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This paper introduces a novel algorithm called Ant-based Feature and Instance Selection. This new algorithm addresses the simultaneous selection of instances and features for mixed, incomplete, and imbalanced data in the context of lazy instance-based classifiers. The proposed algorithm uses a hybrid selection strategy based on metaheuristic procedures and Rough Sets. The Ant-based Feature and Instance Selection algorithm combines Ant Colony Optimization and Generic Extended Rough Sets for Mixed and Incomplete Information Systems. It has five stages: reduct computation, metadata computation, intelligent instance preprocessing, submatrices creation, and fusion. To test the performance of the proposed algorithm, we used 25 datasets from the Machine Learning repository of the University of California at Irvine. All these datasets are imbalanced, with multiple classes and represent real-world classification problems. The number of classes ranges between three and eight classes. Most of them also have mixed or incomplete descriptions. We used several performance measures and computed the Instance Retention ratio and the Feature Retention ratio. To determine the existence or not of significant differences in the performance of the compared algorithms, we used non-parametric hypothesis testing. The statistical analysis results confirm the high quality of the proposed algorithm for selecting features and instances in multiclass imbalanced data.

Using automated models to analyze classroom discourse is a valuable tool for educators to improve their teaching methods. In this paper, we focus on exploring alternatives to ensure the generalizability of models for identifying teaching practices across diverse teaching contexts. Our proposal utilizes artificial intelligence to analyze audio recordings of classroom activities. By leveraging deep learning for speaker diarization and traditional machine learning algorithms for classifying teaching practices, we extract features from the audio diarization using a processing pipeline to provide detailed insights into teaching dynamics. These features enable the classification of three distinct teaching practices: lectures, group discussions, and the use of audience response systems. Our findings demonstrate that these features effectively capture the nuances of teacher-student interactions, allowing for a refined analysis of teaching styles. To enhance the robustness and generalizability of our model, we explore various pipelines for audio processing, evaluating the model’s performance across diverse contexts involving different teachers and students. By comparing these practices and their associated features, we illustrate how AI-driven tools can support teachers in reflecting on and improving their teaching strategies.

Magnetic resonance images are usually corrupted by noise during the acquisition process, which can affect the results of subsequent medical image analysis and diagnosis. This paper presents a denoising recurrent convolutional neural network for Brain MRI denoising. The proposed model consists of a one-level autoencoder architecture with a shortcut, in which the standard convolutional blocks are changed for a new recurrent convolutional denoising block. This block is based on the gated recurrent units combined with local residual learning, allowing us to filter the noisy image recursively. Additionally, we adopt global residual learning to directly estimate the corrupted image’s noise instead of the noise-free image. The proposed model requires less computation than other models based on neural networks and experimentally outperforms state-of-the-art models on clinical brain MRI datasets, particularly for high noise levels.

Federated learning (FL) is a distributed machine learning technique that enables multiple decentralized clients to develop a model collaboratively without exchanging their local data. Heightened privacy risks and the recent strict privacy laws make it even more precarious for the gathering and integration of data in a centralized location for full utilization. Federated learning is compatible with established privacy laws like General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), Health Insurance Portability and Accountability Act (HIPAA), and China’s Cybersecurity Law. Further, there are very few scenarios where centralized, properly labeled, and complete data are available. Federated learning provides a way to solve this problem. As a result, much research has been conducted in several areas within the emerging field of FL. This review paper focuses on decision tree-based FL systems due to their desirable properties of interpretability, parallelism, and high performance. We take a closer look at the motivations, design considerations, tree building algorithms, and security mechanisms used for these systems. We also present the various datasets used in these systems, demonstrated application areas, and the evidence of their benefits. The objective of this paper is to provide an informative overview about the characteristics of FL, privacy and security mechanisms used in them, available open source development frameworks for FL, and the decision tree-based systems developed in FL for researchers in academia and system architects in the industry.

This study focuses on optimizing the efficient operation of standalone direct-current (DC) microgrids with photovoltaic (PV) sources using semi-definite programming (SDP) optimization. The PV source operation model is formulated as a nonlinear programming (NLP) problem with the objective of minimizing daily energy losses and reducing CO2 emissions compared to diesel generators. Transforming the NLP model into convex optimization involves a linear matrix model that combines positive semi-definite matrices with an affine space. This approach enhances robustness by incorporating uncertainties in demand and PV source power. The robust SDP model employs a min–max strategy for worst-case scenario energy management dispatch (EMD). Evaluating a 27-bus standalone DC microgrid, the SDP model outperforms random-based algorithms by achieving global optima in both objectives. Under uncertainties, the energy loss objective increases by 21.6706% with demand uncertainty, 0.3997% with PV source uncertainty, and 22.2009% with both. Meanwhile, the CO2 emissions objective increases by 11.9184%, 1.8237%, and 14.0045%, respectively. Additional simulations on an 85-node DC network confirm the efficacy of SDP in worst-case scenario EMD. All simulations utilized MATLAB’s Yalmip tool with the Mosek solver.

This paper presents a streamlined two-layer control system for effective power sharing and switching control in a DC microgrid designed for electric vehicles. The system integrates Energy Storage Systems and advanced converters to ensure a broad operational range and bidirectional power flow. The Dual active bridge topology is used to integrate the EV to DC MG. Hence, the DC MG system has multiple power converter operating simultaneously. The enhanced droop control strategy is advised for the upper layer and the switching controller is derived using nonlinear controls theory embedding the barrier functions. The enhanced droop strategy shares the power considering the individual dynamics of the storage devices while Barrier-based sliding mode control is applied to converters for current/voltage tracking. Mathematical analysis, leveraging Lyapunov’s theory, confirms the large signal stability of the system. Demonstrated through MATLAB/Simulink-based simulations, the control system exhibits proficient load power sharing, and the adaptive nonlinear controller showcases robustness against unforeseen disturbances. Moreover, the comparative analysis provides insight into the performance of the proposed control methods concerning traditional methods. Hardware-in-loop tests, utilizing Typhoon HIL 404, authentically validate the real-time performance of the proposed control strategies. Different EV and Constant Power Load scenarios ensure a thorough examination, supporting the efficacy of the system. The study contributes valuable insights into the feasibility and efficiency of these control strategies, paving the way for advancements in sustainable electric mobility.

