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2024 International Conference on communication, Computer Sciences and engineering, IC3SE 2024

作者： Jaiswal, Raghevendra Maurya, Rahul Singh, Rajneesh Agarwal, Ruchi Singh, Shyam Pratap Pandey, Vijay Kumar Galgotias College Of Engineering & Technology Department Of Electronics & Communication Engineering Greater Noida India Sharda University India Department Of Computer Science & Applications Sharda School Of Engineering & Technology Greater Noida India Nagpur Campus Pune India Noida Institute Of Engineering & Technology Department Of Electronics & Communication Engineering Greater Noida India

ISBN: (纸本)9798350366846

Diffusive Molecular communication (DMC) enabled bacterial propagation within biological systems is employed in different medical applications. In the other words, DMC is being used to study the propagation of bacteria inside the human body. However, DMC and thus the bacterial propagation model depend on the type of deployment used. Various deployment geometries including Rectangular, Spherical and Cylindrical. In this research, a DMC system within a cylindrical biological deployment is developed for the human respiratory tract. It is expected that the information molecules diffusing across the fluid medium would degrade and be influenced by flow. Further, the Cylindrical Biological Deployments (CBDs) offer versatility in representing different tissues or organs. Therefore, in this research paper different statistical models are derived and analyzed under CBD deployment using bacterial concentration f(y) In particular, statistical models for bacterial propagation in terms of probability (Pb), non-binding probability (Pnb), evade probability (Pe(y)), and the probability of binding rate (Pb(y)) are presented. Also, presented models are analyzed numerically using simulation. The numerical simulation demonstrates excellent agreement with the theoretical foundation. The result has been simulated in MATLAB-20. © 2024 IEEE.

关键词： Fading channels

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Designing a Multipath Rayleigh fading channel simulator Utilizing Filtered Gaussian Noise method and Sum of Sinusoidal method 5

Designing a Multipath Rayleigh fading channel simulator Util...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Sultana, S. Fowzia Sarala, Mangali Sree, L.S.N. Navya Mallika, Shaik Department of Electronics and Communication Engineering G. Pullaiah College of Engineering and Technology Kurnool India

ISBN: (纸本)9798350361155

Wireless communication is rapidly emerging as the leading sector in the communication industry, with mobile communication serving as the most extensively utilized form. Nevertheless, this domain confronts various technical challenges, including issues such as fading, shadowing, interface complications, and propagation path loss. Multipath fading channel simulators play an important role in the physical layer simulations of wireless systems. This project employs MATLAB and introduces an approach based on the Filtered Gaussian Noise (FGN) and Sum of Sinusoidal (SoS) approaches are used to simulate multipath fading channel. FGN and SoS methods represent distinct strategies to simulate multipath Rayleigh fading channels. The Filtered Gaussian Noise approach involves generating a Gaussian noise with complex values, which is subsequently subjected use a low-pass filter. The filter's frequency response is precisely adjusted to mirror the power spectral density of the channel, capturing the characteristic of frequency selective fading. The Sum of Sinusoidal approach involves adding the amplitudes and phases of numerous sinusoidal impulses. To simulate the random nature of the multipath components in a Rayleigh fading channel, the random nature of these parameters is managed by appropriate probability distributions. The simulation results show that both are effective and accurate. The validation of the simulated channels against real world measurements or theoretical models. By comparing simulation results with empirical data or established theoretical frameworks, the accuracy and reliability of the simulation methods can be verified and validated. © 2024 IEEE.

关键词： Rayleigh fading

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Personalized Music Recommendation System with Transformer based Convolutional Recurrent Neural Networks using User Emotion Detection 1

Personalized Music Recommendation System with Transformer ba...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Dineshkumar, R. Aravinda, N.L. Soundarya, M. Sheeba, G. Hussein, Ramy Riad Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India Maisammaguda Department of Electronics and Communication Engineering Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350352931

