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Heartbeat Rate Estimation using Convolutional Neural Network

Heartbeat Rate Estimation using Convolutional Neural Network

引用

International conference on Inventive Computation Technologies (ICICT)

作者： J Calvin Abilash D Albert P Jayabharathi Department of CSE St. Joseph’s College of Engineering Chennai Tamil Nadu

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

Heartbeat rate estimation using Convolutional Neural Network presents an innovative and non-invasive solution for cardiovascular monitoring. This approach harnesses computer vision and signal processing techniques to extract valuable physiological information from facial video data captured by a standard web camera. By analyzing subtle color variations associated with blood flow, the system provides continuous and instantaneous estimations of the user’s heartbeat rate. The proposed method involves the application of advanced signal processing algorithms, specifically utilizing photoplethysmography (PPG) principles, to the facial video stream. These algorithms track changes in pixel intensity over time, correlating with variations in blood volume during each cardiac cycle. To enhance accuracy and adaptability, machine learning models are employed, allowing the system to account for individual differences in skin tones, lighting conditions, and facial features. The result is a real-time analysis that enables continuous and unobtrusive monitoring of the user’s heart rate. A notable advantage of this approach lies in its accessibility and cost-effectiveness, as it leverages widely available web cameras without requiring specialized hardware. The proposed system’s robustness is evaluated across diverse scenarios, including varying lighting conditions and demographic groups, ensuring its reliability in real-world applications. This real-time heartbeat rate estimation using a web camera holds promise for a range of applications, from personalized health tracking to telehealth, marking a significant advancement in accessible cardiovascular monitoring

关键词： Heart beat Telemedicine Estimation signal processing algorithms Lighting Cameras Real-time systems

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VisionaryX-9: Efficient plant disease prediction model

VisionaryX-9: Efficient plant disease prediction model

引用

Communication, Computing and signal processing (IICCCS), IEEE International conference on

作者： Ishwari Singh Rajput Kapil Bhardwaj Deepak Sanwal CSE Department Grahpic Era Hill University Haldwani

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

Agriculture, which is the foundation of worldwide sustenance, is facing substantial obstacles in the twenty-first century, mainly because of increasing demands for food production and the growing menace of plant diseases. Ensuring the well-being of crops is of utmost importance for the security of global food supply, which requires the timely and precise detection of plant diseases. Conventional approaches are becoming less effective in dealing with the changing agricultural dangers, necessitating the development of new and creative solutions. The incorporation of machine learning and deep learning technologies in recent agricultural practices has significantly transformed plant disease diagnosis, providing remarkable levels of accuracy, efficiency, and scalability. This study utilises advanced algorithms such as ResNet, VGG, and EfficientNet to improve the accuracy of disease identification, thereby revolutionising precision agriculture. The main objective of this article is to enhance the process of plant disease detection with the help of a sophisticated model, VisonaryX-9, that can not only properly identify plant diseases but also classify their symptoms. The objective is to empower farmers by furnishing them with timely information, enabling informed decisions that directly influence agricultural output and, consequently, global food production. Our contributions consist of the creation of VisonaryX-9, a hybrid model that merges the ResNet and VGG architectures. The model is validated with a dataset of having 87,295 real-world plant disease images. The results obtained indicates the the model outperforms other baseline model with an accuracy of 99.12%. Our objective is to reveal profound understandings that will enhance the discussion on sustainable and resilient farming methodologies.

关键词： Plant diseases Accuracy Food manufacturing Computational modeling Scalability signal processing algorithms signal processing Predictive models Telecommunication computing Security

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Deep Learning for Future: Advancements and Prospects in signal Denoising for Cognitive Radio

Deep Learning for Future: Advancements and Prospects in Sign...

引用

International conference on Optimization and Applications (ICOA)

作者： Noureddine El-haryqy Abdelmouttalib Bousrout Zhour Madini Younes Zouine Dept. electrical and telecommunication Laboratory of Advanced Systems Engineering (ISA) Kenitra Morocco

ISBN: (数字)9798350387353

ISBN: (纸本)9798350387360

Within communication systems, the challenge of noise and interference presents a formidable barrier to reliable data transmission. While classical denoising methods have provided valuable insights, their limitations impede their effectiveness. However, the emergence of deep learning offers a promising solution, especially within the domain of cognitive radio. Deep learning techniques exhibit remarkable efficacy in addressing noise and interference across various signal types. Recent research emphasizes the transformative potential of deep learning in advancing signal denoising methodologies, highlighting its pivotal role in bolstering the performance of cognitive radio systems.

关键词： Deep learning Noise reduction signal processing algorithms Interference Sensors signal denoising Cognitive radio Reliability Optimization signal to noise ratio

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Fast Erasure Decoding of Reed-Solomon Codes Based on Decomposition of Vandermonde Matrix

Fast Erasure Decoding of Reed-Solomon Codes Based on Decompo...

