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Quantum Computing is drawing a significant attention from the current scientific community. The potential advantages offered by this revolutionary paradigm has led to an upsurge of scientific production in different fields such as economics, industry, or logistics. The main purpose of this paper is to collect, organize and systematically examine the literature published so far on the application of Quantum Computing to routing problems. To do this, we embrace the well-established procedure named as Systematic Literature Review. Specifically, we provide a unified, self-contained, and end-to-end review of 18 years of research (from 2004 to 2021) in the intersection of Quantum Computing and routing problems through the analysis of 53 different papers. Several interesting conclusions have been drawn from this analysis, which has been formulated to give a comprehensive summary of the current state of the art by providing answers related to the most recurrent type of study (practical or theoretical), preferred solving approaches (dedicated or hybrid), detected open challenges or most used Quantum Computing device, among others.

Researchers often need to gather a comprehensive set of papers relevant to a focused topic, but this is often difficult and time-consuming using existing search methods. For example, keyword searching suffers from difficulties with synonyms and multiple meanings. While some automated research-paper recommender systems exist, these typically depend on either a researcher’s entire library or just a single paper, resulting in either a quite broad or a quite narrow search. With these issues in mind, we built a new research-paper recommender system that utilizes both citation information and textual similarity of abstracts to provide a highly focused set of relevant results. The input to this system is a set of one or more related papers, and our system searches for papers that are closely related to the entire set. This framework helps researchers gather a set of papers that are closely related to a particular topic of interest, and allows control over which cross-section of the literature is located. We show the effectiveness of this recommender system by using it to recreate the references of review papers. We also show its utility as a general similarity metric between scientific articles by performing unsupervised clustering on sets of scientific articles. We release an implementation, ExCiteSearch (bitbucket.org/mmmontemore/excitesearch), to allow researchers to apply this framework to locate relevant scientific articles.

In the age of social media, the spread of rumors is becoming easier due to the proliferation of communication and information dissemination platforms. Detecting rumors is a major problem with significant consequences for the economy, democracy, and public safety. Deep learning approaches were used to classify rumors and have yielded state-of-the-art results. Nevertheless, the majority of techniques do not attempt to explain why or how decisions are made. This paper introduces a hybrid attention neural network (HANN) to identify rumors from social media. The advantage of HANN is that it will allow the main user to capture the relative and important features between different classes as well as provide an explanation of the model’s decisions. Two deep neural networks are included in the proposal: CNNs and Bidirectional Long Short Term Memory (Bi-LSTM) networks with attention modules. In this paper, the model is trained using a benchmark dataset containing 3612 distinct tweets crawled from Twitter including several types of rumors related to COVID-19. Each subset of data has a balanced label distribution with 1480 rumors tweets (46.87%) and 1677 non-rumors tweets (53.12%). The experimental results demonstrate that the new approach (HANN model) performs better results in terms of performance and accuracy (about 0.915%) than many contemporary models (AraBERT, MARBEART, PCNN, LSTM, LSTM-PCNN and Attention LSTM). Moreover, a number of software engineering features such as followers, friends, and registration age are used to enhance the model’s accuracy.

One of the most important features of solid-state drives (SSDs) is the ability to update data sectors out of place, performing garbage collection operations within its physical blocks, to mitigate limited flash memory lifespan. In SSDs, a firmware called the Flash Translation Layer (FTL) is used to hide such features from the operating system. One way to extend the lifespan of flash memories is to use data compression at the FTL level. In compression-based FTLs, the address mapping type has a significant effect on the efficiency and read/write speed. The type of address mapping in previous similar schemes is based on page-level address translation and requires high memory space for the mapping table. In this paper, we propose a compression-based hybrid FTL called In Block Update Address Mapping, in which the pages of each logical block are compressed, and fewer physical pages are written in physical blocks. Therefore, a small number of free pages remain. In the proposed scheme, the required memory of the mapping table has been reduced by 78% when compared to similar schemes. Moreover, extensive simulation results reported in this paper show that the proposed FTL scheme outperforms other FTL schemes in the read and write operations under realistic benchmark workloads.

Over the past few decades, the substantial growth in enterprise-data availability and the advancements in Artificial Intelligence (AI) have allowed companies to solve real-world problems using Machine Learning (ML). ML Operations (MLOps) represents an effective strategy for bringing ML models from academic resources to useful tools for solving problems in the corporate world. The current literature on MLOps is still mostly disconnected and sporadic. In this work, we review the existing scientific literature and we propose a taxonomy for clustering research papers on MLOps. In addition, we present methodologies and operations aimed at defining an ML pipeline to simplify the release of ML applications in the industry. The pipeline is based on ten steps: business problem understanding, data acquisition, ML methodology, ML training & testing, continuous integration, continuous delivery, continuous training, continuous monitoring, explainability, and sustainability. The scientific and business interest and the impact of MLOps have grown significantly over the past years: the definition of a clear and standardized methodology for conducting MLOps projects is the main contribution of this paper.

Ensemble methods can be used to identify causal relationships in data for a better understanding and taking the right decision in processes that involve high risk. This paper explores the idea of a causal decision tree forest and proposes a regularized ensemble method by integrating optimal causal trees for improved prediction accuracy while not compromising on accurately estimating heterogeneous treatment effects. The proposed method is based on selecting a subset of the most accurate causal trees from a sufficiently large pool based on their out-of-sample error estimates. The selected trees are integrated to form an ensemble that is used for estimating heterogeneous treatment effect and predicting unseen data. The proposed method is applied on Pakistan’s income function consisting of 27964 observations on wages of workers age 10 and above as an example dataset. The paper gives a detailed simulation study where datasets are generated under 5 different designs. The proposed method is assessed against ordinary least square (OLS), least absolute shrinkage and selection operator (LASSO), Ridge, Causal Tree and the standard decision trees forest (i.e. the causal forest) via mean square error (MSE), root mean square error (RMSE), mean absolute deviation (MAD) and Pearson correlation ( ${r}$ ) as performance metrics. The analyses given in the paper reveal that the proposed method can be used effectively for estimating heterogeneous treatment effects and achieves better prediction performance and as compared to the rest of the methods given in the paper.

This paper presents an improved circuit design of Gallium Nitride (GaN) based phase shifted full bridge (PSFB) converters along with a new adaptive burst mode control strategy, to achieve high efficiency over a wide power range, including light loads. The main challenges of PSFB converters addressed in this paper are: load-dependent zero-voltage switching (ZVS), transformer saturation, and secondary side ringing. Since switching losses of GaN FETs are dominant at light loads, adaptive burst mode control can be employed to improve the efficiency at lower output power. By periodically switching output current between 0 A and minimum ZVS current, adaptive burst mode control can enable both smaller effective switching frequency and lower switching losses. A correction factor ‘k’ is adopted in the adaptive burst mode control’s current loop PI calculation so that the output current can switch fast without any overshoot that increases switches’ current stress. To verify the effectiveness of the proposed circuit and controller design, a 375 V input, 70 V output, 800 W PSIM simulation model as well as experimental prototype are developed and tested. The experimental results demonstrate the practical benefits of the proposed adaptive burst mode control.

