WorldCat Identities

Mississippi State University Department of Electrical and Computer Engineering

Overview
Works: 20 works in 22 publications in 1 language and 24 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
Load flow and state estimation algorithms for three-phase unbalanced power distribution systems by Chiranjeevi Madvesh( )

1 edition published in 2014 in English and held by 1 WorldCat member library worldwide

Distribution load flow and state estimation are two important functions in distribution energy management systems (DEMS) and advanced distribution automation (ADA) systems. Distribution load flow analysis is a tool which helps to analyze the status of a power distribution system under steady-state operating conditions. In this research, an effective and comprehensive load flow algorithm is developed to extensively incorporate the distribution system components. Distribution system state estimation is a mathematical procedure which aims to estimate the operating states of a power distribution system by utilizing the information collected from available measurement devices in real-time. An efficient and computationally effective state estimation algorithm adapting the weighted-least-squares (WLS) method has been developed in this research. Both the developed algorithms are tested on different IEEE test-feeders and the results obtained are justified
Spectral band selection for ensemble classification of hyperspectral images with applications to agriculture and food safety by Sathishkumar Samiappan( )

1 edition published in 2014 in English and held by 1 WorldCat member library worldwide

In this dissertation, an ensemble non-uniform spectral feature selection and a kernel density decision fusion framework are proposed for the classification of hyperspectral data using a support vector machine classifier. Hyperspectral data has more number of bands and they are always highly correlated. To utilize the complete potential, a feature selection step is necessary. In an ensemble situation, there are mainly two challenges: (1) Creating diverse set of classifiers in order to achieve a higher classification accuracy when compared to a single classifier. This can either be achieved by having different classifiers or by having different subsets of features for each classifier in the ensemble. (2) Designing a robust decision fusion stage to fully utilize the decision produced by individual classifiers. This dissertation tests the efficacy of the proposed approach to classify hyperspectral data from different applications. Since these datasets have a small number of training samples with larger number of highly correlated features, conventional feature selection approaches such as random feature selection cannot utilize the variability in the correlation level between bands to achieve diverse subsets for classification. In contrast, the approach proposed in this dissertation utilizes the variability in the correlation between bands by dividing the spectrum into groups and selecting bands from each group according to its size. The intelligent decision fusion proposed in this approach uses the probability density of training classes to produce a final class label. The experimental results demonstrate the validity of the proposed framework that results in improvements in the overall, user, and producer accuracies compared to other state-of-the-art techniques. The experiments demonstrate the ability of the proposed approach to produce more diverse feature selection over conventional approaches
Multihop cognitive cellular networks : Optimization, security, and privacy by Ming Li( )

1 edition published in 2014 in English and held by 1 WorldCat member library worldwide

The exploding growth of wireless devices like smartphones and tablets has driven the emergence of various applications, which has exacerbated the congestion over current wireless networks. Noticing the limitation of current wireless network architectures and the static spectrum policy, in this dissertation, we study a novel hybrid network architecture, called multihop cognitive cellular network (MC2N), taking good advantage of both local available channels and frequency spatial reuse to increase the throughput of the network, enlarge the coverage area of the base station, and increase the network scalability. Although offering significant benefits, the MC2N also brings unique research challenges over other wireless networks. Of note are the problems associated with the architecture, modeling, cross-layer design, privacy, and security issues. In this dissertation, we aim to address these challenging and fundamental issues in MC2Ns. Our contributions in this dissertation are multifold. First, we consider multiradio multi-channel in MC2Ns and propose a multi-radio multi-channel multi-hop cognitive cellular network (M3C2N). Under the proposed architecture, we then investigate the minimum length scheduling problem by exploring joint frequency allocation, link scheduling, and routing. Second, energy consumption minimization problem is further studied for MC2N under physical model. Third, we introduce device-to-device (D2D) communications among cellular users in MC2Ns by bypassing the base stations (BSs) and utilizing local available spectrums, and hence potentially further alleviate network congestion. A secondary spectrum auction market is constructed to dynamically allocate the available licensed spectrums. Fourth, we propose realtime detection, defense, and penalty schemes to identify, defend against, and punish MAC layer selfish misbehavior, respectively, in multihop IEEE 802.11 networks, noticing that most traditional detection approaches are for wireless local area networks only, and rely on a large amount of historical data to perform statistical detection. Last, a new location-based rewarding system, called LocaWard is proposed, where mobile users can collect location-based tokens from token distributors, and then redeem their gathered tokens at token collectors for beneficial rewards. Besides, we also develop a security and privacy aware location-based rewarding protocol for the LocaWard system
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
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
A model-based holistic power management framework : A study on shipboard power systems for navy applications by Ranjit Amgai( )

