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Wind Driven Optimization (WDO) Algorithm is a novel metaheuristic algorithm inspired by the continuous flow of air resulting from differences in air pressure until the air reaches a state of pressure balance. Owing to its simple structure, few parameters, intuitive nature, and straightforward programming, WDO has garnered increasing attention from scholars since its inception. WDO’s novelty lies in its utilization of aerodynamic principles to orchestrate the search process, WDO draws on the dynamics of wind and atmospheric pressure differences to propel the search for optimal solutions. However, WDO has limitations such as sensitivity to algorithm parameters and premature convergence. Consequently, various WDO variants have been proposed to overcome the limitations of the original WDO. To identify potential avenues for further research and to develop WDO for future investigation. This article systematically reviews WDO and its variants from multiple perspectives. Initially, the principle of WDO is outlined. Subsequently, the impact of modifications to the WDO on its overall effectiveness is investigated. Furthermore, the distinctive characteristics of WDO variants and their practical applications are analyzed. Moreover, the conclusions of the review are summarized, and future research directions for WDO variants and their applications are described.

The use of robots to carry out activities in various fields has evolved over the years. Robots can move from one location to another according to a plan and have a good understanding of the environment to perform their assigned tasks. Robot movements are based on motion planning and control algorithms. This paper proposes a new hybrid motion planning and control algorithm called FAStep-Kinematic (Stepping Ahead Firefly algorithm and Kinematic equations). This algorithm includes the Firefly Stepping Ahead algorithm to avoid obstacles, the equation of motion to control the movement of the robot, and a method to detect obstacles. The algorithm starts with the equation of motion and switches to the stepping ahead firefly algorithm when an obstacle is detected. The stepping ahead firefly algorithm then plans the robot’s next step to avoid obstacles. The robot moves to this step. This procedure is repeated until the robot successfully avoids an obstacle and reaches its target. The obstacles used in this article are static and are known to robots. The hybrid algorithm will be used to navigate robots in a cluttered environment. The new algorithm’s effectiveness will be seen with an application to tractor-trailer robotic systems. The results show that the robots are able to reach their destination safely and using a shorter route. Additionally, this research compared the new hybrid algorithm’s performance with that of the ACO-Kinematic (Ant Colony Optimization and Kinematic equations) algorithm in terms of path length and convergence time. The analysis show that the hybrid algorithm is superior to the ACO-Kinematic algorithm. The proposed algorithm improves the path length by 0.37%, 9.22%, and 5.79% compared to the ACO-Kinematic algorithm in three different scenarios.

Optimizing doping profiles has always been a key approach to enhance the performance of silicon modulators. Nevertheless, the pursuit of innovative profiles has encountered barriers in recent times. To tackle this issue, the idea of inverse design, widely adopted in passive photonic devices, can be employed in silicon active devices. As a result, we incorporate the inverse design method with the particle swarm optimization (PSO) algorithm and achieve a G-shaped doping profile for the modulator, exhibiting superior $V_{\pi } L$ of 0.68 V$\cdot$cm and low loss of 9.3 dB/cm. The small-signal frequency response suggests a reliable operation range under reverse biases of 1$\sim$3 V with the bandwidth over 26 GHz. The silicon modulator with a G-shaped design demonstrates remarkable efficiency in modulation and very low loss, suggesting its great potential for application in microwave front-end systems. The use of inverse design shows great potential in enhancing active silicon photonic devices, allowing for faster, higher-capacity, and more reliable data communication systems.

In multilateration, distances between an unknown location and several known points are used to determine the location of the unknown point. Papers on algorithms for multilateration, such as time of arrival (TOA), time difference of arrival (TDOA), and angle of arrival (AOA), have proliferated in the literature. These use the same equations, but are often written in a way that hides the underlying geometry and make it difficult to see the similarity in approaches between different papers. This paper provides a review of TOA and AOA-based localization approaches, with a focus on linear positioning algorithms. This paper presents simple and consistent versions of TOA, TDOA, and AOA position equations with intuitive explanations for how those equations relate to the geometry of the localization problem. This perspective can make it easier to implement these algorithms in a way that produces stable results and provides insight into the underlying geometric problem. We also review the literature relating to TDOA and AOA localization algorithms and multistatic radar.

This paper presents a novel pilot protection algorithm for alternating current (ac) transmission lines that relies on biweight midcorrelation (BWMC) coefficients between the terminals’ currents. The proposed solution was compared with recent and similar literature alternatives, and the results indicate that it is both effective in detecting faults of any type and that, as it is median-based, unlike the techniques used so far, the BWMC-based algorithm may be advantageous over existing methods as it presents higher robustness to not incorrectly trip on external faults, mainly under the critical conditions of CT saturation, noisy measurements, and sample misalignment, typically associated with outliers. The algorithm also proved effective against simultaneous multi-faults, weak infeed, power swing, multiple cases on different lines, and faults with time-varying transition resistance. The BWMC-based solution was finally experimentally tested in real-time with a hardware-in-the-loop system containing a CMC-256 set, an IEC 61850-based IED that embeds the algorithm’s logic, and current measurements recorded during actual faults on existing 500 kV lines from the Brazilian interconnected power system. All analyses indicate that this new algorithm can be a rapid, reliable, and robust solution.

With an increase in the complexity of society, solving multi-objective optimization problems (MOPs) has become crucial. In this study, we introduced a novel method called “quadratic unconstrained binary optimization based on the weighted normal” for solving MOPs using Ising machines, such as quantum annealing and digital annealer (DA), in the field of combinatorial optimization. The proposed method applies the penalty-based boundary intersection method to Ising machines under a setting limited to linear objective functions and maximizes the speed and performance of the DA, which is a quadratic unconstrained binary optimization-specific solver. We demonstrated the effectiveness of the proposed method by solving a real-world problem with a nonconvex shaped Pareto front (component combination problem). The results suggested that the proposed method could handle both convex- and nonconvex-shaped Pareto fronts, expanding the potential applications of Ising machines to solving complex MOPs. This development could significantly enhance decision-making processes, particularly in achieving sustainable development goals.

