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This paper presents a practical step-by-step approach to Frequency Modulated Continuous Wave (FMCW) radar nonlinearity correction (deconvolution), utilizing surface-based Ku- and Ka-band radar data collected over nilas ice within a newly-opened sea ice lead during the 2019/2020 MOSAiC expedition. Two performance metrics are introduced to evaluate deconvolution effectiveness: the spurious free dynamic range (SFDR), which quantifies sidelobe suppression, and the leading edge width (LEW), which quantifies the improvement in surface return clarity. The impact of deconvolution waveforms on different survey dates, radar polarizations, and surface types is examined using echograms and quantitative metrics. Deconvolution results in a maximum SFDR increase of 28 dB, with a maximum 3 dB decline in deconvolution performance observed over an 8-day period and a maximum decline of 15 dB observed over a 71-day period. The LEW values indicate that the effectiveness of deconvolution in enhancing interface clarity depends on the combination of pre-deconvolution sidelobe shape, prominence of the surface return, the influence of snowpack returns, as well as a time-dependent reduction in deconvolution performance. Deconvolution significantly improves surface return clarity for cross-polarized radar data, where weak surface returns are obscured by returns from within the snowpack. The results demonstrate that deconvolution performance is most effective shortly after deconvolution waveform characterization. Therefore, it is recommended to perform at least weekly calibrations using a large metal sheet and ideally calibration before/after data collection to ensure optimal deconvolution performance and effective sidelobe suppression.

Traditional proportional integral derivative (PID) falls short for precise control of DC motor speed under changing conditions. This paper presents a novel FPGA based IP (intellectual property) core for real-time PID parameter adjustment utilizing a multilayer neural network and the back propagation neural network algorithm. The design is implemented entirely in verilog HDL (hardware description language) using the Vivado 2023.1 tool in the Zynq 7z020 SoC (system on chip), with highly efficient resource utilization of 7.37% look-up table (LUT), 1.92% flip-flops, and 3.57% memory. The co-simulation results show a fast rise time of 0.00496s, a settling time of 0.0083s, a lower integral absolute error (IAE) of 0.045, and a minimum overshoot of 0.45%, validating the effectiveness of the proposed controller for comprehensive motion control systems. This dynamic approach outperforms traditional PID controllers by adapting to changing conditions, significantly improving rise time and settling time, which are critical factors in healthcare applications. Other key features of this work include precise duty cycle generation at a 10 kHz switching frequency for accurate motor speed control and an incremental encoder interface for high-resolution position feedback.

Biometric identification technologies are crucial for enhancing security through reliable personal authentication methods. Among these modalities, finger knuckle biometrics stands out for its distinctive and consistent features, offering a valuable alternative to more commonly used biometric traits. Unlike fingerprints, which are easily captured from the surface of the skin, knuckle prints present a unique challenge. Knuckle prints are not as readily accessible from surface scans due to their position and the intricacy of their features, which require specialized techniques for accurate capture and recognition. The paper comprehensively reviews the evolution from traditional geometric methods to advanced deep learning techniques in finger knuckle recognition. Our review covers both unimodal and multimodal approaches, discussing various recognition strategies and their effectiveness. We also discussed the performance of knuckle biometric systems using metrics such as accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR), and Equal Error Rate (EER). The paper also highlights the importance of publicly available knuckle datasets, which are essential for developing and evaluating FKP biometric systems. These datasets enable researchers to benchmark and improve recognition algorithms. This review is aimed at researchers, practitioners, and academics interested in biometric technologies, offering insights into current advancements and future directions in finger knuckle biometrics.