This paper proposes a general analytical formulation for LCL filter design for grid-connected PWM-driven cascaded H-bridge inverters. The novelty of this work deals with providing some easy-of-use analytical expressions that allow for properly sizing the filter inductances and capacitance values considering the number of voltage levels, the DC-link voltage, the adopted multicarrier pulse width modulation strategy, and the switching frequency. Although multilevel inverters performance strongly depends on the adopted modulation strategy and switching frequency, a general mathematical formulation that allows for properly sizing the LCL filter by considering such parameters simultaneously is currently missing. The proposed approach is generalized for the most adopted multicarrier pulse width modulation strategies. To validate the proposed approach, an extended investigation analysis is performed by hardware-in-the-loop real-time tests. According to international standards EN50160 and IEEE Std 1547–2018, the voltage total harmonic distortion and current total rated distortion are adopted to evaluate the LCL filter performance. Tests are carried out in several working conditions, defined in terms of provided apparent power and power factor values. Finally, the proposed analytical formulation is adopted to formulate an optimized LCL filter design algorithm that allows for matching the standard requirements without oversizing the filter parameters.

The Physical Optics (PO) method is used to derive simplified expressions to compute the radiation electric fields of planar graded-index and geodesic lenses based on a parallel-plate waveguide (PPW) implementation. If the PO method is combined with ray-tracing (RT) techniques, the resulting RT-PO procedure is capable of computing radiation patterns and gain of the PPW-based lens antennas when their apertures have a general shape and the electric field is vertically polarized. The RT-PO method is validated by comparing it with the full-wave simulation results of the commercial software ANSYS HFSS for three application cases. The results show that the RT-PO approach is not only very efficient from a computational point of view but also accurate, thus being a very convenient analysis/design tool for this kind of antenna.

The application of lenses combined with array antennas (also known as dome arrays or dome antennas) to the next generation of terrestrial and satellite communication systems brings a wide range of advantages in terms of improved radiation performance, reconfigurability in the use case, and reduction in power consumption. To facilitate the industrial implementation of dome antennas, highly efficient simulation tools are required. In this paper, we present a streamlined implementation of ray tracing for fast and efficient numerical analysis of the far-field radiation performance of 2D multilayer dielectric lenses combined with phased arrays. Unlike commercial physical-optical methods, our proposed ray-tracing method is capable of computing the effects of internal reflections in the dome in a multilayer configuration. In addition, the method estimates the absorption losses as a result of the Joule effect. To demonstrate the effectiveness of the proposed approach, we provide comparisons of the simulated radiation patterns using our proposed ray tracing with the results obtained from commercial full-wave simulation tools.

In a multitude of industrial and biomedical applications, the need for arbitrary waveform generators is essential, serving the purpose of load characterization and excitation, among others. Historically, these generators have had limitations in terms of voltage, current, and frequency output, primarily related with constraints associated with the power devices and circuit topologies. However, notable advancements in semiconductor technology have introduced a new era, enabling the creation of highly versatile waveform generators capable of superior performance, and extended operational capabilities. In this article, a versatile AWG based on switched modules is proposed. In contrast to the previous ones, whose implementation was based on linear amplifiers, it enables arbitrary waveform generation, higher efficiency, and very low output impedance. In addition, it is also presented as a novelty that the voltage in each of the modules is different, following a digital to analog converter (DAC) structure, which allows us to obtain a lower total harmonic distortion (THD) in the output waveform than with conventional methods. The design will take advantage of wide band gap devices to be able to switch in the MHz range to achieve a high bandwidth. Furthermore, in addition to the design and implementation of a high-performance generator, a comparative analysis between the conventional and the proposed DAC-based modulation pattern is performed based on a comparative analysis of the THD and switching losses.

The aim of this observational, cross-sectional study is to evaluate potential differences in kinematics, specifically range of motion (ROM) and velocity, during planar cervical movements between patients with non-traumatic chronic neck pain and disability and asymptomatic controls, while accounting for potential influencing variables of age, sex and fear of movement. The influence of pain intensity, neck disability, age, sex or fear of motion on kinematics was analyzed through robust multivariate Bayesian regression models fitted using the brms library in R. Forty-three patients with neck pain (aged 36.70 ± 13.75 years; 10 men and 33 women) and 42 asymptomatic participants (aged 32.74 ± 13.24 years; 25 men and 17 women) completed the study protocol. The presence of neck pain/disability was associated with lower ROM and peak velocity during all planar movements when considering the influence of age, sex or fear of motion, with standardized regression coefficients that had a small effect size (ranged from 0.11 to 0.28) and estimated differences of less than 2.21° in ROM and 25.61°/s in peak velocity. Although patients with chronic mechanical neck pain showed reduced ROM and peak velocity, the small effect sizes and the low estimated differences between groups question the relevance and clinical usefulness of kinematic analysis of planar movements in samples of patients similar to those included in our study. It is probable that there are differences between the groups, but it is insufficient to rely solely on kinematic variables for patient discrimination. This limitation likely arises from the substantial variability in patient kinematics.

Automated decision support systems are computational tools that have been applied in clinical practice and have many benefits in processes, such as the diagnosis of diseases. Scientific and technological advancements in this area have led to the development of systems for other decision-making processes including medical prescriptions. In this study, we aimed to automate medical prescription making by designing a computer-aided system based on the experience of clinicians. In this case study, we automated the prescription of therapies for ankle fractures in a physical rehabilitation program. The database of the computer-aided prescription systems comprised a set of clinical records, from which the input variables were signs and symptoms related to ankle fracture rehabilitation, and the output variables represented rehabilitation therapies that may be prescribed by clinicians. The system was clinically validated, and its performance was quantified using confusion matrix metrics: 97.4% accuracy, 98.7% precision, 96.6% recall, 98.4% specificity, and 97.6% F-score. Therefore, the proposed system could be a useful tool in decision-making processes as prescription therapies that could contribute to the later motivation regarding traditional physical rehabilitation programs which is the optimization of resources for both patients and physical rehabilitation centers, while rehabilitation objectives are achieved.

In this work, we examine the methodology for numerically computing the dispersion diagram of three-dimensional periodic structures using commercial electromagnetic simulators. Examples of periodic structures based on body-centered cubic, face-centered cubic, and monoclinic lattices are used to illustrate this methodology. We first outline the characteristics of these structures in both physical and reciprocal spaces from a theoretical point of view. On this basis, we provide a comprehensive explanation of how to adjust the setting in simulation software commonly used in microwave engineering to generate the dispersion diagrams of these structures. The appropriate simulation conditions are tabulated to serve as a further guide for other researchers. This study also explores the influence of the elements of the unit cell on the dispersion characteristics. Additionally, we evaluate and contrast the dispersion properties of identical periodic elements when having simple cubic, body-centered cubic, and face-centered cubic arrangements. We found that symmetries, such as those seen in body-centered cubic and face-centered cubic arrangements, can improve the isotropy and maintain low-dispersion characteristics over a wider frequency range. The monoclinic structure is also taken as an example to demonstrate that the reported analysis method can be applied to the dispersion analysis of other more complex noncubic lattices. Our findings offer useful information for the examination and engineering of three-dimensional periodic structures, which can be used to design microwave and antenna devices.

