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Adaptive Sensing
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Adaptive Sensing

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (31 December 2010) | Viewed by 98622

Special Issue Editor

Prof. Dr. Rolf Mueller

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Virginia Tech, Institute for Advanced Learning and Research, 150 Slayton Ave., Danville, VA 24540, USA
Interests: bio-inspired technology and design patterns in nature; integration of structure and sensing; wave-based sensing; biosonar; bat-inspired autonomous robots

Special Issue Information

Dear Colleagues,

Over the past decades, an ever deepening understanding of the basic science principles behind sensor function combined with powerful engineering optimization methods has continuously pushed sensing capabilities forward. In many cases, fundamental physical limits have been reached - and sometimes even circumvented. Nevertheless, delivering satisfactory sensor performance remains an elusive goal for many critical applications scenarios. In the majority of these cases, the root cause for these difficulties lies in complex, dynamic signals of interest or likewise complex, dynamic environments in which these signals have to be sensed. In such situations, no single sensor configuration can be relied upon to continuously meet performance expectations. Adaptive sensing paradigms are designed to address these issues and hence hold great promise to extend technical sensing capabilities to meet the requirements of demanding and critical applications. To reach these goals requires advances in several key areas of sensors research. Examples are insights into the signals and the environments in which they are sensed, novel sensor designs that offer multiple levels of adaptation along with the necessary paradigms to control them, and methods for the interpretation of non-stationary sensor outputs. Making the most out of adaptive sensing strategies will also require intelligent integration of these aspects.

Prof. Dr. Rolf Mueller
Guest Editor

Keywords

  • sensor adaptation
  • adaptive sensor hardware
  • signal understanding
  • sensor control strategies
  • system integration

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Published Papers (11 papers)

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Research

580 KiB  
Article
An Innovations-Based Noise Cancelling Technique on Inverse Kepstrum Whitening Filter and Adaptive FIR Filter in Beamforming Structure
by Jinsoo Jeong
Sensors 2011, 11(7), 6816-6841; https://doi.org/10.3390/s110706816 - 29 Jun 2011
Cited by 8 | Viewed by 8386
Abstract
This paper presents an acoustic noise cancelling technique using an inverse kepstrum system as an innovations-based whitening application for an adaptive finite impulse response (FIR) filter in beamforming structure. The inverse kepstrum method uses an innovations-whitened form from one acoustic path transfer function [...] Read more.
This paper presents an acoustic noise cancelling technique using an inverse kepstrum system as an innovations-based whitening application for an adaptive finite impulse response (FIR) filter in beamforming structure. The inverse kepstrum method uses an innovations-whitened form from one acoustic path transfer function between a reference microphone sensor and a noise source so that the rear-end reference signal will then be a whitened sequence to a cascaded adaptive FIR filter in the beamforming structure. By using an inverse kepstrum filter as a whitening filter with the use of a delay filter, the cascaded adaptive FIR filter estimates only the numerator of the polynomial part from the ratio of overall combined transfer functions. The test results have shown that the adaptive FIR filter is more effective in beamforming structure than an adaptive noise cancelling (ANC) structure in terms of signal distortion in the desired signal and noise reduction in noise with nonminimum phase components. In addition, the inverse kepstrum method shows almost the same convergence level in estimate of noise statistics with the use of a smaller amount of adaptive FIR filter weights than the kepstrum method, hence it could provide better computational simplicity in processing. Furthermore, the rear-end inverse kepstrum method in beamforming structure has shown less signal distortion in the desired signal than the front-end kepstrum method and the front-end inverse kepstrum method in beamforming structure. Full article
(This article belongs to the Special Issue Adaptive Sensing)
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2218 KiB  
Article
Enhancement of Optical Adaptive Sensing by Using a Dual-Stage Seesaw-Swivel Actuator with a Tunable Vibration Absorber
by Po-Chien Chou, Yu-Cheng Lin and Stone Cheng
Sensors 2011, 11(5), 4808-4829; https://doi.org/10.3390/s110504808 - 3 May 2011
Cited by 7 | Viewed by 10985
Abstract
Technological obstacles to the use of rotary-type swing arm actuators to actuate optical pickup modules in small-form-factor (SFF) disk drives stem from a hinge’s skewed actuation, subsequently inducing off-axis aberrations and deteriorating optical quality. This work describes a dual-stage seesaw-swivel actuator for optical [...] Read more.
Technological obstacles to the use of rotary-type swing arm actuators to actuate optical pickup modules in small-form-factor (SFF) disk drives stem from a hinge’s skewed actuation, subsequently inducing off-axis aberrations and deteriorating optical quality. This work describes a dual-stage seesaw-swivel actuator for optical pickup actuation. A triple-layered bimorph bender made of piezoelectric materials (PZTs) is connected to the suspension of the pickup head, while the tunable vibration absorber (TVA) unit is mounted on the seesaw swing arm to offer a balanced force to reduce vibrations in a focusing direction. Both PZT and TVA are designed to satisfy stable focusing operation operational requirements and compensate for the tilt angle or deformation of a disc. Finally, simulation results verify the performance of the dual-stage seesaw-swivel actuator, along with experimental procedures and parametric design optimization confirming the effectiveness of the proposed system. Full article
(This article belongs to the Special Issue Adaptive Sensing)
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1718 KiB  
Article
Dynamic Experiment Design Regularization Approach to Adaptive Imaging with Array Radar/SAR Sensor Systems
by Yuriy Shkvarko, José Tuxpan and Stewart Santos
Sensors 2011, 11(5), 4483-4511; https://doi.org/10.3390/s110504483 - 27 Apr 2011
Cited by 30 | Viewed by 8485
Abstract
We consider a problem of high-resolution array radar/SAR imaging formalized in terms of a nonlinear ill-posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) of the random wavefield scattered from a remotely sensed scene observed through a kernel signal [...] Read more.
We consider a problem of high-resolution array radar/SAR imaging formalized in terms of a nonlinear ill-posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) of the random wavefield scattered from a remotely sensed scene observed through a kernel signal formation operator and contaminated with random Gaussian noise. First, the Sobolev-type solution space is constructed to specify the class of consistent kernel SSP estimators with the reproducing kernel structures adapted to the metrics in such the solution space. Next, the “model-free” variational analysis (VA)-based image enhancement approach and the “model-based” descriptive experiment design (DEED) regularization paradigm are unified into a new dynamic experiment design (DYED) regularization framework. Application of the proposed DYED framework to the adaptive array radar/SAR imaging problem leads to a class of two-level (DEED-VA) regularized SSP reconstruction techniques that aggregate the kernel adaptive anisotropic windowing with the projections onto convex sets to enforce the consistency and robustness of the overall iterative SSP estimators. We also show how the proposed DYED regularization method may be considered as a generalization of the MVDR, APES and other high-resolution nonparametric adaptive radar sensing techniques. A family of the DYED-related algorithms is constructed and their effectiveness is finally illustrated via numerical simulations. Full article
(This article belongs to the Special Issue Adaptive Sensing)
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Graphical abstract

