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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Huang, Shih-Xiu Lung, An Chen, Jia-You Sung, Wu-Min Chu, Yu-Cheng Tu, Jiun-Yu Hu, Chih-Lin National Central University Dept. Physics Taoyuan City320317 Taiwan National Central University Dept. Communication Engineering Taoyuan City320317 Taiwan Southern Taiwan University of Science and Technology Dept. Computer Science and Information Engineering Tainan710301 Taiwan

ISBN: (纸本)9798350340181

Considering the large-scaled field of mountain rescue, a hybrid self-organized network architecture is developed for multi-hop data delivery against weak network connectivity in challenged areas. UAVs and wireless nodes can self-organize unstructured multi-hop networks to distribute urgent and recuse messages in geographic proximity. To seek lost climbers efficiently, this architecture provides two search methods. (1) In a passive method, climbers send messages via MQTT protocols to neighbor nodes that will further forward messages to the cluster head of a self-organized network. (2) In an active method, UAVs hover over disjoint cluster heads to store, carry, and forward messages, and UAVs are equipped with cameras to perform face recognition of detecting target users. All the messages and camera-initiated video streaming can be passed through self-organized networks and then displayed on the rescue website. © 2023 IEEE.

关键词： Network architecture

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Look-Up Table Based Reduced Order Model of Synchronous Motors for Digital Twin Applications

Look-Up Table Based Reduced Order Model of Synchronous Motor...

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IEEE Workshop on electrical Machines Design, Control and Diagnosis (WEMDCD)

作者： Gabriele De Boni Lorenzo Mantione Marcello Minervini Lucia Frosini Dept. of Electrical Computer and Biomedical Engineering University of Pavia SPIN Applicazioni Magnetiche Pavia Italy Dept. of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Dept. of Engineering and Applied Sciences University of Bergamo Dalmine Italy

ISBN: (数字)9798331520748

ISBN: (纸本)9798331520755

Electric motors can be represented as non-linear (NL), multiple input multiple output (MIMO), highly interconnected systems. Finite Element (FE) simulations are powerful tools to represent electric motors. However, the process can be time consuming. A Reduced Order Model (ROM) approach emerged as a possible alternative to describe the physical behavior of an electric motor. This paper presents a reduced order model for a Synchronous Reluctance motor, built from a general method for synchronous machines. Currents from simulations and measurement have been compared in time and frequency domains, to explore the possible application of such generated model in Digital Twin applications.

关键词： Heuristic algorithms Frequency-domain analysis Current measurement Predictive models Harmonic analysis Reduced order systems Digital twins Read only memory Reluctance motors Electric motors

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A Hybrid Approach Using Cryptography and K-Least Significant Bit Steganography Algorithm in Cloud Computing

A Hybrid Approach Using Cryptography and K-Least Significant...

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Advances in computer science, electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Anshika Goyal Bhumika Gupta Dept. of Computer Science & Engineering VMSB Uttarakhand Technical University Dehradun India Dept. of Computer Science & Engineering G.B. Pant Institute of Engineering & Technology Pauri India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

The exponential growth of communication technologies has broadened the cloud computing ecosystem horizon to meet main communication needs. However, in-parallel upsurge in online attacks has alarmed industries to ensure optimal data security. The combination of steganography and cryptosystems has been recognized as an enhanced security model for data transfer among the main security systems now in use. In this paper, a hybrid approach using cryptography and K- Least Significant Bit Steganography algorithm in Cloud Computing (HYBC-KLSBS-CC) are proposed. Initially, input text data is retrieved from cloud storage and compressed using Huffman Compression Coding (HCC) to reduce data size. The compressed data is then encrypted with a Hybrid Cryptography Algorithm (Hyb-MRFC-ALC), combining Modified Random Fibonacci Cryptography and Asymmetric Lightweight Cryptography to ensure robust encryption. Finally, encrypted data is embedded within an image using the K-Least Significant Bit (KLSB) steganography technique, providing additional layer of security. The proposed method enhances data confidentiality, integrity, and storage efficiency in cloud environments. The proposed HYBC-KLSBS-CC approach attains 26.89%, 29.49% and 18.64% lower PSNR and 26.89%, 29.49% and 18.64% lower MSE when analyzed through existing techniques like data security dependent on cryptography steganography related with OTP procedure and Huffman coding in the cloud environment (CS-OTP-CE), a novel hybrid cryptographic framework for secure data storage in cloud computing: Integrating AES-OTP and RSA with adaptive key management and Time-Limited access control (AES-OTP-RSA-CC) and Crypto-Stegno dependent model for securing medical information on IOMT platform (CS-SMI-IOMT).

