Elio Di Claudio - Academia.edu (original) (raw)
Papers by Elio Di Claudio
COST-229 Bayona (Vigo) Spain, 1994
This work addresses the problem of locating spatially coherent wide-band signal sources by an arr... more This work addresses the problem of locating spatially coherent wide-band signal sources by an array of passive sensor
This work addresses the problem of locating spatially coherent wide-band signal sources by an arr... more This work addresses the problem of locating spatially coherent wide-band signal sources by an array of passive sensor
Acoustic array applications are generally characterized by very large signal bandwidth. Most exis... more Acoustic array applications are generally characterized by very large signal bandwidth. Most existing wide-band direction of arrival (DOA) estimators are based on binning in the frequency domain, so that within each bin the signal model is considered approximately narrow-band. In this work the basic inconsistency of the commonly used binning is first shown. It is shown that the recent Space Time MUSIC (ST-MUSIC) method, which estimates a set of narrow-band signal subspaces directly from the space-time array covariance and combines them within a Weighted Subspace Fitting paradigm, can restore wide-band DOA estimation consistency in most scenarios, obtaining a large variance improvement at high signal to noise ratio (SNR). In addition, a refined ST-MUSIC subspace weighting is proposed to improve accuracy, especially at low SNR.
2014 22nd European Signal Processing Conference (EUSIPCO), 2014
This paper presents a novel Full Reference method for image quality assessment based on two indic... more This paper presents a novel Full Reference method for image quality assessment based on two indices measuring respectively detail loss and spurious detail addition. These indices define a two dimensional (2D) state in a Virtual Cognitive State (VCS) space. The quality estimation is obtained as a 2D function of the VCS, empirically determined via polynomial fitting of DMOS values of training images. The method provides at the same time highly accurate DMOS estimates, and a quantitative account of the causes of quality degradation.
IEEE Transactions on Image Processing
In this paper, a novel Full Reference method is proposed for image quality assessment, using the ... more In this paper, a novel Full Reference method is proposed for image quality assessment, using the combination of two separate metrics to measure the perceptually distinct impact of detail losses and of spurious details. To this purpose, the gradient of the impaired image is locally decomposed as a predicted version of the original gradient, plus a gradient residual. It is assumed that the detail attenuation identifies the detail loss, whereas the gradient residuals describe the spurious details. It turns out that the perceptual impact of detail losses is roughly linear with the loss of the positional Fisher information, while the perceptual impact of the spurious details is roughly proportional to a logarithmic measure of the signal to residual ratio. The affine combination of these two metrics forms a new index strongly correlated with the empirical Differential Mean Opinion Score (DMOS) for a significant class of image impairments, as verified for three independent popular databases. The method allowed alignment and merging of DMOS data coming from these different databases to a common DMOS scale by affine transformations. Unexpectedly, the DMOS scale setting is possible by the analysis of a single image affected by additive noise.
In this paper a new approach to the equalization of digital transmission channels is introduced a... more In this paper a new approach to the equalization of digital transmission channels is introduced and described. The proposed solution makes use of a fast neural architecture, coupled with a innovative error functional, and is able to perform the equalization task in a Viterbi-like fashion applied to a Decision Feedback architecture for the purpose of improving the resitance to imperfect
Neural Networks, IEEE Transactions on, Nov 1, 1996
In this paper a general class of fast learning algorithms for feedforward neural networks is intr... more In this paper a general class of fast learning algorithms for feedforward neural networks is introduced and described. The approach exploits the separability of each layer into linear and nonlinear blocks and consists of two steps. The first step is the descent of the error functional in the space of the outputs of the linear blocks (descent in the neuron space), which can be performed using any preferred optimization strategy. In the second step, each linear block is optimized separately by using a least squares (LS) criterion.
