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

Social Determinants of Smoke Exposure During Pregnancy: Findings From Waves 1 & 2 of the Population Assessment of Tobacco and Health (PATH) Study

Maternal smoking during pregnancy (MSDP) and secondhand smoke (SHS) exposure are associated with a myriad of negative health effects for both mother and child. However, less is known regarding social determinants for SHS exposure, which may differ from those of maternal smoking during pregnancy (MSDP). To identify social determinants for SHS exposure only, MSDP only, and MSDP and SHS exposure, data were obtained from all pregnant women (18–54 years; N = 726) in waves 1 and 2 of the Population Assessment of Tobacco and Health Study (2014–2015). Multiple logistic regressions were conducted using SAS 9.4. Smoke exposure during pregnancy was common; 23.0% reported SHS exposure only, 6.1% reported MSDP only, and 11.8% reported both SHS exposure and MSDP. Results demonstrate that relationships between smoke exposure during pregnancy and social determinants vary by type of exposure. Women at risk for any smoke exposure during pregnancy include those who are unmarried and allow the use of combustible tobacco products within the home. Those who are at higher risk for SHS exposure include those who are younger in age, and those who are earlier in their pregnancy. Those who are at higher risk for maternal smoking include those with fair/poor mental health status and those who believe that others\u27 view tobacco use more positively. These results suggest the need for implementing more comprehensive policies that promote smoke-free environments. Implementing these strategies have the potential to improve maternal and fetal health outcomes associated with tobacco smoke exposure

Inverse molecular design from first principles: Tailoring organic chromophore spectra for optoelectronic applications

The discovery of molecules with tailored optoelectronic properties, such as specific frequency and intensity of absorption or emission, is a major challenge in creating next-generation organic light-emitting diodes (OLEDs) and photovoltaics. This raises the following question: How can we predict a potential chemical structure from these properties? Approaches that attempt to tackle this inverse design problem include virtual screening, active machine learning, and genetic algorithms. However, these approaches rely on a molecular database or many electronic structure calculations, and significant computational savings could be achieved if there was prior knowledge of (i) whether the optoelectronic properties of a parent molecule could easily be improved and (ii) what morphing operations on a parent molecule could improve these properties. In this Perspective, we address both of these challenges from first principles. We first adapt the Thomas-Reiche-Kuhn sum rule to organic chromophores and show how this indicates how easily the absorption and emission of a molecule can be improved. We then show how by combining electronic structure theory and intensity borrowing perturbation theory we can predict whether or not the proposed morphing operations will achieve the desired spectral alteration, and thereby derive widely applicable design rules. We go on to provide proof-of-concept illustrations of this approach to optimizing the visible absorption of acenes and the emission of radical OLEDs. We believe that this approach can be integrated into genetic algorithms by biasing morphing operations in favor of those that are likely to be successful, leading to faster molecular discovery and greener chemistry

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

Predictors of Lung Cancer Screening Recommendation in Virginia Using the Community Health Assessment Survey

Purpose: The purpose of this analysis was to determine the factors that may influence the probability of being recommended a lung cancer screening by a health professional in Virginia. Methods: Data were obtained from the Community Health Assessment Survey conducted by the University of Virginia (UVA) Health System and Cancer System in collaboration with Virginia Commonwealth University (VCU) Cancer Center. SAS software was used to conduct a logistic regression with the following variables: age, sex, race, current smoking status, cancer history, education level, income level, insurance, and rurality. Results: Statistically significant positive predictors included being a current smoker (OR: 3.504, CI: 1.576 - 7.794), having previous cancer history (OR: 2.159, CI: 1.090 - 4.278), and living in an urban environment (OR: 1.939, CI: 1.009 - 3.724). Conclusion: Smoking, cancer history, and rurality were considered significant predictors of lung cancer screening recommendations by a health professional in Virginia while age, sex, race, education level, income level, and insurance were not considered significant predictors in this model. This study suggests that key mechanisms underlying lung cancer outcome disparities among racial minorities and socioeconomically disadvantaged groups may lie beyond the level of screening recommendations. Further research investigating when along the disease progression these disparities tend to arise could help in creating more targeted public health interventions and improving health equity

Knowledge of the Human Papillomavirus Vaccine: An Analysis using Together for Health Virginia Population Health Survey

Purpose: The purpose of this analysis was to identify key predictors which impact knowledge of the Human Papillomavirus vaccine in adults aged 21 to 45 in Virginia. Methods: Data was collected from the Together for Health Virginia Population Surveys administered by Virginia Commonwealth University and the University of Virginia. Logistic regression was performed on data using the variables sex, age, rurality, race, education, income, occupation, and type of health insurance coverage. Results: There was a statistically significant positive relationship between knowledge of the HPV vaccine and part-time occupation (OR: 4.288, CI: 1.492-13.325), younger age (OR: 2.31, CI: 1.088-4.905), and higher education (OR: 2.683, CI: 1.227-5.870). There was a statistically significant negative relationship between knowledge of the vaccine and being male (OR: 0.437, CI: 0.248-0.771), living in an urban area (OR: 0.511, CI: 0.267-0.977), and identifying in the lower income category (OR: 0.246, CI: 0.093-0.651). Conclusion: This study identified 6 key predictors in knowledge of the HPV vaccine among adults in Virginia. Future studies should explore, in particular, the category of students and residents of urban areas. Despite these results, knowledge of the HPV vaccine does not translate to intention to receive the vaccine. Therefore, future studies should additionally study attitudes, behaviors, and potential barriers