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Dual Space DTR-net Learning based Heart Disease Prediction 3

Dual Space DTR-net Learning based Heart Disease Prediction

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3rd international conference on Intelligent Data communication Technologies and Internet of Things, IDCIoT 2025

作者： Maragatham, T. Vanitha, K. Sivarajan, R. Department of Computer Technology-PG Kongu Engineerning College Erode India Department of CSE Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

The biggest cause of death worldwide is a heart attack. If a heart attack can be identified early and treated right away, many lives can be spared. In this study, we suggest a wireless ECG sensor connected to an in-dash ECG monitoring system as the basis for a heart attack detection and alert system. We employ an artificial neural network (DTR Net) pretrained model on ECG signals for heart attack detection. When a heart attack is discovered, the system immediately uses a satellite communication system to notify co-passengers, local hospitals, and paramedics. The suggested solution comprises a wireless ECG sensor that is fastened to the driver’s body and sends ECG signals to an ECG monitoring system built into the car’s dashboard. signal processing methods are used to extract the characteristics after the ECG signals have undergone preprocessing to reduce noise and artefacts. The DTR Net model for heart attack detection is then fed the retrieved features. To precisely identify the existence of a heart attack, the DTR Net model is trained using a substantial dataset of ECG signals. When the DTR Net model recognizes a heart attack, the system immediately sends out a satellite communication system alert to co-passengers, local hospitals, and ambulances. This enables quick medical care to be given, possibly saving lives. In conclusion, our suggested approach for early heart attack detection and alerting in automobiles, which combines a wireless ECG sensor with an DTR Net model, is reliable and effective. By automatically alerting local hospitals, ambulances, and fellow passengers, the technology makes sure that immediate medical assistance is given, potentially saving lives. The technology can reduce fatalities and improve road safety by enabling immediate medical care. © 2025 IEEE.

关键词： Satellite communication systems

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Design & Development of Multipliers & Square Circuits Using the Concepts of Quantum-Dot Cellular Automata (QCA) Technology 3

Design & Development of Multipliers & Square Circuits Using ...

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3rd international conference on Integrated Circuits and communication Systems, ICICACS 2025

作者： Manjunath, T.C. Venugopal, Mohankumar Rajarajeswari College of Engineering Department of Computer Science and Engineering Bangalore India Dr. Ambedkar Institute of Technology Department of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798331508456

Quantum dots Cellular Automatas [QCA] represents an groundbreaking advancement in nanoelectronics, characterizeed by their lower power consumptions, higher-speed operations, & enhanced integration capabilities. These attributes position QCA as a formidable contender to the prevalent Complementery Metal-Oxides-Semiconductors (CMOS) technology, which are currently the industry standard. The focus of these researches is on the innovative design of a QCA-based Multiplier and Square circuit. Multipliers are integral to various digital signal processing applications, where they significantly influence system throughput and power efficiency. Square circuits, on the other hand, are vital for performing mathematical computations. The design of the QCA-based multiplier incorporates binary adders and AND gates, optimizing it for efficient performance. To thoroughly assess the viability of QCA over traditional CMOS, a comparative analysis focusing on key metrics in the areas utilization & thier power's dissipations is undertaken. Their QCA Designer tool plays a critical role in this evaluation, allowing for the validation of the proposed model by examining parameters like cell count, area, and energy dissipation. This approach not only highlights the potential enhancements QCA offers over CMOS but, it further gives the router for a no. of future scope in innovations w.r.t. nanoelectronics. © 2025 IEEE.

关键词： CMOS integrated circuits

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Hybrid Beamforming Optimization in Millimeter Wave MIMO Using Deep Deterministic Policy Gradient Based Deep Reinforcement Learning 3

Hybrid Beamforming Optimization in Millimeter Wave MIMO Usin...

