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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Jaichandran, R. Shobana, R. Mohamed Tharick, K. Raja, L. Anandaram, Harishchander Vijaipriya, K. AarupadaiVeedu Institute of Technology Department of Computer Science and Engineering Tamil Nadu Paiyanoor603104 India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore641202 India Centre for Excellence in Computational Engineering and Networking Amrita VishwaVidyapeetham Tamil Nadu Coimbatore641112 India Sri SaiRanganathan Engineering College Department of Electronics and Communication Engineering Tamil Nadu Thondamuthur641109 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Poultry is the kind of bird farming in which birds like geese, ducks, chickens, and turkeys are domesticated to produce eggs and meat for the food. Of these birds, chickens are the most consumed bird by the people. The chickens which are grown up for eggs are called layers and chickens for meat are known as broilers. Hatching eggs naturally by the hens is considered a big problem for poultry farmers. Hatching the eggs fast by using the technology can produce more chicks. To hatch the eggs by technology, a hatching incubator is implemented by using the Internet of Things (IoT), temperature sensor, humidity sensor, NodeMCU, servo motor, and Blynk cloud. Egg hatching manually by hens takes approximately 20 to 21 days. An automatic hatching system will reduce the number of days required for hatching. The cloud displays the data of the incubator like temperature, degree of rotation of egg tray, humidity, and the ON or OFF conditions of the light, fan, and humidifier. © 2022 IEEE.

关键词： Birds

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Monitoring and accelerating plant growth using IoT and Hydroponics

Monitoring and accelerating plant growth using IoT and Hydro...

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International Conference on computer Communication and Informatics (ICCCI)

作者： Abhishek Kumar P Savaridassan Computer Science and Engineering SRM Institute of Science and Technology Chennai India Department of Networking And Communications SRM Institute of Science and Technology Chennai India

Growing plants without soil is known as hydroponics, and it is particularly helpful in places with little space and bad weather. A novel method of growing plants without soil in an inert medium is called hydroponics. By avoiding pesticides and other substances that degrade soil and land quality, hydroponics enables farmers to conserve water while enhancing crop quality. Due to its high efficiency and role as an environmentally benign substitute for soil culture, it has the ability to close the gap left by India’s low agricultural productivity. This farming process has leveraged several new technologies like Internet of Things (IoT), making integration of intelligent management and real-time data monitoring and collection systems possible. This farming process also allows for a greater degree of automation and refinement in the control process of the hydroponic systems. Via this paper, we have discussed the development of a hydroponic subsystem that leverages agents for cloud-based monitoring, data collection, and storage for future analysis and heuristics. This paper presents an intuitive and low-cost approach to monitoring and accelerating plant growth based on Hydroponics and IoT with tools such as automated environment parameter monitoring and error rectification, sub-second streaming of telemetry from ground units to cloud servers and allowing users to access the status of the plant or system of plants being monitored as a plant health index from anywhere in the world, via a mobile application. The end goal of this paper is to understand the benefits one obtains by adopting a Hydroponics based cultivation system, which would show up as lower water consumption, minimal space usage, increased control over nutrients being introduced, and much more effective protection against pests and diseases when coupled with the feature of emphasizing each plant genus being cultivated via plant profile templates. This approach will not only revolutionize the millennial approach

关键词： Sociology Hydroponics Process control Soil Agriculture Real-time systems Mobile applications

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Site-Specific Beam Alignment without Explicit Channel Knowledge via Deep Learning

Site-Specific Beam Alignment without Explicit Channel Knowle...

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Asilomar Conference on Signals, Systems & computers

作者： Jong Woo Kwak Hanju Yoo Ian P. Roberts Chan-Byoung Chae Intelligence Networking Lab. School of Integrated Technology Yonsei University Seoul Korea Department of Electrical and Computer Engineering Wireless Lab UCLA Los Angeles CA USA

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Quickly and accurately steering the highly directional beams of a millimeter-wave (mmWave) base station (BS) toward the intended user(s) is a challenging task known as the beam alignment problem. Prior work has identified machine learning (ML) as a promising tool for developing fast, effective beam alignment solutions through site-specific optimization. However, these approaches typically rely on training datasets containing the propagation channel information between the BS and virtually every possible user within the site. Acquiring such datasets is impractical through channel estimation and time-consuming when using ray-tracing tools. In this paper, we propose a novel ML-driven framework that relies solely on measurements of received signal power, collected by users during standard 5G beam sweeping procedures, for training. By leveraging these beam sweeping measurements, our model implicitly learns features of the surrounding environment, enabling the construction of a sparse probing beam codebook and a corresponding beam selector for rapid beam alignment. Remarkably, our approach matches the speed and effectiveness of state-of-the-art methods trained on perfect explicit channel data, even when the beam sweeping measurements used for training are noisy.

