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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.

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.

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.

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.

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.

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.

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.

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.

Pairwise comparisons constitute a popular and important tool for multiple-criteria decision making. Consistency is one particular aspect of pairwise comparisons that has been studied thoroughly in recent decades since human judgements are seldom fully consistent. The aim of this paper is to propose a new simple and intuitive iterative Step-by-Step (SBS) algorithm for inconsistency reduction of a (multiplicative) pairwise comparison matrix based on step-by-step changes of the most inconsistent matrix element, where the inconsistency is measured by a modified Koczkodaj’s inconsistency index. A comparison of the proposed SBS algorithm with other algorithms for inconsistency reduction is provided as well.

This paper presents a method for optimizing the trajectories of robotic arms having manipulators with six degrees of freedom (DOFs) and spherical wrists. The trajectories are optimized by maximizing the manipulator performance (manipulability). For this purpose, kinematics models of the robot arms are defined, such that they can be integrated into an algorithm based on the Kalman filter. This algorithm is implemented through the simulation of trajectories in a serial industrial robot, which is a robotic arm with six DOFs and a fitted welding tool with a material contribution. During the trajectory optimization for such a manipulator robot, the orientation of the welding gun (e.g., the position of the final effector) must be preserved to guarantee correct welding. Applications of this method to two trajectories in the automotive industry are presented, and remarkable improvements in the performance of the manipulator itself are observed. The results obtained demonstrate that the proposed algorithm is an appropriate method for optimizing trajectories because of its various advantages, such as ease of implementation and states of calculation based only on the previous states. Therefore, this method allows the trajectories to be optimized in the work environment of the robot once the kinematic parameters of a robotic manipulator arm are known.

Long term planning in power transmission network expansion provides a well ordered and profitable extension of power equipment and facilities to meet the expected electric energy demand with an allowable degree of reliability. However, high quality and improved reliability in energy supply have to be balanced with the available funds. The need to expand transmission network can never be over emphasized. Transmission Network Expansion Planning (TNEP) is a periodical measure that must be carried out due to dynamic societies that attract extra energy demands. It is highly important to minimize the network reinforcement and operational costs while satisfying the increase in demand imposed by technical and economic conditions over the planning horizon. Several optimization algorithms for TNEP problems have been developed and applied over the past decades. This paper presents a comprehensive state-of-the-art survey on the TNEP optimization algorithms. The approach of this paper is in the area of highlights of the various available TNEP algorithms, their applications, viability, computational complexities and drawbacks, which can aid in the identifications of the proper methods that can yield an optimal solution to TNEP problem.

Based on the mutual coupling effect among the compressor, the air cooler and pipes in the system of natural gas pipeline, innovatively with the goal of minimum energy consumption, this paper established a combined operation optimization model of the air cooler and compressor through the optimization of the switching scheme of compressors and air coolers, which can greatly reduce the production energy consumption of the pipeline system. Moreover, when the air temperature is taken as an optimization variable, the most proper temperature to start the air cooler of each compressor station can be worked out to guide the optimized operation of the pipeline, which is of high value for promotion and application. The case analysis of west-east natural gas pipeline II showed that among genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) algorithm that are used to solve the optimization model, the genetic algorithm is the fastest, and the simulated annealing algorithm the slowest, but the optimization results of the simulated annealing algorithm is the best, in which the reduced production energy consumption accounted for 33.77%, testifying the practicability and creativity of the optimization model.

Twitter is a leading platform among social media networks. It allows microblogging of up to 140 characters for a single post. Owing to this characteristic, it is popular among users. People tweet about various topics from daily life events to major incidents. Given the influence of this social media platform, the analysis of Twitter contents has become a research area as it gives us useful insights on a topic. Hence, this paper will describe how Twitter data are extracted, and the sentiment of the tweets on a particular topic is calculated. This paper focusses on tweets of two halal products, i.e., halal tourism and halal cosmetics. Twitter data (over a 10-year span) were extracted using the Twitter search function, and an algorithm was used to filter the data. Then, an experiment was conducted to calculate and analyze the tweets' sentiment using deep learning algorithms. In addition, convolutional neural networks (CNN), long short-term memory (LSTM), and recurrent neural networks (RNN) were utilized to improve the accuracy and construct prediction models. Among the results, it was found that the Word2vec feature extraction method combined with a stack of the CNN and LSTM algorithms achieved the highest accuracy of 93.78%.

Determining the affine equivalence of Boolean functions has significant applications in circuit and cryptography. Previous methods for determining this require a large amount of computation when Boolean functions are bent functions or when the truth table is sparse. This paper presents a new method to determine the affine equivalence based on matrix algebra. By transforming Boolean function to the corresponding matrix representation, we first propose and prove the congruent standard form of Boolean function. It lays the foundation for the determination of equivalence because affine Boolean functions must have the same standard form. Then we find the generators of orthogonal matrix group and symplectic matrix group, which greatly reduce the search space. The computation complexity of our method is o(2(r2/2+n*(n-r))), where n is the number of bit operations, and r is the rank of the matrix, which is the product of Boolean-1 matrix of the test Boolean function and its transposition. The experimental results show that our method is useful when the test Boolean function is no more than 7 bits and r is greater than 2.