In recent decades, the data envelopment analysis (DEA) model has been applied to various fields. However, the current research on DEA for land utilization efficiency is based on the assumption that each decision making unit (DMU) is independent and does not distinguish different preference to output indexes. Therefore, this paper proposes a new game cross efficiency model which considers the different levels of concern to different output indexes. In the proposed model, the output indexes are divided into several groups and DMUs present their preferences to these groups. Subsequently, this paper develops an algorithm to solve this model and proves the convergence of the algorithm. Then, taking the evaluation of land utilization efficiency as an example, the importance of game cross efficiency and group indexes in evaluations is presented. The output indexes are separated as two classes including economy output indexes and environment output indexes. The land utilization efficiency of cities in Pearl River Delta in 2019 is evaluated by the proposed model. The analysis shows that when there is a significant difference between different groups of output indexes, the ranking result for decision making units is different from the traditional DEA. The model reveals that the efficiency of some cities depends much on some special output indexes.

In this paper, we propose and study a type of tuple-level constraint that arises from the selection operator $\sigma $ of relational algebra and that closely resembles the concepts of tuple-level denial constraints. We call this type of constraint selection rules and study their concepts and properties in the setting of data consistency management. The main contribution of this paper is the study of rule implication with selection rules in order to solve the error localization problem by means of the set cover method. It turns out that rule implication can be applied more easily if the representation of selection rules is extended in order to allow gaps between attribute values. We show that the properties of selection rules allow to improve the performance of rule implication. Evaluation of our approach compared to HoloClean on four real-world datasets shows promising results. First, repair with selection rules is often faster and less memory-consumable than HoloClean, especially when the amount of work that rule implication has to do is limited. Second, in terms of precision and recall of error detection and correction, repair strategies with selection rules almost always outperform HoloClean.

The paper presents research on the approximation of variable-order fractional operators by recurrent neural networks. The research focuses on two basic variable-order fractional operators, i.e., integrator and differentiator. The study includes variations of the order of each fractional operator. The recurrent neural network architecture based on GRU (Gated Recurrent Unit) cells functioned as a neural approximation for selected fractional operators. The paper investigates the impact of the number of neurons in the hidden layer, treated as a hyperparameter, on the quality of modeling error. Training of the established recurrent neural network was performed on synthetic data sets. Data for training was prepared based on the modified Grünwald-Letnikov definition of variable-order fractional operators suitable for convenient numerical computing without memory effects. The research presented in this paper showed that recurrent network architecture based on GRU-type cells can satisfactorily approximate targeted simple yet functional variable-order fractional operators with minor modeling errors. In addition, the research also compares the presented solution with basic and recurrent neural networks that utilize Tapped Delay Lines (TDL) in their structure. The presented solution is a novel approach to the approximation of VO-FC operators. It has the advantage of automatic selection of neural approximator parameters by optimization based on data customized for specific requirements.

With a large number of distributed generation connected to the distribution network, the total supply capacity of the distribution network has been improved to a certain extent. However, due to the randomness of its output, it also brings some challenges to the reliable operation of the distribution network and the restoration of power supply after failure. Based on this, this paper proposes an active distribution network reconfiguration strategy with uncertain factors for maximum total supply capacity improvement. Firstly, the evaluation index of the maximum total supply capacity of distribution network is constructed, and the maximum total supply capacity index under the current operation mode is evaluated online through the calculation model of maximum total supply capacity based on N-1 security criterion. Then, the uncertainty of wind power, photovoltaic and electric vehicle DG and the stochastic model of prosumer load are considered, random variables are generated by Latin hypercube sampling (LHS) and their correlation is considered. Then, it introduces that the position of sectionalizing switch and tie switch can be adjusted according to the dispatching demand. Based on the evaluation index of maximum total supply capacity and DG output of uncertain factors, the active reconfiguration model of distribution network after fault is proposed. Finally, a calculation example illustrates the effectiveness and correctness of the model in this paper. It is demonstrated that the model can optimize the selection strategy of section switch and tie switch after the fault of distribution network, improve the utilization rate of distributed energy, and give full play to the power supply recovery potential of distribution network and reduce the outage time of prosumer groups.

Musculoskeletal conditions, often requiring rehabilitation, affect one-third of the U.S. population annually. This paper presents rehabilitation assistive technology that includes body-worn motion sensors and a mobile application that extends the reach of a physical rehabilitation specialist beyond the clinic to ensure that home exercises are performed with the same precision as under clinical supervision. Assisted by a specialist in the clinic, the wearable sensors and user interface developed allow the capture of individualized exercises unique to the patient’s physical abilities. Beyond the clinical setting, the system can assist patients by providing real-time corrective feedback to repeat these exercises through a correct and complete arc of motion for the prescribed number of repetitions. An inertial measurement unit (IMU) is used on the body part to be exercised to capture its pose. In this paper, we present a kinematics data processing approach to defining custom exercises with flexibility in terms of where it is worn and the nature of the exercise, as well as real-time corrective feedback parameters. The system is tested on two exercises perfromed by a healthy individual to demonstrate the feasibility and accuracy of the approach. We demonstrate how it can improve exercise adherence by assisting users in reaching the full prescribed range of motion and stay on the ideal plane of motion and improve hold time. Preliminary results from an ongoing clinical trial are presented.

In the sixth generation of mobile communication networks (6G) era, in which the convergence of wireless and Internet technologies will drive the digital economy and automation across industries, the focus of innovation will shift from individual technology or products towards innovation within platforms and ecosystems. This paper provides a comprehensive categorised survey of the 6G business literature and the current state of mobile communication intellectual property value creation across the system architecture layers. The key emerging and enabling technologies of the 6G networks are positioned as applicable to areas of value creation and capture, and an outline of the proposed transformation of technology innovation by considering the 6G system architecture layers is provided. Unlike the existing 6G literature focusing on visions, use cases, KPI requirements, and KVI drivers, this study will extend the discussion to the value-creation-and-capture problem of technology innovators. The role of complementary assets, technology policy, standardisation, and intellectual property in 6G is analysed by profiting from innovation lenses. To gain the full potential and capture value from technology innovation in 6G, it is essential that regulators protect the intellectual property contributions to incentive R&D investments by the developers of open, collaborative enabling technologies. The paper further assesses the business model evolution from the 4G engineering platform to modular 5G and 6G, stemming from ecosystems and open value configurations. The results of the technology value analysis show the emerging 6G business to be an oblique hybrid of vertical and horizontal business models and characterise the future 6G networks as a general-purpose technology.

