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International Conference on ent Computing and Control systems (ICICCS)

作者： R. Sharmila R Kamalitta Moorthy Devesh Pratap Singh Amit Chauhan Purnendu Bikash Acharjee Department of MCA Dhanalakshmi Srinivasan Engineering College Perambalur Tamilnadu India Information Technology K.Ramakrishnan College of Engineering Trichy Tamilnadu India Electronics Engineer Production Dhaksha Unmanned System Pvt ltd Chennai Tamilnadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttrakhand India Department of life Sciences CHRIST (Deemed to be University) Bengaluru Karnataka India Faculty Member Computer Science CHRIST University Pune Maharashtra India

Large losses in output, money, and quality/quantity of agricultural goods are incurred due to plant diseases. Seventy percent of India’s GDP is tied to the agricultural sector, thus protecting plants from diseases is crucial. For this reason, it is important to keep an eye on plants from the moment they sprout. The usual approach for this omission is naked eye inspection, which is more time-consuming, costly, and requires significant skill. Thus, automating the method for detecting diseases is necessary to speed up this process. It is imperative that image processing methods be used in the creation of the illness detection system. Disease detection involves a number of processes, including Weighted Mask R-CNN, GLCM feature extraction, Multi-thresholding image pre-processing, and K means image segmentation classification. The weighted Mask R-CNN outperforms the standard RNN, the Mask R-CNN, and the CNN in terms of accuracy and recall in analytical trials by a significant margin.

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The Development of Self-Service Application at Martabak Air Mancur Restaurant 4

The Development of Self-Service Application at Martabak Air ...

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4th International Conference on Computing and Applied Informatics 2019, ICCAI 2019

作者： Sagala, N.T.M. Andreas Putro, E. Department of Information System Faculty of Engineering and Computer Science Krida Wacana Christian University Jakarta Indonesia

The amount of time spent in requesting and waiting for the meal in Martabak Air Mancur restaurant may permanently multiply the number of customers in the line waiting especially during peak hours and on weekend. It will affect the quality service of the restaurant and the loyalty of the customer. From the observation, most of the customers spent a great deal of time making an order when faced with many proportionate choices. The study proposed a system by using self-service technology and the waterfall model. In the beginning, the time needed from making an order to finishing the payment is 15 minutes. The system built can reduce the transaction time to 10 minutes. © Published under licence by IOP Publishing Ltd.

关键词： Sales

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Smart Home Gas Sensor Optimization Using Kalman Filter for Data Processing

Smart Home Gas Sensor Optimization Using Kalman Filter for D...

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International Conference on Informatics and Computing (ICIC)

作者： Somantri Muhamad Ikhsan Thohir Alun Sujjada Deni Setiawan Ilham Komara Fikri Ardiansyah Efendi Muhamad Muslih Zubaile Bin Abdullah Mohd Zainuri Saringat Department of Software Engineering University Tun Hussein Onn Malaysia Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering University Tun Hussein Onn Malaysia Sukabumi Indonesia Department of Information System Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Bahru Malaysia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The integration of Internet of Things (IoT) technologies into modern homes has enhanced safety and comfort, particularly in detecting gas leaks, which pose serious fire hazards. Gas leaks can often be detected by smell, but this method fails when no one is present. Previous research using microcontrollers and sensors to detect gas leaks faced challenges with accuracy due to noise interference. This paper proposes the use of a Kalman filter, developed by Rudolf Emil Kalman in 1960, to improve gas leak detection accuracy by filtering out noise. The system comprises an Arduino Nano, ESP8266 WiFi module, MQ-2 gas sensor, buzzer, and ThingSpeak cloud platform. By applying the Kalman filter, noise and data oscillations are reduced, enhancing detection accuracy. Experimental results show the system effectively detects gas leaks, provides real-time data, and triggers alarms. Future improvements could include additional sensors and features to further increase the system’s reliability and functionality.