1 edition published in 2014 in English and held by 1 WorldCat member library worldwide

The recent development of Integrated Power Systems (IPS) for shipboard application has opened the horizon to introduce new technologies that address the increasing power demand along with the associated performance specifications. Similarly, the Shipboard Power System (SPS) features system components with multiple dynamic characteristics and require stringent regulations, leveraging a challenge for an efficient system level management. The shipboard power management needs to support the survivability, reliability, autonomy, and economy as the key features for design consideration. To address these multiple issues for an increasing system load and to embrace future technologies, an autonomic power management framework is required to maintain the system level objectives. To address the lack of the efficient management scheme, a generic model-based holistic power management framework is developed for naval SPS applications. The relationship between the system parameters are introduced in the form of models to be used by the model-based predictive controller for achieving the various power management goals. An intelligent diagnostic support system is developed to support the decision making capabilities of the main framework. Naïve Bayes' theorem is used to classify the status of SPS to help dispatch the appropriate controls. A voltage control module is developed and implemented on a real-time test bed to verify the computation time. Variants of the limited look-ahead controls (LLC) are used throughout the dissertation to support the management framework design. Additionally, the ARIMA prediction is embedded in the approach to forecast the environmental variables in the system design. The developed generic framework binds the multiple functionalities in the form of overall system modules. Finally, the dissertation develops the distributed controller using the Interaction Balance Principle to solve the interconnected subsystem optimization problem. The LLC approach is used at the local level, and the conjugate gradient method coordinates all the lower level controllers to achieve the overall optimal solution. This novel approach provides better computing performance, more flexibility in design, and improved fault handling. The case-study demonstrates the applicability of the method and compares with the centralized approach. In addition, several measures to characterize the performance of the distributed controls approach are studied
Target discrimination/classification radar by B. Jeffrey Skinner( Book )

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

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
A data analytic methodology for materials informatics by Osama Yousef AbuOmar( )

1 edition published in 2014 in English and held by 1 WorldCat member library worldwide

A data analytic materials informatics methodology is proposed after applying different data mining techniques on some datasets of particular domain in order to discover and model certain patterns, trends and behavior related to that domain. In essence, it is proposed to develop an information mining tool for vapor-grown carbon nanofiber (VGCNF)/vinyl ester (VE) nanocomposites as a case study. Formulation and processing factors (VGCNF type, use of a dispersing agent, mixing method, and VGCNF weight fraction) and testing temperature were utilized as inputs and the storage modulus, loss modulus, and tan delta were selected as outputs or responses. The data mining and knowledge discovery algorithms and techniques included self-organizing maps (SOMs) and clustering techniques. SOMs demonstrated that temperature had the most significant effect on the output responses followed by VGCNF weight fraction. A clustering technique, i.e., fuzzy C-means (FCM) algorithm, was also applied to discover certain patterns in nanocomposite behavior after using principal component analysis (PCA) as a dimensionality reduction technique. Particularly, these techniques were able to separate the nanocomposite specimens into different clusters based on temperature and tan delta features as well as to place the neat VE specimens in separate clusters. In addition, an artificial neural network (ANN) model was used to explore the VGCNF/VE dataset. The ANN was able to predict/model the VGCNF/VE responses with minimal mean square error (MSE) using the substitution and 3-folds cross validation (CV) techniques. Furthermore, the proposed methodology was employed to acquire new information and mechanical and physical patterns and trends about not only viscoelastic VGCNF/VE nanocomposites, but also about flexural and impact strengths properties for VGCNF/ VE nanocomposites. Formulation and processing factors (curing environment, use or absence of dispersing agent, mixing method, VGCNF fiber loading, VGCNF type, high shear mixing time, sonication time) and testing temperature were utilized as inputs and the true ultimate strength, true yield strength, engineering elastic modulus, engineering ultimate strength, flexural modulus, flexural strength, storage modulus, loss modulus, and tan delta were selected as outputs. This work highlights the significance and utility of data mining and knowledge discovery techniques in the context of materials informatics
Electrical properties degradation of Photovoltaic modules caused by lightning induced voltage by Taosha Jiang( )

1 edition published in 2014 in English and held by 1 WorldCat member library worldwide

