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Research Projects

Below, you can find a short summary of our current and previous research projects. If you want to learn about some of our new exciting projects, contact Prof. Daphney-Stavroula Zois (dzois@albany.edu).

Energy-Efficient Physical Activity Detection

Wireless Body Area Networks (WBANs) that consist of heterogeneous biometric sensors (e.g., heart–rate monitors, accelerometers) and an energy–constrained personal device (e.g., mobile phone, PDA), have the transformative potential to influence a wide range of applications including health–care, sports, military and emergency applications. However, the practical realization of a WBAN is hindered by a number of unique challenges, including energy constraints that significantly impact its lifetime. To address this issue, we have proposed novel resource allocation strategies that maximize the network lifetime by minimizing energy spent at the energy–constrained fusion center. The effectiveness of our proposed methods have been evaluated using extensive simulations on real data collected from a real prototype WBAN, the KNOWME network, used for preventing obesity. We have showed that we can improve energy gains by as much as 68% compared to state–of–the–art schemes, without compromising detection accuracy.

Active Object Detection for Computer Vision

Object detection is a fundamental, yet very challenging task in image analysis. A typical object detection algorithm first generates a set of region proposals. These can be either fixed and class–independent or generated on the fly while searching for a particular object. Each such proposal is then assigned a class label by running a set of detectors. Evaluating a large number of region proposals will certainly lead to high detection accuracy, but will incur high computational costs if each detector evaluation is computationally expensive. In this line of work, we proposed an object detection algorithm that models image regions as vertices and overlap relationships as edges in a directed weighted graph. Information is propagated from labeled vertices through graph edges that operate as noisy channels via message passing over locally informative trees that are extracted from the original graph using an information-theoretic criterion. Influential vertices are determined by an appropriate centrality index. Our algorithm can be applied on top of any state–of–the–art region proposal method as it treats it as a black box. We evaluated the performance of our proposed algorithm on different scenarios and showed that in some cases only 0.45% of the total regions is evaluated with maximum 21.45%.

Active Collision Detection in Freeways

In recent years, urban mobility demand has become highly variable over time challenging the sustainability of transportation networks of major cities. At the same time, various types of incidents such as accidents, construction zone closures and weather hazards exacerbate the already congested transportation network. Timely detection of such events can offer an unprecedented opportunity to mitigate the consequences. Currently, we are developing a framework for continuous active collision detection in a road segment equipped with spatially distributed speed sensors of variable accuracy. Our goal is to design appropriate low-complexity algorithms that can quickly estimate the existence of a collision by appropriately selecting which sensors to query and when.

Active State Tracking: Frameworks, Structural Characterization, Performance Objectives

Sensor heterogeneity necessitates the design of sensing algorithms to achieve inference, the quality of which drives the adaptation of the associated algorithm. Thus, sensing and tracking are tightly interconnected. In our work, we developed a generalized framework for active state tracking with heterogeneous observations, where the decision maker selects which observations to consider at each step based on accuracy and cost criteria. Various criteria were considered along with their effect on accuracy and were generalized appropriately to address the intertwined key problems of information characterization and unified sensing and tracking. Based on this framework, we developed fundamental theory that enabled us to characterize the structure of the optimal sensing algorithm and devise appropriate scalable algorithms.