Neveen Shlayan

Energy Efficient RGBW Pixel Configuration for Light-Emitting Displays

A study on LED displays has been conducted exploring a more efficient method for color generation than the traditional method. The study is comprehensive and thoroughly performed employing various sets of experiments in order to examine the functionality of the new proposed scheme which includes a literature review, theoretical modeling based on a scientific study, experimental data measurements of a developed prototype, and statistical data based on a survey. This study resulted in very interesting outcomes that may lead to a tremendous change in the existing LED display technology.

Dynamic Traffic Assignment: A Survey of Mathematical Models and Techniques

This paper presents a survey of the mathematical methods used for modeling and solutions for the traffic assignment problem. It covers the static (steady-state) traffic assignment techniques as well as dynamic traffic assignment in lumped parameter and distributed parameter settings. Moreover, it also surveys simulation-based solutions. The paper shows the models for static assignment, variational inequality method, projection dynamics for dynamic travel routing, discrete time and continuous time dynamic traffic assignment, and macroscopic dynamic traffic assignment (DTA). The paper then presents the macroscopic DTA in terms of the Wardrop principle and derives a partial differential equation for experienced travel time function that can be integrated with the macroscopic DTA framework.

Formal Language Modeling and Simulations of Incident Management

Traffic incident management (TIM) is a multijurisdictional process. Complications with communications, compatibility, coordination, institutional responsibilities, and legal issues are inherent in TIM systems. Increased delay in incident clearance due to various conflicts has vital economical, safety, environmental, and social impacts. Therefore, a thorough and rigorous modeling of the system is necessary to better understand its properties and systematically discern issues that may arise. The goal of this study is to develop modeling tools for the incident management process. Formal language automata theory is proposed for modeling and analysis since incident management can be viewed as a series of discrete events. Using formal methods allows us to use tools that are well established in this field to systematically study incident management processes. Formal language and automata theory are the foundation for numerous hardware and software development with applications in digital design, compilers, and programming languages. Formal language and automata theory provide us with powerful tools for developing, analyzing, and debugging such models. A systematic structure of an incident management model permits methodical identification of the systems “bugs.” This study demonstrates the development of models of some first response incident management agencies through a case study in the Las Vegas, NV, area using formal languages and automata theory. Sequence properties such as safety and liveness are verified for the developed models.

High-Performance Vehicle Streams: Communication and Control Architecture

This paper presents the overall framework for high-performance vehicle streams that integrates the transportation and the communication layered architectures together. It shows the process in establishing an infrastructure for high-performance vehicles and the theoretical development and deployment of cooperative adaptive cruise control (CACC) for heterogeneous vehicles that is integrated with lateral control. The system is designed to be fault tolerant and uses the concept of subplatoons. Our framework is based on integrating a transportation layered architecture with the Wireless Access in Vehicular Environment (WAVE) communication protocol.

Feedback ramp metering using Godunov method based hybrid model

This paper presents a feedback control design for an isolated freeway ramp that utilizes a hybrid dynamical model for the traffic using Godunovs numerical technique. Feedback ramp metering designs in the past have relied on either discretized linearized method such as ALINEA, or nonlinear feedback designs based on ordinary differential equations for the traffic model. However, lumped parameter models fail to represent the rarefaction wave phenomenon of the distrbuted model. This paper uses Godunov based hybrid lumped model based on which feedback control design is proposed, and simulation results for the model are presented.

Bayesian Safety Analyzer using multiple data sources of accidents

In this study, the authors demonstrate the development of a Bayesian Safety Analyzer (BSA) using multiple accident data sources as well as traffic flow data. Simulations of the developed model are conducted using MATLAB FullBNT toolbox where different parameter estimations are used. This study demonstrates the efficiency as well as flexibility of using Bayesian analysis on large data sets containing a large number of attributes. The developed BSA can be used in the incident management process. It can assist decision makers in estimating the severity of a certain incident based on given attributes for better preparedness. BSA can also be used in order to assess the safety transportation system.

Exploring pedestrian Bluetooth and WiFi detection at public transportation terminals

This is an on-going study that explores the potential benefits of using pedestrian data for evaluation and enhancement of public transportation. The research team proposes the utilization of Bluetooth (BT) and WiFi technologies to estimate time-dependent origin-destination (OD) demands and station wait-times of transit bus and subway users. The detection approach and a complete system design are discussed in this paper. Preliminary results from multiple pilot field studies, that were conducted at some of the major New York City (NYC) public transportation facilities, are also presented. The main objective of this study is to inquire into the various ways this extensive transit rider data can be used and to establish a general framework through data-driven pedestrian modeling within transit stations that renders estimation of key parameters and strategic control of public transportation services possible.

Establishing a cost effective embedded control and robotics engineering program optimal control of a two wheeled robot

The cost of establishing a traditional control systems and robotics program usually runs into many thousands of dollars. As a result many undergraduate and graduate STEM institutions are unable to establish these important STEM programs in their curriculum. This paper introduces an alternative method of teaching important control system and robotics concepts using LEGO kits and ROBOTC and MATLAB software. This paper is the second in a series of papers that explore the extent to which LEGO kits can be used to introduce important control system and robotics concepts to both undergraduate and graduate students. Such a program has been successfully implemented in NYITs school of electrical and computer engineering. In this paper we discuss the control design of a two wheeled mobile robot. The dynamic model of the multi-input-multi-output (MIMO) system is nonlinear in nature. The nonlinear model is first linearized and then controlled by designing a full state feedback, LQR and H2 controllers. The system performance of each controller is simulated and compared with each other. Finally, the controllers are implemented and their performance is validated on the actual system. The establishment for embedded control system of two wheeled robot is design based on a MATLAB environment. Implementation of the software is completed using ROBOTC.

Model-Based Methodology for Validation of Traffic Flow Detectors by Minimizing Human Bias in Video Data Processing

This paper provides a model-based method for analysis and hypothesis testing for paired data where one source of data has to be validated against another source of data that contains subjective and dynamic errors. This study deals with human-observed flow counts collected from traffic videos of freeway cameras. The available videos are mainly used for the purpose of manual observation by transportation personnel in case of emergency. This amounts to a varying inconsistency of the quality of the videos, which presents an additional challenge when analyzing the data. Video processing cannot be performed due to the mentioned issues with regard to the video quality. The processing has to be manually performed by humans who unfortunately have an inherent bias. If the video data have to be used for validating flow detector sensors, then a technique that performs validation with subjective and dynamic erroneous data as a result of the human bias is needed. This paper presents a methodology to deal with this issue. It is based on statistical testing with heteroscedasticity, which is demonstrated through a case study using data from traffic flow detectors and traffic cameras installed on highways in the Southern Nevada Region. A model for the relationship between the video ratings and the distribution of the human errors is developed taking into consideration the human bias. A method for identification of faulty detectors is also demonstrated based on the developed technique.

Simultaneous Stochastic Approximation for the spectroscopic reconstruction of neutron distributions

Various aspects of the neutron spectroscopy problem are studied in this research. Theoretically, the neutron emission problem transforms into a limited angle Radon transform problem. In order to solve this ill posed problem, various algorithms are tested spanning two different techniques; algebraic reconstruction as well as Monte-Carlo methods. The developed algorithms are Stochastic Gradient Approximation (SGA) method, Simultaneous Perturbation Stochastic Approximation (SPSA) method, and Time of Flight (TOF) method.