Our stellar programs attract oustanding students from around the world who work closely with our faculty to advance state-of-the-art research in computing technologies. We attribute our success to a strong tradition of collaborative research, close working relationships with local industries, state-of-the-art facilities and a dedicated committment to student achievement.
We offer bachelor of science degrees in computer science (CS), computer engineering (CPE), and a master's degree in software engineering (MSE). Our MSE program is unique within the UW system and offers graduates very desiriable employment opportunities. We also offer a very popular dual-degree program that awards students a BS in Computer Science and a Master's in Software Engineering within a condensed time frame of only five years. If you have any questions please contact us at email@example.com.
Dr. Lei Wangs' article entitled "ARPAP: A Novel Antenna-Radiation-Pattern-Aware Power-Based Positioning in RF System" was accepted for publication in the highly ranked IEEE Transactions on Mobile Computing. A pre-print can be found at IEEE Xplore. The abstract is shown below.
Traditional power-based localization methods suffer from low accuracy in the practical application environment. The main challenges are the antenna directivity and fading effect. Conventional methods assume omnidirectional antenna directivity such that the solution is the intersections of multiple circle-shape contours. This strong assumption results in significant localization error in practical non-isotropic antenna applications. In this article, a novel antenna radiation-pattern-aware power-based positioning (ARPAP) scheme is proposed. It reduces the antenna directivity effect by including the antenna pattern into the localization system model. It reduces the bias error that introduced by power measurement through estimating the line-of-sight (LoS) component in received signal strength (RSS). Moreover, the error mode for the proposed ARPAP system, along with the theoretical limit, Cramer-Rao Bound (CRB), and bias of the proposed positioning system are derived. The Pearson correlation coefficient between the proposed error model and simulation result shows a high similarity score. The proposed positioning scheme and analytic error model are instantiated for the cellular network. Both analytical model and simulation results demonstrate the superiority of the proposed method over traditional methods.
Dr. David Mathias Dr. David Mathias presented his work in Evolutionary Computing and Genetic Algorithms at The University of Central Florida's Spring 2020 Seminar Series. The presentation was entitled "Evolving Cooperation in the Iterated Prisoner's Dilemma". The abstract of his presentation is given below.
The Prisoner’s Dilemma is a simple two-player game in which two accomplices have been captured by the police and are being interrogated separately. Each has two choices: to cooperate with their accomplice and refuse to confess; or to defect by confessing and implicating their accomplice. The reward (symmetrically, penalty) that each receives depends not only on their own action but also that of the other player. In the Iterated Prisoner’s Dilemma (IPD), the players play multiple rounds of the game allowing them to learn about their opponent’s actions and act accordingly.
The Iterated Prisoner's Dilemma has been studied in great detail in fields as diverse as economics, computer science, psychology, politics, and environmental studies. This is due, in part, to the intriguing property that its Nash Equilibrium is not a globally optimal solution. Many researchers have used evolutionary computation to evolve effective strategies for IPD. Typically treated as a single-objective problem, a player's goal is to maximize their total reward. Mittal and Deb created a multi-objective version of the game by including minimization of the opponent's reward as an additional objective.
Here, we explore the role of mutual cooperation in IPD player performance. We implement a multi-objective genetic algorithm in which each member of the population belongs to one of four sub-populations: selfish, communal, cooperative, and selfless, the last three of which use mutual cooperation as an optimization objective. Game play occurs among all members, without regard to sub-population, while crossover and selection occur only within a sub- population. Our evolved players are tested against a population of Axelrod's strategies and the Gradual strategy. Testing solely using self score, we find that players evolved with mutual cooperation as an objective perform very well. In some cases, our cooperative players completely dominate the competition. Thus, learning to play nicely with others is a successful strategy for maximizing personal reward.
The embedded systems emphasis trains students to design, construct and program small-scale hardware systems for use across a wide variety of platforms. Our departmental "pick and place" machine is able to quickly populate a PC board once it has been configured and calibrated. This video shows the device populating a PCB measuring just over a square inch with 40 components (resistors, LEDs and MOSFETs) in just over a minute.
