Search

Transfemoral amputation is a debilitating condition that leads to long-term mobility restriction and secondary disorders that negatively affect the quality of life of millions of individuals worldwide. Currently available prostheses are not able to restore energetically efficient and functional gait, thus, recently, the alternative strategy to inject energy at the residual hip has been proposed to compensate for the lack of energy of the missing leg. Here, we show that a portable and powered hip exoskeleton assisting both the residual and intact limb induced a reduction of walking energy expenditure in four individuals with above-knee amputation. The reduction of the energy expenditure, quantified using the Physiological Cost Index, was in the range [-10, -17]% for all study participants compared to walking without assistance, and between [-2, -24]% in three out of four study participants compared to walking without the device. Additionally, all study participants were able to walk comfortably and confidently with the hip exoskeleton overground at both their self-selected comfortable and fast speed without any observable alterations in gait stability. The study findings confirm that injecting energy at the hip level is a promising approach for individuals with above-knee amputation. By reducing the energy expenditure of walking and facilitating gait, a hip exoskeleton may extend mobility and improve locomotor training of individuals with above-knee amputation, with several positive implications for their quality of life.

In the context of neurorehabilitation, there have been rapid and continuous improvements in sensors-based clinical tools to quantify limb performance. As a result of the increasing integration of technologies in the assessment procedure, the need to integrate evidence-based medicine with benchmarking has emerged in the scientific community. In this work, we present the experimental validation of our previously proposed benchmarking scheme for upper limb capabilities in terms of repeatability, reproducibility, and clinical meaningfulness. We performed a prospective multicenter study on neurologically intact young and elderly subjects and post-stroke patients while recording kinematics and electromyography. 60 subjects (30 young healthy, 15 elderly healthy, and 15 post-stroke) completed the benchmarking protocol. The framework was repeatable among different assessors and instrumentation. Age did not significantly impact the performance indicators of the scheme for healthy subjects. In post-stroke subjects, the movements presented decreased smoothness and speed, the movement amplitude was reduced, and the muscular activation showed lower power and lower intra-limb coordination. We revised the original framework reducing it to three motor skills, and we extracted 14 significant performance indicators with a good correlation with the ARAT clinical scale. The applicability of the scheme is wide, and it may be considered a valuable tool for upper limb functional evaluation in the clinical routine.

Atmospheric visibility and cloud ceiling forecasts are essential for the safety and efficiency of flight operations and the aviation industry. Routine hourly aviation meteorological observations are recorded at every airport. However, forecasts of these two meteorological parameters using artificial intelligence techniques are limited. This research utilizes data from two study sites in Fiji, Nadi, and Nausori International Airport, and proposes a hybrid Iterative Input Selection – Long Short-Term Memory (IIS-LSTM) integrated model to forecast the consecutive hour’s visibility and ceiling parameters. The IIS algorithm acts as a feature selector from the global predictor matrix of predictor variables with its significant lagged inputs and the significant lagged inputs of the target variable, while the LSTM algorithm acts as the learning model and makes forecasts. The performance of the proposed hybrid IIS-LSTM model is evaluated using seven statistical score metrics and compared with four competing benchmark models. The evaluated results illustrate the superiority of the proposed hybrid IIS-LSTM integrated model and its advanced capability to generate accurate atmospheric visibility and cloud ceiling forecasts for the next consecutive hour compared to the benchmark models. The most important features selected were the second lagged input of visibility and first lagged input of rainfall to improve visibility forecasts while the first and the fifth lagged inputs of the total low cloud cover were paramount for accurate cloud ceiling forecasts. Considering the geography of the study sites, the overall efficacy of the IIS method is strongly advocated to screen most suitable model predictors and the subsequent integration of this input selection method with the LSTM predictive algorithm to attain enhanced performance of the hybrid IIS-LSTM forecast model. This objective model is therefore proposed to be an efficient and cost-effective predictive tool for atmospheric visibility and cloud ceiling forecasts, especially its applications in the aviation industry for aeronautical purposes.

SfM-MVS (Structure from Motion -Multi View Stereo) photogrammetry is a cost-effective and versatile technique used, among other applications, for the three-dimensional (3D) modelling of archaeological heritage sites and artistic objects. Traditional photogrammetry primarily derives the calibration parameters of the camera and the exterior orientation variables from ground control points (GCPs), obtained by means of a GPS, and tie points (TPs). The SfM-MVS approach simultaneously computes both the relative projection geometry and a set of sparse 3D points. However, when these techniques must be used with difficult-to-reach objects, the process is very complex and implies the use of expensive pieces of equipment. For these reasons, this study develops a new procedure to obtain 3D scale models using of a low-cost Unmanned Aerial System (UAS). To achieve good dimensional accuracy, a scaling tool has been designed which can be carried by the aircraft and deployed on the top of object. By means of this tool it is possible to determine the object actual size, scale the 3D model and then verify the operability and quality of the measurements obtained. The new method has been validated by comparing point clouds and distances. The results showed an error of less than 1 mm in almost 90% of the point cloud and a mean relative error of 1.49%. The procedure is therefore simple, effective and allows the use of SfM-MVS photogrammetry techniques indoors or outdoors on objects, which due to their complex location, it is impossible to use scaling references and their reconstruction is only feasible with sophisticated and expensive technical means.