The rise of Telemedicine has revolutionized how patients are being treated, leading to several advantages such as enhanced health analysis tools, accessible remote healthcare, basic diagnostic of health parameters, etc. The advent of the Internet of Things (IoT), Artificial Intelligence (AI) and their incorporation into Telemedicine extends the potential of health benefits of Telemedicine even further. Therefore, the synergy between AI, IoT, and Telemedicine creates diverse innovative scenarios for integrating cyber-physical systems into medical health to provide remote monitoring and interactive assistance to patients. Data from World Health Organization reports that 7.4 million people died because of Atrial Fibrillation (AF), recognizing the most common arrhythmia associated with human heart rate. Causes like unhealthy diet, smoking, poor resources to go to the doctor and based on research studies, about 12 and 17.9 million of people will be suffering the AF in the USA and Europe, in 2050 and 2060, respectively. The AF as a cardiovascular disease is becoming an important public health issue to tackle. By using a systematic approach, this paper reviews recent contributions related to the acquisition of heart beats, arrhythmia detection, IoT, and visualization. In particular, by analysing the most closely related papers on Convolutional Neural Network (CNN) and IoT devices in heart disease diagnostics, we present a summary of the main research gaps with suggested directions for future research.

Image transmission over Low-Power Wide Area Networks (LP-WAN) protocols has always been a difficult task since it necessitates high data rates and high energy consumption. Long Range (LoRa) is one such protocol, which is excellent for transferring data over long distances but has generated severe doubts regarding the viability of image transmission due to its low data rate. This paper demonstrates the application results of an integrated LoRa and Deep Learning-based computer vision system that can efficiently identify grape leaf diseases using low-resolution images. In particular, the focus in this paper is to combine the two technologies, LoRa and Deep Learning, to make the transmission of the images and the identification of the diseases possible. To achieve this objective, the framework utilizes a combination of on-site and simulation experiments along with different LoRa parameters and Convolutional Neural Model (CNN) model fine-tuning. Based on the evaluation, the proposed framework proved that the transmission of images using LoRa is possible within the protocol limitations (such as limited bandwidth and low duty cycle). Our fine-tuned model can efficiently identify grape leaves diseases. The technique is both efficient and adaptive to the specifics of each leaf disease, while it does not need any training data to adjust parameters. It is worth noting that today, end-user trust in Machine and Deep Learning models has increased significantly because of novel solutions in the field of Explainable Artificial Intelligence (XAI). In this study, we use the Grad-CAM method to visualize the output layer judgments of the CNN. The disease’s spot region is highly activated, according to the visualization findings. This is how the network distinguishes between different grape leaf diseases.

IoT sensors and deep learning models can widely be applied for fault prediction. Although\ud deep learning models are considerably more potent than many conventional machine learning models, they\ud are not transparent. This paper first examines different deep learning techniques to carry out univariate time\ud series analysis based on vibration sensors installed on four industrial bearings to predict a fault occurring in\ud a predefined time window. Several recurrent neural networks are used to develop fault prediction models.\ud An empirical evaluation of these models shows that all models perform well; however, hybrid models\ud outperform other models when the time window increases. Then, instance-wise feature selection has been\ud considered to highlight the most contributing features for its outputs regarding any input. In this problem,\ud the main challenge is to propose a trainable feature selection model with the minimum number of selected\ud features whilst its performance is close to the baseline model. This paper develops a novel explainable\ud method called the Gumbel-Sigmoid eXplanator (GSX) to tackle these problems. In a nutshell: (i) we have\ud developed a differentiable and trainable selector, and (ii) we utilize regularization to control the number\ud of features for each instance flexibly. The proposed method is model agnostic, and empirical evaluations\ud on two datasets show that GSX can not only solve the problems identified with two other state-of-the-art\ud methods but also outperform them in terms of accuracy and run-time.

This paper proposes a neural 5G traffic generation model and a methodology for calculating the spectrum requirements of private 5G networks to provide various industrial communication services. To accurately calculate the spectral requirements, it is necessary to analyze the actual data volume and traffic type of industrial cases. However, because there is currently no suitable traffic model to test loads in private 5G networks, we have developed a generative adversarial network (GAN)-based traffic generator that can generate realistic traffic by learning actual traffic traces collected by mobile network operators. In addition, in the case of industrial applications, probability-based traffic models were used in parallel as there were not enough real data to be learned. The proposed 5G traffic generation model is combined with the proposed 5G spectrum calculation methodology, enabling more accurate spectrum requirements calculation through traffic simulation similar to a real-life environment. In this paper, the spectrum requirements are calculated differently according to two types of duplexing, namely frequency division duplexing (FDD) and time division duplexing (TDD). As a guide for companies aiming to provide advanced wireless connectivity for a wide variety of vertical industries using 5G networks, eight use cases defined in the 5G Alliance for Connected Industries and Automation (ACIA) white paper were simulated. The spectrum requirements were calculated under various simulation conditions considering varying traffic loads, deployment scenarios, and duplexing types. Various simulation results confirmed that a bandwidth of at least 22.0 MHz to a maximum of 397.8 MHz is required depending on the deployment scenario.

This paper presents a two-element MIMO antenna system using two dual-beam EMSIW antenna elements. The dual-beam pattern is achieved by creating two weak field areas using EMSIW. The antenna elements are placed in the weak field areas of each other, which results in −31.65 dB measured isolation between the two adjacent antenna elements. Different scenarios for the placement of the elements are predicted, one case is illustrated in this paper. The high isolation, omission of decoupling networks, and use of dual-beam antenna element make the antenna simple, compact, and suitable for MIMO applications.

Due to environmental interference and operational errors, problems such as incomplete and random missing logging data have occurred during the geophysical logging data collection process. Since it is difficult to establish a geophysical model based on logging data and geological information, the data complementation effect of conventional methods is not very satisfactory. In this paper, we propose an MC-GAN-BiLSTM model based on spatiotemporal sequence prediction. In the model, we adopt a generative adversarial network (GAN) as a network framework, and a long short-term memory (LSTM) neural network and a bi-directional long short-term memory (Bi-LSTM) as the basic modules. We use the LSTM instead of a fully-connected layer in the GAN to extract the potential information in the logging data depth domain. We complete the logging data missing values through an encoding-decoding structure that includes the Bi-LSTM. In addition, the generator module also uses multiscale convolution to fully extract the logging data features. We use logging data random missing values and consecutive missing values to simulate a field data acquisition environment and threshold control to simulate a laboratory processing environment for experiments. The experimental results show that the coefficient of determination (R2) of the GAN-LSTM model reaches 0.906 when 30% of random logging data are missing and 0.851 when 30% of consecutive logging data are missing. The effect of the model proposed in this paper is significantly higher than the commonly used random forest (RF), sequence to sequence (seq2seq) and generative adversarial interpolation network (GAIN) models.