Harmonic, dynamic transient, and electromagnetic induction signals can propagate at very high speeds through the electrical network, causing insulation breakdown damage to system equipment or severely damaging sensitive electronic control devices, requiring costly maintenance repairs or replacement due to permanent loss. To prevent possible economically significant damage, it is crucial to have timely measurements of voltages and currents using a high-speed digital phosphor oscilloscope and mass storage in the sampling to obtain an accurate and timely diagnosis. The problem here is that when a very high sampling rate is used with a digital phosphor oscilloscope for the precise recording of the phenomenon, due to uncertainty at the trigger time, or by setting a wide recording aperture on the time scale, the process of extracting information for further signal processing becomes a problem of a lot of information that requires a lot of computing time and computational resources. Although there are different decimation methods for sampling rate, these are usually very slow and inefficient because they partly depend on an engineer analyst’s technical criteria and experience. This paper presents a new application of the compressed sensing method with a new improved and validated basis pursuit reconstruction algorithm for dynamic electrical signals of the harmonic and transient type, drastically reducing the number of samples and the processing time without losing accuracy in the results. The harmonic signal under study of 6.25k samples was reduced to a subsampling range between 100-3.7k samples with a minimum processing time of 0.1053s. The transient signal with 10k samples, was reduced to a range of 500-2.7k samples with a minimum processing time of 0.0861s. In both cases, the accuracy of the reconstructed signals is very high according to their validation using Parseval’s Theorem.

Human Activity Recognition (HAR) has seen remarkable advances in recent years, driven by the widespread use of wearable devices and the increasing demand for personalized healthcare and activity tracking. Federated Learning (FL) is a promising paradigm for HAR that enables the collaborative training of machine learning models on decentralized devices while preserving data privacy. It improves not only data privacy but also training efficiency as it utilizes the computing power and data of potentially millions of smart devices for parallel training. In addition, it helps end-user devices avoid sending users’ private data to the cloud, eliminates the need for a network connection, and saves the latency of back-and-forth communication. FL also offers significant advantages for communication by reducing the amount of data transmitted over the network, alleviating network congestion and reducing communication costs. By distributing the training process across devices, FL minimizes the need for centralized data storage and processing, leading to more scalable and resilient systems. This paper provides a comprehensive survey of the integration of FL into HAR applications. Unlike existing reviews, this paper uniquely focuses on the intersection of FL and HAR, providing an in-depth analysis of recent advances and their practical implications. We explore key advances in FL-based HAR methodologies, including model architectures, optimization techniques, and different applications. Furthermore, we highlight the major challenges and future research questions in this domain, such as model personalization and robustness, privacy concerns, concept drift, and the limited capacity of edge devices.

The preface of the permeated metaverse together with Web 3.0, has confluenced virtual and physical reality and can revolutionize social networks, healthcare, gaming and the educational system. Unfortunately, this assimilation has divulged ways for physical and virtual-reality-synthesized security threats like avatar impersonation and sybil attacks. Furthermore, the divergence in metaverse platforms and the involvement of central intermediaries prompted the demand for interoperability, security provisions and decentralization. Therefore, this paper projects a blockchain-integrated authentication protocol for a seamless interface in a cross-platform metaverse network with Web 3.0. The proposed mechanisms are structured for user and avatar authentication within cross-platforms while furnishing conditional traceability, an avatar-update phase and a revocation phase. The blockchain network hinges on characteristics of a consortium blockchain and the voting-based Practical Byzantine Fault Tolerance (PBFT) consensus algorithm. The security of the proposed network is displayed by harnessing the Real-or-Random (RoR) oracle model and informal analysis. The operational effectiveness of the suggested system is gauged through an assessment of relevant existing work based on metrics such as communication, bandwidth, and computational costs. The study’s results indicate that the proposed system exhibits better computation and communication overhead compared to other systems, making it well-suited for metaverse applications in Web 3.0.

Trajectory prediction plays a key role in modern air traffic management. The ability to predict the future position of aircraft in flight allows for greater predictability, safety and efficiency. In recent years, recurrent neural networks, and particularly LSTM (Long-Short Term Memory), have been successfully applied (alone or in combination with other kinds of network) to this problem. However, the criticality of the supervision of these operations and the difficulty of predicting trajectories in high density traffic zones, such the Terminal Area around the airports, require high accuracy methods that takes into account all factors involved in these operations. In this paper, we propose an architecture based on Temporal Fusion Transformer (TFT) for multi-route, long-term trajectory prediction using surveillance data (Automatic Dependent Surveillance - Broadcast, ADS-B). We conduct our experiments on the case study of the Madrid Barajas-Adolfo Suárez airport (Spain), using nine months worth of data. In particular, we focus on predicting the next 150 seconds at any point in the trajectory for flights arriving at this airport. Compared with other LSTM networks developed in this work, TFT provides an increased accuracy for 2D positioning, with mean absolute errors of 0.0091 and 0.0104 degrees for latitude and longitude, respectively, in the Terminal Area of the destination airport. These results have been shown to be competitive with, or even superior to, more consolidated approaches based on LSTM networks that have been proposed for single route, short-term predictions.