When the pulse-sequence-based image sensor films high-speed moving objects, the image lag problem may appears as blurring in reconstructed images. In this paper, an algorithm is proposed to suppress lags by distinguishing the lag area and calculating ratio of bright to dark part in one trigger interval. The algorithm bases on the sensor's characteristics and image lag principle that the change of light intensity cannot be immediately reflected on the pulse generated from photoelectric accumulation. The effectiveness of the algorithm is verified both on the sensor's behavior-level model and the real image sensor. In both experiments the curves of LSF (line spread function) and MTF (modulation transfer function) show the reconstructed edges after lag elimination algorithm are similar to the real edges both in sharpness and position. The indexes to evaluate image similarity including LMD (location mean deviation), PSNR (peak signal to noise ratio) and SSIM (structural similarity index) improve respectively. In the experiment on sensor's model, when the simulation velocity of edge is low, the algorithm can reconstruct the perfect edges as input images. As velocity increases, the reconstructed edges are still sharp but with slight shifts. In the real experiment to film a turntable with a rotation speed of 6000r/min, the LMD, PSNR and SSIM improve from 22.2833 to 2.5167, 18.2485 to 21.8603 and 0.8345 to 0.9364 respectively. These results show that the algorithm can suppress image lag and improve reconstructed image quality.

Aiming at the low efficiency, poor performance and weak stability of traditional clustering algorithms and the poor response to the processing of massive data in real time, a real-time streaming controllable clustering edge computing algorithm (SCCEC) is proposed. First, the data tuples that arrive in real time are pre-processed by coarse clustering, the number of clusters, and the position of the center point are determined, and a set formed by macro clusters having differences is formed. Secondly, the macro cluster set obtained by the coarse clustering is sampled, and then K-means parallel clustering is performed with the largest and smallest distances, thereby realizing fine clustering of data. Finally, the completely clustering algorithm and the edge-computing algorithm are combined to realize the clustering analysis under the edge-computing framework. The experimental results show that the proposed algorithm has the advantages of high efficiency, good quality, and strong stability. It can quickly obtain the global optimal solution, and deal with massive data with high real-time performance. It can be used for real-time streaming data aggregation under big data background.

In order to make strategic, tactical and operational decisions for the shortest path in an uncertain environment. Firstly, the information transmission time expectation-variance shortest path model in uncertain environment is proposed. Secondly, to avoid the interruption of information transmission caused by sudden situations, a fuzzy shortest path critical edge model is established. Then, an improved ant colony algorithm with traffic congestion factor and random variables is developed. Finally, to assess the performance of Improved ant colony algorithm, experiments on a set of 6 benchmark test functions are conducted, and numerical examples of these models are given to illustrate its effectiveness.

Similarity search in metric spaces finds similar objects to a given object, which has received much attention as it is able to support various data types and flexible similarity metrics. In real-life applications, metric spaces might be combined with graphs, resulting in geo-social network, citation graph, social image graph, to name but a few. In this paper, we introduce a new notion called quasi-metric graph that connects metric data using a graph, and formulate similarity search on quasi-metric graphs based on the combined similarity metric considering both the metric data similarity and graph similarity. We propose two simple efficient approaches, the best-first method and the breadth-first method, which traverse the quasi-metric graph following the best-first and the breadth-first paradigms, respectively, and utilize the triangle inequality to prune unnecessary evaluation. Extensive experiments with three real datasets demonstrate, compared with several baseline methods, the effectiveness and efficiency of our proposed methods.

Nonnegative sparse representation has become highly popular in certain applications in the context of signals and corresponding dictionaries that have nonnegative limitations. Applying an adaptive dictionary for sparse representation of nonnegative signals has been shown to be very effective; but constructing adaptive dictionary, i.e., dictionary learning, remains a challenge. In this paper, we attempt to design an effective and tailored algorithm for the sparse representation of nonnegative signals. We consider the so-called determinant sparsity measure formed with the determinant of the Gram matrix of sparse coefficients. Based on the determinant measure, we formulate the nonnegative dictionary learning problem that is optimization of a non-convex function. For reducing the computational complexity of the optimization, the difference of convex functions (DC) programming is employed since the non-convex function can be set to the form of difference of two convex functions. Because there are two variables in the dictionary learning problem that cannot be solved directly by DC programming, an alternation scheme is employed to treat the sparse coding and dictionary update of the dictionary learning problem alternatively with the DC programming. Our proposed novel dictionary learning algorithm has three advantages: 1) the higher recovery ratio is achieved by the proposed algorithm, even when the signals are weakly sparse; 2) the generalization ability of the proposed algorithm is greater, more often converging to a global solution than to local optimal solutions; and 3) the obtained sparse solution has a low-overlapping property that enables the solutions to be sparse and makes the proposed algorithm robust. Numerical experiments and experiments using real-world data demonstrate the effectiveness and applicability of the proposed algorithm.

