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In forensic document analysis, the authenticity of a document must be properly checked in the context of suspected forgery. Hyperspectral Imaging (HSI) is a non-invasive way of detecting fraudulent papers in a multipage document. The occurrence of a forged paper in a multi-page document may have a substantial difference from rest of the papers in its age, type, color, texture, and so on. Each pixel in an HSI data can be used as the material fingerprint for the spatial point it corresponds to. Hence, hyperspectral data of paper samples made of the same substance have similar characteristics and can be grouped into a single cluster. Similarly, paper samples made of different substances have different spectral properties. This paper relies on this heuristic and proposes a tensor based clustering framework for hyperspectral paper data, with an application to detect the forged papers in multi-page documents. Information embedded in the hyperspectral patches of the papers to be clustered is arranged into individual lateral slices of a third-order tensor in this framework. Further, this work employs the self-expressiveness property of submodules and an objective function is formulated to extract self-expressive representation tensor with low multirank and f-diagonal structure. Objective function of the proposed method incorporates $l_{\frac {1}{2}}$ -induced Tensor Nuclear Norm (TNN) and $l_{\frac {1}{2}}$ regularization to impart better low rankness and f-diagonal structure to the representation tensor. Experimental results of the proposed method were compared to the state-of-the-art subspace clustering approaches. The results demonstrate improved performance of the proposed method over the existing clustering algorithms.

Temperature compensation occupies a significant function in the insulation condition evaluation of converter transformer bushings based on the Frequency Domain Spectroscopy (FDS), which could influence the accuracy of its insulation diagnosis. In order to accurately obtain the variation law of FDS at different temperature, a Resin Impregnated Paper (RIP) insulation bushing for converter transformer was designed by the equivalence of maximum electric field, coaxial cylinder structure and RIP materials. The increment law of dielectric parameters both dissipation factor and capacitance at wideband frequency (1mHz ~ 10kHz) was measured for a quality RIP insulation samples at the temperature from 30°C to 100°C. 1) Under the uniform temperature, it was found that the value of dissipation factor was rising with the frequency from 10 Hz to 0.001Hz, while the tendency was reversed at the frequency from 100Hz to10kHz. 2) The minimum value of tan $\delta $ from 0.001Hz to 10kHz could be driven to higher frequency moving (10Hz to 10kHz) by the action of temperature rising 30°C to 100°C.3) The effect of temperature on the lower frequency 0.001Hz ~ 1Hz tan $\delta $ of RIP insulation closely conformed to the index law of Arrhenius equation, while it was in accordance with the linear decrease at higher frequency 1kHz ~ 10kHz. The effect of temperature on dissipation factor could be eliminated by curve segment shifting based the experiment equation, which it at 0.001Hz dissipation factor could be eliminated by curve shifting based the index equation originated Arrhenius equation. The method is beneficial to eliminate the influence of temperature on the dielectric spectroscopy test results, and then to eliminate the influence of temperature on the dielectric spectroscopy diagnosis of RIP bushings.

The tremendous development of the Internet of Things (IoT) technology in the last decades has fostered advancement in automatic medical assistive devices to support the existing Electronic Health Record (EHR) system. As an integral part of the EHR electronic model, public cloud servers store medical data. Unfortunately, public cloud servers are prone to security and privacy breach. This paper introduces a novel non-cryptographic approach to preserve electrocardiograph (ECG) data confidentiality and integrity in the EHR environment. The main objective of the proposed anonymization algorithm is to obscure the patient’s cardiac information during transmission and to protect information stored in the cloud database. Although we focus on ECG data, generalization to other types of clinical data can be derived using the proposed method. Performance evaluation of the proposed scheme showed that the algorithm conceals both fiducial and non-fiducial features of the data. Therefore, confidentiality feature is preserved. This paper examined confidentiality of the anonymized data using the Percentage Residual Difference (PRD) and investigated the integrity of the reconstructed data in terms of cross-correlation. Security analysis carried out using the PRD, brute force attack, and performance comparison between the proposed algorithm and existing methods. Evaluation showed that the proposed scheme offers a secure non-cryptographic model for transmission and storing clinical data in the cloud. Moreover, in terms of processing time, the proposed algorithm is ten times faster than the existing wavelet packet method when processing long ECG data, 65,536 sample points. In a real-time experimental testbed, the implemented proposed system was successful.