555 KiB  
Communication
Applying Rprop Neural Network for the Prediction of the Mobile Station Location
by Chien-Sheng Chen and Jium-Ming Lin
Sensors 2011, 11(4), 4207-4230; https://doi.org/10.3390/s110404207 - 8 Apr 2011
Cited by 16 | Viewed by 10135
Abstract
Wireless location is the function used to determine the mobile station (MS) location in a wireless cellular communications system. When it is very hard for the surrounding base stations (BSs) to detect a MS or the measurements contain large errors in non-line-of-sight (NLOS) [...] Read more.
Wireless location is the function used to determine the mobile station (MS) location in a wireless cellular communications system. When it is very hard for the surrounding base stations (BSs) to detect a MS or the measurements contain large errors in non-line-of-sight (NLOS) environments, then one need to integrate all available heterogeneous measurements to increase the location accuracy. In this paper we propose a novel algorithm that combines both time of arrival (TOA) and angle of arrival (AOA) measurements to estimate the MS in NLOS environments. The proposed algorithm utilizes the intersections of two circles and two lines, based on the most resilient back-propagation (Rprop) neural network learning technique, to give location estimation of the MS. The traditional Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP) have convergence problems, and even if the measurements are fairly accurate, the performance of these algorithms depends highly on the relative position of the MS and BSs. Different NLOS models were used to evaluate the proposed methods. Numerical results demonstrate that the proposed algorithms can not only preserve the convergence solution, but obtain precise location estimations, even in severe NLOS conditions, particularly when the geometric relationship of the BSs relative to the MS is poor. Full article
(This article belongs to the Special Issue Adaptive Sensing)
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Graphical abstract

