Sensor placement

This paper reviews Frechet sensitivity analysis for partial differential equations with variations in distributed parameters. The Frechet derivative provides a linear map between parametric variations and the linearized response of the solution. We propose a methodology based on representations of the Frechet derivative operator to find those variations that lead to the largest changes to the solution (the most significant variations). This includes an algorithm for computing these variations that only requires the action of the Frechet operator on a given direction (the Gateaux derivative) and its adjoint. This algorithm is applicable since it does not require an approximation of the entire Frechet operator, but only typical sensitivity analysis software for partial differential equations. The proposed methodology can be utilized to find worst case distributed disturbances and is thus applicable to uncertainty quantification and the optimal placement of sensors and actuators.

This paper presents a technique for planning and controlling bevel-tip steerable needles towards a target location in 3-D anatomy under the guidance of partial, noisy sensor feedback. Our approach minimizes the probability that the needle intersects obstacles such as bones and sensitive organs by (1) explicitly taking into account motion uncertainty and sensor types, and (2) allowing for efficient optimization of sensor placement.We allow for needle trajectories of arbitrary curvature through duty-cycled spinning of the needle, which is conjectured to make a needle path small-time locally “trackable” [13]. This enables us to use LQG control to guide the needle along the path. For a given path and sensor placement, we show that a priori probability distributions of the needle state can be estimated in advance. Our approach then plans a set of candidate paths and sensor placements and selects the pair for which the estimated uncertainty is least likely to cause intersections with obstacles. We demonstrate the performance of our approach in a modeled prostate cancer treatment environment.

Over the past few decades many vibration and signal analysis techniques have been investigated, developed, and used to provide operational response information about mechanical power transmission systems for monitoring and diagnosis of components and their worn/faulted condition. The presence of multiple sources of excitation and forcing in a complex distributed mechanical structure (rotor transmission shafting, gearing, bearings, casing, and foundation)

Activities of daily living are important for assessing changes in physical and behavioural profiles of the general population over time, particularly for the elderly and patients with chronic diseases. Although accelerometers are widely integrated with wearable sensors for activity classification, the positioning of the sensors and the selection of relevant features for different activity groups still pose interesting research challenges. This paper investigates wearable sensor placement at different body positions and aims to ...

This paper explores the opportunity of using tri-axial wireless accelerometers for supervised monitoring of sports movements. A motion analysis system for the upper extremities of lawn bowlers in particular is developed. Accelerometers are placed on parts of human body such as the chest to represent the shoulder movements, the back to capture the trunk motion, back of the hand, the wrist and one above the elbow, to capture arm movements. These sensors placement are carefully designed in order to avoid restricting bowler’s movements. Data is acquired from these sensors in soft-real time using virtual instrumentation; the acquired data is then conditioned and converted into required parameters for motion regeneration. A user interface was also created to facilitate in the acquisition of data, and broadcasting of commands to the wireless accelerometers. All motion regeneration in this paper deals with the motion of the human body segment in the X and Y direction, looking into the motio...

This paper addresses the issue of monitoring physical spatial phenomena of interest using information collected by a resource-constrained network of mobile, wireless and noisy sensors that can take discrete measurements as they navigate through the environment. We first propose an efficient novel optimality criterion for designing a sampling strategy to find the most informative locations in taking future observations in order to minimize the uncertainty at all unobserved locations of interest. This solution is proven to be within bounds. The computational complexity of this proposition is shown to be practically feasible. We then prove that under a certain condition of monotonicity property the approximate entropy at resulting locations obtained by our proposed algorithm is within 1 − 1 e of the optimum, which is then utilized as a stopping criterion for the sampling algorithm. The criterion enables the prediction results to be within user-defined accuracies by controlling the number of mobile sensors. The effectiveness of the proposed method is illustrated using a pre-published data set.