Chinese characters have been created into many font styles, such as official script, running script, and regular script. Other than that, some famous calligraphers, such as Ouyang Xun and Yan Zhenqing, have produced fonts with their style. Being able to detect and recognize these font styles quickly and accurately has essential applications in graphic design, page layout, handwriting identification, and other use cases. Distinguishing between font styles requires professional knowledge, which almost inevitably leads to errors for unprofessional people. Therefore, this paper presents a sword-like model based on a convolutional neural network with a sword structure to recognize font styles for Chinese characters. This model includes 15 convolutional layers. For each layer, we gradually increase the number of convolutional kernels to better extract the classification features of the input image. This paper uses four downsampling layers in the model. For each downsampling operation, the length and width of the image become half of their original values while the number of channels gradually increases, leading to a sword-like shape. As a result, we name our model as SwordNet. We also created a Chinese font dataset called the Nankai Chinese Font Style dataset and made it available on Github. Using the above dataset, we compared the accuracy of our model with six other state-of-the-art network models. The experiments showed that SwordNet could achieve an average recognition accuracy of 99.03% in multiple experiments, while the other six models can only achieve accuracy up to 94.91%. So we can conclude that SwordNet could perform better in font style recognition than other models.

Discrete-time dynamic systems demonstrate quite exciting possibilities from the perspective of control as compared with the continuous-time counterpart. Interesting properties of discrete-time dynamic systems include the possibility to algebraically determine previously unknown system parameters by simply measuring the present inputs and outputs of the system. Additionally, achieving a finite settling time with zero steady-state error is only achievable in discrete-time dynamic systems. Deadbeat current control (DBCC) has been used to achieve a finite settling time, especially in grid-connected inverter applications. However, there is no comprehensive study on reviewing or evaluating existing control approaches, to the authors’ best knowledge. This paper systematically examined the existing methods by paying attention to four key research issues: 1) research evidence indicating the adoption of DBCC in grid-connected inverter applications (GCIAs), 2) the types of deadbeat control approaches adopted in GCIAs, 3) the best approach in terms of stability especially regarding grid-impedance variation, and 4) the barriers that might prevent the wide adoption of DBCC in GCIAs. Finally, this paper presents a hypothesis based on the simulated results on which approach is superior at present to give readers a direction for further research classification on deadbeat control.

COVID-19 has dramatically affected various aspects of human society with worldwide repercussions. Firstly, a serious public health issue has been generated, resulting in millions of deaths. Also, the global economy, social coexistence, psychological status, mental health, and the human-environment relationship/dynamics have been seriously affected. Indeed, abrupt changes in our daily lives have been enforced, starting with a mandatory quarantine and the application of biosafety measures. Due to the magnitude of these effects, research efforts from different fields were rapidly concentrated around the current pandemic to mitigate its impact. Among these fields, Artificial Intelligence (AI) and Deep Learning (DL) have supported many research papers to help combat COVID-19. The present work addresses a bibliometric analysis of this scholarly production during 2020. Specifically, we analyse quantitative and qualitative indicators that give us insights into the factors that have allowed papers to reach a significant impact on traditional metrics and alternative ones registered in social networks, digital mainstream media, and public policy documents. In this regard, we study the correlations between these different metrics and attributes. Finally, we analyze how the last DL advances have been exploited in the context of the COVID-19 situation.

The holograms in Star Wars have inspired many researchers to capture the whole human body in real-time and present it as an avatar into Virtual Reality (VR). Facial capture is important to achieve this because facial expressions are essential for social interaction. However, facial capture works well only when the face is not occluded. While a VR headset has been widely used as a display device for VR, it occludes half of the face and becomes an obstacle. This paper presents a systematic literature review on facial capture for VR. The survey aims to: review the current state-of-the-art facial capture technologies for VR; identify the types of technologies and context of the use, the methodologies, and theoretical groundings; identify research gaps in facial capture for VR; and identify solutions of facial capture for VR headsets. A realism index is defined to evaluate and compare the collected papers. The results show several technology trends in the facial capture for VR: tracking facial motion with markers, facial capture in headsets using cameras or sensors, facial performance capture, and hologram/volumetric capture. It is shown that the Modular Codec Avatar is the best facial capture method in a VR headset, whereas Metahuman has the best output effects. This paper also proposes that an open-design VR headset is an effective approach to lower the Total Cost of Ownership (TCO).

Electrification of the transportation sector has originated a worldwide demand towards green-based refueling infrastructure modernization. Global researches and efforts have been pondered to promote optimal Electric Vehicle (EV) charging stations. The EV power electronic systems can be classified into three main divisions: power charging station configuration (e.g., Level 1 (i.e., slow-speed charger), Level 2 (i.e., fast-speed charger), and Level 3 (i.e., ultra-fast speed charger)), the electric drive system, and the auxiliary EV loads. This paper emphasizes the recent development in Power Factor Correction (PFC) converters in the on-board charger system for short-distance EVs (e.g., e-bikes, e-trikes, e-rickshaw, and golf carts) and long-distance EVs (passenger e-cars, e-trucks, and e-buses). The EV battery voltage mainly ranges between 36 V and 900 V based on the EV application. The on-board battery charger consists of either a single-stage converter (a PFC converter that meets the demands of both the supply-side and the battery-side) or a two-stage converter (a PFC converter that meets the supply-side requirements and a DC-DC converter that meets the battery-side requirements). This paper focuses on the single-phase unidirectional non-isolated PFC converters for on-board battery chargers (i.e., Level 1 and Level 2 charging infrastructure). A comprehensive classification is provided for the PFC converters with two main categories: (1) the fundamental PFC topologies (i.e., Buck, Boost, Buck-Boost, SEPIC, Ćuk, and Zeta converters) and (2) the modified PFC topologies (i.e., improved power quality PFC converters derived from the fundamental topologies). This paper provides a review of up-to-date publications for PFC converters in short-/long-distance EV applications.

One-fourth population of the world lives in South Asia. The electricity generation for this population consumes the substantial fossil fuels, resulting in the significant emissions of harmful gases in the global environment. To protect the natural environment, South Asian Association for Regional Cooperation (SAARC) has planned a super smart grid for the power sharing between South Asian countries through the integration of the renewable energy resources (RERs). However, the capability of these countries to construct such a mega power grid is unclear due to the unavailability of proper strategic analysis, lack of information about RERs potential, and the lack of decision making models. This paper analyzes the existing power grids in SAARC countries, identifies their electricity generation potential, and develops an integer linear programming model based on the power generation cost, CO2 emissions, and disruption risk. This study solves the proposed model using goal programming methods. The results exhibited that the SAARC countries have the capability to share the sustainable super smart grids through the substantial integration of RERs. For instance, the model solution based on 100% renewable power (i.e. Case 5) exhibited 36% cost savings, 86% CO2 emission reduction, and 39% risk reduction compared with corresponding non-renewable power based model solution (i.e. Case 1). In addition, this study performs the cost-benefit analysis for the existing and proposed power systems of SAARC countries. The results provide the various insights to policy makers and practitioners for the strategic decision making related to the SAARC super smart grid. In addition, the methodology of this paper can be adopted for other super smart grids around the world.