2002 11th European Signal Processing Conference, Sep 1, 2002
Ieee Transactions on Signal Processing, Feb 1, 2003
Adaptive beamforming of sensor arrays immersed into reverberant fields can easily result in the c... more Adaptive beamforming of sensor arrays immersed into reverberant fields can easily result in the cancellation of the useful signal because of the temporal correlation existing among the direct and the reflected path signals. Wideband beamforming can somewhat mitigate this phenomenon, but adaptive solutions based on the minimum variance (MV) criterion remain nonrobust in many practical applications, such as multimedia systems, underwater acoustics, and seismic prospecting.
Ieee Transactions on Image Processing, May 1, 2010
A method for measuring the orientation of linear (1-D) patterns, based on a local expansion with ... more A method for measuring the orientation of linear (1-D) patterns, based on a local expansion with Laguerre-Gauss circular harmonic (LG-CH) functions, is presented. It lies on the property that the polar separable LG-CH functions span the same space as the 2-D Cartesian separable Hermite-Gauss (2-D HG) functions. Exploiting the simple steerability of the LG-CH functions and the peculiar block-linear relationship among the two expansion coefficients sets, maximum likelihood (ML) estimates of orientation and cross section parameters of 1-D patterns are obtained projecting them in a proper subspace of the 2-D HG family. It is shown in this paper that the conditional ML solution, derived by elimination of the cross section parameters, surprisingly yields the same asymptotic accuracy as the ML solution for known cross section parameters. The accuracy of the conditional ML estimator is compared to the one of state of art solutions on a theoretical basis and via simulation trials. A thorough proof of the key relationship between the LG-CH and the 2-D HG expansions is also provided.
A novel approach to Full Reference objective image quality measurement is presented. It is based ... more A novel approach to Full Reference objective image quality measurement is presented. It is based on modelling the human judgment of image quality as a consequence of the value assumed by a two-dimensional cognitive state. The method presented here is immune to the variability of causes of quality impairment, i.e., it is inherently universal. It is shown in this contribution that this quality meter is fast calibrating, i.e., it may be calibrated on the basis of few subjective experimental data.
In this paper the CRB accuracy of a monopulse receiver parametrized by two (off-boresight and rev... more In this paper the CRB accuracy of a monopulse receiver parametrized by two (off-boresight and revolution) angles obtained by combining polar-separable and angularly periodic Gauss-Laguerre directivity patterns is calculated and compared to the CRB of monopulse receivers based on cartesian separable beams.
The very large relative bandwidth of acoustic sources, coupled with the high number of reflection... more The very large relative bandwidth of acoustic sources, coupled with the high number of reflections of a typical listening room, makes localization a challenging task, since all basic assumptions of classical array processing algorithms constitute at the best viable approximations in real-world environments. In this work, a novel decentralized approach for acoustic localization in reverberant environment is presented. It is based on a two-stage strategy. First, candidate source positions are found by a Time-Delay-Of-Arrivals (TDOA) analysis of signals received by colocated pairs of microphones. Differential delays are estimated by a robust ROOT-MUSIC based technique, applied to the sample cross-spectrum of whitened signals recorded from each microphone pair. A subsequent clustering stage in the spatial coordinates validates the raw TDOA estimates, eliminating most of false detections. The new algorithm is capable of tracking multiple speakers at the same time, exhibits a very good co...
In this paper we introduce an enhanced Decision Feedback Equalizer (DFE), based on the use of a f... more In this paper we introduce an enhanced Decision Feedback Equalizer (DFE), based on the use of a feedforward neural network trained with the Discriminative Least Squares (DLS) algorithm. The DFE is a very common architecture in communications (4) ; its ability to cope with channels characterized by a high Intersymbol Interference (ISI) comes from the degree of nonlinearity and the feeback introduced. In this work we show how Neural Networks can generalize the DFE, giving superior performance in the presence of non-minimum phase and non-linear channels. In this last case, the Neural DFE (NDFE) outperforms a Viterbi decoder with a decision depth of five symbols.
