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This article presents the theoretical study and experimental validation of the multipactor (MP) effect in bandpass filters implemented in groove gap waveguide (GGW) technology. Such filters are based on rectangular cavities and inductive coupling irises, whose optimized dimensions (widths and lengths) are fixed between rows of metal pins. For the effective design of the considered GGW filter, an equivalent counterpart in standard rectangular waveguide (RW) technology will be first studied, using very efficient tools for computing its electrical response and MP discharge breakdown levels. Two prototypes of the GGW and RW bandpass filters, designed to operate at 17.5 GHz (for Ka-band satellite downlink applications), are manufactured for validation and comparison purposes. Both of them are made of aluminium material, whose properties (in terms of secondary electron emission) have also been measured, as well as considered in the prediction of MP threshold levels using available software tools. Proceeding in this way, an outstanding matching between all simulated and experimental results of the MP effect has been achieved.

This work introduces the iSensMEX-POT platform, a potentiostat-based detection system for conducting cyclic voltammetry measurements. It consists of a mixed-mode application-specific integrated circuit (ASIC) and a user digital interface unit based on Raspberry/Python platform. The ASIC consists of three main building blocks: a potentiostat built by three operational amplifiers (OpAmps) working as an analog front-end, a 10-bit R2R digital-to-analog converter (DAC) and a 10-bit successive-approximation analog-to-digital converter (SAR-ADC) composing the mixed signal mode processing stage, and a digital finite state machine using the Inter-Integrated Circuit (I2C) protocol conforming the digital data processing and transmission section. The Raspberry controls the ASIC using the I2C, writes a digital words into the ASIC’s registers to replicate a triangular cyclic voltammetry signal with a scan rate of 200 mV/s over a range of ±1 V, and reads the digitized data from the SAR-ADC. The Raspberry platform displays the resulting waveform on a touch-screen using a graphical user interface (GUI). Also, a precision model based on the open-loop gain of the amplifier and an output noise quantification model were developed and applied to design a highly accurate potentiostat based on three amplifiers. The proposed platform’s functionality and accuracy was validated by comparing the data gathered by the iSensMex-POT with the data obtained with the EVAL-AD5940ELCZ commercial board. The proposed ASIC was mathematically modeled, simulated and fabricated in a 180 nm CMOS standard technology, featuring a silicon area of 1.39 mm2 and a power consumption of 52.99 mW (16.05 mA @ 3.3 V).

The design, manufacture and characterization of a slotted waveguide antenna using substrate-integrated waveguide (SIW) technology combined with a metasurface are described in this paper. The inclusion of the metasurface serves to augment the radiating aperture and narrows the E-plane cut of the antenna pattern, a characteristic that would otherwise be dictated by the slots. For demonstration, an 8-element slotted SIW (S-SIW) antenna array with a −20 dB sidelobe level Chebyshev distribution was designed and its performance was compared to that of a conventional slotted SIW array. Owing to the metasurface, the radiation pattern E-plane narrows and 3 dB gain improvement has been achieved, while keeping matching unaffected.

In recent years, Generative Adversarial Networks (GANs) have gained worldwide interest and have marked a breakthrough in deep learning, encouraging detailed studies in generating artificial images. A new Generative Adversarial Networks (GAN) is proposed to unveil how Human visual perception takes place, focusing on how human beings perceive images, firstly, coarse structures and then their details. The network called 3G-AN consists of three generation stages and a single Discriminator. In this paper, a novel three-branch generator is proposed, which takes into account Coarse, Medium, and Fine structure of a given image. Coarse RGB decomposition image provides the general structure, while Medium RGB stage provides general-fine structure. Finally, Fine RGB decomposition provides fine details of the image. The proposal is tested on MNIST, CIFAR10, and Celebrity faces databases, generating realistic images with almost no anomalies. The RGB decomposition into coarse, medium, and fine, allows to understand the composition of an image from a structural point of view. The qualitative analysis carried out in this research paper outperforms the six most competitive models existing in the literature.

