Real time video pipeline for computer vision using embedded GPUs, A

2016 Fall.Includes bibliographical references.This thesis presents case study confirming the feasibility of real time Computer Vision applications on embedded GPUs. Applications that depend on video processing, such as security surveillance, can benefit from applying optimizations common in scientific computing. This thesis demonstrates the benefit of applying such optimizations to real time Computer Vision applications on embedded GPUs. The primary contribution of this thesis is an optimized implementation of ViBe targeting NVIDIA's Jetson TK1. ViBe is a commonly used background subtraction algorithm. Optimizing a background subtraction algorithm accelerates the task of reducing the field of view to only interesting patches of the frames of the video. Placing portable hardware close to capturing devices in the surveillance system reduces bandwidth requirements and cost. The goals of the optimizations proposed for this algorithm are to 1) reduce memory traffic 2) overlap CPU and GPU usage 3) reduce kernel overhead. The optimized implementation of ViBe achieves a frame rate of almost 55 FPS beating the real time goal standard of 30 FPS for real time video. This is a small portion of the real-time window leaving processing time for additional algorithms like object recognition

Priority selection of agro-meteorological parameters for integrated plant diseases management through analytical hierarchy process

To understand the influence of agro-meteorological parameters to take decisions related to various factors in an integrated plant disease management, it becomes vital to carry out scientific studies on the factors affecting it. The different agro-meteorological parameters namely temperature, humidity, moisture, rain, phenological week, cropping season, soil type, location, precipitation, heat index, and cloud coverage have been considered for this study. Each parameter has been allocated the ranking by using a technique called analytical hierarchical process (AHP). The parameter priorities are determined by calculating the Eigenvalues. This helps to make decisions related to integrated plant disease management where the prediction of plant disease occurrence, yield prediction, irrigation requirements, and fertilization recommendations can be taken. To take these decisions which parameters are good indicators can be identified using this method. The parameters majorly contribute to plant diseases and pest management decision making while delivers minor contribution in irrigation and fertilizer management related decision making. The manual results are compared with software generated results which indicates that both the results correlate with each other. Therefore, AHP technique can be successfully implemented for prioritizing agro-meteorological parameters for integrated plant diseases management as the results for both levels are consistent (consistency ratio < 0.1)

Shortwave Diathermy Machine

In this paper, Diathermy is a most generally prescribed for muscle and joint conditions. It uses a high-frequency electric contemporary to stimulate warmth technology inside frame tissues. Shortwave diathermy makes use of excessive frequency electromagnetic power to generate heat. Diathermy uses high-frequency electric present day to supply warmth deep inner a centered tissue. it can attain regions as deep as two inches underneath the skins surface

Governance Policies in IT Service Support

IT Service support provider, whether outsourced or kept in-house, has to abide by the Service Level Agreements (SLA) that are derived from the business needs. At the heart of IT Service support provider are the human resources that are expected to resolve tickets. It is essential that the policies, which govern the tickets’ movement amongst these resources, follow the business objectives such as service availability and cost reduction. In this study, we propose an agent based model that represents an IT Service Support system. A vital component in the model is the agent ‘Governor’, which makes policy decisions based on the by reacting to changes in the environment. The paper also studies the impact of various behavioural attributes of the Governor on the service objectives

Epidemic Diseases Forestall Module using Data Science and SIR Algorithms

This survey paper is intended to prevent epidemic diseases and pandemic diseases. According to the WHO every year in the world over 17 million people die due to this type of disease. Epidemic diseases have lower transmission rate than pandemic diseases and they spread in a bounded area. On the other hand, pandemic diseases have higher transmission rate and it can easily spread in an immense area. We can control this type of disease in its initial stages before it becomes a fatal disease like covid-19. Lack of knowledge in peoples and inefficient systems used by higher authorities in that region are the main reasons to spread diseases in larger areas. But using data science and the epidemic compartment models it’s possible to control infectious diseases in its initial stages. For different diseases there are different compartment algorithms that are able to estimate the number of cases in the future. These models often use ordinary differential equations for predicting things. Using data science, we are able to find what are key factors responsible for the spreading of that particular disease

Detection of Facial Expression using Fisher, Multi-SVM and Pattern Network and Comparison

Expression of face is very remarkable posture underneath the derma of the face. Expression of faces is one of the ways of human communication, which deliver so many things without talking verbally. The main purpose of this project is to develop a system for detecting facial expression of a given image among the seven basic human emotion expressions such as Angry, Sad, Happy, Contempt, Surprise, Disgust and Fear. This is performed using three different methods. The first method used is based on Eigen faces and Fisher face, using this method the obtained accuracy is 95.81%. The second method used here is HOG feature extraction and using these features to train the multi-SVM, and obtain the expression of test image. Using multi-SVM the obtained accuracy is 99.58%. The third method used is pattern neural network for emotion recognition of face image, for this also HOG features are used for training the network, and the accuracy obtained using pattern neural network is 90.79%

Web Application for Crime Analysis

Crime Analysis is records management and analysis of the crime investigation system. A crime is an unlawful act punishable by some authority or some state. The goal of the project is to develop a reporting and management system that is easily made accessible to the citizen to lodge the complaints online, police department and the administrative department for analysis and start investigation as soon as the complaint has been registered. This is used to record crime information updated by station officer through a website online, which saves the time of the witnessed person and police can start investigation, faster, so that criminal can be punished as soon as possible and helps in reduction of the crimes

A machine learning model for predicting innovation effort of firms

Classification and regression tree (CART) data mining models have been used in several scientific fields for building efficient and accurate predictive models. Some of the application areas are prediction of disease, targeted marketing, and fraud detection. In this paper we use CART which widely used machine learning technique for predicting research and development (R&D) intensity or innovation effort of firms using several relevant variables like technical opportunity, knowledge spillover and absorptive capacity. We found that accuracy of CART models is superior to the often-used linear parametric models. The results of this study are considered necessary for both financial analysts and practitioners. In the case of financial analysts, it establishes the power of data-driven prototypes to understand the innovation thinking of employees, whereas in the case of policymakers or business entrepreneurs, who can take advantage of evidence-based tools in the decision-making process

Design and Development of Saline Monitoring System Using Load cell

As the world population grows, the need for health care increases. In recent years, progress in medical care has been rapid due to the advancements in the field of sensors, microcontrollers and computers. A major reason for this is the combination of the two important disciplines namely medicine and engineering.                      This paper describes the development of an automatic saline monitoring system using a low cost indigenously developed load cell and GSM (Global system for mobile communication) modem. This enables the doctor or nurse on duty to monitor the saline flow rate from a distance. The Atmel16microcontroller is used for providing co-ordination action.                       Load cell which acts as a weight sensor , used to sense the weight of the bottle.  The detection of saline drop rate is quite faithful. Message about the status of the bottle is transmitted through GSM technology to a distant mobile cell for future actions as well as displayed on LCD display