This research presents a multi-objective dispatch (MOD) for energy storage systems (ESS) utilizing batteries and renewable energy resources (RES) in distribution network applications. This proposal employs a convex weighting-based approach. The exact MOD nonlinear programming model, which is non-convex due to the power balance constraint, is approximated using a second-order cone equivalent in order to ensure a global optimum, leveraging the convex properties of the objective functions and the conic equivalent in the complex-variable domain. The MOD, based on the linear combination of the objective functions via the weighting-based method, enables the construction of the optimal Pareto front, as each combination of the weighting factors generates a convex optimization sub-problem. The MOD analysis simultaneously considers the minimization of the expected grid operating costs with regard to energy purchasing at the substation terminals, the reduction of the operating costs associated with the RES and the ESS, and the minimization of the expected daily energy losses. This research makes two contributions: 1) the incorporation of the active and reactive power capabilities of the power electronic converters that interface with the ESS and the RES and 2) a robust analysis via convex optimization to address the uncertainties related to the expected daily generation and demand profiles. Numerical results obtained in the IEEE 33- and 85-bus test system confirm the effectiveness and robustness of the proposed MOD in comparison with the continuous genetic algorithm, the particle swarm optimizer, and the vortex search algorithm. In addition, the best metah-euristic technique was employed for constructing the Pareto front and comparing its performance against the proposed convex approach for the 33-bus grid. In the case of the 85-bus grid, battery power losses were included to demonstrate the effectiveness of this solution methodology in medium-scale distribution grids.INDEX TERMS Energy losses minimization, grid operating cost minimization, multi-objective dispatch, second-order conic equivalent, robust convex optimization, variable power factor operation, weighting-based multi-objective methodology.

Modern electric vehicles require power electronic systems capable of operating under a wide variety of operating conditions, including on-board chargers (OBCs) and dc–dc converters. These systems must function across a wide range of parameters, such as phase number, input voltage, and output battery voltage. Considering modern design standards, achieving a high-performance implementation featuring high efficiency and low cost is also mandatory, adding additional technical challenges. To address these challenges, this article proposes a novel OBC architecture designed to operate in both three-phase and single-phase configurations across the full output power range. This is achieved without requiring additional power components or degrading performance. As a consequence, the proposed solution is a universal-charging single-power-processing block that features a cost-effective implementation while achieving high power density and efficiency. In this article, a bidirectional 11-kW 800-V-battery-voltage prototype of the system is designed and constructed for a 400-V (line-to-line) mains supply.

We verify the moderating effect of managerial ambidexterity on the relationship between intellectual capital and financial performance of small manufacturing companies in Peru. This study used a quantitative, nonexperimental, cross-sectional design. The sample consisted of 506 small manufacturing firms. To determine the validity and reliability of the hypothesized model, we performed exploratory factor analysis using a rotated component matrix to group questions within their corresponding constructs. Next, we assessed convergent and discriminant validity using measures such as Cronbach’s alpha, composite reliability, and average variance extracted. Finally, we tested the model hypotheses using structural equation modelling. SPSS 27 and AMOS 24 were used for all the analyses. This study showed that managerial ambidexterity has a partial moderating effect on the relationship between intellectual capital and the financial performance of small manufacturing firms in Peru. Additionally, statistical analysis showed that managerial ambidexterity moderates the direct relationship between structural capital and relational capital with financial performance, while no moderating effect was observed for human capital. This research constitutes a pioneering study within developing economies, as investigating the moderating effect of managerial ambidexterity on the relationship between intellectual capital and the financial performance of small manufacturing companies in Peru provides valuable information that closes knowledge gaps within this field of study. Additionally, it offers valuable insights for the business sector to analyse the relationship between the hypothesized variables and make informed decisions regarding the benefits and sustainability of companies.

Epilepsy impacts around 6.38 per 1,000 people globally, presenting diagnostic challenges due to the complexity of seizures. Accurate classification of seizure types via Electroencephalogram (EEG) is critical for effective treatment and enhancing patient quality of life. However, the intricate characteristics of EEG data necessitate expert interpretation, a process that is both time-intensive and susceptible to human error. Recent advancements in Deep Learning (DL) have shown promise in EEG analysis, offering new avenues for seizure type classification. This study introduces two innovative deep learning architectures designed for seizure type classification within a seven-class framework: Network 1D Raw, which applies 1D Convolutions to Raw EEG signals, and Network 2D Conv, utilizing 2D convolutions on pre-computed spectrograms. Both architectures employ Separable Convolutions to enhance feature extraction efficiency, with the Network 1D Raw also incorporating a dilation rate technique for expanded analysis. Tested on the Temple University Hospital Seizure (TUSZ) dataset, these methods are evaluated using inter-patient 3-fold cross-validation. The Network 1D Raw achieved a weighted f1-score of $0.611 \pm 0.037$ , while the Network 2D Conv reached $0.599 \pm 0.052$ , both surpassing existing benchmarks. The Network 1D Raw demonstrated superior classification for Absence Seizure (ABSZ), Focal Non-specific Seizures (FNSZ), and Generalized Non-specific Seizures types (GNSZ) with AUPRs of $0.9400 \pm 0.0500$ , $0.8100\pm 0.0300$ , and $0.5500\pm 0.1800$ , with Network 2D Conv excelling in FNSZ Seizures and GNSZ classes with AUPRs of $0.8100 \pm 0.1100$ and $0.4000 \pm 0.0200$ . Our DL models advance seizure type classification, blending efficiency with accuracy. Network 1D Raw’s compact design suits low-resource environments, aiding quick, precise diagnoses. Future work will focus on expanding dataset diversity, particularly for underrepresented seizure types, to further refine classification performance.