The fifth-generation (5G) of networks is being deployed to provide a wide range of new services and to manage the accelerated traffic load of the existing networks. In the present-day networks, data has become more noteworthy than ever to infer about the traffic load and existing network infrastructure to minimize the cost of new 5G deployments. Identifying the region of highest traffic density in megabyte (MB) per $\text {km}^{2}$ has an important implication in minimizing the cost per bit for the mobile network operators (MNOs). In this study, we propose a base station (BS) clustering framework based on unsupervised learning to identify the target area known as the highest traffic cluster (HTC) for 5G deployments. We propose a novel approach assisted by real data to determine the appropriate number of clusters $k$ and to identify the HTC. The algorithm, named as NetClustering, determines the HTC and appropriate value of $k$ by fulfilling MNO’s requirements on the highest traffic density $\text {MB/km}^{2}$ and the target deployment area in $\text {km}^{2}$ . To compare the appropriate value of $k$ and other performance parameters, we use the Elbow heuristic as a benchmark. The simulation results show that the proposed algorithm fulfills the MNO’s requirements on the target deployment area in $\text {km}^{2}$ and highest traffic density $\text {MB/km}^{2}$ with significant cost savings and achieves higher network utilization compared to the Elbow heuristic. In brief, the proposed algorithm provides a more meaningful interpretation of the underlying data in the context of clustering performed for network planning.

The use of ICT tools is broadly extended among people with intellectual disabilities and also, to a lesser degree, the use of learning tools including learning games. Although the use of learning games is widely accepted due to its high engagement capacity, there are few studies that analyze its usability for people with intellectual disabilities. This work presents an evaluation of the impact of adding an autonomous playing mode on the usability of a learning game designed to aid the training of oral skills in people with Down syndrome. A study in which the effectiveness, efficiency and user satisfaction of a learning game are compared when the system is used with different degrees of teacher supervision is carried out. A learning game originally designed to train oral competencies for people with Down syndrome in a teacher supervised scenario is adapted to allow its autonomous use, by including a module that provides the automatic assessment of oral productions. The use of the tool is thus compared in three different scenarios: a supervised environment, autonomous use, and laboratory use with multiple users working in parallel. The different usability evaluation instruments used reveal that, although there are no differences in the degree of engagement, there may be important differences regarding session performance: the quality of the audios is lower in the laboratory sessions and the number of errors increases in the autonomous sessions. We conclude that, although the autonomous use of learning games by users with intellectual disabilities is possible, and this can lead to considerable savings in human resources, if the feedback provided by the game is not comparable with that provided by the teacher, performance may drop considerably although the degree of engagement is maintained.

Learning games have a remarkable potential for education. They provide an emergent form of social participation that deserves the assessment of their usefulness and efficiency in learning processes. This study describes a novel learning game for foreign pronunciation training in which players can challenge each other. Native Spanish speakers performed several pronunciation activities during a one-month competition using a mobile application, designed under a minimal pairs approach, to improve their pronunciation of English as a foreign language. This game took place in a competitive scenario in which students had to challenge other participants in order to get high scores and climb up a leaderboard. Results show intense practice supported by a significant number of activities and playing regularity, so the most active and motivated players in the competition achieved significant pronunciation improvement results. The integration of automatic speech recognition (ASR) and text-to-speech (TTS) technology allowed users to improve their pronunciation while being immersed in a highly motivational game.

Flexible soft exoskeletons, so-called exosuits, are robotic devices that interact with their users to assist or enhance muscle performance. Their lightweight design and lack of rigid parts allow assisting the user's natural motion without any constraints. They are thereby valuable in carrying out daily labour tasks and performing active stances of rehabilitation. Nonetheless, the usage of these devices in long-term applications demands anatomically adaptive designs and mechanisms to tackle textile artefacts and discrepancies in the human constitution. The soft exoskeleton described in this article is a textile-wearable design that assists shoulder and elbow flexion. The cable-driven actuation is embedded in a jacket by using several textiles and deformable parts. The inconveniences of using textile such as slipping, dampening, and pressure sores are tackled by combining textile layers with force-compliant sewing. The design also includes pieces for cable guidance, anchoring and support. These parts employ different tailoring methods so as to ease fabrication, wearing and cleaning. The motors and electronics, whose design is compatible with textiles too, are placed in a backpack. This configuration reduces forces from loads in motion and weight on the arm. Finally, the last part of the document discusses the preliminary results that have been obtained from four subjects who have worn the device.

