WorldCat Identities

Mississippi State University Department of Electrical and Computer Engineering

Overview
Works: 11 works in 13 publications in 1 language and 15 library holdings
Genres: Academic theses 
Classifications: HE5614.3.M7,
Publication Timeline
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Most widely held works by Mississippi State University
Automated accident detection at intersections by Yunlong Zhang( Book )

2 editions published in 2004 in English and held by 3 WorldCat member libraries worldwide

This research aims to provide a timely and accurate accident detection method at intersections, which is very important for the Traffic Management System(TMS). This research uses acoustic signals to detect accident at intersections. A system is constructed that can be operated in two modes: two-class and multiclass. The input to the system is a three-second segment of audio signal. The output of the two-class mode is a label of "crash" or "non-crash". In the multi-class mode of operation, the system identifies crashes as well as several types of non-crash incidents, including normal traffic and construction sounds. The system is composed of three main signal processing stages: feature extraction, feature reduction, and feature classification. Five methods of feature extraction are investigated and compared; these are based on the discrete wavelet transform, fast Fourier transform, discrete cosine transform, real cepstral transform, and mel frequency cepstral transform. Statistical methods are used for feature optimization and classification. Three types of classifiers are investigated and compared: the nearest mean, maximum likelihood, and nearest neighbor methods. This study focuses on the detection algorithm development. Lab testing of the algorithm showed that the selected algorithm can detect intersection accidents with very high accuracy
Dimension reduction for hyperspectral imagery by Nam H Ly( )

1 edition published in 2013 in English and held by 1 WorldCat member library worldwide

In this dissertation, the general problem of the dimensionality reduction of hyperspectral imagery is considered. Data dimension can be reduced through compression, in which an original image is encoded into bitstream of greatly reduced size; through application of a transformation, in which a high-dimensional space is mapped into a low-dimensional space; and through a simple process of subsampling, wherein the number of pixels is reduced spatially during image acquisition. All three techniques are investigated in the course of the dissertation. For data compression, an approach to calculate an operational bitrate for JPEG2000 in conjunction with principal component analysis is proposed. It is shown that an optimal bitrate for such a lossy compression method can be estimated while maintaining both class separability as well as anomalous pixels in the original data. On the other hand, the transformation paradigm is studied for spectral dimensionality reduction; specifically, data-independent random spectral projections are considered, while the compressive projection principal component analysis algorithm is adopted for data reconstruction. It is shown that, by incorporating both spectral and spatial partitioning of the original data, reconstruction accuracy can be improved. Additionally, a new supervised spectral dimensionality reduction approach using a sparsity-preserving graph is developed. The resulting sparse graph-based discriminant analysis is seen to yield superior classification performance at low dimensionality. Finally, for spatial dimensionality reduction, a simple spatial subsampling scheme is considered for a multitemporal hyperspectral image sequence, such that the original image is reconstructed using a sparse dictionary learned from a prior image in the sequence
Meta-heuristic optimization of antennas for biomedical applications by Aaron Zachary Hood( )

1 edition published in 2013 in English and held by 1 WorldCat member library worldwide

Given the proper conditions, antennas applied in medicine can offer improved quality of life to patients. However the human body proves hostile to typical, analytical antenna design techniques as it is composed entirely of frequency- and temperature-dependent lossy media. By combining optimization techniques with numerical methods, many of these challenges may be overcome. Particle swarm optimization (PSO) models the solution process after the natural movement of groups such as swarms of bees as they search for food sources. This meta-heuristic procedure has proven adept at overcoming many challenging problems in the electromagnetics literature. Therefore, this dissertation explores PSO and some of its variants in the solution of two biomedical antenna problems. Recent advances in biosensor technology have led to miniaturized devices that are suitable for in vivo operation. While these sensors hold great promise for medical treatment, they demand a wireless installation for maximum patient benefit, which in turn demands quite specific antenna requirements. The antennas must be composed of biocompatible materials, and must be very small (no more than a few square centimeters) to minimize invasiveness. Here PSO is applied to design a 22.5 mm x 22.5 mm x 2.5 mm implantable serpentine planar inverted-F antenna for dual-band MedRadio and ISM operation. Measurements reveal the accuracy of the models. Hyperthermia is the process of elevating a patient’s temperature for therapeutic gain. Since the ancient Egyptians, physicians have employed hyperthermia in the destruction of cancerous tumors. Modern implementations typically apply electromagnetic radiation at radio and microwave frequencies to induce local or regional heating. In this dissertation PSO is used to evaluate candidate antennas for inclusion in an array of antennas with the aim of local adjuvant hyperthermia for breast cancer treatment. The near-field of the array is then optimized to induce a uniform specific absorption rate throughout the breast
Target discrimination/classification radar by B. Jeffrey Skinner( Book )

1 edition published in 1995 in English and held by 1 WorldCat member library worldwide

Scalable SiC power switches for applications in more electric vehicles : (preprint)( Book )

2 editions published in 2007 in English and held by 1 WorldCat member library worldwide

