Using Reverberation to Improve Range and Elevation Discrimination for Small Array Sound Source Localization

Sound source localization (SSL) is an essential task in many applications involving speech capture and enhancement. As such, speaker localization with microphone arrays has received significant research attention. Nevertheless, existing SSL algorithms for small arrays still have two significant limitations: lack of range resolution, and accuracy degradation with increasing reverberation. The latter is natural and expected, given that strong reflections can have amplitudes similar to that of the direct signal, but different directions of arrival. Therefore, correctly modeling the room and compensating for the reflections should reduce the degradation due to reverberation. In this paper, we show a stronger result. If modeled correctly, early reflections can be used to provide more information about the source location than would have been available in an anechoic scenario. The modeling not only compensates for the reverberation, but also significantly increases resolution for range and elevation. Thus, we show that under certain conditions and limitations, reverberation can be used to improve SSL performance. Prior attempts to compensate for reverberation tried to model the room impulse response (RIR). However, RIRs change quickly with speaker position, and are nearly impossible to track accurately. Instead, we build a 3-D model of the room, which we use to predict early reflections, which are then incorporated into the SSL estimation. Simulation results with real and synthetic data show that even a simplistic room model is sufficient to produce significant improvements in range and elevation estimation, tasks which would be very difficult when relying only on direct path signal components.

Scalable Visualization of Galaxies, Oceans, and Brains

One of the challenges in scientific visualization is to generate software libraries suitable for the large-scale data emerging from tera-scale simulations and instruments. We describe the efforts currently under way at SDSC and NPACI to address these challenges. The scope of the SDSC project spans data handling, graphics, visualization, and scientific application domains. Components of the research focus on the following areas: intelligent data storage, layout and handling, using an associated “Floor-Plan” (meta data); performance optimization on parallel architectures; extension of SDSC’s scalable, parallel, direct volume renderer to allow perspective viewing; and interactive rendering of fractional images (“imagelets”), which facilitates the examination of large datasets. These concepts are coordinated within a data-visualization pipeline, which operates on component data blocks sized to fit within the available computing resources. A key feature of the scheme is that the meta data, which tag the data blocks, can be propagated and applied consistently. This is possible at the disk level, in distributing the computations across parallel processors; in “imagelet” composition; and in feature tagging. The work reflects the emerging challenges and opportunities presented by the ongoing progress in high-performance computing (HPC) and the deployment of the data, computational, and visualization Grids.

Effect of image analysis software on neurofunctional activation during processing of emotional human faces

Functional brain imaging techniques such as functional MRI (fMRI) that allow the in vivo investigation of the human brain have been exponentially employed to address the neurophysiological substrates of emotional processing. Despite the growing number of fMRI studies in the field, when taken separately these individual imaging studies demonstrate contrasting findings and variable pictures, and are unable to definitively characterize the neural networks underlying each specific emotional condition. Different imaging packages, as well as the statistical approaches for image processing and analysis, probably have a detrimental role by increasing the heterogeneity of findings. In particular, it is unclear to what extent the observed neurofunctional response of the brain cortex during emotional processing depends on the fMRI package used in the analysis. In this pilot study, we performed a double analysis of an fMRI dataset using emotional faces. The Statistical Parametric Mapping (SPM) version 2.6 (Wellcome Department of Cognitive Neurology, London, UK) and the XBAM 3.4 (Brain Imaging Analysis Unit, Institute of Psychiatry, Kings College London, UK) programs, which use parametric and non-parametric analysis, respectively, were used to assess our results. Both packages revealed that processing of emotional faces was associated with an increased activation in the brain`s visual areas (occipital, fusiform and lingual gyri), in the cerebellum, in the parietal cortex, in the cingulate cortex (anterior and posterior cingulate), and in the dorsolateral and ventrolateral prefrontal cortex. However, blood oxygenation level-dependent (BOLD) response in the temporal regions, insula and putamen was evident in the XBAM analysis but not in the SPM analysis. Overall, SPM and XBAM analyses revealed comparable whole-group brain responses. Further Studies are needed to explore the between-group compatibility of the different imaging packages in other cognitive and emotional processing domains. (C) 2009 Elsevier Ltd. All rights reserved.

Three-dimensional space-borne synthetic aperture radar (SAR) imaging with multiple pass processing

Three-dimensional (3D) synthetic aperture radar (SAR) imaging via multiple-pass processing is an extension of interferometric SAR imaging. It exploits more than two flight passes to achieve a desired resolution in elevation. In this paper, a novel approach is developed to reconstruct a 3D space-borne SAR image with multiple-pass processing. It involves image registration, phase correction and elevational imaging. An image model matching is developed for multiple image registration, an eigenvector method is proposed for the phase correction and the elevational imaging is conducted using a Fourier transform or a super-resolution method for enhancement of elevational resolution. 3D SAR images are obtained by processing simulated data and real data from the first European Remote Sensing satellite (ERS-1) with the proposed approaches.

