Resource Allocation and Performance Analysis of Wireless Video Sensors

Digital Object Identifier 10.1109/TCSVT.2006.873154Wireless video sensor networks (WVSNs) have been envisioned for a wide range of important applications, including battlefield intelligence, security monitoring, emergency response, and environmental tracking. Compared to traditional communication system, the WVSN operates under a set of unique resource constraints, including limitations with respect to energy supply,on-board computational capability, and transmission bandwidth. The objective of this paper is to study the resource utilization behavior of a wireless video sensor and analyze its performance under the resource constraints. More specifically, we develop an analytic power-rate-distortion (P-R-D) model to characterize the inherent relationship between the power consumption of a video encoder and its rate-distortion performance. Based on the P-R-D analysis and a simplified model for wireless transmission power,we study the optimum power allocation between video encoding and wireless transmission and introduce a measure called achievable minimum distortion to quantify the distortion under a total power constraint. We consider two scenarios in wireless video sensing, small-delay wireless video monitoring and large-delay wireless video surveillance, and analyze the performance limit of the wireless video sensor in each scenario. The analysis and results obtained in this paper provide an important guideline for practical wireless video sensor design.This work was supported in part by the National Science Foundation under Grant DBI-0529082 and Grant DBI-0529012

Effects of different immunosuppressive drugs on the periodontal status and changes in periodontal pathogenic bacterial flora in rheumatoid arthritis patients

Purpose: To investigate the prevalence of periodontal disease(s) and the associated bacteria among rheumatoid arthritis (RA) patients treated with different immunosuppressive drugs.Methods: Patients aged 18 – 65 years who had a 6-month history of RA, and were diagnosed as per the American College of Rheumatology and European League against Rheumatism, were included in the study. Supragingival plaque was removed by dentists. Using sterile paper strips, sub-gingival biofilm samples were collected from 5 of the deepest periodontal pockets. The samples were sent to pathologists for assessment. Polymerase chain reaction was carried out on them. Detection thresholds were >102 for Aggregatibacter actinomycetemcomitans, while the detection threshold for Porphyromonas gingivalis, Tannerella forsythia, Treponema denticola, Prevotella intermedia,  Fusobacterium nucleatum, Campylobacter rectus, Eubacterium nodatum, Eikenellacorrodens, and Capnocytophaga species was 103.Results: There was a higher number of patients with bleeding-on-probing amongst cohorts who received a combination of methotrexate and tumor necrosis factor-α antagonist than in those given leflunomide only (52 vs. 29, p = 0.041, q = 3.064), or methotrexate + rituximab (52 vs. 30, p = 0.041, q = 3.131, Fig. 1). Papilla bleeding index was lowest in patients who were treated with leflunomide. Almost all patients had dental infection with Fusobacterium nucleatum.Conclusion: These results indicate that treatment of RA with methotrexate results in periodontal inflammation

Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems

Given an unknown dynamical system, what is the minimum number of samples needed for effective learning of its governing laws and accurate prediction of its future evolution behavior, and how to select these critical samples? In this work, we propose to explore this problem based on a design approach. Starting from a small initial set of samples, we adaptively discover critical samples to achieve increasingly accurate learning of the system evolution. One central challenge here is that we do not know the network modeling error since the ground-truth system state is unknown, which is however needed for critical sampling. To address this challenge, we introduce a multi-step reciprocal prediction network where forward and backward evolution networks are designed to learn the temporal evolution behavior in the forward and backward time directions, respectively. Very interestingly, we find that the desired network modeling error is highly correlated with the multi-step reciprocal prediction error, which can be directly computed from the current system state. This allows us to perform a dynamic selection of critical samples from regions with high network modeling errors for dynamical systems. Additionally, a joint spatial-temporal evolution network is introduced which incorporates spatial dynamics modeling into the temporal evolution prediction for robust learning of the system evolution operator with few samples. Our extensive experimental results demonstrate that our proposed method is able to dramatically reduce the number of samples needed for effective learning and accurate prediction of evolution behaviors of unknown dynamical systems by up to hundreds of times