This paper presents an alternative methodology for the study of flyrock hazards in mining, utilizing Artificial Intelligence (AI) through machine learning by classification. By using distance as a delineator to denote the consequences of a blast, the models generated two classes of blasts: safe and unsafe. In this study, statistical learning models best suited for classification, that is, K Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Artificial Neural Networks (ANNs), were used, and their classification abilities were assessed. Machine performance was evaluated using a Confusion Matrix (sensitivity and specificity) and Receiver Operating Characteristic (ROC) curve. A higher weight was assigned to the minority class (unsafe blasts). Overfitting assessment was also performed. The Wide Neural Network (WNN) demonstrated the highest classification superiority. During training and validation, 75% sensitivity, 100% specificity, and an ROC of 0.9853 were achieved. In the test phase, perfect stratification (100 %) was maintained, with an ROC of 1. The Cubic SVM exhibited 50% sensitivity, 100% specificity, and an ROC of 0.9412 during training and validation. In the test set, it achieved 100% sensitivity, 100% specificity, and a ROC of 1. Fine KNN showed 50% sensitivity, 94.1% specificity, and an ROC of 0.7206 in the validation set. The test set displayed 100% sensitivity, 100% specificity, and an ROC of 1. Conversely, Coarse DT had a higher misclassification rate, resulting in a 25% sensitivity, 76.5% specificity, and an ROC of 0.5221 during the validation phase. In the test set, it showed 50% sensitivity, 100% specificity, and an ROC of 0.75. A feedforward neural network (FNN) was designed, trained, and demonstrated to be a highly flexible classification tool. The FNN achieved an excellent classification score of 100%. These findings demonstrate the potential for the broad applicability of machine learning through classification in addressing flyrock challenges in open-pit mines.

Based on delay-Doppler maps (DDMs) in raw counts generated by spaceborne global navigation satellite system reflectometry (GNSS-R) receivers, retrieving ocean surface wind speeds is feasible, so several spaceborne GNSS-R missions have been carried out. However, it is currently troubled by global data latency of several hours or even more due to the bottleneck in the satellite downlink. Consequently, this article, for the first time, presents an algorithm for spaceborne GNSS-R receivers to conduct the DDM calibration in orbit and then to retrieve ocean surface wind speeds in real time, which contributes to not only lightening the burden on downloading a wealth of scientific data but also broadcasting real-time ocean surface wind speeds to users. Since there is a power correlation between direct and reflected signals from the same GNSS satellite with respect to the GNSS-R receiver, this algorithm calibrates direct signal power first, and then it estimates the real-time GNSS transmitter effective isotropic radiated power at the reflected signal according to the normalized antenna pattern of the corresponding GNSS satellite. Afterward, DDMs in raw counts are calibrated. Finally, ocean surface wind speeds are computed using pretrained geophysical model functions. Exploiting the scientific data from the GNOS-II onboard China's FY-3E satellite, this algorithm is validated carefully, and final retrieved ocean surface wind speeds against collocated European Centre for Medium-Range Weather Forecasts wind speeds have an overall root-mean-square error of 1.68 m/s and 1.50 m/s for GPS-R and BDS-R, respectively.

Based on delay-Doppler maps (DDMs) in raw counts generated by spaceborne global navigation satellite system reflectometry (GNSS-R) receivers, retrieving ocean surface wind speeds is feasible, so several spaceborne GNSS-R missions have been carried out. However, it is currently troubled by global data latency of several hours or even more due to the bottleneck in the satellite downlink. Consequently, this article, for the first time, presents an algorithm for spaceborne GNSS-R receivers to conduct the DDM calibration in orbit and then to retrieve ocean surface wind speeds in real time, which contributes to not only lightening the burden on downloading a wealth of scientific data but also broadcasting real-time ocean surface wind speeds to users. Since there is a power correlation between direct and reflected signals from the same GNSS satellite with respect to the GNSS-R receiver, this algorithm calibrates direct signal power first, and then it estimates the real-time GNSS transmitter effective isotropic radiated power at the reflected signal according to the normalized antenna pattern of the corresponding GNSS satellite. Afterward, DDMs in raw counts are calibrated. Finally, ocean surface wind speeds are computed using pretrained geophysical model functions. Exploiting the scientific data from the GNOS-II onboard China's FY-3E satellite, this algorithm is validated carefully, and final retrieved ocean surface wind speeds against collocated European Centre for Medium-Range Weather Forecasts wind speeds have an overall root-mean-square error of 1.68 m/s and 1.50 m/s for GPS-R and BDS-R, respectively. [ABSTRACT FROM AUTHOR]

A Multi-Channel Stethograph System (STG system) was designed and developed as an electronic auscultation system for recording heart, lung, and trachea sounds non-invasively through an acoustic sensor array. The STG system consists of 16 acoustic sensors, a signal conditioning board, and a data logger (data acquisition, wireless transmission, sound visualization). The STG system captures breath with any adventitious sound event in 16 locations simultaneously to maximize the information of the specific sound event (for example, detection of the origin of mother adventitious sound and extracting its features), when compared with a single-channel stethoscope. This system can be an efficient tool to aid doctors or physicians in analyzing the adventitious sound from respiration diseases. However, it still requires the need for an experienced doctor or physician in the diagnosis and validation of adventitious sound. This paper presents a computerized method with an intelligent algorithm for detecting various adventitious sounds that are the key characteristics of cardiopulmonary diseases (CD) and assists the doctor/physician in the continuous diagnosis of lungs, which potentially can be beneficial during the COVID-19 progression. The proposed algorithm was able to detect breath patterns using trachea sound; location of the mother adventitious event using lung sounds (14 channels); determine the type of adventitious sound by correlating lung sound with trachea sound. The algorithm consists of breath pattern detection, candidate audio selection, breath pattern extraction, and adventitious sound detection. Digital signal processing techniques such as filtering, windowing, enveloping, discrete Fourier transform, and thresholds were used for identifying and classifying the inhalation and exhalation patterns in the lung sound in an independent (automatic) and intelligent way. The auscultation diagnosis algorithm can identify and distinguish discontinuous adventitious sounds which include wheeze, rhonchi, wheeze & rhonchi, and squawk, with an accuracy of 96.9%, 95.3%, 90%, and 100%. The algorithm was able to fully utilize the advantage of the multichannel system to simultaneously detect breath patterns, types of adventitious sound, and the location of the mother adventitious event that other algorithms cannot achieve. It has the potential to aid doctors/physicians in the early detection and monitoring of any lung disorders by providing objective evidence on adventitious sounds.

Finding connectivity in graphs has numerous applications, such as social network analysis, data mining, intra-city or inter-cities connectivity, neural network, and many more. The deluge of graph applications makes graph connectivity problems extremely important and worthwhile to explore. Currently, there are many single-node algorithms for graph mining and analysis; however, those algorithms primarily apply to small graphs and are implemented on a single machine node. Finding 2-edge connected components (2-ECCs) in massive graphs (billions of edges and vertices) is impractical and time-consuming, even with the best-known single-node algorithms. Processing a big graph in a parallel and distributed fashion saves considerable time to finish processing. Moreover, it enables stream data processing by allowing quick results for vast and continuous nature data sets. This research proposes a distributed and parallel algorithm for finding 2-ECCs in big undirected graphs (subsequently called “ $BiECCA$ ”) and presents its time complexity analysis. The proposed algorithm is implemented on a MapReduce framework and uses an existing algorithm to find connected components (CCs) in a graph as a sub-step. Finally, we suggest a few novel ideas and approaches as extensions to our work.