关键词： Cloud computing Steganography Image coding Computational modeling Data models Encoding Communications technology Encryption Cryptography Huffman coding

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An Intelligent CCTV based Surveillance System for Realtime crimes or life threat detection

An Intelligent CCTV based Surveillance System for Realtime c...

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International Conference on electrical, computer and Communication engineering (ECCE)

作者： Md. Niamatullah Naeem Ongsaiu Marma Mohammad Hasan Sujit Roy Dept. of Computer Science & Engineering Jamalpur Science & Technology University Jamalpur Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

It’s the time when people are more concerned about their safety than ever before. For this reason, sophisticated surveillance systems that can quickly identify any threats and take action to prevent it is essential to save life and property. An intelligent closed-circuit television (CCTV) surveillance system driven by the You Only Look Once (YOLO) object detection model is a robust approach to put the surveillance system to a new dimension. The proposed system is intended for monitoring real-time CCTV footage which generally focusing on identifying any weapons-carrying persons—a vital sign of possible threat and take appropriate action against it. By utilizing the effectiveness and precision of the YOLO model, the system quickly recognizes and detect these people as they enter the CCTV-covered region. Once detected, the system initiates a multi-tiered response mechanism that can involve loud alerts system, emergency police calls, targeted authority phone call and notifications, depending on the perceived level of threat for immediate action. The system is not limited to weapon detection only it is also capable of detecting fires or suspicious activity and triggering the response system to take action. This surveillance system adaptability guarantees monitoring and timely action in emergency situations, strengthening overall security protocols. With the help of this intelligent CCTV surveillance system public safety should be improved by avoiding possible threats, speeding up reaction and response times, and enabling prevent injuries and property from any incoming danger.

关键词： YOLO TV Surveillance Weapons Streaming media Cameras Real-time systems Threat assessment Public security Time factors

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Thin Gold Film Deposited on PDMS for Stretchable electrical Interconnections and Bio-compatible Applications via Textured 3D Printed Mold

Thin Gold Film Deposited on PDMS for Stretchable Electrical ...

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Challenges, Opportunities, Innovations and Applications in Electronic Textiles (E-Textiles), International Conference on the

作者： Gabriel Digregorio Jean-Michel Redouté dept. of Electrical Engineering and Computer Science Montefiore Institute of the University of Liège Liège Belgium

ISBN: (数字)9783000804298

ISBN: (纸本)9798331516871

This study demonstrates that 3D printing significantly enhances the surface roughness of PDMS substrates, leading to a substantial increase in the maximum allowable elongation before a drastic rise in electrical resistance. The PDMS substrates were molded using 3D-printed molds with varying surface treatments to achieve different roughness levels. The untreated samples displayed higher surface roughness, allowing for elongation up to 12.5% before the electrical resistance increased sharply due to the formation of micro-cracks in the gold film. Conversely, smoother surfaces, achieved through ethanol mist treatment, showed higher initial resistance and reduced elongation capacity. This finding underscores the critical role of substrate roughness in stretchable electronics, where optimizing surface characteristics is essential for balancing adhesion and mechanical robustness, thus improving the performance and reliability of bio-compatible and stretchable electrical interconnections.