This paper presents a non-iterative online approach to identify the modeling parameters of a photovoltaic (PV) module. It is motivated by the fact that accurate and reliable modeling of distributed energy resources (DERs) in DC Microgrids improves their stability and efficiency under a wide range of operational conditions. In particular, the case where PV modules are used as DERs in their current-source region is considered. The proposed method addresses the limitations associated with parameter identification in these settings. Specifically, the method works under varying temperature and insolation conditions relying only on current and voltage sensors that already exist in the power electronic converters tying the PV DER to the Microgrid. Also, its algorithm is practical and reliable as it does not rely on a priori knowledge or an initial guess, and it is non-iterative, so it does not risk divergence (or require proof of convergence) as other iterative algorithms. Moreover, it is both fast and of a low computational complexity, which enables its implementation on microcontrollers within PV DER systems. The development of this method is detailed in the paper along with its application steps to facilitate its adoption. Furthermore, an experimental setup was used to test the proposed method under different ambient conditions and demonstrated its efficacy with algorithm execution times of under 1 second and high modeling accuracy on a microcontroller.

Manual inspection of textiles is a long, tedious, and costly method. Technology has solved this problem by developing automatic systems for textile inspection. However, Jacquard fabrics present a challenge because patterns can be complex and seemingly random to systems. Only a few in-depth studies have been conducted on jacquard fabrics despite their important and intriguing nature. Previous studies on jacquard fabrics are of simple patterns. This paper introduces a new and novel field in fabrics defect detection. Complex-patterned jacquard fabrics are much more challenging. In this paper, novel defect detection models for jacquard-patterned fabrics are presented. Owing to the lack of available databases for jacquard fabrics, we compiled and experimented on our own novel dataset. Our dataset was collected from plain, undyed jacquard fabrics with different complex patterns. In this study, we used and tested several deep learning models with image pre-processing and convolutional neural networks (CNNs) for unsupervised detection of defects. We also used multispectral imaging, combining normal (RGB) and near-infrared (NIR) imaging to improve our system and increase its accuracy. We propose two systems: a semi-manual system using a simple CNN network for operation on separate patterns and an integrated automated system that uses state-of-the-art CNN architectures to run on the entire dataset without prior pattern specification. The images are preprocessed using contrast-limited adaptive Histogram Equalization (CLAHE) to enhance their features. We concluded that deep learning is efficient and can be used for defect detection in complex patterns. Proposed method of EfficientNet CNN gave high accuracy reaching 99% approximately. We also found that multispectral imaging is more advantageous and yields higher accuracy.

Visible Light Communication (VLC) is a key technology for the sixth-generation (6G) wireless communication thanks to the possibility of using artificial environmental lights as a data transfer channel. Although VLC systems are more resistant against interference and less susceptible to security vulnerabilities like most wireless networks, VLC is even inherently susceptible to eavesdropping attacks. Moreover, since VLC is considered an enabling technology for 6G, specific mechanisms are needed to enforce data security. This paper considers improving the security of the next generation of wireless communications by using the Watermark Blind Physical Layer Security (WBPLSec) in VLCs. The main intuition is that RGB LEDs offer the possibility for Wavelength Division Multiplexing (WDM) as a useful support for the Spread-Spectrum (SS) watermarking. In this paper, we propose an approach that aims at obtaining VLC Physical Layer Security (PLS) by combining watermarking with an RGB LED jamming. We provide a performance analysis of the proposed security architecture based on the secrecy capacity in terms of its existence and outage probability. We prove that WBPLSec can be used to significantly improve confidentiality in the next generation of wireless communications. The results offer the possibility of creating a secure region around the legitimate receiver by leveraging the jamming optical power.

With the rapidly increasing deployment of distributed energy resources (DERs), electric power systems across the world are moving towards more distributed designs and operations. While not every distributed device will be part of a microgrid, microgrids do offer solutions for scalability and interoperability, and for coordination with centralized utility control systems. The challenge is that the microgrids will be heterogenous with respect to ownership, operational objectives, resource mix, and control systems. Due to these variations within the population of microgrids, there will be significant challenges with coordinating their operations to achieve global objectives using existing centralized controls and optimizations. This paper presents a framework for the coordination of heterogeneous groups of microgrids using consensus algorithms. The framework enables the active engagement of utility and non-utility microgrids using a distributed architecture to support end-use loads during the loss of the bulk power system. Results from laboratory testing and preliminary implementations, as well as details of an ongoing operational deployment at the Electric Power Board of Chattanooga are presented in this paper.