1421 KiB  
Article
Adaptive Marginal Median Filter for Colour Images
by Samuel Morillas, Valentín Gregori and Almanzor Sapena
Sensors 2011, 11(3), 3205-3213; https://doi.org/10.3390/s110303205 - 15 Mar 2011
Cited by 39 | Viewed by 9015
Abstract
This paper describes a new filter for impulse noise reduction in colour images which is aimed at improving the noise reduction capability of the classical vector median filter. The filter is inspired by the application of a vector marginal median filtering process over [...] Read more.
This paper describes a new filter for impulse noise reduction in colour images which is aimed at improving the noise reduction capability of the classical vector median filter. The filter is inspired by the application of a vector marginal median filtering process over a selected group of pixels in each filtering window. This selection, which is based on the vector median, along with the application of the marginal median operation constitutes an adaptive process that leads to a more robust filter design. Also, the proposed method is able to process colour images without introducing colour artifacts. Experimental results show that the images filtered with the proposed method contain less noisy pixels than those obtained through the vector median filter. Full article
(This article belongs to the Special Issue Adaptive Sensing)
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1519 KiB  
Article
Adaptive Sampling for Learning Gaussian Processes Using Mobile Sensor Networks
by Yunfei Xu and Jongeun Choi
Sensors 2011, 11(3), 3051-3066; https://doi.org/10.3390/s110303051 - 9 Mar 2011
Cited by 70 | Viewed by 9730
Abstract
This paper presents a novel class of self-organizing sensing agents that adaptively learn an anisotropic, spatio-temporal Gaussian process using noisy measurements and move in order to improve the quality of the estimated covariance function. This approach is based on a class of anisotropic [...] Read more.
This paper presents a novel class of self-organizing sensing agents that adaptively learn an anisotropic, spatio-temporal Gaussian process using noisy measurements and move in order to improve the quality of the estimated covariance function. This approach is based on a class of anisotropic covariance functions of Gaussian processes introduced to model a broad range of spatio-temporal physical phenomena. The covariance function is assumed to be unknown a priori. Hence, it is estimated by the maximum a posteriori probability (MAP) estimator. The prediction of the field of interest is then obtained based on the MAP estimate of the covariance function. An optimal sampling strategy is proposed to minimize the information-theoretic cost function of the Fisher Information Matrix. Simulation results demonstrate the effectiveness and the adaptability of the proposed scheme. Full article
(This article belongs to the Special Issue Adaptive Sensing)
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Graphical abstract

276 KiB  
Article
ECS: Efficient Communication Scheduling for Underwater Sensor Networks
by Lu Hong, Feng Hong, Zhongwen Guo and Zhengbao Li
Sensors 2011, 11(3), 2920-2938; https://doi.org/10.3390/s110302920 - 4 Mar 2011
Cited by 30 | Viewed by 8440
Abstract
TDMA protocols have attracted a lot of attention for underwater acoustic sensor networks (UWSNs), because of the unique characteristics of acoustic signal propagation such as great energy consumption in transmission, long propagation delay and long communication range. Previous TDMA protocols all allocated transmission [...] Read more.
TDMA protocols have attracted a lot of attention for underwater acoustic sensor networks (UWSNs), because of the unique characteristics of acoustic signal propagation such as great energy consumption in transmission, long propagation delay and long communication range. Previous TDMA protocols all allocated transmission time to nodes based on discrete time slots. This paper proposes an efficient continuous time scheduling TDMA protocol (ECS) for UWSNs, including the continuous time based and sender oriented conflict analysis model, the transmission moment allocation algorithm and the distributed topology maintenance algorithm. Simulation results confirm that ECS improves network throughput by 20% on average, compared to existing MAC protocols. Full article
(This article belongs to the Special Issue Adaptive Sensing)
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Graphical abstract

