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5th World Symposium on communication engineering, WSCE 2022

作者： Utaminingrum, Fitri Mayena, Sri Karim, Corina Wahyudi, Slamet Huda, Fais Al Lin, Chih-Yang Shih, Timothy K. Thaipisutikul, Tipajin Computer Vision Research Groups Brawijaya University Faculty of Computer Science Malang Indonesia Brawijaya University Mathematics Departments Malang Indonesia Brawijaya University Faculty of Computer Science Malang Indonesia Yuan-Ze University Department of Electrical Engineering Taoyuan32003 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan32001 Taiwan Mohidol University Faculty of Information and Communication Technology Thailand

ISBN: (数字)9781665450577

ISBN: (纸本)9781665450577

Today's wheelchair technology is evolving, with options ranging from manual wheelchairs, electric wheelchairs to the latest smart wheelchairs. However, wheelchairs on the market today still lack of the resources to facilitate advanced security for their users, particularly from the risk of injury or death caused by user neglect to differences in the rough and uneven texture surface structure and distinctions in road height levels when crossed by wheelchairs. Which would be at risk of accidents such as the existence of descending stairs. The victim may sustain physically visible physiological effects because of the accident, such as abrasions, bruises, tears, fractures, head injuries, and even death in fatal cases. The goal of this research is to enhance the security and safety of smart wheelchairs by developing autonomous controls with the extraction of gray level cooccurrence matrix (GLCM) texture features and the Naive Bayes Classification in anticipation of irregular road conditions and the existence of levels that are at risk of endangering wheelchair users based on camera input. According to the results of 100 experiments using 2-dimensional imagery data tested at d = 1, 2, 3, 4 and θ = 0°, 45°, 90°, 135° values, the resulting d = 1 and θ = 0° levels have the highest accuracy of 87% when classifying images of descending stairs and floors. © 2022 IEEE.

关键词： Wheelchairs

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Tumor Infiltration of Microrobot using Magnetic torque and AI Technique 2

Tumor Infiltration of Microrobot using Magnetic torque and A...

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2nd IEEE International Conference on Vision Towards Emerging Trends in communication and Networking Technologies, ViTECoN 2023

作者： Shalini, R. Mishra, Laxmi Athulya, S. Chimankar, Abhijeet Gajanan Kandavalli, Sunanda Ratna. Keshav Kumar, K. Selvan, R. Senthamil School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Department of BCA & IT Chennai India Khwaja Moinuddin Chishti Language University Department of Electronics and Communication Engineering Uttar Pradesh India Christ Deemed to Be University Faculty of Engineering Department of Computer Science and Engineering Bangalore India Chatrapati Shivaji BCA and BCS College Department of B.Sc. Computer Science Maharashtra India Sri Padmavati Mahila Visva Vidyalayam Department of Electronics and Communication Engineering Tirupati India G. Narayanamma Institute of Technology and Science Department of Humanities and Mathematics Hyderabad India Annamacharya Institute of Technology & Sciences Department of Electronics and Communication Engineering Tirupati India

ISBN: (纸本)9798350347982

Because of their surroundings and lifestyle alternatives, human beings, these days be afflicted by a huge style of illnesses. thus, early contamination prediction will become crucial. on the other hand, primarily based just on signs, docs warfare to make correct forecasts. The most challenging issue is accurately forecasting illnesses, which is why machine learning is essential to accomplish this task. To identify concealed patterns within vast amounts of medical data, disease information is processed using data mining techniques. We evolved a extensive contamination prediction primarily based on the affected person's signs. We rent the device getting to know techniques Convolutional Neural network (CNN) and ANFIS to exactly count on sickness (adaptive community-based totally fuzzy inference machine). For an correct forecast, this trendy illness prediction considers the character's way of life picks and fitness history. ANFIS outperforms CNN's set of rules in phrases of popular infection prediction, with an accuracy price of 96.7%. additionally, CNN consumes extra memory and processing energy than ANFIS because it trains and assessments on facts from the UCI repository. The Anaconda notebook is a suitable tool for implementing Python programming as it contains a range of libraries and header files that enhance the accuracy and precision of the process. © 2023 IEEE.