Overlap has been identified in previous works as a significant obstacle to automated diagnosis using data mining algorithms, since it makes it impossible to discern how each machine influences product quality. Several solutions that handle overlap have been proposed, but the final result is a list of potential overlapped root causes. The goal of this paper is to develop a solution resilient to overlap that can determine the true root cause from a list of possible root causes, when possible, and determine the conditions in which it is possible to identify the root causes. This allows for a better understanding of overlap, and enables the development of a fully automatic root cause analysis for manufacturing. To do so, we propose an automatic root cause analysis approach that uses causal inference and do calculus to determine the true root cause. The proposed approach was validated on simulated and real case-study data, and allowed for an estimation of the effect of a product passing through a certain machine while disregarding the effect of overlap, in certain conditions. The results were on par with the state-of-the-art solutions capable of handling overlap. The contributions of this paper are a graphical definition of overlap, the identification of the conditions in which is possible to overcome the effect of overlap, and a solution that can present a single true root cause when such conditions are met.

Synthetic datasets are gradually emerging as solutions for data sharing. Multiple synthetic data generators have been introduced in the last decade fueled by advancement in machine learning and by the increased demand for fast and inclusive data sharing, yet their utility is not well understood. Prior research tried to compare the utility of synthetic data generators using different evaluation metrics. These metrics have been found to generate conflicting conclusions making direct comparison of synthetic data generators very difficult. This paper identifies four criteria (or dimensions) for masked data evaluation by classifying available utility metrics into different categories based on the measure they attempt to preserve: attribute fidelity, bivariate fidelity, population fidelity, and application fidelity. A representative metric from each category is chosen based on popularity and consistency, and the four metrics are used to compare the overall utility of four recent data synthesizers across 19 datasets of different sizes and feature counts. The paper also examines correlations between the selected metrics in an attempt to streamline synthetic data utility.

In this paper, a profitable strategy of utilizing solar and bio-energy available in a rural location for meeting the energy deficit is discussed. The proposed model can provide 24-hour continual power supply to the vicinity without the need for any battery. The land requirement is minimal in the proposed model making it cost-effective for implementation. The paper provides a step by step procedure to optimally size the solar and bio-power plants and schedule their operation for maximum utility. A programmable smart meter connected with a central system having a user-friendly control algorithm has also been proposed at every home for end-user profitability. The smart meter will indicate energy consumption in local home, its solar energy generation, vicinity power demand, current tariff of power usage, energy imported/exported and other required details. The proposed model is discussed in detail for comprising of two adjacent villages named Gaddikheri and Tajamajra in Rohtak (Haryana), North India (having 768 houses). The technical and financial analysis at this location is done using RETScreen expert software and the results are presented. The system is found to provide 24-hr power supply and also be profitable with the capital cost being recovered in less than 8 years time. Apart from this, the proposed system also provides other benefits like reduction in greenhouse gas emission (by 93%), auto power theft control, bio-fertilizers, and a clean environment.

Pulswidth modulation (PWM) methods were developed to grant the voltage source inverter (VSI) output signal better quality measures. The PWM method that VSI relies on must consider the utilization of the direct current (DC) source, especially when the drive system works in the overmodulation region. In this paper, different zero-sequence harmonic injection PWM methods are proposed. Two essential objectives are optimized by minimizing total harmonic distortion and maximizing the DC bus utilization. All schemes’ results are compared with each other and with some of the commonly used PWM schemes. This paper concludes the usage of third, ninth, and fifteenth harmonic injections and any combination of them to reach a new PWM method that overtakes the disadvantages of other methods. Meanwhile, it provides a handy tool to operate VSI optimally. Ultimately, this work concludes to what extent is the zero-sequence injection is helpful. The work is evaluated experimentally, and the practical results match the simulation outcomes.

Brain diseases are mainly caused by abnormal growth of brain cells that may damage the brain structure, and eventually will lead to malignant brain cancer. An early diagnosis to enable decisive treatment using a Computer-Aided Diagnosis (CAD) system has major challenges, especially accurate detection of different diseases in the magnetic resonance imaging (MRI) images. In this paper, a three-step preprocessing is proposed to enhance the quality of MRI images, along with a new Deep Convolutional Neural Network (DCNN) architecture for effective diagnosis of glioma, meningioma, and pituitary. The architecture uses batch normalization for fast training with a higher learning rate and ease initialization of the layer weights. The proposed architecture is a computationally lightweight model with a small number of convolutional, max-pooling layers and training iterations. A demonstrative comparison between the proposed architecture and other discussed models in this paper is conducted. An outstanding competitive accuracy is achieved of 98.22% overall, 99% in detecting glioma, 99.13% in detecting meningioma, 97.3% in detecting pituitary and 97.14% in detecting normal images when tested on a dataset with 3394 MRI images. Experimental results prove the robustness of the proposed architecture which has increased the detection accuracy of a variety of brain diseases in a short time.

