MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

Engineering and Technology

The self-organizational ability of ad-hoc Wireless Sensor Networks (WSN) have led them to be the most popular choice. Despite significant advancements in Wireless Sensor Networks, efficient energy consumption in the network remains one of the most important research challenges. Clustering sensor nodes and organizing them hierarchically have proven to be an effective method to provide better data aggregation and scalability for the sensor network while conserving limited energy. Topology control mechanisms promotes efficient energy consumption as it deals with the arrangement of the nodes. Topology acts as virtual backbone of the network, so a good topology control mechanism improves the overall network lifetime. There are various techniques for topology control such as Self-Organizing algorithms, Minimal Spanning Trees and Unit Disk Graphs which are taken as basis for the proposed algorithm. The proposed algorithm performs hierarchical clustering on the network in order establish optimal routing path from sensor nodes to sink node whilst conserving network resources.

School of Computer Engineering & Technology

2018 2nd International Conference on Electronic Systems, Signal Processing and Computing Technologies [ICESC-2018]

Optimal Routing Path Selection to Improve Data Transmission and Lifetime of Wireless Sensor Network

The world over, prevailing technologies to assist physically challenged people are either complex or available at high cost. The recent development in the medical world, however, claim to facilitate comfort and make their life autonomously operational but lack of information, training, and functional intricacies often lead to discouraging users. This paper presents a comprehensive study of an existing system called 'Internet of Things' (ioT); this system is a programmable well-synchronized network of different electronic devices, sensors, mechanical & digital machines and highly active communication network. IoT is used to provide assistance to visually challenged (blind) people and help to extend their self-reliance not only in the known environment but also unknown places would become easily accessible to them. In this preliminary approach, various possibilities to develop multisensory network based on 'Inter of Things' have been proposed. This approach not only targets technical complications associated with available technologies but also introduces inexpensive and user-friendly applications which can easily be employed to provide comfort. © 2018 IEEE.

World University root node

School of Electronics & Communication Engineering

Proceedings of the 2018 3rd IEEE International Conference on Research in Intelligent and Computing in Engineering, RICE 2018

Design and development of multisensory smart assistive technology for blind persons

The echocardiogram is the technique which is used in the diagnosis of most of the heart related diseases. Echocardiogram contains less information hence the diagnosis of the disease from the Echocardiogram videos is time consuming task. It required more human efforts to make a decision. Hence the automatic approach to detect the cardiovascular diseases with minimum computing time and high accuracy is necessary. In this approach, the automatic dilated cardiomyopathy (DCM) and Atrial Septal Defect (ASD) disease detection using machine learning approach has been proposed. The database contains the ultrasound videos of ASD, DCM and normal cases. The features extracted from the image and the classified the extracted features using supervised support vector machine algorithm. The proposed system achieved the accuracy of 98.30%. © 2018 IEEE.

Proceedings - 2018 4th International Conference on Computing, Communication Control and Automation, ICCUBEA 2018

Supervised Machine Learning Algorithm for Detection of Cardiac Disorders

Application of artificially intelligent methods for predictions is a fairly old area, although it is also the one in which there is always room for improvement in performance and in consistency, given the escalating nature of information and the varying efficacy of prediction logics. A hybrid of simple statistical methods coupled with intelligent computing (here artificial neural networks) is most likely to yield the closest prediction values with modest error rates. We propose to build an analytical and predictive model for estimating the stock market indices. This model can guide any kind of a user with or without experience in the stock market to make profitable investments. The forecasting done is by way of three statistical algorithms and an adaptive, intelligent algorithm, thus making the process fairly robust. Training and testing the neural network will be done with two-month stock market index values for some of the companies listed with the Bombay Stock Exchange. A comparative result of the four algorithms is calculated, and the one with best precision is suggested to the user with a sale/buy/hold answer. © Springer Nature Singapore Pte Ltd. 2018.

Advances in Intelligent Systems and Computing

Prediction of stock market indices by artificial neural networks using forecasting algorithms

We have used two methods for earth station power controller is designed by Cortex M3 by using LPC 1768 board and FPGA board. Different loads having different power requirements and different fault conditions occurred. Earth station having 800 W inverter and having two failures named hard failure and soft failure. We have used solar energy as a main source of earth station and diesel generator for failure conditions. Using neural network, we developed algorithm in embedded C as per different types of failure, battery voltage, and solar voltage. © 2018, Springer Nature Singapore Pte Ltd.