In this work new Decision-Feedback (DF) Neural Equalizers (DFNE) are introduced and compared with... more In this work new Decision-Feedback (DF) Neural Equalizers (DFNE) are introduced and compared with classical DF equalizers and Viterbi demodulators. It is shown that the choice of an innovative cost functional based on the Discriminative Learning (DL) technique, coupled with a fast training paradigm, can provide neural equalizers that outperform standard DF equalizers (DFEs) at practical signal to noise ratio (SNR). In particular, the novel Neural Sequence Detector (NSD) is introduced, which allows to extend the concepts of Viterbi-like sequence estimation to neural architectures. Resulting architectures are competitive with the Viterbi solution from cost-performance aspects, as demonstrated in experimental tests.
Performance of TCP connections in high-speed wide-area ATM networks is of great importance due to... more Performance of TCP connections in high-speed wide-area ATM networks is of great importance due to the widespread use of the TCT/IP protocol for data transfers and the increasing deployment of ATM networks. ATM technology promises substantial gains by ...
Identifying and estimating the orientation of monodimensional (linear) patterns found into images... more Identifying and estimating the orientation of monodimensional (linear) patterns found into images is an important task for pattern recognition purposes (e.g., in SAR images) and can greatly improve the efficacy of image restoration, sharpening and de-noising procedures. Existing approaches to linear pattern orientation estimation are based either on pyramid filter banks, steered to a small set of discrete orientations, or on parametric approaches based on the tensor gradient. In the present work, using a local tomography paradigm, the complementary properties of Hermite and Gauss-Laguerre image expansions are exploited for accurately estimating the orientation angles of linear patterns by rooting a polynomial, built with transform coefficients at each analysis point of the image. In particular, as in direction finding with sensor arrays (e.g., ROOT-MUSIC, MODE and ESPRIT), rooting allows a fast and accurate orientation estimation on a continuous set of angles. It is shown that the f...
In this contribution a novel technique for objective full reference quality rating of degraded im... more In this contribution a novel technique for objective full reference quality rating of degraded images using the paradigm of structural distortion measurement is presented. The method is based on the separate evaluation of the edge degradation and of the textural disturbances to measure the quality loss of a given image. Compared with other techniques, the method presents quite uniform behavior with respect to different image defects, responsiveness to image improvements, and diagnostic capabilities.
COST-229 Bayona (Vigo) Spain, 1994
This work addresses the problem of locating spatially coherent wide-band signal sources by an arr... more This work addresses the problem of locating spatially coherent wide-band signal sources by an array of passive sensor
This work addresses the problem of locating spatially coherent wide-band signal sources by an arr... more This work addresses the problem of locating spatially coherent wide-band signal sources by an array of passive sensor
Acoustic array applications are generally characterized by very large signal bandwidth. Most exis... more Acoustic array applications are generally characterized by very large signal bandwidth. Most existing wide-band direction of arrival (DOA) estimators are based on binning in the frequency domain, so that within each bin the signal model is considered approximately narrow-band. In this work the basic inconsistency of the commonly used binning is first shown. It is shown that the recent Space Time MUSIC (ST-MUSIC) method, which estimates a set of narrow-band signal subspaces directly from the space-time array covariance and combines them within a Weighted Subspace Fitting paradigm, can restore wide-band DOA estimation consistency in most scenarios, obtaining a large variance improvement at high signal to noise ratio (SNR). In addition, a refined ST-MUSIC subspace weighting is proposed to improve accuracy, especially at low SNR.
2014 22nd European Signal Processing Conference (EUSIPCO), 2014
This paper presents a novel Full Reference method for image quality assessment based on two indic... more This paper presents a novel Full Reference method for image quality assessment based on two indices measuring respectively detail loss and spurious detail addition. These indices define a two dimensional (2D) state in a Virtual Cognitive State (VCS) space. The quality estimation is obtained as a 2D function of the VCS, empirically determined via polynomial fitting of DMOS values of training images. The method provides at the same time highly accurate DMOS estimates, and a quantitative account of the causes of quality degradation.