The use of finger veins for biometric authentication is increasingly popular; however, low-quality images present significant challenges that necessitate innovative approaches for accurate identification. This study investigates the effectiveness of various image preprocessing techniques to enhance and analyze finger vein images for biometric recognition. We utilized a finger vein dataset from Kaggle, comprising diverse images captured under controlled conditions. Our preprocessing methods included sharpening images using convolution kernels to improve edge definition, employing thresholding techniques (simple binary, adaptive, and Otsu’s) for effective image segmentation, and applying morphological operations (erosion, dilation, opening, and closing) to refine object shapes and reduce noise. We also implemented edge detection methods, including Sobel, Laplacian, and Canny, to identify significant boundaries within the images. Image resizing was performed using linear, cubic, and area interpolation to assess their effects on image quality. Additionally, various filtering techniques-such as Kalman, median, and Gaussian filters-were applied to reduce noise and enhance image clarity. The dataset comprised 3,816 images from 106 individuals, split into two configurations: 80-10-10 and 70-15-15. We assessed models such as VGG16, VGG19, ResNet101, AlexNet, MobileNet, DenseNet201, and EfficientNet based on metrics including accuracy, precision, recall, F1-score, and training time. VGG16, VGG19, and ResNet101 achieved accuracies of 99.9%, 99.8%, and 99.8%, respectively. Data augmentation techniques generated 76,320 augmented images, significantly improving model performance, especially for the 80-10-10 split. Visualizations through radar and bar charts indicated that VGG16, VGG19, and ResNet101 delivered the highest performance metrics, while DenseNet201 exhibited a slight decline in the 70-15-15 split due to increased test data. Overall, the findings demonstrate the models’ efficacy for reliable finger vein biometric recognition, contributing to advancements in biometric authentication systems.

Chronic Kidney Disease (CKD) remains a significant global health challenge, with increasing prevalence and a substantial impact on patient quality of life. Early and accurate prediction of CKD risk is crucial for timely intervention and management. This study presents a comprehensive comparative analysis of both machine learning and deep learning algorithms applied to predict CKD risk. The research involved the application of eight traditional machine learning algorithms: Naive Bayes, K-nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, AdaBoost, and XGBoost, each implemented on a CKD dataset retrieved from the UCI data repository. Furthermore, three neural network-based algorithms, Artificial Neural Network (ANN), Simple Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) were used to compare to the traditional algorithms. This comparative study not only assessed each algorithm’s performance in terms of accuracy, precision, recall, and F1 score but also examined their computational efficiency and applicability in real-world clinical settings. All eleven algorithms were trained with three versions of the dataset. The first version kept the original unbalance between classes and used KNN imputation to fill up missing values (unbalanced). The second dataset used SMOTENC to create new samples to balance the dataset (balanced). The third dataset used feature selection to choose 14 features from the original 24. The results showed that there is almost no performance difference among the classifiers produced with the balanced, unbalanced and feature selection datasets. This means that the best algorithms for this task are the ones with short training and testing runtime, namely RF, SVM, AdaBoost and XGBoost. The experiments also showed that the neural network-based algorithms had no performance advantage and were slower to train due to the small size of samples available in the original dataset.

Technology-enhanced learning (TEL) has been employed in healthcare systems to deliver case-based practical knowledge and prognostic assessments for healthcare professionals. Each medical specialty requires a unique set of skills, knowledge, and experience to ensure optimal patient care, necessitating the identification and assignment of the best medical professionals. We propose a recommendation system that facilitates the selection and assembly of top medical professionals for life-threatening cases on the basis of assessments conducted on a TEL platform for critical care skills. The key contribution of this work is the process of forming a team of the most qualified medical professionals for a critical care case. The system helps learners refine their skills by identifying areas that need improvement and providing timely assistance to struggling learners. A variant of content-based filtering is employed by leveraging the projected clustering (PROCLUS) algorithm on assessment data. The recommendation system identifies clusters of similar performance patterns among healthcare professionals on the basis of key features. Additionally, weak learners deficient in crucial healthcare areas are identified, and the model recommends the most qualified professionals for specific critical care cases.

