Engineering Commons^™

Fighting Child Pornography: A Review Of Legal And Technological Developments, Jasmine V. Eggestein, Kenneth J. Knapp

Journal of Digital Forensics, Security and Law

In our digitally connected world, the law is arguably behind the technological developments of the Internet age. While this causes many issues for law enforcement, it is of particular concern in the area of child pornography in the United States. With the wide availability of technologies such as digital cameras, peer-to-peer file sharing, strong encryption, Internet anonymizers and cloud computing, the creation and distribution of child pornography has become more widespread. Simultaneously, fighting the growth of this crime has become more difficult. This paper explores the development of both the legal and technological environments surrounding digital child pornography. In doing …

A Study Of Forensic Imaging In The Absence Of Write-Blockers, Gary C. Kessler, Gregory H. Carlton

Journal of Digital Forensics, Security and Law

Best practices in digital forensics demand the use of write-blockers when creating forensic images of digital media, and this has been a core tenet of computer forensics training for decades. The practice is so ingrained that the integrity of images created without a write-blocker are immediately suspect. This paper describes a research framework that compares forensic images acquired with and without utilizing write-blockers in order to understand the extent of the differences, if any, in the resultant forensic copies. We specifically address whether differences are superficial or evidentiary, and we discuss the impact of admitting evidence acquired without write blocking. …

Automated Evaluation Of Approximate Matching Algorithms On Real Data, Frank Breitinger, Vassil Roussev

Electrical & Computer Engineering and Computer Science Faculty Publications

Bytewise approximate matching is a relatively new area within digital forensics, but its importance is growing quickly as practitioners are looking for fast methods to screen and analyze the increasing amounts of data in forensic investigations. The essential idea is to complement the use of cryptographic hash functions to detect data objects with bytewise identical representation with the capability to find objects with bytewise similarrepresentations.

Unlike cryptographic hash functions, which have been studied and tested for a long time, approximate matching ones are still in their early development stages and evaluation methodology is still evolving. Broadly, prior approaches have …

Analysis Of Bfsa Based Anti-Collision Protocol In Lf, Hf, And Uhf Rfid Environments, Varun Bhogal

UNF Graduate Theses and Dissertations

Over the years, RFID (radio frequency identification) technology has gained popularity in a number of applications. The decreased cost of hardware components along with the recognition and implementation of international RFID standards have led to the rise of this technology.

One of the major factors associated with the implementation of RFID infrastructure is the cost of tags. Low frequency (LF) RFID tags are widely used because they are the least expensive. The drawbacks of LF RFID tags include low data rate and low range. Most studies that have been carried out focus on one frequency band only. This thesis presents …

Risk Minimization Of Disjunctive Temporal Problem With Uncertainty, Hoong Chuin Lau, Tuan Anh Hoang

Research Collection School Of Computing and Information Systems

The Disjunctive Temporal Problem with Uncertainty (DTPU) is a fundamental problem that expresses temporal reasoning with both disjunctive constraints and contingency. A recent work (Peintner et al, 2007) develops a complete algorithm for determining Strong Controlla- bility of a DTPU. Such a notion that guarantees 100% confidence of execution may be too conservative in practice. In this paper, following the idea of (Tsamardinos 2002), we are interested to find a schedule that minimizes the risk (i.e. probability of failure) of executing a DTPU. We present a problem decomposition scheme that enables us to compute the probability of failure efficiently, followed …

Detection Of Seagrass Scars Using Sparse Coding And Morphological Filter, Ender Oguslu, Sertan Erkanli, Victoria J. Hill, W. Paul Bissett, Richard C. Zimmerman, Jiang Li, Charles R. Bostater Jr. (Ed.), Stelios P. Mertikas (Ed.), Xavier Neyt (Ed.)

