Estimates Of Spatial Location Research Articles

Flexible, monolithic piezoelectric sensors for large-area structural impact monitoring via MUSIC-assisted machine learning

Aircraft smart skin requires the integration of large-scale, ultrathin, and high-sensitive sensor network on the surface for structural health monitoring (SHM). However, it is fairly difficult to fabricate such large-area flexible sensor networks with low cost and high reliability. Although flexible and monolithic sensors are easy to be fabricated, their applications in large-area impact monitoring have yet to be developed and revealed. In this study, an impact monitoring and localization technology based on monolithic small-area sensors is proposed for large-area structures. The pivotal principle lies in the combination of the flexible piezoelectric sensor array and the Multiple Signal Classification (MUSIC)-assisted Artificial Intelligence Network (ANN) algorithm, where the key sorted feature matrix from the output signals to ANN can be captured by the MUSIC algorithm to achieve the spatial location estimation. The results show that the flexible piezoelectric sensors demonstrate excellent performance to monitor structural impacts in a region of more than 7500% of its area. Secondly, excellent conformation between the sensors and complex surfaces is achieved by the ultrathin thickness almost without affecting the surficial flow field. The overall recognition accuracy and robustness of impact location of machine learning is greatly increased by the integration of MUSIC algorithm, which is immune to the shape, material, thickness, or opening holes of the structure. Except for the impact location, impact energy, impact frequency, impact hardness and other structural health parameters can also be monitored. Therefore, a great prospect in the integrated monitoring of the aircraft’s structural and environmental parameters is expected.

Manifold Optimization for High-Accuracy Spatial Location Estimation Using Ultrasound Waves

This paper designs a high accuracy spatial location estimation method using ultrasound waves by exploiting the fixed geometry of the transmitters. Assuming an equilateral triangle antenna configuration, where three antennas are placed as the vertices of an equilateral triangle, the spatial location problem can be formulated as a non-convex optimization problem whose interior is shown to admit a Riemannian manifold structure. The investigation of the geometry of the newly introduced manifold, i.e. the manifold of all equilateral triangles in R^3, allows the design of highly efficient optimization algorithms. Simulation results are presented to compare the performance of the proposed approach against popular methods from the literature. The results suggest that the proposed Riemannian-based methods outperform the state-of-the-art methods. Furthermore, the proposed Riemannian methods require much smaller computation time as compared with popular generic non-convex approaches.

Action Extraction in Continuous Unconstrained Video for Cloud-Based Intelligent Service Robot

Transferring complex computing to the cloud server side leverages cloud-based intelligent service robots that are capable of highly complex computing tasks such as video analysis. In practical behavior surveillance applications, the captured videos from intelligent service robots are continuous. Action extraction from continuous unconstrained video is an important prerequisite for action analysis, such as action classification and recognition, abnormal event detection, and crowd emotion sensing. This paper proposes a novel approach for action extraction in continuous unconstrained video, which has three parts: spatial location estimation, temporal action path searching; and spatial-temporal action compensation. Spatial location estimation utilizes both human appearance and motion cues to obtain frame-level bounding boxes. Then, with the spatial action proposal results as a priori, the searching of temporal action paths is formulated as an optimal estimation problem by accounting for the missed detections and false alarms of the spatial location estimation. To solve the temporal action path searching problem, we propose the Markov Chain Monte Carlo algorithm and illustrate its convergence property. Extensive experiments on the challenging UCF-Sports and UCF-101 data sets show the effectiveness of our approach and obtain superior performance compared with the state-of-the-arts.

On the Perceptual Advantages of Visual Suppression Mechanisms for Dynamic Robot Systems

The use of computer vision based methods to explore the surrounding environment and track individuals, is further enhanced by the ability to move the visual system, a process realized by biological organisms. However, several perceptual issues arise from the rapid movement of the image acquisition system: blurred images and visual-proprioceptive transient delays result in incorrect spatial location estimation. Inspired by the biological mechanism of saccadic suppression, our main contribution is a biologically inspired visual stability mechanism able to deal with problems arising from self-motion. Together with a state-of-the-art pedestrian detection algorithm, our proposed methodology contributes to enhancements in person position estimation, thus improving human-robot interaction behaviors.

Prediction, Postdiction, and Perceptual Length Contraction: A Bayesian Low-Speed Prior Captures the Cutaneous Rabbit and Related Illusions

Illusions provide a window into the brain’s perceptual strategies. In certain illusions, an ostensibly task-irrelevant variable influences perception. For example, in touch as in audition and vision, the perceived distance between successive punctate stimuli reflects not only the actual distance but curiously the inter-stimulus time. Stimuli presented at different positions in rapid succession are drawn perceptually toward one another. This effect manifests in several illusions, among them the startling cutaneous rabbit, in which taps delivered to as few as two skin positions appear to hop progressively from one position to the next, landing in the process on intervening areas that were never stimulated. Here we provide an accessible step-by-step exposition of a Bayesian perceptual model that replicates the rabbit and related illusions. The Bayesian observer optimally joins uncertain estimates of spatial location with the expectation that stimuli tend to move slowly. We speculate that this expectation – a Bayesian prior – represents the statistics of naturally occurring stimuli, learned by humans through sensory experience. In its simplest form, the model contains a single free parameter, tau: a time constant for space perception. We show that the Bayesian observer incorporates both pre- and post-dictive inference. Directed spatial attention affects the prediction-postdiction balance, shifting the model’s percept toward the attended location, as observed experimentally in humans. Applying the model to the perception of multi-tap sequences, we show that the low-speed prior fits perception better than an alternative, low-acceleration prior. We discuss the applicability of our model to related tactile, visual, and auditory illusions. To facilitate future model-driven experimental studies, we present a convenient freeware computer program that implements the Bayesian observer; we invite investigators to use this program to create their own testable predictions.

