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

School of Electronics & Communication Engineering

Speech coding is an application of data compression of digital audio signals containing speech. Speech coding uses speech specific parameter estimation using audio signal processing techniques to model the speech signal combined with generic data compression algorithms to represent the resulting model parameters into a compatible string. The most important applications of speech coding are mobile telephony and voice over Internet Protocol. In this paper a modified Code Excited Linear Prediction technique is proposed and is compared with Linear Predictive Coding, Voice Excited Linear Predictive Coding and Code Excited Linear Predictive Coding. From the results it can be concluded that modified Code Excited Linear Prediction technique is better than its counterparts.

Engineering and Technology

IJARESM

An enhanced CELP Speech Coding Technique

Facilitating the Detection of ASD in Ultrasound Video using RHOOF and SVM

International Journal of Advanced Computer Science and Applications

The automobile and transport industries have always tried to reduce down-time by the way of preventive maintenance. In the past decade, electromechanical sensors have become more accurate along with novel innovations in IoT and Machine Learning, and automobiles have leveraged this. In this paper, an end to end open source predictive maintenance solution is presented to predict the severity of faults in a car using onboard historical and real-time sensor data using IoT and Machine Learning. Sensor data is collected from a Suzuki Swift VXi Model, and classifiers like Logistic Regression, Random Forest , and Gradient Boosting trees are used to train the data with imputed faults. F1 score and AUC are used as evaluation metrics. An end-to-end On-Board Diagnostics (OBD) data to the user dashboard pipeline is proposed with final predicted faults visible on a real-time dashboard.

World University root node

Maintenance of Automobiles by predicting System Fault Severity using Machine Learning

The purpose of this paper is toreview Motion Amplification Techniques in video sequences. Various computational techniques are used to visualize, analyze and efficiently represent imperceptible variation in image and video sequences. These computational methods which are used reveal temporal changes in videos which are not visible to the naked eyes. In these techniques standard video sequence is used as an input on which spatial decomposition is applied then temporal filtering is applied. The filtered output is then amplified to expose the invisible content in the input video. This paper contains four techniques which are used for enhancement of temporal variations in the video: 1. Linear Approximation Method,Eulerian Video Magnification (EVM), 2. Phase Based Video Processing 3. Fast Phase Based Video Processing (Riesz Pyramid) and 4. Enhanced Eulerian Video Magnification (EEVM). Using these techniques, it is possible to magnify and see tiny motions and subtle color variations.After review it is observed that, the noise level in EVM technique is more which reduced in EEVM technique using motion analysis and image warping. The other two phase based techniques supports larger amplification factor and better noise performance.

International Journal of Future Generation Communication and Networking

Comparative Study of Motion Amplification Techniques for Video Sequences

The purpose of this paper is to review Motion Amplification Techniques in video sequences. Various computational techniques are used to visualize, analyze and efficiently represent imperceptible variation in image and video sequences. These computational methods which are used reveal temporal changes in videos which are not visible to the naked eyes. In these techniques standard video sequence is used as an input on which spatial decomposition is applied then temporal filtering is applied. The filtered output is then amplified to expose the invisible content in the input video. This paper contains four techniques which are used for enhancement of temporal variations in the video: 1. Linear Approximation Method, Eulerian Video Magnification (EVM), 2. Phase Based Video Processing 3. Fast Phase Based Video Processing (Riesz Pyramid) and 4. Enhanced Eulerian Video Magnification (EEVM). Using these techniques, it is possible to magnify and see tiny motions and subtle color variations. After review it is observed that, the noise level in EVM technique is more which reduced in EEVM technique using motion analysis and image warping. The other two phase based techniques supports larger amplification factor and better noise performance.

IJFGCN

<a href="http://sersc.org/journals/index.php/IJFGCN/article/view/29174">Comparative Study of Motion Amplification Techniques for Video Sequences</a>

Remote sensing image scene classification has a wide scope of applications and hence has been receiving remarkable attention. Nowadays, Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing image analysis over the past few years. This paper first provides a comprehensive review of the recent progress in remote sensing applications. The existing remote-sensing classiﬁcation methods are categorized into four main categories according to the features they use: manually feature-based methods, unsupervised feature learning methods, supervised feature learning methods, and object based methods[15]. This article then focuses on evaluating the available and public remote-sensing datasets. Finally, a conclusion regarding the current state of art methods and directions for future research are presented.

Review on Deep Learning in Remote Sensing Image Classification

Remote sensing image scene classification has a wide scope of applications and hence has been receiving remarkable attention. Nowadays, Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing image analysis over the past few years. This paper first provides a comprehensive review of the recent progress in remote sensing applications. The existing remote-sensing classification methods are categorized into four main categories according to the features they use: manually feature based methods, unsupervised feature learning methods, supervised feature learning methods, and object based methods[15]. This article then focuses on evaluating the available and public remote-sensing datasets. Finally, a conclusion regarding the current state of art methods and directions for future research are presented.

Review on Deep Learning in Remote Sensing Image Classification

Critical Research o Tracking of Blood Movement in the Heart Using Elongated Horizontal Large Diamond Search Pattern and Optical Flow Technique for enabling the detection of Atrial Septal Defect

Recent Developments in Engineering Research

This project includes the designing of 16-Bit RISC processor and modeling of its components using Verilog HDL. The processor is based on Harvard architecture. The instruction set adopted here is extremely simple that gives an insight into the kind of hardware which should be able to execute the set of instructions properly. Along with sequential and combinational building blocks of a processor such as adders and registers more complex blocks such as ALU and memories have been designed and simulated. The modeling of ALU which has been done in this project is fully structural starting from half adders. At the end the semi-custom layout has been developed for ALU. Complex blocks such as memories have been modeled using behavioral approach, whereas simple blocks such as adders have been done through structural approach. The tools which have been used throughout the project work are ModelSim and Intel Quartus prime 18.1. For synthesis purpose the targeted FPGA device technology is ALTERA.

International Journal of Creative Research Thoughts

Design and Implementation of 16 bit RISC Processor on FPGA

Automobiles are perhaps the domain that represents the 21st century. The vehicles play a major role in our day to day life for transportation. The term Cruise Control refers to the concept of assisting drivers in the task of longitudinal vehicle control to avoid any accident or collision. With the increase in the world population the need for automobiles as well as its usage daily has increased drastically. This leads to heavy traffic, rush, collisions, and accidents. The automobile black box is used to analyze the cause of vehicular accidents and prevent a loss of life and property from vehicle accidents. This Paper is based on the Project topic of Adaptive Cruise Control. In our proposed system, we tried to implement ACC using the less expensive devices, also increasing the ease to use it and adding a few other features in it. The project aims to build a Semi-Autonomous system; that assists the driver for the longitudinal control on their vehicle while driving by using the ultrasonic sensor (HCSR_04). An alert (led or buzzer) indication is given when the inter-vehicle gap approaches the safety limit. A switch is provided to ensure the semi-autonomous use of the vehicle. Also, it aims to use IoT to keep track of the distance readings, speed readings, and stability of the vehicle using the NodeMCU and ThnigSpeak platform. This system also uses the GPS (Neo-6m) and GSM (SIM 800A) to send a text message to a pre-defined number in case of detection of an accident.