Chang, Kai-Cheng Wang, Pin-Jyun Shuai, Hong-Han Cheng, Wen-Huang National Yang Ming Chiao Tung University Department of Electronics and Electrical Engineering Hsinchu300 Taiwan National Yang Ming Chiao Tung University Institute of Electronics Hsinchu300 Taiwan Department of Computer Science and Information Engineering Taipei106 Taiwan

Despite notable advancements in the field of computer vision (CV), the precise detection of tiny objects continues to pose a significant challenge, largely due to the minuscule pixel representation allocated to these objects in imagery data. This challenge resonates profoundly in the domain of geoscience and remote sensing, where high-fidelity detection of tiny objects can facilitate a myriad of applications ranging from urban planning to environmental monitoring. In this article, we propose a new framework, namely, DeNoising feature pyramid network (FPN) with Trans R-CNN (DNTR), to improve the performance of tiny object detection. DNTR consists of an easy plug-in design, DeNoising FPN (DN-FPN), and an effective Transformer-based detector, Trans region-based convolutional neural network (R-CNN). Specifically, feature fusion in the FPN is important for detecting multiscale objects. However, noisy features may be produced during the fusion process since there is no regularization between the features of different scales. Therefore, we introduce a DN-FPN module that utilizes contrastive learning to suppress noise in each level's features in the top-down path of FPN. Second, based on the two-stage framework, we replace the obsolete R-CNN detector with a novel Trans R-CNN detector to focus on the representation of tiny objects with self-attention. The experimental results manifest that our DNTR outperforms the baselines by at least 17.4% in terms of AP_vt on the AI-TOD dataset and 9.6% in terms of average precision (AP) on the VisDrone dataset, respectively. Our code will be available at https://***/hoiliu-0801/DNTR. © 1980-2012 IEEE.

关键词： Noise abatement

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A Study on Spiking Neural Network Design 14

A Study on Spiking Neural Network Design

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14th International Conference on electrical and electronics engineering, ELECO 2023

作者： Erkan, Yasemin Erkan, Erdem Bartin University Department of Electrical and Electronics Engineering Bartin Turkey Bartin University Department of Computer Engineering Bartin Turkey

ISBN: (纸本)9798350360493

A Spiking Neural Network (SNN) is defined as an Artificial Neural Network (ANN) that has developed rapidly in the last decade. There are some difficulties in implementing these networks, especially in supervised machine learning. The main limitation is that classical learning approaches such as backpropagation cannot be directly applied to SNNs. We created a model to enable backpropagation, using a biological neuron model instead of the activation function of the ANN network. This study, a deep neural network that can work in both ANN and SNN modes was designed to classify the feature vectors obtained from the wavelet coefficient of magnetoencephalography signals taken from the human brain. Thus we propose a hybrid network that can operate in both conventional and firing modes by replacing the activation function of the traditional neural network with Izhikevich neurons. Our proposed network has been tested in classifying 4-class motor imaginary signals and the results are presented comparatively. We hope this work, which blends computational neuroscience and machine learning, will bring a different perspective to fired network design. © 2023 IEEE.

关键词： Backpropagation

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IoT Enabled Smart Mess with Nutrition Benefits and Waste Management 4

IoT Enabled Smart Mess with Nutrition Benefits and Waste Man...

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4th International Conference on Advancement in electronics and Communication engineering, AECE 2024

作者： Utsav, Ankur Kumari, Annu Awasthi, Shivam Kumar, Aniket Rahman, Md Atiqur Galgotias College of Engineering Department of Electronics and Communication Engineering Greater Noida India Iit Patna Department of Computer Science and Engineering Patna India Iilm University Department of Computer Science and Engineering Greater Noida India Gec Vaishali Department of Electronics and Communication Engineering Vaishali India Gec Vaishali Department of Electrical Engg. Vaishali India

ISBN: (纸本)9798350364729

This research explores the implementation of a smart mess system tailored to meet fitness enthusiast dietary needs through the use of RFID technology integrated with a mobile application. The system begins with people inputting basic information such as height, weight, and any food allergies into the app, which calculates their BMI and stores this data on an RFID card. At the mess gate, an RFID receiver grants access to the automated door, allowing students to enter and receive meal recommendations based on their BMI, including what to eat and in what quantities. In the second scenario, the app provides a solution for people who fall ill by enabling them to order food suitable for their symptoms, thus ensuring they receive proper nutrition even when they cannot physically visit the mess. The third case introduces a smart dishwasher system, where leftover waste is efficiently disposed of, and plates are automatically transported via a conveyor belt to the washing section, streamlining the cleaning process. This smart mess system aims to enhance student health, convenience, and operational efficiency within educational institutions. © 2024 IEEE.

关键词： Belt conveyors

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Novel Three-Phase Buck-Boost Inverter With Reduced Input Current Ripple and No Short-Circuit and Open-Circuit Faults

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IEEE Transactions on Power electronics 2024年第5期39卷 5695-5706页

作者： Khan, Shah Nawaz Khan, Ashraf Ali Jamil, Mohsin Khan, Usman Ali Ahmed, Hafiz Furqan Ahmed, Shehab Memorial University of Newfoundland Department of Electrical and Computer Engineering St. JohnNLA1C 5S7 Canada Yonsei University Department of Electrical and Electronics Engineering Seoul03722 Korea Republic of National Sun Yat-sen University Department of Electrical Engineering Kaohsiung894 Taiwan King Abdullah University of Science and Technology CEMSE Divisions Thuwal23955 Saudi Arabia

This article proposes a new single-stage three-phase buck-boost inverter and control scheme, which remarkably reduces both the low and high-frequency ripple components in the input current. The low-frequency ripple component is eliminated by phase-shifting the output three-phase voltages by 120°. The high-frequency ripple component is decreased by doubling the effective switching frequencies and lowering the voltages of the input inductors. The proposed inverter has buck-boost dc-ac conversion ability and contains only six active switches. It is short-circuiting free and can avoid the reverse recovery losses of the body diodes of the mosfets. The proposed inverter has better spectral performance and has a lower distortion in the output waveforms due to no delays (equivalent to dead-time in the traditional three-phase inverter) in the switching signals. To validate the performance and advantages of the proposed inverter, detailed analytical, simulation, and experimental results are reported. Experiments have been conducted with a 1.5-kW laboratory prototype at a 50 kHz switching frequency for the line-line output voltage of 110-220 Vrms/60. At the end, integration of inductors is also suggested to decrease the magnetic volume. © 1986-2012 IEEE.

关键词： Electric inverters

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