This paper addresses the issue of monitoring physical spatial phenomena of interest utilizing the information collected by a network of mobile, wireless and noisy sensors that can take discrete measurements as they navigate through the environment. The spatial phenomenon is statistically modelled by a Gaussian Markov Random Field (GMRF) with hyperpa-rameters that are learnt as the measurements accumulate over time. In this context, the GMRF approximately represents the spatial field on an irregular lattice of triangulation by exploiting a stochastic partial differential equation (SPDE) approach, which benefits remarkably in computation due to the sparsity of the precision matrix. A technique of the one-step-ahead forecast is employed to predict the future measurements that are required to find the optimal sampling locations. It is shown that optimizing the sampling path problem with the logarithm of the determinant either of a covariance matrix using a GP model or of a precision matrix using a GMRF model for mobile robotic wireless sensor networks (MRWSNs) even by a greedy algorithm is impractical. This paper proposes an efficient novel optimality criterion for the adaptive sampling strategy to find the most informative locations in taking future observations that minimize the uncertainty at unobserved locations. The computational complexity of our proposed method is linear, which makes the MRWSN scalable and practically feasible. The effectiveness of the proposed approach is compared and demonstrated using a pre-published data set with appealing results.

A test protocol is defined for the purpose of measuring rectus femoris mechanomyographic (MMG) signals. The protocol is specified in terms of the following: measurement equipment, signal processing requirements, human postural requirements, test rig, sensor placement, sensor dermal fixation, and test procedure. Preliminary tests of the statistical nature of rectus femoris MMG signals were performed, and Gaussianity was evaluated by means of a two-sided Kolmogorov-Smirnov test. For all 100 MMG data sets obtained from the testing of two volunteers, the null hypothesis of Gaussianity was rejected at the 1%, 5%, and 10% significance levels. Most skewness values were found to be greater than 0.0, while all kurtosis values were found to be greater than 3.0. A statistical convergence analysis also performed on the same 100 MMG data sets suggested that 25 MMG acquisitions should prove sufficient to statistically characterize rectus femoris MMG. This conclusion is supported by the qualitative characteristics of the mean rectus femoris MMG power spectral densities obtained using 25 averages.

Currently, the only high-quality, high-resolution temperature and precipitation data sets for the continental United States suitable for use on climatological time scales are for mean values. None yet exist that represent sequential monthly values over an extended historical ...

Much of the analysis of the potential to save energy and reduce emissions of greenhouse gases in the buildings sector has focused on the energy savings that can be achieved through incremental improvements in the efficiency of individual energy-using devices (motors, fans, pumps, boilers, chillers) but without changing the way in which they are put together as systems. However, much larger savings are possible through changes in building systems, and further, these savings can be achieved at much smaller incremental investment cost and sometimes at lower first cost. This paper will focus on the savings that can be achieved through a systems approach to buildings. The systems approach begins with a consideration of building shape and form and the specification of a high-performance envelope so as to minimize heating and cooling loads. This leads to a number of synergies that further reduce energy requirements, such as permitting cooler temperatures for distributing heat and warmer te...

A methodology is presented for designing cost-effective optimal sensor configurations for structural model updating and health monitoring purposes. The optimal sensor configuration is selected such that the resulting measured data are most informative about the condition of the structure. This selection is based on an information entropy measure of the uncertainty in the model parameter estimates obtained using a statistical system identification method. The methodology is developed for the uncertain excitation case encountered in practical applications for which data are to be taken either from ambient vibration tests or from other uncertain excitations such as earthquake and wind. Important issues related to robustness of the optimal sensor configuration to uncertainties in the structural model are addressed. The theoretical developments are illustrated by designing the optimal configuration for a simple 8-DOF chain-like model of a structure subjected to an unmeasured base excitat...

Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ensures that the object of interest is visible in the camera's field of view (FOV). However, visibility, which is a fundamental requirement of object tracking, is insufficient for automated persistent surveillance. In such applications, a continuous consistently labeled trajectory of the same object should be maintained across different camera views. Therefore, a sufficient uniform overlap between the cameras' FOVs should be secured so that camera handoff can successfully and automatically be executed before the object of interest becomes untraceable or unidentifiable. In this paper, we propose sensor-planning methods that improve existing algorithms by adding handoff rate analysis. Observation measures are designed for various types of cameras so that the proposed sensor-planning algorithm is general and applicable to scenarios with different types of cameras. The proposed sensor-planning algorithm preserves necessary uniform overlapped FOVs between adjacent cameras for an optimal balance between coverage and handoff success rate. In addition, special considerations such as resolution and frontal-view requirements are addressed using two approaches: 1) direct constraint and 2) adaptive weights. The resulting camera placement is compared with a reference algorithm published by Erdem and Sclaroff. Significantly improved handoff success rates and frontal-view percentages are illustrated via experiments using indoor and outdoor floor plans of various scales.