Dr. Lu gained his PhD from the multidesciplinary Geospatial Engineering program at Viriginia Tech, and Master's degrees in Computer Science and Transportation Engineering at University of Massachusetts Amherst. Dr. Lu is specialized in best first searches. His dissertation is on bivariate best first searches to process multi-category based queries in a graph and their applications in GIS-T. His work significantly extends the functionality of best first searches. He proposed multivariate best first searches to process queries with multiple categories/points of interest, and created a set of novel challenges both in research and in practice. Dr. Lu joined MacroSys as a Research Analyst/GIS Specialist in July, 2009. He continues his work in best first search and takes a strong role in implmenting GISs that use best first searches for optimization and to provide advanced routing services in transportation, logistics, and computer networking. Research Interests: Dr. Lu has broad interests in GIS-T, informed search, optimization, logistics, Location Based Services, data mining, wireless and geosensor network, and transportation engineering. Publications:
Patents Selected Projects: National Corridor Freight Performance Measure Web System The web system is developed to provide FHWA, State DOT, MPOs, and researchers freight performance measures along national corridors. Queris over time and locations can be customized and linear referencing is used to provide performances over 3-mile segments with ArcGIS server.
Real Time Micro Traffic Simulation It is crucial to incorporate real time traffic information into micro simulations to timely manage event-related activities in traffic operations. This project used TransModeler and real time data sets to monitor possible events along the corridor Route 50 at Arlington, VA.
Micro Traffic Simulation of Truck Activities Transportation professionals have long realized the importance of incorporating truck characteristics into transportation design and traffic operations. Large trucks differ dramatically from passenger cars in many physical and operational characteristics. They are longer, wider, heavier, less maneuverable, and have slower acceleration and deceleration rates and higher emissions outputs than passenger cars. Thus they can cause traffic disturbances leading to the generation of shock waves, traffic delays, adverse variations in vehicle speeds, etc. In addition, their larger size and inertia pose safety concerns in case of crashes and may influence the response of passenger vehicles drivers. On the other hand, their movements also can be impacted by road conditions such as intersection geometric characteristics. These impacts can be more serious and complex at tight intersections on surface street networks. This project prevides a framework to simulate the impacts of truck activities at tight intersections on surface streets, proposes simulation methods to quantify those impacts, and presents the results from the study on those impacts on the south Boston local street network near the Boston Container Terminal using a modified surface street network simulation model in Traffic Software Integrated System (TSIS) 5.1.
Developing a Logical
Model for a Geo-Spatial
Right-of-Way Land Management System Right-of-way (ROW) requirements are significant components of project schedule and cost. Manually recorded ROW information includes agency ownership, appraisal information acquisition status, and property -management functions that are important for addressing real estate issues, utilities, environmental permitting and mitigation, access management, outdoor advertising control, and programming. Electronic management of this information improves coordination and consistency of data, leading to reduced project delivery delays caused by ROW acquisition. In addition, the ability to retrieve these data electronically provides fast, convenient, and consistent access to all users, reducing the time and expense needed to ship documents; eliminating repetitive entries; minimizing data-entry errors caused by multiple formats; and ultimately saving money for the DOTs. Electronic management of real estate information could improve coordination with local jurisdictions and provide appropriate data to the public on agency ownership of property.
Biker Routing on a Trail Network To facilitate the decision making of a state Department of Transportation (DOT) on non-motorized transportation assets, a transportation network model, TNM, is developed. Based on this model, a web-based routing service is implemented to provide bikers routing services on the trail network.
Alexandria Archeology GIS Mapping Digitally preserve/rectify important historic maps for the Alexandria Archaeology Museum beginning with the area outside of Old Town Alexandria where archaeological resources are most threatened by expanding development.Deploy the digital maps through arcIMS.
SenseEye SenseEye is a multi-tier network of heterogeneous wireless nodes and cameras, which was employed to show a multi-tier sensor network can reconcile the traditionally conflicting systems goals of latency and energy efficiency. A surveillance application was employed using SensEye comprising three tasks: object detection, recognition and tracking. Novel mechanisms were designed for low-power low-latency detection, low-latency wakeups, efficient recognition and tracking. An experimental evaluation shows that, when compared to a single-tier prototype, our multi-tier SensEye can achieve an order of magnitude reduction in energy usage while providing comparable surveillance accuracy.
Acromegaly Acromegaly is a hormonal disorder. Acromegaly means extremities and enlargements in Greek. Its typical symtoms are disfiguring growth of the bones of the skull and swelling of the face, hands, and feet. A facial features enlargement is the second most prevalent symptom with a prevalence over 95%. It includes protruded jaws, eye brows, and cheekbones, and enlarged lips and nose. The goal in this project is to detect acromegaly automatically from generic frontal facial photographs so that it can be diagnosed and recognized in a timely manner. In collaboration with Volker Blanz, Erik G. Learned-Miller and others, a classification system which prescreens patients for acromegaly using the Morphable model and Support Vector Machines (SVMs) was developed. Be aware: The following image can only be used for research purpose! Violation may result in serious consequences including but not limited to lawsuits!
A Probabilistic Upper Bound for Differential Entropy To provide a probabilistic MAXIMUM for differential entropy.
Below are some links to some friends, co-authors, and affiliated groups. | |||