Most conventional data association based multi-target tracking (MTT) algorithms typically suffer from intractable computational complexities and could not perform in an environment where large number of closely spaced multiple targets move across each other in clutters. Unlike to the existing MTT systems, the linear multi-target (LM) algorithm modifies the measurement detection followed by neighbored tracks as a clutter, hence, it updates the track without the influence of other tracks. Thus, LM technique is a computationally efficient algorithm that allows the multi-target system to play like a single target tracking algorithm. Smoothing maximizes the state estimation accuracy and reduces estimation error based on future scan measurement. However, only few research paper focused on the LM algorithm without utilizing the benefits of the smoothing. This paper presents Rauch-Tung-Striebel Smoothing in the linear multi-target based on integrated probabilistic data association (RTS-LMIPDA). The RTS-LMIPDA algorithm fuses forward and backward LM track predictions to obtain the smoothing prediction which is required to calculate the smoothing multi-target state estimates in the forward track. Numerical analysis is presented to illustrate the estimation accuracy and false track discrimination (FTD) performances of RTS-LMIPDA in comparison to the existing MTT algorithms using the simulations.

Lately, gamification (i.e., employing game-design elements and game principles in non-game contexts) has gained massive popularity and widespread usage in various areas, including education. However, gamification deployment in education in general and remote education in particular still faces many challenges that mainly influence user quality of experience. These challenges include lack of dedicated communication-based systems, potential additional load on teachers, absence of customization and personalization for users, and no support for advanced technology-enhanced learning (TEL). This paper investigates the use of gamification for networked delivery of science, technology, engineering and mathematics (STEM) subjects. It proposes an innovative gamification framework, the NEWTON-enhanced gamification model (N-EGM), which was designed as part of the European Horizon 2020 project NEWTON. The paper also describes the proposed N-EGM model deployment in the gamification engine of a real learning management system and its associated communication and networking solution. The communication support provides easy-to-use gamification configuration functionality and efficient data collection and processing in a heterogeneous technology context. Finally, the paper evaluates the proposed N-EGM model as part of a NEWTON project pilot deployed in a Romanian school. The results demonstrate the effectiveness of the proposed gamification solution in improving both students’ learning experience and their engagement, while also increasing student knowledge gain.

Smart cities often rely on technological innovations to improve citizens’ safety and quality of life. This paper presents a novel smart mobility system that facilitates people’s access to public mobility while preserving their privacy. In contrast to several well-known smart mobility systems discussed in this paper, the one we propose combines privacy guarantees with user friendliness. Specifically, the system is based on a zero-interaction approach whereby a person can use public transport services without any need to perform explicit actions. Operations related to ticket purchases and validation were fully automated. The system is also designed with the privacy-by-design paradigm to preserve user privacy as much as possible. Throughout the paper, several technical details are discussed as well to describe a prototype version of the system that was implemented. The prototype was successfully tested in the city of Imola (Emilia Romagna, Italy) to prove the validity of the system in the field.

Targets of cyber crime are not exclusive to the private sector. Successful cyber attacks on nation-states have proved that cyber threats can jeopardize significant national interests. In response, nation-states have begun to handle cyber threats at the national defense level, which is titled ‘cyber defense.’ The cyber defense sector is related to national security, therefore requires robust security technology. Contrary to normal systems, blockchain provides strong security properties without a centralized control entity, and as such its application in the cyber defense field is under the spotlight. In this paper, we present opportunities blockchain provides for cyber defense, research and national projects, and limitations. We constructed a survey of government documents, interviews, related news, technical reports, and research papers from 2016 to 2021. As a result, our research contributes to reducing the gap in blockchain for cyber defense by systematically conducting research and analysis. In our research, we found that not only research but also government-led plans are actively promoting blockchain, which demonstrates that blockchain will play a remarkable role in cyber defense. This paper concludes with suggestions for future research in aspects of the blockchain technology, evaluation, and survey.

DevOps is a set of practices and a cultural movement that aims to break down barriers between development and operation teams to improve collaboration and communication. Different organizations have embraced DevOps principles due to the massive potential, such as a much shorter time to production, increased reliability and stability. However, despite the widespread adoption of DevOps and its infrastructure, there is a lack of understanding and literature on the key concepts, practices, tools, and challenges associated with implementing DevOps strategies. The main goal of this research paper is to explore and discuss challenges related to DevOps culture and practices. Moreover, it describes how DevOps works in an organization, provides a detailed explanation of DevOps, and investigates the cultural challenges that organizations face when implementing DevOps. The proposed paper reveals ten critical challenges that need to be addressed in adopting the DevOps culture. The challenges are further analyzed on the basis of the various continents. According to the findings, the following critical challenges are considered during the implementation of a DevOps culture: lack of collaboration and communication, Lack of skill and knowledge, complicated infrastructure, Lack of management, Lack of DevOps approach, and trust confidence problems.

