WorldCat Identities

Mississippi State University Department of Electrical and Computer Engineering

Overview
Works: 27 works in 29 publications in 1 language and 31 library holdings
Genres: Academic theses 
Classifications: HE5614.3.M7,
Publication Timeline
.
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
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
Target discrimination/classification radar by B. Jeffrey Skinner( Book )

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

Cybersecurity testing and intrusion detection for cyber-physical power systems by Shengyi Pan( )

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

Power systems will increasingly rely on synchrophasor systems for reliable and high-performance wide area monitoring and control (WAMC). Synchrophasor systems greatly use information communication technologies (ICT) for data exchange which are vulnerable to cyber-attacks. Prior to installation of a synchrophasor system a set of cyber security requirements must be developed and new devices must undergo vulnerability testing to ensure that proper security controls are in place to protect the synchrophasor system from unauthorized access. This dissertation describes vulnerability analysis and testing performed on synchrophasor system components. Two network fuzzing frameworks are proposed; for the IEEE C37.118 protocol and for an energy management system (EMS). While fixing the identified vulnerabilities in information infrastructures is imperative to secure a power system, it is likely that successful intrusions will still occur. The ability to detect intrusions is necessary to mitigate the negative effects from a successful attacks. The emergence of synchrophasor systems provides real-time data with millisecond precision which makes the observation of a sequence of fast events feasible. Different power system scenarios present different patterns in the observed fast event sequences. This dissertation proposes a data mining approach called mining common paths to accurately extract patterns for power system scenarios including disturbances, control and protection actions and cyber-attacks from synchrophasor data and logs of system components. In this dissertation, such a pattern is called a common path, which is represented as a sequence of critical system states in temporal order. The process of automatically discovering common paths and building a state machine for detecting power system scenarios and attacks is introduced. The classification results show that the proposed approach can accurately detect these scenarios even with variation in fault locations and load conditions. This dissertation also describes a hybrid intrusion detection framework that employs the mining common path algorithm to enable a systematic and automatic IDS construction process. An IDS prototype was validated on a 2-line 3-bus power transmission system protected by the distance protection scheme. The result shows the IDS prototype accurately classifies 25 power system scenarios including disturbances, normal control operations, and cyber-attacks
Stochastic and robust optimal operation of energy-efficient building with combined heat and power systems by Ping Liu( )

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

Energy efficiency and renewable energy become more attractive in smart grid. In order to efficiently reduce global energy usage in building energy systems and to improve local environmental sustainability, it is essential to optimize the operation and the performance of combined heat and power (CHP) systems. In addition, intermittent renewable energy and imprecisely predicted customer loads have introduced great challenges in energy-efficient buildings' optimal operation. In the deterministic optimal operation, we study the modeling of components in building energy systems, including the power grid interface, CHP and boiler units, energy storage devices, and appliances. The mixed energy resources are applied to collaboratively supply both electric and thermal loads. The results show that CHP can effectively improve overall energy efficiency by coordinating electric and thermal power supplies. Through the optimal operation of all power sources, the daily operation cost of building energy system for generating energy can be significantly reduced. In order to address the risk due to energy consumption and renewable energy production volatility, we conduct studies on both stochastic programming and robust optimizations to operate energy-efficient building systems under uncertainty. The multistage stochastic programming model is introduced so that the reliable operation of building energy systems would be probabilistically guaranteed with stochastic decisions. The simulation results show that the stochastic operation of building systems is a promising strategy to account for the impact of uncertainties on power dispatch decisions of energy-efficient buildings. In order to provide absolute guarantee for the reliable operation of building energy systems, a robust energy supply to electric and thermal loads is studied by exploring the influence of energy storage on energy supply and accounting for uncertainties in the energy-efficient building. The robustness can be adjusted to control the conservativeness of the proposed robust operation model. For the purpose of achieving adaptability in the robust optimal operation and attaining robustness in the stochastic optimal operation of building energy systems, we also develop an innovative robust stochastic optimization (RSO) model. The proposed RSO model not only overcomes the conservativeness in the robust operation model, but also circumvents the curse of dimensionality in the stochastic operation model
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
Model-based autonomic security management of networked distributed systems by Qian Chen( )

