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In this demonstration, we aim at presenting our recent implementation results and provide an evaluation testbed through which users can experiment and compare the outputs of the distributed speech enhancement algorithms in [1–3]. The system allows a user to assess the merits of these algorithms in any acoustic setup. The multi-channel Wiener filter (MWF) is a well-known noise reduction algorithm for...
We consider the problem of inferring the hidden structure of high-dimensional time-varying data. In particular, we aim at capturing the dynamic relationships by representing data as valued nodes in a sequence of graphs. Our approach is motivated by the observation that imposing a meaningful graph topology can help solving the generally ill-posed and challenging problem of structure inference. To capture...
We propose an algorithm to uncover the intrinsic low-rank component of a high-dimensional, graph-smooth and grossly-corrupted dataset, under the situations that the underlying graph is unknown. Based on a model with a low-rank component plus a sparse perturbation, and an initial graph estimation, our proposed algorithm simultaneously learns the low-rank component and refines the graph. The refined...
Random sinusoidal features are a popular approach for speeding up kernel-based inference in large datasets. Prior to the inference stage, the approach suggests performing dimensionality reduction by first multiplying each data vector by a random Gaussian matrix, and then computing an element-wise sinusoid. Theoretical analysis shows that collecting a sufficient number of such features can be reliably...
In 2013, Nguyen and Yamada proposed Adaptive normalized quasi-Newton algorithm and its adaptive step size for accurate and stable extraction of the first generalized eigenvector. The adaptive step size is determined by an upper bound of the condition number of a time-varying matrix. However, the employed upper bound is fairly tight only when the size of matrix is small, which degrades the performance...
In this paper, we study the problem of distributed blind equalization in single-input multi-output (SIMO) systems, wherein the channels of networked systems share some similarities. This corresponds to a multi-task optimization problem. To tackle this problem, an adaptive distributed generalized Sato algorithm (d-GSA) using the diffusion cooperation rule is proposed. In the proposed d-GSA, only the...
It is known that accurate partition of subspaces is important to fourth-order statistics based multiple signal classification (FO-MUSIC) algorithm. However, when the number of signals exceeds the number of array elements, the error of conventional subspace partition method will increase, and the performance of FO-MUSIC algorithm will decrease. A novel subspace partition method for FO-MUSIC algorithm...
It has been recently shown that judicious use of correlation priors can lead to significant improvement in the performance of sparse estimation algorithms. This happens primarily due to two reasons: (i) second order statistics or covariance matrix of signals can possess unique structures that are not captured in the raw measurements (ii) these structures involve non linear functions of the underlying...
A new theory known as compressed sensing considers the problem to acquire and recover a sparse signal from its linear measurements. In this paper, we propose a new support recovery algorithm from noisy measurements based on the linear programming (LP). LP is widely used to estimate sparse signals, however, we focus on the problem to recover the support of sparse signals rather than the problem to...
This paper presents an innovative technique for 3D depth estimation from a single holoscopic 3D image (H3D). The image is captured with a single aperture holoscopic 3D camera, which mimics a fly's eye technique to acquire an optical 3D model of a true 3D scene. The proposed method works by extracting of optimum viewpoints images from a H3D image and it uses the shift and integration function in up-sampling...
Contrast and color of the captured images are degraded under bad weather conditions mostly in fog due to attenuation and airlight of the scene radiance coming towards the observer. To minimize road accidents through vision enhancement in turbid weather, an efficient visibility enhancement using fog removal technique plays a very significant role as fog greatly reduces the contrast and hence affects...
In this article, we introduce a direction-of-arrival (DOA) estimation by combining a Lüneburg lens and a frequency tunable metamaterial absorber. The Lüneburg lens is used to physically separate the multiple incident waves with different DOAs and focus them into the different focal points near the lens surface. The metamaterial absorber is set on the focal plane as a sensor array. Therefore, the DOA...
In estimating DOA of incident waves with high accuracy, we often have to take into consideration the angular spread (AS) of each wave due to reflection, diffraction, and scattering. As a method of estimating DOA and AS simultaneously, DOA-Matrix method was proposed. In this paper, we extend the array configuration from the linear array to the planar array for simultaneous estimation of DOA and AS...
A novel digital beamforming (DBF) algorithm for spaceborne synthetic aperture radar (SAR) system based on sparse direction-of-arrive (DOA) estimation is presented, which can solve the beam mispointing caused by the terrain height. Compared to the traditional subspace-based algorithms, the approach has no strict requirement for the number of snapshots. Finally, the simulated results validate the proposed...
The beamforming technique usually employs multiple sensors or sensor array to enhance receiving signals such that the generalized sidelobe canceler (GSC) is commonly applied for eliminating interfering signals. In this paper, a new direction-of-arrival (DOA) estimation method is studied for the GSC with a moving terminal in the arrayed wireless sensor network. In our simulations, the beamformer's...
The paper presents a new method to detect the forgery in Copy-Move images using feature comparison to find the similar parts and sharpness estimation to collect the suspicious edges. Besides, one-level DWT decomposition, with the role of multiresolution, is used to limit the computational complexity and morphological operation is applied for presenting counterfeit objects. Both feature comparison...
By introducing the nonholonomic constraints, the nonholonomic natural gradient algorithm is effective to overcome the shortcomings of traditional natural gradient algorithm. Namely, when the source signal amplitude changes rapidly over time or is equal to zero in a certain period of time, it can still work well. In addition, selecting the different estimate function in different stage can get the...
In order to function reliably, synthetic molecular circuits require mechanisms that allow them to adapt to environmental disturbances. Least mean squares (LMS) schemes, such as commonly encountered in signal processing and control, provide a powerful means to accomplish that goal. In this paper we show how the traditional LMS algorithm can be implemented at the molecular level using only a few elementary...
This paper presents an iterative Variational Bayes (VB) algorithm that allows sparse recovery of the desired transmitted vector. The VB algorithm is derived based on the latent variables introduced in the Bayesian model in hand. The proposed algorithm is applied to the Angle-of-Arrival (AoA) estimation problem and simulations demonstrate the potential of the proposed VB algorithm when compared to...
We consider the robust PCA problem of recovering a low-rank matrix corrupted by Gaussian noise and large elementlevel outliers. Motivated by the sparse estimation literature, we consider outlier rejection schemes that apply hard or soft thresholding, respectively, to the elements of the data matrix to efficiently estimate the sparse component and then apply an SVD on the residual matrix to estimate...
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