Wind, being a clean and sustainable resource, boasts environmental advantages. However, its electricity generation faces challenges due to unpredictable variations in wind speed (WS). Accurate predictions of these variations would allow mixed grids to adjust their energy mix in real-time, ensuring overall stability. For this purpose, the paper develops a new hybrid gated additive tree ensemble (H-GATE) model for accurate multi-step-ahead WS predictions. First, the multivariate empirical mode decomposition (MEMD) simultaneously demarcates the multivariate data into intrinsic mode functions (IMFs) and residuals. These components represent underlying trends, periodicity, and stochastic patterns in WS variations. The IMF and residual components are pooled in respective sets, and an opposition-based whale optimization algorithm (OBWOA) is applied for dimensionality reduction. The selected features are used by GATE tuned with Bayesian optimization (BO) to forecast the individual IMF and residual components. The outputs are summed to obtain the final multi-step-ahead WS forecasts. The proposed H-GATE is benchmarked against standalone (S-GATE, S-CLSTM, and S-ABR) and hybrid (H-CLSTM and H-ABR) models. Based on all statistical metrics and diagnostic plots, H-GATE outperforms all comparative models at all forecast horizons, accumulating the lowest mean absolute percentage error (MAPE) of 6.13 - 9.93% (at $t_{L+1}$ ), 8.67 - 14.07% (at $t_{L+2}$ ), and 11.60 - 18.37% (at $t_{L+3}$ ) across all three sites. This novel multi-step-ahead WS forecasting strategy can significantly benefit grid operators by helping anticipate fluctuations in wind power generation. This can assist in optimizing energy dispatch schedules, reducing reliance on backup power sources, and enhancing overall grid stability. Practical implementation of this method can help meet the rising energy demands through renewable wind energy.

In this article, we introduce a novel methodology for characterizing viral genetic features: the Unified Methodology of recombinant virus Identification (UMI). Our methodology converts genomic sequences into spectrograms, applies transfer learning using a pre-trained Convolutional Neural Network (CNN), and employs interpretability tools to identify the genomic regions relevant for characterizing a viral sequence as recombinant. The UMI methodology does not necessitate multiple sequence alignment or manual adjustments. As a result, it operates much faster, has low computational demands, and is capable of handling substantial amounts of data. To validate this, we applied UMI to one extensively studied and documented case: HIV-1 genetic recombination. We worked with all identified HIV-1 complete sequences (13554 sequences up to 2020), searching for mathematical patterns, signatures, that characterize an HIV-1 sequence as recombinant. CNN’s hit rate (test accuracy) is 94%, with consistent and differentiated decision areas in each category. Using interpretability tools, we verified that the hot zones were similar for sequences of the same subtype and phylogenetic proximity. The leading areas for classifying a sequence as recombinant or non-recombinant are coincident with genomic regions that play a key role in genetic recombination processes. By applying UMI methodology we found that there is indeed a genome mathematical pattern that assesses an HIV-1 sequence as recombinant. In addition, we located its position. Considering expert knowledge, our results showed a substantial, robust and biologically-consistent hit rate. This type of solution can successfully guide the location and subsequent characterization of relevant areas, avoiding the heavy analysis of multiple sequence alignment and manual adjustments.

This paper introduces novel dual-polarized/duplex filtering antennas with a novel filtering feeding network. The feeding network consists of an all-resonator structure, which shares a common dual-mode resonator. This structure facilitates the isolation of two well-isolated channels through the exploitation of the even and odd modes of the dual-mode resonator. By applying balanced excitation to the odd mode and unbalanced excitation to the even mode, the two modes can be separated, creating a hybrid-like feeding structure. Despite the common resonator, the channels exhibit high isolation, and the overall size of the filtering circuit is much smaller than conventional cascaded-resonator designs. To demonstrate the effectiveness of the proposed feeding network, we have designed, fabricated, and measured a dual-polarized and a duplex filtering antenna. The simulated and measured results agree well, showing isolations greater than 34 dB and 38 dB for dual-polarized and duplex cases, respectively. Our proposed antennas are compared with state-of-the-art developments, demonstrating the advantages of compact size, flexible decoupling frequency ratio, and high isolation.

A Mobile Networks Digital Twin (MNDT) is a virtual replica of a mobile communication network that accurately models the devices, the communication links, the operating environment, and the applications that run on the physical network. By replicating different environments in a laboratory and running multiple scenarios, Digital Twins provide a cost-effective way to evaluate performance, predict the effects of network changes, optimize network management, and make appropriate decisions. This paper presents a methodology for automatically creating and using Network Digital Twins, along with a proposed architecture for performing this implementation. This work is framed in the context of the B5GEMINI project, whose goal is to develop an MNDT applied to a 5G core environment and its evolution towards 6G, being able to apply it to use cases in advanced scenarios such as cybersecurity or Industry 4.0. The proposed methodology covers the entire lifecycle of the MNDT, from the initial phases of data acquisition and modeling to the phases of use and bidirectional connection between physical and digital elements.

This paper discusses the increasing amount of data handled by companies and the need to use Big Data and Data Analytics to extract value from this data. However, due to the large amount of data collected, challenges related to the computational capacity of machines often arise when performing this analysis to acquire relevant information for the organization, especially when we are using edge computing. The paper aims to train machine learning models using compressed data, with two compression techniques applied to the original data. The results show that models trained with compressed data achieved similar accuracy to those trained with uncompressed data, and different compression techniques were compared. The research extended a previous study by analyzing the use of autoencoders for compression and reducing both instances and dimensionality of the dataset. The accuracy rate of the models when trained with compressed data instead of original data was maintained.