关键词： Training Power measurement Millimeter wave technology Ray tracing Millimeter wave propagation Structural beams Noise measurement Millimeter wave communication Standards Optimization

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Enhancing Insect Species Identification in Agriculture using Fusion BiLSTM Network and VGG-16 CNN Architecture

Enhancing Insect Species Identification in Agriculture using...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Dharani Devi Sterlin Rani Devakadacham D. Saveetha J Manikandan Computer Science and Engineering Rajalakshmi Engineering College Chennai India Computer Science and Engineering R. M. D. Engineering College Kavaraipettai India Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur India Information Technology St. Joseph’s College of Engineering Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

In agricultural sectors like wheat, paddy, flowers, crops and maize production, insect infestations lead to significant financial losses. Swift identification of the insect species is vital to mitigate economic impacts. However, farmers often struggle to distinguish between various agricultural pests due to their limited experience and knowledge. Tackling this challenge involves harnessing advanced technologies such as the VGG-16 convolutional neural network (CNN) architecture, alongside other computational methods. VGG-16, renowned for its effectiveness in image classification tasks, can be employed to automatically extract features from the data. In our study, we propose integrating VGG-16 as part of a fusion BiLSTM network, which incorporates bidirectional long short-term memory (BiLSTM) layers, considering temporal information. This approach offers a promising solution to insect species identification in agricultural settings.

关键词： Recurrent neural networks Insects Computational modeling Transfer learning Crops computer architecture Feature extraction

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Differential Privacy Preserving Distributed Quantum Computing

arXiv

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arXiv 2024年

作者： Zhong, Hui Ju, Keyi Shen, Jiachen Zhang, Xinyue Qin, Xiaoqi Ohtsuki, Tomoaki Pan, Miao Han, Zhu Department of Electrical and Computer Engineering University of Houston Houston United States State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Computer Science Kennesaw State University Marietta United States Department of Information and Computer Science Keio University Tokyo Japan

Existing quantum computers can only operate with hundreds of qubits in the Noisy Intermediate-Scale Quantum (NISQ) state, while quantum distributed computing (QDC) is regarded as a reliable way to address this limitation, allowing quantum computers to achieve their full computational potential. However, similar to classical distributed computing, QDC also faces the problem of privacy leakage. Existing research has introduced quantum differential privacy (QDP) for privacy protection in central quantum computing, but there is no dedicated privacy protection mechanisms for QDC. To fill this research gap, our paper introduces a novel concept called quantum Rényi differential privacy (QRDP), which incorporates the advantages of classical Rényi DP and is applicable in the QDC domain. Based on the new quantum Rényi divergence, QRDP provides delicate and flexible privacy protection by introducing parameter α. In particular, the QRDP composition is well suited for QDC, since it allows for more precise control of the total privacy budget in scenarios requiring multiple quantum operations. We analyze a variety of noise mechanisms that can implement QRDP, and derive the lowest privacy budget provided by these mechanisms. Finally, we investigate the impact of different quantum parameters on QRDP. Through our simulations, we also find that adding noise will make the data less usable, but increase the level of privacy protection. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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Satellite Selection for In-Band Coexistence of Dense LEO Networks

arXiv

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arXiv 2025年

作者： Kim, Eunsun Roberts, Ian P. Lee, Taekyun Andrews, Jeffrey G. 6G@UT Research Center Wireless Networking and Communications Group University of Texas Austin United States Wireless Lab Department of Electrical and Computer Engineering UCLA United States

We study spectrum sharing between two dense low-earth orbit (LEO) satellite constellations, an incumbent primary system and a secondary system that must respect interference protection constraints on the primary system. In particular, we propose a secondary satellite selection framework and algorithm that maximizes capacity while guaranteeing that the time-average interference and absolute interference inflicted upon each primary ground user never exceeds specified thresholds. We solve this NP-hard constrained, combinatorial satellite selection problem through Lagrangian relaxation to decompose it into simpler problems which can then be solved through subgradient methods. A high-fidelity simulation is developed based on public FCC filings and technical specifications of the Starlink and Kuiper systems. We use this case study to illustrate the effectiveness of our approach and that explicit protection is indeed necessary for healthy coexistence. We further demonstrate that deep learning models can be used to predict the primary satellite system associations, which helps the secondary system avoid inflicting excessive interference and maximize its own capacity. Copyright © 2025, The Authors. All rights reserved.

