One of the most promising technologies to be employed in beyond 5G networks is based on the concept of cell-free (CF) massive MIMO, in which a predetermined set of access points (APs) jointly cooperate in the data transmission and reception to/from the user equipment (UE). In order to efficiently manage radio resources, the CF central processing unit implement uplink power control policies. These policies aim to optimize a given network utility function. In this paper, we investigate the max-min fairness optimization problem, in which the spectral efficiency performance of the UE with the worst channel conditions is prioritized, taking into account a per-UE power constraint and assuming linear maximum ratio combining at APs. Existing solutions have typically relied on second-order cone programming with convex approximations, which exhibit high computational complexity and scalability issues. Therefore, meta-heuristics (MHs) are explored as alternative optimization schemes, capable of providing (near)-optimal solutions with reasonable computational effort. Three MH approaches with different operation principles are compared. Numerical results show that the differential evolution algorithm exhibits the best trade-off between solution quality and run time, being able to reach (near)-optimal solutions faster than the bisection approach while coping with the scalability issues of the geometric programming-based algorithm.

Objective: To early detect nuclear cataract in vivo and automatically classify its severity degree, based on the ultrasound technique, using machine learning. Methods: A 20-MHz ophthalmic ultrasound probe with a focal length of 8.9 mm and an active diameter of 3 mm was used. Twenty-seven features in time and frequency domain were extracted for cataract detection and classification with support vector machine (SVM), Bayes, multilayer perceptron, and random forest classifiers. Fifty rats were used: 14 as control and 36 as study group. An animal model for nuclear cataract was developed. Twelve rats with incipient, 13 with moderate, and 11 with severe cataract were obtained. The hardness of the nucleus and the cortex regions was objectively measured in 12 rats using the NanoTest. Results: Velocity, attenuation, and frequency downshift significantly increased with cataract formation (P <; 0.001). The SVM classifier showed the higher performance for the automatic classification of cataract severity, with a precision, sensitivity, and specificity of 99.7% (relative absolute error of 0.4%). A statistically significant difference was found for the hardness of the different cataract degrees (P = 0.016). The nucleus showed a higher hardness increase with cataract formation (P = 0.049). A moderate-to-good correlation between the features and the nucleus hardness was found in 23 out of the 27 features. Conclusion: The developed methodology made possible detecting the nuclear cataract in-vivo in early stages, classifying automatically its severity degree and estimating its hardness. Significance: Based on this work, a medical prototype will be developed for early cataract detection, classification, and hardness estimation.

The 5G and beyond future wireless networks aim to support a large variety of services with increasing demand in terms of data rate and throughput while providing a higher degree of reliability, keeping the overall system complexity affordable. One of the key aspects regarding the physical layer architecture of such systems is the definition of the waveform to be used in the air interface. Such waveforms must be studied and compared in order to choose the most suitable and capable of providing the 5G and beyond services requirements, with flexible resource allocation in time and frequency domains, while providing high spectral and power efficiencies. In this paper, several beyond 5G waveforms candidates are presented, along with their transceiver architectures. Additionally, the associated advantages and disadvantages regarding the use of these transmission techniques are discussed. They are compared in a similar downlink transmission scenario where three main key performance indicators (KPIs) are evaluated. They are the peak-to-average power ratio, the overall system spectral efficiency (wherein the out of band emissions are measured, along with the spectral confinement of the power spectral density of the transmitted signals) and the bit error rate performance. Additionally, other KPIs are discussed.

This paper addresses the use of computer-aided diagnosis (CAD) system for the cataract classification based on ultrasound technique. Ultrasound A-scan signals were acquired in 220 porcine lenses. B-mode and Nakagami images were constructed. Ninety-seven parameters were extracted from acoustical, spectral and image textural analyses and were subjected to feature selection by Principal Component Analysis (PCA). Bayes, K Nearest-Neighbors (KNN), Fisher Linear Discriminant (FLD) and Support Vector Machine (SVM) classifiers were tested. The classification of healthy and cataractous lenses shows a good performance for the four classifiers (F-measure ≥92.68%) with SVM showing the highest performance (90.62%) for initial versus severe cataract classification

State-of-the-art decoders for Low-Density Parity-Check (LDPQ codes adopted by the DVB-S2 standard, explore the periodicity M = 360 features of the selected special LDPC- IRA codes. This paper addresses the generalization of a well known M-kernel parallel hardware structure and proposes an efficient partitioning by any factor of M, without memory addressing overhead and keeping unchanged the efficient message mapping scheme. The method provides a simple and efficient way to reduce the decoder complexity. Synthesizing the proposed decoder architecture for N = {45,90,180} parallel processing units using an FPGA family from Xilinx shows a minimum throughput above the minimal 90 Mbps.

This paper presents a new technique for providing the analysis and comparison of wiretap codes in the small blocklength regime over the binary erasure wiretap channel. A major result is the development of Monte Carlo strategies for quantifying a code's equivocation, which mirrors techniques used to analyze forward error correcting codes. For this paper, we limit our analysis to coset-based wiretap codes, and give preferred strategies for calculating and/or estimating the equivocation in order of preference. We also make several comparisons of different code families. Our results indicate that there are security advantages to using algebraic codes for applications that require small to medium blocklengths.