For bifacial solar photovoltaic panels, surface albedo plays a crucial role in estimating the radiant energy. Since land surfaces are heterogeneous, the actual albedo of the surface where the solar photovoltaic panel is placed can vary widely and its temporality and sparsity present a significant challenge for renewable energy engineers. This paper develops a new image super-resolution deep learning model based on convolutional neural network to generate high resolution spatial representations of surface albedo from coarse resolution remote sensing-based data. For selected Australian locations, we generated a higher resolution surface albedo using imagery from PROBA-V/SPOT Earth Observation satellites. We proposed a Deep Downscaling Spectral Model with Attention (DDSA) with the capability of processing 10-day albedo images captured at a relatively low (≈ 1 km) resolution. The proposed DDSA was then applied to downscale observed surface albedo and generate predicted albedo at 500 m, 333 m and 250 m resolutions. The proposed model was benchmarked with alternative deep learning, super-resolution approaches: Super-Resolution Convolution Neural Network (SRCNN), Enhanced Deep Super-Resolution network (EDSR), Efficient Sub-Pixel Convolutional Neural Network (ESPCN) and Residual Dense Network (RDN). The results showed that the proposed DDSA model outperformed all comparative models in terms of the mean square error (MSE) $\approx ~0.0041$ , signal-to-noise ratio (PSNR) $\approx ~39.471$ , Structural Similarity Index (SSIM) $\approx ~0.999$ vs. an MSE $\approx $ [0.0140-0.0387], PSNR $\approx $ [29.761-33.850], SSIM $\approx $ [0.9994-0.999]). We also cross-validated the downscaled images with satellite imagery and ground-based observations, which reaffirmed the proposed DDSA model’s ability to produce high resolution surface albedo maps and its potential applications for granular scale tracking and mapping solar energy where bifacial solar photovoltaic panels are placed.

In the next 10 years, the telecommunications sector will be dominated by the deployment of pervasive networking available in a wide range of heterogeneous scenarios applying 5G (and beyond) technologies. SDN, NFV as well as Network Sliding Technologies used by 5G will provide a “softwarization” of the network functional elements, opening the ecosystem of network providers to a vast number of suppliers from traditional manufacturers to start-ups focusing on specialized functions. Due to the need to assert the expected interoperability of the introduction and evolution of this set of Network Functions, a set of validation tools is needed to explore traffic patterns according to different scenarios and thus allow the performance of the software modules to be evaluated. This research work presents the design of software tools that aim to provide a way for generating signaling and user traffic in 5G networks, enabling the definition of novel pure-software testing environments for 5G that includes real signaling and user traffic. The tools have been designed to support a set of testing signaling procedures that cover the basic main functioning of 5G networks and that allow for the transmission of user traffic over the network. The tools have been implemented and licensed as Open Source and have been validated through their deployment in a virtualized 5G environment that includes a 5G core network.

Reliable and accurate measurement methods are necessary for the clinical assessment of wounds. Repeated measure of a wound indicates whether its healing is progressing or deteriorating, and if alternate treatment must be initiated. Many wound measurement techniques lack accuracy and reliability. Technology: We developed a software prototype that calculates 3-D wound measurements from 3-D scans. We conducted a study to compare the software prototype to physical and 2-D image measurement techniques commonly used by clinicians. We compared inter-rater reliability between the techniques and measurements (i.e., length, width, depth, perimeter, and surface area). Results: Inter-rater reliability was good or excellent for the physical, image, and software measurement techniques; however, there were significant differences in measurements between the techniques. For complex measurements (i.e., perimeter and surface area), the reliability of the software exceeded that of the physical and image techniques. Conclusion: Although inter-rater reliability was high for all measurement techniques, there was significant variability between the techniques. The software was overall most reliable, especially for calculation of complex measurements. Clinical Impact: Reducing the variability of wound measurements may improve patient outcomes, reduce wound prevalence, and mitigate the associated morbidity, mortality, and costs of these occurrences.

Deep Learning methods have produced good carrier frequency offset estimations for short message sequences in comparison with methods based on the Fast Fourier Transform. However, these performance gains were observed for short ranges of frequency offsets, sequences with predefined pilot symbols and periodic modulation schemes. Chaotic modulation has an advantage over periodic signals in offering security through the continuous changes produced by parameterising the chaotic map function. However, synchronisation of chaotic map parameters in coherent receivers is dependent on the carrier recovery of phase and frequency which dramatically reduces the demodulation performance under high noise levels. This article presents a stacked sequence-to-sequence neural network architecture for blind carrier frequency offset estimation of both periodic and chaotic modulation schemes. The results obtained demonstrate better performance than conventional methods in low SNR for the Additive White Gaussian Noise channel. While this technique operates without feature engineering, the results demonstrate that data augmentation produces a higher degree of accuracy for such models, indicating the benefit of integration with conventional signal pre-processing steps as part of the deep learning pipeline. The proposed neural network architecture is shown to perform carrier frequency offset estimation, not only for the selected periodic modulations, but also in the case of highly non-linear chaotic maps. This suggests the applicability of deep learning methods for synchronisation in waveforms that employ chaotic modulation schemes for secure communication and for applications where short and sporadic messaging are required (e.g., Internet of Things).

This paper proposes a robust non-linear generalized path-loss propagation model, the Extended Weighted ABG (EWABG), which efficiently allows generating a path-loss propagation model by combining several available path-loss datasets (from measurements campaigns) and other previously proposed state-of-the-art 5G path-loss propagation models. The EWABG model works by integrating individual path-loss models into one single model in the least-squares sense, allowing to extend knowledge from frequencies and distances covered by path-loss datasets or path-loss propagation models. The proposed EWABG model is the first non-linear extension of the common ABG-based approach, which surpasses the non-uniformity problem between the low and high 5G frequencies (as most measurements campaigns have taken place in low frequencies). The EWABG also addresses the problem of removing outlier measurements, a step not included in previous propagation path-loss models. In this case, we have compared the most recent techniques for avoiding outliers, and we have adopted the Theil-Sen method, due to its strong robustness demonstrated in the experiments carried out. In addition, the proposed model specifically considers non-linear attenuation by atmospheric gases, in order to improve its estimations. The good performance of the proposed EWABG model has been tested and compared against recent 5G propagation path-loss models including the ABG and WABG models. The exhaustive experimentation carried out includes the 5G non-line-of-sight environment in different 5G scenarios, UMiSC, UMiOS and UMa. The proposed EWABG obtains the best accuracy, specially in noisy environments with outliers, reporting negligible increment error rates (with respect to the non-outliers situation), lower than 1%, compared to the ABG and WABG.

