Luis Weruaga

Khalifa University of Science and Technology

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Rafael Verdú‐Monedero

Universidad Politécnica de Cartagena

Juan Morales‐Sánchez

Universidad Politécnica de Cartagena

Marián Képesi

Signal Processing (United States)

Shihab Jimaa

Khalifa University of Science and Technology

Abdullah Al-Shabili

Purdue University West Lafayette

Rafael Berenguer-Vidal

Universidad Católica San Antonio de Murcia

Raed M. Shubair

New York University Abu Dhabi

A. Goian

Khalifa University of Science and Technology

Mohamed I. AlHajri

Massachusetts Institute of Technology

R. AlMemari

Khalifa University of Science and Technology

Cooperative Institutions

Khalifa University of Science and Technology

Universidad Politécnica de Cartagena

Graz University of Technology

BT Group (United Kingdom)

Signal Processing (United States)

Korea Advanced Institute of Science and Technology

Institut für Informationsverarbeitung

Forschungszentrum Telekommunikation Wien

Kootenay Association for Science & Technology

Massachusetts Institute of Technology

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Sparse NLMS adaptive algorithms for multipath wireless channel estimation

Abdullah Al-Shabili Bilal Taha Hadeel Elayan Fatima Al-Ogaili Leen Alhalabi

Embedding a sparse penalty in conventional Least Mean Square (LMS) adaptive algorithms is an established strategy to enhance the performance and robustness against noise in the estimation of sparse plants, such as wireless mul-tipath channels. In this paper we review the most prominent NLMS-based algorithms with ℓ _p -norm constraint, discussing the underlying mechanisms that lead to improvement gains in sparse scenarios. Simulation results validate the analysis and comparative discussion. Given that adaptive algorithms operating in time domain deteriorate with correlated signals, we propose hereby a novel frequency-domain (FD) ℓ _p -NLMS that performs in such situations. Simulation results indicate that the proposed method outperforms its time-domain counterparts not only in convergence rate but more importantly in residual misalignment. This important result has not been echoed so far.

Robustness

10.1109/wimob.2015.7348049

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Citations (8)

Active training on the CMAC nonlinear adaptive system

European Signal Processing Conference (2004)

Luis Weruaga Juan Morales‐Sánchez Rafael Verdú‐Monedero

The CMAC neural network presents a rigid architecture for learning and generalizing simultaneously, a limitation stressed with sparse or non-dense training datasets, and hardly solved by the current training algorithms. This paper proposes a novel training algorithm that overcomes the mentioned tradeoff. The training mechanism is based on the minimization of the energy of curvature of the output, solution based on the active deformable model theory. This leads to a cell-interaction-based internal update that preserves the efficient hashed indexing and the original learning capabilities, and delivers a higher generalization degree than the apriori embedded in the CMAC architecture. The theoretical analysis is supported with comparative results on the inverse kinematics of a robotic arm.

Minification

Source

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Estimating volumetric motion in human thorax with parametric matching constraints

IEEE Transactions on Medical Imaging (2003)

Luis Weruaga Juan Morales‐Sánchez Luis Núñez Rafael Verdú‐Monedero

In radiotherapy (RT), organ motion caused by breathing prevents accurate patient positioning, radiation dose, and target volume determination. Most of the motion-compensated trial techniques require collaboration of the patient and expensive equipment. Estimating the motion between two computed tomography (CT) three-dimensional scans at the extremes of the breathing cycle and including this information in the RT planning has been shyly considered, mainly because that is a tedious manual task. This paper proposes a method to compute in a fully automatic fashion the spatial correspondence between those sets of volumetric CT data. Given the large ambiguity present in this problem, the method aims to reduce gradually this uncertainty through two main phases: a similarity-parametrization data analysis and a projection-regularization phase. Results on a real study show a high accuracy in establishing the spatial correspondence between both sets. Embedding this method in RT planning tools is foreseen, after making some suggested improvements and proving the validity of the two-scan approach.