关键词： Gold Surface resistance Three-dimensional printing Surface roughness Rough surfaces Surface treatment Substrates Elongation Sputtering Immune system

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Advanced Tool Learning and Selection System (ATLASS): A Closed-Loop Framework Using LLM

arXiv

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arXiv 2025年

作者： Haque, Mohd Ariful Williams, Justin Siddique, Sunzida Islam, Md. Hujaifa Ali, Hasmot Gupta, Kishor Datta George, Roy Dept. of Cyber Physical Systems Clark Atlanta University United States Dept. of Computer Science and Engineering Daffodil International University Bangladesh Dept. of Computer Science and Engineering Ahsanullah University of Science and Technology Bangladesh

The combination of LLM agents with external tools enables models to solve complex tasks beyond their knowledge base. Human-designed tools are inflexible and restricted to solutions within the scope of pre-existing tools created by experts. To address this problem, we propose ATLASS, an advanced tool learning and selection system designed as a closed-loop framework. It enables the LLM to solve problems by dynamically generating external tools on demand. In this framework, agents play a crucial role in orchestrating tool selection, execution, and refinement, ensuring adaptive problem-solving capabilities. The operation of ATLASS follows three phases: The first phase, Understanding Tool Requirements, involves the Agents determining whether tools are required and specifying their functionality;the second phase, Tool Retrieval/Generation, involves the Agents retrieving or generating tools based on their availability;and the third phase, Task Solving, involves combining all the component tools necessary to complete the initial task. The Tool Dataset stores the generated tools, ensuring reusability and minimizing inference cost. Current LLM-based tool generation systems have difficulty creating complex tools that need APIs or external packages. In ATLASS, we solve the problem by automatically setting up the environment, fetching relevant API documentation online, and using a Python interpreter to create a reliable, versatile tool that works in a wider range of situations. OpenAI GPT-4.0 is used as the LLM agent, and safety and ethical concerns are handled through human feedback before executing generated code. By addressing the limitations of predefined toolsets and enhancing adaptability, ATLASS serves as a real-world solution that empowers users with dynamically generated tools for complex problem-solving. © 2025, CC BY-SA.

关键词： Reusability

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Gamification of RF Data Acquisition for Classification of Natural Human Gestures

Gamification of RF Data Acquisition for Classification of Na...

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IEEE National Conference on Radar

作者： Emre Kurtoğlu Kenneth DeHaan Caroline Kobek Pezzarossi Darrin J. Griffin Chris Crawford Sevgi Z. Gurbuz Dept. of Electrical and Computer Engineering The University of Alabama Dept. of American Sign Language Gallaudet University Dept. of Psychology Gallaudet University Dept. of Communication Studies The University of Alabama Dept. of Computer Science The University of Alabama

ISBN: (数字)9798350329209

ISBN: (纸本)9798350329216

In recent years, there have been significant developments in radio frequency (RF) sensor technology used in human-computer interaction (HCI) applications, specifically in areas like gesture recognition and more broadly, human activity recognition. Although extensive research has been conducted on these subjects, most experiments involve controlled settings where participants are instructed on how to perform specific movements. However, when such experiments are conducted on sign language recognition they lack capturing dialectal and background-related diversities. In this work, we explore the differences in RF datasets acquired under controlled experimental settings and in free form environments where users were not constrained by the experimental instructions and limitations. We show that directed (i.e., controlled) data acquisition approaches result in over-optimistic performances which do not perform well on naturally acquired data samples in a real-world use case. We evaluate different approaches on generating synthetic samples from directed dataset, but show that such methods do not offer much benefit over collecting natural data. Therefore, we propose an interactive data acquisition paradigm through gamification. We show that the proposed approach enables the recognition of American Sign Language (ASL) in real-world settings by achieving 69% accuracy on 29 words.