Music recommendation based on user's priority is the most important way to increase the listening experience. Due to the relationship between user data and music is one of major challenging task. In the existing Support Vector Machine (SVM) classifier based recommendation system, the model mostly suffered with poor performance and limited classification. In this paper, Transformer Based Convolutional Recurrent Neural Networks (TCRNN) is proposed for effective music recommendation. Initially, data is taken from speech emotion recognition dataset and preprocessed using Hamming Window (HW) to extract pitches from sampled signals. Then, End-Point (EP) detection is used to differentiate the speech and non-speech intervals to extract reliable features vectors from only speech intervals to enhance performance of recommendation system. The reliable feature vectors are further processed with Mel-Frequency Cepstral Coefficient (MFCC) to extract mean, average and interquartile range (IQR) features. After that, these extracted features are further processed with Particle Swarm Optimization (PSO) to select features. Finally, the classification is done through TCRNN dense layer and collaborative filtering model is introduced to recommend music based on user emotion. From the analysis, the proposed TCRNN performed better compared to existing SVM by providing results including accuracy, precision, recall and f1-score of 95.29%,96.47%,95.92% and 97.15% respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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AI Based Early Flood Monitoring and Detection

AI Based Early Flood Monitoring and Detection

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Nihal, Mohamed Aji, Deena Prabhakaran, Arjun Siby, Kens Philip Anand, Leno Simon Vidyamol, K. Sahrdaya College of Engineering and Technology Department of Electronics & Communication Engineering Thrissur kerala Kodakara India

ISBN: (纸本)9798350394399

Advanced technology solutions are necessary in light of the increasing natural disasters that are linked to climate change, such as unpredictable rainfall and flooding. Automation, as a key technology, emerges to streamline processes with minimal human intervention. This paper investigates existing flood monitoring systems, recognising the critical impact of flooding on communities residing in vulnerable areas. The primary objective is to contribute to the existing literature by developing an automated flood monitoring system as an extension of the review process. The proposed system seeks to integrate automation into a community-based early warning system, providing continuous water level updates through an LCD display. The focus is on preemptive measures to mitigate the adverse effects of floods by implementing timely warnings. The flood early warning system is designed to alert residents through warning messages prior to the occurrence of floods. This encompasses a comprehensive overview of community-based early warning systems, involving a survey of such systems in a specific region. The initial phase involves an extensive literature review covering IoT-based hardware and software, as well as early flood warning systems. Subsequently, a small-scale working model of an early flood warning system has been developed utilising IoT-based hardware and software. The model incorporates float and flow sensors as sensing devices to gauge water level and flow, transmitting this data to a microprocessor. The microprocessor processes the information and relays the output to a water level indicator, serving as an early warning mechanism. © 2024 IEEE.

关键词： Water levels

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Comparative investigation of group velocity dispersion with nonlinear phase modulation in fiber optic WDM transmission

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International Journal of Information technology (Singapore) 2024年 1-10页

作者： Sultana, Nasrin Islam, M.S. Department of Electronics and Communication Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur 5200 Bangladesh Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka 1000 Bangladesh

WDM system transmission efficiency is deteriorated by the combined influence of cross phase modulation (XPM) and group velocity dispersion (GVD) of first and second order. This degradation occurs as the channel bulk, light intensity, speed of transmission, and wavelength count frequencies increase. Analysis of the pulse broadening factor, standardized outturn, and resolving the nonlinear Schrödinger equation (NLSE) is conducted in this study. The influence of XPM on higher order GVD is reflected. The impact of broadcast limit and different absorbed powers (10 mW to 120 mW) at various transmission speeds (10 Gbps and 40 Gbps) are assessed utilizing large effective area fiber (LEAF) and standard single mode fiber (SSMF). The first- and second order GVD XPM impacts are the only ones that influence emitted oscillation. GVD's second-order consequences are not perceptible at close grips (⁓10 km) and low throughput (10 Gbps) but become perceptible and affect system performance at bit rates of 40 Gbps and above. The study found that transmission rate and fiber span have a stronger impression on duration than input dominance. The SSMF and LEAF consequences are obtained by rigorous derivation and numerical simulation at the consistent throughput and absorbed power managing the split-phase Fourier method. XPM has a stronger optimistic impact on GVD in SSMF fibers than LEAF fibers by 2 km. Due to their ability to quantify the degree of performance degradation emanating from XPM effects with first- and second order GVD, the findings of this work may prove useful in the design of high-speed, long-distance WDM fiber-optic transmission links. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Cross phase modulation Fiber optic infrastructure Group velocity dispersion Self-phase modulation Split step fourier method

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Design of a Hybrid Frequency and Pattern Reconfigurable Antenna for 5G Sub-6 GHz Applications

Design of a Hybrid Frequency and Pattern Reconfigurable Ante...

