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;

&nbsp;

MIT World Peace University

Engineering and Technology

World University root node

School of Computer Engineering & Technology

A Survey of Comparative Analysis of Secure Passwords using CaRP by Different Techniques

Super resolution technique gives an effective way to increase image resolution. Lower resolution is converted into higher resolution. By proposed super resolution algorithm, image is resolved sixteen times. This paper presents a FPGA (Field Programmable Gate Array) implementation of super resolution algorithm. FPGA is used because of its various advantages. In super resolution image size is increased by adopting information from input image itself. Using this algorithm image can be resolved up-to 2x, 4x, 8x and 16x.

School of Electronics & Communication Engineering

2015 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2015

Implementation of super-resolution algorithm on FPGA

Powerful automatic speech recognition system (ASR)is matter of commercial importance as many leading companies are sprinting at industry and consumer level production. One of the major reasons for speech quality to hamper is environmental noise. Speech gets obscured by the loud background sound. This adversely affects the performance of automatic speech recognition system. We also know that human auditory system is comparatively more capable of managing noise than the machine. So as to improve the performance of ASR, auditory properties of human system is studied and modeled with the help of psychoacoustic filter. The filter is labeled as 2D P-filter as its parameter has values zero or positive. Also to remove noise, masking effect is implemented where the sounds falling under predetermined masking threshold are modified. Therefore the enhanced set of features are extracted by applying this filter to the Mel filter bank. The novelty of the paper is use of different distance metrics for classification and testing the performance of Automatic speech recognition system. Experiments are carried out on database of recording of rhyming words by articulatory disabled children in a studio. Expected results obtained after testing phase for noisy speech signals would be considerably improved.

Conference on Advances in Signal Processing, CASP 2016

2-D psychoacoustic modeling for automatic speech recognition in noisy environment

A smart antenna is actually a fusion of an array that changes individual antenna elements patterns in response of a dedicated signal to improve performance. Moving one step beyond of beam forming is adaptive algorithm which allows for the computation of continuously amend weights. The process of adaptation must satisfy a specified optimization principle. In this project two Adaptive algorithm Least Mean Square and Recursive Least Mean Square are implemented to revamp beam. Added optimization of beam is accomplished with the help of genetic algorithm which makes beam more optimize. This optimization method is used for the synthesis of antenna array radiation pattern in adaptive beam forming. The optimum value of weights of each antenna element is determined which produces a radiation pattern with minimum side lobe level. Analysis is being carried out with varying different element and space in between element to obtain the minimum weight with the help of genetic algorithm. This improves the performance to achieve the maximum reduction in side lobe level (of the order -20dB) with minimum function calls.

2016 International Conference on Internet of Things and Applications (IOTA)

Recursive least mean square algorithm for linear antenna array optimization for beamforming using genetic algorithm

In this paper, Motion Estimation (ME) and Motion Compensation (MC) techniques are used for video compression which eliminates the temporal redundancy of video sequence. For motion estimation, Block Matching (BM) algorithm has been used among the various motion estimation algorithms because of its simplicity and efficiency. In this paper, the performance of block matching algorithm such as Four Step Search (4SS) has been improved using Block Transform Scaling (BTS). In BTS all the varying nature frames of video transform into uniform frames using adaptive filter, which fills the pixel information where pixel value is missing within the frame. The proposed work mainly focuses on the compression of videos which have medical information in it. At the end of this paper the result analysis has been done using the parameters such as Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), Computation Time (CT) and Compression Ratio (CR). © 2015 IEEE.

Proceedings of the 2015 International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2015

Improved motion estimation using block transform scaling technique for video compression

Cloud computing that is based on Infrastructure as a Services model, allows the system administrators to identify resources or services to deploy their applications. Selection of the most appropriate provider for a particular application is a difficult task as there are huge numbers of services offered by considerable number of providers that are not directly comparable and have similar functionality. Additionally these systems must comply with non-functional requirements as they evolve. Certain traditional techniques are employed to verify the non-functional properties of critical systems at design time. In this paper, a new framework is suggested that supports the process of deploying applications in cloud providers, using a comparison of high level requirements predominantly non-functional requirements (NFR). Experiments have already been conducted with conclusive results wherein matching of user constraints during service selection process is done. The experiment uses Aspect Oriented Paradigm (AOP), XML and graph transformation. The relevance of the framework is illustrated using the Remote Patient Monitoring (RPM) scenario. The results demonstrate that the model considerably improves the process by identifying conflicting NFR. Based on this work done, the next logical step is to extend it to Cloud Computing. © 2016 IEEE.

Proceedings - 2016 5th IEEE International Conference on Cloud Networking, CloudNet 2016

Validation of non-functional requirements in cloud based systems (short paper)

Content-based image retrieval system nowadays use color histogram as a common color descriptor. We consider color as one of the important features during image representation process. Different transformations such as changing scale of image, rotating an image and translations of image to other forms does not make any alterations to the color content of image. If we need to focus on differentiation or similarity between two images we usually deal with various color features of image. To extract color features of image we consider on color space, color reduction, color feature extraction process. In image retrieval applications, user specifies desired image as query image and wants to search for the most similar image in database of his interest. Application then identifies similar relevant images from database based on different color features of database images and query image. To achieve this we compute color features of database images and those for query image. We use local color features of different regions and combine them to represent color histogram as a color feature. These color features are compared using Euclidean distance as a metric to define similarity between the query image and the database images. For calculations of local color histogram we divide image into different blocks of size 8 × 8 as fixed, so that for each block of image spatial color feature histogram of image is obtained. Our experimental work shows that local hybrid color histogram produced more accurate image retrieval results than global color moments color histogram.

Advances in Intelligent Systems and Computing

Local and global color histogram feature for color content-based image retrieval system

Due to robustness feature color of object has been proved as low level unique feature. If we change viewpoint looking at a color we note change in its appearance. Color image processing requires dealing with each and every pixel of an image. In this paper spatial color distribution features are implemented. We focus on its calculation and comparison of retrieval efficiency using histogram quadratic distance measure for HSV color Space. Our experiments show hybrid histograms produced better results.