The increased availability and affordability of 3D scanning technology due to recent advancements have made pre and post-operative analysis for facial surgeries more convenient and accurate through the use of 3D patient scans. However, to perform a more comprehensive analysis using area and volume measurements, it is necessary to develop new software tools that include the appropriate algorithms. Without the right algorithms, any software tool cannot perform its intended tasks effectively. In this study, we introduce two algorithms along with their open-source code to calculate the surface area and volume of a specific subsection of a 3D model. The first algorithm partitions the area/volume into smaller segmentations and applies raycasting to find the points on the face surface, and then finds an approximate measurement depending on the resolution selected. In contrast, the second algorithm utilizes the vertex information stored in the 3D model file to calculate the area and volume measurements for the designated region. The advantage of the second algorithm is that it examines every point in the model, resulting in the highest degree of precision possible. We have also used ChatGPT to generate the code for area/volume calculation and found that the logic of the ChatGPT-generated code is surprisingly similar to the second algorithm. We have presented the results of the measurements on certain regions of the face for both of the algorithms and compared the temporal performance of the algorithms.

Classical data cleaning performs a minimal set of operations on the data to satisfy the given integrity constraints. Often, this minimization is equivalent to vertex cover, for example when tuples can be removed due to the violation of functional dependencies. Classically, the uncertainty of tuples and constraints is ignored. We propose not to view data as dirty but the uncertainty information about data. Since probabilities are often unavailable and their treatment is limited due to correlations in the data, we investigate a qualitative approach to uncertainty. Tuples are assigned degrees of possibility with which they occur, and constraints are assigned degrees of certainty that say to which tuples they apply. Our approach is non-invasive to the data as we lower the possibility degree of tuples as little as possible. The new resulting qualitative version of vertex cover remains NP-hard. We establish an algorithm that is fixed-parameter tractable in the size of the qualitative vertex cover. Experiments with synthetic and real-world data show that our algorithm outperforms the classical algorithm proportionally to the available number of uncertainty degrees. By mining the certainty degrees with which constraints hold, our framework becomes applicable even when uncertainty information is unavailable. [ABSTRACT FROM AUTHOR]

Many datasets including social media data and bibliographic data can be modeled as graphs. Clustering such graphs is able to provide useful insights into the structure of the data. To improve the quality of clustering, node attributes can be taken into account, resulting in attributed graphs. Existing attributed graph clustering methods generally consider attribute similarity and structural similarity separately. In this paper, we represent attributed graphs as star-schema heterogeneous graphs, where attributes are modeled as different types of graph nodes. This enables the use of personalized pagerank (PPR) as a unified distance measure that captures both structural and attribute similarities. We employ DBSCAN for clustering, and we update edge weights iteratively to balance the importance of different attributes. The rapidly growing volume of data nowadays challenges traditional clustering algorithms, and thus, a distributed method is required. Hence, we adopt a popular distributed graph computing system Blogel, based on which, we develop four exact and approximate approaches that enable efficient PPR score computation when edge weights are updated. To improve the effectiveness of the clustering, we propose a simple yet effective edge weight update strategy based on entropy. In addition, we present a game theory based method that enables trading efficiency for result quality. Extensive experiments on real-life datasets offer insights into the effectiveness and efficiency of our proposals. [ABSTRACT FROM AUTHOR]

Production of clean, green solar PV (SPV) power in developing countries now becomes a trend because of their economic and technical benefits. Therefore, generating maximum power out of the SPV is a key searchable area. The SPV must produce power at its terminal at their maximum possible power. To reach to the maximum possible power, maximum power point tracker(MPPT) is used in conjunction with SPV. Extracting maximum power from SPV under varying partial shading condition is one of the important factor in performance improvement of SPV. The characteristics of classical MPPT controller is not acceptable under variable shading condition. A clear distinction between global maxima power point from global minima using MPPT technique must be needed for extracting maximum power. This paper proposes a P&O MPPT based particle swarm optimization with improved search space, optimised through Fuzzy Fokker Planck solution. The pre-defined search space has been introduced to provide fine tune to membership function used in Fuzzy logic controller. The partial shading performance has been examined under four different condition such as active partial shading, colour spectrum, dust level and green house gas (GHG) concentration. Both hardware and simulation studies has been carried out for the proposed techniques. The MATLAB simulation result and that of proposed MPPT, offer more and better performance in terms of algorithm convergence by enhancing the efficiency of system under varying shading condition.

A good exploration ability can ensure that the method jumps out of local optimum in multimodal problems and a good exploitation can ensure an algorithm converge faster to global optimum values. So, this paper proposes a new hybrid sperm swarm optimization and genetic algorithm to obtain global optimal solutions termed HSSOGA which is developed based on the concept of balancing the exploration and exploitation capability by merging Sperm Swarm Optimization (SSO), which has a fast convergence rate, and a Genetic Algorithm (GA) that can explore a search domain efficiently. To ensure that the proposed method delivers good performance, it is evaluated with 11 standard test function problems consisting of 5 unimodal and 6 multimodal functions. The proposed HSSOGA set is compared with conventional GA and SSO methods, as well as with several hybrid methods such as Hybrid Firefly and Particle Swarm Optimization (HFPSO), hybrid Simulated Annealing and Genetic Algorithm (SAGA), Hybrid Particle Swarm Optimization and Genetic Algorithm (HFPSO), hybrid Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO), and closely related Hybrid Sperm Swarm Optimization and Gravitational Search Algorithm (HSSOGSA). The results are evaluated in terms of each method’s best fitness, mean, standard deviation, and convergence rates. The numerical experiment results show that HSSOGA has better convergence towards the true global optimum values as compared to the conventional and existing hybrid methods in most unimodal and multimodal test function problems.

Sorting algorithms usually generate strictly descending sequences. However, almost descending sequences may be sufficient to fulfill our needs in practice, and the number of movements needed for generating such sequences can be greatly reduced. We call a sequence $S$ almost descending if $S[j]-S[i]< C$ for any $i< j$ and a positive number $C$ , which depends on the practical circumstances. Unless the difference between $S[j]$ and $S[i]$ exceeds the bound $C$ , we deem them indistinguishable. For example, when people are arranged in a line from tall to short, two people with heights of 1.751 meters and 1.752 meters can be considered visually indistinguishable (we can set the bound to $C=0.005$ meters in this case). Therefore, we do not need to adjust their positions, which leads to fewer movements in sorting. In this paper, three algorithms are provided to generate almost descending sequences, and their correctness is proven. The experimental results of the proposed algorithms demonstrate that our technique can reduce the number of movements in sorting. In addition, almost descending sequences are more stable than strictly descending sequences in dynamic sorting, which is useful in online sorting and sorting in noisy environments.

