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Noise pollution has a large negative influence on the health of humans, especially in case of long-term exposure. Various passive hearing protection approaches are available. However, they often lack good protection against low frequency noise. For these applications, the principle of Active Noise Cancellation (ANC) offers a promising supplement. It relies on anti-phase compensation of the noise signal...
In Big Data Processing we typically face very large data sets that are highly structured. To save the computation and storage cost, it is desirable to extract the essence of the data from a reduced number of observations. One example of such a structural constraint is sparsity. If the data possesses a sparse representation in a suitable domain, it can be recovered from a small number of linear projections...
As the world's largest radio telescope, the Square Kilometer Array (SKA) will provide radio interferometric data with unprecedented detail. Image reconstruction algorithms for radio interferometry are challenged to scale well with TeraByte image sizes never seen before. In this work, we investigate one such 3D image reconstruction algorithm known as MUFFIN (MUlti-Frequency image reconstruction For...
Estimation of the number of endmembers existing in a scene constitutes a critical task in the hyperspectral unmixing process. The accuracy of this estimate plays a crucial role in subsequent unsupervised unmixing steps i.e., the derivation of the spectral signatures of the endmembers (endmembers' extraction) and the estimation of the abundance fractions of the pixels. A common practice amply followed...
Given a collection of M experimentally measured subspaces, and a model-based subspace, this paper addresses the problem of finding a subspace that approximates the collection, under the constraint that it intersects the model-based subspace in a predetermined number of dimensions. This constrained subspace estimation (CSE) problem arises in applications such as beamforming, where the model-based subspace...
In this paper, we focus on tracking the signal subspace under a sparsity constraint. More specifically, we propose a two-step approach to solve the considered problem whether the sparsity constraint is on the system weight matrix or on the source signals. The first step uses the OPAST algorithm for an adaptive extraction of an orthonormal basis of the principal subspace, then an estimation of the...
A distortionless speech extraction in a reverberant environment can be achieved by an application of a beamforming algorithm, provided that the relative transfer functions (RTFs) of the sources and the covariance matrix of the noise are known. In this contribution, we consider the RTF identification challenge in a multi-source scenario. We propose a successive RTF identification (SRI), based on a...
In this paper a new direct nonparametric estimation of the period and the shape of a periodic component in short duration signals is proposed and evaluated. Classical Fourier Transform (FT) methods lack precision and resolution when the duration of the signal is very short and the signal is noisy. The proposed method is based on the direct description of the problem as a linear inverse problem and...
In Compressed Sensing, a real-valued sparse vector has to be recovered from an underdetermined system of linear equations. In many applications, however, the elements of the sparse vector are drawn from a finite set. Adapted algorithms incorporating this additional knowledge are required for the discrete-valued setup. In this paper, turbo-based algorithms for both cases are elucidated and analyzed...
As electric network frequency is sometimes embedded in audio signals when the recording is carried out with the equipment connected to an electrical outlet, electric network frequency estimation is an important task in audio authenticity. After the theoretical analysis, a novel electric network frequency estimation method based on linear canonical transform is proposed from anti-multipath interference...
It is indispensable to take a low complexity and high accuracy channel estimation algorithm into account under rapidly growth in wireless communication. In this paper, we propose an algorithm for channel estimation using complementary sequence (CS). By utilizing the fantastic autocorrelation property of CS, time domain channel estimation can be easily achieved. We analyzed the complementary sequence,...
In the last decade, modified subspace DoA estimation methods such as G-MUSIC have been proposed, in the context where the number of available snapshots N is of the same order of magnitude than the number of sensors M. In this context, the conventional MUSIC algorithm fails in presence of close sources because the empirical covariance matrix is a poor estimate of the true covariance matrix. The G-MUSIC...
Direction-of-arrival (DOA) estimation using non-uniform linear arrays is considered. We focus on the so called "fully augmentable arrays" (FAAs) with full set of covariance lags. In FAAs, the number of covariance lags is usually larger than the number of sensors in the array. Thus, with FAAs more sources than the number of sensors can be identified. Existing DOA estimation algorithms for...
The problem of localizing an IR-UWB transmitter from the signals received at several anchors is considered. The positioning problem is typically solved in a two-step approach where in the first step the Time of Arrival (TOA) is estimated independently at each anchor, and the position estimate is found in a second step. However, this approach can be improved, especially in challenging scenarios, if...
HEVC has emerged as the new video coding standard promising increased compression ratios compared to its predecessors. This performance improvement comes at a high computational cost. For this reason, HEVC offers three coarse grained parallelization potentials namely, wave front, slices and tiles. In this paper we focus on tile parallelism which is a relatively new concept with its effects not yet...
The paper addresses the problem of joint signal separation and estimation in a single-channel discrete-time signal composed of a wandering baseline and overlapping repetitions of unknown (or known) signal shapes. All signals are represented by a linear state space model (LSSM). The baseline model is driven by white Gaussian noise, but the other signal models are triggered by sparse inputs. Sparsity...
We present a density-based clustering method producing a covering of the dataset by ellipsoidal structures in order to detect possibly entangled clusters. We first introduce an unconstrained version of the algorithm which does not require any assumption on the number of clusters. Then a constrained version using a priori knowledge to improve the bare clustering is discussed. We evaluate the performance...
Quaternion-valued adaptive filters based on the mean square error (MSE) criterion have been extensively studied in recent years. However, the MSE cost function has only one degree of freedom, and to circumvent this problem, we propose another criterion which enables separate control of the magnitude and phase. Next, a quaternion least mean magnitude phase (QLMMP) filtering algorithm is introduced...
The limited availability and the lack of continuity in the service of Global Positioning Satellite Systems (GNSS) in harsh environments is a critical issue for Intelligent Transport Systems (ITS) applications relying on the position. This work is developed within the framework of vehicle-to-everything (V2X) communication, with the aim to guarantee a continuous position availability to all the agents...
The curse of outlier measurements in estimation problems is a well-known issue in a variety of fields. Therefore, outlier removal procedures, which enables the identification of spurious measurements within a set, have been developed for many different scenarios and applications. In this paper, we propose a statistically motivated outlier removal algorithm for time differences of arrival (TDOAs),...
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