The parasitic capacitances of modern Si SJ MOSFETs are characterized by their non-linearity. At high voltages the total stored energy Eoss(VDC) in the output capacitance Coss(v) differs substantially from the energy in an equivalent linear capacitor Coss(tr) storing the same amount of charge. That difference requires the definition of an additional equivalent linear capacitor Coss(er) storing the same amount of energy at a specific voltage. However, the parasitic capacitances of current SiC and GaN devices have a more linear distribution of charge along the voltage. Moreover, the equivalent Coss(tr) and Coss(er) of SiC and GaN devices are smaller than the ones of a Si device with a similar Rds,on. In this work, the impact of the non-linear distribution of charge in the performance and the design of resonant ZVS converters is analyzed. A Si SJ device is compared to a SiC device of equivalent Coss(tr), and to a GaN device of equivalent Coss(er), in single device topologies and half-bridge based topologies, in full ZVS and in partial or full hard-switching. A prototype of 3300 W resonant LLC DCDC converter, with nominal 400 V input to 52 V output, was designed and built to demonstrate the validity of the analysis.

The fifth-generation (5G) of cellular communications is expected to be deployed in the next years to support a wide range of services with different demands of peak data rates, latency and quality of experience (QoE). In this work, we propose a novel approach for radio network dimensioning (RND), named as Heuristic RND (HRND), which uses real open data in the network dimensioning process. This procedure, named as NetDataDrilling, provides the dimensioning target area by means of network data selection and visualization from the existing infrastructure. Moreover, the proposed NetDimensioning heuristic considers the necessary parameters of numerology and bandwidth parts (BWP) supported by New Radio (NR) to provide a balanced network design mediating among the requirements of coverage, capacity, QoE and cost. The proposed HRND is based on the novel quality of experience (QoE) parameter $\zeta $ by probabilistically characterizing the 5G radio resource control ( $rrc$ ) states to ensure the availability of peak data rates for the MNO’s required percentage of the time. The simulation results show the fulfilment of QoE and load balancing parameters with significant cost savings compared to the conventional RND methodology.

ultra-wideband (UWB) technology enables centimeter-level localization systems based on the accurate estimation of the actual distance between transmitter and receiver, by means of the precise estimation of the signal time-of-flight. However, this is only possible when correctly detecting the first path of the incoming signal instead of a bounce or a reflection, which becomes challenging in non line-of-sight (NLOS) situations. There are many different approaches in the literature to alleviate the wrong detection of the first incoming UWB signal path. One of them considers machine learning techniques to design classifiers capable of distinguishing between line-of-sight (LOS) and NLOS propagation from available signal features. However, the performance and complexity of the obtained classifiers depend largely on the size of the input data associated to such features. Thus, features such as the channel impulse response (CIR) produce large amounts of data, yielding very complex classifiers. In this paper, we propose using a downsampled power delay profile (PDP) as an alternative feature consisting of input data much smaller than the CIR, although sufficiently representative, hence resulting in a lower computational cost while exhibiting a similar classification performance. Furthermore, another of the tasks addressed in this work is the study of the impact on the classification results of using a dataset for training where the samples of each class are not balanced from the point of view of energy. Finally, this work also studies how the classifiers based on the CIR or the PDP improve their performance when considering additional signal features such as the estimated range value or its energy level.

Higher order tensor decompositions have hardly been used in muscle activity analysis despite multichannel electromyography (EMG) datasets naturally occurring as multi-way structures. Here, we seek to demonstrate and discuss the potential of tensor decompositions as a framework to estimate muscle synergies from the third-order EMG tensors built by stacking repetitions of multi-channel EMG for several tasks. We compare the two most widespread tensor decomposition models-parallel factor analysis (PARAFAC) and Tucker-in muscle synergy analysis of the wrist's three main degrees of freedom (DoF) using the public first Ninapro database. Furthermore, we proposed a constrained Tucker decomposition (consTD) method for efficient synergy extraction building on the power of tensor decompositions. This method is proposed as a direct novel approach for shared and task-specific synergy estimation from two biomechanically related tasks. Our approach is compared with the current standard approach of repetitively applying non-negative matrix factorization (NMF) to a series of movements. The results show that the consTD method is suitable for synergy extraction compared with PARAFAC and Tucker. Moreover, exploiting the multi-way structure of muscle activity, the proposed methods successfully identified shared and task-specific synergies for all three DoFs tensors. These were found to be robust to disarrangement with regard to task-repetition information, unlike the commonly used NMF. In summary, we demonstrate how to use tensors to characterize muscle activity and develop a new consTD method for muscle synergy extraction that could be used for shared and task-specific synergies identification. We expect that this paper will pave the way for the development of novel muscle activity analysis methods based on higher order techniques.

