Fewer Number Of Sensors Research Articles

Significance of group delay spectrum in re-weighted sparse recovery algorithms for DOA estimation

Sparse Recovery (SR) algorithms have been widely used for direction of arrival (DOA) estimation. At low values of signal to noise ratio (SNR) i.e. beyond -10 dB and with adequate number of sensors [1], their estimates are incorrect. The magnitude spectrum-based Re-weighted sparse recovery (RWSR) algorithms improve the robustness by re-weighting the sparse estimates. But their efficiency degrades significantly with a fewer number of sensors. The significance of phase spectrum in the form of Group delays for robust DOA estimation using RWSR algorithms for spatially contiguous sources is explored in this paper. An optimal re-weighting methodology based on simultaneously minimizing average root mean square error (ARMSE) and maximizing the probability of separation is proposed. The simulations are carried for Gaussian and Laplacian noise to demonstrate the superior performance of the proposed method with a few sensors at low values of SNR.

Extraction of respiratory rate from PPG using ensemble empirical mode decomposition with Kalman filter

This Letter suggests a simple but effective approach for accurate estimation of respiratory rate (RR) from the photoplethysmogram (PPG). In the suggested technique, the PPG signal is first decomposed into a number of intrinsic mode functions (IMFs) using the ensemble empirical mode decomposition (EEMD). The IMFs comprising respiratory information are selected and used to reconstruct the signal. A Kalman filter coupled with the signal quality measure of PPG is utilised to process the reconstructed signal to derive the respiratory signal for RR estimation. Experiments are conducted on two independent datasets, namely CapnoBase and MIMIC. Based on experimental results, the technique is found to be outperforming the existing methods by achieving lower error in the estimated RRs on both datasets. This work demonstrates the potential of the EEMD method with Kalman filter for precise estimation of RR from PPG to contribute to the advancement of portable healthcare systems with a fewer number of sensors.

Investigation of Channel Selection for Gesture Classification for Prosthesis Control Using Force Myography: A Case Study.

Background: Various human machine interfaces (HMIs) are used to control prostheses, such as robotic hands. One of the promising HMIs is Force Myography (FMG). Previous research has shown the potential for the use of high density FMG (HD-FMG) that can lead to higher accuracy of prosthesis control.Motivation: The more sensors used in an FMG controlled system, the more complicated and costlier the system becomes. This study proposes a design method that can produce powered prostheses with performance comparable to that of HD-FMG controlled systems using a fewer number of sensors. An HD-FMG apparatus would be used to collect information from the user only in the design phase. Channel selection would then be applied to the collected data to determine the number and location of sensors that are vital to performance of the device. This study assessed the use of multiple channel selection (CS) methods for this purpose.Methods: In this case study, three datasets were used. These datasets were collected from force sensitive resistors embedded in the inner socket of a subject with transradial amputation. Sensor data were collected as the subject carried out five repetitions of six gestures. Collected data were then used to asses five CS methods: Sequential forward selection (SFS) with two different stopping criteria, minimum redundancy-maximum relevance (mRMR), genetic algorithm (GA), and Boruta.Results: Three out of the five methods (mRMR, GA, and Boruta) were able to decrease channel numbers significantly while maintaining classification accuracy in all datasets. Neither of them outperformed the other two in all datasets. However, GA resulted in the smallest channel subset in all three of the datasets. The three selected methods were also compared in terms of stability [i.e., consistency of the channel subset chosen by the method as new training data were introduced or some training data were removed (Chandrashekar and Sahin, 2014)]. Boruta and mRMR resulted in less instability compared to GA when applied to the datasets of this study.Conclusion: This study shows feasibility of using the proposed design method that can produce prosthetic systems that are simpler than HD-FMG systems but have performance comparable to theirs.

Thinned Coprime Array for Second-Order Difference Co-Array Generation With Reduced Mutual Coupling

In this work, we present a new coprime array structure termed thinned coprime array (TCA), which exploits the redundancy in the structure of existing coprime array and achieves the same virtual aperture and degrees of freedom (DOFs) as the conventional coprime array with much fewer number of sensors. In comparison to other sparse arrays, thinned coprime arrays possess more unique lags (total number of difference co-arrays) than the nested arrays, while the number of consecutive lags (connected co-arrays) generated is close to 75% of the consecutive lags of the nested arrays with hole-free co-arrays. The resulting structure is much sparser and the number of sensor pairs with small separation is significantly reduced. Theoretical properties and proofs are provided and simulations are presented to demonstrate its robustness against heavy levels of mutual coupling using compressive sensing based direction of arrival estimation as well as certain additional desirable characteristics.

Structural sensing of interior sound for active control of noise in structural-acoustic cavities.