引用

3rd international conference on Integrated Circuits and communication Systems, ICICACS 2025

作者： Riadhusin, Rame Sudhakar, K. Prakash, D. Bhanu Kumar, S. Senthil Rajasubha, J. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Cvr College of Engineering Department of Electronics and Communication Engineering Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India Dhanalakshmi Srinivasan College of Engineering Technology Department of Computer Science and Engineering Mamallapuram India

ISBN: (纸本)9798331508456

In recent years, the rapid growth of Hybrid Beamforming (HB) technology has developed rapidly to improve high spectrum efficiency and data transmission in wireless communication systems. Massive Multiple Input Multiple Output (MIMO) systems are integrated with HB to encourage wireless network performance by improving signal quality. However, combination of digital and analog beamforming increases hardware complexity and quantization errors in multiple streams. Hence, this research proposes Deep Reinforcement Learning (DRL)-based hybrid beamforming optimization framework to learn relevant Channel State Information (CSI) by learning complex non-linear relationships and beam forming weights dynamically. Initially, Deep Multiple Input Multiple Output (Deep MIMO) dataset which consists of channel measurements and capacity to receive and transmit signal processing is used. After that, preprocessing is performed using Amplitude Scaling (AS) for signal compression and Phase Normalization (PN) to reduce phase noise in multiple frequencies. Finally, Deep Deterministic Policy Gradient (DDPG)-based DRL model is used to perform HB optimization through adaptive learning policy by improving sum rate and spectral efficiency while reducing quantization errors. Proposed method results with the Sum-rate (5.35 bps/HZ), Accuracy (99.26%) and Inference time (0.03 sec) when compared with existing CNN-Bi-directional Long Short-Term Memory (CNNBiLSTM). © 2025 IEEE.

关键词： Beamforming

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Improved Support Vector Machine for ECG signal Classification in Implantable Biomedical Devices 3

Improved Support Vector Machine for ECG Signal Classificatio...

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3rd international conference on Integrated Circuits and communication Systems, ICICACS 2025

作者： Patchamatla, Pavan Srikanth SubbaRaju Balsem, Zayd Ajsan Parthiban, K.G. Ramachandra, A.C. Latha Bhuvaneswari, S. RKInfo-Tech Llc India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Al Diwaniyah Iraq Dhaanish Ahmed Institute of Technology Department of Biomedical Engineering Coimbatore India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Dhanalakshmi Srinivasan College Of Engineering Technology Department of Electronics and Communication Engineering Mamallapuram India

ISBN: (纸本)9798331508456

Electrocardiogram (ECG) signal classification in implantable biomedical devices plays a significant role for continuous heart monitoring and detecting cardiac abnormalities such as arrhythmias. These devices analyse ECG patterns to enable accurate diagnostics and provide necessary interventions. However, traditional ECG monitoring tools faces certain challenges like low power transfer efficiency, failure in capturing most paroxysmal Atrial Fibrillation (AFib), and difficulty in automatic determination of AFib. To solve this issue, the implantable ECG sensor with improved Support Vector Machine (SVM) is proposed to monitor the patient continuously and detect AFib effectively. The QRS complex is observed from ECG signals of 29 AFib patients is extracted by utilizing signal processing techniques such as wavelet transform and statistical analysis. The Minimum Description Length (MDL) method is applied to extract and select 12 features of variability-based heart rate parameters. Then, the improved SVM classifier with a Radial Basis Function (RBF) is used to classify the AFib in ECG data accurately. Experimental results demonstrate that the proposed implantable ECG sensor with optimized SVM achieves a better accuracy of 99.2%, sensitivity of 98.8%, specificity of 99.5%, and an F1-score of 98.9% which outperforms the existing methods like traditional SVM and CNN-based models. © 2025 IEEE.

关键词： Electrocardiograms

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An Adaptive Steering Vector - Based Dual Microphone System’s Evaluation 3rd

An Adaptive Steering Vector - Based Dual Microphone System’...

引用

3rd international conference on Advances in Information and communication Technology, ICTA 2024

作者： The, Quan Trong Anh, Pham Tuan Hanoi Viet Nam Vietnam - Korea University of Information and Communication The University of Da Nang Danang Viet Nam

ISBN: (纸本)9783031809422

Nowadays, due to the development of smart home and smart voice-controlled devices, the concern of noise reduction and speech enhancement are getting much attention. Basically, most signal processing algorithms include separating the noisy speech in each frame, estimating, tracking and updating noise statistics for mitigating noise. Recently, scholars and researchers are focusing on studying the space domain in the scenario of multichannel case with an appropriate microphone array (MA). MA owns the high directivity beampattern toward the speech source at a certain direction while eliminating all interference, which come from other directions. Minimum Variance Distortionless Response (MVDR) is commonly installed in most acoustics devices, such as: surveillance equipment, conferencing systems or mobile phones. Because of the different sensitivities of MA, the error of direction of arrival of interest signal or imprecise MA distribution, the overall MVDR beamformer’s evaluation often corrupted. In this paper, the authors introduce a new method for adaptive updating of the steering vector to enhance MVDR beamformer’s performance. The illustrated experiment has confirmed the advantage of suggested techniques in improving the speech quality in the term of signal-to-noise ratio (SNR)from 8.8 to 10.3 (dB), reducing speech distortion to 6.9 (dB). The proposed methods are suitable for applying into multi-channel systems for signal processing in realistic recording environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Noise abatement

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Channel Modeling for Underwater Acoustic communication Systems Using Liquid State Machines with Reservoir Computing 3

Channel Modeling for Underwater Acoustic Communication Syste...