In this paper, a robust, multi-modal deep-learning-based fault identification method is proposed for solar photovoltaic (PV) systems, capable of detecting a wide range of faults at PV arrays, inverters, sensors, and grid connections. The proposed method combines residual convolutional neural networks (CNNs) and gated recurrent units (GRUs) to effectively extract both spatial and temporal features from raw PV data. To enhance the proposed model’s robustness and accuracy, a probabilistic loss function based on the entropy theory is formulated. The proposed method is validated using both experimental data obtained from a PV emulator-based test system and simulation data, achieving over 98% accuracy in fault identification under various noise conditions. The results indicate that the proposed model outperforms conventional CNN- and MSVM-based methods, demonstrating its potential in providing precise fault diagnostics in PV systems.

Microarray image analysis is an algorithmic and analytical approach used in gene expression profiling in the study of biological and zoological taxonomy. Gene expression data is a structured matrix which represents gene data corresponding to specific conditions. The intricacy of biological networks, along with the volume of imprecise and outlier-containing data, makes dealing with them challenging. Hence, clustering algorithms are crucial for identifying patterns in massive genetic data. This paper presents Microarray spot segmentation using Hybrid Symbolic Fuzzy C-Means (HSFCM) clustering method which exhibits effective handling of uncertainty and imprecision. Even though the microarray spot data points are similar, they are not indistinguishable and demonstrate some dissimilarity. To capture this disparity, HSFCM method uses symbolic interval valued object instead of crisp data points to resolve imprecision present in the spot data. The proposed novel approach involves fuzzification of input data and later converting it to interval based symbolic object which is fed to clustering model. To assess the performance of the proposed method, experimentation is carried out on Lymphoma Leukemia Molecular Profiling Project microarray dataset and results are evaluated using Cluster Validity index and Mean Square Error (MSE). The results exhibit that the proposed approach has outperformed the modern contemporary clustering techniques.

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

A novel dual-band circularly polarized MIMO wearable antenna is screen-printed on a cotton polyester fabric to operate at 2.45 and 5.8 GHz wireless body area network communications. The MIMO fabric antenna is realized with two circularly polarized wearable antennas arranged in orthogonal directions, which provides a less than −10 dB bandwidth of 400 MHz from 2.2 to 2.6 GHz and 800 MHz from 5.3 to 6.1 GHz, with impedance bandwidth of 17 and 14% for lower and higher bands, respectively. The antenna provides an axial ratio bandwidth (below 3 dB) of 3% from 2.4 to 2.47 GHz and 8% from 5.45 to 5.94 GHz. The proposed MIMO antenna is integrated with an artificial magnetic conductor, which helps to create isolation between the human body and antenna, which also minimizes the specific absorption rate (SAR). The antenna SAR performance is tested on the HUGO model, which gives 0.025, 0.035 W/Kg for 1g tissue at 2.45 and 5.8 GHz. Furthermore, the antenna provides good MIMO characteristics such as envelope correlation coefficient, diversity gain, and channel capacity. The antenna is suitable for flexible wearable applications due to fabric substrate, screen printing, low SAR, and lightweight.

This paper presents wideband channel measurements for indoor Non-Line-of-Sight (NLoS) fixed links operating at sub-Terahertz (sub-THz) frequencies (92.110 GHz) with single scattered reflections. Seemingly “smooth” surfaces generate substantial internal multipath causing frequency selective scattered reflections owing to the short wavelength. Unlike specular reflections, no single well-defined reflection point aligns the incidence angle ( $\theta_{{\mathrm {i}}}$ ) and reflection angle ( $\theta_{{\mathrm {r}}}$ ) to form the lowest path loss. This means that the scattering effect of the reflective surface gives reason to search for the best angular alignment of the Receiver (Rx) antenna given a specific angle from the Transmitter (Tx) antenna. The wideband effective Radar Cross Section (RCS) is derived and computed based on the bi-static radar equation, which specifically accounts for the effect of alignment angle on the defined frequency selective RCS. The measurements reveal alignment-variant angular scattering from large-scale discontinuities such as wall partitions, television screens and metallic reinforcement studs, offering valuable insights for the design and deployment of future wireless communications systems operating in the sub-THz band.