IEEE Transactions on Image Processing
In this paper, a novel Full Reference method is proposed for image quality assessment, using the ... more In this paper, a novel Full Reference method is proposed for image quality assessment, using the combination of two separate metrics to measure the perceptually distinct impact of detail losses and of spurious details. To this purpose, the gradient of the impaired image is locally decomposed as a predicted version of the original gradient, plus a gradient residual. It is assumed that the detail attenuation identifies the detail loss, whereas the gradient residuals describe the spurious details. It turns out that the perceptual impact of detail losses is roughly linear with the loss of the positional Fisher information, while the perceptual impact of the spurious details is roughly proportional to a logarithmic measure of the signal to residual ratio. The affine combination of these two metrics forms a new index strongly correlated with the empirical Differential Mean Opinion Score (DMOS) for a significant class of image impairments, as verified for three independent popular databases. The method allowed alignment and merging of DMOS data coming from these different databases to a common DMOS scale by affine transformations. Unexpectedly, the DMOS scale setting is possible by the analysis of a single image affected by additive noise.
In this paper a new approach to the equalization of digital transmission channels is introduced a... more In this paper a new approach to the equalization of digital transmission channels is introduced and described. The proposed solution makes use of a fast neural architecture, coupled with a innovative error functional, and is able to perform the equalization task in a Viterbi-like fashion applied to a Decision Feedback architecture for the purpose of improving the resitance to imperfect
Neural Networks, IEEE Transactions on, Nov 1, 1996
In this paper a general class of fast learning algorithms for feedforward neural networks is intr... more In this paper a general class of fast learning algorithms for feedforward neural networks is introduced and described. The approach exploits the separability of each layer into linear and nonlinear blocks and consists of two steps. The first step is the descent of the error functional in the space of the outputs of the linear blocks (descent in the neuron space), which can be performed using any preferred optimization strategy. In the second step, each linear block is optimized separately by using a least squares (LS) criterion.
2002 11th European Signal Processing Conference, Sep 1, 2002
Ieee Transactions on Signal Processing, Feb 1, 2003
Adaptive beamforming of sensor arrays immersed into reverberant fields can easily result in the c... more Adaptive beamforming of sensor arrays immersed into reverberant fields can easily result in the cancellation of the useful signal because of the temporal correlation existing among the direct and the reflected path signals. Wideband beamforming can somewhat mitigate this phenomenon, but adaptive solutions based on the minimum variance (MV) criterion remain nonrobust in many practical applications, such as multimedia systems, underwater acoustics, and seismic prospecting.
Ieee Transactions on Image Processing, May 1, 2010
A method for measuring the orientation of linear (1-D) patterns, based on a local expansion with ... more A method for measuring the orientation of linear (1-D) patterns, based on a local expansion with Laguerre-Gauss circular harmonic (LG-CH) functions, is presented. It lies on the property that the polar separable LG-CH functions span the same space as the 2-D Cartesian separable Hermite-Gauss (2-D HG) functions. Exploiting the simple steerability of the LG-CH functions and the peculiar block-linear relationship among the two expansion coefficients sets, maximum likelihood (ML) estimates of orientation and cross section parameters of 1-D patterns are obtained projecting them in a proper subspace of the 2-D HG family. It is shown in this paper that the conditional ML solution, derived by elimination of the cross section parameters, surprisingly yields the same asymptotic accuracy as the ML solution for known cross section parameters. The accuracy of the conditional ML estimator is compared to the one of state of art solutions on a theoretical basis and via simulation trials. A thorough proof of the key relationship between the LG-CH and the 2-D HG expansions is also provided.
A novel approach to Full Reference objective image quality measurement is presented. It is based ... more A novel approach to Full Reference objective image quality measurement is presented. It is based on modelling the human judgment of image quality as a consequence of the value assumed by a two-dimensional cognitive state. The method presented here is immune to the variability of causes of quality impairment, i.e., it is inherently universal. It is shown in this contribution that this quality meter is fast calibrating, i.e., it may be calibrated on the basis of few subjective experimental data.
In this paper the CRB accuracy of a monopulse receiver parametrized by two (off-boresight and rev... more In this paper the CRB accuracy of a monopulse receiver parametrized by two (off-boresight and revolution) angles obtained by combining polar-separable and angularly periodic Gauss-Laguerre directivity patterns is calculated and compared to the CRB of monopulse receivers based on cartesian separable beams.