New Sixth-generation (6G) networks rely heavily on the Intelligence Internet of Things (IIoT) to store and process data more efficiently. 6G is desired to offer ultra-low latency, high bandwidth, and improvised quality of service that can effectively handle the communication among the nodes. All the healthcare facilities must be outfitted with cutting-edge technology to assist the individual with intelligent diagnosis, patient-centric treatment, and a range of other healthcare services both in the hospital and remotely. To make the system ready and adaptable to the technology and provide services to divergent applications ranging from robotic surgeries to remote monitoring of the patients through wearable technologies in an Ambient Assistive Living (AAL) environment over the intelligent networking platform. Various networking nodes and terminal devices provide the services for applications in the healthcare domain, which needs a backbone framework to deliberate the time-intensive services. This paper proposes a reference layered communication framework for the nodes and devices in real-time communication. The feature perspective aspects of 6G technology present the futuristic healthcare application for effective treatment and smart integration of services.

The study of metagenomic sequences brings a deep understanding of microbial communities. One of the crucial steps in metagenomic projects is to classify sequences into different organisms, named the binning problem. In the emerging methods for classification, deep learning is a potential technology to be applicable with high accuracy. However, it is well-known that reference databases, which are highly required by deep learning based methods, are not always available. As a result, some existing binning solutions have applied unsupervised learning processes, but utilizing the strength of deep learning in an unsupervised model is still a challenging problem. This work proposes a binning algorithm for metagenomic sequences, called MetaDEC, which applies a deep unsupervised learning approach. By following the two-phase paradigm, the algorithm firstly divides sequences into groups of overlapping sequences. The groups are then classified into clusters using an adversarial deep embedded clustering technique. Experimental results show that MetaDEC achieves competitive performance compared to existing methods on both simulated and real metagenomic data.

It has become routing work to detect and correct for population structure in genome-wide association analysis. A variety of methods have been proposed. Particularly, the methods based on spectral graph theory have shown superior performance. We discovered that the inherent nonlinear distribution of high-dimensional genotypic data was a possible source of confounding factors in population structure analysis, and was also the possible underlying reason that accounted for the superiority of these spectral-based methods. We verified this hypothesis by validating a variation of the Laplacian Eigen analysis: LAPMAP. The method could faithfully reveal the underlying population structures of HapMap II and III data sets. The inferred top eigenvectors together with minor eigenvectors were used to segregate samples by their ancestries. We found that the top 3 eigenvectors differentiated the 4 populations in phase II data set; the top 3 eigenvectors clustered the populations into 4 clusters, reflecting their continental origins. In phase III populations, 9 populations were well recognized. Next, we estimated admixture proportions for simulated individuals. The method showed comparable or better performance in capturing and correcting for modelled population structures. All experimental results showed that LAPMAP was robust, efficient and scalable to genome-wide association studies. [ABSTRACT FROM AUTHOR]

Data similarity (or distance) computation is a fundamental research topic which underpins many high-level applications based on similarity measures in machine learning and data mining. However, in large-scale real-world scenarios, the exact similarity computation has become daunting due to “3V” nature (volume, velocity and variety) of big data. In this case, the hashing techniques have been verified to efficiently conduct similarity estimation in terms of both theory and practice. Currently, MinHash is a popular technique for efficiently estimating the Jaccard similarity of binary sets and furthermore, weighted MinHash is generalized to estimate the generalized Jaccard similarity of weighted sets. This review focuses on categorizing and discussing the existing works of weighted MinHash algorithms. In this review, we mainly categorize the weighted MinHash algorithms into quantization-based approaches, “active index”-based ones and others, and show the evolution and inherent connection of the weighted MinHash algorithms, from the integer weighted MinHash ones to the real-valued weighted MinHash ones. Also, we have developed a Python toolbox for the algorithms, and released it in our github. We experimentally conduct a comprehensive study of the standard MinHash algorithm and the weighted MinHash ones in the similarity estimation error and the information retrieval task. [ABSTRACT FROM AUTHOR]

The capacity for renewable energy generation, particularly solar and wind, tends to grow over the years. However, locating regions favorable to the development of solar and wind generation is not trivial and is subject to errors due to the social, environmental, economic, political and administrative aspects of the regions. Therefore, this article proposes an algorithm based on digital image processing, capable of performing a variable weight multi-parameter evaluation and quantifying how favorable a given territorial region is for the implantation of wind or solar plants. Maps of the region under analysis are taken as input information. The region taken as a case study is the Brazilian state of Pernambuco, due to the access to data related to the evaluated characteristics: solar radiation, solar insolation, precipitation, air humidity, temperature, electrogeography, demographic density, environmental protection areas, slope, hydrography, wind speed, urban spot and the presence of aerodromes. It was possible to list for each type generation (wind or solar, exclusively), the 15 most favorable regions for the implementation plants. Furthermore, the proposed technique presents a practical indifference to the territorial extension of the evaluated region, which can comprehend a micro-region or an entire continent, requiring only that sufficiently detailed maps of the evaluated characteristics be provided. [ABSTRACT FROM AUTHOR]

Sample entropy is a widely used method for assessing the irregularity of physiological signals, but it has a high computational complexity, which prevents its application for time-sensitive scenes. To improve the computational performance of sample entropy analysis for the continuous monitoring of clinical data, a fast algorithm based on OpenCL was proposed in this paper. OpenCL is an open standard supported by a majority of graphics processing unit (GPU) and operating systems. Based on this protocol, a fast-parallel algorithm, OpenCLSampEn, was proposed for sample entropy calculation. A series of 24-hour heartbeat data were used to verify the robustness of the algorithm. Experimental results showed that OpenCLSampEn exhibits great accelerating performance. With common parameters, this algorithm can reduce the execution time to 1/75 of the base algorithm when the signal length is larger than 60,000. OpenCLSampEn also exhibits robustness for different embedding dimensions, tolerance thresholds, scales and operating systems. In addition, an R package of the algorithm is provided in GitHub. We proposed a sample entropy fast algorithm based on OpenCL that exhibits significant improvement for the computation performance of sample entropy. The algorithm has broad utility in sample entropy when facing the challenge of future rapid growth in the quantity of continuous clinical and physiological signals.

In the strapdown inertial navigation system, the accuracy of the solving attitude and position is closely related to the quaternion update algorithm. For this reason, what method is used to update the quaternion has become an important project. At present, the primary method is to update the quaternion by solving the equivalent rotation vector. A new quaternion update method is proposed in this paper. By using the third-order and fifth-order Taylor series to approximate the solution of the quaternion differential equation, two attitude quaternion update equations are derived. For single sample and two samples two cases, the derived formula is completely consistent with the common formula derived based on the equivalent rotation vector method. For the three samples in the coning motion environment, the fifth-order formula is derived in this paper, the error analysis method is applied to assess the accuracy of the formula, the two coefficients of the formula correction is optimized, and the drift rate error and drift rate are calculated, which are compared with the drift rate errors of the optimized three-sample equivalent rotation vector algorithm. The expressions for calculating the drift rate errors of the two are the same, and the calculation results are the same. In order to simplify the calculation, the established fifth-order formula is simplified to fourth-order one in this paper, but the drift rate error and drift rate remain unchanged. Finally, simulations and experiments demonstrate the correctness of the attitude quaternion update method proposed in this paper. The attitude quaternion update method proposed in this paper has certain theoretical research and application value for the attitude and position calculation of strapdown inertial navigation application, as well as the further studies of integrated navigation algorithm, and enriches the theoretical system of inertial navigation.

