Secret Image Sharing Scheme Based on Compressed Sensing
Implementation of HOG Based Feature Extraction Method
Soldier Health and Position Monitoring System using GPS and IOT Technology
A Review on the Closed-Loop Strategy for Speed Control of BLDC Motors
V/F Based Speed Controller of Induction Motor using Ann Controller - A Review
Blockchain 3.0: Towards a Secure Ballotcoin Democracy through a Digitized Public Ledger in Developing Countries
Fetal ECG Extraction from Maternal ECG using MATLAB
Brief Introduction to Modular Multilevel Converters and Relative Concepts and Functionalities
Detection of Phase to Phase Faults and Identification of Faulty Phases in Series Capacitor Compensated Six Phase Transmission Line using the Norm of Wavelet Transform
A Novel Approach to Reduce Deafness in Classical Earphones: MUEAR
A novel mathematical ECG signal analysis approach for features extraction using LabVIEW
Filtering of ECG Signal Using Adaptive and Non Adaptive Filters
Application of Polynomial Approximation Techniques for Smoothing ECG Signals
A Novel Approach to Improve the Wind Profiler Doppler Spectra Using Wavelets
Implementation of HOG Based Feature Extraction Method
In traditional secret image-sharing schemes, all the data of a secret image has to be processed, which prolongs the algorithm execution. Meanwhile, the inflated data become a burden for network transmission and disk storage when many secret images need to be routinely shared. Compressed sensing technology measures the original image perceptually through a proper measurement matrix, and the measured data cover the vast majority of the useful information of the original image. While ensuring precise reconstruction, the original image is compressed from high dimensional to low dimensional, and the amount of image data decreases dramatically. Thus, a number of problems caused by the large amount of data in traditional secret image-sharing schemes could be solved by compressed sensing. In this paper, we combine traditional secret image-sharing with compressed sensing technology and show, through experiments, that the proposed method can clearly reduce the amount of data that needs to be processed and effectively shorten the algorithm execution time. The experimental results reveal that our method can shorten the image-sharing time by 2.7% to 57.3% and the image restoration time by 3.3% to 57.7% under different compression ratios.
Human detection on emerging intelligent transportation systems is a challenging task in hardware implementation. The Histogram of Oriented Gradients (HOG)-based human detection is the most successful algorithm due to its superior performance. Unfortunately, more intensive computations and poor performance at a multi-scale and low-contrast make human detection more difficult and unreliable. To address the aforementioned problems, an efficient histogram of edge-oriented gradients-based human detection is proposed to preserve the edge gradients at low-contrast and support multi-scale detection. The proposed algorithm uses approximation methods and adopts a pipelined structure that utilizes low-cost and high-speed, respectively. Experiments conducted on various challenging human datasets show that the proposed method provides efficient detection. This algorithm has been synthesized on Xilinx Spartan 3 FPGA software and board, achieving better hardware utilization compared to other state-of-the-art approaches.
The fighter Wellbeing and Position Global Positioning Framework allows the military to track the current GPS position of officers and also monitors their health status, including internal temperature and heartbeat. This system also includes an additional feature that enables a soldier to request physical assistance or send a distress signal to the military if needed. The GPS modem transmits the latitude and longitude positions in a connected pattern, aiding the military in tracking the officer's current location. The system is highly beneficial for obtaining real-time health status information and providing immediate assistance to soldiers. In today's world, all nations prioritize their security, and soldiers, as the backbone of any armed forces, often lose their lives due to lack of medical aid during emergencies. Furthermore, soldiers engaged in missions or special operations might become disoriented on war fields, losing contact with authorities. To address these concerns, we developed this project. By utilizing a Wireless Body Area Sensor Network (WBANS) consisting of temperature and heartbeat sensors, we can monitor the soldiers' health status whenever necessary. Additionally, through GPS, we can track the precise location of the soldiers as needed. The use of an oxygen level sensor enables us to monitor the environmental conditions so that necessary aid can be provided by medical professionals. Communication between the soldiers and medical personnel is established via the Internet of Things (IoT). Any anomalies detected by the Remote Body Area Sensor Network (WBASNs) act as triggers for the IoT device to establish a connection between the soldier and the base unit, transmitting current location and health status to the receiver. To effectively convey this data, we have employed this equipment to create a cost-effective, lightweight, portable, and accurate defense system for soldiers.
The advancements in the field of materials and force gadgets, coupled with the increased affordability of highperformance processors, have led to a widespread adoption of Brushless Direct Current (BLDC) motors across various industries. These applications range from household appliances to automotive, aerospace, and medical sectors. The widespread use of may be attributed to its manifold advantages over various types of engines, such as its superior efficiency, robust power output, extended operational lifespan, relatively quiet operation, and broader range of achievable speeds. Due to the increasing adoption of Brushless Direct Current (BLDC) motors in many real-life applications as a replacement for traditional motors, this paper aims to compile and evaluate a comprehensive list of control algorithms employed for BLDC motor control. This study discusses several ways for managing speed and current, including hysteresis band control, variable DC-link voltage control, and Pulse Width Modulation (PWM) control schemes. The optimization of Proportional-Integral-Derivative (PID) gains for these controlling techniques is achieved through the utilization of the particle swarm optimization (PSO) algorithm. By employing Fast Fourier Transform (FFT) analysis to examine the controller behavior through frequency analysis of the output signals and calculating the Total Harmonic Distortion (THD), a more advantageous control method may be determined.
Induction motors are typical motors with a constant speed that do not function at synchronous rates. Industrial mechanical loads must not only be powered but also operated at the specified velocity. Consequently, it is necessary to have ways of controlling the speed of induction motors. Multiple techniques exist for regulating the velocity of an induction motor. The objective of this paper is to critically examine the existing literature on different speed control techniques and evaluate their effectiveness in the presence of harmonics. Specifically, the focus will be on the utilization of SPWM inverter, harmonics reduction, and speed-torque characteristics to identify the most efficient methods. Additionally, the paper aims to explore strategies for minimizing odd harmonics through the use of an inverter.