Coordinate-based voxel-wise meta-analysis: dividends of spatial normalization. Report of a virtual workshop.

Spatial normalization transforms a brain image from its natural form (“native space”) into a standardized form defined by a reference brain [Fox, 1995a]. The original motivation for introducing this technique was to allow the brain locations of task-induced functional activations to be reported in a “precise and unambiguous” manner, thereby “facilitating direct comparison of experimental results from different laboratories” [Fox et al., 1985]. The prospect of clear communication as a “dividend” from a community commitment to spatial normalization, however, proved largely unconvincing to the still-nascent brain mapping community of the middle 1980s. Improvement in the signal-to-noise ratio of functional brain maps that could be achieved by intersubject image averaging in standardized space [Fox et al., 1988; Friston et al., 1991] proved to be a very salient motivation, leading to widespread adoption of this data analysis standard. We estimate the human functional brain mapping (HFBM) literature reporting brain activations as x-y-z coordinates in standardized space to be no less than 2,500 articles (!10,000 experiments) with !500 new articles (2,000 experiments) published per year (Fig. 1). Fortunately, regardless of the motivation for adoption of this standard, the widespread use of spatial standardization makes the HFBM literature fertile ground for quantitative meta-analysis methods based on spatial concordance [Fox and Lancaster, 1996a,b; Fox et al., 1998]. In reference to the title of this article, voxel-based, function-location meta-analysis can be considered a dividend that the HFBM community is now receiving from its long-term investment in the development and promulgation of community standards for data-analysis and, in particular, spatial normalization. Meta-analysis is defined most generally as the post-hoc combination of results from independently performed studies to estimate better a parameter of interest. The original and by far the most prevalent form of meta-analysis pools studies with nonsignificant effects to test for significance in the collective, using the increase in n to increase statistical power [Pearson, 1904]. Effect-size meta-analyses have come under criticism for a variety of misuses, but are growing steadily in power and acceptance [Fox et al., 1998]. In the HFBM community, fundamentally new forms of meta-analysis are emerging, in which statistically significant effects are pooled and contrasted to estimate better such parameters as the spatial location, spatial distribution, activation likelihood, co-occurrence patterns, and underlying cognitive operations for specific categories of task. In the first published meta-analysis in cognitive neuroimaging, coordinates from three prior reports were tabulated and plotted to guide interpretation of results in a primary (non-meta-analytic) study [Frith et al., 1991]. Shortly thereafter, “stand-alone” HFBM meta-analyses began to appear in the literature [Buckner and Petersen, 1996; Fox, 1995b; Paus, 1996; Picard and Strick, 1996; Tulving et al., 1994]. To date, more than 50 meta-analyses of coordinate-based HFBM studies have appeared in the peer-reviewed literature. Although most of these meta-analyses are semiquantitative and statistically informal, this is changing. The trend toward quantitative, statistically formal HFBM meta-analysis began with Paus [1996], who computed and interpreted means and standard deviations of the x-y-z addresses in a review of studies of the frontal eye fields. Fox et al. [1997, 2001] extended this initiative by correcting raw estimates of spatial location and variance for sample size to create scalable models of location probabilities (functional volumes models; FVM) and suggesting uses of such models *Correspondence to: Peter Fox, University of Texas Health Science Center, 7703 Floyd Curl Drive, San Antonio, TX 78284. E-mail: fox@uthscsa.edu Received for publication 7 February 2005; Accepted 8 February 2005 DOI: 10.1002/hbm.20139 Published online in Wiley InterScience (www.interscience.wiley. com). ! Human Brain Mapping 25:1–5(2005) !

A binaural processor for missing data speech recognition in the presence of noise and small-room reverberation

In this study we describe a binaural auditory model for recognition of speech in the presence of spatially separated noise intrusions, under small-room reverberation conditions. The principle underlying the model is to identify time–frequency regions which constitute reliable evidence of the speech signal. This is achieved both by determining the spatial location of the speech source, and by grouping the reliable regions according to common azimuth. Reliable time–frequency regions are passed to a ‘missing data’ speech recogniser, which performs decoding based on this partial description of the speech signal. In order to obtain robust estimates of spatial location in reverberant conditions, we incorporate some aspects of precedence effect processing into the auditory model. We show that the binaural auditory model improves speech recognition performance in small room reverberation conditions in the presence of spatially separated noise, particularly for conditions in which the spatial separation is 20° or larger. We also demonstrate that the binaural system outperforms a single channel approach, notably in cases where the target speech and noise intrusion have substantial spectral overlap.

Temporal masking level differences for transients: further evidence for a short-term integrator.

Studies of binaural perception have indicated that subjects are able to use temporal information available in high-frequency regions of the spectrum to lateralize high-frequency waveforms but not to detect these waveforms in masking noise. The present experiments demonstrate that although high-frequency interaural difference cues are relatively ineffective in simultaneous and forward masking, they can be utilized in backward masking. In Experiment 1, large maskinglevel differences were found in backward masking for high-frequency transients presented either monaurally or with an interaural temporal delay. Experiments 2–4 examined fringe masking, effects of masker duration, and combined forward-backward masking for both high- and lowfrequency transients presented with interaural differences in phase and intensity. The results are interpreted as support for the view that the auditory system is organized into parallel shortand long-term integration systems specialized for processing transient and sustained aspects of acoustic stimulation. It is suggested that information from the two integrators is combined when analysis of interaural differences within each of the systems yields similar estimates of spatial location.

Convergence Conditions for Ascent Methods

Convergence Conditions for Ascent Methods