@inproceedings{liang_wang_krirn_2016, title={A behavior-based evaluation of product quality}, DOI={10.1109/icassp.2016.7472010}, abstractNote={In the pharmaceutical industry, quality is often measured by the impact of a product on a population. Knowledge about the behaviour of mosquitos responding to a repellent is a case in point in helping to improve the effect of insect repellent. It is ideally carried out using 3D videos which require a stereoscopic apparatus. To do so using 2D video and effectively evaluate the repellent is an difficult problem as is known in the biotechnology research field. In this paper, we propose a general framework for the swarm motion analysis of multiple mosquitos based on 2D videos. The effectiveness and robustness of our algorithm are verified by multiple 2D videos capturing mosquitos behavior in different experimental conditions.}, booktitle={International conference on acoustics speech and signal processing}, author={Liang, W. and Wang, H. and Krirn, H.}, year={2016}, pages={1916–1920} }
 @article{mahdizadehaghdam_wang_krim_dai_2016, title={Information Diffusion of Topic Propagation in Social Media}, volume={2}, ISSN={["2373-776X"]}, DOI={10.1109/tsipn.2016.2618324}, abstractNote={Real-world social and/or operational networks consist of agents with associated states, whose connectivity forms complex topologies. This complexity is further compounded by interconnected information layers, consisting, for instance, documents/resources of the agents which mutually share topical similarities. Our goal in this paper is to predict the specific states of the agents, as their observed resources evolve in time and get updated. The information diffusion among the agents and the publications themselves effectively result in a dynamic process which we capture by an interconnected system of networks (i.e., layered). More specifically, we use a notion of a supra-Laplacian matrix to address such a generalized diffusion of an interconnected network starting with the classical “graph Laplacian.” The auxiliary and external input update is modeled by a multidimensional Brownian process, yielding two contributions to the variations in the states of the agents: one that is due to the intrinsic interactions in the network system, and the other due to the external inputs or innovations. A variation on this theme, a priori knowledge of a fraction of the agents' states is shown to lead to a Kalman predictor problem. This helps us refine the predicted states exploiting the estimation error in the agents' states. Three real-world datasets are used to evaluate and validate the information diffusion process in this novel-layered network approach. Our results demonstrate a lower prediction error when using the interconnected network rather than the single connectivity layer between the agents. The prediction error is further improved by using the estimated diffusion connection and by applying the Kalman approach with partial observations.}, number={4}, journal={IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS}, author={Mahdizadehaghdam, Shahin and Wang, Han and Krim, Hamid and Dai, Liyi}, year={2016}, month={Dec}, pages={569–581} }