Due to the randomness of load and renewable energy generation (REG), microgrids face multiple uncertainties. These uncertainties lead to the uncertainty of microgrid operation and bring more challenges to the economic evaluation of microgrids. In this paper, an economic evaluation method for determining microgrid revenue distribution is proposed. Considering the dual uncertainties of source-load and forecast, and temporal autocorrelation of time series, the probabilistic model of uncertainties is established by multivariate kernel density estimation (KDE). Then the random scenarios including forecasting values are generated and used in optimal dispatch calculation for the detailed production simulation. The probabilistic revenue is derived with a method based on Monte Carlo method. Finally, a case study is carried out based on the real data of an industrial park. The results demonstrate the necessity and effectiveness of the probabilistic revenue analysis proposed in this paper. This method can reveal the actual values of each component of a microgrid (e.g., device or algorithm) in specific scenes and provides more insights into investment decisions.

In software debugging, fault localization is the most difficult, expensive, tedious, and time-consuming task, particularly for large-scale software systems. This is due to the fact that it requires significant human participation and it is difficult to automate its sub-tasks. Therefore, there is a high demand for automatic fault localization techniques that can help software engineers effectively find the locations of faults with minimal human intervention. This has led to the proposal of implementing different types of such techniques. However, Spectrum Based Fault Localization (SBFL) is considered amongst the most prominent techniques in this respect due to its efficiency and effectiveness. In SBFL, the probability of each program element (e.g., statement, block, or function) being faulty is calculated based on the results of executing test cases and their corresponding code coverage information. However, SBFL techniques are not yet widely adopted in the industry. The rationale behind this is that they pose a number of issues and their performance is affected by several influential factors. For example, the characteristics of bugs, target programs, test suites, and supporting tools make their effectiveness differ dramatically from one case to another. There are massive studies on SBFL that cover its usage, formulas, performance, etc. So far, no dedicated survey points out comprehensively the issues of SBFL. In this paper, various SBFL challenges and issues have been identified, categorized, and discussed alongside many directions. Also, the paper raises awareness of the works being achieved to address the identified issues and suggests some potential solutions too.

This paper provides a comprehensive literature review of various technologies and protocols used for medical Internet of Things (IoT) with a thorough examination of current enabling technologies, use cases, applications, and challenges. Despite recent advances, medical IoT is still not considered a routine practice. Due to regulation, ethical, and technological challenges of biomedical hardware, the growth of medical IoT is inhibited. Medical IoT continues to advance in terms of biomedical hardware, and monitoring figures like vital signs, temperature, electrical signals, oxygen levels, cancer indicators, glucose levels, and other bodily levels. In the upcoming years, medical IoT is expected replace old healthcare systems. In comparison to other survey papers on this topic, our paper provides a thorough summary of the most relevant protocols and technologies specifically for medical IoT as well as the challenges. Our paper also contains several proposed frameworks and use cases of medical IoT in hospital settings as well as a comprehensive overview of previous architectures of IoT regarding the strengths and weaknesses. We hope to enable researchers of multiple disciplines, developers, and biomedical engineers to quickly become knowledgeable on how various technologies cooperate and how current frameworks can be modified for new use cases, thus inspiring more growth in medical IoT.

This paper addresses the problem of super-resolution of environment matting of transparent objects. In contrast to traditional methods of environment matting of transparent objects, which often require a large number of input images or complex camera setups, recent approaches using convolutional neural networks are more practical. In particular, after training, they can generate the environment mattes using a single image. However, they still do not have super-resolution capabilities. This paper first proposes an encoder-decoder network with restoration units for super-resolution environment matting, called Enhanced Transparent Object Matting Network (ETOM-Net). Then, we introduce a refinement phase to improve the details of the output further. The ETOM-Net effectively recovers lost features in the LR input images and produces visually plausible HR environment mattes and the corresponding reconstructed images, demonstrating our method’s effectiveness.

This paper studies four patterns for the maneuverability of robotic dolphins: turning with flapping flippers, turning with swing flippers, turning with head offset, and turning with head-flipper coordination. Turning maneuverability is one of the important evaluation indexes for the performance of underwater bionic robots, and existing literature has rarely theoretically or experimentally evaluated turning maneuverability for the latter two turning patterns. In this paper, we establish a dynamic model for the high maneuverability of underwater bionic robots and discuss the influence of motion design parameters, including the frequency of the pectoral fins and the offset angle of the head, on the turning maneuverability, focusing on the turning angular velocity and turning radius. To verify the effectiveness and control accuracy of the dynamic model, we develop a multidegree-of-freedom (multi-DOF) and high-maneuverability bionic robotic dolphin and test its maneuverability through experiments. Extensive experimental results demonstrate that the established dynamic model can successfully predict the turning maneuverability of robotic dolphins. In addition, the turning methods in this paper have better turning performance than some robotic fish. This paper provides a reference for the establishment of a maneuvering dynamics model for underwater bionic robots and a theoretical basis for the design of a turning maneuver control system.

In this paper, a novel electric machine, Induction Switched Reluctance Machine (US patent-US20170370296A1) is introduced, and its preliminary performance is evaluated. The innovation is based on placing windings on the rotor, in order to create short magnetic flux paths in the rotor and stator core of the electric machine. As a result, a higher torque with lower copper and iron losses is generated. Like other ideas, there are unknown aspects of this electric machine, such as thermal and mechanical properties, that should be studied. A comprehensive research program is required to analyze and design an Induction Switched Reluctance Machine with an optimized configuration. This electric machine could be designed with different pole numbers and various winding strategies, and it is not possible to consider all configurations and aspects of it in this paper. Moreover, the size and the rated power of the machine can affect its properties. The purpose of this paper is to introduce, simulate and prototype a simple Induction Switched Reluctance Machine. This electric machine has an excellent potential to be implemented in the propulsion system of electric vehicles.

Ocean waves that impact along the coast are among the most promising renewable energy sources that can be extracted from the environment. The ocean’s energy can be extracted by a few methods and converted from kinetic energy into electrical energy using turbine blades and power generators installed in areas with strong waves. The best and most widely established wave energy converters use an Oscillating Water Column (OWC) conversion concept. During the past few years, numerous research projects have been conducted to ensure a continuous development of OWC technologies that could provide more systematic, consistent, and environmentally friendly electrical supply sources to the nearby communities and electric powered facilities. OWC has distinctive techniques for every section of the energy converting process, differing from other methods. Several things need to be considered to ensure that a maximum amount of energy can be generated from the established system. This paper discusses the OWC system design characteristics and functions. This paper also summarizes the current technology developments for the OWC and their achievements.

