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

In today's scenario, congestion has become a major issue. Mainly the urban cities hit the hardest and its ever- increasing nature, real time knowledge of automobile road traffic density is essential for improved control of traffic signals and efficient management of traffic. The factors that contribute to traffic congestion are inadequate capacity, constrained demand, significant delays at red lights, etc. Capacity constraints and demand constraints are somehow related, but the delay for each signal is hard coded and traffic independent. So, to better meet this growing demand, the traffic control needs to be simulated and optimized. In recent times, image processing and surveillance systems are established in passenger information, access measurement and traffic control for real- time updates. Estimation of traffic bulkiness can also be performed using image processing. Tracing images of the moving automobiles can provide a quantitative explanation of traffic flow. This paper demonstrates how to utilize real-time live video feeds from cameras at intersections to perform instantaneous traffic bulkiness calculations using image processing and vehicle detection using EfficientDet architecture and TensorFlow lite. It also aims to reduce traffic jams and accidents by focusing on algorithms that switch signal lights on the basis of density of vehicles on the road and the priority set for specific emergency vehicles. In turn, it provides people with safe transportation, reduces fuel consumption and waiting times. Vehicle recognition is done by the system from images and not by using electronic sensors mounted on the roadway. Installation of camera is done next to the traffic light, which captures the video feed sent to the Raspberry Pi. The proposed algorithm aids to manage the timing of the traffic lights which in turn helps to achieve the goal of automatic control of traffic situations. © 2023 The Author(s)

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School of Electronics & Communication Engineering

Engineering and Technology

e-Prime - Advances in Electrical Engineering, Electronics and Energy

Real-time traffic control and monitoring

Potholes are a threat on roads, and their presence compromises driver, vehicle, and pedestrian safety. In developing countries, the primary reason for road accidents is bad road conditions, resulting in human life and property loss. In countries like India, Road maintenance is a challenging activity. Accidents rates are increasing year by year due to the up-surging potholes count. This paper presents the system as ``Forward View Guidance for pothole detection for Indian passenger Car.{''} The camera captures video images, and a deep learning algorithm is used to classify the images as potholes and regular roads. The camera will also provide a view of the vehicle's front, highlighting the pothole. Deep learning YOLOv3 and YOLOv5 algorithms are used to train the model and tested with the Kaggle pothole detection datasets to predict the model's accuracy for detection. The proposed system will help monitor the road's condition, count the number of potholes on the road, and generate an alert signal. The performance of the proposed system is evaluated using precision, recall, and average precision (AP). The experimentation results show that the YOLOv5 algorithm performs best than the YOLOv3 algorithm. The precision, recall, and average precision (AP) for YOLOv5 are obtained as 0.763, 0.548, and 0.635, respectively. The system algorithm is implemented on the Raspberry Pi 4B model, which can be easily fitted as an addon system in the vehicle.

INTERNATIONAL JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS RESEARCH

Smart Pothole Detection System using Deep Learning Algorithms

Transportation services for safe travel are critical to a country's growth. The dangerous pavement surface is caused by a variety of asphalt pavement distresses. Potholes are the primary cause of pavement distress. Many methods for detecting and correcting potholes in asphalt pavement have been presented in this paper. Potholes are essential indicators of structural difficulties in the asphalt road, and recognition of the potholes is a significant issue for smooth and safe driving. On the other hand, manually discovering and evaluating strategies is expensive and time-consuming. Consequently, several efforts have been made to develop technology for the detection and recognition of potholes automatically, which may reduce vehicle damage, driver stress with a smaller number of accidents. In this review paper, we explored and reviewed the existing pothole detection systems based on sensor, vision, machine learning, and deep learning algorithms. The systems based on equipment and methods are presented in this paper. © Grenze Scientific Society, 2022.

13th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2022

Survey on Pothole Detection Methods

Health monitoring and technologies related to health monitoring is an appealing area of research. The electrocardiogram (ECG) has constantly been the mainstream estimation plan to evaluate and analyse cardiovascular diseases. Heart health is important for everyone. Heart needs to be monitored regularly and early warning can prevent the permanent heart damage. Also, heart diseases are the leading cause of deaths worldwide. Hence, this work presents a design of a mini wearable ECG system and is interfacing with the Android application. This framework is created to show and analyse the ECG signal obtained from the ECG wearable system. The ECG signals will be shipped off an Android application via Bluetooth device and Wi-Fi. This system will automatically alert the user through notification. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes on Data Engineering and Communications Technologies

ECG Acquisition Analysis on Smartphone Through Bluetooth and Wireless Communication

