Mepco Schlenk Engineering College

About: Mepco Schlenk Engineering College is a based out in . It is known for research contribution in the topics: Wavelet & Wavelet transform. The organization has 1307 authors who have published 1665 publications receiving 18690 citations.

Simulation of friction welding of alumina and steel with aluminum interlayer

Abstract: Friction welding is a complicated metallurgical process that is accompanied by frictional heat generation and plastic deformation. Since the thermal cycle of friction welding is very short, simulation becomes very significant. In the present work, a finite element-based numerical model has been developed to understand the thermo-mechanical phenomenon involved in the process of friction welding. The developed model is capable of predicting thermal distribution during friction welding of ceramics with metal using an aluminum interlayer for various time increments. Frictional heating at the interfacial region consumes the aluminum interlayer and establishes a bond between alumina and mild steel. Though there is mechanical mixing, the bond is incomplete in the aluminum-alumina interface. Due to the variation of thermal properties between alumina and mild steel, more amount of thermal stress is induced at the joint interface. Numerical simulation predicts the formation of residual stress in the alumina-mild steel side of the interface. This leads to incomplete interlocking that results in poor joint strength.

A deep learning approach for person identification using ear biometrics

Abstract: Automatic person identification from ear images is an active field of research within the biometric community. Similar to other biometrics such as face, iris and fingerprints, ear also has a large amount of specific and unique features that allow for person identification. In this current worldwide outbreak of COVID-19 situation, most of the face identification systems fail due to the mask wearing scenario. The human ear is a perfect source of data for passive person identification as it does not involve the cooperativeness of the human whom we are trying to recognize and the structure of ear does not change drastically over time. Acquisition of a human ear is also easy as the ear is visible even in the mask wearing scenarios. Ear biometric system can complement the other biometric systems in automatic human recognition system and provides identity cues when the other system information is unreliable or even unavailable. In this work, we propose a six layer deep convolutional neural network architecture for ear recognition. The potential efficiency of the deep network is tested on IITD-II ear dataset and AMI ear dataset. The deep network model achieves a recognition rate of 97.36% and 96.99% for the IITD-II dataset and AMI dataset respectively. The robustness of the proposed system is validated in uncontrolled environment using AMI Ear dataset. This system can be useful in identifying persons in a massive crowd when combined with a proper surveillance system.

Respiratory monitoring system for asthma patients based on IoT

Abstract: Nowadays peoples are suffered from breathing problems. The diseases which include various ailments in the form of sleep apnea, chronic obstructive pulmonary disease and asthma. These diseases are the main cause of hospitalization for elder people. People who have suffered from chronic diseases are needed to repeatedly monitor the vital signs periodically. The vital signs are the basic measurements of body functions. The vital signs of our body are heart rate, temperature, blood pressure, respiratory rate, oxygen saturation and blood glucose level. The respiratory rate monitor is one of the important vital signs which is useful for patient's who admitted in Intensive Care Unit (ICU). The respiratory rate is calculated by using LM35 temperature sensor and monitored the patient's respiration continuously based on voltage value of inhaled and exhaled air. NRF24l01 is used to transmit the sensor data from home to medical center. Then the data are published in a webserver using Ethernet to know the patient's status which is useful for doctor. And also the data is displayed in a Liquid Crystal Display (LCD). Once the threshold is reached, the alarm is generated and also a message generated in a webpage. Thereby doctor or medical professionals know the patient's conditions without delay. By using data mining techniques, people may know their condition without medical professionals help.