Powerline inspection and modelization using airborne light detection and ranging (LiDAR) data have been widely studied through the years. However, to the best of our knowledge, the proposed methods rely on intentional flights carried out along the high-voltage powerline. Thus, the state-of-the-art studies focus on detecting and characterizing a single powerline whose presence and location are known beforehand. We propose a method to detect and model powerlines of any voltage from airborne LiDAR point clouds not necessarily acquired for this purpose. Also, the method is suitable to be applied to those point clouds whose density is usually lower than that obtained using specific purpose flights over the powerlines. Our solution starts filtering out most of the points that do not belong to electric conductors. Then, the Hough transform is used to detect straight lines. Its output is then used to cluster the electric conductors. Also, we propose a solution to bypass a common issue regarding the nonmaxima suppression often used in object detection algorithms. Furthermore, a robust method for clustering conductors sharing the same vertical plane is presented, being able to return good results even in the absence of parts of any electrical conductor. The algorithm is tested in several datasets containing high-voltage powerlines and others, comprising mid- and low-voltage electric conductors. Finally, a study of the computational performance shows that the algorithm can efficiently take advantage of manycore systems, which is essential to determine the feasibility of our approach on massive LiDAR point clouds.

This paper presents the methodology followed for the design of a quad-ridged flared horn (QRFH) feed for the VLBI Global Observing System (VGOS) antennas. The study demonstrates the potential undesired effects caused by the presence of non-fundamental modes at the throat of the horn antenna, and how they can be avoided following some design considerations. The approach consists of a separate design of the feeding transition and the flared section of the horn, and it can be extrapolated to other implementations. Firstly, the geometry of the coaxial to quad-ridged waveguide (QRWG) transition is defined. The proposed design technique ensures negligible propagation of higher order modes at the waveguide interface. Secondly, the ridges and sidewall profiles of the flared part are optimized to achieve the desired performance in terms of input reflection and aperture efficiency on the VGOS 13.2-meter displaced-axis two-reflector radio telescope. Following the optimized design, five QRFH antenna units have been manufactured and tested, all showing reflection coefficients better than -10 dB and predicted aperture efficiencies better than 55 % in the band from 2.2 to 14 GHz. The excellent agreement between simulations and experimental results demonstrates the validity of the proposed methodology.

The growing demand for electric power and the need for an energy transition that contributes to the reduction of global greenhouse gas emissions have driven the development of various energy generation, storage, and offset technologies. These technologies are known as distributed energy resources. Their integration into distribution power systems not only contributes to improving operating aspects, but also allows supplying electricity to areas that do not have access to large-scale power systems. Therefore, the integration and management of these resources has become a topic of interest, and several studies seek to optimize their impact on technical, economic, and environmental aspects. However, this optimization poses specific challenges related to the type and number of variables related to the operation of a distribution power system. This review article aims to describe the main challenges posed by three-phase AC three-phase distribution power systems under scenarios involving the integration of distributed energy resources. In addition, it presents some approaches proposed by different authors to improve the technical, economic, and environmental aspects of power grids. It can be stated that the strategies presented in the literature fail to consider scenarios that simultaneously integrate different types of technologies and optimize them while following a multi-objective approach and considering three-phase systems in a context of variable generation and demand. Therefore, future work in this field should address these aspects in a holistic manner, taking into account the computation efforts and processing times required by intelligent algorithms.

In the era of Industry 4.0, digital transformation has spurred the swift advancement of technologies, including intelligent predictive maintenance scheduling, prognostics and health management. The accurate prediction of remaining useful life plays a crucial role in these technologies as it extends power equipment’s safe operational duration and decreases the maintenance costs associated with unforeseen shutdowns. Also, the increased accessibility of data for monitoring system conditions has paved the way for the more immense adoption of machine learning models in prognostics and health management for power transformers. At the moment, with the ever-increasing demand for electricity, there is a corresponding increase in the degradation processes of power transformers. Transformers insulation system and more importantly, the paper insulation happens to be the principal part where the degradation is prominent. Therefore, an accurate prediction of the insulating paper condition through its degree of polymerization is required to guarantee the reliability of power transformers. In this regard, the predictions, reliability, and health monitoring of this power equipment can be actualized by modeling the degradation of transformer insulation paper through several machine learning frameworks. In this view, this review paper has been drafted not only to serve as a guide for researchers interested in the fields of transformer insulation system fault prognosis but also to offer insights into potential research directions as existing literature in modeling and evaluating transformer paper insulation is presented.

Popular comments suggest that continuous exposure of children and adolescents to video games yields a non-benefit behavior in the players’ mental health. Contrarily, several studies have proven that commercial and serious games improve mental activity; some are used in treating psychological and physical disorders. This paper presents a method based on electroencephalogram signals analysis to classify multiple emotions recorded from subjects’ gameplay seasons. In the core of this study, a self-assessed labeling method is evaluated using the Force, EEG, and Emotion Labelled Dataset (FEEL) for emotion recognition tasks. Besides, a 1-D Local Binary Pattern (LBP) method transforms the EEG temporal behavior to extract time-frequency features. Complementarily, the database artifacts were removed using a novel Conflict Learning approach for machine learning models, associating the extracted samples with the subjects’ emotion labeling. A semi-supervised clustering method was employed to show the similarity between self-assessed subjects’ labels. Finally, numerical results suggested a conflict between 23 original labels, improving the classification by over 92% in accuracy for 19 self-assessed classes.

This research proposes an effective energy management dispatch (EMD) using a convex approximation for battery-based energy storage systems (ESS) and renewable sources in medium-voltage distribution networks. By means of the branch flow formulation, the exact nonlinear programming model representing the EMD is transformed into a convex model through a second-order cone programming relaxation. Two contributions are presented in this work. The first involves analyzing battery-based EES and renewable sources, specifically focusing on their operation with a variable power factor. This analysis considers the reactive power control capabilities of the power electronic converters that connect distributed energy resources to the grid. The second contribution involves evaluating a robust operation scenario by incorporating uncertainties in the availability of renewable generation and the expected daily demand profile. Numerical validations are conducted on two radial distribution networks composed of 27 and 33 nodes. These networks were chosen to represent the characteristics of rural and urban networks in the Colombian distribution system. The results are then compared against those obtained with a classical unitary power factor. The main goal of the proposed EMD is to minimize the expected annual grid energy losses and operating costs through a single-objective analysis. A comparison with metaheuristic optimization algorithms, such as the parallel version of the particle swarm optimizer, the vortex search algorithm, and a genetic algorithm, demonstrates the effectiveness of our convex EMD model for both objective functions under study. All numerical simulations, including the robust model, were run using MATLAB’s Yalmip tool.

