Surface Water Quality Assessment of Panchagnaga River and Development of DO-BOD Relationship Using Empirical Approach

Surface water samples were collected from selected locations along river Panchaganga,from Kolhapur to Narsobawadi during April 2019.Physicochemical parameters were determined in the laboratory and chemical mass balance approach was adopted to estimate the individual ionic loads in the river water.Streeter-Phelps equation was applied to derive a relationship between DO and BOD_(5).Model parameters such as De-oxygenation Rate(K_(d))and Re-aeration Rates(K_(r))were optimized using different empirical methods.The result of chemical mass balance showed an increase in the loading of various ions from upstream to downstream which could be attributed to agricultural and industrial wastes that enter the main stream.De-oxygenation rate and re-aeration constants were calculated using various empirical methods.DO sag curve was developed using Streeter Phelp’s model and compared with the observed parameters which showed a significant correlation.DO-BOD concentration observed along the course of the river indicated that the self-purification capacity of the river is high due to which the river regains the lost DO level at a distance less than 50 meters.

An optimized design of seizure detection system using joint feature extraction of multichannel EEG signals

The detection of seizure onset and events using electroencephalogram(EEG) signals are important tasks in epilepsy research.The literature available on seizure detection has discussed the implementation of advanced signal processing algorithms using tools accessed over the cloud.However,seizure monitoring application needs near sensor processing due to privacy and latency issues.In this paper,a real time seizure detection system has been implemented using an embedded system.The proposed system is based on ensemble empirical mode decomposition(EEMD) and tunable-Q wavelet transform(TQWT) algorithms.The analysis and classification of non-stationary EEG signals require the wavelet transform with high Q-factor.However,direct use of TQWT increases the computational complexity of feature extraction from multivariate EEG signals.In this paper,the first step is to process the signal by using EEMD to obtain 8 intrinsic mode functions(IMFs).The Kraskov(KraEn),sample(SampEn),and permutation(PermEn) entropy features of IMFs are extracted and based on optimum values,and 4 IMFs are decomposed using TQWT.Secondly,centered correntropy(CenCorrEn) features of the 1^(st)and 16^(th) sub-band of TQWT have been used as classifier inputs.The performance of multilayer perceptron neural networks(MLPNN),least squares support vector machine(LSSVM),and random forest(RF) classifiers has been tested on the multichannel EEG data recorded from a local hospital.The RF classifier has produced the highest accuracy of 96.2% in classifying the signals.The proposed scheme has been employed in developing an embedded seizure detection system to assist neurologists in making seizure diagnostic decisions.

A Study on Automatic Latent Fingerprint Identification System

Latent fingerprints are the unintentional impressions found at the crime scenes and are considered crucial evidence in criminal identification.Law enforcement and forensic agencies have been using latent fingerprints as testimony in courts.However,since the latent fingerprints are accidentally leftover on different surfaces,the lifted prints look inferior.Therefore,a tremendous amount of research is being carried out in automatic latent fingerprint identification to improve the overall fingerprint recognition performance.As a result,there is an ever-growing demand to develop reliable and robust systems.In this regard,we present a comprehensive literature review of the existing methods utilized in latent fingerprint acquisition,segmentation,quality assessment,enhancement,feature extraction,and matching steps.Later,we provide insight into different benchmark latent datasets available to perform research in this area.Our study highlights various research challenges and gaps by performing detailed analysis on the existing state-of-the-art segmentation,enhancement,extraction,and matching approaches to strengthen the research.

Unmanned Drug Delivery Vehicle for COVID-19 Wards in Hospitals

The prime reason for proposing the work is designing and developing a low-cost guided wireless Unmanned Ground Vehicle(UGV)for use in hospitals for assistance in contactless drug delivery in COVID-19 wards.The Robot is designed as per the requirements and technical specifications required for the healthcare facility.After a detailed survey and tests of various mechanisms for steering and structure of UGV,the best mechanism preferred for steering articulated and for body structure is hexagonal as this approach provides decent performance and stability required to achieve the objective.The UGV has multiple sensors onboard,such as a Camera,GPS module,Hydrogen,and Carbon Gas sensor,Raindrop sensor,and an ultrasonic range finder on UGV for the end-user to understand the circumferential environment and status of UGV.The data and control options are displayed on any phone or computer present in the Wi-Fi zones only if the user login is validated.ESP-32 microcontroller is the prime component utilized to establish reliable wireless communication between the user and UGV.These days,the demand for robot vehicles in hospitals has increased rapidly due to pandemic outbreaks as using this makes a contactless delivery of the medicinal drug.These systems are designed specifically to assist humans in the current situation where life can be at risk for healthcare facilities.In addition,the robot vehicle is suitable for many other applications like supervision,sanitization,carrying medicines and medical equipment for delivery,delivery of food and used dishes,laundry,garbage,laboratory samples,and additional supply.

Analytical review and study on object detection techniques in the image

Object detection is the most fundamental but challenging issues in the field of computer vision.Object detection identifies the presence of various individual objects in an image.Great success is attained for object detection/recognition problems in the controlled environment,but still,the problem remains unsolved in the uncontrolled places,particularly,when the objects are placed in arbitrary poses in an occluded and cluttered environment.In the last few years,a lots of efforts are made by researchers to resolve this issue,because of its wide range of applications in computer vision tasks,like content-enabled image retrieval,event or activity recognition,scene understanding,and so on.This review provides a detailed survey of 50 research papers presenting the object detection techniques,like machine learning-based techniques,gradient-based techniques,Fast Region-based Convolutional Neural Network(Fast R-CNN)detector,and the foreground-based techniques.Here,the machine learning-based approaches are classified into deep learning-based approaches,random forest,Support Vector Machine(SVM),and so on.Moreover,the challenges faced by the existing techniques are explained in the gaps and issues section.The analysis based on the classification,toolset,datasets utilized,published year,and the performance metrics are discussed.The future dimension of the research is based on the gaps and issues identified from the existing research works.