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Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2018. / The DCS invested in the ICT solution to drive their Enterprise Architectural strategic and objective goals. Government departments uses ICT on their day to day business activities and to pursue for competitive progression compare to other parts of the world. The challenges are entirely depending on the user adoption of new technology. Other challenges that might delay the progress in government department would be the financial constraints and the socio inequality among our community in the developing countries. Government has a growth in e-government ICT’s infrastructure used in everyday activities and online functionality. These emerge from private entities that the government does business with, to force them to move away from manual function to electronic function and processes. Electronic monitoring system has been there in some parts of the world. Hence the South African government has opt to make use of this tool as it has been have a success results in some parts of the world. Even though there are some challenge the department has decided to implement EM system for monitoring of parolees. This study explores the factors that influence the adoption of electronic monitoring systems of parolees in the Department of Correctional Services (DCS) in South Africa, which will assist the DCS to monitor parolees effectively under budgetary constraints. Also to overcome the challenges of overcrowding, saving more cost of building more new facilities.

Thesis(M.S.)--Auburn University, 2007. / Abstract. Vita. Includes bibliographic references.

In order to achieve binding Government targets towards the decarbonisation of the electricity network, the GB power system is undergoing an unprecedented amount of change. A series of new technologies designed to integrate massive volumes of renewable generation, predominantly in the form of offshore wind, asynchronously connecting to the periphery of the transmission system, are transforming the requirements of the network. This displacement of traditional thermal generation is leading to a significant reduction in system inertia, thus making the task of system operation more challenging. It is therefore deemed necessary to develop tools and technologies that provide far greater insight into the state of the power system in real-time and give rise to methods for improving offline modelling practices through an enhanced understanding of the systems performance. To that extent PMUs are seen as one of the key enablers of the Smart Grid, providing accurate time-synchronised measurements on the state of the power system, allowing the true dynamics of the power system to be captured and analysed. This thesis provides an analysis of the existing PMU deployment on the GB transmission system with a view to the future system monitoring requirements. A critical evaluation and comparison is also provided on the suitability of a University based Low Voltage PMU network to further enhance the visibility of the GB system. In addition a novel event detection algorithm based on Detrended Fluctuation Analysis is developed and demonstrated, designed to determine the exact start time of a transmission event, as well as the suitability of such an event for additional transmission system analysis, namely inertia estimation. Finally, a reliable method for the estimation of total system inertia is proposed that includes an estimate of the contribution from residual sources, of which there is currently no visibility. The proposed method identifies the importance of regional inertia and its impact to the operation of the GB transmission system.

Plasma cleaning is the most effective dry process to remove surface contaminates from a SAW (Surface Acoustical Wave) device. Consistent gas pressures, flows, and good electrical connections between the chamber shelves are necessary for the process to function predictably. In addition, operation of the monitoring system must be transparent to the plasma cleaning unit. This thesis describes a simple solution to the complex problem of monitoring a plasma cleaning system. The monitoring system uses the LabVIEW® G programming language and hardware, both products of National Instruments, Inc.®, to monitor critical parameters necessary to achieve a consistent process when cleaning these devices.

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With the advancement of computer technology, demand for more accurate and intelligent monitoring systems has also risen. The use of computer vision and video analysis range from industrial inspection to surveillance. Object detection and segmentation are the first and fundamental task in the analysis of dynamic scenes. Traditionally, this detection and segmentation are typically done through temporal differencing or statistical modelling methods. One of the most widely used background modeling and segmentation algorithms is the Mixture of Gaussians method developed by Stauffer and Grimson (1999). During the past decade many such algorithms have been developed ranging from parametric to non-parametric algorithms. Many of them utilise pixel intensities to model the background, but some use texture properties such as Local Binary Patterns. These algorithms function quite well under normal environmental conditions and each has its own set of advantages and short comings. However, there are two drawbacks in common. The first is that of the stationary object problem; when moving objects become stationary, they get merged into the background. The second problem is that of light changes; when rapid illumination changes occur in the environment, these background modelling algorithms produce large areas of false positives. / These algorithms are capable of adapting to the change, however, the quality of the segmentation is very poor during the adaptation phase. In this thesis, a framework to suppress these false positives is introduced. Image properties such as edges and textures are utilised to reduce the amount of false positives during adaptation phase. The framework is built on the idea of sequential pattern recognition. In any background modelling algorithm, the importance of multiple image features as well as different spatial scales cannot be overlooked. Failure to focus attention on these two factors will result in difficulty to detect and reduce false alarms caused by rapid light change and other conditions. The use of edge features in false alarm suppression is also explored. Edges are somewhat more resistant to environmental changes in video scenes. The assumption here is that regardless of environmental changes, such as that of illumination change, the edges of the objects should remain the same. The edge based approach is tested on several videos containing rapid light changes and shows promising results. Texture is then used to analyse video images and remove false alarm regions. Texture gradient approach and Laws Texture Energy Measures are used to find and remove false positives. It is found that Laws Texture Energy Measure performs better than the gradient approach. The results of using edges, texture and different combination of the two in false positive suppression are also presented in this work. This false positive suppression framework is applied to a smart house senario that uses cameras to model ”virtual sensors” to detect interactions of occupants with devices. Results show the accuracy of virtual sensors compared with the ground truth is improved.