Time Series Analysis (TSA) is a critical workload to extract valuable information from collections of sequential data, e.g., detecting anomalies in electrocardiograms. Subsequence Dynamic Time Warping (sDTW) is the state-of-the-art algorithm for high-accuracy TSA. We find that the performance and energy efficiency of sDTW on conventional CPU and GPU platforms are heavily burdened by the latency and energy overheads of data movement between the compute and the memory units. sDTW exhibits low arithmetic intensity and low data reuse on conventional platforms, stemming from poor amortization of the data movement overheads. To improve the performance and energy efficiency of the sDTW algorithm, we propose MATSA, the first Magnetoresistive RAM (MRAM)-based Accelerator for TSA. MATSA leverages Processing-Using-Memory (PUM) based on MRAM crossbars to minimize data movement overheads and exploit parallelism in sDTW. MATSA improves performance by $7.35\times /6.15\times /6.31\times $ and energy efficiency by $11.29\times /4.21\times /2.65\times $ over server-class CPU, GPU, and Processing-Near-Memory platforms, respectively.

Modeling the probability of blocking User Equipment (UE) sessions within a cell is a crucial aspect within the management of 5G services with Guaranteed Bit Rate (GBR) requirements, especially in the process of planning in advance the deployment of such services. The complexity of modeling the UE blocking probability arises from the dependency of this performance indicator on multiple factors, including the UE channel quality within the cell, the MAC scheduling discipline, the statistical distributions of the traffic demand and session duration, and the GBR requirements of the corresponding service. In this vein, we propose an analytical model to assess the UE blocking probability for a GBR service. The proposed model is based on a Markov chain which is insensitive to the holding time distribution of the UE data sessions. Furthermore, it may consider as input any continuous distribution for the average Signal-to-Interference-plus-Noise Ratio (SINR) within the cell. The simulation results demonstrate the execution time of the proposed model is on the order of tens of milliseconds, which makes it suitable for testing multiple network configurations in a short term, training ML models or detecting traffic anomalies in real time. Additionally, the results show that our model exhibits an estimation error for the UE blocking probability below 2.6%.

Due to rising consumer demand and traffic congestion, last-mile logistics is becoming more challenging. To optimize urban distribution networks, digital image processing plays a key role in addressing these challenges through efficient traffic monitoring systems, an essential component of intelligent transportation systems. This paper introduces the Hyper-heuristic Genetic Algorithm based on Thompson Sampling with Diversity (HHGATSD), a novel approach to efficiently solving complex optimization and versatility problems in image segmentation. We evaluate its efficiency and robustness using the IEEE CEC2017 benchmark function set in general optimization problems with 30 and 50 dimensions. HHGATSD’s applicability extends beyond optimization to computer vision in traffic management. First, the multilevel thresholding segmentation is performed on images extracted from the Berkeley Segmentation Dataset with minimum cross-entropy as the objective function, and its performance is compared using PSNR, SSIM, and FSIM metrics. Following that, the proposed methodology addresses the task of vehicle segmentation in traffic camera videos, reaffirming HHGATSD’s effectiveness, adaptability, and consistency by consistently outperforming alternative segmentation methods found in the state-of-the-art. The results of comprehensive experiments, validated by statistical and non-parametric analyses, show that the proposed hyper-heuristic and methodology produce accurate and consistent segmentations for road traffic surveillance compared to the other methods in the literature.

In this paper, we have investigated the flicker noise (1/ $f$ ) in 45-nm RFSOI NFETs for quantum computing applications. 1/ $f$ noise characterization and analysis were performed in linear region at cryogenic temperatures down to 10K. A Lorentzian-like noise is also observed depending on bias conditions, possibly due to the floating body of PDSOI. The extracted frequency exponent $(\gamma)$ shows an inverted U-shape behavior with temperature mainly attributed to non-uniform energy distribution of traps in the gate dielectric. The dominant source of 1/ $f$ noise is carrier number fluctuation. Thermal-activation behavior of 1/ $f$ noise is studied, which shows the traps responsible for noise are not thermally activated. Volume trap densities are also extracted from 300K down to 10K. The volume trap density increases with a decrease in temperature, but no significant increase in normalized noise is observed at cryogenic temperatures in the measured NFETs. The non-uniform distribution along with the thermal inactive behavior of traps over the studied temperature range is expected to be a plausible reason for the temperature-independent behavior of normalized noise.

This paper presents a novel and simple procedure for the determination of a Fault-Tolerant Algorithm (namely FTA) for the adequate working operation of an electric machine equipped with a general $m$ -phase winding under possible open circuits occurring in some of its phases. The FTA is firstly theoretically derived from the analysis of the magnetic field distribution in polyphase systems, then simulated through finite-element analysis and finally implemented via software for real-time validation. The effectiveness of the proposed procedure is confirmed through experimental tests on a laboratory setup. Key contributions include the flexibility of the algorithm, which can be applied to any $m$ -phase electric machine with various winding configurations. Both the obtained FEA and experimental results demonstrate that the proposed control algorithm can be easily used and applied in electric drives supplied by $m$ -phase motors under fault conditions without significantly affecting the magnetic performance of the whole system and ensuring the continuity of operation even in the presence of faults.

Power electronics plays a key role in electric vehicle technology in areas such as the battery charging and managing, dc distribution and motor drive systems, among others. There are, however, significant challenges to be addressed in terms of cost, performance, reliability and power density. This paper aims at proposing an improved on-board charger architecture taking advantage of a power pulsating buffer topology. The proposed architecture will enable to significantly reduce the dc-link capacitor size, enabling a change in its technology, and leading to a higher power density and higher reliability implementation. The proposed architecture is analyzed, its main design considerations are discussed and, finally, a 3.6-kW experimental prototype is designed and implemented to prove the feasibility of this proposal. As a conclusion, the proposed topology is recommended as a high-performance high-power-density OBC implementation for future EVs.

In this paper, a novel dual-slant polarized three-dimensional (3D) periodic Luneburg lens with a diameter of 390 mm ( $4.6\lambda _{0}$ at 3.55 GHz) is presented. Copper-plated cubes with truncated corners are placed in a body-centered cubic (BCC) lattice and held together with layers of Rohacell foam. The sizes of the cubes are varied to realize the gradient refractive index (GRIN) profile of a generalized Luneburg lens at a low cost, with a low weight and loss. The designed Luneburg lens operates from 3.3 to 3.8 GHz with a total weight of 1 kg. The quasi-isotropic response of the proposed periodic structure allows a wide angle coverage. Measured results show that the lens antenna can achieve a peak gain of 22 dBi with a scanning loss of less than 0.4 dB in a wide angular range in both the azimuth and the elevation plane. Importantly, the proposed lens antenna design achieves a cross-polarization level below peak gain of less than −19 dB at all angles for the two slant polarizations of the feed, while comparable designs report up to −11 dB at given angular directions. This high-gain multi-beam antenna with a commercially viable design is suitable for wireless communications at low microwave frequencies.

