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

School of Electronics & Communication Engineering

Restoration of MR Images using a hybrid model consisting of two stages is implemented. Application of Fourth Order PDE and the application of relaxed median filter are pre and post stages respectively. The results were calculated using standard parameters like PSNR, SSIM and RMSE. It was found that the hybrid mm gives acceptable results when compared with standard non-linear filters when applied to T1 weighted Thorax Images. For Brain MR Images, the hybrid model filter performs better for a finite range of input noise level (% N ). The algorithm was implemented on synthetic images of T1 weighted Thorax and Brain MRI using MATLAB 2015a. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

World University root node

Engineering and Technology

Lecture Notes in Electrical Engineering

Restoration of Rician Corrupted MR Data Using Improved Hybrid Model

Localization technologies have their own challenges dependent on the applications and surrounding environment. Few additional applications and open challenges of IoT node localization in the smart world are introduced in this chapter. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

SpringerBriefs in Applied Sciences and Technology

Localization in Smart Applications

Traffic congestion due to the increasing number of vehicles is an alarming problem globally and aggravating day by day. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Node Localization for Smart Parking Systems

&nbsp;The crime rate is increasing at a high rate in India. Terrorist attacks like Mumbai 26/11, Pulwama attack, Pune German Beckary attacks have created terrific fear amongst Indian Society. Video analytics plays a significant role in detecting and predicting such suspicious&nbsp;human activities using deep learning models It will help in reducing the increasing crime rate by preventing treacherous actions.&nbsp;Video analytics&nbsp;analyzes the video content and adds brains to eyes i.e. analytics to the camera. It extracts contents from the video by monitoring the video in real-time. There is a huge advancement in human action recognitions and prediction-related research but still, state of art algorithms needs improvement in classifying it. The main objective here is to create a dataset for Indian scenarios for different crime actions like Fighting, Abuse, Arrest, Arson&nbsp; &amp; Burglary. It can then be used to train a deep learning model to detect and predict criminal actions reducing terrorism in India.

IEEE Dataport

CRIME VIDEO DATASET FOR INDIAN SCENARIOS

The hearing aid system is used for hearing impair patient communication, but the hearing aid's main issues are improper communication, power dissipation, delay, matching error, and low complexity. Many existing filter bank techniques are developed to overcome these issues but still have the problem of delay, unwanted noise, etc. This paper has designed a Tuned Feed forward Variable Bandwidth Filter (TFVBF) to enhance the Hearing Aid System (HAS) by reducing unwanted noise, matching error, and complexity. Moreover, the developed technique is implemented in MATLAB and it achieve low rate in power consumption and delay. Thus, the gained performance metrics has enhanced the HAS's performance by reducing, unwanted noise, matching error, and complexity. Finally, the attained performance metrics of the developed technique using 50 bands are compared with existing replicas in terms of matching error, delay, power consumption, and complexity for checking the effectiveness of developed replica.&nbsp;

The International Journal of Intelligent Engineering and Systems

Design, Development and Analysis of Variable Bandwidth Filter Bank for Enhancing the Performance of Hearing Aid System

With an increase in defined carrier frequency for the 5G new radio system, the need for synchronization also increases. The transceiver loses synchronization due to the occurrence of timing and carrier frequency offsets. Carrier frequency offsets often occur due to mismatch between transmitter and receiver oscillator frequency as well as the occurrence of doppler shifts due to transmitter/receiver movements. When frequency offsets exceed subcarrier spacing, integer frequency offset occurs that results in performance loss due to subcarrier indices shifts. Conventional approach i.e. maximum likelihood and sequential method is already employed to estimate integer frequency offset and to detect sector id. In this paper, the deep learning-based method is demonstrated to estimate the integer frequency offset and sector id detection. The neural network containing multiple convolution layers with activation layers is used to find the optimum received signal. Then, by calculating the number of cyclic shifts in the optimum received signal, the integer frequency offset is estimated. Using the corrected optimum received signal, the primary synchronization signal is also detected that gives sector id. This proposed estimator is tested for different profiles of tapped delay line models with different desired delay spread and compared with conventional methods i.e. maximum likelihood estimation method and sequential estimation method. Simulation results show that the proposed Neural Network based estimator outperforms in all delay profiles.