Lightning is one of the main factors that cause Photovoltaic (PV) systems to fail. The PV modules inside PV systems, like any other electric equipment, will be degraded under electrical stress. The effect of electrical degradation of the PV modules caused by lightning induced voltage has been rarely reported. In the dissertation, the electrical properties degradation of a polycrystalline silicon module was studied. Firstly, lightning impulse voltages of positive polarity ranging from low to high are applied on different groups of the testing modules. All these lightning impulse voltage tests are conducted in the same experimental condition except for their stress voltage magnitudes. The maximum power output, I-V characteristics, and dark forward I-V curve are measured and reported periodically during the lightning impulse voltage tests. By comparing the maximum output power and changes in the internal electrical properties, it could be concluded that lightning impulse voltages, even medium voltage levels, will cause degradation to the sample. The relationship of the maximum output power and the number of applied impulses for different testing voltage levels are compared. An analysis of the electrical property changes caused by the lightning impulse voltages is presented. Secondly, a group of samples are tested with lightning impulse voltage of negative polarity. A comparison of the impulse voltage aging effects at the same voltage level with positive polarity is made. The maximum power output drop caused by positive and negative lightning impulses are compared. Laboratory results revealed that positive and negative lightning impulses will not only influence the degree of degradation, but also lead to different electrical property changes. Finally, a comparison of the effect of lightning impulses combined with other stress factors are discussed. The study simulates a field-aged sample's behavior at lightning impulse voltage testing conditions. The result suggests that the degradation caused by lightning impulse voltage is greatly accelerated when the sample has bubbles and delamination. Electrical breakdown of the module is caused by the failure of the insulation
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
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
Security and privacy in online social networks by Arun Thapa( )

1 edition published in 2014 in English and held by 1 WorldCat member library worldwide

The explosive growth of Online Social Networks (OSNs) over the past few years has redefined the way people interact with existing friends and especially make new friends. OSNs have also become a great new marketplace for trade among the users. However, the associated privacy risks make users vulnerable to severe privacy threats. In this dissertation, we design protocols for private distributed social proximity matching and a private distributed auction based marketplace framework for OSNs. In particular, an OSN user looks for matching profile attributes when trying to broaden his/her social circle. However, revealing private attributes is a potential privacy threat. Distributed private profile matching in OSNs mainly involves using cryptographic tools to compute profile attributes matching privately such that no participating user knows more than the common profile attributes. In this work, we define a new asymmetric distributed social proximity measure between two users in an OSN by taking into account the weighted profile attributes (communities) of the users and that of their friends'. For users with different privacy requirements, we design three private proximity matching protocols with increasing privacy levels. Our protocol with highest privacy level ensures that each user's proximity threshold is satisfied before revealing any matching information. The use of e-commerce has exploded in the last decade along with the associated security and privacy risks. Frequent security breaches in the e-commerce service providers' centralized servers compromise consumers' sensitive private and financial information. Besides, a consumer's purchase history stored in those servers can be used to reconstruct the consumer's profile and for a variety of other privacy intrusive purposes like directed marketing. To this end, we propose a secure and private distributed auction framework called SPA, based on decentralized online social networks (DOSNs) for the first time in the literature. The participants in SPA require no trust among each other, trade anonymously, and the security and privacy of the auction is guaranteed. The efficiency, in terms of communication and computation, of proposed private auction protocol is at least an order of magnitude better than existing distributed private auction protocols and is suitable for marketplace with large number of participants
Ultra-wideband microwave ablation applicators by Mustafa Asili( )

1 edition published in 2014 in English and held by 1 WorldCat member library worldwide

The increasing demand for efficient cancer treatment inspired the researchers for new investigations about an alternative treatment of cancer. Microwave ablation is the newest ablation technique to cure cancer. This method is minimally noninvasive and inexpensive compared to the other methods. However, current microwave ablation systems suffer due to narrowband nature of the antenna (dipole or slot) placed at the tip of the probe. Therefore, this study developed an ultra-wideband ablation probe that operates from 300MHz to 10 GHz. For this purpose, a small wideband antenna is designed to place at the tip of the probe and fabricated. These probes are tested at ISM frequencies (2.4 GHz and 5.8GHz) in skin mimicking gels and pig liver. Microwave ablation probe design, simulation results, and experiment results are provided in this thesis
A virtual hydroelectric power system for distributable industrial control system security research by David Brian Mudd( )

1 edition published in 2014 in English and held by 1 WorldCat member library worldwide

Cyber security for industrial control systems (ICS) has been a rapidly growing area of interest and research for the last several years. The lack of an easily distributable platform on which ICS components can be built for use in security testing and result comparison among researchers presents a major issue. This thesis details the use of a virtual testbed environment to build a representative virtual hydroelectric power system (VHPS). The VHPS generates realistic Modbus/TCP network traffic between two separate ICS devices, a Master and a Slave, located on separate VMs. For security testing purposes, a method of session hijacking has been implemented as well as a Function Code Scan attack and a Setpoint Manipulation attack. The virtual environment, the VHPS, and the attacks have been packaged into an LXDE-based Fedora Spin VM for easy distribution
 
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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 (22)