Peer-to-peer electricity markets allow small producers and consumers of energy to trade directly. Energy imbalances, the difference between predicted and actual supply or demand for energy, may be higher in peer-to-peer markets than in traditional markets. High energy imbalances could increase the total cost of the electricity system, both inside and outside the peer-to-peer market, because the system operator must take expensive corrective actions at short notice. This paper examines the effect of imbalance charges on peer-to-peer electricity markets. A new symmetric imbalance charge mechanism is proposed which penalises market participants irrespective of the direction of their energy imbalance. The symmetric imbalance charge mechanism provides a financial incentive for peer-to-peer market participants to reduce their energy imbalances. For illustrative purposes, this paper uses a simulation to show that the current imbalance charge mechanism in Great Britain does not provide a financial incentive for peer-to-peer market participants to reduce their energy imbalances. The imbalance charge is close to zero under the current British imbalance charge mechanism when averaged over hundreds of settlement periods or more. When subject to the symmetric imbalance charge mechanism, the simulation shows that peer-to-peer market participants are given a strong financial incentive to reduce their energy imbalances. Finally, this paper discussed how the symmetric imbalance charge mechanism could be implemented only within the bounds of a peer-to-peer market, or within the whole electricity system.

This work was supported in part by the Ministry of Higher Education under the Fundamental Research Grant Scheme under Grant FRGS/1/2018/ICT04/UTM/01/1; and in part by the Faculty of Informatics and Management, University of Hradec Kralove, through SPEV project under Grant 2102/2022., Phishing has become an increasing concern and captured the attention of end-users as well as security experts. Existing phishing detection techniques still suffer from the de ciency in performance accuracy and inability to detect unknown attacks despite decades of development and improvement. Motivated to solve these problems, many researchers in the cybersecurity domain have shifted their attention to phishing detection that capitalizes on machine learning techniques. Deep learning has emerged as a branch of machine learning that becomes a promising solution for phishing detection in recent years. As a result, this study proposes a taxonomy of deep learning algorithm for phishing detection by examining 81 selected papers using a systematic literature review approach. The paper rst introduces the concept of phishing and deep learning in the context of cybersecurity. Then, taxonomies of phishing detection and deep learning algorithm are provided to classify the existing literature into various categories. Next, taking the proposed taxonomy as a baseline, this study comprehensively reviews the state-of-the-art deep learning techniques and analyzes their advantages as well as disadvantages. Subsequently, the paper discusses various issues that deep learning faces in phishing detection and proposes future research directions to overcome these challenges. Finally, an empirical analysis is conducted to evaluate the performance of various deep learning techniques in a practical context, and to highlight the related issues that motivate researchers in their future works. The results obtained from the empirical experiment showed that the common issues among most of the state-of-the-art deep learning algorithms are manual parameter-tuning, long training time, and de cient detection accuracy., Ministry of Higher Education under the Fundamental Research Grant Scheme FRGS/1/2018/ICT04/UTM/01/1, Faculty of Informatics and Management, University of Hradec Kralove, through SPEV project 2102/2022

UML templates are possibly the most neglected and misused piece of knowledge in UML modelling. This subject has been disregarded in the research and practice literature and even by modelling tools providers. This paper suggests that such oblivion results from a general misunderstanding that UML templates are just graphical representations of genericity like it is found in programming languages, and from the insufficient support from the modelling tools, with a consequence of poor usage of UML templates in practice. Indeed, the capabilities and potential of UML templates are far-reaching. Increasing awareness around them could bring significant benefits for UML users, namely, higher-level abstraction and reuse. Therefore, this paper provides a distilling tutorial on UML templates to highlight their flexibility and advantages. That presentation follows a tutorial style and is supported by several illustrative examples, varying from simpler to more complex ones. This tutorial reviews the Template construct’s core concepts and terminology, presents constraining classifiers and shows how to define properties and operations as template parameters. Then, it presents and discusses advanced aspects such as operation templates, parameter defaults, the relationship between binding and generalization, and the specific semantics of package templates. Furthermore, the paper discusses the related work and uncovers some of the UML templates’ limitations and opportunities for improvement.

Traditional denoising methods for seismic exploration data design a corresponding mathematical denoising model batch according to the different properties of different random noises, which is a tedious and time-consuming process. To solve this problem, this paper proposes a deep convolutional neural network denoising model based on noise estimation (MCD-DCNN). This model is primarily composed of two modules, the noise estimation module and the denoising module. The noise estimation module uses a multiscale convolutional neural network to better extract the characteristics of random noise in the seismic data. To make full use of the extracted features, a dense connection method is adopted between the multiscale convolutions in the noise estimation module. In the denoising module, we use multiscale convolutions and dense connections to replace the original convolutional neural network and use the residual structure (ResNet) and batch normalization (BN) to improve the denoising effect and running speed of the model. In this experiment, single trace and simple and complex profile data are used as input to simulate the real data processing environment. Finally, we compare the denoising effects of the MCD-DCNN model proposed in this paper with the current mainstream feed-forward denoising convolutional neural network (DnCNN) and a fast and flexible denoising convolutional neural network (FFDNet) models. The comprehensive results show that under the condition of a given prior noise level, the denoising performance of the FFDNet and MCD-DCNN models are comparable. In the absence of a priori noise level, the denoising performance of the FFDNet model drops sharply, while the denoising performance of MCD-DCNN is not affected; therefore, MCD-DCNN is more in line with actual seismic denoising.

In order to reduce the influence of strong halo effect on dim small target detection in daytime, a dim small target detection algorithm based on spiral gradient optimization estimation and high-order correlation enhancement is proposed in this paper. In this paper, we first design a spiral motion model to obtain the local gradient information in the central image point by perturbing the designed motion direction, then estimate the optimal background by establishing a gradient optimization model to achieve background suppression while effectively removing the halo phenomenon. Considering the original high-order correlation model only uses a single pixel for motion correlation, there is insufficient information utilization, an improved high-order correlation energy enhancement model is proposed to enhance the target signal, the algorithm first constructs an attention discrimination model based on inner and outer windows to obtain the salient region of the image, and then carries out multi frame high-order motion correlation of neighborhood blocks to enhance the target energy. After experiments, it is shown that compared with the traditional algorithm, the algorithm proposed in this paper can effectively weaken the halo effect while suppressing most of the background and can effectively enhance the local signal-to-noise ratio of the target.