The UWL Computer Science Department will offer a new undergraduate degree in Computer Engineering beginning in Fall 2020. Computer engineering graduates can expect to receive very good starting salaries; placing among the highest in engineering (refer to the Burea of Labor Statistics for details). Computer engineers work at the hardware-software boundary and are able to move into either hardware or software positions.
Graduates can expect to fill a wide variety of in-demand jobs including roles in low-level software development (device drivers, firmware, operating systems and virtual machines) in addition to roles in hardware design (digital circuit design and verification, computer architecture, control systems and signal processing).
More information information about the program can be found under the Programs tab above.
Dr. Lei Wang was recently awarded a UWL Faculty Research Grant for his proposal entitled Advanced Intelligent Flying Robot Tracking in Comprehensive Environments. The objective of this research is to to provide accurate positioning and tracking for mobile, autonomous drones when a GPS signal is either not available or when the environment distorts GPS readings. Dr. Wang will build a low-cost tracking and navigation system that integrates computer-vision based motion detection algorithms with a real-time video stream obtained from a drone-mounted camera. In addition to a set of mobile drones, Dr. Wang will also construct a large, external 3D tracking environment to provide ground-truth localization data. This project is an excellent experimental environment and platform for interdisciplinary research, including singificant contributions from the fields of artificial intelligence, deep/machine learning, robotics, networking, security, control, embedded system, and wireless communication. Funding will span July 2020 through June 2021.
Dr. David Mathias was recently awarded a prestigious NSF Research Grant for his proposal entitled Modeling Intensity and Duration Variations in Multi-agent Systems. Swarm-based systems consist of large groups or swarms of agents that have a common goal and work collectively to solve the tasks associated with that goal. In general, each agent in a swarm is capable of performing multiple, perhaps all, of the tasks required of the swarm. Thus, the division of labor, selecting which agents perform which tasks, is neither predetermined nor obvious. A self-organizing swarm is one in which coordination of the activities of agents in the swarm is decentralized. This decentralized task allocation problem is the primary focus of the work outlined here.
Complex multi-agent swarms that must achieve critical tasks are common in nature. For example, bee hives are highly sensitive to temperature variation. Members of the hive act collectively to maintain the hive's temperature. When the temperature drops, bees intentionally shiver to generate heat. When the temperature rises, bees flap their wings to move air and cool the hive. Their actions are decentralized: each individual bee decides if and when to shiver or flap. If members of the hive were uniform in their decisions, the hive temperature would oscillate over time. This is avoided due to variation in the bees' behaviors under the same conditions. Some bees will, for example, begin shivering or flapping before others.
This research will explore two sources of inter-agent variation in artificial swarms: duration of activity and intensity of activity. While variation in activation thresholds models the times at which agents begin working on a task, variability in duration models how long an agent works on a task. This might take the form of a fixed length of time for each member to participate, or perhaps a deactivation threshold, mirroring the activation threshold. Variation in duration among agents desynchronizes the agents' decision making schedule such that agents make decisions about whether or not to change tasks at different points in time. This desynchronization reduces the likelihood of over-response by the swarm and is expected to increase the stability of the swarm. Funding spans August 2019 to July 2020.
Dr. Elliott Forbes participated in a panel discussion entitled Including Embedded Systems in CS: Why? When? and How? as part of SIGCSE 2019. As noted on the SIGCSE web site, The SIGCSE Technical Symposium is the largest computing education conference worldwide organized by ACM SIGCSE. It attracts over 1,500 researchers, educators, and others interested in improving computing education in K-12 and higher education.
Dr. Forbes served as a domain expert in both Computer Engineering and Compter Science Education where he presented the way in which the UWL CS department developed its well received embedded systems emphasis. Dr. Forbres argued that within the last 10-15 years the barriers to entry in embedded systems development have dramatically decreased. Development platforms of the past were often vendor-locked, required expensive hardware and software developer kits (SDKs), were difficult to use, and lacked a community that was accessible to newcomers. Now, however, the open-source hardware and software communities have rallied, largely solving these issues. The CS department recognized the new opportunities afforded by these lowered barriers and developed a hands-on curriculum revolving around the Internet of Things (IoT).