关键词： Forecasting

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QuripfeNet: Quantum-Resistant IPFE-Based Neural Network

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IEEE Transactions on Emerging Topics in Computing 2024年

作者： Han, KyungHyun Lee, Wai-Kong Karmakar, Angshuman Yi, Myung-Kyu Hwang, Seong Oun Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of Universiti Tunku Abdul Rahman Faculty of Information and Communication Technology Malaysia Indian Institute of Technology Computer Science and Engineering Kanpur India

In order to protect the sensitive information in many applications involving neural networks,several privacy-preserving neural networks that operate on encrypted data have been developed. Unfortunately,existing encryption-based privacy-preserving neural networks are mainly built on classical cryptography primitives,which are not secure from the threat of quantum computing. In this paper,we propose the first quantum-resistant solution to protect neural network inferences based on an inner-product functional encryption scheme. The selected state-of-the-art functional encryption scheme based on lattice-based cryptography works with integer-type inputs,which is not directly compatible with neural network computations that operate in the floating point domain. We propose a polynomial-based secure convolution layer to allow a neural network to resolve this problem,along with a technique that reduces memory consumption. The proposed solution,named QuripfeNet,was applied in LeNet-5 and evaluated using the MNIST dataset. In a single-threaded implementation (CPU),QuripfeNet took 107.4 seconds for an inference to classify one image,achieving accuracy of 97.85%,which is very close to the unencrypted version. Additionally,the GPU-optimized QuripfeNet took 25.9 seconds to complete the same task,which is improved by 4.15 × compared to the CPU version. © 2013 IEEE.

关键词： Differential privacy

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Underwater visible light communication: recent advancements and channel modeling

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Optical and Quantum Electronics 2024年第10期56卷 1-45页

作者： Elfikky, Abdelrahman Boghdady, Ayman I. Mumtaz, Sajid Elsayed, Ebrahim E. Singh, Mehtab Abd El-Mottaleb, Somia A. Mohsan, Syed Agha Hassnain Aly, Moustafa H. Electrical and Computer Engineering Department Baskin School of Engineering University of California Santa Cruz United States Arab Academy for Science Technology and Maritime Transport Alexandria1029 Egypt Institute of Nano Optoelectronics Research and Technology Universiti Sains Malaysia Pulau Malaysia Department of Electronics and Communications Engineering Faculty of Engineering Mansoura University Mansoura Egypt Department of Electronics and Communication Engineering University Institute of Engineering Chandigarh University Punjab Mohali India Alexandria Higher Institute of Engineering and Technology Alexandria Egypt Ocean College Zhejiang University Hangzhou China

Underwater Visible Light communication (UVLC) is a promising technology for high-speed data transmission in aquatic environments. However, the performance and reliability of UVLC systems are often challenged by factors such as absorption, scattering, and oceanic turbulence. In this paper, we explore various UVLC modulation techniques, analyzing both Single Carrier Modulation and Multi-Carrier Modulation schemes, including their respective advantages and limitations. Through simulations and experimental evaluations, we investigate the effectiveness of modulation strategies such as Non-Return-to-Zero On-Off Keying, Pulse Position Modulation, and Quadrature Amplitude Modulation-Carrierless Amplitude Phase Modulation in addressing the inherent challenges of UVLC. Additionally, we examine advanced techniques like Orthogonal Frequency Division Multiplexing and space-domain index modulation, highlighting their potential to achieve higher data rates and improved reliability in UVLC systems. Our findings underscore the importance of selecting optimal modulation schemes to enhance spectral efficiency, reduce interference, and improve Bit Error Rate performance. Recent experimental advancements have showcased remarkable achievements, such as data rates exceeding 30 Gbps over distances up to 21 m, demonstrating significant progress in system reliability and transmission quality. This paper also identifies potential challenges and open research directions in UVLC, such as the need for efficient channel estimation and system configuration algorithms, contributing to the design and optimization of robust and scalable underwater communication systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Orthogonal frequency division multiplexing

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Decentralized Optimization of Coupled Power-Transportation Network considering Environment-aware User Behavior 8

Decentralized Optimization of Coupled Power-Transportation N...