关键词： Orbits

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Retraction Note: Optoelectronic-aided machine learning-based stable routing protocol for MANET and beyond massive MIMO systems in 5G networks

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Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Gnanasekaran, P. Varunkumar, K. A. Rajendran, N. Priyadarshini, R. Macherla, Sivudu Department of Information Technology B.S. Abdur Rahman Crescent Institute of Science and Technology Vandalur India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur District India

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Disaster Assistance Intelligent Agent

Disaster Assistance Intelligent Agent

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International Conference on Computing and networking Technology (ICCNT)

作者： Siddhant Priyadarshi Mudit Rastogi D. R. V. M. Gayathri Computer Science Engineering SRM Institute of Science and Technology Chennai India Department of Networking With Comunication SRM Institute of Science and Technology Chennai India

Virtual characters are controlled by artificial intelligence in video games and movies. Controlling these characters when they are surrounded by people is known as crowd simulation. In this work, we propose to employ intelligent agents and decision trees to simulate a realistic scenario in which our agent will lead the human behavioural AI and identify the best route to the goal or the best circumstances to the objective avoiding all the obstacles in the path. This crowd simulation will be used to make intelligent agents which can find the best path during the situation of any disaster and show humans the safest path which the humans can follow to get to the nearest safe points. Multiagent simulation, commonly referred to as a Crowd and Agent Simulation, is an approach used to simulate many people, animals or other characters interacting with each other in one place. These actors are expected to pursue their objectives, engage in the environment and coordinate with each other. It is expected that the simulated actors will pursue their goals, interact with the environment, and communicate among themselves. Crowd simulations find utility in various areas, including the enhancement of training environments and virtual worlds, the optimization of architectural planning, and the refinement of AI-driven characters' driving behaviors in video games and movies.

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An Optimized Learning Model for Image Denoising using Modern Deep Learning Approaches

An Optimized Learning Model for Image Denoising using Modern...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： A. Karthikram M. Saravanan Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Networking and Communications Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

In the domain of image denoising, the existence of several forms of noise, such as Gaussian, impulse, salt, pepper, and speckle noise, presents considerable obstacles. A viable strategy for resolving this issue is optimized convolutional neural networks (O − CNN) which are receiving more and more attention. Numerous standard CNN techniques have been developed and applied to denoise images with different datasets being used to evaluate their performance. This paper seeks to offer a thorough overview of several CNN image-denoising methods with a meta-heuristic optimization approach known as adaptive wolf optimizer (AWO). The approaches are organized and looked at, and the most popular datasets for analyzing O − CNN image-denoising algorithms are reviewed. A selection of O − CNN-based image-denoising techniques was carefully examined and analyzed in this study. The goals and underlying principles of the O − CNN approach were elucidated. In addition to detailed explanations, certain O – CNN image-denoising techniques were represented graphically. With O − CNN, the suggested reviewing image denoising. The studies on image denoising using O − CNN from the past and present were chosen. Future research directions and potential obstacles were also explained.

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Leaf Disease Identification using ResNet

Leaf Disease Identification using ResNet

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Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： P. Sirenjeevi J. Madhu Karthick K. Agalya R. Srikanth T. Elangovan R. Nareshkumar Department of Artificial Intelligence and Data Science SRM Valliammai Engineering College Kattankulathur Chennai Department of Computer Science and Engineering A.V. C. College of Engineering Mayiladuthurai Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Department of Computer Science and Engineering Karpagam Academy of Higher Education Eachanari Coimbatore Department of Networking and Communication SRM Institute of Science and Technology Kattankulathur Chennai

The study of leaf diseases, as well as their detection and diagnosis, has been the subject of an increasing amount of research and attention as intelligent agricultural systems have become increasingly common and widely used. In order to explore the detection and classification of apple leaf illnesses, we made use of data sets containing examples of apple grey-spot disease, black star disease, cedar rust disease, and healthy leaves. SVM classifier for image segmentation, ResNet and VGG convolutional neural network models were utilized for comparison and improvement respectively. In our prosed method ResNet-18, which had less layers of the ResNet network, achieved greater recognition effects by obtaining an accuracy rate of 98.5 percent.

关键词： Support vector machines Deep learning Image segmentation Stars Transforms Knowledge discovery Hardware

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