Time filter

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Reutlingen, Germany

Jensen J.,University of Aalborg | Taal C.H.,Applied Sensor
IEEE Transactions on Audio, Speech and Language Processing | Year: 2014

This paper deals with the problem of predicting the average intelligibility of noisy and potentially processed speech signals, as observed by a group of normal hearing listeners. We propose a model which performs this prediction based on the hypothesis that intelligibility is monotonically related to the mutual information between critical-band amplitude envelopes of the clean signal and the corresponding noisy/processed signal. The resulting intelligibility predictor turns out to be a simple function of the mean-square error (mse) that arises when estimating a clean critical-band amplitude using a minimum mean-square error (mmse) estimator based on the noisy/processed amplitude. The proposed model predicts that speech intelligibility cannot be improved by any processing of noisy critical-band amplitudes. Furthermore, the proposed intelligibility predictor performs well (ρ > 0.95) in predicting the intelligibility of speech signals contaminated by additive noise and potentially non-linearly processed using time-frequency weighting. © 2013 IEEE. Source

Theiss H.J.,Applied Sensor
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2012

Sensor builders in the digital era have design limitations due to the constraint of maximum available digital array size. A straightforward solution exists, for example, when four cameras that each simultaneously captures an image from essentially the same perspective centre; they can be re-sampled to form a virtual large format image that can be exploited using a single (instead of four separate) instantiation of a frame model. The purpose of this paper is to address the less trivial time-dependent cases where the sensor scans the ground and the detector arrays obtain chips of imagery that need to be stitched together to form a single conveniently exploitable image. Many operational techniques warp the imagery to form a mosaic, or ortho-rectify it using an imperfect digital surface model (DSM), thus eliminating the possibility for accurate geolocation and uncertainty estimation. This algorithm, however, forms a single virtual image with associated smooth metadata, which can be exploited using a simple physical sensor model. The algorithm consists of four main steps: 1) automated tie point matching; 2) camera calibration (once per sensor); 3) block adjustment; and 4) pixel re-sampling based on an "idealized" virtual model. The same geometry model used to form the image, or its true replacement, must be used to exploit it. This paper verifies the algorithm using real imagery acquired from the Global Hawk (GH) UAV. Registration of the virtual image to a WorldView1 stereopair using four tie points yielded an RMS below 0.6 meters per horizontal axis. Source

Dolloff J.T.,Applied Sensor
American Society for Photogrammetry and Remote Sensing Annual Conference 2012, ASPRS 2012 | Year: 2012

Commercial satellite imagery contains meta-data for the RPC sensor model. In addition to the RPC ground-to-image polynomial, this meta-data contains two uncertainty parameters, termed ErrRand and ErrBias, that are used for error propagation in support of mono and stereo extraction. These parameters describe the uncertainty in the RPC ground-to-image relationship on a per image basis due to errors in the underlying physical sensor model and its meta-data used by the image vendors to generate the RPC ground-to-image polynomial, along with RPC polynomial fit error. The definition and proper application of these uncertainty parameters are not clear throughout the geopositioning community. This paper presents their recommended definition, optimal algorithms for their generation, and optimal algorithms for their use. In addition, it recommends the addition of a priori inter-image and intra-image correlation functions to be published by the image vendors for optimal flexibility and performance. These functions are image independent for images from the same sensor. They account for temporal correlation of RPC errors between same-pass images as well as correlation of RPC errors for two points within the same image. This paper also describes the effects of RPC uncertainty parameters on geopositioning, and in particular, error propagation and accuracy prediction. Various examples are included. Source

Dolloff J.T.,Applied Sensor
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2012

A video-stream associated with an Unmanned System or Full Motion Video can support the extraction of ground coordinates of a target of interest. The sensor metadata associated with the video-stream includes a time series of estimates of sensor position and attitude, required for down-stream single frame or multi-frame ground point extraction, such as stereo extraction using two frames in the video-stream that are separated in both time and imaging geometry. The sensor metadata may also include a corresponding time history of sensor position and attitude estimate accuracy (error covariance). This is required for optimal down-stream target extraction as well as corresponding reliable predictions of extraction accuracy. However, for multi-frame extraction, this is only a necessary condition. The temporal correlation of estimate errors (error cross-covariance) between an arbitrary pair of video frames is also required. When the estimates of sensor position and attitude are from a Kalman filter, as typically the case, the corresponding error covariances are automatically computed and available. However, the cross-covariances are not. This paper presents an efficient method for their exact representation in the metadata using additional, easily computed, data from the Kalman filter. The paper also presents an optimal weighted least squares extraction algorithm that correctly accounts for the temporal correlation, given the additional metadata. Simulation-based examples are presented that show the importance of correctly accounting for temporal correlation in multi-frame extraction algorithms. © 2012 SPIE. Source

Bur C.,Saarland University | Bastuck M.,Saarland University | Spetz A.L.,Applied Sensor | Andersson M.,Saarland University | Schutze A.,Saarland University
Sensors and Actuators, B: Chemical | Year: 2014

tIn this paper temperature modulation and gate bias modulation of a gas sensitive field effect transis-tor based on silicon carbide (SiC-FET) are combined in order to increase the selectivity. Data evaluationbased on extracted features describing the shape of the sensor response was performed using multivari-ate statistics, here by Linear Discriminant Analysis (LDA). It was found that both temperature cycling andgate bias cycling are suitable for quantification of different concentrations of carbon monoxide. However,combination of both approaches enhances the stability of the quantification, respectively the discrim-ination of the groups in the LDA scatterplot. Feature selection based on the stepwise LDA algorithm aswell as selection based on the loadings plot has shown that features both from the temperature cycle andfrom the bias cycle are equally important for the identification of carbon monoxide, nitrogen dioxide andammonia. In addition, the presented method allows discrimination of these gases independent of the gasconcentration. Hence, the selectivity of the FET is enhanced considerably. © 2013 Elsevier B.V. All rights reserved. Source

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