This paper provides a concise overview of recent developments in sparse antenna arrays, with a specific focus on techniques for reducing mutual coupling. It explores the concept and definitions of sparse arrays in different applications, highlighting their historical significance in antenna theory. The paper addresses the mutual coupling problem and presents reduced coupling geometrical configurations through illustrative examples. Various mutual coupling compensation techniques are discussed. The paper conducts a comprehensive comparison of multiple array design optimisation techniques, including genetic algorithm, covariance matrix adaptation evolution strategy, particle swarm optimisation, trust-region framework, Nelder-Mead simplex algorithm, interpolated Quasi-Newton, and classic Powell. The comparison emphasizes achieving desired radiation performance and evaluates the mutual coupling coefficient using $4\times 4$ planar arrays and $16\times 1$ linear arrays with typical patch antennas in the mmWave bands. Notably, the paper considers optimised non-uniform antenna element positioning within the arrays, which has shown promising results in reducing mutual coupling. The study also introduces the application of beam steering to these optimised non-uniform arrays, demonstrating resilience to beam steering degradation and potential performance improvements. The findings indicate that particle swarm optimisation generally provides the most consistent performance across the discussed optimisation problems.

As one of the most significant part of carbon neutralisation, the rapid growth of electric vehicle (EV) market in past few years has greatly expedited the transport electrification, which, however, has brought in new challenges to power system including isolated distribution network for commercial and industrial set up. Stochastic and complex EV behaviours would violate network permissible operation region and increase costs for system operators. To address these problems, a chance-constrained smart EV charging strategy in a DC microgrid (DCMG) supporting large office complex is proposed to minimize system cost from distribution network and fleet battery degradation cost from EVs providing ancillary service to the DCMG. When dealing with uncertainties from EVs, a Markov Chain Monte Carlo (MCMC) model is built to couple different parameters in load profiles and characterize the time series of likelihood of charging and discharging. A state-of-charge (SOC) space random walk method is then proposed to solve the resultant massive recursive probabilistic charging requirements. Based on that, a three-stage optimization framework is established to illustrate the work flow in system level. Numerical results verifying the effectiveness of the proposed method are also presented.

Linear and symbol-level precoding in satellite communications have received increasing research attention thanks to their ability to tackle inter-beam interference, allowing the use of spectral resources more efficiently. However, there are still challenges and open questions regarding the implementation of practical precoding systems taking the phase uncertainties in estimating the channel state information into account. This work assesses the impact of phase variations and uncertainties inherent to the satellite communication system operating a precoded forward link. Specifically, we address the inability to measure at the user terminal, the absolute phase rotation introduced by the channel, and the transponder local oscillator phase noise effects on the precoding operations considering the use of frequency division multiplexing in the forward-uplink transmission. We formally demonstrate that the system performance for linear and non-linear precoding operations is not affected by the uncertainty in the phase measurements at the user terminal. Additionally, we show that using a single frequency reference for all the local oscillators at the transponder does not avoid the phase variations related to the frequency division multiplexing in the forward-uplink. This work demonstrates that these phase variations would not affect the system performance for an ideal zero-delay precoding loop. However, this is not feasible in practical scenarios, where the phase noise of the frequency reference at the transponder and the loop delay determine the impact on the system performance. We validate our results by simulations considering three frequency references with different stability levels in a typical geosynchronous orbit (GEO) satellite system. Our results suggest that practical implementations of multiuser-MISO precoding systems must include a differential phase synchronization loop to compensate for this performance degradation.