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

This research focuses on the development and validation of an autonomic security management (ASM) framework to proactively protect distributed systems (DSs) from a wide range of cyber assaults with little or no human intervention. Multi-dimensional cyber attack taxonomy was developed to characterize cyber attack methods and tactics against both a Web application (Web-app) and an industrial control system (ICS) by accounting for their impacts on a set of system, network, and security features. Based on this taxonomy, a normal region of system performance is constructed, refined, and used to predict and identify abnormal system behavior with the help of forecasting modules and intrusion detection systems (IDS). Protection mechanisms are evaluated and implemented by a multi-criteria analysis controller (MAC) for their efficiency in eliminating and/or mitigating attacks, maintaining normal services, and minimizing operational costs and impacts. Causes and impacts of unknown attacks are first investigated by an ASM framework learning module. Attack signatures are then captured to update IDS detection algorithms and MAC protection mechanisms in near real-time. The ASM approach was validated within Web-app and ICS testbeds demonstrating the effectiveness of the self-protection capability. Experiments were conducted using real-world cyber attack tools and profiles. Experimental results show that DS security behavior is predicted, detected, and eliminated thus validating our original hypothesis concerning the self-protection core capability. One important benefit from the self-protection feature is the cost-effective elimination of malicious requests before they impede, intrude or compromise victim systems. The ASM framework can also be used as a decision support system. This feature is important especially when unknown attack signatures are ambiguous or when responses selected automatically are not efficient or are too risky to mitigate attacks. In this scenario, man-in-the-loop decisions are necessary to provide manual countermeasures and recovery operations. The ASM framework is resilient because its main modules are installed on a master controller virtual machine (MC-VM). This MC-VM is simple to use and configure for various platforms. The MC-VM is protected; thus, even if the internal network is compromised, the MC-VM can still maintain "normal" self-protection services thereby defending the host system from cyber attack on-the-fly
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
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
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
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
The multi-stress aging of 15 kV EPR power cables by Linfeng Cao( )

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

This research is focused on the multi-stress aging phenomena and lifetime estimation of 15 kV EPR cable. In order to gain the suitable parameters for the lifetime estimation, the aging study on the EPR cable samples as well as on the cable layers' dielectrics samples was carried out at the High Voltage Laboratory of Mississippi State University. During the multi-stress aging study of 15 kV EPR cable samples, the EPR cable samples underwent electrical stress, thermal stress, and environmental effects. The aging time for the EPR cables varied from 650 hrs to 1300 hrs. An empirical aging model describing the cables' lifetime was derived from the partial discharge measurements results. The aging study on the EPR cable layers' dielectrics was achieved as well. The EPR insulation material samples were aged by combined electrical and thermal stress, while the material samples of inner semi-conducting layer, outer semi-conducting layer, and outer low-density polyethylene (LDPE) jacket were aged by thermal stress. The measurement data was used for the newly proposed lifetime estimation method. A new lifetime estimation method was introduced for the EPR cables. The method assumed that the failures of cables results from the expansion of voids/cavities initiated from the defects in the EPR insulation layer. The proposed lifetime estimation method applied the finite element method (FEM) to solve the electric field distribution inside the EPR cable with the existence of voids/cavities. The parameters were derived from the aging study on the EPR insulation material samples. Assuming the voids/cavities would expand in the direction of the maximum electric field stress, the lifetime of the EPR cables was then estimated through the iteration. The introduced method helped to establish a relationship between the aging study of insulation material samples and the aging of EPR cable samples, which was long missing in the past studies. It also provided a new way to assess the reliability of the EPR cable
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
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
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
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
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
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
Electrical breakdown strength of 5 kV ethylene propylene rubber (EPR) cable under ac voltage by Bishal Pradhan( )

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

Ethylene propylene rubber (EPR) cable has been extensively used for distribution of power. The insulation of cable should withstand electrical, thermal, mechanical, and chemical stresses during its operation. It is imperative to measure the data providing dielectric strength of EPR cable for these stresses. Significant improvements on the quality of insulation have been progressing for better performance of cable under these stresses. This study deals with ac voltage stress imposed on the cable. The electrical breakdown strength of 5kV EPR cable under ac voltage has been measured by constructing a suitable test set in Mississippi State University High Voltage Lab
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
 
moreShow More Titles
fewerShow Fewer Titles
Audience Level
0
Audience Level
1
  Kids General Special  
Audience level: 0.59 (from 0.51 for Transmissi ... to 0.99 for Scalable S ...)

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 (25)