A novel low-power sensor wireless system for continuous and simultaneous measurement of the electrocardiogram (ECG) and the impedance plethysmogram (IPG) from a single limb has been designed and tested. This system is made up of an analog subsystem composed of two signal-conditioning channels, one for the ECG and one for the IPG that share the same voltage pickup electrodes, and a digital subsystem based on the BGM220PC22WGA microcontroller that is responsible for digitizing the signals and sending their data via Bluetooth Low Energy (BLE) to a processing unit. The consumption of the system has been analyzed using different settings, obtaining values that would allow a battery duration of up to 131 h, by performing four 30-s measurements every hour. The system was tested in one arm and one leg in a group of healthy volunteers to verify the functionality and to identify the best electrode positions to get high quality signals. Non-standard ECG leads are obtained in both one arm and one leg with an SNR of more than 30 dB. In the worst case, for measurements on the legs, the mean error of the RR intervals from each recording was 1.7 ms and the 95% confidence interval (±SD) was ±7.3 ms versus a reference system. This has made it possible to accurately obtain the Pulse Arrival Time (PAT) between the ECG and the simultaneously measured IPG signal, which allows more cardiovascular information to be obtained from the patient than with systems that acquire only the ECG signal.

The popularity of current communication technologies has boosted the spread of polarization and radical ideologies, which can be exploited by terrorist organizations. Building upon previous research, this work focuses on the task of automatic radicalization detection in texts using natural language processing and machine learning techniques. In this way, we investigate the effectiveness of integrating moral values through the Moral Foundations Theory (MFT). Moral values play a crucial role in identifying ideological inclinations and can have a significant impact on the radicalization detection task. Our approach distinguishes itself in the feature extraction stage, leveraging moral values, emotions, and similarity-based features that utilize word embeddings. Additionally, we thoroughly evaluate the proposed representations with three distinct datasets that model radicalization and use the SHAP method to gain relevant insight into the models’ reasoning.

This article investigates the dispersion properties of metasurfaces with hexagonal lattices, including potential higher symmetric configurations. We explore the relationships between the periodicity of hexagonal lattices and their dispersion properties, paying special attention to how hexagonal periodic structures can be analyzed with either a hexagonal primitive unit cell or a rectangular supercell. We also study the possibility of introducing higher symmetries into hexagonal periodic structures, including glide symmetry and mirrored half-turn symmetry. To complement and validate the analysis, we designed a graded-index Luneburg lens antenna with a dielectric-filled hexagonal holey structure working in the K$_{\mathrm{a}}$-band. The antenna generates steerable highly directive beams from 26 GHz to 30 GHz, which corroborates our analysis. Our findings provide valuable insight into the dispersion properties of hexagonal-lattice metasurfaces and demonstrate the feasibility of using such structures in practical applications.

This paper presents a new method for identifying dynamical systems to get fractional-reduced-order models based on the process reaction curve. This proposal uses information collected from the process. It can be applied to processes with an S-shaped step response that can be considered with fractional behavior and a fractional order range of $\alpha \in [{0.5, 1.0}]$ . The proposed approach combines obtaining the fractional order of the model using asymptotic properties of the Mittag-Leffler function with time-based parameter estimation by considering two arbitrary points on the process reaction curve. The improvement in terms of accuracy of the identified FFOPDT model is obtained due to a more accurate estimation of $\alpha $ parameter. This method is characterized by its effectiveness and simplicity of implementation, which are key aspects when applying at an industrial level. Several examples are used to illustrate the effectiveness and simplicity of the proposed method compared to other well-established methods and other approaches based on the process reaction curve. Finally, it is also implemented on microprocessor-based hardware to demonstrate the applicability of the proposed method to identify the fractional model of a thermal process.

This study presents a method based on elementary logic and arithmetic operations to enhance the cryptographic properties of Substitution Boxes (S-Boxes). S-Boxes are a crucial component of cryptosystems, as they apply the confusion principle to information before it is encrypted, making them vital for ensuring the security of sensitive information transmitted through insecure channels. The proposed method employs bitwise XOR, Modular Addition, and Circular Shift operations, which are applied to selected S-Boxes, resulting in numerous S-Box variants that have no fixed points or reverse fixed points. We found that some of these variants can increase nonlinearity when using modular addition or circular shift operations and are therefore more suitable for use in cryptosystems. Our study contributes to the understanding of how S-Boxes can be enhanced by elementary logic and arithmetic operations. We recommend using the proposed method with the bitwise XOR operation when the S-Box has high nonlinearity (112) but requires removing fixed points and reverse fixed points. Otherwise, first use modular addition or circular shift operations to increase nonlinearity.

Volcanic seismicity is one of the most relevant parameters for the evaluation of volcanic activity and consequently the prognosis of eruptions. Earthquakes of volcanic origin are of different classes, directly related to the physical process that generates them. The distribution of the data between classes of seismic-volcanic signals generally presents an unbalanced profile (imbalanced datasets), which can hinder the performance of the classification in machine learning models. Therefore, this research presents a characterization technique (feature extract) that, in addition to reducing the dimension of each seismic record, allows a representation of the signals with the most relevant and significant information. This work proposes the use of a dual autoencoder feature, which is compared with conventional characterization techniques, such as linear prediction coefficients and principal component analysis. The training of the model was performed with a dataset containing volcano-tectonic (VT) earthquakes, long period events, and Tornillo-type events of the Galeras volcano, one of the most active volcanoes in Colombia. The classification results reach 99% of the classification of the mentioned classes.

Land-cover classification is an important topic for remotely sensed hyperspectral (HS) data exploitation. In this regard, HS classifiers have to face important challenges, such as the high spectral redundancy, as well as noise, present in the data, and the fact that obtaining accurate labeled training data for supervised classification is expensive and time-consuming. As a result, the availability of large amounts of training samples, needed to alleviate the so-called Hughes phenomenon, is often unfeasible in practice. The class-imbalance problem, which results from the uneven distribution of labeled samples per class, is also a very challenging factor for HS classifiers. In this article, a comprehensive review of oversampling techniques is provided, which mitigate the aforementioned issues by generating new samples for the minority classes. More specifically, this article pursues a twofold objective. First, it reviews the most relevant oversampling methods that can be adopted according to the nature of HS data. Second, it provides a comprehensive experimental study and comparison, which are useful to derive practical conclusions about the performance of oversampling techniques in different HS image-based applications.