Monitoring the spatiotemporal distribution of invasive aquatic plants is a challenge in many regions worldwide. One of the most invasive species on Earth is the water hyacinth. These plants are harmful to biodiversity and create negative impacts on society and economy. The Guadiana river (one of the most important ones in Spain) has suffered from this problem since the early 2000s. Several efforts have been made to mitigate it. However, invasive plants, such as the water hyacinth, are still present in seed banks at the bottom of the river and can germinate even more than a decade after. In this article, we propose an automatic methodology, based on remote sensing and deep learning techniques, to monitor the water hyacinth in the Guadiana river. Specifically, a multitemporal analysis was carried out during two years using images collected by ESA's Sentinel-2 satellite, analyzed with a convolutional neural network. We demonstrate that, with our strategy, the river can be monitored every few days, and we are able to automatically detect the water hyacinth. Three experiments have been carried out to predict the presence of water hyacinth from a few scattered training samples, which represent invasive plants in different phenological stages and with different spectral responses.

Smart networks such as active distribution network (ADN) and microgrid (MG) play an important role in power system operation. The design and implementation of appropriate protection systems for MG and ADN must be addressed, which imposes new technical challenges. This paper presents the implementation and validation aspects of an adaptive fault detection strategy based on neural networks (NNs) and multiple sampling points for ADN and MG. The solution is implemented on an edge device. NNs are used to derive a data-driven model that uses only local measurements to detect fault states of the network without the need for communication infrastructure. Multiple sampling points are used to derive a data-driven model, which allows the generalization considering the implementation in physical systems. The adaptive fault detector model is implemented on a Jetson Nano system, which is a single-board computer (SBC) with a small graphic processing unit (GPU) intended to run machine learning loads at the edge. The proposed method is tested in a physical, real-life, low-voltage network located at Universidad del Norte, Colombia. This testing network is based on the IEEE 13-node test feeder scaled down to 220 V. The validation in a simulation environment shows the accuracy and dependability above 99.6%, while the real-time tests show the accuracy and dependability of 95.5% and 100%, respectively. Without hard-to-derive parameters, the easy-to-implement embedded model highlights the potential for real-life applications.

Objective: Colorectal cancer (CRC) patients respond differently to treatments and are sub-classified by different approaches. We evaluated a deep learning model, which adopted endoscopic knowledge learnt from AI-doscopist, to characterise CRC patients by histopathological features. Results: Data of 461 patients were collected from TCGA-COAD database. The proposed framework was able to 1) differentiate tumour from normal tissues with an Area Under Receiver Operating Characteristic curve (AUROC) of 0.97; 2) identify certain gene mutations (MYH9, TP53) with an AUROC > 0.75; 3) classify CMS2 and CMS4 better than the other subtypes; and 4) demonstrate the generalizability of predicting KRAS mutants in an external cohort. Conclusions: Artificial intelligent can be used for on-site patient classification. Although KRAS mutants were commonly associated with therapeutic resistance and poor prognosis, subjects with predicted KRAS mutants in this study have a higher survival rate in 30 months after diagnoses.

Network human operators’ decision-making during grid outages requires significant attention and the ability to perceive real-time feedback from multiple information sources to minimize the number of control actions required to restore service, while maintaining the system and people safety. Data-driven event and alarm management have the potential to reduce human operator cognitive burden. However, the high complexity of events, the data semantics, and the large variety of equipment and technologies are key barriers for the application of Artificial Intelligence (AI) to raw SCADA data. In this context, this paper proposes a methodology to convert a large volume of alarm events into data mining terminology, creating the conditions for the application of modern AI techniques to alarm data. Moreover, this work also proposes two novel data-driven applications based on SCADA data: (i) identification of anomalous behaviors regarding the performance of the protection relays of primary substations, during circuit breaker tripping alarms in High Voltage (HV) and Medium Voltage (MV) lines; (ii) unsupervised learning to cluster similar events in HV line panels, classify new event logs based on the obtained clusters and membership grade with a control parameter that helps to identify rare events. Important aspects associated with data handling and pre-processing are also covered. The results for real data from a Distribution System Operator (DSO) showed: (i) that the proposed method can detect unexpected relay pickup events, e.g., one substation with nearly 41% of the circuit breaker alarms had an ‘atypical’ event in their context (revealed an overlooked problem on the electrification of a protection relay); (ii) capability to automatically detect and group issues into specific clusters, e.g., SF6 low-pressure alarms and blocks with abnormal profiles caused by event time-delay problems.

