Energy-efficient data acquisition for accurate signal estimation in wireless sensor networks

Long-term monitoring of an environment is a fundamental requirement for most wireless sensor networks. Owing to the fact that the sensor nodes have limited energy budget, prolonging their lifetime is essential in order to permit long-term monitoring. Furthermore, many applications require sensor nodes to obtain an accurate estimation of a point-source signal (for example, an animal call or seismic activity). Commonly, multiple sensor nodes simultaneously sample and then cooperate to estimate the event signal. The selection of cooperation nodes is important to reduce the estimation error while conserving the network’s energy. In this paper, we present a novel method for sensor data acquisition and signal estimation, which considers estimation accuracy, energy conservation, and energy balance. The method, using a concept of ‘virtual clusters,’ forms groups of sensor nodes with the same spatial and temporal properties. Two algorithms are used to provide functionality. The ‘distributed formation’ algorithm automatically forms and classifies the virtual clusters. The ‘round robin sample scheme’ schedules the virtual clusters to sample the event signals in turn. The estimation error and the energy consumption of the method, when used with a generalized sensing model, are evaluated through analysis and simulation. The results show that this method can achieve an improved signal estimation while reducing and balancing energy consumption

The nature of the Galactic Center source IRS 13 revealed by high spatial resolution in the infrared

High spatial resolution observations in the 1 to 3.5 micron region of the Galactic Center source known historically as IRS 13 are presented. They include ground-based adaptive optics images in the H, Kp (2.12/0.4 micron) and L bands, NICMOS data in filters between 1.1 and 2.2 micron, and integral field spectroscopic data from BEAR, an Imaging FTS, in the HeI 2.06 micron and the Br$\gamma$ line regions. Analysis of all these data provides a completely new picture of the main component, IRS 13E, which appears as a cluster of seven individual stars within a projected diameter of ~0.5'' (0.02 pc). The brightest sources, 13E1, 13E2, 13E3 (a binary), and 13E4, are all massive stars, 13E1 a blue object, with no detected emission line while 13E2 and 13E4 are high-mass emission line stars. 13E2 is at the WR stage and 13E4 a massive O-type star. 13E3A and B are extremely red objects, proposed as other examples of dusty WR stars. All these sources have a common westward proper motion. 13E5, is a red source similar to 13E3A/B. This concentration of comoving massive hot stars, IRS 13E, is proposed as the remaining core of a massive star cluster, which could harbor an intermediate-mass black hole (IMBH) of ~1300 M_sol. This detection plays in favor of a scenario in which the helium stars and the other hot stars in the central pc originate from the stripping of a massive cluster formed several tens of pc from the center. The detection of a discrete X-ray emission (Baganoff et al. 2003) at the IRS~13 position is examined in this context.Comment: 14 pages, 6 figures (3 in color), LaTeX2e, accepted in A&

Self-adjustable domain adaptation in personalized ECG monitoring integrated with IR-UWB radar

To enhance electrocardiogram (ECG) monitoring systems in personalized detections, deep neural networks (DNNs) are applied to overcome individual differences by periodical retraining. As introduced previously [4], DNNs relieve individual differences by fusing ECG with impulse radio ultra-wide band (IR-UWB) radar. However, such DNN-based ECG monitoring system tends to overfit into personal small datasets and is difficult to generalize to newly collected unlabeled data. This paper proposes a self-adjustable domain adaptation (SADA) strategy to prevent from overfitting and exploit unlabeled data. Firstly, this paper enlarges the database of ECG and radar data with actual records acquired from 28 testers and expanded by the data augmentation. Secondly, to utilize unlabeled data, SADA combines self organizing maps with the transfer learning in predicting labels. Thirdly, SADA integrates the one-class classification with domain adaptation algorithms to reduce overfitting. Based on our enlarged database and standard databases, a large dataset of 73200 records and a small one of 1849 records are built up to verify our proposal. Results show SADA\u27s effectiveness in predicting labels and increments in the sensitivity of DNNs by 14.4% compared with existing domain adaptation algorithms

Adaptive sampling in context-aware systems: a machine learning approach

As computing systems become ever more pervasive, there is an increasing need for them to understand and adapt to the state of the environment around them: that is, their context. This understanding comes with considerable reliance on a range of sensors. However, portable devices are also very constrained in terms of power, and hence the amount of sensing must be minimised. In this paper, we present a machine learning architecture for context awareness which is designed to balance the sampling rates (and hence energy consumption) of individual sensors with the significance of the input from that sensor. This significance is based on predictions of the likely next context. The architecture is implemented using a selected range of user contexts from a collected data set. Simulation results show reliable context identification results. The proposed architecture is shown to significantly reduce the energy requirements of the sensors with minimal loss of accuracy in context identification

Self-organizing nonlinear output (SONO): A neural network suitable for cloud patch-based rainfall estimation at small scales

Accurate measurement of rainfall distribution at various spatial and temporal scales is crucial for hydrological modeling and water resources management. In the literature of satellite rainfall estimation, many efforts have been made to calibrate a statistical relationship (including threshold, linear, or nonlinear) between cloud infrared (IR) brightness temperatures and surface rain rates (RR). In this study, an automated neural network for cloud patch-based rainfall estimation, entitled self-organizing nonlinear output (SONO) model, is developed to account for the high variability of cloud-rainfall processes at geostationary scales (i.e., 4 km and every 30 min). Instead of calibrating only one IR-RR function for all clouds the SONO classifies varied cloud patches into different clusters and then searches a nonlinear IR-RR mapping function for each cluster. This designed feature enables SONO to generate various rain rates at a given brightness temperature and variable rain/no-rain IR thresholds for different cloud types, which overcomes the one-to-one mapping limitation of a single statistical IR-RR function for the full spectrum of cloud-rainfall conditions. In addition, the computational and modeling strengths of neural network enable SONO to cope with the nonlinearity of cloud-rainfall relationships by fusing multisource data sets. Evaluated at various temporal and spatial scales, SONO shows improvements of estimation accuracy, both in rain intensity and in detection of rain/no-rain pixels. Further examination of the SONO adaptability demonstrates its potentiality as an operational satellite rainfall estimation system that uses the passive microwave rainfall observations from low-orbiting satellites to adjust the IR-based rainfall estimates at the resolution of geostationary satellites. Copyright 2005 by the American Geophysical Union

ShaneAO: wide science spectrum adaptive optics system for the Lick Observatory

A new high-order adaptive optics system is now being commissioned at the Lick Observatory Shane 3-meter telescope in California. This system uses a high return efficiency sodium beacon and a combination of low and high-order deformable mirrors to achieve diffraction-limited imaging over a wide spectrum of infrared science wavelengths covering 0.8 to 2.2 microns. We present the design performance goals and the first on-sky test results. We discuss several innovations that make this system a pathfinder for next generation AO systems. These include a unique woofer-tweeter control that provides full dynamic range correction from tip/tilt to 16 cycles, variable pupil sampling wavefront sensor, new enhanced silver coatings developed at UC Observatories that improve science and LGS throughput, and tight mechanical rigidity that enables a multi-hour diffraction- limited exposure in LGS mode for faint object spectroscopy science.Comment: 11 pages, 10 figures. Presented at SPIE Astronomical Telescopes + Instrumentation conference, paper 9148-7