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8th International Conference on computer and communication Systems, ICCCS 2023

作者： Cao, Zhiao Han, Yinghua Zhao, Qiang Wang, Jinkuan Zhao, Shuyi School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China College of Information Science and Engineering Northeastern University Shenyang China Taylor's University Faculty of Business and Law Subang Jaya Malaysia

ISBN: (纸本)9781665456128

The increasing popularity of electric vehicles (EVs) has led to a growing need for electrified transportation, which has further deepened the interconnectedness of power and transportation networks. To account for EV drivers' preferences in route selection, this paper proposes an environment-aware dispatch model that coordinates the integrated power and transportation network to achieve greater economic benefits and increased utilization of renewable energy. The model employs discrete choice models with an environmental factor to represent user behavior. The constructed user behavior models serve as the basis for the integrated optimal traffic-power flow (IOTPF) model, which aims to optimize the flow of both power and traffic while minimizing the total cost of EV travel and maximizing the use of renewable energy sources. The paper employs a decentralized optimization algorithm to solve the equilibrium flows of the IOTPF model. Numerical results illustrate how the environment-aware user behavior impacts travel costs and the use of renewable energy, demonstrating the effectiveness of the IOTPF model in coordinating the optimal strategy of the integrated power-transportation network. © 2023 IEEE.

关键词： Electric load flow

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Sparse Channel Representation and Estimation in Near Field communications

Sparse Channel Representation and Estimation in Near Field C...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xing Zhang Haiyang Zhang Yonina C. Eldar School of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China Faculty of Math and Computer Science Weizmann Institute of Science Israel

The employment of extremely large antenna arrays and high-frequency signaling makes future communication systems likely to operate in the near-field region, where the conventional planar wave assumption is invalid. Instead, a spherical wave assumption which provides both the user angle and distance information is more accurate. Spherical wave-based channel representation and estimation is still under investigation. In this work, we propose a distance-parameterized angular-domain sparse model to represent the near-field channel, followed by a joint dictionary learning and sparse recovery based channel estimation algorithm. The proposed sparse representation model overcomes challenges such as the storage burden and high dictionary coherence that arise in the existing polar-domain method. Simulations in multi-user communication scenarios support the superiority of the proposed near-field channel sparse representation and estimation over the polar-domain method in channel estimation error and pave the way to efficient and accurate modeling in the near-field regime.

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Parkinson's Disease Early Detection and Classification based on EMG Signal using Spherical Convolutional Neural Network 8

Parkinson's Disease Early Detection and Classification based...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Nandankar, Praful V. Kothiyal, Arnav Kiran Kumar, D. Patil, Anuradha Patil, Harshal Maranan, Ramya Government College of Nagpur Department of Electrical Engineering Maharashtra 441108 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Bgs Institute of Technology Department of Information Science and Engineering Karnataka B G Nagar571448 India Sharnbasva University Faculty of Engineering and Technology Exclusively for Women Department of Electronics and Communication Engineering Karnataka Kalaburagi585103 India Iilm University School of Computer Science & Engineering Uttar Pradesh Greater Noida201 306 India Simats Saveetha School of Engineering Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350376425

Parkinson's Disease (PD) is a degenerative neurological condition that impairs motor function. Tremors, stiffness, sluggishness, and trouble balancing are its hallmarks. Degeneration of dopamine-producing neurons in the brain is the disease's primary cause, resulting in progressive motor and non-motor symptoms. But the existing methods have lot of disadvantages such as over fitting, quality control and limited data availability. To overcome the aforementioned problem, A Spherical Convolutional Neural Network with Mantis Search Algorithm (SCNN-MSA) is proposed for accurately classifying PD. In this input image is taken from two datasets such as ADL dataset and REMPARK dataset. The Wavelet Decomposition Threshold Anisotropic Filter is used for the pre-processing (WDTAF) is proposed, for the purpose of removing noises. Following that segmentation are done using Spike-Driven Transformer-based U-Net (SDT-Unet). After that classification and feature extraction are done using Spherical Convolutional Neural Network (SCNN) and optimization are done using Mantis Search Algorithm (MSA) for classifying the Parkinson's Disease. The efficiency of the proposed SCNN-MSA is analyzed using a dataset and attains 99.93% accuracy, 98.76% recall and attains better results compared with the existing methods. This demonstrates the approach's increased efficiency and potential advancement in the industry. The model's accuracy at identifying PD in EMG signals might allow doctors to treat patients considerably faster. © 2024 IEEE.