Large amounts of data have been produced at a rapid rate since the invention of computers. This condition is the key motivation for up-to-date and forthcoming research frontiers. Replication is one of the mechanisms for managing data, since it improves data accessibility and reliability in the distributed database environment. In recent years, the amount of various data grows rapidly with widely available low-cost technology. Although we have been packed with data, we still have lacked of knowledge. Nevertheless, if the impractical data is used in database replication, this will cause waste of data storage and the time taken for a replication process will be delayed. This paper proposes Binary Vote Assignment on Grid Quorum with Association Rule (BVAGQ-AR) algorithm in order to handle fragmented database synchronous replication. BVAGQ-AR algorithm is capable for partitioning the database into disjoint fragments. Fragmentation in distributed database is very useful in terms of usage, reliability and efficiency. Managing fragmented database replication becomes a concern for the administrator because the distributed database is disseminated into split replica partitions. The result from the experiment shows that handling fragmented database synchronous replication through proposed BVAGQ-AR algorithm able to preserve data consistency in distributed environment.

Containers are a form of software virtualization, rapidly becoming the de facto way of providing edge computing services. Research on container-based edge computing is plentiful, and this has been buoyed by the increasing demand for single digit, milliseconds latency computations. A container scheduler is part of the architecture that is used to manage and orchestrate multiple container-based applications on heterogenous computing nodes. The scheduler decides how incoming computing requests are allocated to containers, which edge nodes the containers are placed on, and where already deployed containers are migrated to. This paper aims to clarify the concept of container placement and migration in edge servers and the scheduling models that have been developed for this purpose. The study illuminates the frameworks and algorithms upon which the scheduling models are built. To convert the problem to one that can be solved using an algorithm, the container placement problem in mostly abstracted using multi-objective optimization models or graph network models. The scheduling algorithms are predominantly heuristic-based algorithms, which are able to arrive at sub-optimal solutions very quickly. There is paucity of container scheduling models that consider distributed edge computing tasks. Research in decentralized scheduling systems is gaining momentum and the future outlook is in scheduling containers for mobile edge nodes.

Essential tremor is the most common pathological tremor, with a prevalence of 6.3% in people over 65 years of age. This disorder interferes with a patient’s ability to carry out activities of daily living independently, and treatment with medical and surgical interventions is often insufficient or contraindicated. Mechanical orthoses have not been widely adopted by patients due to discomfort and lack of discretion. Over the past 30 years, peripheral electrical stimulation has been investigated as a possible treatment for patients who have not found other treatment options to be satisfactory, with wearable devices revolutionizing this emerging approach in recent years. In this paper, an overview of essential tremor and its current medical and surgical treatment options are presented. Following this, tremor detection, measurement and characterization methods are explored with a focus on the measurement options that can be incorporated into wearable devices. Then, novel interventions for essential tremor are described, with a detailed review of open and closed-loop peripheral electrical stimulation methods. Finally, discussion of the need for wearable closed-loop peripheral electrical stimulation devices for essential tremor, approaches in their implementation, and gaps in the literature for further research are presented.

In this paper, a novel metaheuristic algorithm called Social Network Search (SNS) is developed for solving optimization problems. The SNS algorithm simulates the attempts of users in social networks to gain more popularity by modeling the moods of users in expressing their opinions. These moods are named Imitation, Conversation, Disputation, and Innovation, which are real-world behaviors of users in social networks. These moods are used as optimization operators and model how users are affected and motivated to share their new views. To evaluate the performance of the SNS algorithm, two comparative studies with different properties were conducted. In the first step, 210 mathematical functions have been chosen, which include 120 fixed-dimension, 60 N-dimension, and 30 CEC 2014 problems. Seven metaheuristics are selected from the literature, and the statistical results of these methods are calculated and analyzed. Also, to provide a valid judgment about the performance of the new algorithm, four nonparametric statistical tests have been used. In the next step, the performance of the proposed algorithm is compared to some state-of-the-art algorithms in dealing with CEC 2017 problems. According to the performance of algorithms, the SNS method is capable of achieving better results compared to the other metaheuristics in 101 cases (48%) and performed the same or comparatively in dealing with the other problems.

The remote location of agricultural fields leads to the difficulty of deploying Precision Agriculture (PA) systems as there is no Internet access in those areas. Therefore, the use of long-range wireless technologies such as LoRa can provide connectivity to rural areas and allow monitoring PA systems remotely. In this paper, a heterogeneous architecture and protocol that allows communication with both WiFi and LoRa, including multiple hops in LoRa are presented. The design is based on a tree topology comprised of electronic devices deployed on different areas of interest for PA systems such as the canals of irrigation water, the fields, and the urban areas that generate wastewater. A set of practical tests with different configurations have been performed to determine the correct operation of the proposed protocol. The results show that the consumed bandwidth for both 433 MHz and 868 MHz frequency bands remained within the limits for the most restrictive LoRa configurations. Therefore, different deployment needs can be addressed with the implementation of this proposal. Furthermore, the use of packet transmission delays of 500 ms at the CH node results in high successful packet delivery rates.

The article describes an innovative algorithm of a radar north correction estimation. The uniqueness of the algorithm is the pure data/software nature without need for any additional north-seeking equipment. This procedure is a key step of a registration local coordinate system to a global one. The presented method is an application of cognitive radar approach, matching of radar ground clutter echoes with a-priori information, vector map data, and terrain elevation profile. The algorithm is based on matching observed ground clutter areas within received radar signal, with (corresponding) areas visible upon terrain profile data. The designed north correction algorithm was tested on real radar signal recorded on the 2D surveillance short-range radar ReVISOR made by RETIA, a.s. The current implementation utilizes SRTM height profile data.

Two sets $A$ and $B$ , whose elements fulfill a total order on operator $\le $ , can have a binary relation $R \subseteq A \times B$ represented by the k2-tree compact data structure, which greatly improves storage space. Currently, $Count$ query is managed by either using $Range$ query or to modify the structure to have aggregate information, implying additional time or space in order to perform the query. This article presents Compact Count, which exploits the k2-tree properties to reduce the paths to be scanned to count the numbers in a range $r$ , thus ensuring an expected runtime of $O(\log _{k} r \log _{k} n)$ and storage of $O(\log _{k} r)$ with the k2-tree parameters $n$ and $k$ . Our algorithm was compared through a series of experiments that consider both synthetic data with different distributions and real data, with a solution based on the Range algorithm. Experimental results show that Compact Count is 250 to 1,000 times faster than Range on synthetic and real data, respectively, with a small additional storage cost, as expected by the theoretical analysis.