关键词： Radio frequency Human computer interaction Sign language Data acquisition Transforms Radar Predictive models

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NoxTrader: LSTM-Based Stock Return Momentum Prediction for Quantitative Trading

arXiv

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arXiv 2023年

作者： Wei-Ning, Chiu Hsiang-Hui, Liu Han-Jay, Shu Data dept. NoxLab Computer Science dept. NTHU Hsinchu Taiwan Infrastructure dept. NoxLab Computer Science dept. NTHU Hsinchu Taiwan Data dept. NoxLab Electrical Engineering and Computer Science dept. NTHU Hsinchu Taiwan

We introduce NoxTrader, a sophisticated system designed for portfolio construction and trading execution with the primary objective of achieving profitable outcomes in the stock market, specifically aiming to generate moderate to long-term profits. The underlying learning process of NoxTrader is rooted in the assimilation of valuable insights derived from historical trading data, particularly focusing on time-series analysis due to the nature of the dataset employed. In our approach, we utilize price and volume data of US stock market for feature engineering to generate effective features, including Return Momentum, Week Price Momentum, and Month Price Momentum. We choose the Long Short-Term Memory (LSTM) model to capture continuous price trends and implement dynamic model updates during the trading execution process, enabling the model to continuously adapt to the current market trends. Notably, we have developed a comprehensive trading backtesting system — NoxTrader, which allows us to manage portfolios based on predictive scores and utilize custom evaluation metrics to conduct a thorough assessment of our trading performance. Our rigorous feature engineering and careful selection of prediction targets enable us to generate prediction data with an impressive correlation range between 0.65 and 0.75. Finally, we monitor the dispersion of our prediction data and perform a comparative analysis against actual market data. Through the use of filtering techniques, we improved the initial -60% investment return to 325%. © 2023, CC BY-NC-SA.

关键词： Momentum

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Automated Sign Detection in a Self-Driving Car 9

Automated Sign Detection in a Self-Driving Car

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9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Afor, Mary Echabuo Ravulakollu, Kiran Chhabra, Megha Sharma, Pratik Dewingong, Tarcisius Forjong Dewan, Ritu School of Engineering and Technology Sharda University Dept. of Computer Science and Engineering Greater Noida India School of Technology Woxsen University Hyderabad India Galgotia College of Engineering and Technology Dept. of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9789380544441

Self-driving cars are a recent trending area of research in the automation industry. The advantages of using self-driving cars are certainly on efficient resource utilization along with reduction in manual effort and danger to manual labour if any. Among other advantages, it also includes long working hours, supervision and repetition of the tasks. These cars, hence, can be used in factories and warehouses with large spaces and/ or geographical disparity where conditions are not suitable for humans to survive. The proposed work uses wireless software and hardware technologies such as a wireless transceiver module, Raspberry Pi as a microcontroller, H-bridge L293D IC for motor controller, and 4 DC motors used to move the automobile. The system uses the Open CV-Library for object detection and image processing. Also, the Firebase database and file storage service is used to send images from the car to the app. In order to make it hybrid, the system also proposes an IOS application that connects to the car so it can be moved manually. The system has been tested and the result shows that the car can move in any direction with the app-controlled remote and detect signs with an accuracy of 78%. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Object detection

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Enhanced Realtime Sign Language Detection and Multilingual Translation System

Enhanced Realtime Sign Language Detection and Multilingual T...

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Computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： Madhur Kakkar John Daniel D. Sudha Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

Sign language is the communication medium that is utilized by the hearing-impaired community. The medium is visual and it uses hand signs and symbols for communication. Over 300 different types of sign languages present in the world today. Sign language uses gestures and hand movements for the majority of the words and finger spellings for the rest. Nonverbal speakers face a lot of challenges while interacting with verbal speakers. Even non-verbal speakers who speak different sign languages face difficulties communicating with each other. This project proposes an enhanced real-time system which detects gestures and finger spellings of multiple sign languages, and translates them into multiple spoken languages. The proposed system aims to utilize CNN network for finger spelling recognition and Long Short-Term Memory (LSTM) network for gesture interpretation. The system will use computer vision for accurate gesture detection and Google Translator API for recognizing signs which will be translated into multiple spoken languages. Initial tests with isolated datasets indicate promising accuracy for both finger spelling and gesture recognition. The goal of the completed system is to help aid communication between verbal and non-verbal speakers and to provide an efficient mode of communication between the two communities.

关键词： Hands Deep learning Sign language computer vision Translation Face recognition Medical services Real-time systems Multilingual Long short term memory

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