An important concern that 5G networks face is supporting a wide range of services and use cases with heterogeneous requirements. Radio access network (RAN) slices, understood as isolated virtual networks that share a common infrastructure, are a possible answer to this very demanding scenario and enable virtual operators to provide differentiated services over independent logical entities. This article addresses the feasibility of forming 5G slices, answering the question of whether the available capacity (resources) is sufficient to satisfy slice requirements. As spectral efficiency is one of the key metrics in 5G networks, we introduce the minislot-based slicing allocation (MISA) model, a novel 5G slice resource allocation approach that combines the utilization of both complete slots (or physical resource blocks) and mini-slots with the adequate physical layer design and service requirement constraints. We advocate for a probabilistic characterization that allows to estimate feasibility and characterize the behavior of the constraints, while an exhaustive search is very computationally demanding and the methods to check feasibility provide no information on the constraints. In such a characterization, the concept of phase transition allows for the identification of a clear frontier between the feasible and infeasible regions. Our method relies on an adaptation of the Wang-Landau algorithm to determine the existence of, at least, one solution to the problem. The conducted simulations show a significant improvement in spectral efficiency and feasibility of the MISA approach compared to the slot-based formulation, the identification of the phase transition, and valuable results to characterize the satisfiability of the constraints.

Fluctuation-based dispersion entropy (FDispEn) is a new approach to estimate the dynamical variability of the fluctuations of signals. It is based on Shannon entropy and fluctuation-based dispersion patterns. To quantify the physiological dynamics over multiple time scales, multiscale FDispEn (MFDE) is developed in this paper. MFDE is robust to the presence of baseline wanders or trends in the data. We evaluate MFDE, compared with popular multiscale sample entropy (MSE), multiscale fuzzy entropy (MFE), and the recently introduced multiscale dispersion entropy (MDE), on selected synthetic data and five neurological diseases' datasets: 1) focal and non-focal electroencephalograms (EEGs); 2) walking stride interval signals for young, elderly, and Parkinson's subjects; 3) stride interval fluctuations for Huntington's disease and amyotrophic lateral sclerosis; 4) EEGs for controls and Alzheimer's disease patients; and 5) eye movement data for Parkinson's disease and ataxia. The MFDE avoids the problem of the undefined MSE values and, compared with the MFE and MSE, leads to more stable entropy values over the scale factors for white and pink noises. Overall, the MFDE is the fastest and most consistent method for the discrimination of different states of neurological data, especially where the mean value of a time series considerably changes along with the signal (e.g., eye movement data). This paper shows that MFDE is a relevant new metric to gain further insights into the dynamics of neurological diseases' recordings. The MATLAB codes for the MFDE and its refined composite form are available in Xplore.

Forecasting of stock market returns is a challenging research activity that is now expanding with the availability of new data sources, markets, financial instruments, and algorithms. At its core, the predictability of prices still raises important questions. Here, we discuss the economic significance of the prediction accuracy. To develop this question, we collect the daily series prices of almost half of the publicly traded companies around the world over a period of ten years and formulate some trading strategies based on their prediction. Proper visualization of these data together with the use of the No Free Lunch theoretical framework gives some unexpected results that show how the a priori less accurate algorithms and inefficient strategies can offer better results than the a priori best alternatives in some particular subsets of data that have a clear interpretation in terms of economic sectors and regions.

