Sensor Management for Tracking in Sensor Networks

We study the problem of tracking an object moving through a network of wireless sensors. In order to conserve energy, the sensors may be put into a sleep mode with a timer that determines their sleep duration. It is assumed that an asleep sensor cannot be communicated with or woken up, and hence the sleep duration needs to be determined at the time the sensor goes to sleep based on all the information available to the sensor. Having sleeping sensors in the network could result in degraded tracking performance, therefore, there is a tradeoff between energy usage and tracking performance. We design sleeping policies that attempt to optimize this tradeoff and characterize their performance. As an extension to our previous work in this area [1], we consider generalized models for object movement, object sensing, and tracking cost. For discrete state spaces and continuous Gaussian observations, we derive a lower bound on the optimal energy-tracking tradeoff. It is shown that in the low tracking error regime, the generated policies approach the derived lower bound

Sensor Scheduling for Energy-Efficient Target Tracking in Sensor Networks

In this paper we study the problem of tracking an object moving randomly through a network of wireless sensors. Our objective is to devise strategies for scheduling the sensors to optimize the tradeoff between tracking performance and energy consumption. We cast the scheduling problem as a Partially Observable Markov Decision Process (POMDP), where the control actions correspond to the set of sensors to activate at each time step. Using a bottom-up approach, we consider different sensing, motion and cost models with increasing levels of difficulty. At the first level, the sensing regions of the different sensors do not overlap and the target is only observed within the sensing range of an active sensor. Then, we consider sensors with overlapping sensing range such that the tracking error, and hence the actions of the different sensors, are tightly coupled. Finally, we consider scenarios wherein the target locations and sensors' observations assume values on continuous spaces. Exact solutions are generally intractable even for the simplest models due to the dimensionality of the information and action spaces. Hence, we devise approximate solution techniques, and in some cases derive lower bounds on the optimal tradeoff curves. The generated scheduling policies, albeit suboptimal, often provide close-to-optimal energy-tracking tradeoffs

Techniques for the Regeneration of Wideband Speech from Narrowband Speech

This paper addresses the problem of reconstructing wideband speech signals from observed narrowband speech signals. The goal of this work is to improve the perceived quality of speech signals which have been transmitted through narrowband channels or degraded during acquisition. We describe a system, based on linear predictive coding, for estimating wideband speech from narrowband. This system employs both previously identified and novel techniques. Experimental results are provided in order to illustrate the system’s ability to improve speech quality. Both objective and subjective criteria are used to evaluate the quality of the processed speech signals

Smart sleeping policies for energy efficient tracking in sensor networks

We study the problem of tracking an object that is moving randomly through a dense network of wireless sensors. We assume that each sensor has a limited range for detecting the presence of the object, and that the network is sufficiently dense so that the sensors cover the area of interest. In order to conserve energy the sensors may be put into a sleep mode with a timer that determines the sleep duration. We assume that a sensor that is asleep cannot be communicated with or woken up. Thus the sleep duration needs to be determined at the time the sensor goes to sleep based on all the information available to the sensor. The objective is to track the location of the object to within the accuracy of the range of the sensor. However, having sleeping sensors in the network could result in tracking errors, and hence there is a tradeoff between the energy savings and the tracking errors that result from the sleeping actions at the sensors. We consider the design of sleeping policies that optimize this tradeoff

Techniques for The Regeneration of Wideband Speech from Narrowband Speech

This paper addresses the problem of reconstructing wideband speech signals from observed narrowband speech signals. The goal of this work is to improve the perceived quality of speech signals which have been transmitted through narrowband channels or degraded during acquisition. We describe a system, based on linear predictive coding, for estimating wideband speech from narrowband. This system employs both previously identified and novel techniques. Experimental results are provided in order to illustrate the system&#8242;s ability to improve speech quality. Both objective and subjective criteria are used to evaluate the quality of the processed speech signals.</p

Spatially Varying Associations of Neighborhood Disadvantage with Alcohol and Tobacco Retail Outlet Rates

More than 30% of cancer related deaths are related to tobacco or alcohol use. Controlling and restricting access to these cancer-causing products, especially in communities where there is a high prevalence of other cancer risk factors, has the potential to improve population health and reduce the risk of specific cancers associated with these substances in more vulnerable population subgroups. One policy-driven method of reducing access to these cancer-causing substances is to regulate where these products are sold through the placement and density of businesses selling tobacco and alcohol. Previous work has found significant positive associations between tobacco, alcohol, and tobacco and alcohol retail outlets (TRO, ARO, TARO) and a neighborhood disadvantage index (NDI) using Bayesian shared component index modeling, where NDI associations differed across outlet types and relative risks varied by population density (e.g., rural, suburban, urban). In this paper, we used a novel Bayesian index model with spatially varying effects to explore spatial nonstationarity in NDI effects for TROs, AROs, and TAROs across census tracts in North Carolina. The results revealed substantial variation in NDI effects that varied by outlet type. However, all outlet types had strong positive effects in one coastal area. The most important variables in the NDI were percent renters, Black racial segregation, and the percentage of homes built before 1940. Overall, more disadvantaged areas experienced a greater neighborhood burden of outlets selling one or both of alcohol and tobacco

Examining the Association of Food Insecurity and Being Up-to-Date for Breast and Colorectal Cancer Screenings.

BackgroundFood insecurity (FI) has been associated with poor access to health care. It is unclear whether this association is beyond that predicted by income, education, and health insurance. FI may serve as a target for intervention given the many programs designed to ameliorate FI. We examined the association of FI with being up-to-date to colorectal cancer and breast cancer screening guidelines.MethodsNine NCI-designated cancer centers surveyed adults in their catchment areas using demographic items and a two-item FI questionnaire. For the colorectal cancer screening sample (n = 4,816), adults ages 50-75 years who reported having a stool test in the past year or a colonoscopy in the past 10 years were considered up-to-date. For the breast cancer screening sample (n = 2,449), female participants ages 50-74 years who reported having a mammogram in the past 2 years were up-to-date. We used logistic regression to examine the association between colorectal cancer or breast cancer screening status and FI, adjusting for race/ethnicity, income, education, health insurance, and other sociodemographic covariates.ResultsThe prevalence of FI was 18.2% and 21.6% among colorectal cancer and breast cancer screening participants, respectively. For screenings, 25.6% of colorectal cancer and 34.1% of breast cancer participants were not up-to-date. In two separate adjusted models, FI was significantly associated with lower odds of being up-to-date with colorectal cancer screening [OR, 0.7; 95% confidence interval (CI), 0.5-0.99)] and breast cancer screening (OR, 0.6; 95% CI, 0.4-0.96).ConclusionsFI was inversely associated with being up-to-date for colorectal cancer and breast cancer screening.ImpactFuture studies should combine FI and cancer screening interventions to improve screening rates