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For the Sinusoid Signals with additive Gauss white noise, a frequency estimation algorithm based on discrete Fourier transform (DFT) interpolation algorithm is proposed in this paper. Based on the classical interpolation algorithm, the algorithm of this paper takes full use of the Peak Spectral Frequency and its neighbor spectral lines to estimate the frequency of the signal. The analysis and simulation...
A low rate of signal element transmissions in a communication system and a low SNR at the receiving side results in a normalized large frequency offset. To address this problem, the paper compares the performance from the aspect of the computational complexity, stability, noise immunity, and estimation accuracy. The performance is evaluated under different carrier frequency synchronization algorithms...
This paper proposes a new neural fusion algorithm for fast robust image restoration without requiring the optimal regularization parameter. The new neural fusion algorithm is based on a new reduced dimension neural network (RDNN). The RDNN is guaranteed to obtain an optimal fusion weight. The proposed RDNN-based neural fusion algorithm uses only a very small solution space to compute the optimal fusion...
A new algorithm based on the coherent signal-subspace method (CSM) is developed to estimate the angle and range parameters of the wideband near-field linear frequency modulated (LFM) sources. The algorithm decomposed the wideband LFM signals into several narrowband LFM signals, and focused the every narrowband signals to the same reference frequency by the coherent subspace algorithms, and then constructed...
It is a pervasive problem to accurately estimate the frequency of sinusoids contaminated by random noise, which has existed in many signal processing areas, including the application in mechanical fault diagnosis and prognostics. The interpolation discrete Fourier transform (DFT) method, employed in frequency domain, is one of the most well studied frequency estimation methods. In this paper, a comparison...
Haze is mainly occurred by atmospheric phenomena. Recently, many researchers in haze removal algorithm area are using single image. At the single image, we can't use depth information. To estimate the thickness of haze without depth information is not easy. As a result, single image haze removal method includes halo effect. In this paper, we propose halo effect suppression for single image haze removal...
For multi-sensor data fusion applications the accurate alignment of different sensor data is essential for the proper combination of matching features. In food inspection system the boxing often is in a rectangular shape. This knowledge can be used to rectify the image data, an important step in the alignment stage. In case of low contrast between boxing and background, the detected contour may differ...
Synthetic aperture radar is a very popular and widely used instrument for various remote sensing tasks. One of the most challenging problems is to obtain high-quality images in the case of unstable flight conditions. In the paper the problem of full platform motion compensation is discussed. A particular attention is given to the analysis of moving targets. Algorithm for estimation of moving target...
This paper presents a recursive parameter estimation algorithm based on a matchable-observable parameterization of multivariable process models. As a consequence of the properties of the models used, no undesired pole-zero cancellations appear, the number of model parameters is not excessive, linear least-squares estimation methods are applicable, and parameter estimation can be accomplished without...
Analysis of harmonic and interharmonic phasors is a promising smart grid measurement and diagnostic tool. This creates the need to deal with multiple phasor components having different amplitudes, including interharmonics with unknown frequency locations. The Compressive Sensing Taylor-Fourier Multifrequency (CSTFM) algorithm provides very accurate results under demanding test conditions, but is computationally...
The Normalized Least-Mean Squares (NLMS) algorithm is a widely used method for linear system identification (e.g., for Acoustic Echo Cancellation (AEC), where the acoustic path between loudspeaker and microphone needs to be estimated). As soon as interferers or background noise are active, step size control becomes a crucial task in order to ensure a fast but stable adaptation. Conventional step size...
Noise energy estimation is widely used as a pre-process in speech enhancement and speech recognition systems. While many signal processing algorithms have been proposed to estimate the additive noise energy, they are generally based on some statistical hypothesis and have high computation complexity, which is crucial in mobile devices. When the hypothesis does not hold, the estimation performance...
In the problem of acoustic source localization, time difference of arrival (TDOA) among multiple sensors is needed, which is often obtained through time delay estimation (TDE) techniques. Among the multiple TDE methods developed in the literature, the normalized multichannel frequency-domain least-mean-square (NMCFLMS) algorithm is shown robust to reverberation. The performance of this algorithm,...
A combine-then-adapt (CTA) diffusion proportionate affine projection algorithm (DP APA) is proposed for distributed estimation, which uses gain matrices in the CTA diffusion affine projection algorithm (DAPA) to proportionately adapt the weight vectors of agents in the network. Then, a variable step-size (VSS) is presented for the DPAPA to address the problem of tradeoff between fast convergence rate...
The elastic net (EN) is a popular regularization and variable selection method that overcomes the shortcomings of Lasso such as poor recovery in the face of high mutual coherence. In this paper, we develop an efficient algorithm to solve the weighted EN criterion for complex-valued measurements applying the cyclic coordinate descent approach. We illustrate that usage of smartly chosen adaptive (i...
For low-angle targets, the performance of altitude measurement is affected by multipath phenomenon. Generally, the response at the receiver is contributed by the echoes of target and its image. Such sparsity of signals in the elevation direction provides a foundation for parameter estimation based on compressive sensing with parameterized dictionary. As the dictionary atoms are multi-parameter, it...
Automatic identification of jump Markov systems (JMS) is known to be an important but difficult problem. In this work, we propose a new algorithm for the unsupervised estimation of parameters in a class of linear JMS called “conditionally Gaussian pairwise Markov switching models” (CGPMSMs), which extends the family of classic “conditionally Gaussian linear state-space models” (CGLSSMs). The method...
A Speaker Localization algorithm based on Neural Networks for multi-room domestic scenarios is proposed in this paper. The approach is fully data-driven and employs a Neural Network fed by GCC-PHAT (Generalized Cross Correlation Phase Transform) Patterns, calculated by means of the microphone signals, to determine the speaker position in the room under analysis. In particular, we deal with a multi-room...
The possibility of studying multiple objects at once for forensic analysis has paved the way to the development of multimedia phylogeny algorithms. Concerning video phylogeny, a fundamental step at the base of many applications is multiple video alignment. This is, given a pool of near-duplicate video sequences partially overlapping in the temporal domain, find the relative time delay between all...
A sparsity-aware least-mean mixed-norm (LMMN) adaptive filter algorithm is proposed for sparse channel estimation applications. The proposed algorithm is realized by incorporating a sum-log function constraint into the cost function of a LMMN which is a mixed norm controlled by a scalar-mixing parameter. As a result, a shrinkage is given to enhance the performance of the LMMN algorithm when the majority...
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