引用

3rd international conference on Integrated Circuits and communication Systems, ICICACS 2025

作者： Rana Veer Samara Sihman Bharattej, R. Hossain, Ramy Read Divya, S. Natesan, M. Dhilipkumar, R. National Louis University FL United States College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Electrical and Electronics Engineering Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Aeronautical Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331508456

In recent years, Underwater Acoustic communication (UAC) system is defined as Acoustic signals (AS) by which information between underwater devices exchanged. UAC environment faces several challenges including dispersion and multipath distortion due to nature of channel modeling for reservoir computing. Hence, this research proposes Liquid State Machine-based Reservoir Computing (LSM-RC) for channel modeling with fixed input data called a 'liquid' reservoir for processing input signals. Initially, the data is collected from Watkins Marine Mammal Sound Database (WMMSD) to analyze human activities. After that, preprocessing is performed using Quadrature Phase Shift Keying (QPSK) modulation to generate continuous signals through amplifier circuit. A pretrained model, Liquid State Machine (LSM) is used for modulation recognition of input signal to produce output signal. For prediction, RC is used to memorize and produce network modulation features which are capable of forecasting large-scale systems in UAC. Finally, LSM-RC predicts UAC signals with enhanced robustness and adaptability in underwater environments. The proposed LSM-RC achieved superior performance in terms of Mean Absolute Error (MAE) (0.0270) and Mean Absolute Percentage Error (MAPE) (0.4320) when compared to existing Orthogonal Time-Frequency Space (OTFS). © 2025 IEEE.

关键词： Phase shift keying

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IoT-Integrated Convolutional Neural Network for Accurate Vehicle Acoustic signal Analysis 3

IoT-Integrated Convolutional Neural Network for Accurate Veh...

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3rd international conference on Integrated Circuits and communication Systems, ICICACS 2025

作者： Sasikala, P. Sharmini, K Subha Ponnappan, Venkatesan Sorakka Kavitha, K.R. Gunasekaran, K. Srinivasan, C. Department of Mathematics Salem India Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur Chennai India Sathyabama Institute of Science and Technology Department of Mechanical Engineering Chennai India Sona College of Technology Department of Electronics and Communication Engineering Salem India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331508456

This paper examines the use of IoT-integrated Convolutional Neural Networks (CNNs) for precise analysis of vehicle audio signals. The context highlights the growing intricacy of vehicle systems and the need for advanced monitoring methods. Acoustic signals generated by different vehicle components may provide significant insights about vehicle health;nevertheless, traditional analysis techniques are often ineffective. The Internet of Things (IoT) technology allows real-time data collection from sensors installed on vehicles, which is then analyzed using CNNs to identify certain patterns and abnormalities in auditory signals. The approaches used include using IoT sensors to record various vehicle noises and utilizing CNNs for feature extraction and classification, providing great defect detection precision. The results indicate the system's capacity to detect engine faults, braking deficiencies, and other mechanical concerns early, enabling quick treatments. It presents a novel methodology for vehicle monitoring, enhancing predictive maintenance, safety, and performance, with prospects for future developments in autonomous diagnostics. The proposed system improved vehicle acoustic signal processing using IoT and CNN models, achieving high accuracy of 99% in anomaly detection, preventive maintenance, and real-time traffic monitoring. © 2025 IEEE.