关键词： Accuracy Noise Sensor phenomena and characterization Sensor systems Real-time systems Kalman filters Internet of Things Reliability Leak detection Gas detectors

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Biocompatible Paper Based Humidity Sensor Using Paper Graphene Oxide for Environment Monitoring and Human Health

SSRN

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SSRN 2023年

作者： Khan, Muhammad Umair Abbas, Yawar Abunahla, Heba Rezeq, Moh'd Alazzam, Anas Alamoodi, Nahla Mohammad, Baker Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi127788 United Arab Emirates System on Chip Lab Khalifa University Abu Dhabi127788 United Arab Emirates Department of Physics Khalifa University Abu Dhabi127788 United Arab Emirates Department of Quantum and Computer Engineering TU Delft Netherlands Department of Chemical Engineering Khalifa University Abu Dhabi127788 United Arab Emirates

Environmentally friendly humidity sensors with high sensing performance are considered crucial components for various wearable electronic devices. We developed a rapid-response and durable Paper Graphene Oxide (PGO) humidity sensor using an all-carbon functional material. The fabricated sensor demonstrated a high sensitivity to humidity through an electrical impedance measurement, with an increase in response to humidity ranging from 10% to 90% at 1 kHz and 10 kHz, respectively, along with a response time of 1.2 s and a recovery time of 0.8 s. The stability of the sensor was also examined, with consistent performance over a period of 24 hours. This novel sensor was employed in several applications, including non-contact proximity sensing, environmental humidity detection, and human respiration detection, to showcase its potential. Moreover, this work represents a significant milestone in developing inexpensive and eco-friendly humidity sensors, given the abundance of paper and graphene in nature and their biocompatibility. © 2023, The Authors. All rights reserved.

关键词： Graphene

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Parallel chaotic sources based on microcombs

Parallel chaotic sources based on microcombs

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Shen, Bitao Shu, Haowen Xie, Weiqiang Chen, Ruixuan Liu, Zhi Zhang, Xuguang Wang, Yimeng Zhang, Yunhao Cheng, Buwen Yu, Shaohua Bowers, John E. Chang, Lin Wang, Xingjun State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Frontiers Science Center for Nano-Optoelectronics Peking University Beijing100871 China Peng Cheng Laboratory Shenzhen518055 China State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China

Massively parallel optical chaotic sources with large bandwidth are demonstrated based on chaotic microcombs in the AlGaAsOI platform. 30-channel parallel random bit generation with 30 Gbit/s per channel is realized b... 详细信息

ISBN: (纸本)9781957171258

关键词： Gallium compounds

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Orchid: A Creative Approach for Authoring LLM-Driven Interactive Narratives 25

Orchid: A Creative Approach for Authoring LLM-Driven Interac...

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Proceedings of the 2025 Conference on Creativity and Cognition

作者： Zhen Wu Serkan Kumyol Shing Yin Wong Xiaozhu Hu Xin Tong Tristan Braud Division of Integrative System and Design Hong Kong University of Science and Technology HongKong Hong Kong Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong Department of Mathematics Hong Kong University of Science and Technology Hong Kong Hong Kong Division of Integrative Systems and Design Hong Kong University of Science and Technology Hong Kong Hong Kong Thrust of Computational Media and Arts Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

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A Cooperative Co-evolutionary Genetic Algorithm for Multi-Robot Path Planning Having Multiple Targets 1st

A Cooperative Co-evolutionary Genetic Algorithm for Multi-Ro...

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1st International Conference on Computational Intelligence in Pattern Recognition, CIPR 2019

作者： Sarkar, Ritam Barman, Debaditya Chowdhury, Nirmalya Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Department of Computer and System Sciences Visva-Bharati Santiniketan731235 India

ISBN: (纸本)9789811390418

A cooperative co-evolutionary genetic algorithm based method has been proposed in this work to plan multiple robots’ path. A domain knowledge-based operator named "Deletion Operator" and a method to find out the elite set of chromosomes have been developed for our proposed algorithm. A number of works have been carried out on multi-robot path planning, but most of them are suitable for robots having a single target only. Our proposed method can deal with the situation where each of the robots can have multiple targets without any predefined order. Hence, our objective is to get the shortest path from source to goal nodes considering that the goal nodes can appear in any order and the robots should not collide with any obstacles or other robots. The start nodes can also be the same as well as different for each of the robots. We have deployed our proposed method on a number of simulated environments to measure the efficacy of our proposed method. The experimental results reveal the effectiveness of the proposed method. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Secure and Privacy-preserving Data-sharing Framework based on Blockchain Technology for Al-Najaf/Iraq Oil Refinery 19