In this paper, the analysis and simulation of shunt reactor turn-to-turn faults, as well as the algorithms for their detection, are presented. A simple equivalent circuit of a shunt reactor applicable for a small number of short-circuited turns was developed and applied to simulate such faults. It is shown why the commonly applied differential relays are not able to detect turn-to-turn faults. Then three new algorithms are introduced, and their performance is analyzed for simulated internal faults. The proposed methods are based on zero-sequence reactance, negative-sequence reactance, and phase impedance. The sensitivity and security of the presented methods are discussed. The proposed algorithms’ performance has been tested with the signals generated with the use of the MATLAB/Simulink program. The results confirm that the proposed algorithm can effectively improve the detection of turn-to-turn faults. The paper ends with recommendations for the setting of the proposed protection schemes.

Noise and variation are the two major challenges for the reliability of digital circuits, especially multiple-valued logic (MVL) circuits where the entire voltage range is divided into some narrow zones. In spite of few correct examples, many ternary inverters with reduced noise margins have been presented in the literature. The defect is mainly because of their improperly shaped voltage transfer characteristic (VTC). With proper transistor sizing, we can rectify the problem and provide uniformly wide noise margin values while maintaining power-delay product (PDP) low. As far as we know, none of the previous ternary inverters has been given based on a methodical transistor sizing procedure. In this paper, a systematic transistor sizing through physical equations is suggested for an existing standard ternary inverter (STI), whose original sizes for the carbon nanotube FETs (CNFETs) are inappropriate. This paper includes a comprehensive investigation to determine appropriate values for the physical parameters of the CNFET-based STI. Compared with the original design, with a negligible increase in circuit delay and area, simulation results show that the proposed ternary inverter can increase noise margin and static noise margin by up to 47.7% and 83.3%, respectively.

This paper presents a cloud energy storage (CES) architecture for reducing energy costs for residential microgrid users. The former of this article concentrates on identifying an appropriate battery technology from various battery technologies with the aid of a simulation study. The later part addresses the economic feasibility of the storage architecture with three different scenarios namely grid connected energy storage, distributed energy storage (DES) and CES. The performance of the proposed architecture has been evaluated by considering five residential users with suitable battery technology identified from the former part of the study. For the purpose of the analysis, PV and load profiles including seasonal effects and grid price were taken from IIT Mumbai, India and IEX portal, respectively. In addition, this article also examines the impact of increased number of users with CES. The value of this study is that the proposed CES architecture is capable of reducing the cost of electricity experienced by the user by 11.37% as compared to DES. With this, CES operator’s revenue can be increased by 6.70% in summer and 16.97% in winter in the case of fixed number of users. Finally, based on the analysis and simulation results, this paper recommends CES with Li-ion battery technology for residential application.

Wireless mesh networks are getting adopted in the domain of network communication. Their main benefits include adaptability, configuration, and flexibility, with added efficiency in cost and transmission time. Traffic prediction refers to forecasting the traffic volumes in a network. The traffic volume includes incoming requests and outgoing data transmitted by the network nodes. The previous logs of traffic in the network are used for extracting patterns that help for accurate predictions. In this paper, an analysis of various existing traffic prediction methods is done. Specifically, the analysis of a case study where the performance of the High-Speed Diesel (HSD) pump is predicted by observing its output. A network of sensors form a less mesh network; sensors act as nodes while reading the parameters, namely, three-phase Current, Voltage, Temperature, and Vibration. In this case study, a High-Speed Diesel pumps’ performance is predicted by predicting the vibration parameter as the output parameter. Other parameters affecting the performance of the High-Speed Diesel pump which are causing the change in vibration value are identified. Various algorithms, including Statistical Auto-Regressive Integration and Moving Average, Poisson’s regression, and a few Machine Learning and Deep Learning algorithms like Decision Tree Regressor, Multi-Layer Perceptron, Linear Regression, and Long Short-Term Memory are implemented and evaluated for this purpose. Along with the comparison, a novel architecture using Convolution Neural Network and Long Short-Term Memory is described in this paper. The result and comparison between these give the clear understanding that the suggested novel Convo-LSTM model gives better performance and helps to predict the performance of the High-Speed Diesel pump. The proposed system makes a strong case for the network traffic prediction, where the use of historical data is collected over the wireless mesh network. A similar analogy can be used where this model could be implemented further for network monitoring tasks.

Electrical power converters use semiconductor switching devices for larger loss reduction compared with non-switching power linear amplifier such as a class-B amplifier. However, it is well known that the switching operation produces harmonics and EMI noise, thus passive components are usually required for the suppression of these harmful sub-products. Use of recent fast power devices such as silicon carbide (SiC) and gallium nitride (GaN) devices produce larger dv/dt and di/dt than that of Si power devices and as a result above mentioned problem becomes more tangible. In this paper, the authors propose a concept of a family of multi-level linear amplifier (MLLA), which makes no switching power conversion possible. MLLA consist of series-connected switching devices and only one of them operates as a linear amplifier and the loss is much less than that of the class-B amplifier. The concept of the family is very useful when a proper MLLA topology might be selected for applications. In this paper, three kinds of MLLA—diode-clamped linear amplifier (DCLA), flying-capacitor linear amplifier (FCLA), and novel modular-cascaded linear amplifier (MCLA)—with four series connection of devices are investigated for the viewpoint of merits and demerits clarification and the efficiency over 82 % is demonstrated in the experiments.

Fixed-beam, high-gain antenna systems can be used for a finer partitioning of the currently used cell-sectoring. This partitioning has the benefit of reducing the number of users seen per antenna beam, which reduces interference. Furthermore, the high antenna gain allows for a high effective isotropic radiated power while keeping the transmit power low. In this paper, we study the performance of such a fixed-beam, high gain antenna system design for millimeter-wave mobile communications. The antenna system is designed to keep the inter-sector interference in a multi-site scenario low. The performance is analyzed for single- and multi-user environments. In single-input single-output mode, the 50th percentile of the signal-to-interference-plus-noise ratio lies between 12.5 dB to 39.7 dB if 3 to 0 interferers are present, respectively. For multiple-input multiple-output transmission using zero-forcing, the signal-to-interference-plus-noise ratio increases and the 50th percentile ranges from 36.1 dB to 43.3 dB if 3 to 0 interferes are present, respectively. By using maximum ratio transmission, the best performance is achieved with no interferers present, while a plunge in performance is observed with interferers. Furthermore, the study revealed that the narrow beam antenna system can also provide a clear signal separation for small spatial separations. In the given example, the signal-to-interference-plus-noise ratio is larger than 32.1 dB with 11 active antenna elements, where 2.8 meters separate the users. Hence, the paper shows that the cell-partitioning antenna systems provide coverage in the desired area while keeping the inter-sector interference low, and the considered transmission techniques can be used for situation optimized mobile communication links.