This paper presents the design and analysis of a special Rectifier Unit (RU) composed of a Delta-Differential Symmetric 18-pulse Autotransformer and cascade Boost converters for applications in More Electric Aircraft (MEA), which is denominated as RU-DDS18PATR+Boost. The cascaded Boost converters provide dc-link regulation, while eliminating the need to use interphase transformers (IPTs), interphase rectors (IPRs), and zero-sequence blocking transformers (ZSBTs), thus contributing toward decreasing the complexity and at the same time increasing reliability. The proposed RU-DDS18PATR+Boost operates with an active current shaping strategy in order to meet the harmonic content restrictions imposed by the DO-160F standard, while allowing for the design of a DDS18PATR with a notable kVA rating of 21.03%. The performance of the RU-DDS18PATR+Boost was analyzed operating in a system with a supply voltage of 115VAC and variable frequency in the range of 360Hz to 800Hz, which is converted to 540VDC. Experimental results are presented corroborating the theory put forward herein, especially with regard to meeting the limits imposed by the DO-160F standard.

In this paper, we evaluate the selective combination of algorithms for fiber nonlinearity compensation, transceiver impairments mitigation and variable-rate coding in unrepeatered optical transmission. A post-emphasis filter and a two-stage 4×4 multiple-input and multiple-output equalizer compensate the impact of bandwidth limitations and in-phase and quadrature skew. Nonlinear compensation is accomplished by a fast-converging adaptive digital back-propagation algorithm and maximum likelihood sequence estimation. Forward error correction is provided by a variable-rate spatially-coupled low-density parity-check code. The performance and complexity of the proposed digital subsystems are experimentally evaluated by the unrepeatered wavelength division multiplexing transmission of 17×200-Gb/s (DP-16QAM 32-GBd) channels over 350-km of large effective area and low-loss single-mode fibers. The experimental results for different combinations of algorithms elucidate the trade-off of complexity and performance in unrepeatered optical transmission.

A middleware software can be seem as an abstraction layer between hardware and user applications, that facilitates the development and deployment of services in various scenarios, such as those found in Home Energy Management Systems (HEMS). There are several middleware proposals for HEMS, with most of them taking the cloud computing approach. This approach is unconcerned about computing resources but raises a dependency on external connections. This paper presents a middleware for energy management systems, based on the concept of edge computing for smart homes. The paper presents a reference model for the proposed architecture, considering specific requirements for this type of application. The proposed architecture employs the concept of microservices for data access and system configuration. The proposed middleware is designed to work with embedded systems under computational constraints, such as processing capability and storage, to reduce costs and allow its application closer to the user. The middleware is open and customizable to meet the developer’s needs. The proposed solution was implemented and tested in a university laboratory, as well as at the Eldorado Research Institute to confirm the effectiveness of the middleware. The proposal stands out from others found in the literature as it can be implemented using low cost hardware. In addition to using microservices concepts, the proposed middleware is a valuable option for applications that need an edge computing approach. A performance analysis was carried out, using low cost hardware with limited resources. The results show that the proposal can handle a significant number of devices, offering low latency and low error rate, and consuming few processing resources and memory.

Congestion issues are becoming more prevalent as the share of variable renewable energy increases. Conventional methods to mitigate them, such as building new lines, require significant lead time and upfront investment costs. Thus, alternative approaches that deal with congestion problems faster and in a more efficient manner have gained significant interest. In this paper, a modular mobile power flow controller (MPFC) is proposed that can redirect power flow from congested lines to adjacent ones by modifying the line reactance, but also offers additional significant advantages: a) scalability due to the modular nature that allows the MPFC to be sized optimally for addressing specific system needs, b) redeployability due to the mobile deployment method that allows the MPFC to be rapidly deployed and easily removed and redeployed to another location, and c) replicability stemming from the minimal construction requirements for connecting the MPFC to the transmission grid. The whole process of planning, installing, operating and redeploying the MPFC on a 150kV transmission system is thoroughly documented and studied. Overall, this paper presents a novel technology that can offer a quicker and at a lower cost solution to congestion issues, while reducing the impact of new infrastructure on communities and the environment.