The aim of this paper is to compare selected iterative algorithms for inconsistency reduction in pairwise comparisons by Monte Carlo simulations. We perform simulations for pairwise comparison matrices of the order $n = 4 $ and $n = 8 $ with the initial inconsistency $0.10 < CR < 0.80 $ and entries drawn from Saaty’s fundamental scale. Subsequently, we evaluate the algorithms’ performance with respect to four measures that express the degree of original preference preservation. Our results indicate that no algorithm outperforms all other algorithms with respect to every measure of preference preservation. The Xu and Wei’s algorithm is the best with regard to the preservation of an original priority vector and the ranking of objects, the Step-by-Step algorithm best preserves the original preferences expressed in the form of a pairwise comparison matrix, and the algorithm of Szybowski keeps the most matrix entries unchanged during inconsistency reduction.

This paper investigates the partial anti-synchronization in a class of chaotic and hyper-chaotic systems. Firstly, the existence of the partial anti-synchronization for the chaotic system is proved. By a systematic method including two algorithms, all solutions of the partial anti-synchronization for a given chaotic system are then derived, and physical controllers are designed. Finally, some illustrative examples with numerical simulations are used to verify the validity and effectiveness of the theoretical results.

Feature selection plays an important role in pattern recognition and smart computing. The full set of typical testors constitutes a useful tool for solving feature selection problems, especially those problems in which the objects are described by both quantitative and qualitative features. However, finding the typical testors involves a high computational cost. That is why even the most efficient methods become unsuitable to solve some problems. In this work, a new algorithm was introduced in order to reduce the long runtimes involved in the search of typical testors. The performance of the proposed algorithm was evaluated by means of several tests, which use both real-world and simulation data. MATLAB and Java language on Eclipse SDK platform were used to build the simulation dataset and to perform the tests, respectively. The runtimes achieved by the proposed algorithm were significantly shorter than those obtained by fast-BR and GCreduct (the two fastest algorithms) mainly when the latter ones exhibited excessively long runtimes.

Motifs are the evolutionarily conserved patterns which are reported to serve the crucial structural and functional role. Identification of motif patterns in a set of protein sequences has been a prime concern for researchers in computational biology. The discovery of such a protein motif using existing algorithms is purely based on the parameters derived from sequence composition and length. However, the discovery of variable length motif remains a challenging task, as it is not possible to determine the length of a motif in advance. In current work, a k-mer based motif discovery approach called Pr[m], is proposed for the detection of the statistically significant un-gapped motif patterns, with or without wildcard characters. In order to analyze the performance of the proposed approach, a comparative study was performed with MEME and GLAM2, which are two widely used non-discriminative methods for motif discovery. A set of 7,500 test dataset were used to compare the performance of the proposed tool and the ones mentioned above. Pr[m] outperformed the existing methods in terms of predictive quality and performance. The proposed approach is hosted at https://bioserver.iiita.ac.in/Pr[m]. [ABSTRACT FROM AUTHOR]

With the best basis function that compactly describes a discrete-time signal, the Discrete Hirschman Transform (DHT) has been proved to perform better than the Discrete Fourier Transform (DFT) in terms of high resolution and computational complexity. It is reasonable to develop fast algorithms for the DHT computation since the DHT has applied to multiple signal processing applications. In this letter, we propose a split-radix DHT (SRDHT) including mathematical decomposition and comparison of computation complexity. The SRDHT is computationally superior to the DFT and performs more efficiently than our previously developed radix-2/-4 DHTs, with further reduced arithmetic operations. We regard this proposed SRDHT as a more attractive candidate to compute the DHT for those existing and future Hirschman-based applications. [ABSTRACT FROM AUTHOR]

A nonnegative latent factorization of tensors (NLFT) model precisely represents the temporal patterns hidden in multichannel data emerging from various applications. It often adopts a single latent factor-dependent, nonnegative and multiplicative update on tensor (SLF-NMUT) algorithm. However, learning depth in this algorithm is not adjustable, resulting in frequent training fluctuation or poor model convergence caused by overshooting. To address this issue, this study carefully investigates the connections between the performance of an NLFT model and its learning depth via SLF-NMUT to present a joint learning-depth-adjusting scheme for it. Based on this scheme, a Depth-adjusted Multiplicative Update on tensor algorithm is innovatively proposed, thereby achieving a novel depth-adjusted nonnegative latent-factorization-of-tensors (DNL) model. Empirical studies on two industrial data sets demonstrate that compared with the state-of-the-art NLFT models, a DNL model achieves significant accuracy gain when performing missing data estimation on a high-dimensional and incomplete tensor with high efficiency. Note to Practitioners—Multichannel data are often encountered in various big-data-related applications. It is vital for a data analyzer to correctly capture the temporal patterns hidden in them for efficient knowledge acquisition and representation. This article focuses on analyzing temporal QoS data, which is a representative kind of multichannel data. To correctly extract their temporal patterns, an analyzer should correctly describe their nonnegativity. Such a purpose can be achieved by building a nonnegative latent factorization of tensors (NLFT) model relying on a single latent factor-dependent, nonnegative and multiplicative update on tensor (SLF-NMUT) algorithm. But its learning depth is not adjustable, making an NLFT model frequently suffer from severe fluctuations in its training error or even fail to converge. To address this issue, this study carefully investigates the learning rules for an NLFT model’s decision parameters using an SLF-NMUT and proposes a joint learning-depth-adjusting scheme. This scheme manipulates the multiplicative terms in SLF-NMUT-based learning rules linearly and exponentially, thereby making the learning depth adjustable. Based on it, this study builds a novel depth-adjusted nonnegative latent-factorization-of-tensors (DNL) model. Compared with the existing NLFT models, a DNL model better represents multichannel data. It meets industrial needs well and can be used to achieve high performance in data analysis tasks like temporal-aware missing data estimation [ABSTRACT FROM AUTHOR]

This article reinterprets algorithmic rationality by looking at the interaction between mathematical logic, mechanized reasoning, and, later, computing in the Russian Imperial and Soviet contexts to offer a history of the algorithm as a mathematical object bridging the inner and outer worlds, a humanistic vision that we, following logician Vladimir Uspensky, call the "culture of the impossible." We unfold the deep roots of this vision as embodied in scientific intelligentsia. In Part I, we examine continuities between the turn-of-the-twentieth-century discussions of poznaniye—an epistemic orientation towards the process of knowledge acquisition—and the postwar rise of the Soviet school of mathematical logic. Establishing this connection allows us to explain, in Part II, the role of the algorithm in disciplinary dynamics between mathematical logic and cybernetics and a characteristic understanding of programming, not as a narrow skill, but as a matter of consciousness. [ABSTRACT FROM AUTHOR]

