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The availability of fast processors with architectures tailored to meet the computational demand of digital signal processing algorithms is widely applied to demodulation and decodification of CPM signals in some scenes: Mobiles, AWGN channels,… In this application the number of floating point operations executed by each processed symbol is a critical parameter to be designed, this is to be minimized...
In this paper, compressed sensing (CS) is investigated as a denoising tool in bioimaging. Multiple reconstructions at low sampling rates are combined to generate high quality denoised images using total-variation spar-sity constraints. The validity of the proposed method is first assessed on a synthetic image with a known ground truth and then applied to real biological images.
Sparse estimation has received a lot of attention due to its broad applicability. In sparse channel estimat ion, the parameter vector with sparsity characteristic can be well estimated from noisy measurements through sparse adaptive filters. In previous studies, most works use the mean square error (MSE) based cost to develop sparse filters, which is rat ional under the assumption of Gaussian distributions...
We consider networks of agents cooperating to minimize a global objective, modeled as the aggregate sum of regularized costs that are not required to be differentiable. Since the subgradients of the individual costs cannot generally be assumed to be uniformly bounded, general distributed subgradient techniques are not applicable to these problems. We isolate the requirement of bounded subgradients...
We study a distributed node-specific signal estimation problem where the node-specific desired signals and/or the sensor observations can have partially-overlapping latent signal subspaces. First, we provide the minimum number of linear combinations of observed sensor signals that each node can broadcast to still let all other nodes achieve the network-wide Linear Minimum Mean-Square Error (LMMSE)...
A widely linear quaternion recursive total least squares (WL-QRTLS) algorithm is introduced for the processing of ℚ-improper processes contaminated by noise. The total least squares for quaternions (QTLS) is a generalisation of the real-valued total least squares and is introduced rigorously, starting from the existence condition for low-rank approximation of quaternion matrices. Then, a quaternion...
Identifying “interesting” features, such as faults, unconformities, and other events in subsurface images is a challenging task in seismic data processing. Existing state-of-the-art methods usually involve manual intervention in the form of a visual inspection by an expert, but this is time-consuming, expensive, and error-prone. In this paper, we propose an efficient, automatic approach for seismic...
This paper investigates the variable tap-length algorithm for structure adaptation. Among existing algorithms, the Segmented Filter (SF) and Gradient Descent (GD) algorithms are of interest as both can track the tap-length variations quickly. In this paper, we first compare the SF and GD algorithms and show that each has advantages/disadvanges relative to the other. Then we propose an improved variable...
In some acoustic echo cancellation scenarios, such as an automatic gain adjustment application, near-end noise may be continuously present. In this case a double-talk detector cannot be applied and the adaptive algorithm should behave in a robust way w.r.t. the disturbing near-end signal. From linear estimation theory it is known that the variance of the room impulse response estimate may be decreased...
In this paper we present a new algorithm for blind source separation (BSS) based on the Constant Norm (CN) criterion for Multiple-Input Multiple-Output (MIMO) communication systems. The treated problem consists in blindly recovering (i.e. without the use of training sequences) the signals transmitted over a linear MIMO memoryless system, which introduces only Inter Stream Interference (ISI). From...
In this paper we propose a novel adaptive filtering algorithm. Using the Set Theoretic Estimation framework, the algorithm exploits the information given by the power spectral density of the noise extracted from the periodogram of filtering error. With this information new appropriate sets are built and projections onto them are computed. The simulations results show that the algorithm has excellent...
This paper addresses asymptotically (in the number of measurements) minimum variance (AMV) estimators within the class of estimators based on a mixture of real and complex-valued sequence of statistics whose first covariance of its asymptotic distribution is singular. Thanks to two conditions, we extend the standard AMV estimator. We prove that these conditions are satisfied for the estimates of orthogonal...
In this article we consider the Data Projection Method (DPM), which constitutes a simple and reliable means for adaptively estimating and tracking subspaces. Specifically we propose a fast and numerically robust implementation of DPM. Existing schemes can track subspaces corresponding either to the largest or the smallest singular values. DPM, on the other hand, with a simple change of sign in its...
This Paper presents an object tracking system based on FPGA using canny edge detection Algorithm. The system consists of canny edge detection algorithm implemented on FPGA kit to identify edges of real time object. Also a tracking and reorganization of object is done by Smartphone camera. Canny edge detection algorithm is key stage in image processing and object recognizing application. The field...
A novel estimation algorithm which assures stability of a discrete FIR system with time-varying dynamics is derived by using loop transformation and the small-gain theorem. The proposed estimation algorithm incorporates an internal model of unknown dynamics for the time-varying channel. The stability condition is given by designing the estimation algorithm based on the small-gain theorem instead of...
Adaptive filters with suitable nonlinear devices are very effective in suppressing the adverse effect due to impulse noise. In a previous work, the authors have proposed a new class of nonlinear adaptive filters using the concept of robust statistics [1,2]. The robust M-estimator is used as the objective function, instead of the mean square errors, to suppress the impulse noise. The optimal coefficient...
A parameter estimation algorithm is developed for the identification of an input output quadratic model. The excitation is a zero mean white Gaussian input and the output is corrupted by additive measurement noise. Input output crosscumulants up to fifth order are employed and the identification problem of the unknown model parameters is reduced to the solution of succesive linear systems of equations...
Based on the adaptive filtering mode, we discussed and analyzed how to improve the quality of microphone array speech enhancement and reduce the complexity of the algorithm. This paper described a new variable step least mean square (LMS) algorithm which could reduce the effect of input noise to step factor, improve the performance of the algorithm. The algorithm smoothed the step factor in time domain...
Edge pixels are the key feature widely used for the processing of the digital images. Edges will be lost due to filtering operation carried out on digital images. The structure comprises of alpha trimmed mean filter and edges are preserved with the specialized technique. Lost edge details due to the mean filtering operation are restored with the help of the preserved edges. Balanced histogram technique...
Electrocardiogram (ECG) signals are affected by various types of noises that are differed based on frequency content. In order to improve accuracy and reliability, it is essential to remove such a disturbance. The denoising of ECG signals is challenging as it is difficult to apply filters with fixed coefficients. Adaptive filtering techniques can be used, in which the filter coefficients can be modified...
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