For the dynamic spectrum sharing (DSS), this paper proposes a spectrum sensing scheme that measures packet lengths of interfering systems. DSS requires spectrum sensing techniques to measure a length of time for which the interfering systems use a channel during observation time. A ratio of the length of time to the observation time is referred to as the channel occupation ratio (COR). When the observation time is limited, a conventional estimation scheme suffers from large errors of the measured COR. To cope with this problem, another conventional scheme divides the observation time into multiple short slots so as to estimate mean squared estimation errors of COR. However, this conventional scheme cannot accurately estimate errors, because it does not consider the time length of packets. Therefore, this paper proposes a scheme to estimate the variance of measured COR by measuring how long packets from the interfering systems can be observed, which can estimate errors much more accurately than the conventional scheme. Computer simulations evaluate how the observation time affects the standard deviation of the measured COR. It is also demonstrated that the theoretical results of the proposed scheme agree with those of the simulations when the true value of COR is less than 0.4.

Image classification needs to consider image degradations in practice because an image classification network trained with clean images works poorly for degraded images while digital images usually include some degradations such as JPEG compression. To tackle this problem, a common approach is training classification networks on degraded images with various levels of degradation, e.g. various quality factors for JPEG compression. However, the classification networks do not usually have enough accuracy for clean images because the classification networks have been averagely trained for various levels of degradation. This paper aims to construct a classification network of degraded images without sacrificing the performance of clean images. This paper proposes a network to learn the classification of degraded images and degradation levels of degraded images as multi-task learning. Learning the classification of degraded images is the main task of multi-task learning. On the other hand, learning degradation levels is a sub-task of multi-task learning and reinforces the classification ability. In the proposed network, a feature extractor of the classification is trained with image features acquired from a classification network of clean images by using consistency regularization with the cosine similarity loss. The experimental results using different types of degradations, including JPEG compression, Gaussian blur, Gaussian noise, and salt-and-pepper noise, show that our proposed network has enough ability to classify degraded images without sacrificing the performance for clean images.

Distress image retrieval for infrastructure maintenance via self-trained deep metric learning using experts’ knowledge is proposed in this paper. Since engineers take multiple images of a single distress part for inspection of road structures, it is necessary to construct a similar distress image retrieval method considering the input of multiple images to support determination of the level of deterioration. Thus, the construction of an image retrieval method while selecting an effective input from multiple images is described in this paper. The proposed method performs deep metric learning by using a small number of effective images labeled by experts’ knowledge with information about their effectiveness and a large number of unlabeled images via a self-training approach. Specifically, an end-to-end learning approach that performs retraining of the model by assigning pseudo-labels to these unlabeled images according to the output confidence of the model is achieved. Thus, the proposed method can select an effective image from multiple images that are input at the retrieval as a query image. This is the main contribution of this paper. As a result, the proposed method realizes highly accurate retrieval of similar distress images considering the actual situation of inspection in which multiple images of a distress part are input.

A human-centric emotion estimation method based on correlation maximization with consideration of changes with time in visual attention and brain activity when viewing images is proposed in this paper. Owing to the recent developments of many kinds of biological sensors, many researchers have focused on multimodal emotion estimation using both eye gaze data and brain activity data for improving the quality of emotion estimation. In this paper, a novel method that focuses on the following two points is introduced. First, in order to reduce the burden on users, we obtain brain activity data from users only in the training phase by using a projection matrix calculated by canonical correlation analysis (CCA) between gaze-based visual features and brain activity-based features. Next, for considering the changes with time in both visual attention and brain activity, we obtain novel features based on CCA-based projection in each time unit. In order to include these two points, the proposed method analyzes a fourth-order gaze and image tensor for which modes are pixel location, color channel and the changes with time in visual attention. Moreover, in each time unit, the proposed method performs CCA between gaze-based visual features and brain activity-based features to realize human-centric emotion estimation with a high level of accuracy. Experimental results show that accurate human emotion estimation is achieved by using our new human-centric image representation.

The variability of wind speed is modeled in the literature using probability distribution functions (PDFs). In many papers, the selection of PDFs is based solely on goodness-of-fit metrics without giving proper consideration to whether these PDFs can model the wind speed range. A PDF’s ability to model the wind speed range ensures that it can synthesize both the minimum and the maximum wind speed at a site. A methodology to select PDFs that can be used to model wind speed is presented in this paper. The proposed methodology considers not only the goodness-of-fit metrics when selecting PDFs, but also their ability to model the wind speed range. The proposed methodology is compared with a commonly used methodology in the literature that selects PDFs based solely on goodness-of-fit metrics. Simulation results show that the proposed methodology chooses better PDFs than the commonly used methodology. Furthermore, it is shown that selecting PDFs using the commonly used methodology does not guarantee that the chosen PDFs will model the wind speed range.

In this paper, we designed a registration framework that can be used to develop augmented reality environments, where all the real (including the users) and virtual elements are co-localized and registered in a common reference frame. The software is provided together with this paper, to contribute to the research community. The developed framework allows us to perform a quantitative assessment of interaction and egocentric perception in Augmented Reality (AR) environments. We assess perception and interaction in the peripersonal space through a 3D blind reaching task in a simple scenario and an interaction task in a kitchen scenario using both video (VST) and optical see-through (OST) head-worn technologies. Moreover, we carry out the same 3D blind reaching task in real condition (without head-mounted display and reaching real targets). This provides a baseline performance with which to compare the two augmented reality technologies. The blind reaching task results show an underestimation of distances with OST devices and smaller estimation errors in frontal spatial positions when the depth does not change. This happens with both OST and VST devices, compared with the real-world baseline. Such errors are compensated in the interaction kitchen scenario task. Thanks to the egocentric viewing geometry and the specific required task, which constrain the position perception on a table, both VST and OST have comparable and effective performance. Thus, our results show that such technologies have issues, though they can be effectively used in specific real tasks. This does not allow us to choose between VST and OST devices. Still, it provides a baseline and a registration framework for further studies and emphasizes the specificity of perception in interactive AR.

Heavy equipment operation is a responsible and difficult task causing mental workload on a human operator and exposing the operator to a range of harmful factors. Human factors and ergonomics in heavy equipment design have traditionally been focused on anthropometry and questionnaires. More advanced techniques involving biosignal measurements were not applied to heavy equipment, mainly due to the diversity of real working conditions that were hard to reproduce in a laboratory environment and that prevented ambulatory studies. Recent advances in wearable biosensors and real-time simulators produce the capability of using biosignals for improving the ergonomics of heavy equipment operation. The present paper reviews the use of biosignals in human factors and the ergonomics of heavy machines by focusing on stress detection for the last ten years. The aim of the paper is analyzing the previous implemented algorithms to find a set of biosignals and methods of stress identification that could be suitable for identifying stress in heavy equipment operators both in laboratory and ambulatory studies. The conclusion emphasizes successful stress identification methods and a combination of the algorithms from different studies that facilitate the use of heavy equipment operator’s applications. Also, feasible methods and directions for future research are considered.