MR image-based measurement of rates of change in volumes of brain structures. Part 1: Method and validation

A detailed analysis procedure is described for evaluating rates of volumetric change in brain structures based on structural magnetic resonance (MR) images. In this procedure, a series of image processing tools have been employed to address the problems encountered in measuring rates of change based on structural MR images. These tools include an algorithm for intensity non-uniforniity correction, a robust algorithm for three-dimensional image registration with sub-voxel precision and an algorithm for brain tissue segmentation. However, a unique feature in the procedure is the use of a fractional volume model that has been developed to provide a quantitative measure for the partial volume effect. With this model, the fractional constituent tissue volumes are evaluated for voxels at the tissue boundary that manifest partial volume effect, thus allowing tissue boundaries be defined at a sub-voxel level and in an automated fashion. Validation studies are presented on key algorithms including segmentation and registration. An overall assessment of the method is provided through the evaluation of the rates of brain atrophy in a group of normal elderly subjects for which the rate of brain atrophy due to normal aging is predictably small. An application of the method is given in Part 11 where the rates of brain atrophy in various brain regions are studied in relation to normal aging and Alzheimer's disease. (C) 2002 Elsevier Science Inc. All rights reserved.

Fluorescence Microscopy Imaging Denoising with Log-Euclidean Priors and Photobleaching Compensation

Fluorescent protein microscopy imaging is nowadays one of the most important tools in biomedical research. However, the resulting images present a low signal to noise ratio and a time intensity decay due to the photobleaching effect. This phenomenon is a consequence of the decreasing on the radiation emission efficiency of the tagging protein. This occurs because the fluorophore permanently loses its ability to fluoresce, due to photochemical reactions induced by the incident light. The Poisson multiplicative noise that corrupts these images, in addition with its quality degradation due to photobleaching, make long time biological observation processes very difficult. In this paper a denoising algorithm for Poisson data, where the photobleaching effect is explicitly taken into account, is described. The algorithm is designed in a Bayesian framework where the data fidelity term models the Poisson noise generation process as well as the exponential intensity decay caused by the photobleaching. The prior term is conceived with Gibbs priors and log-Euclidean potential functions, suitable to cope with the positivity constrained nature of the parameters to be estimated. Monte Carlo tests with synthetic data are presented to characterize the performance of the algorithm. One example with real data is included to illustrate its application.

Non-selective optical wavelength-division multiplexing devices based on a-SiC:H multilayer heterostuctures

In this paper we present results on the optimization of multilayered a-SiC:H heterostructures for wavelength-division (de) multiplexing applications. The non selective WDM device is a double heterostructure in a glass/ITO/a-SiC:H (p-i-n) /a-SiC:H(-p) /a-Si:H(-i')/a-SiC:H (-n')/ITO configuration. The single or the multiple modulated wavelength channels are passed through the device, and absorbed accordingly to its wavelength, giving rise to a time dependent wavelength electrical field modulation across it. The effect of single or multiple input signals is converted to an electrical signal to regain the information (wavelength, intensity and frequency) of the incoming photogenerated carriers. Here, the (de) multiplexing of the channels is accomplished electronically, not optically. This approach offers advantages in terms of cost since several channels share the same optical components; and the electrical components are typically less expensive than the optical ones. An electrical model gives insight into the device operation.

ITO/SiOx/Si optical sensor with internal gain

A visible/near-infrared optical sensor based on an ITO/SiOx/n-Si structure with internal gain is presented. This surface-barrier structure was fabricated by a low-temperature processing technique. The interface properties and carder transport were investigated from dark current-voltage and capacitance-voltage characteristics. Examination of the multiplication properties was performed under different light excitation and reverse bias conditions. The spectral and pulse response characteristics are analysed. The current amplification mechanism is interpreted by the control of electron current by the space charge of photogenerated holes near the SiOx/Si interface. The optical sensor output characteristics and some possible device applications are presented.

Stacked pin Devices for Imaging Applications

In this paper we present results on the optimization of device architectures for colour and imaging applications, using a device with a TCO/pinpi'n/TCO configuration. The effect of the applied voltage on the color selectivity is discussed. Results show that the spectral response curves demonstrate rather good separation between the red, green and blue basic colors. Combining the information obtained under positive and negative applied bias a colour image is acquired without colour filters or pixel architecture. A low level image processing algorithm is used for the colour image reconstruction.

