Auto Encoders based Neural Networks to Predict Faultiness of VLSI Circuits
Smart Electrical Vehicle
Development of Smart Electronic System to Implement Smart Home
Multilingual Speaker Identification System through Multiple Features Analysis of Speech Signal in Multilingual Environment
Photographing a Black Hole
Development of an Intelligent Battery Charging System Based on PIC16F877A Microcontroller
Blockchain 3.0: Towards a Secure Ballotcoin Democracy through a Digitized Public Ledger in Developing Countries
Brief Introduction to Modular Multilevel Converters and Relative Concepts and Functionalities
Fetal ECG Extraction from Maternal ECG using MATLAB
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
Wearable Health Monitoring Smart Gloves
Nowadays, heart diseases are the major cause of death toll. Sometimes, a heart attack results in immediate death and in some case, patients need to be monitored after their first attack to reduce the risk of an attack. This paper is aimed to improve the patient monitoring with minimal number of staffs. In this paper, smart hand gloves are introduced to monitor the patient heart rate and the body temperature. Physiological health parameters are sensed which after collecting and processing, is displayed on the LCD screen. The system is also connected via IoT platform named Blynk, which assists the caretaker in remote monitoring. The data collected can be stored for future diagnostic purposes. Moreover, the system helps the patients to do basic activity on their own. It is equipped with buttons to enable the patient to communicate with the family members and doctors as well. The fitted buzzer will alert family members about emergency situations where the heart rate or body temperature is higher than normal range. Additionally, further advances can be made by real time camera setup to guard the patient's activity.
In the present time, there is a strong need to develop an efficient health monitoring system to diagnose air-gap eccentricity fault of the induction motor at early stages. If the fault is diagnosed in the early stages then one can save the industry for millions of dollars. The main aim of the researchers is to develop a non-intrusive health monitoring system for induction motor health detection in relatively low cost and also ought to be powerful for detection of developing online faults in the early stages. In the induction motor, due to unbalanced magnetic pull, the airgap eccentricity faults occur and if this fault is not diagnosed in the early stages then it will lead to large revenue losses for the industry. This issue has been addressed in this research paper and an effort is made to give an competent health monitoring technique for this kind of fault detection purpose. To achieve better results, hybrid technique has been used to extract relevant information of the fault from the raw signal in the developing stage. The EMD and wavelet algorithm has been used jointly for efficient health monitoring purpose for inverter fed induction motor machine. Two techniques have been used for fault diagnosis purpose, one is FFT technique and the other is hybrid technique. It has been observed that the hybrid technique has given encouraging results over FFT technique.
This study investigates the relationship between sampling frequency and SNR of Electroencephalogram (EEG) signal. The EEG is a standard technique for investigating the electrical activity of brains in different psychological and pathological states. At the time of EEG recording, various artifacts such as muscle activity, eye blinks, eye movements and electrical noise corrupt the EEG signal. Normally, EEG signals fall in the frequency range of DC to 60 Hz and amplitude of 1-5 µv. Ocular artifacts have the similar statistical properties of EEG signals, and often interfere with EEG signal, making the analysis of EEG signals more complex. In this research paper, two different datasets were taken from Physionet data base. The sampling frequency of one dataset is 100Hz and the sampling frequency of another dataset is 250Hz. The research paper attempts to establish the relationship between sampling frequency and SNR of EEG signal. In this paper, the collected EEG signals are normalized and then mixed linearly with the normalized Electrooculography (EOG) signals, resulting in noisy EEG signals. Later soft and hard thresholding techniques were applied for detail coefficients and to estimate the SNR of the denoised EEG signals. This research paper concludes that signals with lower sampling rates provide better SNR than the signals with higher sampling rates. In addition to this, Haar wavelet provided better SNR compared to dB10 and Sym8 wavelets.
Nowadays, object detection is widely used for various purposes. There is a need for detecting living objects so that various problems like humans getting stuck in an undesirable place, and to find the suspected objects out of a group of other objects. Image segmentation is a process of dividing a digital picture into many segments, which helps in getting meaningful and well analyzed data that can be used in obtaining a well-defined output. Detection of living beings during the natural disasters is the most challenging task. Our proposed system makes use of You Only Look Once (YOLOv3), the quickest and most accurate among object detection system. During natural disasters, objects/living beings are detected using YOLOv3 model and the same information will be sent to the rescue team so that lives of human beings will be saved quickly and effectively.
Distributed Arithmetic or Dispersed Arithmetic (DA) is named so considering the way that the number shuffling activities that give up in indication planning (e.g., addition, multiplication) are not "lumped" as a strong helpful component, but instead are passed on in a consistently unrecognizable way. Distributed Arithmetic is the procedure or technique, which is used most for the computation of the inner product between fixed and variable data vector. In Digital Signal Processing, the most often form met is sum of product, dot product, or inner product generation. This is the computation that is seen in DA. It is well suited for the implementation in Field Programmable Gate Array because of its usage in lookup tables. The main motivation of using Distributed Arithmetic is to increase the efficiency in computation whereas drawback raised here is for each added input line size of the ROM used increases exponentially. To overcome this, various techniques are preferred, among them Offset Binary Coding is suggested. By designing carefully, it is possible to reduce the total gate count in a signal processing arithmeticunit.