Conventional theory for designing strictly nonblocking networks, such as crossbars or Clos networks, assumes that these networks have a centralized topology. Many new applications, however, require networks to have a distributed topology, like 2-D mesh or torus. In this paper, we present a new theoretical framework for designing such nonblocking networks. The framework is based on a linear programming formulation originally proposed for solving the hose-model traffic routing problem. The main difference, however, is that the link bandwidths in our problem must be discrete. This makes the problem much more challenging. We show how to apply the developed theorems to tackle the problem of designing bufferless NoCs (networks-on-chip). The proposed bufferless NoCs are deadlock/livelock-free and consume significantly less power than their buffered counterparts. We also present a multi-slice technique to reduce node capacity variations. This can make the proposed NoC architecture more cost efficient in a VLSI (very large-scale integration) implementation. In addition, we offer a detailed delay/throughput performance evaluation of the proposed bufferless NoC in the paper.

Perception is the fundamental task of any autonomous driving system, which gathers all the necessary information about the surrounding environment of the moving vehicle. The decision-making system takes the perception data as input and makes the optimum decision given that scenario, which maximizes the safety of the passengers. This paper surveyed recent literature on autonomous vehicle perception (AVP) by focusing on two primary tasks: Semantic Segmentation and Object Detection. Both tasks play an important role as a vital component of the vehicle’s navigation system. A comprehensive overview of deep learning for perception and its decision-making process based on images and LiDAR point clouds is discussed. We discussed the sensors, benchmark datasets, and simulation tools widely used in semantic segmentation and object detection tasks, especially for autonomous driving. This paper acts as a road map for current and future research in AVP, focusing on models, assessment, and challenges in the field.

This paper first reviews the related concepts of agricultural supply chain and the current research status of agricultural inventory models; proposes the classification standard of supply chain structure from four different aspects: the complexity of the supply chain node enterprises, the reliability of the supply chain and the optimization goal. In addition, this article starts from the characteristics of agriculture itself, according to the actual needs of the agricultural supply chain, considers the feasibility of the solution strategy from different sides and gives related models, which solves the related problems in the agricultural supply chain to a certain extent. At present, there are many researches on resource allocation algorithms at the edge of mobile networks, but the existing resource allocation algorithms still have the problem of high computational complexity and can still solve the above problems in resource utilization optimization. In order to solve these problems, this paper proposes a system architecture of the agricultural industry Internet of Things based on edge computing. The main motivations of this article are to improve the stability and usability of the system, and to enhance agricultural wisdom, so as to provide a reference for the development of precision agriculture. According to this method, Firstly, from the perspective of how to improve the energy efficiency of mobile terminals, methods to improve the energy efficiency of mobile terminals are studied in multi-user systems where mobile terminals compete for MEC servers with limited computing resources. Then, this paper focuses on the waste of computing resources caused by user movement under the scenario of limited server coverage in MEC system, and studies the problem from the perspective of effective management of computing resources. Experimental results show that the proposed algorithm has good performance.

This paper presents a new method for computationally effective implementation of a discrete-time fractional-order proportional–integral–derivative (FOPID) controller. The proposed method is based on a unique representation of the FOPID controller, where fractional properties are modeled by a specific finite impulse response (FIR) filter. The balanced truncation model order reduction method is applied in the proposed approach to obtain an effective, low-order model of the FOPID controller. The time-invariant FOPID controller implementation is presented first, and then the methodology is extended to the controller with time-varying gains. A comparative analysis shows that the proposed methodology leads to the effective modeling of discrete-time FOPID controllers. In addition to simulation runs, the effectiveness of the introduced methodology is confirmed in a real-life experiment involving the control of the DC motor servo system. The paper concludes with the implementation tools developed in the Matlab/Simulink environment.