关键词： Convolutional neural networks

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High-Speed Area Efficient Approximate Kogge–Stone Adder 3rd

High-Speed Area Efficient Approximate Kogge–Stone Adder

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3rd international conference on Computational Electronics for Wireless communications, ICCWC 2023

作者： Dantla, Sudhakar Reddy Tummala, Prudhvi Musala, Sarada Kanapala, Satish Department of Electronics and Communication Engineering Vignan’s Foundation for Science Technology & Research Andhra Pradesh Vadlamudi India

ISBN: (纸本)9789819719426

Approximate parallel prefix adders are a type of circuit that can perform addition operations on binary numbers with high speed, low area, and less power. These circuits are designed to provide approximate results, which means that they sacrifice accuracy for efficiency. This trade-off makes them ideal for use in applications where speed is more important than precision. In recent years, there has been a growing interest in the improvement of these circuits, as they have the potential to revolutionize the field of digital signal processing. Kogge–Stone (KS) adder is a kind of parallel prefix adder that has the strong point of quickest addition primarily based on design time. Approximation Kogge–Stone (AxKS) adder is a variant of basic Kogge–Stone Adder that introduces a level of approximation in its operation. In this paper, a new approximate KS Adder is proposed with high-speed performance and less area. The KS Adder and its AxKS Adder are compared for bits 8, 16, 32 using software tool Xilinx Vivado. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Digital signal processing

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Robust signal processing Approaches for Enhancing communication Reliability in Ultra-Dense Massive MIMO Networks Facing Fading Challenges 3

Robust Signal Processing Approaches for Enhancing Communicat...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Moray, Rashmy Kumari, Savita Upadhyay, Deepak Sharma, Rashmi Ranswal, Aakash Singh Venu, Nookala Symbiosis International Deemed University India School of Computing Science and Engineering Galgotias University Greater Noida India Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Department of Cse Nagpur Campus Pune India Madhav Institute of Technology & Science Deemed University Centre for Internet of Things Gwalior India

ISBN: (纸本)9798331542375

The goal of this paper is to introduce robust signal processing techniques for realistic communication scenarios with fading in ultra-dense massive MIMO communications. The proposed techniques tackle major challenges like channel estimation accuracy, interference management, and energy efficiency, which are essential for sustaining high performance in highly dense and dynamic scenarios. The proposed system outperforms traditional algorithms through advanced techniques and pilot contamination mitigation, interference-to-signal ratio optimization, and hybrid relaying. It is shown by the simulation results those considerable gains in spectral efficiency, energy efficiency and packet errors are achieved, even in the presence of varying user mobility and hardware impairments. These approaches provide reliable communication and optimize the network performance and thus are desirable for future ultradense 6G networks. © 2025 IEEE.

关键词： Network coding

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3rd international conference on Machine Vision and Augmented Intelligence, MAI 2023

3rd International Conference on Machine Vision and Augmented...

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3rd international conference on Machine Vision and Augmented Intelligence, MAI 2023

ISBN: (纸本)9789819743582

The proceedings contain 76 papers. The special focus in this conference is on Machine Vision and Augmented Intelligence. The topics include: Survey on Robustness of Deep Learning Techniques on Adversarial Attacks in WBAN;synergizing Collaborative and Content-Based Filtering for Enhanced Movie Recommendations;exploring Transformer-Based Approaches for Hyperspectral Image Classification: A Comparative Analysis;deep Learning for Cognitive Task and Seizure Classification with Hilbert–Huang Transform and Variational Mode Decomposition;tracking of Ship and Plane in Satellite Videos Using a Convolutional Regression Network with Deep Features;Tumor Detection and Analysis from Brain MRI Images Using Deep Learning;software Maintenance Prediction Using Stack Ensemble Deep Learning Algorithms;resource Allocation in 6G Network for High-Speed Train Using D2D Outband communication;controlling the Band-to-Band Tunneling Effect in Charge Plasma Based Dopingless Transistor;Comparison of Different CIC Filter Architectures on the Basis of a Novel Parameter Called Noise Factor for Sigma-Delta Based ADCs;the Scientific Analysis on Effective Yoga Posture Recognition Techniques;impact of Gamma Rays on Emerging Devices for Photonic Applications;shaft Rotation Monitoring Using Radar signal processing and Wavelet Transform;gysel Power Divider Miniaturization Using an Inter-Digital Capacitor-Based Slow-Wave Structure;noise Estimation and Removal in Fundus Images Using Pyramid Real Image Denoising Network;evaluation of Hybrid Encryption Method to Secure Healthcare Data;multimodal Face Recognition System Using Hybrid Deep Learning Feature;Classification of Copy and Move Image by Using HELM-FSK Method: An Efficient Approach;analysis of Energy Efficient Smart Home Based on IoT System;role of Explainable Artificial Intelligence Approaches in Cybersecurity.

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