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1st International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2022

作者： Krishnan, Arun K. Panicker, Senju Thomas Sandeep, S. Jithin, S. Daniel, Jerry J. Sajjad, Tarique Centre for Development of Advanced Computing Control and Instrumentation Group Kerala Thiruvananthapuram India Central Mine Planning and Design Institute Mining Electronics Department Jharkhand Ranchi India

ISBN: (纸本)9781665436472

Air quality monitoring is mandatory in many industries like mining, refining and semiconductor industries owing to detrimental impact of air pollutants on the health of human beings, animals and the environment. There is a growing concern regarding the health impact of air pollutants on personal working in coal mines, both opencast and underground. Toxic gases in the ambient air can rapidly become a danger if not kept within safe permissible limits. The first step in maintaining air quality in coal mining areas is continuous monitoring of the atmospheric air. Toxic gases like carbon monoxide, nitric oxide, nitrogen dioxide and sulphur dioxide produced by various mining activities contribute to polluting the air. Several techniques are being used worldwide to sense the presence of various pollutant gases. This paper explores the various techniques for sensing of toxic gases in ambient air and sheds light into their pros and cons. It showcases a simple and cost-effective way of sensing toxic gases in areas near opencast mines by using electrochemical gas sensors. The paper also sheds light on the design of an ambient air quality monitoring instrument which uses electrochemical gas sensors for sensing the concertation of SO2, NO2, NO and CO at areas near opencast coal mines © 2022 IEEE.

关键词： Sulfur dioxide

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Remote Photoplethysmography (rPPG) for Contactless Blood Oxygen Saturation Monitoring

Remote Photoplethysmography (rPPG) for Contactless Blood Oxy...

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IEEE International Conference on Electronics, computing and Communication Technologies (CONECCT)

作者： Yaythish Kannaa G S Sayantani Bhattacharya N Aishwarya Department of Electronics and Communication Engineering Amrita School of Engineering Chennai India Centre for Development of Advanced Computing (C-DAC) Chennai India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

The increasing demand for healthcare technologies in Ambient Assisted Living, accentuated by recent global health challenges, necessitates advanced solutions for continuous and non-invasive vital sign monitoring. This research addresses this imperative by introducing a novel application utilizing remote photoplethysmography-based techniques. Focused on frail and elderly individuals, the application aims to measure key vital signs, like SpO2. Unlike traditional methods, our approach requires no wearable sensors, providing a comfortable and convenient monitoring solution. The application's development involves a comprehensive pipeline for vital sign estimation, overcoming challenges posed by diverse skin tones and facial characteristics in elderly subjects. The system had achieved notable results in terms of accuracy measuring 1.2 in MSE and 1.09 in RMSE. This innovative application represents a significant step forward in non-invasive vital sign monitoring, offering a promising avenue for enhancing healthcare in Ambient Assisted Living environments.

关键词： Patient monitoring Ambient assisted living Telemedicine Refining Medical services Cameras Skin

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A Quantum Machine Learning framework for Driver Drowsiness Detection using Biopotential Signals and Head Movement Analysis

A Quantum Machine Learning framework for Driver Drowsiness D...

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Women in Innovation, Technology & Entrepreneurship (ICWITE), IEEE International Conference for

作者： Sagnik De Anil Kumar Gupta Institute of Radio Physics and Electronics University of Calcutta Kolkata India Centre for Development of Advanced Computing (C-DAC) Pune Maharashtra India

Road accidents claim numerous lives annually, with drowsiness identified as a primary catalyst for a substantial portion of these incidents. This study addresses this critical issue by introducing an innovative approach to gauge human drowsiness levels during driving. The primary objective of this study is to introduce a novel deep-learning technique capable of detecting various alertness levels—awake, drowsy, and very sleepy—while driving. For this purpose, a hybrid model is proposed, leveraging Convolutional Neural Networks (CNN) in conjunction with an Attention-based Quantum Long Short-Term Memory (QLSTM) network. The designed model employs different biopotential signals, including electroencephalogram (EEG), facial electromyography (EMG), pulse rate, and head movement, to discern a person’s alertness level. Demonstrating remarkable accuracy, the proposed model achieves detection rates of 99%, 98.5%, and 99% for awake, drowsy, and very sleepy states, respectively, thus offering a promising solution to mitigate the impact of drowsiness-related accidents.

