A Smart Browsing System with Colour Image Enhancement for Surveillance Videos

Surveillance cameras have been widely installed in large cities to monitor and record human activities for different applications. Since surveillance cameras often record all events 24 hours/day, it necessarily takes huge workforce watching surveillance videos to search for specific targets, thus a system that helps the user quickly look for targets of interest is highly demanded. This paper proposes a smart surveillance video browsing system with colour image enhancement. The basic idea is to collect all of moving objects which carry the most significant information in surveillance videos to construct a corresponding compact video by tuning positions of these moving objects. The compact video rearranges the spatiotemporal coordinates of moving objects to enhance the compression, but the temporal relationships among moving objects are still kept. The compact video can preserve the essential activities involved in the original surveillance video. This paper presents the details of browsing system and the approach to producing the compact video from a source surveillance video. At the end we will get the compact video with high resolution. DOI: 10.17762/ijritcc2321-8169.15038

Fabric Fault Detection Using Digital Image Processing

This paper helps to detect the fault in fabric. For the good quality of fabric the inspection of fabric is very important .The faults in fabric causes poor quality in fabric. This may affects the economical growth of the Industry. The old methods which are used for fault detection such as Human Visual Inspection, Regular Band based Methodology, Gabor Wavelet Filter Methodology etc which are time consuming &stressful. So to reduce time and stress the new method introduced is Automatic Fabric fault inspection .Due to this method, at the time of manufacturing itself we get high quality fabric it implies the high speed of production.The detection of local fabric defects is one of the most problems in computer vision.For this problem the solution is that at the time of manufacturing fabric in textile the faults present on fabric are identified by MATLAB software using some Image Processing techniques. Image Processing is very helpful because all the techniques applied on the faulty image is useful to acquire fault free image

Zigbee Technology

This paper aims at presenting the concept of ZigBee, the name of a specification for a suite of high level communication protocols using small, low - power digital radios based on the IEEE802.15.4 - 2006 standard for wireless personal area networks (WPANs),such as wireless headphones connecting with cell phones via short - range radio. The technology is intended to be simpler and less expensive than other WPANs, such as Bluetooth. ZigBee is targeted at radio - frequency (RF) applications that require a low data rate, long battery life, and secure networking. The ZigBee communication is a communication technology to connect local wireless nodes and provides high stability an d transfer rate due to data communication with low power. In the nodes away from coordinator in one PAN, the signal strength is weak causing the net work a shortage of low performance and inefficient use of resources due to transferring delay and increasing delay time and thus cannot conduct seamless communication

Spin-charge and spin-orbital coupling effects on spin dynamics in ferromagnetic manganites

Correlation-induced spin-charge and spin-orbital coupling effects on spin dynamics in ferromagnetic manganites are calculated with realistic parameters in order to provide a quantitative comparison with experimental results for spin stiffness, magnon dispersion, magnon damping, anomalous zone-boundary magnon softening, and Curie temperature. The role of orbital degeneracy, orbital ordering, and orbital correlations on spin dynamics in different doping regimes is highlighted.Comment: 19 pages, 9 figure

Bioactive Components of Magical Velvet Beans

The plant Mucuna is an annual climbing shrub with long vines that can reach over fifteen meters in length. About 100–150 Mucuna species are found in the tropic and subtropic regions of both hemispheres of the earth. The genus Mucuna belongs to the family Leguminosae. It is commonly known as Kewanch, velvet bean, cowhage and kappikachhu and is found widely in India as a hardy, herbaceous, vigorous, twining annual plant. The size and dimension of the Mucuna seeds, pods, platelets and leaves change from species to species. The hair present on pods is anthelmintic, which causes itching. People are seeking great attention towards Mucuna due to its several medicinal properties, including L-DOPA (L-3, 4-dihydroxyphenylalanine) along with supplementary antioxidants that are used for treating Parkinson’s disease and many neurodegenerative diseases. Thus it is being used in about 200 medicinal formulations. The current chapter outlines the work that determines the influence of different nutritional, anti-nutritional and medicinal values and bioactive agents from different parts of the Mucuna species present in India and its importance in medicine

Metal Oxide-based Gas Sensor Array for the VOCs Analysis in Complex Mixtures using Machine Learning

Detection of Volatile Organic Compounds (VOCs) from the breath is becoming a viable route for the early detection of diseases non-invasively. This paper presents a sensor array with three metal oxide electrodes that can use machine learning methods to identify four distinct VOCs in a mixture. The metal oxide sensor array was subjected to various VOC concentrations, including ethanol, acetone, toluene and chloroform. The dataset obtained from individual gases and their mixtures were analyzed using multiple machine learning algorithms, such as Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree, Linear Regression, Logistic Regression, Naive Bayes, Linear Discriminant Analysis, Artificial Neural Network, and Support Vector Machine. KNN and RF have shown more than 99% accuracy in classifying different varying chemicals in the gas mixtures. In regression analysis, KNN has delivered the best results with R2 value of more than 0.99 and LOD of 0.012, 0.015, 0.014 and 0.025 PPM for predicting the concentrations of varying chemicals Acetone, Toluene, Ethanol, and Chloroform, respectively in complex mixtures. Therefore, it is demonstrated that the array utilizing the provided algorithms can classify and predict the concentrations of the four gases simultaneously for disease diagnosis and treatment monitoring