Adaptive DCS-SOMP for Localization Parameter Estimation in 5G Networks.

In this work, we model a 5G downlink channel using millimeter-wave (mmWave) and massive Multiple-Input Multiple-Output (mMIMO) technologies, considering the following localization parameters: Time of Arrival (TOA), Two-Dimensional Angle of Departure (2D-AoD), and Two-Dimensional Angle of Arrival (2D-AoA), both encompassing azimuth and elevation. Our research focuses on the precise estimation of these parameters within a three-dimensional (3D) environment, which is crucial in Industry 4.0 applications such as smart warehousing. In such scenarios, determining the device localization is paramount, as products must be handled with high precision. To achieve these precise estimations, we employ an adaptive approach built upon the Distributed Compressed Sensing-Subspace Orthogonal Matching Pursuit (DCS-SOMP) algorithm. We obtain better estimations using an adaptive approach that dynamically adapts the sensing matrix during each iteration, effectively constraining the search space. The results demonstrate that our approach outperforms the traditional method in terms of accuracy, speed to convergence, and memory use.

Compressed Sensing Technique for the Localization of Harmonic Distortions in Electrical Power Systems.

The present work proposes to locate harmonic frequencies that distort the fundamental voltage and current waves in electrical systems using the compressed sensing (CS) technique. With the compressed sensing algorithm, data compression is revolutionized, a few samples are taken randomly, a measurement matrix is formed, and according to a linear transformation, the signal is taken from the time domain to the frequency domain in a compressed form. Then, the inverse linear transformation is used to reconstruct the signal with a few sensed samples of an electrical signal. Therefore, to demonstrate the benefits of CS in the detection of harmonics in the electrical network of this work, power quality analyzer equipment (commercial) is used. It measures the current of a nonlinear load and issues its results of harmonic current distortion (THD-I) on its screen and the number of harmonics detected in the network; this equipment acquires the data based on the Shannon-Nyquist theorem taken as a standard of measurement. At the same time, an electronic prototype senses the current signal of the nonlinear load. The prototype takes data from the current signal of the nonlinear load randomly and incoherently, so it takes fewer samples than the power quality analyzer equipment used as a measurement standard. The data taken by the prototype are entered into the Matlab software via USB, and the CS algorithm run and delivers, as a result, the harmonic distortions of the current signal THD-I and the number of harmonics. The results obtained with the compressed sensing algorithm versus the standard measurement equipment are analyzed, the error is calculated, and the number of samples taken by the standard equipment and the prototype, the machine time, and the maximum sampling frequency are analyzed.

A Compressed Sensing Approach for Multiple Obstacle Localisation Using Sonar Sensors in Air.

Methods for autonomous navigation systems using sonars in air traditionally use the time-of-flight technique for obstacle detection and environment mapping. However, this technique suffers from constructive and destructive interference of ultrasonic reflections from multiple obstacles in the environment, requiring several acquisitions for proper mapping. This paper presents a novel approach for obstacle detection and localisation using inverse problems and compressed sensing concepts. Experiments were conducted with multiple obstacles present in a controlled environment using a hardware platform with four transducers, which was specially designed for sending, receiving and acquiring raw ultrasonic signals. A comparison between the performance of compressed sensing using Orthogonal Matching Pursuit and two traditional image reconstruction methods was conducted. The reconstructed 2D images representing the cross-section of the sensed environment were quantitatively assessed, showing promising results for robotic mapping tasks using compressed sensing.

MAPL1: q-space reconstruction using ℓ1 -regularized mean apparent propagator.

PURPOSE: To improve the quality of mean apparent propagator (MAP) reconstruction from a limited number of q-space samples. METHODS: We implement an ℓ1 -regularised MAP (MAPL1) to consider higher order basis functions and to improve the fit without increasing the number of q-space samples. We compare MAPL1 with the least-squares optimization subject to non-negativity (MAP), and the Laplacian-regularized MAP (MAPL). We use simulations of crossing fibers and compute the normalized mean squared error (NMSE) and the Pearson's correlation coefficient to evaluate the reconstruction quality in q-space. We also compare coefficient-based diffusion indices in the simulations and in in vivo data. RESULTS: Results indicate that MAPL1 improves NMSE in 1 to 3% when compared to MAP or MAPL in a high undersampling regime. Additionally, MAPL1 produces more reproducible and accurate results for all sampling rates when there are enough basis functions to meet the sparsity criterion for the regularizer. These improved reconstructions also produce better coefficient-based diffusion indices for in vivo data. CONCLUSIONS: Adding an ℓ1 regularizer to MAP allows the use of more basis functions and a better fit without increasing the number of q-space samples. The impact of our research is that a complete diffusion spectrum can be reconstructed from an acquisition time very similar to a diffusion tensor imaging protocol.