OES Faculty Publications

We present a two-step algorithm for the detection of seafloor propeller seagrass scars in shallow water using panchromatic images. The first step is to classify image pixels into scar and non-scar categories based on a sparse coding algorithm. The first step produces an initial scar map in which false positive scar pixels may be present. In the second step, local orientation of each detected scar pixel is computed using the morphological directional profile, which is defined as outputs of a directional filter with a varying orientation parameter. The profile is then utilized to eliminate false positives and generate the final …

Strategic Decision Support System Using Heuristic Algorithm For Practical Outlet Zones Allocation To Dealers In A Beer Supply Distribution Network, Michelle Lee Fong Cheong

Research Collection School Of Computing and Information Systems

We consider a two-echelon beer supply distribution network with the brewer replenishing the dealers and the dealers serving the outlet zones directly, for multiple product types. The allocation of the outlet zones to the dealers will determine the quantity of products the brewer replenishes each dealer, which will in turn impact the total warehousing and transportation costs. The non-linear optimization model formulated is difficult to solve to optimality, and the model itself does not include practical business considerations in the distribution business. A heuristics algorithm is designed and easily implemented using spreadsheets with Visual Basic programming to effectively and efficiently …

On The Security Of Auditing Mechanisms For Secure Cloud Storage, Yong Yu, Lei Niu, Guomin Yang, Yi Mu, Willy Susilo

Research Collection School Of Computing and Information Systems

Cloud computing is a novel computing model that enables convenient and on-demand access to a shared pool of configurable computing resources. Auditing services are highly essential to make sure that the data is correctly hosted in the cloud. In this paper, we investigate the active adversary attacks in three auditing mechanisms for shared data in the cloud, including two identity privacy-preserving auditing mechanisms called Oruta and Knox, and a distributed storage integrity auditing mechanism.We show that these schemes become insecure when active adversaries are involved in the cloud storage. Specifically, an active adversary can arbitrarily alter the cloud data without …

Automated Classification Of Malignant Melanoma Based On Detection Of Atypical Pigment Network In Dermoscopy Images Of Skin Lesions, Nabin K. Mishra

Doctoral Dissertations

“Melanoma causes more deaths than any other form of skin cancer. Early melanoma detection is important to prevent progression to a more deadly stage. Automated computer-based identification of melanoma from dermoscopic images of skin lesions is the most efficient method in early diagnosis. An automated melanoma identification system must include multiple steps, involving lesion segmentation, feature extraction, feature combination and classification. In this research, a classifier-based approach for automatically selecting a lesion border mask for segmentation of dermoscopic skin lesion images is presented. A logistic regression based model selects a single lesion border mask from multiple border masks generated by …

Crystallization In Nano-Confinement Seeded By A Nanocrystal -- A Molecular Dynamics Study, Heng Pan, Costas Grigoropoulos

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Seeded crystallization and solidification in nanoscale confinement volumes have become an important and complex topic. Due to the complexity and limitations in observing nanoscale crystallization, computer simulation can provide valuable details for supporting and interpreting experimental observations. In this article, seeded crystallization from nano-confined liquid, as represented by the crystallization of a suspended gold nano-droplet seeded by a pre-existing gold nanocrystal seed, was investigated using molecular dynamics simulations in canonical (NVT) ensemble. We found that the crystallization temperature depends on nano-confinement volume, crystal orientation, and seed size as explained by classical two-sphere model and Gibbs-Thomson effect.

Localization And Tracking Of Objects Using Cross-Correlation Of Shadow Fading Noise, M. R. Basheer, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Multipath and shadow fading are the primary cause for positioning errors in a Received Signal Strength Indicator (RSSI) based localization scheme. While fading, in general, is detrimental to localization accuracy, cross-correlation and divergence properties of shadow fading residuals may be utilized to improve localization and tracking accuracy of mobile IEEE 802.15.4 transmitters. Therefore, this paper begins by presenting a stochastic filter that models the fast-changing multipath fading as a mean reverting Ornstein-Uhlenbeck (OU) process followed by a Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) filtering to isolate the slow changing shadow fading residuals from measured RSSI values. Subsequently, a novel wireless …

A Fault Prediction Scheme For Takagi-Sugeno Fuzzy Systems With Immeasurable Premise Variables And Disturbance, Balaje T. Thumati, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