In modern electronic warfare, both the radar and the jammer are capable of changing their waveforms to achieve better performance. The conflict relationship between the radar and jammer can be modeled as a non-cooperative game. In this paper, the optimal strategies of a monostatic radar and a jammer are investigated in terms of both Stackelberg game and symmetric game. Instead of using the widely used mutual information as the utility function, signal-to-jamming-plus-noise ratio (SJNR) is used in formulating the utility functions which is directly related to the target detection performance. In Stackelberg game, the Stackelberg equilibria solutions are derived when the leader is the radar and the jammer, respectively. In symmetric game, the existence of Nash equilibrium (NE) is investigated and the corresponding strategies are obtained when Nash equilibrium exists. If Nash equilibrium does not exist, the Stackelberg equilibria strategies are still useful as they can be regarded as the robust strategies which could optimize the worst case performance. The relationship between the mutual information utility function and the proposed SJNR utility function is investigated as well. Simulation results show the effectiveness of the radar and jammer strategies. The investigation in this paper can provide optimal waveform solutions for the monostatic radar and the jammer in the confrontation scenario.

Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.

Wearing safety helmets can effectively protect workers safety on construction sites. However, workers often take off the helmets because of weak security-conscious and discomfort, then hidden dangers will be brought by this behaviour. Workers without safety helmets will suffer more injuries in accidents such as falling human body and vertical falling matter. Hence, detecting safety helmet wearing is a vital step of construction sites safety management and a safety helmet detector with high speed and accuracy is urgently needed. However, traditional manual monitor is labour intensive and methods of installing sensors on safety helmet are difficult to popularize. Therefore, this paper proposes a deep learning-based method to detect safety helmet wearing at a satisfactory accuracy with high detection speed. Our method chooses YOLO v5 as the baseline, then the fourth detection scale is added to predict more bounding boxes for small objects and the attention mechanism is adopted in the backbone of the network to construct more informative features for following concatenation operations. In order to overcome the defects caused by insufficient data, targeted data augmentation and transfer learning are used. Improvements caused by every modification are discussed in this paper. Finally, our model achieves 92.2% mean average precision, up 6.3% compared to the original algorithm, and it only takes 3.0 ms to detect an image at $640 \times 640$ . These results demonstrate the robustness and feasibility of our model. Meanwhile, the size of our trained model is only 16.3 m, which means the model is easy to be deployed. At last, after obtaining a satisfactory model, a graphical user interface (GUI) is designed to make our algorithm more user-friendly.

5G wireless communication systems provide massive system capacity with high data rates, very short low-latency, and ultra-high reliability, in addition to high connection density with a positive experience on smart cities and the Internet of Things (IoT). Future networks are anticipated to revolutionize typical applications such as the enhanced mobile broadband services (EMBB), ultra-reliable low latency communication (uRLLC), and massive machine-type communications (mMTC) anywhere and everywhere. This rationalizes the need to investigate the sustainable elements of 5G networks in smart cities to understand how 5G networks can be more environmentally- friendly and energy-efficient. This paper aims to investigate how 5G networks can act as key enablers in achieving sustainability in smart cities, using a macroscopic review. An overview of 5G communication networks and several 5G technologies used in smart city applications to enhance sustainability is presented. This is followed by investigating the indicators that measure sustainability in 5G networks across the environmental, social, and economic dimensions; and sub-dimensions such as energy efficiency, power consumption, carbon footprint, pollution, cost, health, safety, and security. The results show that the majority of research papers focus on the environmental dimensions of sustainability (42%) when attempting to address sustainability in 5G systems and smart cities. The findings also showed a huge interest in the economic (37%) and social (21%) dimensions as well. Further, when examining the sub-dimensions, it was found that most of the studies focused on energy efficiency (20%), power consumption (17%), and cost (15%).

This work presents a comprehensive study, from an industrial perspective, of the process between the collection of raw data, and the generation of next-item recommendation, in the domain of Video-on-Demand (VoD). Most research papers focus their efforts on analyzing recommender systems on already-processed datasets, but they do not face the same challenges that occur naturally in industry, e.g., processing raw interactions logs to create datasets for testing. This paper describes the whole process between data collection and recommendation, including cleaning, processing, feature engineering, session inferring, and all the challenges that a dataset provided by an industrial player in the domain posed. Then, a comparison on the new dataset of several intent-based recommendation techniques in the next-item recommendation task follows, studying the impact of different factors like the session length, and the number of previous sessions available for a user. The results show that taking advantage of the sequential data available in the dataset benefits recommendation quality, since deep learning algorithms for session-aware recommendation are consistently the most accurate recommenders. Lastly, a summary of the different challenges in the VoD domain is proposed, discussing on the best algorithmic solutions found, and proposing future research directions to be conducted based on the results obtained.