Fuzzy entropy (FuzEn) was introduced to alleviate limitations associated with sample entropy (SampEn) in the analysis of physiological signals. Over the past decade, FuzEn-based methods have been widely used in various real-world biomedical applications. Several fuzzy membership functions (MFs), including triangular, trapezoidal, Z-shaped, bell-shaped, Gaussian, constant-Gaussian, and exponential functions have been employed in FuzEn. However, these FuzEn-based metrics have not been systematically compared yet. Since the threshold value r used in FuzEn is not directly comparable across different MFs, we here propose to apply a defuzzification approach using a surrogate parameter called 'center of gravity' to re-enable a fair and direct comparison. To evaluate these MFs, we analyze several synthetic and three clinical datasets. FuzEn using the triangular, trapezoidal, and Z-shaped MFs may lead to undefined entropy values for short signals, thus providing a very limited advantage over SampEn. When dealing with an equal value of the center of gravity, the Gaussian MF, as the fastest algorithm, results in the highest Hedges' g effect size for long signals. Our results also indicate that the FuzEn based on exponential MF of order four better distinguishes short white, pink, and brown noises, and yields more significant differences for the short real signals based on Hedges' g effect size. The triangular, trapezoidal, and Z-shaped MFs are not recommended for short signals. We propose to use FuzEn with Gaussian and exponential MF of order four for characterization of short (around 50-400 sample points) and long data (longer than 500 sample points), respectively. We expect FuzEn with Gaussian and exponential MF as well as the concept of defuzzification to play prominent roles in the irregularity analysis of biomedical signals. The MATLAB codes for the FuzEn with different MFs are available at https://github.com/HamedAzami/FuzzyEntropy_Matlab.

Group decision making is a topic of growing interest in today's complex societies. One of the key technologies in this area is the prediction market, where a group of experts plays a fake stock market with assets that represent the outcomes of an uncertain event. The particular problem we address in this paper is the expert selection in these markets to improve their reliability. To aggregate decisions from a particular group of experts, instead of using prices as is typically done, we define a market deconstruction considering player portfolios. This decision technology makes the behaviors of experts toward their decisions available through their portfolios evolution. Our main contribution is the identification of two Persistent Homological Invariants able to classify experts in groups based on the histories of their portfolios. Interestingly, this translates into the definition of essentially two dominant groups. A simulation of the Prediction Market with artificial agents allow us to interpret these two classes as rational and irrational players, following the Microeconomic jargon. Four experiments with experts in the insurance sector help us to illustrate the relationship between these two player types with the prediction reliability of the market.

Being involved in war experiences may have severe consequences in mental health. This exposure has been associated in Colombian ex-combatants with risk of proactive aggression modulating emotional processing. However, the extent of the cognitive processes underlying aggressive behavior is still an open issue. In this paper, we propose a support vector machine-based processing pipeline to identify different cognitive phenotypes associated with atypical emotional processing, based on canonical correlation analysis of EEG network features, and cognitive and behavioral evaluations. Results show the existence of cognitive phenotypes associated with differences in the mean value of leaf fraction and diameter of EEG networks across groups. The ability of identifying phenotypes in these otherwise healthy subjects opens up the possibility to aid in the development of specific interventions aimed to reduce expression of proactive aggression in ex-combatants and assessing the efficacy of such interventions.

We examine whether modeling of the causal dynamic relationships between frontal and occipital electroencephalogram (EEG) time-series recordings reveal reliable differentiating characteristics of Alzheimer's patients versus control subjects in a manner that may assist clinical diagnosis of Alzheimer's disease (AD). The proposed modeling approach utilizes the concept of principal dynamic modes (PDMs) and their associated nonlinear functions (ANF) and hypothesizes that the ANFs of some PDMs for the AD patients will be distinct from their counterparts in control subjects. To this purpose, global PDMs are extracted from 1-min EEG signals of 17 AD patients and 24 control subjects at rest using Volterra models estimated via Laguerre expansions, whereby the O1 or O2 recording is viewed as the input signal and the F3 or F4 recording as the output signal. Subsequent singular value decomposition of the estimated Volterra kernels yields the global PDMs that represent an efficient basis of functions for the representation of the EEG dynamics in all subjects. The respective ANFs are computed for each subject and characterize the specific dynamics of each subject. For comparison, signal features traditionally used in the analysis of EEG signals in AD are computed as benchmark. The results indicate that the ANFs of two specific PDMs, corresponding to the delta-theta and alpha bands, can delineate the two groups well.