Given a geo-textual dataset $\mathbb {O}$ O , a set $\varphi$ φ of keywords, a query object $q \in \mathbb {O}$ q ∈ O , and an integer $k$ k , a reverse top- $k$ k keyword-based location query returns the influence zone $\mathcal {R}$ R of $q$ q such that $q$ q belongs to the result of a top- $k$ k spatial keyword query with query keywords $\varphi$ φ and any location in $\mathcal {R}$ R as arguments. For a query object $q$ q , the influence zone of $q$ q varies for different keywords $\varphi$ φ . Users may be interested in identifying the maximum influence zone of the query object. To this end, in this paper, we study the problem called MaxiZone that finds the keyword set maximizing the influence zone of a specified query object. The MaxiZone problem has many real-life applications, e.g., a business owner would like to identify the maximum influence zone so as to attract as many customers as possible. A straightforward way to tackle the MaxiZone problem is to compute the influence zone for every candidate keyword set. Obviously, this is infeasible if there are a large number of candidate keyword sets. We propose a more efficient index-centric algorithm together with a series of optimizations as well as a sampling-based algorithm, to facilitate the query processing. Moreover, we extend the proposed algorithms to address a variant of MaxiZone problem called $\tau$ τ -MaxiZone problem, which finds top- $\tau$ τ keyword sets having the maximum influence zones. Extensive empirical study using real-world datasets demonstrates the effectiveness and efficiency of our proposed algorithms. [ABSTRACT FROM AUTHOR]

Graph pattern matching has been extensively investigated on general graphs without time information over decades. Nevertheless, few studies focus on temporal graphs, where a relationship between two vertices takes place at a specific moment and lingers for some time. In this paper, we propose a new notion so-called time-respecting flow graph, in which all paths are time-respecting (i.e., a sequence of contacts with non-decreasing time), and one vertex is distinguished as the root, from which other vertices can be reached via a time-respecting path. Based on this, we explore the problem of time-respecting flow graph pattern matching on temporal graphs. This problem motivates important applications in epidemiology, information diffusion, crime detection, etc. To address it, we present one baseline algorithm as well as two optimized algorithms that utilize several efficient matching strategies and topological sort based technique to boost efficiency. Extensive experimental evaluation using both real and synthetic data sets demonstrates the effectiveness and efficiency of our proposed algorithms. Compared with baseline method, our optimized algorithms could achieve up to three orders of magnitude speedup. [ABSTRACT FROM AUTHOR]

Dynamic and Adaptive Load Balancing (DALB) and Controller Adaption and Migration Decision (CAMD) frameworks are the recently developed efficient controller selection frameworks that solved the challenge of load-imbalance in Software-Defined Networking (SDN). While CAMD framework was established to be efficient over DALB framework yet it was not efficient when the incoming-traffic load was elephant flow, hence, leading to a significant reduction in the overall system performance. This study had proposed an Improved Switch Migration Decision Algorithm (ISMDA) that solved the network challenge when the incoming load is elephant flow. The balancing module of the switch migration framework, which runs on each controller, is initiated during the controller load imbalance phase. The improved framework used the controller variance and controller average load status to determine the set of underloaded controllers in the network. The constructed efficient migration model was used to, simultaneously, identify both the migration cost and load-balancing variation for the optimal selection of controller among the set of underloaded controllers. The controller throughput, response time, number of migration space and packet loss were used as the performance comparison metrics. The average controller throughput of ISMDA increased with 7.4% over CAMD framework while average response time of the proposed algorithm improved over CAMD framework with 5.7%. Similarly, the proposed framework had 5.6% average improved migration space over CAMD framework and the packet-loss of ISMDA had average 6.4% performance over the CAMD framework. It was concluded that ISMDA was efficient over CAMD framework when the incoming traffic load is elephant flow.

Cyber-attacks are evolving at a disturbing rate. Data breaches, ransomware attacks, crypto-jacking, malware and phishing attacks are now rampant. In this era of cyber warfare, the software industry is also growing with an increasing number of software being used in all domains of life. This evolution has added to the problems of software vendors and users where they have to prevent a wide range of attacks. Existing watermark detection solutions have a low detection rate in the software. In order to address this issue, this paper proposes a novel blind Zero code based Watermark detection approach named KeySplitWatermark, for the protection of software against cyber-attacks. The algorithm adds watermark logically into the code utilizing the inherent properties of code and gives a robust solution. The embedding algorithm uses keywords to make segments of the code to produce a key-dependent on the watermark. The extraction algorithms use this key to remove watermark and detect tampering. When tampering increases to a certain user-defined threshold, the original software code is restored making it resilient against attacks. KeySplitWatermark is evaluated on tampering attacks on three unique samples with two distinct watermarks. The outcomes show that the proposed approach reports promising results against cyber-attacks that are powerful and viable. We compared the performance of our proposal with state-of-the-art works using two different software codes. Our results depict that KeySplitWatermark correctly detects watermarks, resulting in up to 15.95 and 17.43 percent reduction in execution time on given code samples with no increase in program size and independent of watermark size.

Conurbations around the globe are encountering the challenges of traffic congestion. Miscellaneous smart systems have been developed to help control and improve traffic flow in a cost-effective and measurable way. Yet, the existing systems solve the traffic congestion problem with cost-intensive traffic lights and mostly handle emergency cases poorly. In this article, to minimize the traffic congestion problems for any kind of vehicles without using traffic lights, we propose three deadlock-free algorithms namely: (i) Mutual exclusion algorithm based on single instruction (MEASIR), (ii) Mutual exclusion algorithm based on priority (MEAPRI), and (iii) Mutual exclusion algorithm based on multi-agent systems (MEAMAS). Communication inside a group is accomplished via a queue structure, while an external element (e.g., a router) is used for internal communications. Besides the depiction of experimental and simulation results, a complete statistical analysis has been performed to compare the performance of MEASIR, MEAPRI, and MEAMAS with their alternatives. Our proposed deadlock-free algorithms are not only efficient but also functional with a computational cost of O(n) to enter the critical section, where n represents the number of all vehicles in a particular intersection.

In this paper, by integrating Pythagorean fuzzy set with N-soft set, we propose a generalization of N-soft set called the theory of Pythagorean fuzzy N-soft set and explore some of related operations concerning this theory including the weak complement, extended union and intersection, restricted union and intersection. Then two algorithms are introduced to Pythagorean fuzzy N-soft sets for dealing with multi-attribute group decision-making problems. Finally, a practical example is provided to illustrate the validity and practicality of Pythagorean fuzzy N-soft sets in multi-attribute group decision-making problems. Compared with the existing models, we also elaborate the advantages of this model.