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International conference on Wireless Communications and signal processing (WCSP)

作者： Zebing Lin Jingjie Lv Pingping Li Linqi Song Hui Liang Hanxu Hou School of Computer Science and Technology Dongguan University of Technology School of Electrical Engineering & Intelligentization Dongguan University of Technology Shenzhen Institute for Advanced Study University of Electronic Science & Technology of China Department of Computer Science City University of Hong Kong City University of Hong Kong Shenzhen Research Institute

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

Reed-Solomon (RS) codes are widely applied in storage systems. The main bottleneck of RS codes is that the high decoding complexity defined as the number of multiplications and additions. In this paper, we propose a fast decoding algorithm for RS codes that requires fewer number of multiplications. Our fast decoding algorithm is designed by a decomposition of Vandermonde matrices. We show that our decoding algorithm requires fewer number of multiplications than the existing best known algorithm when the number of parities $T$ is large. When the codeword length $N=16$ , our decoding algorithm reduces the number of multiplications by 7% and 29% when $T=4$ and $T=7$ , respectively, compared with the best known decoding algorithm.

关键词： Reed-Solomon codes Wireless communication Codes Upper bound signal processing algorithms Transforms signal processing Decoding Matrix decomposition Galois fields

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Beamforming Optimization: Extended to Bidirectional Integrated Sensing and Communication System

Beamforming Optimization: Extended to Bidirectional Integrat...

引用

European signal processing conference (EUSIPCO)

作者： Siyang Guan Zehan Liu Rui Duan Jian Zhang Jun Bao School of Information and Communication Engineering University of Electronic Science and Technology of China Institute of Advanced Millimeter-Wave Technology University of Electronic Science and Technology of China

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

This paper investigates the joint transmit and receive beamforming of the bidirectional integrated sensing and communication (BD-ISAC) system. Through in-band full-duplex (FD), the BD-ISAC system aims to achieve the coexistence of bidirectional communication and radar detection. An optimization problem is proposed with the object of maximizing the weighted sum of communication rates, subject to the constraints of radar performance and interference suppression. To be more specific, the similarity between BD-ISAC beamforming weights and referential ones with ideal radar performance is constrained, therefore taking radar properties into account while optimizing communication rates. Nulling of interference is also introduced to tackle the challenges of self-interference within a node and mutual interference between nodes. An algorithm based on the weighted minimum mean square error (WMMSE) method is developed to tackle the non-convex problem. Numerical results demonstrate the effectiveness of the proposed beamforming method for BD-ISAC systems.

关键词： Array signal processing System performance signal processing algorithms Radar detection Radar Bidirectional control Transforms Integrated sensing and communication Transceivers Optimization

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A CNN-based Method for Human Activity Recognition Using Inertial Measurement Units

A CNN-based Method for Human Activity Recognition Using Iner...

引用

International conference on Image and signal processing and their Applications (ISPA)

作者： Lotfi Madaoui Malika Kedir-Talha Oussama Kerdjid Department of Electrical Engineering LINS Laboratory University of Sciences and Technology Houari Boumedien Algiers Algeria CEIT University of Dubai Dubai United Arab Emirates Center for Development of Advanced Technologies Algiers Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Human Activity Recognition using Inertial Measurement Units (IMU) has received particular attention in recent years due to the widespread availability of wearable devices. In this article we present a Convolutional Neural Network-based technique for HAR using IMUs to recognize six human activities. Data from 30 subject was used to validate the system using UCI HAR Dataset. The time series signals were filtered, segmented then transformed into a two-dimensional matrix format, where each row represents a specific time step and each column corresponds to the IMU axes; these data were then fed into the convolutional neural network as input. The results of the experiments demonstrate that the suggested model can effectively capture the spatial and temporal features of IMU data, leading to superior recognition accuracy and relatively lower complexity in comparison to conventional 2-dimensional CNN approaches.

关键词： Measurement units Image recognition Time series analysis signal processing algorithms Inertial navigation signal processing Data models

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Secrecy Energy Efficiency Maximization for RSMA-Based UAV Communication with Multiple Colluding Eavesdropperse

Secrecy Energy Efficiency Maximization for RSMA-Based UAV Co...

引用

IEEE International conference on Information Communication and signal processing (ICICSP)

作者： Shuzhen Liu Yi Wang Ziheng Sheng Zhiyu Huang Hongwen Yu Zhichao Sheng School of Communication and Information Engineering Shanghai University Shanghai China Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China The School of Civil and Environmental Engineering UNSW Sydney Australia School of Civil and Environmental Engineering UNSW Sydney Australia

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

This paper explores a rate splitting multiple access (RSMA)-based unmanned aerial vehicle (UAV) communication system aimed at enhancing secrecy energy efficiency. An UAV communicates with ground users while multiple colluding eavesdroppers intercept the signals. The secrecy energy efficiency is optimized by designing the RSMA precoding matrix and UAV trajectory, subject to constraints on UAV energy consumption and data requirements. To address the complexity of coupled variables, an iterative algorithm combining the Dinkelbach algorithm and block coordinate descent method is proposed. Simulation results indicate that the secrecy performance of the proposed algorithm outperforms that of the benchmark schemes.