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2024 IEEE Wireless Antenna and Microwave Symposium, WAMS 2024

作者： Kaur, Manpreet Singh, Hari Shankar Agarwal, Mayank Thapar Institute of Engineering and Technology Department of Electronics and Communication Engineering Punjab Patiala147004 India

ISBN: (纸本)9798350358926

A low profile hybrid reconfigurable antenna for sub-6 GHz applications has been reported in this paper. A hybrid, frequency-and pattern-reconfigurable antenna with a semi-elliptical patch make up the design. An inverted L-shaped structure and a defective ground structure (DGS) are put onto the antenna's ground plane. Two PIN diodes are employed in the upper layer to reconfigure frequency, and two diodes are used in the lower layer (ground plane) to achieve pattern reconfigurability. Moreover, the suggested design has a -10 dB impedance bandwidth of 260 MHz for 3.4 GHz-3.6 GHz when the top layer diodes are on and 270 MHz for 3.6 GHz-3.8 GHz when they are off. © 2024 IEEE.

关键词： Slot antennas

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AI-Powered Demand Response Mechanisms for Sustainable Smart Grids 5

AI-Powered Demand Response Mechanisms for Sustainable Smart ...

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5th International Conference on electronics and Sustainable communication Systems, ICESC 2024

作者： Krishnan, R. Santhana Ashokkumar, C. Nithila, Ezhil E. Soundiraraj, N. Malar, P.Stella Rose Rani, P. Alice Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Chennai Kattankulathur India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai Avadi India Psna College of Engineering and Technology Department of Electronics and Communication Engineering Dindigul India Jp College of Engineeringology Department of Electronics and Communication Engineering Thenkasi India Sarah Tucker College Department of Tamil Tirunelveli India

ISBN: (纸本)9798350379945

The integration of renewable energy sources into smart grids is a critical strategy for achieving sustainable energy systems. This necessitates effective management of energy resources, demand, and grid operations to fully exploit the potential of renewables while ensuring grid stability and reliability. Demand response (DR) mechanisms play a key role in optimizing energy use and grid stability within smart grids. This research explores the application of artificial intelligence (AI) for developing advanced DR mechanisms. The proposed approach leverages machine learning techniques to analyze energy consumption patterns and predict future demand. This enables the implementation of intelligent DR programs that incentivize consumers to adjust their energy usage during peak demand periods. By optimizing energy consumption in response to grid conditions, AI-powered DR mechanisms can significantly contribute to the integration of renewable energy sources and the creation of a sustainable energy future. The research investigates the design and implementation of these AI-powered DR mechanisms. In the proposed research, Machine learning algorithms are employed to analyze historical energy consumption data and predict future demand with high accuracy. This enables the development of DR programs that are tailored to anticipated grid conditions. Also, AI algorithms continuously monitor grid conditions and dynamically adjust DR incentives in real-time. This ensures that DR programs effectively address fluctuations in energy demand and supply. The research explores methods to incentivize consumer participation in DR programs. This may involve offering rebates or other benefits to consumers who adjust their energy consumption during peak demand periods. The effectiveness of the proposed AI-powered DR mechanisms will be evaluated through extensive simulations. The simulations will compare the performance of the proposed approach with existing DR methods. Key metrics such as forecas

关键词： Demand response

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Outage probability and ABER over shadowed Beaulieu-Xie fading channels under the dual-SC scheme

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International Journal of electronics Letters 2024年第3期12卷 304-315页

作者： Hawaibam, Sisira Singh, Aheibam Dinamani Department of Electronics and Communication Engineering National Institute of Technology Manipur Langol Imphal India