This paper proposes a method for structural sensing of acoustic potential energy for active control of noise in a structural-acoustic cavity. The sensing strategy aims at global control and works with a fewer number of sensors. It is based on the established concept of radiation modes and hence does not add too many states to the order of the system. Acoustic potential energy is sensed using a combination of a Kalman filter and a frequency weighting filter with the structural response measurements as the inputs. The use of Kalman filter also makes the system robust against measurement noise. The formulation of the strategy is presented using finite element models of the system including that of sensors and actuators so that it can be easily applied to practical systems. The sensing strategy is numerically evaluated in the framework of Linear Quadratic Gaussian based feedback control of interior noise in a rectangular box cavity with a flexible plate with single and multiple pairs of piezoelectric sensor-actuator patches when broadband disturbances act on the plate. The performance is compared with an "acoustic filter" that models the complete transfer function from the structure to the acoustic domain. The sensing performance is also compared with a direct estimation strategy.

Localization of a sound source in a noisy environment by hyperbolic curves in quefrency domain

Time Difference of Arrivals (TDOAs) of sound waves between microphones have to do with source localization. How well a sound source can be localized depends on how precisely the TDOAs are estimated. Although many ways to estimate TDOA have been proposed, noise always prevents us from finding exact time differences more or less in practice. Cross correlation has been the most prevalent way to estimate time difference, and various cross correlations robust to noise have also been developed. Nevertheless, much remains to be done for exact TDOA estimation under noisy environments. A novel way to show time delays in quefrency domain by removing noise has been proposed, which is called Minimum Variance Cepstrum (MVC). In particular, it is practically desirable to visualize source position with as few number of sensors as possible. Once TDOAs are obtained precisely, it is enough to show the source position in a 2-D plane using hyperbolic curves with only three sensors. In this work, the MVC is adopted to accurately estimate TDOAs under noise, and a way to localize an acoustic source by intersecting hyperbolic curves using the TDOAs between three microphones is proposed. Numerical simulations on TDOA estimation and source localization with white Gaussian noise demonstrated that the proposed method worked well under the noisy environment, and we compared the results with those of other old but well-established cross correlation estimators. In addition, experiments to detect a leaking point on a pipe successfully showed where the leak sound was generated.

Layout Optimization of Sensor-based Reconstruction of Explosion Overpressure Field Based on the Genetic Algorithm

In underwater blasting experiment, the layout of the sensor has always been highly concerned. From the perspective of reconstruction with explosion overpressure field, the paper presents four indicators, which can obtain the optimal sensor layout scheme and guide sensor layout in practical experiment, combining with the genetic algorithm with global search. Then, a multi-scale model in every subregion of underwater blasting field was established to be used simulation experiments. By Matlab, the variation of these four indicators with different sensor layout, and reconstruction accuracy are analyzed and discussed. Finally, a conclusion has been raised through the analysis and comparison of simulation results, that the program can get a better sensor layout. It requires fewer number of sensors to be able to get good results with high accuracy. In the actual test explosions, we can refer to this scheme laid sensors.

A strain amplitude-based algorithm for impact localization on composite laminates

Automated detection of damage due to low energy impacts in composite structures is very important for aerospace structural health monitoring applications. Low-velocity impact creates subsurface damage that can significantly reduce the stiffness of a component, yet show barely visible damage. This article proposes a novel methodology for impact localization based on the maximum strain amplitude measured by fiber Bragg grating (FBG) sensors during an impact event. The approach correlates the strain amplitude of each sensor pair to find the location of highest strain corresponding to the impact location. This approach requires minimal knowledge of the structure and fewer number of sensors as opposed to current localization methods. Both simulation and experimental data are used as proof of concept. Since FBG sensors measure strain in only one direction, the effect of sensor orientation on the performance of the algorithm is also studied. The algorithm is tested on graphite/epoxy composite plates and shows good localization results in all impact cases considered.

Optimal Control Based Sensor Deployment Incorporating Centralized Data Fusion

Data fusion methods that involve combining information from multiple sensors can be used to satisfy detection and false alarm requirements in a distributed sensor network (DSN) based detection system. However, due to limited sensing and communication capabilities of sensors, the positions at which sensors are deployed play an important role in determining overall system performance. We study the problem of determining the positions where a fixed number of sensors are to be deployed in order to 1) meet false alarm requirements, and 2) minimize the squared error (SE) between achieved and required detection probabilities. Towards this end, we propose a novel treatment of the deployment problem within the optimal control theory framework. Specifically, we model the deployment problem as a linear quadratic regulator (LQR) problem, where the sensor positions serve as control vectors. Based on this formulation, we develop two sequential sensor deployment algorithms and study the impact of dynamically updating the collaboration radius. Simulation results show that, in comparison with existing heuristic algorithms, our proposed algorithms use up to 30% fewer number of sensors to satisfy detection and false alarm requirements when using a fixed collaboration radius. Additionally, we show that the use of a dynamic collaboration radius can save up to 45% in the number of sensors used relative to the fixed collaboration case.