A Systematic Literature Review on Human Activity Recognition - Journal of Electrical Systems

https://journal.esrgroups.org/jes/article/view/2848 

Abstract

Human Activity Recognition (HAR) plays a significant role in several fields by automatically identifying and monitoring human activities using advanced techniques. It enhances safety, improves healthcare services, optimizes fitness routines, and enables context-aware applications in various fields. HAR contributes to a more efficient and intelligent interaction between humans and technology. It has emerged as an essential research domain with applications in healthcare, smart environments, and human-computer interaction. This study aims to provide a comprehensive survey of the evolving landscape of HAR, including key methodologies, techniques, and trends in existing research. The study discusses various applications of HAR and their significance in modern smart environments. The survey also highlights different types of HAR and data collection techniques. Additionally, it explores various methods for analyzing the collected data and provides a comprehensive analysis of existing human activity classification datasets. It offers valuable insights into understanding the strengths and limitations of various HAR techniques. The study also discusses various challenges and future directions for HAR.    

Suresh TechLabs

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Multimodal medical image fusion using residual network 50 in non subsampled contourlet transform - indexed in SCI

K. Koteswara Rao & K. Veera Swamy

To cite this article: K. Koteswara Rao & K. Veera Swamy (2023): Multimodal medical image

fusion using residual network 50 in non subsampled contourlet transform, The Imaging

Science Journal, DOI: 10.1080/13682199.2023.2175426

To link to this article: https://doi.org/10.1080/13682199.2023.2175426

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Journal of Electrical System : SCI-mago Published

Abstract: - Unmanned Aerial Vehicles (UAVs) have grown into a more powerful type of data transmission due to this rapid progress of evolution of wireless communication technology. In addition, UAVs have been proven to be effective in a variety of applications, including intelligent transport, disaster risk management, surveillance, and environmental monitoring. When UAVs are deployed randomly, however, they can effectively accomplish challenging tasks because of the UAVs’ has low battery capacity, quick mobility, and dynamic in nature orientation. Due to this reason, a new technique must be designed for an optimal energy efficient UAV clustering as well as data routing protocols. In this work proposes a new hybrid model of Emperor penguin-based Generalized Approximate Reasoning Based Intelligent Control (EP-GARIC) cluster-based network topology. Furthermore, the optimal routing function is achieved by the proposed Artificial Jellyfish Optimization (AJO). The implementation of this research is carried out using Network Simulator (NS2). The simulation results displays the effective performance of the suggested approach in terms of reduced energy consumption, improved packet delivery ratio, reduced loss, and so on over compared to the conventional approaches. 

 Keywords: Clustering, Neural Network, Fuzzy method, Energy Efficiency, Parameter Tuning.

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ACTA GEOPHYSICA - Publisher SPRINGER

Tropical cyclone detection in South Pacific and Atlantic coastal area using optical flow estimation and RESNET deep learning model

Tropical cyclones (TC) are among the worst natural disasters, that cause massive damage to property and lives. The meteorologists track these natural phenomena using Satellite imagery. The spiral rain bands appear in a cyclic pattern with an eye as a center in the satellite image. Automatic identification of the cyclic pattern is a challenging task due to the clouds present around the structure. Conventional approaches use only image data to detect the cyclic structure using deep learning algorithms. The training and testing data consist of positive and negative samples of TC. But the cyclic structure's texture pattern makes it difficult for the deep learning algorithms to extract useful features. This paper presents an automatic TC detection algorithm using optical flow estimation and deep learning algorithms to overcome this draw-back. The optical flow vectors are estimated using the Horn-Schunck estimator, the Liu-Shen estimator, and the Lagrange multiplier. The deep learning algorithms take the optical flow vectors as input during the training stage and extract the features to identify the cyclone's circular pattern. The software used for experimental analysis is MATLAB 2021a. The proposed method increases the accuracy of detecting the cyclone pattern through optical flow vectors compared to using the pixel intensity values. By using proposed method 98% of accuracy will be achieved when compared with the existing methods.

Author Keywords

Tropical cycloneConventional approachesDeep learning algorithmHorn-schunk estimatorLiu-shen estimatorThe Lagrange multiplier

Keywords Plus

RISK

Tropical cyclone detection in South Pacific and Atlantic coastal area using optical flow estimation and RESNET deep learning model-Web of Science Core Collection

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Published in SCI - Cybernetics and Systems

A Novel Intelligent Ground Moving Target Indication Using Meta-Heuristic-Based Simplified Fractional Fourier Transform: Cybernetics and Systems: Vol 0, No 0 (tandfonline.com)

Abstract

One of the most significant subfields in “Synthetic Aperture Radar (SAR)” research is considered to be target detection. Numerous studies have been conducted on target identification, with the majority of them favoring filter-oriented methods. The fundamental goal of radar systems is to “detect moving targets on the ground.” Decomposing a complex matrix into a structured sparse matrix and a low-rank matrix is a fundamental mathematics issue. Surveillance and reconnaissance rely heavily on “Ground Moving Target Indication (GMTI),” but it's not a simple task. The SAR ATI was first developed for calculating the radial velocity of ground-moving objects. Yet, overlapping stationary clutter can corrupt the recorded differential phase, resulting in mistakes in position and velocity calculations. The main concept of this paper is to propose a novel “Adaptive Simplified Fractional Fourier Transform (A-SFrFT)” using the intelligent meta-heuristic improvement. This adaptive SFrFT efficiently estimates the “Doppler parameters of the moving targets.” The improved “Harris Hawks Optimization (HHO)” termed Trio Updating HHO (TU-HHO) is used as the meta-heuristic algorithm that enhances the performance of the SFrFT-based target estimation. The mathematical analysis and simulation findings show that the suggested methods recommended strategy is successful.

Suresh Tech Labs

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Published In SCI - The Imaging Science Journal

SCI Journal: Imaging Science Journal

Publisher: Taylor & Francis

Multimodal medical image fusion using residual network 50 in non-subsampled contourlet transform

Abstract : Medical image fusion technology and its collective diagnosis are becoming crucial day by day. This task confers the latest algorithm for image fusion of medical images to many diagnostic complications. Firstly, transform is employed on input source images. The result of the application of transform is the decomposition of source images into various subbands. Eminent features are extracted from these subbands by using resnet50. These features are fused by phase congruency and guided filtering fusion rules. Finally, inverse transform gives the original image. The experiment results of this algorithm are compared with different methods by taking some pairs of medical images. Subjective and objective outcomes prove that the proposed algorithm exceeds the current methods by giving optimal performance measures in the area of medical diagnosis. Thus, it is revealed that the suggested multimodal image fusion model provides elevated performance over existing models via diverse diseases using MRI-SPECT and MRI-PET.

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Published in SCI - Low Power VLSI Design Techniques: A Review

Published :  SCI Journal 

Low Power VLSI Design Techniques: A Review

Abstract: Since CMOS technology consumes less power it is a key technology for VLSI circuit design. With technologies reaching the scale of 10 nm, static and dynamic power dissipation in CMOS VLSI circuits are major issues. Dynamic power dissipation is increased due to requirement of high speed and static power dissipation is at much higher side now a days even compared to dynamic power dissipation due to very high gate leakage current and subthreshold leakage. Low power consumption is equally important as speed in many applications since it leads to a reduction in the package cost and extended battery life. This paper surveys contemporary optimization techniques that aims low power dissipation in VLSI circuits.

Keywords: Power dissipation, dynamic power, static power, clock gating, adiabatic logic

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