A well-known problem of visual object tracking is the difficulty of accurately estimating the object trajectory under conditions of environmental disturbances in the bounding box (BB) of a video camera. In this paper, we consider BB variations as Gaussian-Markov colored measurement noise (CMN). In order to perform accurate tracking in the presence of CMN, we use measurement differencing and develop a robust general unbiased finite impulse response (GUFIR) filter and use the general Kalman filter (GKF) as a benchmark. The GUFIR and GKF algorithms are tested by the “Car4” benchmark. It is shown that, in terms of the tracking precision and under the heavy disturbance with the $0.65 \leqslant \Psi \leqslant 0.95$ coloredness factor, the best tracking performance is achieved using the robust GUFIR filter. When $\Psi < 0.6$ , the GUFIR and GKF algorithms perform near equally. In the extreme point of $\Psi = 1.0$ , where the Gauss-Markov CMN loses the stationarity, both algorithms provide zero precision and become inefficient. In general, it is concluded that the GUFIR filter, which ignores any zero mean disturbance and initial values, is much more suitable for applications in visual object tracking than Kalman-like algorithms relying on complete object information.

Recently the grid-following inverter-based power generation systems (IBPGSs) dominate over synchronous generators (SGs) in load sharing in some areas of power grids in many countries worldwide. The trend of increasing penetration of IBPGSs to grid results in decreasing number of SGs in operation. This can result in a slowly decreasing system inertia which results in fast dynamics during transient conditions, that may lead to failures of phase-locked loops (PLLs), which are widely used in grid-following IBPGSs. Consequently, the grid-following IBPGS-dominated grids are facing stability issues in their operations. To support these grid-following IBPGSs for their stable operations, a stable grid-forming IBPGS is required that can operate as SGs. In this paper, a grid-forming controller is designed for IBPGSs that can enable IBPGSs to mimic SGs and work as grid-forming inverters that operate like SGs to maintain the grid dynamics suitable for the stable operation of the IBPGSs. For this purpose, a detailed mathematical model of the SG and its associated controller has been used with the proposed grid-forming controller. From the steady-state and transient dynamics, a mathematical relationship between the inertia support from the grid-forming controller and an SG has been developed to control the inverter in the grid-forming IBPGS. The performance of the grid-forming IBPGS during steady-state and transients due to load change, fault, and grid-isolation has been compared with the SG. The simulation results have validated the effectiveness of the proposed controller and grid-forming IBPGSs to help control the voltage and frequency of the IBPGS-dominated power systems.

For the first time, commercial grade smart meters have been subjected to cyber security attacks to understand their operation and security resilience under different attack scenarios. Cyber security is a matter of top concern for utility companies installing smart meters for remote collection of power usage data from customer premises. Keeping power-usage data secure and to maintain system’s resiliency under cyber security attacks is very important. In Smart electric grids, the power usage data from smart meters are periodically reported to the utility company. Reporting and remote monitoring of power usage data requires the use of data network protocols, which introduces security vulnerabilities. Cyber security attacks can impact reporting mechanisms in the smart grid, which may result in alteration or complete loss of power usage data as reported to the utility company. Despite the wide deployment of smart meters, there has not been much experimental work done to evaluate resiliency and data integrity of smart meters under security attacks. It is not clear how the operation of smart meters or the remote collection of power-usage data can be affected under security attacks. In this paper, we present our experimental work to test security resiliency of the commercial grade smart meters. We conducted real experiments, using smart electric meters from leading manufacturers to investigate data integrity under common data security attacks in a lab environment using real network equipment. Based on the experimental observations, it was discovered that the common cybersecurity attacks were able to adversely impact the data reporting operation of the smart electric meters. Cybersecurity attacks in some cases were found to cause complete loss of reporting of the power-usage data to the remote monitoring station.

Accurate synthetic aperture radar-optical matching is essential for combining the complementary information from the two sensors. However, the main challenge is overcoming the different heterogeneous characteristics of the two imaging sensors. In this article, we propose an end-to-end machine learning pipeline inspired by recent advances in image segmentation. We develop a siamese multiscale attention-gated residual U-Net for feature extraction from satellite images. The siamese architecture shares weights and transforms the heterogeneous images into a homogeneous feature space. Fast Fourier transform is used to compute the cross-correlation between the feature maps and produce a similarity map. A contrastive loss is introduced to aid the training procedure of the model and maximize the discriminability of the model. The experimental results on a benchmark dataset show that the proposed method has superior matching accuracy and precision compared to other state-of-the-art methods.

The unbiased finite impulse response (UFIR) filter, Kalman filter (KF), and game theory $H_{\infty} $ filter are developed for vehicle tracking in wireless sensor networks (WSNs) with multi-step random delays and multiple dropouts. The problem with data delays is solved for given delay probabilities by converting a model with delays to another without delays. The dropouts are detected at the receiver by a data sensor, and lost data are compensated for by prediction. The filters developed are experimentally tested for accuracy and robustness using the Global Positioning System-based vehicle tracking database, where the measured coordinates are transmitted over WSN with delays. It is shown that the UFIR filter is the most robust to uncertain delays and the KF is the most accurate when the delay probabilities are known exactly. The game theory $H_{\infty }$ filter is the most accurate under ideal conditions and prone to divergency otherwise.

This study utilizes inverse optimal control (IOC) theory to address the issue of output voltage regulation in a boost converter feeding an unknown direct current (DC) load. The proposed approach involves developing a general feedback control law through IOC to ensure asymptotic stability in closed-loop operation, with the added advantage of incorporating an integral gain without compromising stability. Two estimators are introduced to minimize the number of sensors required for implementing the IOC controller with integral action. The first estimator, based on the immersion and invariance (I&I) method, determines the current demand of the DC load by measuring the boost converter’s output voltage. While the second estimator, using the disturbance observer (DO) method, estimates the voltage input value by measuring the inductor’s current flow. Both methods guarantee exponential convergence to the precise value of the estimated variable, irrespective of the initial estimation points. Experimental validation using varying DC loads and estimation techniques confirms the proposed IOC approach’s effectiveness and robustness in regulating voltage for DC loads connected to a boost converter. Furthermore, the proposed controller is compared to the sliding mode control and presents a better performance with a more straightforward design, and the stability in closed-loop ensured.

This paper presents a generalized controller design to regulate the output voltage of second-order DC-DC converters feeding an unknown constant power load (CPL). Passivity-based control plus damping injection theory is employed to design a generalized action control to stabilize DC-DC converters. Furthermore, starting from the immersion and invariance (I&I) method, a generalized observer for second-order DC-DC converters is implemented to estimate the CPL value. By mixing the proposed controller with the I&I method, an adaptive generalized control approach is presented, which guarantees the locally asymptotic stability of the closed-loop for each converter. The main advantage of the nonlinear adaptive control design is its nonparametric dependence on the capacitance and inductance values, which makes it robust against parametric uncertainties. Phase portrait and sensitivity analyses are performed, and simulation and experimental results are examined to evaluate the performance of the proposed approach, which is also compared against feedback linearization and sliding mode control. Simulation and experimental results show the robustness and effectiveness of the adaptive proposed control approach.