The fast deployment of distributed energy resources (DERs) is creating a series of challenges that should be addressed in the coming years. In particular, distribution grids are playing an increasingly important role in the electricity system. Moreover, the three-phase four-wire structure of this network contribute to the appearance of imbalances and a series of problems derived from them. In this context, distribution system operators (DSOs), as the main responsible for the distribution grid, must ensure the quality of supply to consumers. This paper takes advantage of a four-legs D-STATCOM to remove current imbalances in low-voltage power lines. A 35-kVA prototype has been developed and installed in an urban distribution grid. The effect of the D-STATCOM has been analyzed during its first month of operation, studying and measuring the advantages of providing DERs the ability to perform active balancing to the utility grid. The results show a reduction in current imbalances from 21 % to 0 % and neutral current from 10.3 A to 0.4 A. In addition, a 13 % decrease in cable losses has been estimated and a slight improvement in voltage unbalance factor can be noted.

Currently, the most popular health indicator used to assess the degradation of lithium-ion batteries (LIBs) is the state-of-health (SOH). This indicator is necessary to ensure the safety, degradation management, and good operation of the battery, for example, the correct estimate of the state-of-charge (SOC). In this paper, a new health indicator is proposed as an alternative to the use of the SOH because it has a high correlation and similarity with the SOH and has the advantage that it can be calculated and/or estimated very easily. The new health indicator, named “Degradation Speed Ratio” (DSR) is calculated with variables directly measured (voltage and time), and it is not necessary to spend any time on the total charging cycle, therefore reducing waiting times about 84%. In addition, due to its high correlation with capacity, it is a significant marker of battery end-of-life (EOL). In this study, the obtained DSR and a Gaussian process regression (GPR) model were used to estimate the lost capacity and to compare it with existing models in the literature. The accuracy achieved using the DSR indicator as input is very high. Similarly, the results of a multilayer perceptron neural network (MLPNN) model are shown using the new indicator (DSR) as input to estimate the degradation. The sensitivity and precision of this NN model with unknown data are also very high.

In the last years, our society’s high energy demand has led to the proposal of novel ways of consuming and producing electricity. In this sense, many countries have encouraged micro generation, including the use of renewable sources such as solar irradiation and wind generation, or considering the insertion of electric vehicles as dispatchable units on the grid. This work addresses the Optimal active–reactive power dispatch (OARPD) problem (a type of optimal power flow (OPF) task) in microgrids considering electric vehicles. We used the modified IEEE 57 and IEEE 118 bus-systems test scenarios, in which thermoelectric generators were replaced by renewable generators. In particular, under the IEEE 118 bus system, electric vehicles were integrated into the grid. To solve the OARDP problem, we proposed the use and improvement of the Canonical Differential Evolutionary Particle Swarm Optimization (C-DEEPSO) algorithm. For further refinement in the search space, C-DEEPSO relies on local search operators. The results indicated that the proposed improved C-DEEPSO was able to show generation savings (in terms of millions of dollars) acting as a dispatch controller against two algorithms based on swarm intelligence.

The use of energy storage systems in standalone photovoltaic installations is essential to supply energy demands, independently of solar generation. Accurate prediction of the battery state is critical for the safe, durable, and reliable operation of systems in this type of installations. In this study, an installation located in the area of Aragon (Spain) has been considered. Two methods, based on different types of Recurrent Neural Networks (RNN), are proposed to predict the battery voltage of the installation two days ahead. Specifically, the Nonlinear Auto Regressive with Exogenous Input (NARX) network and the Long Short-Term Memory (LSTM) network are studied and compared. The implemented algorithms process battery voltage, temperature and current waveforms; and rely on the selection of different future scenarios based on weather forecasting to estimate the future voltage of the battery. The proposed methodology is capable of predicting the voltage with a Root Mean Squared Error (RMSE) error of 1.2 V for batteries of 48 V, in critical situations where the installation is running out of energy. The study contributes to the ongoing research of developing preventive control systems that help reduce costs and improve the performance of remote energy storage systems based on renewable energies with a positive outcome.

Underwater Inspection, Maintenance, and Repair operations are nowadays performed using Remotely Operated Vehicles (ROV) deployed from dynamic-positioning vessels, having high daily operational costs. During the last twenty years, the research community has been making an effort to design new Intervention Autonomous Underwater Vehicles (I-AUV), which could, in the near future, replace the ROVs, significantly decreasing these costs. Until now, the experimental work using I-AUVs has been limited to a few single-vehicle interventions, including object search and recovery, valve turning, and hot stab operations. More complex scenarios usually require the cooperation of multiple agents, i.e., the transportation of large and heavy objects. Moreover, using small, autonomous vehicles requires consideration of their limited load capacity and limited manipulation force/torque capabilities. Following the idea of multi-agent systems, in this paper we propose a possible solution: using a group of cooperating I-AUVs, thus sharing the load and optimizing the stress exerted on the manipulators. Specifically, we tackle the problem of transporting a long pipe. The presented ideas are based on a decentralized Task-Priority kinematic control algorithm adapted for the highly limited communication bandwidth available underwater. The aforementioned pipe is transported following a sequence of poses. A path-following algorithm computes the desired velocities for the robots' end-effectors, and the on-board controllers ensure tracking of these setpoints, taking into account the geometry of the pipe and the vehicles' limitations. The utilized algorithms and their practical implementation are discussed in detail and validated through extensive simulations and experimental trials performed in a test tank using two 8 DOF I-AUVs.