Secure and Privacy-preserving Data-sharing Framework based o...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Umran, Samir M. Lu, SongFeng Abduljabbar, Zaid Ameen Lu, Zhi Feng, Bingyan Zheng, Lu Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Iraqi Cement State Company Ministry of Industry and Minerals Baghdad10011 Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China University of Basrah College of Education for Pure Sciences Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China Industrial Internet Research Institute Wuhan Huazhong Numerical Control Co. Ltd Wuhan430074 China South-Central University for Nationalities College of Computer Science Wuhan430074 China

ISBN: (纸本)9798350346558

The Industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network;provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality. © 2022 IEEE.

关键词： Digital storage

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Traffic Light Control Using Reinforcement Learning

Traffic Light Control Using Reinforcement Learning

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Integrated Circuits and Communication systems (ICICACS), IEEE International Conference on

作者： S M Masfequier Rahman Swapno SM Nuruzzaman Nobel Ramachandra A C Md Babul Islam Rezaul Haque Mohammad Mominur Rahman Dept of CSE Bangladesh University of Business and Technology Dhaka Bangladesh Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru Dept. of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Dept of CSE East West University Dhaka Bangladesh College of Science and Engineering Hamad Bin Khalifa University Qatar

Nowadays, one of the biggest issues in urban areas is traffic. This problem wastes important time and contributes to air and sound pollution. This affects people’s general quality of life in addition to posing health dangers. Our study attempts to mitigate these problems, effectively cutting down on wait times and delays. Our Reinforcement Learning method creates intelligent agents that can adjust traffic lights at crossings instantly. Our objective is to minimize delays, reduce congestion, reduce travel times, improve safety, and improve traffic flow. We implemented the Deep Q Learning algorithm which activities yield the greatest benefits under various traffic scenarios. Our model can the sequence time since the Green signal (GS) lasts 10 seconds and the Red signal (RS) lasts 5 seconds. The waiting period is shortened by 50% as a result. This study suggests reinforcement learning may improve traffic signal controller synchronization and urban traffic congestion. This novel method may improve transport efficiency and sustainability.

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Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles

arXiv

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arXiv 2025年

作者： You, Jiahao Jia, Ziye Dong, Chao Wu, Qihui Han, Zhu The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing211106 China National Mobile Communications Research Laboratory Southeast University Jiangsu Nanjing211111 China Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

The increasing deployment of unmanned surface vehicles (USVs) require computational support and coverage in applications such as maritime search and rescue. Unmanned aerial vehicles (UAVs) can offer low-cost, flexible aerial services, and ground stations (GSs) can provide powerful supports, which can cooperate to help the USVs in complex scenarios. However, the collaboration between UAVs and GSs for USVs faces challenges of task uncertainties, USVs trajectory uncertainties, heterogeneities, and limited computational resources. To address these issues, we propose a cooperative UAV and GS based robust multi-access edge computing framework to assist USVs in completing computational tasks. Specifically, we formulate the optimization problem of joint task offloading and UAV trajectory to minimize the total execution time, which is in the form of mixed integer nonlinear programming and NP-hard to tackle. Therefore, we propose the algorithm of generative artificial intelligence-enhanced heterogeneous agent proximal policy optimization (GAI-HAPPO). The proposed algorithm integrates GAI models to enhance the actor network ability to model complex environments and extract high-level features, thereby allowing the algorithm to predict uncertainties and adapt to dynamic conditions. Additionally, GAI stabilizes the critic network, addressing the instability of multi-agent reinforcement learning approaches. Finally, extensive simulations demonstrate that the proposed algorithm outperforms the existing benchmark methods, thus highlighting the potentials in tackling intricate, cross-domain issues in the considered scenarios. © 2025, CC BY.

关键词： Unmanned surface vehicles

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