As explained in the literature, it is very hard to measure premise variables of a Takagi-Sugeno (TS) fuzzy system. Therefore, in this paper, a fault detection and prediction (FDP) scheme is designed for a class of TS fuzzy systems with immeasurable (unknown) premise variables and external disturbances. A fault detection (FD) observer is designed to approximate the system output and the premise variables. Subsequently, a FD residual is generated by comparing the observer output with respect to the system output. The FD residual is evaluated to detect any faults in the system. Further, time-to-failure (TTF) of the TS fuzzy system …

Extremum-Seeking For Nonlinear Discrete-Time Systems With Application To Hcci Engines, H. Zargarzadeh, S. Jagannathan, J. A. Drallmeier

Electrical and Computer Engineering Faculty Research & Creative Works

For many control applications, identifying an optimal operating point by maximizing/minimizing a performance function is important. This paper applies the extremum-seeking method to nonaffine, nonlinear discrete-time systems stabilized by an optimal adaptive controller. First, a novel averaging method is used for the nonlinear discrete-time systems to show that their output unique extrema are stable equilibrium points. Then, a singular perturbation method in discrete time is employed to show that the overall closed loop system will dynamically converge to the extremum. The applicability of this scheme is numerically verified on a Homogeneous Charge Compression Ignition (HCCI) model validated experimentally and expressed …

Fixed Final-Time Near Optimal Regulation Of Nonlinear Discrete-Time Systems In Affine Form Using Output Feedback, Qiming Zhao, Hao Xu, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, the fixed final-time near optimal output regulation of affine nonlinear discrete-time systems with unknown system dynamics is considered. First, a neural network (NN)-based observer is proposed to reconstruct both the system state vector and control coefficient matrix. Next, actor-critic structure is utilized to approximate the time-varying solution of the Hamilton-Jacobi-Bellman (HJB) equation or value function. To satisfy the terminal constraint, a new error term is defined and incorporated in the NN update law so that the terminal constraint error is also minimized over time. A NN with constant weights and time-dependent activation function is employed to approximate …

An Online Outlier Identification And Removal Scheme For Improving Fault Detection Performance, Hasan Ferdowsi, Sarangapani Jagannathan, Maciej Jan Zawodniok

Electrical and Computer Engineering Faculty Research & Creative Works

Measured data or states for a nonlinear dynamic system is usually contaminated by outliers. Identifying and removing outliers will make the data (or system states) more trustworthy and reliable since outliers in the measured data (or states) can cause missed or false alarms during fault diagnosis. In addition, faults can make the system states nonstationary needing a novel analytical model-based fault detection (FD) framework. In this paper, an online outlier identification and removal (OIR) scheme is proposed for a nonlinear dynamic system. Since the dynamics of the system can experience unknown changes due to faults, traditional observer-based techniques cannot be …

Adaptive Neural Network-Based Optimal Control Of Nonlinear Continuous-Time Systems In Strict-Feedback Form, H. Zargarzadeh, T. Dierks, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper focuses on neural network (NN) based optimal control of nonlinear continuous-time systems in strict-feedback form when the system dynamics are known by using an adaptive backstepping approach. A single NN-based adaptive approach is designed to learn the solution of the infinite horizon continuous-time Hamilton-Jacobi-Bellman (HJB) equation while the corresponding optimal control input that minimizes the HJB equation is calculated in a forward-in-time manner without using value and policy iterations. First, the optimal control problem is solved for a generic multi-input and multi-output nonlinear system with a state feedback approach. Then the approach is extended to a single-input and …

Neural Network-Based Finite-Horizon Approximately Optimal Control Of Uncertain Affine Nonlinear Continuous-Time Systems, Hao Xu, Qiming Zhao, Travis Dierks, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper develops a novel neural network (NN) based finite-horizon approximate optimal control of nonlinear continuous-time systems in affine form when the system dynamics are complete unknown. First an online NN identifier is proposed to learn the dynamics of the nonlinear continuous-time system. Subsequently, a second NN is utilized to learn the time-varying solution, or referred to as value function, of the Hamilton-Jacobi-Bellman (HJB) equation in an online and forward in time manner. Then, by using the estimated time-varying value function from the second NN and control coefficient matrix from the NN identifier, an approximate optimal control input is computed. …

