My research focus is Ambient Intelligence (AmI) that is to build human-like, interactive discovery systems. In my study, AmI is about common sense and biologically inspired cognition that is beyond the scope of ubiquitous computing technology [SIGCHI 2004]. With growing data streams and the complexity of discovery tasks, we see demand for integrating novel digital media and communications (e.g. Bodymedia, Capsule Camera, WiFi, etc.) and the opportunities for ambient intelligence using interaction methods that are usually taken for granted such as perception, insight and analogy. I want to search for solutions to interesting questions such as: How do we significantly reduce information while maintaining meaning? How to extract patterns from massive and growing data resources? How to design common information spaces for collaboration enabled by ambient interfaces? It is foreseeable that AmI based discovery systems have many applications, such as remote sensing, scientific visualization, security surveillance, navigation, aesthetic perception, and distance learning.
For the past five years, I have been working with a broad range of data for developing new discovery systems. For example, the novel tongue imaging and diagnostic system based on Traditional Chinese Medicine principles [1], the moving object tracking from head mounted camera video for landmine detection. [2], the game-based biological discovery system [3], and on-chip satellite lidar image indexing and recognition system [4,5]. In April, 2004, I co-chaired the Workshop on Ambient Intelligence for Scientific Discovery, at SIGCHI, Vienna. As a result from the Workshop, I am editing the book for Lecture Notes in Computer Science (LNCS) subseries Lecture Notes in Artificial Intelligence (LNAI) to be published by Springer [6]. In addition, I am guest editor for special issues on International Journal of Human-Computer Studies, Journal of Information Visualization and Journal of Axiomathes. To learn more about the scientific discovery in real-world, I spent three summers in different research centers: Juelich Research Center in Germany (2002), NASA Langley Center (2003) and NASA Goddard Space Flight Center in Washington DC (2004).
The followings are highlights of my recent funded projects:
1. Spatiotemporal Data Mining System for Tracking and Modeling Ocean Object Movement
The project is sponsored by NASA, starting October 1, 2004 to September 30, 2006. Tracking and modeling spatiotemporal dynamics of ocean objects are essential to NASA and NOAA missions in oceanographic studies, such as monitoring and predicting harmful algal blooms along the coastline, or river-based plume discharged to the open ocean. Despite of successful case studies in detecting ocean objects based on satellite images, most of work has been done manually, such as area marking and measurement, which is expensive and time consuming. We still lack general data mining algorithms for automatically monitoring the movement of an ocean object and forecasting the object movement. Besides, most of current studies are based on a single database rather than multiple data resources. In this project, we develop a spatiotemporal data mining system for following objectives: 1) tracking the movement of ocean objects that have been identified; 2) discovering the correlations between the object attributes and satellite readings from multiple databases; 3) predicating the movement of identified objects. This generalized spatiotemporal data mining tool enables monitoring and modeling for multiple oceanographic objects, such as plume and harmful algal blooms. This may also be applied to other spatiotemporal problems, such as monitoring dust storms. We use SeaWiFS database as our main source. Meanwhile, we will explore the use of other remote sensing databases such as MODIS. The technology would be based on our lab prototypes of multi-sensor data mining framework with the entrance Technical Readiness Level 4. The project deliverable would reach TRL 5 to 6. The total time for this project is for two years. The Co-PI Dr. Richard P. Stumpf, Oceanographer from NOAA will specify the requirements for the data mining tool and validate the product with field data. Dr. Han-Shou Liu, Geophysicist of GSFC, will support computational models for data mining. Dr. Horace Mitchell, Director of SVS, GSFC will support visualization of data mining results. Dr. Judith Devaney, Group Leader, Scientific Applications and Visualization Group, NIST, will participate the study on high fidelity virtual reality system for spatiotemporal data mining.2. Mobile Video Intelligence for Transit Security
This project is a joint R&D with Bombardier Transportation and Transportation Research Board (TRB), National Academy of Sciences (NAS). So far TRB and Bombarider have committed grants for supporting this project. The new phase will be started from October, 2004 to December 2006. According to a Public Transit Association report, there is a need for $5.3 billion dollars for upgrading transit security systems, including real-time surveillance video and face recognition. Unfortunately, technology for those applications does no exist because of the mobile environment and complexity of targets. This project includes two phases: 1) developing a demo system that can transfer wireless video from a moving vehicle to the control center; 2) develop intelligence algorithms for face recognition, sensor fusion and abnormal event detection. Those algorithms are designed to be seamlessly integrated into the existing control and communication software systems. The academic challenges include: 1) face detection in a moving and light-variation environment, 2) feature representation for normal or abnormal events, 3) human-camera interaction and adaptive display design for maximal vigilance and minimal bandwidth both for human attention and ad hoc wireless networks.References:
1. Yang Cai, "A Novel Imaging System for Tongue Inspection", IEEE IMTC'02, Alaska, May, 20022. Yang Cai, "Trajectory Mapping for Landmine Detection," Lecture Notes in Computer Science, Edited by Peter M.A. Sloot, et al, LNCS 2657, Computational Science, ICCS 2003, Part III, Springer-Verlag
3. Yang Cai, Y. Hu and M. Tomzack, "Onboard Multisensor Inversion with High Performance FPGA Computer," Field Programming Technology conference, 2003, Tokyo.
4. Yang Cai, Y. Hu, Mel Sigel et al, "Onboard Feature Indexing from Satellite Images" to appear on Journal of Measurement, Elsevier, 2004
5. Yang Cai, et al, "Character-Based Biomedical Problem Solving Environment," Journal of Future Generation Computer Systems, in print, online version available, Elsevier, 2004
6. Yang Cai. (ed), "Ambient Intelligence for Scientific Discovery," Lecture Notes in Computer Science (LNCS) and Lecture Notes in Artificial Intelligence (LNAI), Springer, 2004
7. Yang Cai, "Minimalism Context-Aware Display," Journal of CyberPsychology and Behavior, 7(3) 2004, also invited paper for Adaptive Displays, SIGGRAPH, Los Angeles, August 2004, indexed by PubMed
8. Yang Cai and Mel Siegel, "Texture Characterization of Visual Appearance of Paintings", IEEE IMTC'02
Cai interacted with CAVE-based discovery systems at NIST and NASA in 2004