Repository logo

Information Science and Technology Center (ISTeC)

Permanent URI for this community

https://hdl.handle.net/10217/195492
These digital collections include materials from ISTeC Distinguished Lectures and Industrial Advisory Council presentations, and publications, grants, and student research posters from ISTeC.

Browse

Browsing Information Science and Technology Center (ISTeC) by Author "Chong, Edwin Kah Pin, author"

Now showing 1 - 3 of 3
  • Thumbnail Image
    ItemOpen Access
    Active sensing in an urban environment: closing the loop
    (Colorado State University. Libraries, 2006) Barbosa, Patricia de Rezende, author; Li, Yun, author; Chong, Edwin Kah Pin, author
    When compared to tracking airborne targets, tracking ground targets on urban terrain brings a new set of challenges. Target mobility is constrained by road networks, and the quality of measurements is affected by dense clutter, multipath, limited line-of-sight, and other interferers. In order to improve the accuracy of track estimates under such complex scenarios, it is important to use prior knowledge of the environment. We investigate the integration of detection, signal processing, tracking, and scheduling by exploiting distinct levels of diversity. This poster is approved by DARPA (Defense Advanced Research Projects Agency) for public release, distibution unlimited.
  • Thumbnail Image
    ItemOpen Access
    Opportunistic scheduling for wireless networks
    (Colorado State University. Libraries, 2006) Zhang, Zhi, author; Chong, Edwin Kah Pin, author
    We consider the problem of downlink scheduling for multi-user OFDM (Orthogonal Frequency Division Multiplexing) systems. We derive optimal scheduling policies under three QoS/fairness constraints -- temporal fairness, utilitarian fairness, and minimum-performance guarantees. To calculate these optimal policies, we interpret the problem as a maximal bipartite matching problem. To solve this problem, we apply the modified Hungarian algorithm and a practical suboptimal algorithm. The simulation results show that our schemes achieve significant improvement in system performance compared with a non-opportunistic scheme.
  • Thumbnail Image
    ItemOpen Access
    Portfolio management using partially observable Markov decision process
    (Colorado State University. Libraries, 2006) Zahedi, Ramin, author; Chong, Edwin Kah Pin, author
    Portfolio theory is concerned with how an investor should divide his wealth among different securities. This problem was first formulated by Markowitz in 1952. Since then, other more sophisticated formulations have been introduced. However, practical issues like transactions costs and their effects on the portfolio choice in multiple stages have not been widely considered. In our work, we show that the portfolio management problem is appropriately formulated as a Partially Observable Markov Decision Process. We use a Monte Carlo method called "rollout" to approximate an optimal strategy for making decisions. To capture the behavior of stock prices over time, we use two well known models.