In the software development process, traceability links between unit tests and code are not explicitly maintained, and dependencies in most cases are manually identified. As a result, a large amount of effort and time is required during the comprehension process to establish the links between these artifacts. Although there are several methods that can infer such links based on different phenomenons, these methods usually produce different set of traceability links. This work expands upon previous traceability link recovery and visualization studies by implementing a combination of traceability recovery methods that automatically retrieve the links, and visualizing them to help developers to overview the links inferred by various recovery techniques, and also to select the right relations for analyses. The results of the usability study show that the visualization model presented herein can effectively support browsing, comprehension, and maintenance of Test-to Code Traceability (TCT) links in a system with enhanced efficiency, as well as visualization of TCT links from multiple sources is better than a visualization of single source of traceability links.

This paper presents an efficient and practical approach for a car navigation system (CVM-Car) based on the velocity space optimization paradigm. The method calculates the velocity control commands to keep the car in the lane while avoiding the obstacles detected by the proximity sensors. The car has to follow a road path consisting of a sequence of lanelets. This approach is a lower-level reactive control that combines the pure pursuit method to obtain a reference curvature and a reactive control algorithm that keeps the vehicle in the center of the lane’ s free space while avoiding obstacles that can partially block it. CVM-Car formulates local obstacle avoidance as a constrained optimization problem in the velocity space of the car. In addition to the vehicle dynamics and obstacles constraints included by the curvature method, car-shape and non-holonomic restrictions are considered in the CVM-Car velocity space. The method has been applied to an autonomous vehicle prototype.

The challenge of Nigeria’s food insecurity in the era of the Covid-19 pandemic, insecurity, climate change, population growth, food wastage, etc., is a demanding task. This study addresses Nigeria’s food insecurity challenges by adopting agriculture 4.0 and commercial farming. Using data from six digital libraries, the Nigerian Bureau of Statistics, and other internet sources, we conducted a Systematic Literature Review (SLR using PRISMA) on Nigeria’s agriculture, food security, and agriculture 4.0. Our results show Nigeria’s current agricultural state, threats to food security, and modern digital agriculture technologies. We adapted our SLR findings to develop an implementation framework for agriculture 4.0 in solving Nigeria’s food insecurity challenge in the post-Covid-19 era. Our proposed framework integrates precision agriculture in Nigeria’s food production and the necessary enabling digital technologies in the agri-food supply chain. We analyzed the critical implementation considerations during each agri-food supply chain stage of farming inputs, farming scale, farming approach, farming operation, food processing, food preservation/storage, distribution/logistics, and the final consumers. This study will help researchers, investors, and the government address food security in Nigeria. The implementation of agriculture 4.0 will substantially contribute to SDG 2 (zero hunger), SDG 3 (good health and well-being), and SDG 8 (decent work and economic growth) of #Envision 2030 of the United Nations, for the benefit of Nigeria, Africa, and the entire world.

The application of alternative dielectric oils as esters in power transformers is hindered by the lack of knowledge regarding their properties and respecting which are the best techniques to ensure their proper performance. In this sense one of the fields needing an impulse is the impregnation processes of transformers cellulosic materials with these alternative oils, currently impregnated in most of the cases with mineral oils. This paper studies the impregnation behavior of eight usual dielectric solids, with two esters and a traditional mineral oil. Empirical equations of the impregnation evolution with time have been obtained, from these the rigid cellulosic materials present in the transformers and the viscosity of the dielectric oils have been identified as the key materials and properties to consider during impregnation. An adaptation of the current impregnation processes to the alternative oils have been proposed by increasing their temperature from ambient temperature up to 61-74°C, depending on the viscosity of the oil used.

In contrast with conventional microgrids (MGs) with fixed boundaries, a smart and flexible MG with dynamic boundary is introduced in this paper. Such a MG can dynamically change its boundary by picking up or shedding load sections of a distribution feeder depending on its available power, leading to more flexible operation, better utilization of renewables, smaller size of energy storage system, higher reliability, and lower cost. To achieve a flexible MG, the main challenges in MG design are addressed, including recloser placement, MG asset sizing considering resilience, system grounding design, and protection system design. Meanwhile, a hierarchical structure is employed to design and implement the MG controller. On top of the functions defined in IEEE 2030.7-2018, a few new functions, e.g., online topology identification and PQ balance, are added, while the planned/unplanned islanding and reconnection functions are enhanced. The controller is implemented on a CompactRIO, a general-purpose hardware platform provided by National Instruments (NI), and tested on a controller hardware-in-the-loop setup based on an OPAL-RT real-time simulator and a reconfigurable power electronic converter-based hardware testbed. The test results have validated the performance of the developed controllers. Such a flexible MG and its controller have been deployed at a municipal utility, and part of the controller’s functions have been tested on-site.

The appearance of the painted surface of the vehicle is key in the quality that the automotive customer perceives. The assurance of this quality starts in the automotive paint shop and compromises the effectiveness of the painting process as every paint defect is reworked. This entails material and labour costs, reducing the efficiency of the process and affecting the competitiveness of the product. To improve the efficiency while guaranteeing the quality, predictive control rather than corrective must be implemented. In order to achieve this control, a predictive model of quality is needed. As a first step to generate said model, this article demonstrates the correlation between the variables of the enamel coating process and the quality of the paint film of the vehicle. As there are no available application examples in the industry, a procedure is proposed in which the necessary steps for the creation of an industrial data set and a predictive model of quality are defined. The procedure is tested in an automotive paint shop. As a result, relevant variables for the quality assurance are identified and the correlation between process variables and the resulting quality is verified, concluding that the implementation of predictive control in the process is feasible.

