Rafael Perez

Dynamic Management of Real-Time Location Data on GPS-Enabled Mobile Phones

Advanced location based services have the ability to track users with Global Positioning System (GPS)-enabled cell phones in real-time. These applications share a common problem; the continuous calculation and transmission of GPS fixes from the mobile phone to a server consumes a considerable amount of energy and increases data transmission costs. Therefore, an application-level algorithm is necessary to reduce the number of GPS fixes calculated and transmitted, while continuing to track the user in real-time and record an accurate representation of his or her travel path. In this paper, two complementary algorithms are presented: the critical point (CP) algorithm, which filters data points to be transmitted to the server, and the location-aware state machine, which dynamically manages the frequency of the location re-calculation update rate. Both algorithms were implemented in TRAC-IT, a Java Micro Edition (Java ME) application designed to automatically collect user travel behavior; the proposed algorithms allow TRAC-IT to build an accurate representation of the users path with a considerably reduced number of fixes while significantly extending mobile device battery life.

The schema theorem considered insufficient

The purpose of this paper is to attempt to verify how the schema theorem affects the behavior of a genetic algorithm (GA) and uncover the relationships between building blocks and increasing schemata. From the analysis of schema tracing, the schema theorem should be interpreted carefully with precise definitions of building block and increasing schema. In addition, we find that an important type of above-average schemata that are not building blocks, which we call phenotypic schemata, always exists among the schemata receiving increasing evaluations during the GA search.<<ETX>>

A location-aware framework for intelligent real-time mobile applications

The Location-Aware Information Systems Client (LAISYC) supports intelligent, real-time, mobile applications for GPS-enabled mobile phones by dynamically adjusting platform parameters for application performance while conserving device resources such as battery life.

Increasing the Participation of Under-represented Minority Student Groups in Computer Science and Engineering: An REU Site Experience

Statistics indicate that under-represented minority student groups continue to experience high dropout rates, low graduation rates, low enrollment in graduate programs and low participation in computer science and engineering (CS&E). At the same time, trends indicate that these groups will constitute an important part of the population of the United States by year 2050. Given these facts, it is crucial to help these minority groups become an integral part of the economic fabric of the United States. It has been shown that undergraduate research is one of the best vehicles to address several of these important issues. This paper describes our experience running a NSF-sponsored research experiences for undergraduates (REU) site in CS&E for under-represented minority student groups. We describe the most important aspects that need to be included in these programs and some strategies for running them successfully. Finally, we include the results of our evaluation and show the effectiveness of the program

Quantifying Position Accuracy of Multimodal Data from Global Positioning System–Enabled Cell Phones

The emergence of cell phones with embedded Global Positioning System (GPS) chips provides opportunities to push personalized real-time travel information subject to an individuals current location. One such application, a travel assistant device, notifies cognitively disabled public transportation users when it is time to request a stop and exit the vehicle. GPS-enabled cell phones must provide highly accurate real-time location data for this type of service. The components used in the data-collection process are identified, and a quantitative analysis of real-time GPS data obtained with a cell phone while walking, driving a vehicle, and riding public transportation is provided. The expectation was that position accuracy would suffer when the GPS signal was obstructed by a vehicle or the users clothing. The obtained data demonstrate the results of location fix attempts over different transportation modes in an urban environment. The highest percentage of GPS fixes (79.0%) was obtained by users walking with the cell phone open and unobstructed; walking also produced valid GPS data (i.e., location data estimated to be accurate within 30 m of the true position) 66.2% of the time. For bus trips, GPS and valid fix percentages were 71.7% and 66.1%, respectively, when the phone was held near the window; when the phone was placed in the travelers lap, these numbers fell to 51.3% and 27.8%, respectively. Car trips provided higher numbers: 77.7% and 71.6%, respectively. Location-based transportation applications are feasible using current technology, but predictive algorithms may be required to deliver highly accurate and timely location-aware services to cell phone users in highly obstructed environments.