Regularization

Similarity (geometry)

10.1109/tmi.2003.814782

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Citations (29)

Minimum mean square deviation in ZA-NLMS algorithm

Abdullah Al-Shabili Shihab Jimaa Luis Weruaga

The ZA-NLMS (for zero-attractor) represents arguably the seminal sparsity-aware gradient adaptive algorithm. As it is constraint by the ℓ ₁ -norm of the filter weights, the underlying problem turns convex, hence with unique solution (in expected sense). Despite these friendly properties, the algorithm convergence and, more important, the best-performing sparsity tradeoff are yet to be effectively studied. This paper presents a comprehensive analytical study on ZA-NLMS' convergence, which results in the optimal (constant) sparsity tradeoff. The value of this decisive hyperparameter from a practitioner point of view turns out related to the 3/2-power of the adaptive filter length. This outcome, difficult to argue intuitively, as well as the convergence model, have been exhaustively validated with numerical experiments.

Hyperparameter

10.1109/icassp.2017.7952881

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Tikhonov training of the CMAC neural network

IEEE Transactions on Neural Networks (2006)

Luis Weruaga B. Kieslinger

The architecture of the cerebellar model articulation controller (CMAC) presents a rigid compromise between learning and generalization. In the presence of a sparse training dataset, this limitation manifestly causes overfitting, a drawback that is not overcome by current training algorithms. This paper proposes a novel training framework founded on the Tikhonov regularization, which relates to the minimization of the power of the sigma-order derivative. This smoothness criterion yields to an internal cell-interaction mechanism that increases the generalization beyond the degree hardcoded in the CMAC architecture while preserving the potential CMAC learning capabilities. The resulting training mechanism, which proves to be simple and computationally efficient, is deduced from a rigorous theoretical study. The performance of the new training framework is validated against comparative benchmarks from the DELVE environment.

Overfitting

Regularization

Early stopping

10.1109/tnn.2006.872348

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Citations (9)

Time–Frequency Analysis of Vietnamese Speech Inspired on Chirp Auditory Selectivity

Lecture notes in computer science (2008)

Ha Q. Nguyen Luis Weruaga

Vietnamese

Chirp

Frequency selectivity

10.1007/978-3-540-89197-0_28

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Adaptive sparsity tradeoff for ℓ<inf>1</inf>-constraint NLMS algorithm

Abdullah Al-Shabili Luis Weruaga Shihab Jimaa

Embedding the norm in gradient-based adaptive filtering is a popular solution for sparse plant estimation. Even though the foundations are well understood, the selection of the sparsity hyper-parameter still remains today matter of study. Supported on the modal analysis of the adaptive algorithm near steady state, this paper shows that the optimal sparsity tradeoff depends on filter length, plant sparsity and signal-to-noise ratio. In a practical implementation, these terms are obtained with an unsupervised mechanism tracking the filter weights. Simulation results prove the robustness and superiority of the novel adaptive-tradeoff sparsity-aware method.

Robustness

10.1109/icassp.2016.7472570

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Detailed Multidimensional Analysis of our Acoustical Environment

Marián Képesi Luis Weruaga Ed Schofield

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Speech analysis with the short-time chirp transform

Luis Weruaga Marián Képesi

Chirp

10.21437/eurospeech.2003-14

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Citations (7)

DynCT: real-time motion-compensated reconstruction and visualisation for 3D CT fluoroscopy [in image-guided surgery]

Luis Weruaga J.M. Cavanillas

Real-time Computed Tomography Fluoroscopy (CTF) has been during the last decade the medical imaging technique for guidance of surgical tools in percutaneous interventions. However, with CTF the guidance is done in a slice-by-slice basis, whereas other imaging techniques, such as MRI, shows a true volume of the region of interest, despite of a lower quality. The research in CTF is focused towards the development of CT scanners with 2D array of X-ray sensors and novel reconstruction algorithms. A European initiative in this field, called DynCT, is working in the development of that real-time true-volume CTF scanner. Moreover, the objective of DynCT is to develop a novel CT system in fluoroscopy mode that addresses two hot issues in this medical scanning modality: real-time 3D cone-beam reconstruction and organ motion-independent CT reconstruction. Innovative user interface and virtual-reality-based image visualisation devices will be developed to give the surgeon the experience of real immersion in the patient's body.

Modality (human–computer interaction)

Image-guided surgery

10.1109/iembs.2000.900614

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