关键词： Convolutional neural networks

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Ensemble Deep Learning for Human-Object Interaction Detection 2

Ensemble Deep Learning for Human-Object Interaction Detectio...

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2nd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2022

作者： Mansour, Ahmed E. Mohammed, Ammar Elsayed, Hussein Abd El Atty Ramly, Salwa H. El Ain Shams University Faculty of Engineering Electronics and Electrical Communication Department Cairo Egypt Cairo University Faculty of Graduate Studies of Statistical Researches Computer Science Department Cairo Egypt

ISBN: (纸本)9781665466776

Human-object interaction (HOI) detection is the task of predicting the visual relationships between humans and their surroundings in images and videos by locating and inferring the interactions between human-object pairs. The majority of existing models have approached this task by detecting human and object instances and predicting interactions between them using a single model and relying on visual features to differentiate between different actions. In this article, we propose a novel method for solving the HOI detection problem by employing ensemble voting on simple models that use only spatial features to predict human-object pair actions. Additionally, we solve the false-positive pairs generated by mis-grouping and non-interaction objects in the image using a bipartite matching. Our proposed approach outperforms many state-of-the-art models on the V-COCO dataset while requiring less inference time. © 2022 IEEE.

关键词： Visualization Computational modeling Object detection Predictive models Feature extraction Ubiquitous computing Prediction algorithms

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Smart Ports Solutions for Cargo Container Tracking and Vessel Traffic Counting Systems using IoT and Cloud Computing 10

Smart Ports Solutions for Cargo Container Tracking and Vesse...

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10th International Conference on communication and Signal Processing, ICCSP 2024

作者： Priya, S. Sairam, A. Azath, H. Manivannan, S.K. Mohankumar, N. Vedasundara Vinayagam, P. Deemed To Be University Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Bhopal India Faculty of Management Srm Institute of Science and Technology Chennai Tamil Nadu Kattankulathur India Symbiosis Institute of Technology Maharashtra Nagpur India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

This research examines current port operations that use Internet of Things (IoT) technologies for cargo container monitoring and vessel traffic counts. Effective port management is crucial for smooth commodities movement and congestion reduction as global commerce grows. Addressing these issues with IoT solutions is promising. It analyzes cargo container IoT sensors and devices for real-time location, condition, and security monitoring. These IoT-enabled containers provide port status data for better logistical planning and security. IoT devices are used to count and control vessel traffic in the research. Sensors and data analytics at important port locations provide real-time vessel tracking and traffic monitoring. This data improves safety and optimizes vessel traffic flow, lowering congestion and wait times. The pros and downsides of using IoT in port operations include data privacy, security, and infrastructure needs. Smart port technologies may enhance marine efficiency, cost savings, and sustainability. The IoT can revolutionize port operations, notably cargo container monitoring and vessel traffic counts. Port capacity, security, and the environmental and economic viability of worldwide trade may all be improved with the help of the IoT. © 2024 IEEE.

关键词： Internet of things

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Smart Energy Management System Using Machine Learning

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computers, Materials & Continua 2024年第1期78卷 959-973页

作者： Ali Sheraz Akram Sagheer Abbas Muhammad Adnan Khan Atifa Athar Taher M.Ghazal Hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science COMSATS University IslamabadLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeSharjah1797United Arab Emirates Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Energy management is an inspiring domain in developing of renewable energy ***,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and *** major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental *** energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user *** address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve *** inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy *** Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)*** IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource *** this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management *** proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

关键词： Intelligent energy management system smart cities machine learning

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