International Journal of Intelligent Engineering and Systems

A Neural Network based Integer Frequency Offset Estimation and PSS Detection in 5G NR Systems

The brain tumor is among the most hazardous and destructive diseases. The mortality rate in brain cancer is more at a later stage. Also, the brain tumor's misdiagnosis will produce danger and reduce the patient's chances of survival. The early diagnosis of a brain tumor aids in saving the life of the affected person by providing the right treatment. The computer-aided medical imaging techniques like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) aids in diagnosing the disease. Hence brain MRI classification became an active research area in recent years. Numerous methods have been presented in the earlier period for MRI categorization, right from classical methods to advance Deep Learning (DL) algorithms such as Convolutional Neural Network (CNN). The conventional Machine Learning (ML) techniques required handcrafted features while CNN performs classification by drawing features from unprocessed images directly via the convolution and pooling layer's parameter tuning. The feature extraction using the CNN algorithm is mostly influenced by the size of the training process's images. CNN models overfit after some epoch if the training dataset size is small. Therefore, transfer learning techniques have evolved. In the proposed system, conduct five studies using five transfer learning architectures such as AlexNet, Vgg16, ResNet18, ResNet50, and GoogLeNet to classify the clinical dataset of brain MRI into benign and malignant. Data augmentation techniques are applied over the brain MRI to generalize the results and reduce overfitting possibilities. In this proposed system, fine-tuned AlexNet architecture achieved the highest precision, recall, and f-measure value of 0.937, 1, and 0.96774, respectively

Journal of Engineering Science and Technology (JESTEC)

COMPARATIVE ANALYSIS OF PERFORMANCE OF DEEP CNN BASED FRAMEWORK FOR BRAIN MRI CLASSIFICATION USING TRANSFER LEARNING

Smart Campus is approach that combines the modern technology with the human innovation in order to use resources efficiently. There are many different individual smart solutions which altogether makes smart campus with some approaches, in which the main approach is the smart parking. Locating parking space in campus, especially during the peak hours, is clumsy for drivers. We have developed a prototype to ease the problem of parking in the campus. This project comprises of an on-site placement of Internet of Things (IOT) module that is used to look after and provide the status of availability of every single parking spot which helps the user to locate the closest parking spot and gives information of available parking spots in that respective parking lot. This system uses functional sensors in parking area which are used to get the real time status of the parking using the deployed active data processing units. To get the real time available slots we have used the Google firebase real-time database. The Django web framework is used to create the web application so that user can get the information of the free available parking slot in the campus using Wi-Fi or Internet. Proposed approach mostly focuses on controlling the time in searching the parking spots and it also avoids the additional travelling through full parking spots in a parking area.

International Research Journal of Engineering and Technology

Smart Campus Parking Using IOT

Human actions consist of a sequence of similar patterns which are difficult to classify using traditional image processing algorithms. Video Analytics is a major research area that adds brains to eyes which means analytics to the camera. It monitors the video contents and extracts intelligent information from it. The human action analysis and its detection is a challenging task. The proposed method focuses on detections of normal human activity using Long-Short Term Memory (LSTM) as a deep neural architecture. The preprocessing technique of redundant frame detection along with pretrained Convolutional Neural Network (CNN) is implemented for classifying the activities efficiently. Transfer learning approach is used followed by Long-Short Term Memory (LSTM) network to generate hybrid framework which further enhances the activity detection. Proposed method shows improvement in accuracy as compared to reference method. This method can be further implemented for on edge processing in embedded platforms for real time applications.

International Journal of Computing and Digital Systems

Intelligent video analytics for human action detection: a deep learning approach with transfer learning

Identification of the crop is an important aspect for preventing the losses of the agricultural product. The study of crop diseases means studying the visually observable patterns observed on the crop. Disease detection on crops is very unfavourable for sustainable agriculture. It is very difficult to monitor the crop diseases manually. It requires an enormous amount of work, professionals in the crop diseases, and requires extreme processing time. Hence, image processing is used for the detection of crop diseases. The proposed system involves five important steps: a) image capturing, b) image pre-processing, c) image segmentation, d) feature extraction and e) classification. Finally, features are trained with the machine learning algorithm and tested on unknown images. Results are being calculated based on qualitative and quantitative analysis. In qualitative analysis, pre-processing and segmentation methods are operated on the whole data set. In quantitative analysis, the accuracy of all machine learning algorithms is computed. This paper discussed the methods used for the detection of crop diseases using their leaf image. This paper also discussed some segmentation and feature extraction algorithms used in crop disease detection. The classification is done using Gradient Boosting Classifiers with the highest accuracy.