关键词： Simulation Precoding signal processing algorithms Benchmark testing signal processing Autonomous aerial vehicles Energy efficiency Trajectory Iterative methods Optimization

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Digital Modulation Identification in Spatial Modulation MIMO Systems

Digital Modulation Identification in Spatial Modulation MIMO...

引用

International conference on Image and signal processing and their Applications (ISPA)

作者： Dalil Beknadj Hakim Tayakout Mohamed Tounsi Fatma Zohra Bouchibane Elhocine Boutellaa LIMed Laboratory University of Bejaia Bejaia Algeria Center for Development of Advanced Technologies (CDTA) Algiers Algeria LAMOS Laboratory University of Bejaia Bejaia Algeria Institute of Electrical Engineering and Electronics University Mhamed Bougara Boumerdes Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Cognitive radio (CR) is a game-changing technology that allows devices to adjust transmission and/or reception parameters in real time, improving wireless communication efficiency. Digital modulation identification (DMI) plays a crucial role in in CR, wireless communications, and spectrum management, providing adaptive and efficient signal processing. In this paper, we introduce a DMI approach designed for spatial modulation (SM) within the context of wireless multiple input multiple output (MIMO) technologies. In this study, we use the higher order statistics (HOS) as feature-based digital modulation identification (FB-DMI) to assess the performance of frequently utilized classifiers namely, k-nearest neighbor (KNN), and decision tree (DT). To train these classifiers, several configurations of the SM-MIMO network were evaluated, using the zero forcing (ZF) equalizer. Experiments show that KNN outperforms DT in identifying modulation schemes across all SNR levels.

关键词： Resistance Digital modulation signal processing algorithms Interference Robustness Real-time systems Object recognition

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Continuous and Non-Contact Extraction of Respiratory signal and Rate from Thermal Video

Continuous and Non-Contact Extraction of Respiratory Signal ...

引用

International conference of signal processing and Intelligent Systems (ICSPIS)

作者： Maryam Saidi Mohammad Rezaei Maryam Beigzadeh Amin Mohammadian Abdolaziz Azizi Lari Bioelectric Department Electrical Engineering Faculty Shahrood University of Technology Shahrood Iran Biosignals processing Group Research Center for Development of Advanced Technologies Tehran Iran

ISBN: (数字)9798331532543

ISBN: (纸本)9798331532550

Achieving a simple, non-contact, non-invasive, and non-disruptive method for measuring and monitoring vital signs, including respiratory signals, is crucial in this field of research. This paper aims to implement algorithms for extracting respiratory signals and breathing rates from thermal facial images. Unlike many previous studies, where the algorithm was validated under resting conditions, this paper designs a protocol to capture a diverse range of respiratory activity datasets in varying conditions. Data were collected from six healthy individuals, with thoracic and abdominal contact-based respiratory signals used as reference signals. processing steps, including robust facial tracking in thermal images and precise selection of target areas around the nose is implemented to extract respiratory signal and rates from the thermal video. The effectiveness of the proposed computational method is assessed through both quantitative and qualitative metrics, considering a variety of breathing patterns beyond just resting conditions

关键词： Training Protocols Correlation Target tracking Shape Neural networks Nose Infrared imaging Feature extraction Recording

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Adaptive Neural Mode Selection for Optimized D2D Communication in LTE-advanced Networks

Adaptive Neural Mode Selection for Optimized D2D Communicati...

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Next Generation Communication & Information processing (INCIP), International conference on

作者： V. Asha S. Sreekanth Ginni Nijhawan Abhishek Kaushik Mohammed Wael Mohammed Department of Computer Applications New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

In LTE-advanced networks, D2D communication comes out as a critical technology to increase the number of connections, decrease delay while improving spectral efficiency. Therefore, this paper focuses on developing an enhanced mode selection algorithm, Adaptive Neural Mode Selector (ANMS), to enhance D2D communication on LTE-advanced network that considers the performance parameters such as throughputs, interference, and energy consumption. The ANMS framework involves machine learning where the receivers adjust the mode of communication to either direct D2D, relay-assisted D2C, or cellular depending on network and the user's preferences. Combining deep learning with conventional signal processing strategies, the ANMS framework learns and foresees the transition of different states of the network for stabilized and effective D2D communication. Cognitive mode selection approach is the one proposed in this paper, and the extensive simulations prove its effectiveness in increasing a network's spectral efficiency, decreasing the interferences, and improving energy consumption compared to conventional methods. These results raise the extremely positive outlook on the role of IMS in enhancing the practical D2D communication in the LTE-advanced context and create the prospect for the much more reliable and faster future wireless networks.

关键词： Deep learning Energy consumption Adaptive systems Spectral efficiency Wireless networks signal processing algorithms Throughput Device-to-device communication Reliability Long Term Evolution

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