Artificial intelligence algorithms have a leading role in the field of cybersecurity and attack detection, being able to present better results in some scenarios than classic intrusion detection systems such as Snort or Suricata. In this sense, this research focuses on the evaluation of characteristics for different well-established Machine Leaning algorithms commonly applied to IDS scenarios. To do this, a categorization for cybersecurity data sets that groups its records into several groups is first considered. Making use of this division, this work seeks to determine which neural network model (multilayer or recurrent), activation function, and learning algorithm yield higher accuracy values, depending on the group of data. Finally, the results are used to determine which group of data from a cybersecurity data set are more relevant and representative for the intrusion detection, and the most suitable configuration of Machine Learning algorithm to decrease the computational load of the system.

This work introduces a Medium Access Control (MAC) protocol designed to allow a group of underwater robots that share a wireless communication channel to effectively communicate with each other. The goal of the Underwater Multirobot Cooperative Intervention MAC (UMCI-MAC) protocol presented in this work is to minimize the end to end delay and the jitter. The access to the medium in UMCI-MAC follows a Time Division Multiple Access (TDMA) strategy which is arbitrated by a master, which also has the capability to prioritize the transmission of some nodes over the rest of the network. Two experiments have been carried out with a team of four Autonomous Underwater Vehicles (AUV) in order to compare this protocol with Aloha-CS and S-FAMA MAC protocols used in Underwater Wireless Sensor Networks (UWSN). In the first experiment, the communications and the AUVs have been simulated using UWSim-NET. The objective of this experiment was to evaluate all three protocols in terms of delay, jitter, efficiency, collisions and throughput depending on the size of the data packet and the rate of packet delivery in the application layer for each robot. The results of this experiment proved that UMCI-MAC successfully avoids packet collisions and outperforms the other two protocols in terms of delay, jitter and efficiency. The second experiment consisted of a Hardware In The Loop (HIL) teleoperation of a team of four robots. One of the AUVs was a real BlueROV in a water tank, while the remaining AUVs and the communications were simulated with UWSim-NET. It demonstrates the impact of the MAC protocols in underwater acoustic links. Of the three MAC protocols evaluated in this work, UMCI-MAC was the only one which succeeded in the proposed teleoperation experiment. Thus demonstrating its suitability as a communications protocol in underwater cooperative robotics.

We examine the problem of learning to cooperate in the context of wireless communication. In our setting, two agents must learn modulation schemes that enable them to communicate across a power-constrained additive white Gaussian noise channel. We investigate whether learning is possible under different levels of information sharing between distributed agents which are not necessarily co-designed. We employ the “Echo” protocol, a “blind” interactive learning protocol where an agent hears, understands, and repeats (echoes) back the message received from another agent, simultaneously training itself to communicate. To capture the idea of cooperation between “not necessarily co-designed” agents we use two different populations of function approximators - neural networks and polynomials. We also include interactions between learning agents and non-learning agents with fixed modulation protocols such as QPSK and 16QAM. We verify the universality of the Echo learning approach, showing it succeeds independent of the inner workings of the agents. In addition to matching the communication expectations of others, we show that two learning agents can collaboratively invent a successful communication approach from independent random initializations. We complement our simulations with an implementation of the Echo protocol in software-defined radios. To explore the continuum of co-design, we study how learning is impacted by different levels of information sharing between agents, including sharing training symbols, losses, and full gradients. We find that co-design (increased information sharing) accelerates learning. Learning higher order modulation schemes is a more difficult task, and the beneficial effect of co-design becomes more pronounced as the task becomes harder.

This article describes the design, development and implementation of a set of microservices based on an architecture that enables detection and assisted clinical diagnosis within the field of infectious diseases of elderly patients, via a telemonitoring system. The proposed system is designed to continuously update a medical database fed with vital signs from biosensor kits applied by nurses to elderly people on a daily basis. The database is hosted in the cloud and is managed by a flexible microservices software architecture. The computational paradigms of the edge and the cloud were used in the implementation of a hybrid cloud architecture in order to support versatile high-performance applications under the microservices pattern for the pre-diagnosis of infectious diseases in elderly patients. The results of an analysis of the usability of the equipment, the performance of the architecture and the service concept show that the proposed e-health system is feasible and innovative. The system components are also selected to give a cost-effective implementation for people living in disadvantaged areas. The proposed e-health system is also suitable for distributed computing, big data and NoSQL structures, thus allowing the immediate application of machine learning and AI algorithms to discover knowledge patterns from the overall population.

Each time more companies seek to differentiate themselves from their competitors. For this, they resort to company certification through standards such as ISO 9001 or EFQM. The quality norms permit business to improve process management and production and reduce the lack of knowledge. Therefore, the main objective of this research is to propose a framework to support the process of improving customer's satisfaction according to ISO 9001. To achieve the main objective, the methodology of the design science research process has been followed. First, the problems in the evaluation of customer's satisfaction required by ISO 9001 have been analysed. Second, an expert system has been developed to evaluate the process. To end, the system has been validated. The results suggest that this system facilitates continuous improvement in organizations, providing the customer's vision of the organization, and making possible the implementation of actions oriented to retain customers. This study presents important managerial implications. Insights from this research allow us to offer recommendations that can help practitioners and managers interested in the implementation of expert systems, to focus in quality processes that can increase the chances of a successful adoption.

In this paper we propose a novel path-loss model, the Weighted ABG (WABG), which suitably allows integrating or combine different available datasets, or previously proposed 5G propagation path-loss models from the state-of-the-art. Our proposal is therefore a new ABG-based approach which integrates other existing models, leading to the best possible approximation in least-square sense, considering different weighting policies. We evaluate the performance of the WABG in several 5G scenarios, and we carry out a complete comparison of the proposed method against several recently published ABG models, showing that the WABG obtains the best results in terms of model's accuracy.