This paper focuses on the unsupervised segmentation of images, which is an essential topic in the field of computer vision. In the absence of prior knowledge, it is challenging to generate semantic segmentation regions based on image content automatically. In this paper, we consider unsupervised image segmentation from the perspective of sub-region clustering and graph convolution. We over-segment the source image into disjoint sub-regions and generate multiscale representative maps for each sub-region. To explore the potential contextual correlation between different sub-regions, we build a graph model that establishes the dependency relationship and use a graph convolution network to transfer long-range contextual information. The segmentation results are obtained by combining sub-regions clustering and graph convolution training. We conduct extensive experiments on three image datasets, and the results show that the proposed method can provide consistent and meaningful segmentation results.

Relational databases are storage for a massive amount of data. Knowledge of structured query language is a prior requirement to access that data. That is not possible for all non-technical personals, leading to the need for a system that translates text to SQL query itself rather than the user. Text to SQL task is also crucial because of its economic and industrial value. Natural Language Interface to Database (NLIDB) is the system that supports the text-to-SQL task. Developing the NLIDB system is a long-standing problem. Previously they were built based on domain-specific ontologies via pipelining methods. Recently a rising variety of Deep learning ideas and techniques brought this area to the attention again. Now end to end Deep learning models is being proposed for the task. Some publicly available datasets are being used for experimentation of the contributions, making the comparison process convenient. In this paper, we review the current work, summarize the research trends, and highlight challenging issues of NLIDB with Deep learning models. We discussed the importance of datasets, prediction model approaches and open challenges. In addition, methods and techniques are also summarized, along with their influence on the overall structure and performance of NLIDB systems. This paper can help future researchers start having prior knowledge of findings and challenges in NLIDB with Deep learning approaches.

Radio frequency energy harvesting (RFEH) and wireless power transmission (WPT) are two emerging alternative energy technologies that have the potential to offer wireless energy delivery in the future. One of the key components of RFEH or WPT system is the receiving antenna. The receiving antenna’s performance has a considerable impact on the power delivery capability of an RFEH or WPT system. This paper provides a well-rounded review of recent advancements of receiving antennas for RFEH and WPT. Antennas discussed in this paper are categorized as low-profile antennas, multi-band antennas, circularly polarized antennas, and array antennas. A number of contemporary antennas from each category are presented, compared, and discussed with particular emphasis on design approach and performance. Current design and fabrication challenges, future development, open research issues of the antennas and visions for RFEH and WPT are also discussed in this review.

The performance of the solar photovoltaic system is affected by the unpredictable phenomenon of partial shading. This causes the mismatch losses that suppress the power generation of healthy PV modules in it. The objective of the proposed work in this paper is to bring out the maximum power from each PV module in the PV array by reducing the mismatch losses. A new array configuration method is proposed in this paper, which follows the screw pattern in the row formation. Each PV row is created with distinct PV modules from the rows of the conventional array configuration. This proposed work allows the PV system to operate with minimum mismatch losses by even shade dispersion over the PV array. The proper mathematical expression with all necessary constraints was derived for the array formation of the proposed work. The output analysis is been validated in the simulation of the $9\times 9$ PV array in MATLAB/Simulink Ⓡ. The mismatch loss generation and output power enhancement are measured and compared with the various conventional array configuration methods under six kinds of partial shading patterns. The proposed array configuration is 40% more efficient than the conventional series-parallel array configuration and also performs better than the total cross-tied and sudoku puzzle pattern methods. The shade dispersion rate of the proposed array configuration has highly reduced the mismatch losses in the PV system and hence, it improves the power output.

Novel personalized and affordable approaches are needed in order to provide efficient therapeutic interventions for people with autism spectrum disorder. In this paper, we introduce a new category of socially assistive robots, i.e., socially assistive drones (SADs) for therapeutic interactions with humans. SADs autonomously perform and engage humans in dance movement therapy (DMT), which has proven to be highly effective for people with autism spectrum disorder when it is personalized and adapted carefully. The main focus of this paper is on developing adaptive, personalized, and interactive control approaches based on fuzzy logic, which efficiently deals with nonlinear dynamics and directly introduces the expert (linguistic) knowledge into the control system. The developed approaches are implemented via a small quadcopter in real-life interactions with 10 participants. The results of the case study prove the excellent performance of the SAD in adapting and personalizing its decisions with respect to each user and thus keeping them engaged in the proposed DMT plans.