Lecture Notes in Networks and Systems

Controlling power system of earth station using embedded system

A flexible, easily configurable data acquisition system (DAS) is designed and built for the testing of engine. It is an Ethernet based data acquisition system. Primary requirements that necessitate the upgradation was the ability to easily configure, save, recall, and modify test configurations, The ability to configure the gain, channel name, and engineering units for each analog channel and to design and develop a DAS that was user friendly and could be operated easily by test cell operators. This is achieved by selecting and installing the appropriate data acquisition software and hardware at Test Cells. The hardware selection comprises the computer type, data acquisition hardware (Opto and Gantner), instrumentation (thermocouple, transducers etc.) and software selection focus on operating system and different application software. Raw data readings (Voltage, Current etc.) are made through data acquisition equipment and those are changed over to engineering units (weights, temperatures, speeds, PSI and so on) by software application to calculate various performance parameters of engine. This data is also checked for the range of acceptable limits (flag, abort, emergency stop) to trigger necessary alarm messages or buzzer where applicable. Test data are stored and archived for future analysis. The described system is intended to set up a fast operating and configurable DAS at low cost consisting of various sensors, acquisition hardware and software applications for acquisition and processing. This system gives improved methods of collecting, recording, and reducing test data for increased test cell utilization at engine test cell. This system provides better data, faster, and at a lower cost. © 2017 IEEE.

2017 International Conference on Computing, Communication, Control and Automation, ICCUBEA 2017

Data Acquisition System Upgradation at Engine Test Cell

Video Error Concealment is the error hiding technique in videos. In recent years, there is huge requirement of error concealment in video applications such as in video streaming, entertainment, advertisement, media, security, etc. The simulation on MATLAB for the error videos using Block matching algorithm (BMA) has been performed to achieve the concealed videos. From an error video, error frame is detected using Histogram and correlation. This frame is corrected using BMA. First step of BMA is to divide the current, frame, of a video into macroblock. Second step is to compare each of the macroblocks with a corresponding block and its adjacent neighbors in the frame or previous frame. Third step is to models the movement in a macroblock from one location to another. Last step is to calculate this movement for all the macro blocks that is comprising a frame. This error block is replaced by correct reference block. The quality of the error video and concealed video is measured using PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index Method). An improvement in quality is observed in concealed video. © 2018, Springer International Publishing AG.

Smart Innovation, Systems and Technologies

Implementation of video error concealment using block matching algorithm

Bending fatigue is one of the failure modes of spur gear tooth. Crack initiates at the root of a tooth on the active side. Crack due to bending fatigue results in tooth breakage. It may cause a sudden or catastrophic failure of spur gears. A major concern for design engineers is to prevent this type of failure, because it will cause accidents, casualties and financial losses. Many researchers have done excellent work on crack propagation in gears due to bending fatigue. Different crack growth models, based on the linear elastic fracture mechanics (LEFM), are used for crack propagation analysis and prediction of the service life of gears. The study in this paper includes the factorial design approach used for gear tooth bending fatigue crack propagation for specific gear configurations. Experiments are performed on the gears with optimized parameters for three different materials. Crack propagation life is estimated for these gears materials. Finite element (with Mechanical APDL 17.0) analysis for crack propagation is also done on optimized gear with SAE8620 materials.

School of Mechanical Engineering

International Review of Mechanical Engineering

Experimental and Analytical Analysis of Crack Propagation on Spur Gear Due to Bending Fatigue and Service Life Estimation

In the present study, a novel method of active vibration control of machines subject to base excitation using air spring is proposed. The study also includes proposes of a feedback control system, for active vibration control with mass flow rate as a function of the velocity of the mass, and/or base of air spring. Earlier study shows application of air spring as a force actuator. The mass flow introduces additional input excitation into the system by means of which the stiffness or resultant excitation force on the mass can be changed. A mathematical model is developed and response to steady state and transient base input are obtained. The use of appropriate value of gain in the system can ensure that the amplitude of response to excitation from the base is lower than the amplitude of base excitation. Some suitable values of gain depending on the special cases considered are suggested. An active control system with mass flow rate as a function of base velocity and gain equal to − 1 is most effective in controlling the vibration. This is highly desirable in case of high precision equipment. The interesting observation is that the spring rate can be changed and even made negative.

Journal of The Institution of Engineers (India) Series C

Active Vibration Control using Air Spring

The NUTRIC score (Nutrition Risk in the Critically Ill) is a method designed to calculate the risk of critically ill patients developing adverse conditions that is likely be improved by optimal nutritional therapy. The objective of this study is to validate the NUTRIC score for aggressive nutrition therapy and identify critically ill patients that are most likely to benefit from macronutrients with an aim to improving mortality rate. A logistic model is proposed to identify the relationship between the observed parameters of NUTRIC score like age, APACHE II score, SOFA score and number of co-morbidities using statistical methods and further authenticate it. The relationship between these parameters is often aggravated in the ICU due to the hypermetabolic nature of critical illness. This care can be extended by use of IoT devices which may be wearable for monitoring patients health. It will help not only to keep track patient's health, but to improve how the physicians deliver care as well. New technology innovations can be leveraged in nutritional health care context to monitor patients remotely. © 2018 Association for Computing Machinery.