Versatile video coding (VVC) will be released by 2020, and it is expected to be the nextgeneration video coding standard. One of its enhancements is multiple transform selection (MTS) for core transform. MTS uses three different types of 2D discrete sine/cosine transforms (DCT-II, DCT-VIII and DST-VII) and up to 64 × 64 transform unit sizes. With this schema, significant enhancements of the compression ratio are obtained at the expense of more computational complexity on both encoders and decoders. In this paper, a deeply pipelined high-performance architecture is proposed that implements the three transforms for sizes from 4 × 4 to 64 × 64 according to working draft 4 of the standard. The design has been described in very high-speed integrated circuit hardware description language (VHDL), and it has been prototyped in a system on a programmable chip (SoPC). It is able to process up to 64 fps@3840 × 2.160 for 4 × 4 transform sizes. To the best of our knowledge, this is the first implementation of an architecture for VVC MTS supporting the 64 × 64 size.

Low-terahertz (Low-THz, 100 GHz-1.0 THz) technology is expected to provide unprecedented data rates in future generations of wireless system such as the 6th generation (6G) mobile communication system. Increasing the carrier frequencies from millimeter wave to THz is a potential solution to guarantee the transmission rate and channel capacity. Due to the large transmission loss of Low-THz wave in free space, it is particularly urgent to design high-gain antennas to compensate the additional path loss, and to overcome the power limitation of Low-THz source. Recently, with the continuous updating and progress of additive manufacturing (AM) and 3D printing (3DP) technology, antennas with complicated structures can now be easily manufactured with high precision and low cost. In the first part, this paper demonstrates different approaches of recent development on wideband and high gain sub-millimeter-wave and Low-THz antennas as well as their fabrication technologies. In addition, the performances of the state-of-the-art wideband and high-gain antennas are presented. A comparison among these reported antennas is summarized and discussed. In the second part, one case study of a broadband high-gain antenna at 300 GHz is introduced, which is an all-metal model based on the Fabry-Perot cavity (FPC) theory. The proposed FPC antenna is very suitable for manufacturing using AM technology, which provides a low-cost, reliable solution for emerging THz applications.

Risk-driven requirements elicitation represents an approach that allows assignment of appropriate countermeasure for the protection of the Information System (IS) depending on the risk level. Elicitation of safety requirements based on risk analysis is essential for those IS which will run on the open and dynamic Internet platform. Traditionally, misuse cases are used to find the weak points of an IS but cannot differentiate between the weak point that can lead to lenient hazard and/or serious hazard. In this paper, we present an enhanced misuse case approach to support IS safety risk assessment at the early stages of software process. We extensively examined and identified concepts which constitute a modelling technique for IS safety risk assessment and build a conceptual model for achieving IS safety risk assessment during the requirement analysis phase of software process. The risk assessment process follows an approach of consequential analysis based on misuse cases for safety hazard identification and qualitative risk measurement. The safety requirements are elicited according to the results of the risk assessment. A medical IS is used as a case study to validate the proposed model.

The development of Airborne and Ground systems is framed by specific regulations, usually expressed as standards. A disadvantage of those standards is the inherent complexity for its application and the verification of compliance given the high number of requirements to be checked in many different situations of the application and which are highly dependent on the applicable level of criticality. When the development of this type of system requires the incorporation of new personnel without enough knowledge about the standards, risks of mistakes in their application grow exponentially. The objective of this work is to develop an Expert System (ES) that helps to evaluate the application of the standards DO-178C and DO-278A throughout the project life cycle, at the same time it serves to facilitate both its use and the learning of its application to a wide group of professionals. The proposed underlying method for the ES will allow evaluating the set of development processes to check coverage of the standards DO-178C and DO-278A without depending on a specific life cycle model. The method involves a model of the set of processes, so they can be evaluated by the ES. Additionally, the ES will require a minimum configuration to evaluate the development of systems based on these two standards. The main result is a new generic Expert System based on rules capable of being adapted to different environment of evaluation, whichh minor configuration operations thus allowing that a Generic ES can act as a Specific ES for each situation. This configurable ES has been customized to evaluate the software life cycle based on the standards under study.

A heat sink is a specific type of heat exchanger integrated with heat generating devices - mostly electronics - for the sake of thermal management. In the design procedure of heat sinks, several considerations such as manufacturing cost, reliability, thermal and hydraulic performance have to be included. In the past few decades, the prevailing trend of electronics design miniaturization has led to high-power-density systems necessitating high performance cooling concepts. This paper intends to provide a comprehensive review on various employed heat transfer enhancement techniques in cooling procedures of electronics thermal management devices, with a focus on core ideas. The main motivation is to give a rapid overview on the key concepts in different high-performance cooling designs along with a quantitative comparison between the different concepts all in one reference which is missing in literature. For this, the key idea of each design is firstly categorized, and then a detailed description is provided for each case. The discussed categories consist of concepts based on channel cooling in various scales, phase transition, jet impingement, spray cooling and hybrid design. At the end, quantitative comparison is illustrated for thermal and hydraulic performance of a selection of the reviewed references covering all these different categories. Based on this comparison, an overview on thermo-hydraulic performance of the presented categories is provided, and recommendations for future studies are given based on this and the detailed review of references.