Optoelectronic Digital Capture Device Based on Si/C Multilayer Heterostructures

Combined tunable WDM converters based on SiC multilayer photonic active filters are analyzed. The operation combines the properties of active long-pass and short-pass wavelength filter sections into a capacitive active band-pass filter. The sensor element is a multilayered heterostructure produced by PE-CVD. The configuration includes two stacked SiC p-i-n structures sandwiched between two transparent contacts. Transfer function characteristics are studied both theoretically and experimentally. Results show that optical bias activated photonic device combines the demultiplexing operation with the simultaneous photodetection and self amplification of an optical signal acting the device as an integrated photonic filter in the visible range. Depending on the wavelength of the external background and irradiation side, the device acts either as a short- or a long-pass band filter or as a band-stop filter. The output waveform presents a nonlinear amplitude-dependent response to the wavelengths of the input channels. A numerical simulation and two building-blocks active circuit is presented and gives insight into the physics of the device.

Fatty liver characterization and classification by ultrasound

Steatosis, also known as fatty liver, corresponds to an abnormal retention of lipids within the hepatic cells and reflects an impairment of the normal processes of synthesis and elimination of fat. Several causes may lead to this condition, namely obesity, diabetes, or alcoholism. In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis from ultrasound images. The features are selected in order to catch the same characteristics used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The algorithm, designed in a Bayesian framework, computes two images: i) a despeckled one, containing the anatomic and echogenic information of the liver, and ii) an image containing only the speckle used to compute the textural features. These images are computed from the estimated RF signal generated by the ultrasound probe where the dynamic range compression performed by the equipment is taken into account. A Bayes classifier, trained with data manually classified by expert clinicians and used as ground truth, reaches an overall accuracy of 95% and a 100% of sensitivity. The main novelties of the method are the estimations of the RF and speckle images which make it possible to accurately compute textural features of the liver parenchyma relevant for the diagnosis.

Value of a lower limb immobilization device for SPECT/CT image fusion optimization

The foot and the ankle are small structures commonly affected by disorders, and their complex anatomy represent significant diagnostic challenges. SPECT/CT Image fusion can provide missing anatomical and bone structure information to functional imaging, which is particularly useful to increase diagnosis certainty of bone pathology. However, due to SPECT acquisition duration, patient’s involuntary movements may lead to misalignment between SPECT and CT images. Patient motion can be reduced using a dedicated patient support. We aimed at designing an ankle and foot immobilizing device and measuring its efficacy at improving image fusion. Methods: We enrolled 20 patients undergoing distal lower-limb SPECT/CT of the ankle and the foot with and without a foot holder. The misalignment between SPECT and CT images was computed by manually measuring 14 fiducial markers chosen among anatomical landmarks also visible on bone scintigraphy. Analysis of variance was performed for statistical analysis. Results: The obtained absolute average difference without and with support was 5.1±5.2 mm (mean±SD) and 3.1±2.7 mm, respectively, which is significant (p<0.001). Conclusion: The introduction of the foot holder significantly decreases misalignment between SPECT and CT images, which may have clinical influence in the precise localization of foot and ankle pathology.

Tunnig optical A-Sic/A-Si active filters by UV bias light in the visible and infrared spectral ranges

Visible range to telecom band spectral translation is accomplished using an amorphous SiC pi'n/pin wavelength selector under appropriate front and back optical light bias. Results show that background intensity works as selectors in the infrared region, shifting the sensor sensitivity. Low intensities select the near-infrared range while high intensities select the visible part according to its wavelength. Here, the optical gain is very high in the infrared/red range, decreases in the green range, stays close to one in the blue region and strongly decreases in the near-UV range. The transfer characteristics effects due to changes in steady state light intensity and wavelength backgrounds are presented. The relationship between the optical inputs and the output signal is established. A capacitive optoelectronic model is presented and tested using the experimental results. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

In-plane displacement and strain image analysis

Measurements in civil engineering load tests usually require considerable time and complex procedures. Therefore, measurements are usually constrained by the number of sensors resulting in a restricted monitored area. Image processing analysis is an alternative way that enables the measurement of the complete area of interest with a simple and effective setup. In this article photo sequences taken during load displacement tests were captured by a digital camera and processed with image correlation algorithms. Three different image processing algorithms were used with real images taken from tests using specimens of PVC and Plexiglas. The data obtained from the image processing algorithms were also compared with the data from physical sensors. A complete displacement and strain map were obtained. Results show that the accuracy of the measurements obtained by photogrammetry is equivalent to that from the physical sensors but with much less equipment and fewer setup requirements. © 2015Computer-Aided Civil and Infrastructure Engineering.

Estimation of signal subspace on hyperspectral data

Dimensionality reduction plays a crucial role in many hyperspectral data processing and analysis algorithms. This paper proposes a new mean squared error based approach to determine the signal subspace in hyperspectral imagery. The method first estimates the signal and noise correlations matrices, then it selects the subset of eigenvalues that best represents the signal subspace in the least square sense. The effectiveness of the proposed method is illustrated using simulated and real hyperspectral images.