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A general superdirective beamforming method which is suitable for arbitrary-shape sensor arrays is proposed, and its performance for linear arrays is studied in this paper. Based on Gram-Schmidt orthogonalization, the optimal solutions of superdirectivity are all expressed in closed-form without any approximations. The optimal beampattern and the maximum directivity factor are decomposed into mode-beams...
A coverage control algorithm in unknown environment is proposed for the multi-vehicle systems in this paper. The measurement white-noise is taken into consideration while learning the interest information online. The Kalman Filter (KF) is introduced to eliminate the noise disturbance and provide us a set of accurately sampled-data. Then, we describe an adaptive algorithm to approximate the sensory...
We present a fast unsupervised myopic deconvolution method dedicated to quasi-real time processing of video sequences such as angiograms. Our method is based on a Bayesian approach of which the tuning parameters are automatically set thanks to the marginalized likelihood of the observed image. We demonstrate the effectiveness of our approach on simulated and empirical images.
This article gives a discussion on impulse noise, its models and how it affects communications systems. We discuss the different impulse noise models in the literature, looking at their similarities and differences in communications systems. The impulse noise models discussed are memoryless (Middleton Class A and Bernoulli-Gaussian), and with memory (Markov- Middleton and Markov-Gaussian). We then...
In this paper, a direct connection between the covariance debiasing methodology for the distributed Kalman (DKF) filter in [1] and the federated Kalman filter is shown. In particular, it can be seen that for a unique choice of the information gain hypothesis of the DKF, the covariance debiasing becomes equivalent to the federated Kalman filter. As the complexity of the covariance calculation for the...
This paper proposes a new Subspace State Estimator (SSE) algorithm for facial biometric verification. In the proposed method, a sequential estimator is being designed in the image subspace which addresses the challenges due to nonlinear, no stationary, and heterogeneous noise. The proposed model includes a subspace method that overcomes the computational complexity associated with the sequential estimator...
This work presents a new method for noise reduction in binaural hearing aid applications that preserves the interaural level difference. A bounded symmetrical approximation of the logarithm is employed to estimate the interaural level difference, resulting in identical values for symmetrical (left/right) frontal angles. It proposes a new cost function to be used in association with the multichannel...
Parametric representation for curves is important in computer-aided geometric design, medical imaging, computer vision, computer graphics, shape matching, and face/object recognition. They are far better alternatives to free form representation, which are plagued with unboundedness and stability problems. This paper deals with the problem of fitting approximating B-spline curves with high compression...
We investigated the distribution change of the intensity noise after passing the gain-saturated semiconductor amplifier. We also investigated the effect of the distribution change on BER.
A 460 kS/s 10-bit successive approximation register (SAR) analog-to-digital converter (ADC) with rail-to-rail input range is proposed for acquiring capacitive sensor. The specifications of ADC are optimized at system level, emphasizing the ADC following a switched-capacitor (SC) capacitance-to-voltage converter (C2V). A bootstrap switch with body effect reduction is adopted to provide the rail-to-rail...
We propose a new algorithm for despeckling multiframe Optical Coherence Tomography (OCT) data based on wavelet shrinkage using anisotropic diffusion and similarity comparison between frames. In this algorithm detail coefficients are weighted for noise reduction, where these weights are calculated based on similarity comparison between approximation coefficients. This comparison is based on the assumption...
Estimating an image M* ∈ ℝ+m1×m2 from its linear measurements under Poisson noise is an important problem arises from applications such as optical imaging, nuclear medicine and x-ray imaging [1]. When the image M* has a low-rank structure, we can use a small number of linear measurements to recover M*, also known as low-rank matrix recovery. This is related to compressed sensing, where the goal is...
A noise-enhanced contrast stretching algorithm for enhancement of dark images in SVD-DWT domain has been presented in this paper. A dark or low-contrast image is considered to be comprising a weak signal (information) and noise (due to insufficient illumination). Since singular values of an image represent luminance of independent image layers, the internal noise may be considered to be inherent in...
Recently it has been proposed that two-dimensional (2D) oversampled received arrays could be used to provide signal-to-external noise ratio (SENR) gains for over-the-horizon radar applications which are strongly externally noise limited. These array configurations can be used to exploit superdirective adaptive beamforming techniques. A key element of the superdirective adaptive beamforming process...
In this paper we extend the previous work on positioning problems using the Circular Interval-based scaling (CIS) algorithm previously proposed by the authors. In particular it is shown how the radius that the CIS algorithm associates to the targets is related to the average statistic of the set of measurements related to them. Together with a factor computed on the basis of the geometric dilution...
Motivated by applications of wireless sensor networks to seismic field monitoring, we propose a method that integrates in-situ lightweight temporal compression with random access communication and compressive sensing for recovery of spatially-sparse phenomena. This method of spatio-temporal compression offers savings in terms of energy consumption and bandwidth usage, does not require sensors to be...
In this paper, we formulate the Motion capture (MoCap) data denoising problem as the concatenation of piecewise motion matrix recovery problem, in which the moving trajectories of each piecewise motion always share the similar subspace representation. To this end, we present an automatic MoCap data denoising approach based on the filtered local subspace affinity (LSA) and low rank approximation. The...
This paper revisits sampling-filters for signals having a finite rate of innovations. Such filters arise in many applications including digital communications and compressive sensing, and mulitchannel versions of these systems have been considered in the past. The main focus of this paper is on sampling-filters that result in perfect reconstruction (PR), or zero-forcing (ZF). Conditions for existence...
In this paper, the Cramér-Rao Lower Bound (CRLB) on the variance of time interval duration estimators is derived for synchronous Time-to-Digital (TDC) converters, based on a coarse counter, keeping into account the effect of noise affecting the TDC clock period. The model has been validated using Montecarlo analysis. By comparing the CRLB to the variance of the TDC output, also theoretically modeled...
This paper analyzes the rate-reliability trade-off for non-ergodic fading channels with state information available at the receiver, specifically the second-order coding rate with a fixed length corresponding to one channel coherence time and a target average error probability. Achievability is developed using random coding and modified typicality decoding, and the converse is developed using generalized...
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