Electric Vehicles are becoming the future of the automotive industry and battery is the main component of Electric vehicles. The life span of the electric vehicle mainly depends on the performance of the battery which can be increased by the proper design of the battery management system (BMS). One of the factors that affect the battery performance and life span of an electric vehicle is the driving style. So, the supporting system to the BMS is developed that considers the effect of driving style on the performance of the battery. The system is implemented using MATLAB/Simulink tool which takes different driving cycles as input and obtained state of charge (SoC) as the output parameter. The main focus is to analyze the battery performance and its effect based on the different driving styles. The parameters like acceleration and deceleration account to determine the driving style like very aggressive, aggressive, and gentle. Finally based on battery capacity, the state of charge (SoC) is calculated for a different driving cycle. These observations are stored and analyzed to generate reports. These reports would be used to avoid the driving styles that are resulting in a decrease in battery performance and thus increases life span. © 2020 IEEE.

2020 IEEE Pune Section International Conference (PuneCon)

Impact of Driving Style on Battery Life of the Electric Vehicle

The disturbed electrical activity of the heart which leads to Atrial Fibrillation (AF) poses a threat to the health and a lot of people in the country are suffering because of this. Its timely detection and treatment can increase the longevity of patients with AF. The proposed, Smartphone based inventive method of detection of AF utilizes Photoplythosmography (PPG) signal obtained from the fingertip using the camera in a smartphone. It differentiates Normal sinus rhythm (NSR) from AF, Premature Ventricular contraction (PVC) and Premature Atrial Contractions (PAC), with the combination of statistical methods like Root mean square of successive differences (RMSSD), in combination with Shannon Entropy (ShE) and Turning point ratio (TPR) method. The reformulated TPR is used along with pulse rise and fall times to increase the accuracy of detection. The acquired results show that this method can detect NSR with sensitivity of 0.9155, and gives overall accuracy of classification of 92% © 2018 IEEE.

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

Discrimination of Arrhythmia Using a Smartphone

Cardiac event detection is one of the essential steps in cardiac signal processing, analysis and disease diagnosis. Complete morphology of cardiac waves (P–QRS–T) extracted from the location of R-peak is helpful for feature extraction of many applications related to cardiac diseases classification. Therefore cardiac event detection is a prerequisite for reliable cardiac disease diagnosis, and hence it should be robust and time-efficient so that it can be used for real-time signal processing. This work proposes a novel method for R-peak detection using curvelet transform (CT). It demonstrates the use of curvelet energy with an adaptive threshold to estimate the boundaries around R-peak. The exact R-peak locations are then detected from the input signal with the predefined estimated boundaries. The proposed method is evaluated and analysed with all 48 records from the MIT-BIH arrhythmia database. The experimental analysis result yields an average sensitivity of 99.62%, average positive productivity of 99.74% and average detection error rate of 0.6%. The results obtained have higher than or comparable indices to those in literature. Thus, the proposed system yields high accuracy, low complexity and high processing speed.

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Cardiac events detection using curvelet transform

Heart disease is canopy term for any disorder that distress health of an individual. There are heart diseases like Ischemic heart disease, arrhythmia etc. These diseases can be classified using Electrocardiography(ECG) signal. ECG is preferred to the display condition of the patient and for the diagnosis and treatment of various types of cardiac diseases. The variations in P-wave, QRS complex and T-wave parameters in ECG are used to identify the type of illness of the human heart. some heart diseases are not served though other may prove fatal. Thus, there is need of classification of different heart diseases. To classify various heart diseases, it is intended to implement CNN algorithm in the proposed work. © 2017 IEEE.

2017 2nd International Conference on Communication and Electronics Systems (ICCES)

Heart diseases classification using convolutional neural network

Electrocardiogram (ECG) is a non-stationary signal which constitutes a lineal recording that provides an insight into heart's electrical activity. Because Cardio Vascular diseases (CVD) are numbered one as reason of mortality globally, detection of abnormalities in ECG at an early stage is crucial for diagnosis and accordingly the treatment. A system can help the cardiologists in diagnosing the arrhythmia present in a patient. The proposed architecture uses Adaptive Neuro-Fuzzy Inference System (ANFIS) with the input preprocessed with subtractive clustering method to learn fuzzy logic. Five morphological and five statistical ECG features are utilized to determine if the patient's heartbeats are normal or irregular and classify them accordingly. During statistical feature extraction, Principal Component Analysis (PCA) on detail coefficients is implemented for optimization. For classification, four classes of ECG are considered, Left Bundle Branch Block (LBBB), normal, Atrial Premature Contraction (APC) and Paced Beats (PB). The proposed system gives an overall classification accuracy of 97.75%. The overall sensitivity, average specificity and average false prediction ratio obtained are 0.9775, 0.9925 and 0.0075 respectively. © 2017 IEEE.