Concurrent programs are widespread in modern systems. They make better use of processor resources but inevitably introduce a new set of problems in terms of reliability and security. Concurrency bugs usually lead to program crashes and unexpected behavior, and are an active research topic. From a security perspective, concurrency vulnerabilities are those that exhibit harmful behavior exclusively in concurrent executions. They can take place in a diverse range of environments, such as in operating system kernels, file system operations, or general-purpose multithreaded programs. A particular characteristic of concurrency is that it not only introduces new problems, but also enables traditional vulnerabilities to be triggered in concurrent-specific ways. Those that lead to dangerous security vulnerabilities usually cause memory corruption, a strong and flexible primitive for exploitation, and are known as concurrency memory corruption vulnerabilities. In this paper, we systematically analyze concurrency vulnerabilities in C and C++ programs, their exploitation and their detection, focusing on concurrency memory corruption vulnerabilities. We organize previous work on concurrency bug characteristics and detection, and highlight the differences in relation to vulnerabilities. Then, we examine the existence of concurrency vulnerabilities in real-world programs by searching the CVE database and point out a growing trend. Further, we analyze and compare existing detection approaches towards concurrency memory corruption.

This paper uses a convex reformulation to deal with the robust, optimal energy management of battery energy storage systems (BESS) and renewable energies in DC microgrids. This relaxed mathematical model guarantees the global optimum regarding energy management problems, even when including uncertainties in demand and renewable energy. The proposed robust model can reach the best scheduling for energy management in worst-case cost scenarios while satisfying all requirements. Furthermore, a model for power transfer losses in converter devices is added to the proposed model by using a binary polynomial representation, which is convexified. This convexification is performed by transforming the nonlinear non-convex equations of the optimal energy management model into second-order cone constraints. Four scenarios implemented in the modified IEEE 123-bus radial distribution feeder are proposed in order to analyze the robust optimal energy management strategy: the deterministic model, demand uncertainty, uncertainty in solar and wind generators, and uncertainty in demand and solar and wind generators. In all scenarios, the energy management model reduces the total energy costs. Simulation scenario 1 showed daily operating costs of about US $\$ $ 4376.82, while simulation scenarios 2, 3, and 4, including demand and generation uncertainties, increased the daily operating costs by about US $\$ $ 5243.23 (19.79%), US $\$ $ 5072.23 (15.88%), and US $\$ $ 5738.75 (31.12%), respectively. Scenario 4 showed the highest costs, as it involves more uncertainty. Hence, the robust optimal energy management strategy dispatches more energy from conventional generators, increasing the operating costs to satisfy those of the worst case.

To avoid leg short-circuit in inverters, dead time must be introduced on leg gate signals. Dead time affects the inverter output voltage fundamental harmonic amplitude, voltage harmonic distortion and inverter efficiency by introducing additional voltage drops. In this regard, dead time effects have been widely investigated for traditional two-level three-phase voltage source inverters in the literature but not extensively for multilevel topology structures. This paper provides a detailed analysis of dead time impact on the harmonic distortion and efficiency of Cascaded H-Bridges Multilevel Inverters (CHBMIs). For this purpose, a general mathematical formulation to determine voltage drop due to dead time effects, also taking into account the adopted Multicarrier PWM strategy, has been provided and experimentally validated for a five-level three-phase CHBMI structure. As a comparison tool between expected and ideal inverter output voltage, the percentage voltage error $e_{\%}$ is introduced. In most of the cases, $e_{\%} $ is lower than 5%, and it starts increasing for very low amplitude modulation index or for specific working points where nonlinearities occur. Furthermore, several experimental investigations have been carried out to evaluate the CHBMI performance in terms of harmonic distortion and efficiency by changing, the values of dead time, modulation index and switching frequency for ten different multi-carried PWM strategies. Experimental results confirm the strong dependency between the dead time impact on the converter performance and the adopted Multi Carrier-PWM (MC-PWM) strategy: as a way of example, converter efficiency can be reduced from 80% to 60% when dead time is increased from $0.5 \mu \text{s}$ to $1.5 \mu \text{s}$ and Phase Shifted-PWM (PS-PWM) is adopted.

3D-printing is leading to a major transformation in many industrial and application sectors, ranging from medical to aerospace. In the space sector, 3D-printing has firstly been adopted for the development of structural and thermal parts, such as brackets and heat pipes. In more recent years, thanks to improvements in precision and surface roughness, 3D-printing technologies have steadily been investigated also for the development of radio frequency components and advanced payloads. Indeed, the unique features of 3D-printing, including the free-form capability and the ease for customization, make these manufacturing technologies very appealing in the development of complex radio-frequency front-ends for space applications, including New Space constellations and next generation software-defined communication satellites. They also enable a drastic simplification of mechanical designs by reducing the number of interfaces and assembling screws of large radio-frequency front-ends, thus leading to significant mass savings and higher integration. This paper briefly reports on recent advances in 3D-printing that have been carried out according to the current trends in radio-frequency front-ends for satellite communications.

This paper discusses the three-wheeled omnidirectional robot (TWOR) self-localization in radio frequency identification (RFID) tag environments. The nonlinear TWOR model is significantly improved by using geometric interpretation and incremental time representation in discrete time. The TWOR position and heading are self-estimated using distance measurements to RFID tags and a digital gyroscope in the presence of typical colored measurement noise (CMN). The extended unbiased finite impulse response (EFIR) is developed along with the extended Kalman filter (EKF) and their versions, cEKF and cEFIR, modified for Gauss-Markov CMN. A particle filter is used as a benchmark. It is shown that the cEFIR filter is more robust than the cEKF and almost as robust as the particle filter, although the latter is less accurate in real time.

The use of Silicon carbide (SiC) devices represents an improvement in terms of size, weight and efficiency of power converters. However, SiC-based solutions present high dv/dt and di/dt on the switching events, increasing the common-mode noise injected into the grid. To reduce the common-mode noise, three-phase inverters with a DC-link referenced output filter are widely considered in photovoltaic (PV) inverters connected to the grid. However, if the filter is DC-link referenced the inductor ripple is larger, and this must be considered for the AC inductor filter design. This work shows, on a PV inverter, the impact of that DC-link referenced filter on the current ripple of the inductor, and the improvement achieved with the use of SiC devices, increasing the switching frequency. A comparison in terms of weight, size, losses and materials costs is presented for different core materials and configurations.