Shadowed Beaulieu-Xie (SBX) is a composite fading model suitable for an environment with a random number of non-line-of-sight and line-of-sight signals encountered in the wireless communication system. There are many effects that undergo in the wireless channel that may degrade the performance of the system. Diversity is one popular technique to mitigate the effects of fading. Investigation of the influence of various parameters of SBX fading channels on the performance of dual-branch selection combining (SC) diversity receiver is carried out. In this paper, mathematical expressions for the probability density function (PDF) of the output signal-to-noise ratio of dual-SC diversity receiver are calculated for SBX fading channels. Outage probability and average bit error rate (ABER) for different kinds of modulation methods are obtained using the PDF-based method. A unified ABER expression for coherent and non-coherent modulations for dual-SC diversity receivers over SBX fading channels is also derived. The effect of shadowing and fading parameters on the performance of dual-SC diversity receivers is studied. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Average bit error rate outage probability probability density function selection combining Shadowed Beaulieu-Xie fading

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Enhancing Agricultural IoT with Predictive Analytics and Reinforcement Learning Models 2

Enhancing Agricultural IoT with Predictive Analytics and Rei...

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2nd IEEE International Conference on Integrated Circuits and communication Systems, ICICACS 2024

作者： Krishnan, R. Santhana Raj, J. Relin Francis Christopher, V. Bibin Nandini, N. Muthu, A. Essaki Puvanadevi, C. SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Ssveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Electronics and Communication Engineering Chennai India Faculty of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies School of Computing College of Engineering and Technology Kattankulathur Chennai India RajaRajeswari College of Engineering Department of Electrical and Electronics Engineering Bangalore India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Adhiyamaan College of Engineering Department of Information Technology Hosur India

ISBN: (纸本)9798350317558

This research explores the augmentation of Agricultural Internet of Things (IoT) systems through the integration of advanced predictive analytics and reinforcement learning models. A novel algorithm, termed "CropQL,"is proposed to optimize crop yield prediction and resource allocation in precision agriculture. CropQL amalgamates the strengths of the CropNet IoT model with Q-Learning for reinforcement learning and Random Forest for predictive analytics. Simulation analyses are conducted to evaluate the performance of CropQL against established algorithms, employing diverse metrics such as precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The proposed CropQL algorithm exhibits superior predictive accuracy, demonstrating a significant enhancement in crop yield forecasting when compared to baseline algorithms. Precision and recall metrics reveal the algorithm's efficacy in minimizing false positives and negatives, ensuring precision agriculture systems make informed decisions. F1 score indicates the balanced optimization of precision and recall, reinforcing the algorithm's robustness. AUC-ROC analysis further corroborates the superior discriminative power of CropQL in distinguishing between positive and negative instances. In comparison to existing algorithms, including AgriSensNet, PrecisionCropNet, and IoTFarmGuard, CropQL consistently outperforms in predictive accuracy and resource optimization. The simulation results underscore the efficacy of the proposed algorithm in enhancing the efficiency of agricultural IoT systems, providing a promising avenue for sustainable precision farming. © 2024 IEEE.

关键词： Agricultural IoT CropQL Precision Agriculture Predictive Analytics Reinforcement Learning Sustainable Farming

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Anomaly Detection Using Feature Encoding and Trajectory Association on Edge Devices for Residential Video Surveillance

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SN Computer Science 2024年第5期5卷 481页

作者： Parate, Mayur Sahare, Parul Department of Electronics and Communication Engineering Indian Institute of Information Technology Maharashtra Nagpur India

In recent years, intelligent surveillance becomes one of the most important community services for residential societies. Intelligent surveillance systems detect anomalies in surveillance scenes, which meet the growing demands of security. At residential societies, installation of high-capacity computational equipment for surveillance purpose is found to be very expensive and unfeasible. In this paper, a system is proposed consisting of CPU-only edge devices to detect anomalies. A modular framework is presented to record object-level inferences and tracking for detecting any freak in surveillance scene. Feature encoding and trajectory association guided by two complementary metrices are employed. These are used to deal with partial occlusions, posture deformations, and complicated scenarios. Proposed anomaly detection framework is tailored to run on CPU-only edge devices to achieve desirable Frames Per Second (FPS) parameter that suits for residential surveillance. Experimental results show that the proposed system is viable and produces satisfactory results in real-world situations when compare to other state of the art methods. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Anomaly detection Edge computing Feature encoding Only-CPU Edge device Trajectory association

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