193 KiB  
Article
An MILP-Based Cross-Layer Optimization for a Multi-Reader Arbitration in the UHF RFID System
by Jinchul Choi and Chaewoo Lee
Sensors 2011, 11(3), 2347-2368; https://doi.org/10.3390/s110302347 - 24 Feb 2011
Cited by 10 | Viewed by 8455
Abstract
In RFID systems, the performance of each reader such as interrogation range and tag recognition rate may suffer from interferences from other readers. Since the reader interference can be mitigated by output signal power control, spectral and/or temporal separation among readers, the system [...] Read more.
In RFID systems, the performance of each reader such as interrogation range and tag recognition rate may suffer from interferences from other readers. Since the reader interference can be mitigated by output signal power control, spectral and/or temporal separation among readers, the system performance depends on how to adapt the various reader arbitration metrics such as time, frequency, and output power to the system environment. However, complexity and difficulty of the optimization problem increase with respect to the variety of the arbitration metrics. Thus, most proposals in previous study have been suggested to primarily prevent the reader collision with consideration of one or two arbitration metrics. In this paper, we propose a novel cross-layer optimization design based on the concept of combining time division, frequency division, and power control not only to solve the reader interference problem, but also to achieve the multiple objectives such as minimum interrogation delay, maximum reader utilization, and energy efficiency. Based on the priority of the multiple objectives, our cross-layer design optimizes the system sequentially by means of the mixed-integer linear programming. In spite of the multi-stage optimization, the optimization design is formulated as a concise single mathematical form by properly assigning a weight to each objective. Numerical results demonstrate the effectiveness of the proposed optimization design. Full article
(This article belongs to the Special Issue Adaptive Sensing)
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368 KiB  
Article
Model-Free Adaptive Sensing and Control for a Piezoelectrically Actuated System
by Hung-Yi Chen and Jin-Wei Liang
Sensors 2010, 10(12), 10545-10559; https://doi.org/10.3390/s101210545 - 24 Nov 2010
Cited by 6 | Viewed by 7464
Abstract
Since the piezoelectrically actuated system has nonlinear and time-varying behavior, it is difficult to establish an accurate dynamic model for a model-based sensing and control design. Here, a model-free adaptive sliding controller is proposed to improve the small travel and hysteresis defects of [...] Read more.
Since the piezoelectrically actuated system has nonlinear and time-varying behavior, it is difficult to establish an accurate dynamic model for a model-based sensing and control design. Here, a model-free adaptive sliding controller is proposed to improve the small travel and hysteresis defects of piezoelectrically actuated systems. This sensing and control strategy employs the functional approximation technique (FAT) to establish the unknown function for eliminating the model-based requirement of the sliding-mode control. The piezoelectrically actuated system’s nonlinear functions can be approximated by using the combination of a finite number of weighted Fourier series basis functions. The unknown weighted vector can be estimated by an updating rule. The important advantage of this approach is to achieve the sliding-mode controller design without the system dynamic model requirement. The update laws for the coefficients of the Fourier series functions are derived from a Lyapunov function to guarantee the control system stability. This proposed controller is implemented on a piezoelectrically actuated X-Y table. The dynamic experimental result of this proposed FAT controller is compared with that of a traditional model-based sliding-mode controller to show the performance improvement for the motion tracking performance. Full article
(This article belongs to the Special Issue Adaptive Sensing)
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448 KiB  
Article
Adaptive AOA-Aided TOA Self-Positioning for Mobile Wireless Sensor Networks
by Chih-Yu Wen and Fu-Kai Chan
Sensors 2010, 10(11), 9742-9770; https://doi.org/10.3390/s101109742 - 1 Nov 2010
Cited by 18 | Viewed by 8205
Abstract
Location-awareness is crucial and becoming increasingly important to many applications in wireless sensor networks. This paper presents a network-based positioning system and outlines recent work in which we have developed an efficient principled approach to localize a mobile sensor using time of arrival [...] Read more.
Location-awareness is crucial and becoming increasingly important to many applications in wireless sensor networks. This paper presents a network-based positioning system and outlines recent work in which we have developed an efficient principled approach to localize a mobile sensor using time of arrival (TOA) and angle of arrival (AOA) information employing multiple seeds in the line-of-sight scenario. By receiving the periodic broadcasts from the seeds, the mobile target sensors can obtain adequate observations and localize themselves automatically. The proposed positioning scheme performs location estimation in three phases: (I) AOA-aided TOA measurement, (II) Geometrical positioning with particle filter, and (III) Adaptive fuzzy control. Based on the distance measurements and the initial position estimate, adaptive fuzzy control scheme is applied to solve the localization adjustment problem. The simulations show that the proposed approach provides adaptive flexibility and robust improvement in position estimation. Full article
(This article belongs to the Special Issue Adaptive Sensing)
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1088 KiB  
Article
Adaptive Control of a Vibratory Angle Measuring Gyroscope
by Sungsu Park
Sensors 2010, 10(9), 8478-8490; https://doi.org/10.3390/s100908478 - 9 Sep 2010
Cited by 16 | Viewed by 8022
Abstract
This paper presents an adaptive control algorithm for realizing a vibratory angle measuring gyroscope so that rotation angle can be directly measured without integration of angular rate, thus eliminating the accumulation of numerical integration errors. The proposed control algorithm uses a trajectory following [...] Read more.
This paper presents an adaptive control algorithm for realizing a vibratory angle measuring gyroscope so that rotation angle can be directly measured without integration of angular rate, thus eliminating the accumulation of numerical integration errors. The proposed control algorithm uses a trajectory following approach and the reference trajectory is generated by an ideal angle measuring gyroscope driven by the estimate of angular rate and the auxiliary sinusoidal input so that the persistent excitation condition is satisfied. The developed control algorithm can compensate for all types of fabrication imperfections such as coupled damping and stiffness, and mismatched stiffness and un-equal damping term in an on-line fashion. The simulation results show the feasibility and effectiveness of the developed control algorithm that is capable of directly measuring rotation angle without the integration of angular rate. Full article
(This article belongs to the Special Issue Adaptive Sensing)
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