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Benchmarking Matrix Multiplications For Variable Qubit Size And Depth

Benchmarking Matrix Multiplications For Variable Qubit Size ...

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Telecom World (ITU WT), Technical Symposium at ITU

作者： Md Imam Mazhar Abhishek Tiwari Nasir Ali Britant Akshay Patil Rahul Kumar Neiwal Embedded Systems Division Centre for Development of Advanced Computing (C-DAC) Noida Ministry of Electronics and Information Technology (MeitY) New Delhi

ISBN: (数字)9789261390914

ISBN: (纸本)9798350367799

To emulate a quantum computation on a classical computer i.e. the evolution of the unitary operations on the wave function of the particle in quantum mechanics, we have to perform unitary matrix and normalized vector multiplications in the high-level programming languages of Python, C++, Java, etc. Quantum Libraries already available perform the matrix-vector multiplication in the backend using the numpy libraries of Python like Qiskit or use a C++ wrapper to further optimize the runtime it as in Qiskit-Aer Simulators. Since a fully functioning fault-tolerant computer is decades away, it is in our best interest to design new quantum algorithms and develop accelerator test beds for Quantum Emulations. All the quantum computer operations can be emulated on a classical computer, with the only downside being that the matrix multiplications scale up as O (N 3 ) in runtime. In contrast, the quantum computer scales it up as O (N 2 log 2 N), where N = 2 n , where N is the matrix dimension, where n is the number of qubits, so the runtime for quantum emulations on the classical computer increases exponentially with increase in number of qubits and increases linearly with increase in number of depths, complexity wise. Though it is not possible to change the exponential index, it is possible to reduce the runtime for quantum emulations on classical computers by use of GPU and Alveo Accelerator Cards, and also code optimization on the software side like using a C++ wrapper. In this paper, we will benchmark the matrix-matrix multiplications on HPC Accelerator Cards varying the qubit size and the depth of the quantum circuit and provide a universal mathematical equation for the runtime on the GPU and Alveo Vector Cards for two variables of qubit size and quantum circuit depth. So a theoretical limit on qubit size and qubit depth exactly can be established for quantum emulations on present classical supercomputers.

关键词： Runtime Quantum computing Qubit Emulation Graphics processing units Quantum mechanics C++ languages Mathematical models Vectors Quantum circuit

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Investigating Quantum-Inspired Evolutionary Algorithm with Restarts for Solving IEEE CEC 2022 Benchmark Problems 5th

Investigating Quantum-Inspired Evolutionary Algorithm with R...

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5th International Conference on Recent developments in Control, Automation and Power Engineering, RDCAPE 2023

作者： Siddiqui, Lubna Mani, Ashish Singh, Jaspal Amity School of Engineering and Technology Amity University Noida India Amity Innovation and Design Centre Amity University Noida India Centre for Development of Advanced Computing Chandigarh India

ISBN: (纸本)9789819600465

This paper investigates the effectiveness of two restart methods, local and global restarts, in enhancing the performance of the quantum-inspired evolutionary algorithm (QiEA) to solve optimization problems. The study compares the result of these modifications by testing their performance against the widely recognized and challenging IEEE CEC 2022 benchmark functions. The objective is to analyze the impact of the restart methods on the convergence and optimization outcomes of the QiEA. Through experimental evaluation, it was sought to determine whether the inclusion of restart methods in the QiEA framework would lead to improved convergence toward the optimal solution. Also, a novel Greedy Local Restart algorithm is proposed. The results demonstrate that the QiEA variants consistently outperform the basic QiEA, achieving superior optimization results in terms of convergence and accuracy. Restart approaches are thus observed as an indispensable asset in handling complex optimization problems, greatly enhancing the effectiveness and applicability of evolutionary algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Evolutionary algorithms