Corpus Christi Bay (TX, USA) is a shallow wind-driven bay which is designated as a National Estuary due to its impact on the economy. But this bay experiences periodic hypoxia (dissolved oxygen <2 mg/l) which threatens aerobic aquatic organisms. Development of the Coastal Margin Observation and Assessment System (CMOAS) through integration of real-time observations with numerical modeling helps to understand the processes causing hypoxia in this energetic bay. CMOAS also serves as a template for the implementation of observational systems in other dynamic ecosystems for characterizing and predicting other episodic events such as harmful algal blooms, accidental oil spills, sediment resuspension events, etc. State-of-the-art sensor technologies are involved in real-time monitoring of hydrodynamic, meteorological and water quality parameters in the bay. Three different platform types used for the installation of sensor systems are: 1) Fixed Robotic, 2) Mobile, and 3) Remote. An automated profiler system, installed on the fixed robotic platform, vertically moves a suite of in-situ sensors within the water column for continuous measurements. An Integrated Data Acquisition, Communication and Control system has been configured on our mobile platform (research vessel) for the synchronized measurements and real-time visualization of hydrodynamic and water quality parameters at greater spatial resolution. In addition, a high frequency (HF) radar system has been installed on remote platforms to generate surface current maps for Corpus Christi (CC) Bay and its offshore area. This data is made available to stakeholders in real-time through the development of cyberinfrastructure which includes establishment of communication network, software development, web services, database development, etc. Real-time availability of measured datasets assists in implementing an integrated sampling scheme for our monitoring systems installed at different platforms. With our integrated system, we were able to capture evidence of an hypoxic event in Summer 2007. Data collected from our monitoring systems are used to drive and validate numerical models developed in this study. The analysis of observational datasets and developed 2-D hydrodynamic model output suggests that a depth-integrated model is not able to capture the water current structure of CC Bay. Also, the development of a threedimensional mechanistic dissolved oxygen model and a particle aggregation transport model (PAT) helps to clarify the critical processes causing hypoxia in the bay. The various numerical models and monitoring systems developed in this study can serve as valuable tools for the understanding and prediction of various episodic events dominant in other dynamic ecosystems.

Abstract According to the development of industry, conventional surveillance system has not already satisfied by the demand today. The combination application of the CCD camera and image analysis technology in personal computer could enhance the functionality and more and more get a lot of attraction on it. Therefore, CCD camera is gradually popular in people live and industry application. However, CCD camera depends on the process ability of personal computer very much. Furthermore, it can¡¦t work well independently on the application. Based on these problems, this paper proposes an effective monitoring technique to establish an image servo module through using the embedding Ethernet controller with CMOS image sensor. The ¡§image servo module¡¨ can execute independently the designate image monitor work. Eventually, an ¡§Internet-based remote visual monitoring system¡¨ was built by integrating ¡§image servo module¡¨ and ¡§database service system¡¨ to widely promote the application on surveillance system. From the experiment system, user can use the IE explore or Netscape to supervise the internet-based image monitor system from web-server. In addition, a real-time monitor system based on VB program tool was also developed for any computer to monitor or setup the remote image server module to achieve the designated work. The transfer speed of Internet image server system is 7~8 frames/s. Keywords¡Gimage servo module, Internet-based remote visual monitoring systems