An integrated machine-learning based adaptive circuit for sensor calibration implemented in standard 0.18μm CMOS technology with 1.8V power supply is presented in this paper. In addition to linearizing the device response, the proposed system is also capable to correct offset and gain errors. The building blocks conforming the adaptive system are designed and experimentally characterized to generate numerical high-level models which are used to verify the proper performance of each analog block within a defined multilayer perceptron architecture. The network weights, obtained from the learning phase, are stored in a microcontroller EEPROM memory, and then loaded into each of the registers of the proposed integrated prototype. In order to verify the proposed system performance, the non-linear characteristic of a thermistor is compensated as an application example, achieving a relative error er below 3% within an input span of 130°C, which is almost 6 times less than the uncorrected response. The power consumption of the whole system is 1.4mW and it has an active area of 0.86mm2. The digital programmability of the network weights provides flexibility when a sensor change is required.

An investigation on antenna solutions for expendable origami paper flapping robots is presented. An origami flapping robotic bird that can be produced using standard A4 paper is employed. Antennas resonating at the two commonly used frequency bands, 2.4 GHz and 5.2 GHz, are designed for the limited space available on the folded origami structure. Two developments of the same dual-band monopole antenna are discussed. The first antenna is located on the robot's spine and the second on its tail. A space diversity configuration is also studied. The antennas are printed directly onto a photo paper substrate and then folded into an origami robotic crane's body structure. An ordinary desktop inkjet printer fitted with silver nanoparticle conductive ink cartridges has been employed. CST Microwave StudioTM has been used to design the antennas. A good agreement between the measured and simulated S11 results is achieved with a reasonable -10dB impedance bandwidth realized in the three cases studied. The radiation patterns are omnidirectional in the XZ plane which is desirable for the specific application. The diversity configuration has a mutual coupling of

3D printing of antennas on removable fingernail for on-body communications at microwave and millimetre waves is proposed. Aerosol Jet technology, a fine-feature material deposition solution, has been used to directly print microstrip patch antennas on an acrylonitrile butadiene styrene (ABS) removable finger nail substrate. Two antennas have been printed and assessed, one operating at 15 GHz and the other at 28 GHz. Nanoparticle conductive silver ink has been employed to create the microstrip patch antennas and corresponding transmission line using an Optomec machine. The inks are then cured using a PulseForge machine. A further copper layer is added to the millimeter wave antenna via an electroplating process. The antennas have been simulated and measured off-the-finger and on-the-finger. Simulated and measured reflection coefficients (S11 ) and radiation patterns are found to be in good agreement. The proposed on-body antennas can find application in the Internet of Things (IoT) where large amount of sensing data can be shared at the microwave and millimetre wave spectrum of future 5G communications. The removable finger nails could include other electronic devices such as on-body sensors, computational, storage and communication systems.

The Unequal Area Facility Layout Problem (UA-FLP) is a relevant optimization problem related to industrial design, that deals with obtaining the most effective allocation of facilities, that make up the rectangular manufacturing plant layout. The UA-FLP is known to be a hard optimization problem, where meta-heuristic approaches are a good option to obtain competitive solutions. Many of these computational approaches, however, usually fall into local optima, and suffer from lack of diversity in their population, mainly due to the huge search spaces and hard fitness landscapes produced by the traditional representation of UA-FLP. To solve these issues, in this paper we propose a novel hybrid meta-heuristic approach, which combines a Coral Reefs Optimization algorithm (CRO) with a Variable Neighborhood Search (VNS) and a new representation for the problem, called Relaxed Flexible Bay Structure (RFBS), which simplifies the encoding and makes its fitness landscape more affordable. Thus, the use of VNS allows more intensive exploitation of the searching space with an affordable computational cost, as well as the RFBS allows better management of the free space into the plant layout. This combined strategy has been tested over a set of UA-FLP instances of different sizes, which have been previously tackled in the literature with alternative meta-heuristics. The tests results show very good performance in all cases.

An investigation of the use of Aerosol jet 3D Printing of frequency selective surface for millimetre and low-THz applications is presented in this article. This 3D printing technique allows the fabrication of intricate details of the designs with high resolution. Band-stop and band-pass FSS are designed and tested. The band stop FSS consisted of a Square loop array that operated in the 26-28 GHz sub-millimetre band. This design is printed on glass substrate and can be used for deployment in windows. The bandpass FSS arrays consisted of simple slot elements arranged in a square lattice and operated at 125 GHz and 280 GHz. The slot arrays were printed on Kapton. Surface profiles demonstrated the uniformity and precision of this printing technique. Simulated and measured results compared well and offered good performances at both the millimetre wave and low-THz bands. The designs find applications in 5G and imminent 6G communications. This printing technique also provides environmentally friendly, rapid, and sustainable alternative for development of highly customised FSS which can be deployed to improve communications in buildings and in future Terahertz applications.

This paper discusses the performance of a multi-method ensemble meta-heuristic for optimization in a problem of submerged arches design. Specifically, the Coral Reefs Optimization algorithm with Substrate Layers (CRO-SL) is proposed. It is a multi-method evolutionary ensemble which combines different searching structures in a single population. In this case, some novel searching strategies such as the Firefly algorithm and the Water Wave Optimization have been proposed to be included in the CRO-SL algorithm. In addition, some other traditional searching structures such as classical crossovers, Gaussian mutation operators, Harmony search and Differential Evolution search strategies have also been implemented in the algorithm. The proposed CRO-SL has been applied to the optimal design of submerged arches in deep waters, considering several design aspects such as the bending moment of the beam and the airspace of the arch. The evaluation of the submerged arches quality is obtained after the simulation of the arches by means of a finite elements computational software, which has been hybridized with the CRO-SL algorithm in the process of fitness calculation. It will be shown that the CRO-SL is able to obtain excellent solutions to submerged arches design problems. For this, the computational performance of each substrate (different searching strategies) in this optimization problem will be discussed and the results obtained will be compared with those in alternative works for similar experiments.