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Maestro: A Robust Multi-Head Attention Enhanced CNN Architecture for Heat-Induced Stress Recognition Using EEG Signals 8

Maestro: A Robust Multi-Head Attention Enhanced CNN Architec...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： De, Sagnik Pavuluri, Sreenija Sayyad, Amaan Gupta, Anil Kumar Institute of Radio Physics and Electronics University of Calcutta Kolkata India Department of Computer Science Engineering Gvpce AP Visakhapatnam India Department of Computer Engineering Meswcoe Maharashtra Pune India Hpc I&e Group Centre for Development of Advanced Computing Maharashtra Pune India

ISBN: (纸本)9798331505462

Heat-induced stress impacts various physiological parameters in the body. Elevated temperature can cause tachycardia (an increase in heart rate), as the body attempts to dissipate heat through vasodilation, leading to dehydration and electrolyte imbalances. In addition, the hypothalamus triggers sweating in order to regulate body temperature;that can culminate in fluid and electrolyte loss, which could impact metabolic processes and blood pressure. A prolonged exposure to high temperatures can cause heat stroke, heat exhaustion, and other ailments including organ damage and systemic dysfunction. Existing electroencephalography (EEG)-based heat-induced stress detection often considers the entire EEG frequency range (delta to gamma), concealing redundant and lossy information and increasing the likelihood of false detection rates. To address the limitations of conventional handcrafted feature engineering approaches in heat stress detection, this paper introduces MAESTRO, a novel model comprising two blocks: Convolutional and Multi-head Attention. The Convolutional block extracts precise information from individual EEG frequency bands, while the Multi-head Attention block enhances feature representation through attention mechanism. Finally, two dense layers are employed to classify heat stress into three classes: Acute, Chronic, and Control. The proposed framework undergoes validation using EEG data obtained from 40 rodents in a simulated laboratory environment. The outcomes illustrate the viability of the method in classifying heat-induced stress, yielding remarkable results for overall accuracy, precision, recall, and F1 score of 98.88 %, 98.54 %, 98. 67 %, and 98.60 %, respectively. © 2024 IEEE.

关键词： Blood pressure

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Study on Vision System for Disease Detection Using CNN and LabVIEW of an Agricultural Robot 12th

Study on Vision System for Disease Detection Using CNN and L...

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12th International Conference on Soft computing and Pattern Recognition, SoCPaR 2020 and 16th International Conference on Information Assurance and Security, IAS 2020

作者： Nahak, Pradeep Jana, Atanu Pratihar, Dilip Kumar Deb, Alok Kanti Ray, Hena Indian Institute of Technology Kharagpur Kharagpur India Centre for Development of Advanced Computing Kolkata India

ISBN: (纸本)9783030736880

Farmers face a lot of problems, while cultivating agricultural plants. They do not get the desired output in agriculture due to the different types of diseases from which the plants suffer. Therefore, it is important to detect the disease(s) of the plant. In this study, a vision-based plant disease detection method has been developed, where convolutional neural network (CNN) model has been used for classification of the plant disease and vision assistant toolbox of LabVIEW software is utilized for disease detection. This vision-based disease detection through a smart camera will be one of the important modules of an intelligent agricultural robot. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Neural network models

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Exploring 2D representation and transfer learning techniques for indoor localization

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Multimedia Tools and Applications 2025年 1-13页

作者： Kerdjidj, O. Himeur, Y. Atalla, S. Copiaco, A. Sohail, S.S. Amira, A. Fadli, F. Mansoor, W. Gawanmeh, A. Centre of Development of Advanced Techniques Algiers Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Department of Computer Science University of Sharjah Sharjah United Arab Emirates Institute of Artificial Intelligence De Montfort University Leicester United Kingdom School of Computing Science and Engineering VIT Bhopal University Kothri Kalan India Department of Architecture & Urban Planning Qatar University Doha2713 Qatar