The very large relative bandwidth of acoustic sources, coupled with the high number of reflection... more The very large relative bandwidth of acoustic sources, coupled with the high number of reflections of a typical listening room, makes localization a challenging task, since all basic assumptions of classical array processing algorithms constitute at the best viable approximations in real-world environments. In this work, a novel decentralized approach for acoustic localization in reverberant environment is presented. It is based on a two-stage strategy. First, candidate source positions are found by a Time-Delay-Of-Arrivals (TDOA) analysis of signals received by colocated pairs of microphones. Differential delays are estimated by a robust ROOT-MUSIC based technique, applied to the sample cross-spectrum of whitened signals recorded from each microphone pair. A subsequent clustering stage in the spatial coordinates validates the raw TDOA estimates, eliminating most of false detections. The new algorithm is capable of tracking multiple speakers at the same time, exhibits a very good co...
In this paper we introduce an enhanced Decision Feedback Equalizer (DFE), based on the use of a f... more In this paper we introduce an enhanced Decision Feedback Equalizer (DFE), based on the use of a feedforward neural network trained with the Discriminative Least Squares (DLS) algorithm. The DFE is a very common architecture in communications (4) ; its ability to cope with channels characterized by a high Intersymbol Interference (ISI) comes from the degree of nonlinearity and the feeback introduced. In this work we show how Neural Networks can generalize the DFE, giving superior performance in the presence of non-minimum phase and non-linear channels. In this last case, the Neural DFE (NDFE) outperforms a Viterbi decoder with a decision depth of five symbols.
In this work new Decision-Feedback (DF) Neural Equalizers (DFNE) are introduced and compared with... more In this work new Decision-Feedback (DF) Neural Equalizers (DFNE) are introduced and compared with classical DF equalizers and Viterbi demodulators. It is shown that the choice of an innovative cost functional based on the Discriminative Learning (DL) technique, coupled with a fast training paradigm, can provide neural equalizers that outperform standard DF equalizers (DFEs) at practical signal to noise ratio (SNR). In particular, the novel Neural Sequence Detector (NSD) is introduced, which allows to extend the concepts of Viterbi-like sequence estimation to neural architectures. Resulting architectures are competitive with the Viterbi solution from cost-performance aspects, as demonstrated in experimental tests.
Performance of TCP connections in high-speed wide-area ATM networks is of great importance due to... more Performance of TCP connections in high-speed wide-area ATM networks is of great importance due to the widespread use of the TCT/IP protocol for data transfers and the increasing deployment of ATM networks. ATM technology promises substantial gains by ...
Identifying and estimating the orientation of monodimensional (linear) patterns found into images... more Identifying and estimating the orientation of monodimensional (linear) patterns found into images is an important task for pattern recognition purposes (e.g., in SAR images) and can greatly improve the efficacy of image restoration, sharpening and de-noising procedures. Existing approaches to linear pattern orientation estimation are based either on pyramid filter banks, steered to a small set of discrete orientations, or on parametric approaches based on the tensor gradient. In the present work, using a local tomography paradigm, the complementary properties of Hermite and Gauss-Laguerre image expansions are exploited for accurately estimating the orientation angles of linear patterns by rooting a polynomial, built with transform coefficients at each analysis point of the image. In particular, as in direction finding with sensor arrays (e.g., ROOT-MUSIC, MODE and ESPRIT), rooting allows a fast and accurate orientation estimation on a continuous set of angles. It is shown that the f...
In this contribution a novel technique for objective full reference quality rating of degraded im... more In this contribution a novel technique for objective full reference quality rating of degraded images using the paradigm of structural distortion measurement is presented. The method is based on the separate evaluation of the edge degradation and of the textural disturbances to measure the quality loss of a given image. Compared with other techniques, the method presents quite uniform behavior with respect to different image defects, responsiveness to image improvements, and diagnostic capabilities.