The effect of back-end of line (BEOL) process on cell performance and reliability of Phase-Change Memory embedded in a 28nm FD-SOI platform (ePCM) is discussed. The microscopic evolution of the Ge-rich GST alloy during process is the focus of the first part of the paper. A new metric for quantification of active material modifications is introduced to better follow its evolution with process sequence. Ge clustering has been shown to occur during the fabrication, impacting the pristine resistance and the after forming cell performance. Two different BEOL processes are then benchmarked in terms of key performance. An optimized process is identified, and an extensive electrical characterization of array performance and reliability is done on the full 16MB chip. The optimized BEOL process results in a memory cell fully compatible with the requirements for demanding automotive applications.

HVDC has been chosen as an economical and technical solution for power transmission through long distances, asynchronous interconnections and long submarine cables crossing. Despite DC transmission benefits to power systems, the converters non-linearity produces undesirable effects to the converter transformer in service, mainly listed in the technical standard IEC/IEEE 60076-57-129. However, additional discussions and complementary information can be found in a plurality of references, which are brought in the article under a comprehensive overview perspective. Some design solutions deal with these effects increasing the technical margins, which have direct influence on manufacturing costs and transformer reliability and availability levels. This article goes through the main topics pointed by the standard and the references, investigating their consequences in the converter transformer operation, in order to provide a comprehensive tutorial on design solutions and considerations to deal with those undesirable effects.

The goal of achieving decarbonized operation of power systems in response to climate change has led to an increase in the number of sources connected to electrical grids through inverters, of which photovoltaic systems are an example. The operating characteristics of these systems in steady-state or transient operation are different from those of synchronous machines, giving rise to issues related to power system stability. The vast majority of grid codes stipulate that fault ride-through operating requirements should be enabled during severe contingencies in the transmission network, prioritizing delivery of reactive power to the power system to support voltage stability. However, this control action may contribute to rapid collapse of the system voltage under adverse operating conditions, such as when the critical voltage in the power vs. voltage curve is near the reliable voltage operating bound. Supporting power system stability under these circumstances represents a new challenge for inverter control schemes. This paper adapts a recently published fault ride-through control scheme for PV inverters for use in a multimachine power system and shows that the scheme can successfully maintain transient and voltage stability even under adverse voltage operating conditions.

Cardamom is a queen of spices. It is indigenously grown in the evergreen forests of Karnataka, Kerala, Tamil Nadu, and the northeastern states of India. India is the third largest producer of cardamom. Plant diseases cause a catastrophic influence on food production safety; they reduce the eminence and quantum of agricultural products. Plant diseases may cause significantly high loss or no harvest in dreadful cases. Various diseases and pests affect the growth of cardamom plants at different stages and crop yields. This study concentrated on two diseases of cardamom plants, Colletotrichum Blight and Phyllosticta Leaf Spot of cardamom and three diseases of grape, Black Rot, ESCA, and Isariopsis Leaf Spot. Various methods have been proposed for plant disease detection, and deep learning has become the preferred method because of its spectacular accomplishment. In this study, U2-Net was used to remove the unwanted background of an input image by selecting multiscale features. This work proposes a cardamom plant disease detection approach using the EfficientNetV2 model. A comprehensive set of experiments was carried out to ascertain the performance of the proposed approach and compare it with other models such as EfficientNet and Convolutional Neural Network (CNN). The experimental results showed that the proposed approach achieved a detection accuracy of 98.26%.

Self-supervised learning has shown value for uncovering informative movement features for human activity recognition. However, there has been minimal exploration of this approach for affect recognition where availability of large labelled datasets is particularly limited. In this paper, we propose a P-STEMR (Parallel Space-Time Encoding Movement Representation) architecture with the aim of addressing this gap and specifically leveraging the higher availability of human activity recognition datasets for pain-level classification. We evaluated and analyzed the architecture using three different datasets across four sets of experiments. We found statistically significant increase in average F1 score to 0.84 for pain level classification with two classes based on the architecture compared with the use of hand-crafted features. This suggests that it is capable of learning movement representations and transferring these from activity recognition based on data captured in lab settings to classification of pain levels with messier real-world data. We further found that the efficacy of transfer between datasets can be undermined by dissimilarities in population groups due to impairments that affect movement behaviour and in motion primitives (e.g. rotation versus flexion). Future work should investigate how the effect of these differences could be minimized so that data from healthy people can be more valuable for transfer learning. [ABSTRACT FROM AUTHOR]