Indoor localization (IL) plays a critical role in various applications, including asset tracking, emergency response, and indoor navigation. Traditional methods relying on one-dimensional (1D) representations of received signal strength indicator (RSSI) data often suffer from limitations such as low accuracy, environmental sensitivity, and susceptibility to interference. To overcome these challenges, this paper proposes Scalogram-Based Transfer Learning for Indoor Localization (STL-IL), a novel framework that significantly enhances localization performance. The proposed approach transforms 1D RSSI signals into two-dimensional (2D) representations using scalogram-based time-frequency analysis, coupled with advanced transfer learning (TL) techniques. By leveraging the scalogram method to generate image representations of the data and employing diverse wavelet types. Furthermore, pre-trained deep learning models, including GoogleNet and SqueezeNet, are utilized to achieve state-of-the-art performance. Experimental results demonstrate that the STL-IL system achieves remarkable accuracy levels of 95% and 96% for SqueezeNet and GoogleNet, respectively. The proposed framework not only improves localization accuracy but also enhances robustness in dynamic and challenging environments, making it a promising solution for real-world indoor localization applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Deep learning

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Real-Time Robotic Vision Application for Health Monitoring of Paddy Plants

Real-Time Robotic Vision Application for Health Monitoring o...

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Conference on Information and Communication Technology (CICT)

作者： Gopinath Bej Abhra Pal Tamal Dey Sabyasachi Majumdar Tapas Sutradhar Hena Ray Amitava Akuli Alokesh Ghosh Centre for Development of Advanced Computing (C-DAC) Kolkata India

ISBN: (数字)9781665473125

ISBN: (纸本)9781665473132

This paper proposes real-time inspection of the paddy field using robotic vision technology. Chlorophyll content in the plant is the measure of sufficient or deficit amount of nutrients in the field as well as the health condition of the plant. The proposed work estimates the chlorophyll content of the plant while navigating the robotic vehicle in the field. An image acquisition arrangement has been mounted facing the ground on the robotic vehicle which captures on-field images. Color information from these images is compared with the digitized reference leaf color chart. Mahalanobis distance has been calculated from the extracted color with all colors in the reference color chart. Using the minimum Mahalanobis distance the best match with the reference chart has been decided. The robotic vehicle has the provision of spraying fertilizer if the matched color is the last two colors (e.g. having a light green shade) from the reference color chart. The result of this chlorophyll estimation approach is encouraging and the accuracy is to the tune of 92%.

关键词： Image color analysis Navigation Estimation Spraying Inspection Real-time systems Information and communication technology

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Self-supported Dynamic Voltage Restorer for Power Distribution System 5

Self-supported Dynamic Voltage Restorer for Power Distributi...

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5th International Conference on Control, Communication and computing, ICCC 2023

作者： Manju, A.S. Manita, P.V. Nair, Manjula G Joseph, Aby Centre for Development of Advanced Computing Power Electronics Group Thiruvananthapuram India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electrical and Electronics Engineering Bengaluru India Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Amritapuri India

ISBN: (纸本)9798350334128

Voltage power quality issues are the most severe and common power quality issues in an electrical power distribution network. Sophisticated equipment for industrial and domestic consumers is sensitive to voltage quality issues and hence to be mitigated effectively. Dynamic voltage restorer (DVR) provides a versatile and cost-effective solution for compensating voltage-related power quality issues. This paper presents the design and simulation of a 50kVA three-phase self-supported DVR. Each phase sag is compensated individually by using three single-phase inverters. The DC voltage for the single-phase inverters is maintained by a three-phase front-end converter. Line frequency transformer is avoided for connecting the three-phase active front-end converter to reduce the size of DVR. The topology, configuration, and control strategy are examined with simulation in PSIM. © 2023 IEEE.

关键词： Power quality

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