Scale-Up And On-Line Monitoring Of Gas-Solid Systems Using Advanced And Non-Invasive Measurement Techniques, Muthanna H. Al-Dahhan, Shreekanta Aradhya, Faraj Zaid, Neven Ali, Thaar Aljuwaya

Chemical and Biochemical Engineering Faculty Research & Creative Works

Industry relies on gas-solid systems for numerous processes. Flow dynamics play an important role in achieving the desired results. The present study proposes, validates and demonstrates a novel mechanistic scale-up approach based on maintaining similar radial profile or cross sectional distribution of gas holdup in two different gas-solid systems in order to achieve hydrodynamics similarity using advanced measurement techniques. This new methodology for scale-up and design has been implemented on gas-solid spouted bed which has been used for drying, granulation and coating. The development can be extrapolated to other gas-solid systems encountered in phosphate processes.

Measuring And Modelling The Thermal Performance Of The Tamar Suspension Bridge Using A Wireless Sensor Network, Nicholas De Battista, James M. W. Brownjohn, Hwee-Pink Tan, Ki Young Koo

Research Collection School Of Computing and Information Systems

A study on the thermal performance of the Tamar Suspension Bridge deck in Plymouth, UK, is presented in this paper. Ambient air, suspension cable, deck and truss temperatures were acquired using a wired sensor system. Deck extension data were acquired using a two-hop wireless sensor network. Empirical models relating the deck extension to various combinations of temperatures were derived and compared. The most accurate model, which used all the four temperature variables, predicted the deck extension with an accuracy of 99.4%. Time delays ranging from 10 to 66 min were identified between the daily cycles of the air temperature and …

Hidden Markov Model With Information Criteria Clustering And Extreme Learning Machine Regression For Wind Forecasting, Dao Lam, Shuhui Li, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes a procedural pipeline for wind forecasting based on clustering and regression. First, the data are clustered into groups sharing similar dynamic properties. Then, data in the same cluster are used to train the neural network that predicts wind speed. For clustering, a hidden Markov model (HMM) and the modified Bayesian information criteria (BIC) are incorporated in a new method of clustering time series data. to forecast wind, a new method for wind time series data forecasting is developed based on the extreme learning machine (ELM). the clustering results improve the accuracy of the proposed method of wind …

Adaptive Resonance Theory And Diffusion Maps For Clustering Applications In Pattern Analysis, Donald C. Wunsch, David J. Morris, Rui Xu

Electrical and Computer Engineering Faculty Research & Creative Works

Adaptive Resonance is primarily a theory that learning is regulated by resonance phenomena in neural circuits. Diffusion maps are a class of kernel methods on edge-weighted graphs. While either of these approaches have demonstrated success in image analysis, their combination is particularly effective. These techniques are reviewed and some example applications are given.

Using Software-Based Decision Procedures To Control Instruction-Level Execution, William B. Kimball

AFIT Patents

An apparatus, method and program product are provided for securing a computer system. A digital signature of an application is checked, which is loaded into a memory of the computer system configured to contain memory pages. In response to finding a valid digital signature, memory pages containing instructions of the application are set as executable and memory pages other than those containing instructions of the application are set as non-executable. Instructions in executable memory pages are executed. Instructions in non-executable memory pages are prevented from being executed. A page fault is generated in response to an attempt to execute an …

Data Management In Cloud Environments: Nosql And Newsql Data Stores, Katarina Grolinger, Wilson A. Higashino, Abhinav Tiwari, Miriam Am Capretz

Electrical and Computer Engineering Publications

: Advances in Web technology and the proliferation of mobile devices and sensors connected to the Internet have resulted in immense processing and storage requirements. Cloud computing has emerged as a paradigm that promises to meet these requirements. This work focuses on the storage aspect of cloud computing, specifically on data management in cloud environments. Traditional relational databases were designed in a different hardware and software era and are facing challenges in meeting the performance and scale requirements of Big Data. NoSQL and NewSQL data stores present themselves as alternatives that can handle huge volume of data. Because of the …