Load forecasting is a critical aspect for power systems planning, operation and control. In this paper, as part of research efforts of an ambitious project at Memorial University of Newfoundland in St. John’s, Canada, to achieve more energy efficient and environmental friendly “Sustainable Campus”, we present a day-ahead load forecasting approach for the energy management system of the project. The hourly load consumption dataset from January 1, 2016 to March 31, 2020 is used in the paper, which was collected from two power meters on campus. Using the load consumption dataset along with the collected meteorological dataset, a total of 19 regression model-based day-ahead load forecasting algorithms for Memorial University of Newfoundland’s campus load are developed and evaluated in this paper. These 19 models belong to five families of regression models in MATLAB Regression Toolbox: Linear Regression, Regression Trees, Support Vector Machines (SVM), Gaussian Process Regression (GPR), and Ensemble of Trees. It is found that the family of GPR models shows the best load forecasting performance because they are nonparametric kernel-based probabilistic models. Two GPR models, Rational Quadratic GPR and Exponential GPR, are recommended as the best models for load forecasting through this study.

In this paper, we propose a novel speech enhancement paradigm which can effectively solve the problem of retrieving a desired speech signal in a multi-talker environment. The proposed speech enhancement paradigm involves a three-step procedure consisting of separation, ranking, and enhancement. First, a speech separation system – which could be a conventional spatial filter bank or more advanced separation systems – separates mixtures of speech signals captured by microphones into speech signals from candidate speakers. Next, novel ranking algorithms – proposed in this paper – are applied to determine the talker-of-interest amongst the separated speech signals. Finally, the speech signal of the talker-of-interest is estimated as a linear combination of the separated signals, whose weights are determined by the ranking algorithms. We propose ranking algorithms, which exploit turn-taking patterns between conversational partners in order to determine the talker-of-interest amongst competing speakers. Unlike some existing solutions, our ranking algorithms do not require access to additional sensors, e.g., EEG electrodes, cameras, etc., but only rely on microphone signals. Specifically, the proposed algorithms rank the separated speech signals based on the probability of speech overlaps and gaps with the user’s own voice. The speech signal with highest ranking is the talker with minimum probability of speech overlap and gap with the user’s own voice. The proposed ranking algorithms are shown highly effective at determining the talker-of-interest, since conversational partners, i.e., the user and the talker-of-interest, behaviorally avoid speech overlaps and gaps. We evaluate the proposed speech enhancement paradigm in two practical hearing aid related applications, where the objective is to enhance a speech signal of a conversational partner in a multi-talker environment. The results of the evaluation demonstrate that the proposed speech enhancement systems in both applications significantly outperform conventional speech enhancement systems.

Waste incineration plants are complex dynamical systems that rely on expert human operators to maintain steady combustion, by observing real-time in-chamber video feeds. Real-time plant forecasting provides vital operational support in decision making, and applying machine learning to automatically learn dynamics forecast models from video feeds is an attractive means to realise this. However, learning complex dynamics in systems that requires cost-efficiency remains an open research problem. Specifically, modelling plant dynamics in real-time is challenging due to uncertainties caused by inhomogeneous waste inputs, requiring complex learning that impedes real-time modelling. To address this, this paper presents a real-time data-driven framework for generating video forecasts, by incorporating task-relevant domain-knowledge, during learning. Specifically, this method combines dynamics modelling and forecasting using dynamic mode decomposition, with Fourier transformations informed by expert operator heuristic knowledge for encoding task-relevant frequency information inside the learning process. Experiments in this paper demonstrate that the proposed framework captures intuitive physical aspects of the underlying physiochemical process, with a greatly reduced computational runtime in comparison to standard approaches, allowing for application in real-time domains. Forecasted video predictions are accurate over short time horizons, and capture important system characteristics over longer time periods.

Traditional shallow machine learning algorithms cannot effectively explore the relationship between the fault data of oil-immersed transformers, resulting in low fault diagnosis accuracy. This paper proposes a transformer fault diagnosis method based on Multi-class AdaBoost Algorithms in response to this problem. First, the AdaBoost algorithm is combined with Support Vector Machines (SVM), The SVM is enhanced through the AdaBoost algorithm, and the transformer fault data is deeply explored. Then the dynamic weight is introduced into the Particle Swarm Optimization (PSO); through the real-time update of the particle inertia weight, the search accuracy and optimization speed of the particle swarm optimization algorithm is improved, and the improved particle swarm optimization algorithm (IPSO) is used to optimize the parameters of the SVM. Finally, by analyzing the relationship between the dissolved gas in the transformer oil and the fault type, the uncoded ratio method forms a new gas group cooperation. The improved ratio method is constructed as the input feature vector. Simulations based on 117 sets of IECTC10 standard data and 419 sets of transformer fault data collected in China show that the diagnosis method proposed in this paper has strong search ability and fast convergence speed and has a significant improvement in diagnostic accuracy compared with traditional methods.

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.

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.

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.

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.

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.

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).

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 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.

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.

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.

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.

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.