Network-on-chip (NoC) has appeared to be an impending substitute for the communication paradigm in modern very large scale integration embedded systems. Apart from many design challenges, application mapping on the NoC system is one of the most intractable and challenging optimization problems. In this paper, we propose a hybrid, branch & bound (BB)-based exact mapping (BEMAP) algorithm, for mapping real-time embedded applications on the NoC architecture. The BEMAP optimizes the latency and throughput of the NoC system and minimizes power consumption under the bandwidth constraint. This method utilizes the modular exact and systematic search optimization techniques to obtain a multi-objective optimized solution to the mapping problem of the NoC designs. The proposed algorithm exploits the state-of-the-art BB algorithm, in order to obtain optimized results against its competitors. Experimental results under the benchmarks of several real-time embedded applications show that the proposed algorithm achieves up to 19.93% savings in power consumption and 61.10% improvement in network latency for two dimension mesh and torus topologies.

In this paper, we study the reliability of a novel deep learning framework for internal gross target volume (IGTV) delineation from 4-D computed tomography (4DCT), which is applied to patients with lung cancer treated by stereotactic body radiation therapy (SBRT). Seventy seven patients who underwent SBRT followed by 4DCT scans were incorporated in this retrospective study. The IGTV_DL was delineated using a novel deep machine learning algorithm with a linear exhaustive optimal combination framework. For the purpose of comparison, three other IGTVs based on common methods was also delineated. We compared the relative volume difference (RVI), matching index (MI), and encompassment index (EI) for the above IGTVs. Then, multiple parameter regression analysis was performed to assess the tumor volume and motion range as clinical influencing factors in the MI variation. The results demonstrated that the deep learning algorithm with linear exhaustive optimal combination framework has a higher probability of achieving optimal MI compared with other currently widely used methods. For patients after simple breathing training by keeping the respiratory frequency in 10 breath per minute (BPM), the four phase combinations of 0%, 30%, 50% and 90% can be considered as a potential solution for an optimal combination to synthesize IGTV in all respiration amplitudes.

Network-on-chip (NoC) is an emerging alternative to address the communication problem in embedded system-on-chip designs. One of the key and major issues is the optimized mapping of the embedded applications on the underlined NoC platform. In this paper, we propose the bandwidth-constrained multi-objective segmented brute-force mapping (SBMAP) algorithm, which minimizes the communication energy consumption and reduces the computational complexity of the NoC designs. The algorithm generates efficient mapping of the embedded applications on the processing elements of the NoC system by segregating the application into multiple segments. It utilizes the property of modular systematic search, which produces high-performance results with optimized simulation time. We compared the SBMAP algorithm with the state-of-the-art mapping techniques, such as branch and bound (BB), near-optimal mapping (NMAP), and random mapping algorithms for mapping real-world application workloads. The experimental results validated the efficiency of the proposed algorithm against its competitors for most of the performance parameters of the NoC designs. The improvement in energy consumption of the SBMAP algorithm is up to 53% for 2-D mesh and 62% for torus topology as compared with the NMAP, BB, and random algorithms for video object plane decoder, picture in picture, Wi-Fi receiver, and multimedia system real-time application benchmarks.

In this paper, we have proposed altogether different and new approaches for the computation of all possible spanning trees of a simple, undirected, and connected graph. Our proposed algorithms have the capability to solve the major bottlenecks in this area, namely, generation of duplicate trees and circuit checking. In the first algorithm, the given graph has been converted to its corresponding weighted complete graph, which proposes to have novelties and limitations of its own. In addition, we have also proposed another related algorithm, and as a result, we have been able to come up with new ideas in this research domain of graph theory.

This paper addresses land surface temperature (LST) retrieval from Landsat-8 data using the generalized split-window (GSW) algorithm. First, radiative transfer modeling experiment is conducted using the moderate spectral resolution atmospheric transmittance algorithm and computer model fed with SeeBor V5.0 atmospheric profile database to build a data set of LST related to brightness temperatures in Thermal Infrared Sensor (TIRS) bands 10 and 11, land surface emissivities (LSEs), and total precipitable water (TPW). Then, the GSW algorithm's coefficients are obtained through linear regression, in which the simulated data are grouped into several sub-ranges to improve the accuracy. The impacts of noise equivalent temperature difference and uncertainty of LSEs and TPW on derived LST are evaluated. Next, the TIRS channels 10 and 11 are inter-calibrated against the channels of infrared atmospheric sounding interferometer on board Metop-B. After that, LST is retrieved from the re-calibrated and clear sky Landsat-8 data using the GSW algorithm, where LSEs are estimated from the measurements of operational land imager on Landsat-8 by the modified normalized difference vegetation index (NDVI) based emissivity method, and TPW is extracted from the european centre for medium-range weather forecasts reanalysis data. Finally, the retrieved LST is cross-validated with the MOD11_L2 V6 product. The results show that the GSW algorithm can accurately retrieve LST from Landsat-8 data, and errors mainly come from the uncertainty of LSEs and TPW. Against the MOD11_L2 V6 product, the LST errors are -1.45 ± 0.80 K and -0.49 ± 0.78 K before and after the correction of LSEs and TPW, respectively.