2017 3rd International Conference on Advances in Computing, Communication &amp; Automation (ICACCA)(Fall)

Arrhythmia classification using neuro fuzzy approach

Biometric technologies are the automated procedures for validating and identifying the originality of an individual. The biometric systems for automatic authentication are fingerprint, iris, retina, voice, hand geometry recognition. Nowadays, the retina recognition has received an importance, as the unique characteristics of the retina. In the acquisition method, the retinal images might experience some deformations like a change in background intensity, rotation and scale etc. These troubles may disturb the feature extraction process, as a result of the identification process. The Optical Disc Ring (ODR) is used as a preprocessing method. Subsequently, the Speeded Up Robust Feature (SURF) is employed. It is fast as well as vigorous. Oriented FAST and Rotated and BRIEF (ORB) is also used as a feature extraction process. ORB is efficient, unaffected by noise and rotation invariant. The true acceptance rate for SURF and ORB is 98.148 percent. © 2017 IEEE.

2017 International conference on Microelectronic Devices, Circuits and Systems (ICMDCS)

Retina based biometric identification using SURF and ORB feature descriptors

ECG(Electrocardiogram) signals get corrupted due to different noises and artifacts such as power line interference, baseline wander, motion artifacts, contact noise that hide lot of required information. This information is required for detecting various cardiac diseases. Hence before processing ECG signal denoising plays important role to obtain significant features of ECG signal. For denoising, Independent Component Analysis (ICA) is applied on contaminated ECG signal. ICA is a blind source separation technique used to find the independent source signal from non-gaussian noisy signal. The ICA technique deals with maximization of non-gaussian source signal using higher order parameters such as kurtosis and Negentropy. The process of maximization is based on the central limit theorem. ICA can be used for ECG denoising only because ECG signal has super Gaussian shape. This technique is capable to remove noise and artifacts even though they have same frequency as original signal. The FastICA with different granularity functions like tanh, power 3 isolates noise and original signal using kurtosis values of separated signal. Analysis of FastICA shows tanh granularity has faster convergence as compared to power 3 granularity and SNR ratio is of 7db. © 2016 IEEE.

2016 Conference on Advances in Signal Processing (CASP)

Noise analysis of ECG signal using fast ICA

Soft computing techniques have emerged as a highly synergistic, computationally appealing, and conceptually unified framework supporting intelligent system design and analysis. The key contributing technologies of soft computing are neural network computing, fuzzy inference system, genetic algorithm or fusion of these techniques. This work proposes a method of analyzing cardiac signal (ECG) to diagnose cardiac events using adaptive neuro fuzzy inference system (ANFIS) with scaled conjugate gradient (SCG). The wavelet energy gradient algorithm is used for delineation and extraction of QRS complex from ECG signal. Seven morphological features and three statistical features are extracted from QRS complex. Optimization of the feature set is done using K-means clustering. These optimized features are used for training the ANFIS network. The experimentation is carried out for six class classifications, analysis of classifier results into average sensitivity of 95.5%, specificity of 99.13%, positive productivity of 95.52% and classification rate 98.51% The proposed methods results are compared with traditional ANFIS method with subtractive clustering, and it is found that there is a significant improvement in performance parameters of adaptive neuro-fuzzy inference system. © 2015 IEEE.

2015 IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)

A multiclass cardiac events classifier using clustering and modified adaptive neuro-fuzzy inference system

The Electrocardiogram (ECG) is most widely used techniques to detect cardiac diseases. In this paper we propose ECG signal analysis and classification method using wavelet energy histogram method and support vector machine (SVM). The classification of cardiac arrhythmia in the ECG signal consists of three stages including ECG signal preprocessing, feature extraction and heartbeats classification. The discrete wavelet transform is used as preprocessing tool for signal denoising and feature extraction such as R point location, QRS complex detection. Morphological features extracted from the QRS complex are employed as input to the classifier. Binary SVM is used as a classifier to classify the input ECG beat into four classes i.e. Normal, Left bundle branch block, Right bundle branch block and Premature ventricular contraction. MIT-BIH arrhythmia database is used for performance analysis. The proposed classifier performs well with an average sensitivity of 100%, specificity of 99.66%, positive prediction of 99%, false prediction of 0.0033, and average classification rate of 99.75%. © 2015 IEEE.