SiC VJFETs are an ideal device for a number of power electronics applications, including, but not limited to, high temperature motor drives, switch modules, and DC-DC or DC-AC inverters/converters. These applications are relevant to a number of military applications, such as shipboard power systems, more electric vehicles (including hybrid vehicles), and power conditioning systems in hostile and/or high temperature environments. The SiC VJFETs combine the switching speed of Si MOSFETs with the voltage and current handling properties of IGBTs and the thermal properties of SiC material. Since the VJFET is a unipolar device, it can easily be paralleled over the entire operating temperature range of the device. The SiC VJFET has a lower specific on resistance than the best Si IBGT and lacks the gate oxide problems of the SiC MOSFET. Because of the thermal properties of SiC and the lack of a gate oxide, they are capable of higher temperature operation than either device. The vertical channel structures provide for excellent packing density on the wafer and low per-unit production costs
Cyberthreats, attacks and intrusion detection in supervisory control and data acquisition networks by Wei Gao( )

1 edition published in 2013 in English and held by 1 WorldCat member library worldwide

Supervisory Control and Data Acquisition (SCADA) systems are computer-based process control systems that interconnect and monitor remote physical processes. There have been many real world documented incidents and cyber-attacks affecting SCADA systems, which clearly illustrate critical infrastructure vulnerabilities. These reported incidents demonstrate that cyber-attacks against SCADA systems might produce a variety of financial damage and harmful events to humans and their environment. This dissertation documents four contributions towards increased security for SCADA systems. First, a set of cyber-attacks was developed. Second, each attack was executed against two fully functional SCADA systems in a laboratory environment; a gas pipeline and a water storage tank. Third, signature based intrusion detection system rules were developed and tested which can be used to generate alerts when the aforementioned attacks are executed against a SCADA system. Fourth, a set of features was developed for a decision tree based anomaly based intrusion detection system. The features were tested using the datasets developed for this work. This dissertation documents cyber-attacks on both serial based and Ethernet based SCADA networks. Four categories of attacks against SCADA systems are discussed: reconnaissance, malicious response injection, malicious command injection and denial of service. In order to evaluate performance of data mining and machine learning algorithms for intrusion detection systems in SCADA systems, a network dataset to be used for benchmarking intrusion detection systems was generated. This network dataset includes different classes of attacks that simulate different attack scenarios on process control systems. This dissertation describes four SCADA network intrusion detection datasets; a full and abbreviated dataset for both the gas pipeline and water storage tank systems. Each feature in the dataset is captured from network flow records. This dataset groups two different categories of features that can be used as input to an intrusion detection system. First, network traffic features describe the communication patterns in a SCADA system. This research developed both signature based IDS and anomaly based IDS for the gas pipeline and water storage tank serial based SCADA systems. The performance of both types of IDS were evaluates by measuring detection rate and the prevalence of false positives
Model-based autonomic performance management of distributed enterprise systems and applications by Rajat Mehrotra( )

1 edition published in 2013 in English and held by 1 WorldCat member library worldwide

Distributed computing systems (DCS) host a wide variety of enterprise applications in dynamic and uncertain operating environments. These applications require stringent reliability, availability, and quality of service (QoS) guarantee to maintain their service level agreements (SLAs). Due to the growing size and complexity of DCS, an autonomic performance management system is required to maintain SLAs of these applications. A model-based autonomic performance management structure is developed in this dissertation for applications hosted in DCS. A systematic application performance modeling approach is introduced in this dissertation to define the dependency relationships among the system parameters, which impact the application performance. The developed application performance model is used by a model-based predictive controller for managing multi-dimensional QoS objectives of the application. A distributed control structure is also developed to provide scalability for performance management and to eliminate the requirement of approximate behavior modeling in the hierarchical arrangement of DCS. A distributed monitoring system is also introduced in this dissertation to keep track of computational resources utilization, application performance statistics, and scientific application execution in a DCS, with minimum latency and controllable resource overhead. The developed monitoring system is self-configuring, self-aware, and fault-tolerant. It can also be deployed for monitoring of DCS with heterogeneous computing systems. A configurable autonomic performance management system is developed using model-integrated computing methodologies, which allow administrators to define the initial settings of the application, QoS objectives, system components' placement, and interaction among these components in a graphical domain specific modeling environment. This configurable performance management system facilitates reusability of the same components, algorithms, and application performance models in different deployment settings
Transmission shift map optimization for reduced electrical energy consumption in a pre-transmission parallel plug-in hybrid electric vehicle by Jonathan Dean Moore( )

1 edition published in 2013 in English and held by 1 WorldCat member library worldwide

The use of an automatic transmission in pre-transmission parallel hybrid electric vehicles provides greater potential for powertrain optimization than conventional vehicles. By modifying the shift map, the transmission's gear selection can be adjusted to reduce the energy consumption of the vehicle. A method for determining the optimal shift map for this hybrid vehicle has been implemented using global optimization and software-in-the-loop vehicle simulation. An analysis of the optimization has been performed using software-in-the-loop and hardware-in-the-loop simulation and evaluates two vehicle modes: regenerative braking active and regenerative braking disabled. The results of these two modes illustrate the successful implementation of the global optimization algorithm. However, the evaluation results raise practical concerns about implementing the optimized shift maps in a vehicle and illustrate a problem which must be overcome for future development
 
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Alternative Names

controlled identityMississippi State University

controlled identityMississippi State University. Department of Electrical Engineering

Mississippi State University. Dept. of Electrical and Computer Engineering

Languages
English (13)