On teaching AI and expert systems courses

The experiences of faculty members from departments of engineering, mathematics, and computer science throughout the United States and Puerto Rico who came together for three weeks to discuss effective ways to teach artificial intelligence to undergraduates are outlined. The paper describes the rationale for the development of three main topic areas: artificial intelligence, expert systems, and symbolic and logic programming, and it includes syllabi for these topics. Also included is a discussion of the results obtained after a year of using the materials and techniques gathered during the original meeting. >

Unifying program-level ABET assessment data collection, analysis, and presentation

Assessment of engineering and computer science programs involves a significant effort in data collection, analysis, and formatting of results for presentation to an ABET evaluation team. At the University of South Florida, we have developed a web-based system to unify program-level data collection, evaluation, and presentation for all programs in the College of Engineering. The goal of our web-based Accreditation Portal is to both improve the efficacy of assessment activities in the College and provide a clearer view of continuous improvement results to constituents and ABET evaluation teams. For each defined program educational objective and student outcome there are links to pages for Performance Indicators, Methods of Assessment, Assessment Data, Evaluation, Changes, and Closing the Loop. For each of these assessment steps Word, Excel, PDF, text, and/or image files can be uploaded. The portal also includes survey tools to enable data collection. We describe the development and deployment of the portal. We show a full sample assessment loop for a mock objective. Our evaluation of the portal is based on a trade study with comparison to several existing web-based assessment systems. Faculty members in the College are using the portal.

Why is problem-dependent and high-level representation scheme better in a genetic algorithm?

New guidelines for selecting a binary or a symbolic representation scheme in GAs are presented. Based on Markov chain models for a Binary GA (BinGA) and a Symbolic GA (SymGA), the effects of different representation schemes on a GA are compared in terms of four criteria: state fitness evaluation time, fully meaningful state ratio, diversity pressure ratio, and convergence pressure. According to these criteria, we show that symbolic representation, which is a problem-dependent and high-level representation scheme, is better than binary representation as long as the mutation probability is not 0.

A Command Language for Multiple Robot Arm Coordination

A high-level robot command language has been developed as an extension to the C programming language and the UNIXTM operating system. It provides a common interface for programming three robot arms made by two different manufacturers and sharing a common work space. This command language allows task description in Cartesian coordinates and it makes concurrency of the movements of the arms transparent to the user. Simultaneous movement of the three arms is obtained by creating different background processes running in parallel under the UNIX system. Synchronization commands are included in the language to allow coordination of the movements of all three arms. The language can be used either in a compiled mode where a task can be described by a sequence of commands, or in an interpreted mode with each command executed immediately. It is convenient to use for both teaching and research, and it can be easily integrated with a vision system. It is also easily transferable to other Unix systems and it can be expanded to include other robot arms.

PREDICTING CHANGE IN AVERAGE VEHICLE RIDERSHIP ON THE BASIS OF EMPLOYER TRIP REDUCTION PLANS

Artificial neural network (ANN) models are described, and efforts to build a model to predict changes in average vehicle ridership using about 7,000 employer trip reduction plans from three cities are highlighted. The development of the application is summarized; the neural network model performance is compared with other analytical approaches; and the results of the field test are summarized. Researchers at the Center for Urban Transportation Research combined the three data sets, identified model inputs and outputs from the data, and built the neural network model. This step also included building alternative models using regression and discriminant analysis to measure relative ANN performance. These models were compared with the FHWA’s transportation demand management model. The ANN model built only with data from Los Angeles was validated using a separate data set and evaluated according to the model’s ability to classify the change in average vehicle ridership (AVR) within an acceptable range. The final step was the validation of the model using data from other sites. The result was a model and software built on data from Los Angeles and Tucson that performed well when tested with data from Phoenix. On the basis of this project, the ANN model predicted an acceptable range of changes in AVR and was proven to be transferable to another city. Furthermore, the ANN model outperformed other analysis tools and was easier to use. Finally, the model provides a basis for helping to assess the impacts of employer trip reduction programs with minimal data collection requirements.