Hydrogen economy is one of the recently opened alternatives in the field of non-polluting energy. Hydrogen fuel cells show high performance, high reliability in stationary applications and minimal environmental impact. To increase the efficiency of the hydrogen fuel cell it is very important to have a good model to predict its dynamic behavior. In addition, this model must be able to adapt iteratively to the changes that occur in its performance due to operating conditions and even to the degradation through its lifespan. This paper presents the application of an iterative fuzzy modeling methodology based on the extended Kalman filter applied to a real hydrogen fuel cell. Two algorithms based on the Kalman filter will be compared with the well-known backpropagation algorithm from three different initializations: by uniform partitioning, subtractive clustering and CMeans clustering. The used data have been collected during the actual operation of a real 3.4 kW proton exchange membrane fuel cell. As the article experimentally shows, the Takagi-Sugeno type fuzzy model allows to create a very accurate nonlinear dynamic model of the fuel cell, which can be very useful to design an efficient fuel cell control system.

This paper provides an in-depth discussion of human motion recognition in Virtual Reality (VR) video sequences through hidden Markov models, which are four steps from VR video acquisition and pre-processing, foreground detection, extraction of human feature parameters, and hidden Markov model human motion recognition. A hybrid Gaussian model was used to build a background model in real-time based on changes in VR video information, and the image was subtracted by the background differential method. The optical flow method was used for foreground detection of the target, and the effects of sparse and dense optical flow were compared to obtain the motion characteristics and optical flow information of the target human body, respectively. Features were extracted for human motion, in terms of common geometric features of the body and optical flow information, respectively. In terms of common geometric information, the width-to-height ratio, perimeter-to-area ratio, center of mass, eccentricity, and feature angle were extracted, respectively. For the optical flow information, optical flow descriptors were constructed using a grid-based approach. And feature fusion was performed for the above two parameters by the k-means method to construct the word pocket model. The hidden Markov model parameters were obtained by using the hidden Markov model for the recognition of human motion and training the human feature parameters for each of the four motions. The recognition of the four common human body movements was realized by the forward-backward algorithm. The test results show that the motion recognition method in this paper has high recognition performance and good anti-interference performance. The time-sequence pooling is used to sort the effective video frame feature sequences to obtain the feature vectors that can represent the dynamic changes of video time sequence; finally, the time-sequence feature vectors are used to train the support vector machine for classification recognition. The recognition accuracy is 65.2% and 89.4% for the HMDB51 and UCF101 datasets, respectively.

Traffic congestion is a major issue in most big cities, resulting in longer travel time and increased greenhouse gas emission. Various factors can cause traffic congestion, and includes not only traffic events on roads (e.g., car accidents) but also urban events (e.g., football games, concerts, and festivals), where a large number of human activities happen in a certain place and at a certain time. The technology of connected vehicles (CV) has provided a crowd-souring platform enabling communication between vehicles and surrounding information share to be more timely and effective. Taking the advantage of that, in this paper we focus on navigation during urban events, and present an approach to find feasible routes avoiding traffic congestion caused by the different types of events. Using 12-month geo-tagged tweets, we create a human activity network to capture certain types of human activities across cities. Based on that, an event estimation algorithm is developed to find the possible events that would occur in the near future, and to estimate their probabilities. These detected events are represented in the form of obstacle polygons with timestamps, and are used by the routing algorithm to generate congestion avoidance routes. We apply our approach to the road network of Toronto, Ontario, Canada, and the experimental results show the capability of our approach in supporting routing during urban events.

This article argues that an efficient artificial intelligence control algorithm needs the built-in symmetries of an industrial robot manipulator to be further characterized and exploited. The product of this enhancement is a four-dimensional (4D) discrete cylindrical grid space that can directly replace complex robot models. A* is chosen for its wide use among such algorithms to study the advantages and disadvantages of steering the robot manipulator within the 4D cylindrical discrete grid. The study shows that this approach makes it possible to control a robot without any specific knowledge of the robot kinematic and dynamic models at planning and execution time. In fact, the robot joint positions for each grid cell are pre-calculated and stored as knowledge, then quickly retrieved by the pathfinding algorithm when needed. The 4D cylindrical discrete space has both the advantages of the configuration space and the three-dimensional Cartesian workspace of the robot. Since path optimization is the core of any search algorithms, including A*, the 4D cylindrical grid provides for a search space that can embed further knowledge in form of cell properties, including the presence of obstacles and volumetric occupancy of the entire industrial robot body for obstacle avoidance applications. The main trade-off is between a limited capacity for pre-computed grid knowledge and the path search speed. This innovative approach encourages the use of search algorithms for industrial robotic applications, opens up to the study of other robot symmetries present in different robot models and lays a foundation for the application of dynamic obstacle avoidance algorithms.

Airborne raw light detection and ranging (LiDAR) measurements are georeferenced three-dimensional coordinates of ground surface, including all natural and man-made features. Extracting terrain surface measurements from raw LiDAR data is referred to as “filtering.” Many filtering algorithms have been published, indicating the difficulty of the task; however, none performs equally well on all kinds of landscapes. This article presents a new algorithm, automatic weighted splines filter (AWSF), to extract the terrain points from raw LiDAR measurements. The mathematical model of the AWSF algorithm utilizes both the cubic smoothing splines (to interpolate and fit raw LiDAR data) and z-shaped function (to estimate the weight value of each point). The AWSF algorithm performance is compared against 14 filtering algorithms published between 1998 and 2019, as well as one unpublished (proprietary) algorithm designed by the world-leading company in processing remote sensing data, Harris Geospatial Solutions. Diverse landscape scenarios are used, ranging from open fields, rural land, and urban areas to dense forests on mountains where most of the filtering algorithms struggle as these areas represent the most difficult and challenging landscapes because the canopy prevents LiDAR pulses from reaching the ground surface. A total of 19 samples were tested; the results clearly show that the filtered terrain measurements are accurate and that the performance of the AWSF algorithm is stable for all the LiDAR data samples in comparison with the other filtering approaches.

A new rational bi-quartic spline interpolation scheme is constructed in this study. This rational bi-quartic spline has six free parameters that can be used to refine the final interpolating surface. The degree of smoothness attained is C1 continuity. Image interpolation is one of the methods in image processing that is used to rescale the images, either downscaling or upscaling. The proposed rational bi-quartic spline will be used to interpolate the missing pixel in image interpolation. We employ an algorithm that will speed up the process as well as be able to produce the quality upscaling images. We measure the quality of the proposed method by calculating peak-signal-to-noise-ratio (PSNR). Based on numerical comparison, the proposed scheme gives higher PSNR (consequently smaller RMSE) unlike some existing schemes. Thus, the new rational bi-quartic spline with six parameters is better than the existing scheme via an efficient algorithm. All numerical simulations and graphical results are presented using MATLAB.