A novel approach for accelerating mouse abdominal MRI by combining respiratory gating and compressed sensing.

PURPOSE: To combine the technique of respiratory gating and compressed sensing (CS) with the objective of accelerating mouse abdominal magnetic resonance imaging (MRI). MATERIALS AND METHODS: To obtain the maximum acceleration, phase-encoding data from a phantom and mouse were obtained on a 4.7 Tesla scanner using the respiratory gating technique. The fully sampled data (FSD) were used to construct reference images and to provide samples to simulate retrospective undersampled data (UD) acquisition using respiratory gating. The UD and 95% of the UD on acceleration 2-5 rates were acquired and used for image reconstruction by CS. Quantitative assessment of reconstructed images was performed by structural similarity index (SSIM), peak signal-to-noise ratio (PSNR) and root mean square error (RMSE). RESULTS: The proposed method can accelerate phantom and mouse abdominal MRI acquisition between 2 and 4 rates by reducing the amount of FSD. For phantom UD acquisition, the mean time was reduced in 45.9% and for the acquisition of 95% of UD in 67.8%. For mouse abdominal image UD acquisition, the mean time was reduced in 44.6% and for the acquisition of 95% of UD in 62.5%. The metrics results show that the reconstructed image from UD and 95% of UD by using CS maintains an optimal agreement with their reference images (similarity above 0.88 for phantom and 0.93 for mouse). CONCLUSION: This study presents a novel approach to accelerate mouse abdominal MRI combining respiratory gating technique and CS without the use of expensive hardware and capable of achieving up to 4 acceleration rate without image degradation.

Reducción de los tiempos de adquisición de imágenes por resonancia magnética utilizando técnicas de compressed sensing / Reduction of the acquisition time in magnetic resonance imaging using compressed sensing

Introduction: The acquisition process in magnetic resonance images (MRI) is slow. One approach to reduce the acquisition times is the reconstruction of undersampled data. i.e. to acquire less samples that those needed for standard application, and to reconstruct the unknown samples using mathematical algorithms. We propose to used reconstruction techniques for undersampled data based on Compressed Sensing (CS) to decrease the acquisition times, obtaining identical MRI as those obtained with all samples. Methods: We performed reconstructions of undersampled data obtained from phantoms and MRI with 60 percent, 55 percent and 50 percent of the samples. Results: When the number of samples was more that the double of pixels with non cero intensity, the reconstructions where identical to the original ones. For the MRI experiment, this was achieved with 60 percent of the samples, therefore obtaining a 40 percent of reduction in the acquisition time. Discussion: Our reconstruction technique based on CS is an effective way for reducing the acquisition times in MRI.

Introducción: El proceso de adquisición de imágenes por resonancia magnética (IRM) es lento. Una forma para disminuir los tiempos de adquisición es a través de reconstrucciones de datos submuestreados, es decir tomar menos muestras que las necesarias en aplicaciones estándares, y reconstruir las muestras faltantes a través de algoritmos matemáticos. Proponemos utilizar técnicas de reconstrucción de datos submuestreados basadas en técnicas de Compressed Sensing (CS) para disminuir los tiempos de adquisición, obteniendo imágenes idénticas a las obtenidas con todas las muestras. Métodos: Realizamos reconstrucciones de datos submuestreados de fantomas y IRM con 60 por ciento, 55 por ciento y 50 por ciento de las muestras. Resultados: Cuando el número de muestras fue mayor al doble del número de pixeles con intensidad cero, las reconstrucciones obtenidas fueron idénticas a las originales. Para las IRM esto se logró con 60 por ciento de las muestras, logrando reducciones del 40 por ciento en los tiempos de adquisición. Discusión: Nuestra técnica de reconstrucción basada en CS es una forma efectiva para reducir los tiempos de adquisición de IRM.