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

Advancement in technology makes great efforts in health care system. Smart health monitoring will reduce effort of people by automating the patient monitoring task which improves patient work flow management. This study reviews enhancement in health care system. It focuses on portable, wearable and android technology based health monitoring system. This study reveals different approaches for health monitoring. © 2018 IEEE.

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Engineering and Technology

Proceedings of the 2nd International Conference on Intelligent Computing and Control Systems, ICICCS 2018

Study of Health Monitoring System

The most essential work in a communication system is reconstructing a clean speech from a noisy environment which is a challenging problem in speech processing. The performance of speech processing system could increase by utilizing an effective speech enhancement technique in case of mobile phone, hearing aids, etc. An innovative speech enhancement technique may help in increasing the accuracy of speech recognition methods. In this paper a new approach has been introduced which deals with the implementation of dual channel speech enhancement technique utilizes the coherence function computed from the dual input signals without prior noise statistics for noise reduction. For post-processing, discrete wavelet transform (DWT) is used as a gain function which is set up by SNR values using coherence function. The proposed algorithm is applied in an environment where interfering speakers are present and gives a better result than other algorithms when the speech is corrupted by different noise types. © 2018 IEEE.

Second International Conference on Intelligent Computing and Control Systems(ICICCS)

Dual Channel Coherence Based Speech Enhancement with Wavelet Denoising

Raga, one of the major pillars of Indian Classical music is arrangement of swaras in specific order and time. In western music a lot of research has been done on notes detection and scale identification. But being swar based, very less work is done in Indian classical music. We propose a raga identification system with different methods like Fourier transform, chromogram and spectrogram with database of few santoor samples and few songs in light music. Many variations in expressions and timings at the time of performance limit the accuracy of the system and we get reasonable accuracy for the raga identification. © 2019 IEEE.

2019 IEEE Pune Section International Conference, PuneCon 2019

Towards Raga Identification of Hindustani Classical Music

Clock gating is a prominent and an efficacious methodology adopted to decrease the dynamic power (clock power) utilization in complementary metal oxide semiconductor (CMOS) based circuits. The sole intent of gating a clock signal is to minimize its switching activity and thereby reduce significant amount of power utilization of the clock signal. Memories or storage elements are the integral part of the complex designs used in the modern day devices enabling storage of exhaustive and crucial values being processed. In this paper, we present the design and implementation of Random Access Memory (RAM) with reduced power consumption using clock gating technique on a Field Programmable Gate Array (FPGA). These memory elements can be either of synchronous or asynchronous nature. The memories discussed in the proposed work are synchronous in nature and hence reading and writing operations take place on the positive or rising edge of the clock. A gating logic is applied to lessen the superfluous transitions of the clock signal propagating along the clock network of the circuit. The target device (FPGA) for this work is Xilinx Spartan 6 and the design tool is Xilinx ISE 14.7 with the memories being modelled in Verilog HDL and simulation outputs shown in ISim. © 2019, Blue Eyes Intelligence Engineering and Sciences Publication. All rights reserved.

International Journal of Innovative Technology and Exploring Engineering

Implementation of low power memory on FPGA

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Object tracking and monitoring is used for detection purpose. This research is found since last two decades. However, the scope is there for further improvement. Cur-rent deep neural network model is found suitable in this area exclusively. We examine the problem of vehicle discov-ery by the deep learning method. In addition, we have shown that the use of deep features extracted from a pre-formed network produces a more efficient and ac-curate means of monitoring. Fast Recurrent CNN, the use of these systems becomes bottleneck process with relevance to CNN's operation. Fast R-CNN solves this downside by applying the projected mechanism to use the region and then CNN. An RPN is a fully convex network that predicts object boundaries and object scores at each location simultaneous-ly. © 2019 IEEE.

2019 International Conference on Applied Machine Learning (ICAML)

Application of Recurrent Convolution Neural Network for Vehicle Detection

Heavy vehicles develop technical snag and traffic jam on streets. Accidents between heavy vehicle and road users, for example, pedestrians often result in severe injuries of the weaker street users. The highway safety and traffic jams can be secured with detection of heavy and overloaded vehicles on the highway to facilitate light motor vehicles like cars, scooters. A model for heavy vehicle detection using fine-tuned based on deep learning is proposed to deal with entangled transportation scene. This model comprises two parts, vehicle detection model and vehicle fine-grained detection. This step provides data for the next classification model. Experiments show that vehicle’s make and model can be recognized from transportation images effectively by using our method. Experimental results demonstrate that the proposed detection system performs accurately with other simple and complex scenarios in detecting heavy vehicles in comparison with past vehicle detection systems. © Springer Nature Switzerland AG 2019.

Lecture Notes in Computational Vision and Biomechanics

Heavy vehicle detection using fine-tuned deep learning

Presently there is major interest in visual surveillance systems for crowd analysis. For preventing the problems caused by large crowd, proper control and management is necessary. In this paper a novel individual feature is introduced and used in combination with the holistic features, to describe crowd density. Various databases are analyzed to check the validity of the feature extraction process. In order to cluster the crowd frames according to congestion degree Support Vector Machine and Artificial Neural Network are used as classifiers. Results obtained shows that the proposed feature performs well in classifying real-world crowd scenes. © 2019 IEEE.

2019 International Conference on Innovative Trends and Advances in Engineering and Technology (ICITAET)

Feature based approach for Efficient Crowd Density Estimation

In India, the concept of smart city has evolved since lastfew years. Smart city includes smart electricity distributions,smart parking, smart lighting on streets, smart water distribution,smart drainage system, smart pipe gas system, smart trafficcontrol system etc. All smart systems listed need smart use oftechnical solution so that all systems will play critical role inmaking city as smart. As far as smart traffic control is concerned,there were few solutions suggested and implanted such as sensorwith CCTV, camera with IR sensor and tags etc. The technicalsolution may include software, hardware, communicationmodels, networking, usage of data and of-course data analytics.As large amount of data may be generated by theobjects/components involved in the system, it must be analyzedproperly. The data may be in structured or un-structured format.In this paper, smart traffic control system with efficient algorithmhas been proposed with data analytics to control traffic, whichcontrols the timing of the signal dynamically. At a junction, thereis need to control the traffic and signal timing such that air andnoise pollution also will be monitored and controlled. In thismodel, IoT system has been proposed with ultrasonic sensors tocontrol the traffic. The signal timing will be dynamicallymonitored and adjusted with traffic density within a region. Thiswill give solution to control, monitor the traffic at every signal ina city

Fulltext

IJEAT

Proposed Algorithm for Smart Traffic Control using Ultrasonic Sensors

The number of mobile users is increasing every day. Increased user cause extra stress on base trans-receiver station (BST) covering particular area, which result into extra power requirement of that antenna. Because of less energy, developing an intelligent energy management system for switch mode power supply (SMPS) is more important to use power efficiently. Objective is to distribute the energy to four different telecom operator effectively. Measure energy consumption by each of the telecom operator using that SMPS. Another objective is to monitor energy consumptions at remote location for each operator using Internet of the things (lOT). © 2018 IEEE.