Reduced Row Echelon Form And Non-Linear Approximation For Subspace Segmentation And High-Dimensional Data Clustering, Akram Aldroubi, Ali Sekmen

Computer Science Faculty Research

Given a set of data W={w1,…,wN}∈RD drawn from a union of subspaces, we focus on determining a nonlinear model of the form U=⋃i∈ISi, where {Si⊂RD}i∈I is a set of subspaces, that is nearest to W. The model is then used to classify W into clusters. Our approach is based on the binary reduced row echelon form of data matrix, combined with an iterative scheme based on a non-linear approximation method. We prove that, in absence of noise, our approach can find the number of subspaces, their dimensions, and an orthonormal basis for each subspace Si. We provide a comprehensive analysis …

A Decentralized Fault Accommodation Scheme For Nonlinear Interconnected Systems, H. Ferdowsi, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a novel decentralized detection and accommodation (FDA) methodology is proposed for interconnected nonlinear continuous-time systems by using local subsystem states alone in contrast with traditional distributed FDA schemes where the entire measured or the estimated state vector is needed. First, the detection scheme is revisited where a network of local fault detectors (LFD) is proposed. A fault is detected by generating a residual from the measured and estimated state vectors locally and the fault dynamics are estimated by using an online approximator upon detection. Subsequently, a fault accommodation scheme is initiated in the subsystem by using a …

A Neural Network Based Outlier Identification And Removal Scheme, H. Ferdowsi, S. Jagannathan, M. Zawodniok

Electrical and Computer Engineering Faculty Research & Creative Works

Identifying and removing the outliers is important in order to make the data more trustworthy and improve the reliability of fault detection, since outliers in the measured data can cause false alarms. An online outlier identification and removal (OIR) scheme, suitable for nonlinear dynamic systems, is proposed in this paper. A neural network (NN) is utilized to estimate the actual outlier-free system states using only the measured system states which involve outliers. Outlier identification is performed online by finding the difference between measured and estimated states and comparing it with its median and standard deviation over a dynamic time window. …

College Of Engineering Senior Design Competition Fall 2013, University Of Nevada, Las Vegas

Fred and Harriet Cox Senior Design Competition Projects

Part of every UNLV engineering student’s academic experience, the senior design project stimulates engineering innovation and entrepreneurship. Each student in their senior year chooses, plans, designs, and prototypes a product in this required element of the curriculum. A capstone to the student’s educational career, the senior design project encourages the student to use everything learned in the engineering program to create a practical, real world solution to an engineering challenge. The senior design competition helps focus the senior students in increasing the quality and potential for commercial application for their design projects. Judges from local industry evaluate the projects on …

Situational Awareness/Triage Tool For Use In A Chemical, Biological, Radiological Nuclear Explosive (Cbrne) Environment, John N. Scarlett, Heather L. Gallup, David A. Smith

AFIT Patents

A method of managing patient care and emergency response following a Chemical, Biological, Radiological, or Nuclear Explosive (CBRNE) attack and maintaining compliance with the Health Insurance Portability and Accountability Act (HIPAA). The method including identifying each patient with a unique patient identifier, the identifier based upon the geospatial location of the patient, the geospatial location including at least the latitude and longitude of the patient when first treated, the unique patient identifier being part of patient data. Providing a collection point of patient data to form a patient data database where in the patient location data may be used to …

Synergy Of The Developed 6d Bim Framework And Conception Of The Nd Bim Framework And Nd Bim Process Ontology, Shawn Edward O'Keeffe

Dissertations

The author developed a unified nD framework and process ontology for Building Information Modeling (BIM). The research includes a framework developed for 6D BIM, nD BIM, and nD ontology that defines the domain and sub-domain constructs for future nD BIM dimensions. The nD ontology defines the relationships of kinds within any new proposed dimensional domain for BIM. The developed nD BIM framework and ontology takes into account the current 2D-5D BIM dimensions. There is a synergy between the 6D and nD framework that allows the nD framework and ontology to be utilized as a unified template for future dimensional development. …

Scitech Book News Reviews, Susan Fingerman

Sci-Tech News

No abstract provided.