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In the paper we continue the study of precipitation regions of high-energy charged particles, carried out by the authors since 2002. In contrast to previous papers, where a stationary source of electrons was considered, it is assumed that the source moves along a low circular near-earth orbit with a constant velocity. The orbit position is set by the inclination angle of the orbital plane to the equatorial plane and the longitude of the ascending node. The total number of injected electrons is determined by the source strength and the number of complete revolutions that the source makes along the circumference. Construction of precipitation regions is produced using the computational algorithm based on solving of the system of ordinary differential equations. The features of the precipitation regions structure for the dipole approximation of the geomagnetic field and the symmetrical arrangement of the orbit relative to the equator are noted. The dependencies of the precipitation regions on different orbital parametres such as the incline angle, the ascending node position and kinetic energy of injected particles have been considered. [ABSTRACT FROM AUTHOR]

The face of the status quo that rampant illegal content in the Internet, the result of traditional way to filter information, keyword recognition and manual screening, is getting worse. Based on this, this paper uses OCAT algorithm nested by KNN classification algorithm to construct a corpus training library that can dynamically learn and update, which can be improved on the filter corpus for constantly updated illegal content of the network, including text and pictures, and thus can better filter and investigate illegal content and its source. After that, the research direction will focus on the simplified updating of recognition and comparison algorithms and the optimization of the corpus learning ability in order to improve the efficiency of filtering, save time and resources. [ABSTRACT FROM AUTHOR]

In this paper, the stability properties of the predictor-corrector mode are investigated in the case when both the predictor and the corrector are symmetric linear multistep methods of the same order. The stability polynomial of the mode is obtained. Conditions are given under which the stability polynomial has at least one root on the unit circle, which makes it possible to develop a constructive algorithm for determining the stability interval of the mode. The stability of the predictor-corrector mode is investigated for a large class of symmetric methods. We compare accuracy and computational costs of the symmetric methods and the Everhart integrator. [ABSTRACT FROM AUTHOR]

In this paper we propose and analyse different stochastic methods for solving a class of integral equations, namely the second kind Fredholm integral equations. We study and compare different possible approaches to compute linear functionals of the integral under consideration. Error balancing of both stochastic and systematic errors has been discussed and applied during the numerical implementation of the algorithms. An almost optimal Monte Carlo algorithm for integral equations in a combination with the idea of balancing of both systematic and stochastic errors is analysed. Conclusions about the applicability and efficiency of the algorithms have been drawn. Meaningful numerical examples and experiments with experimental and theoretical relative errors are presented. It is shown that the balancing of errors reduce the computational complexity if the error is fixed. [ABSTRACT FROM AUTHOR]

A new algorithm for solving the nonlinear equation is shown. It combines Bisection method and False Position method which absolutely give a real solution. The propose is algorithm will be tested to find the numerical results. It is also observed that the new hybrid algorithm performs better than Bisection Method and False Position method. A numerical example is provided to illustrate the proposed method in this paper. [ABSTRACT FROM AUTHOR]

The paper describes an own development of a universal solution for automated transformation from relational databases into semi-structured data (JSON or XML). The aim is to create a tool, that will be able to transform the data, retrieved from relational database with arbitrary SQL query into semi-structured data (JSON or XML). This tool must be able not only to "pack" data into the required output format but, it must be able to recognize the hidden "hierarchical nature" of the "flat" relational data and automatically transform this kind of data into JSON or XML format, preserving existing relationships between them. This transformation must be done without (or with very little) intervention from the user. [ABSTRACT FROM AUTHOR]

Navigation methods of autonomous underwater vehicles (AUVs) present a challenge caused by the fast attenuation of high frequency signals and by the demanding underwater environment. The development within the last years in AUV navigation sensors has led to a different approach in the implementation of traditional AUV navigation algorithms. This paper presents the conclusion for the implementation of different navigation AUV algorithms after the analysis of state-of-the-art solutions through recent improvements in this matter. [ABSTRACT FROM AUTHOR]

Datasets that are gathered from multiple sources are called multi-view datasets. These types of datasets represent different sets of feature attributes to form different views. Advanced computing and information technology have made it possible to collect and store a massive amount of data. As more features are added to each view, the data becomes sparse, and analysis suffers from the curse of dimensionality. Exploring and integrating diverse information from different views have been the focus of many approaches to improve clustering performance. The relationships among distinct views should not only be explored and analyze but should also consider the emerging high-dimensionality of each view to further improve the clustering performance. Recently, Yang and Benjamin proposed a clustering algorithm called "Feature Weighted Reduction PCM (FW-R-PCM) that calculates feature weights to identify relevant features and consequently, eliminates features that are irrelevant to reduce feature dimension from the entire feature space. In this paper, we propose a feature-weighted multi-view possibilistic c-means (FW-R-MVPCM) clustering algorithm, which extends FW-R-PCM to consider clustering high-dimensional multi-view datasets and perform feature reduction simultaneously. Our proposed FW-R-MVPCM can effectively improve the clustering performance using a weighting scheme that identifies, and selects relevant features from each view, and eliminate the irrelevant features, thus reducing the dimension of features in each view. Experiments on real datasets are performed to analyze the theoretical behavior of FW-R-MVPCM and to show its usefulness and effectiveness. Furthermore, FW-R-MVPCM is compared with FW-R-PCM, and other multi-view clustering algorithms such as WMCFS, SWVF, W-MV-PCM-L2, and WV-Co-FCM. [ABSTRACT FROM AUTHOR]

This paper demonstrates the application mechanism of a novel evolutionary algorithm to evaluate the financial performance of a power system while dealing with a combined economic and emission dispatch problem posed by various technical constraints. In particular, as a constrained objective function is associated with weighting factor scenarios, this problem considers the two dispatches for fuel and environmental aspects. Running simulations indicate that minimum costs are determined by weighting factors applied to the problem as a whole. Reducing total fuel costs with a dispatching priority and a pollutant target based on emission production has presented different implications as far as its contribution to the financial performance. Moreover, the increasing load demand results in generated power, costs, and emission discharges linked to the parameters and unit commitments. [ABSTRACT FROM AUTHOR]

In this paper, a new approach for personal identification using finger vein image is presented. Finger vein is an emerging type of biometrics that attracts attention of researchers in biometrics area. As compared to other biometric traits such as face, fingerprint and iris, finger vein is more secured and hard to counterfeit since the features are inside the human body. So far, most of the researchers focus on how to extract robust features from the captured vein images. Not much research was conducted on the classification of the extracted features. In this paper, a new classifier called fuzzybased k-nearest centroid neighbor (FkNCN) is applied to classify the finger vein image. The proposed FkNCN employs a surrounding rule to obtain the k-nearest centroid neighbors based on the spatial distributions of the training images and their distance to the test image. Then, the fuzzy membership function is utilized to assign the test image to the class which is frequently represented by the k-nearest centroid neighbors. Experimental evaluation using our own database which was collected from 492 fingers shows that the proposed FkNCN has better performance than the k-nearest neighbor, k-nearest-centroid neighbor and fuzzy-based-k-nearest neighbor classifiers. This shows that the proposed classifier is able to identify the finger vein image effectively. [ABSTRACT FROM AUTHOR]

This paper introduces the parameter-reduction of Pythagorean fuzzy soft sets. Parameter-reduction for a soft set is the process of reducing the quantity of parameters that determine a given soft set not changing its choice objects. There has been a rapid development in the investigation of more notions that are capable of expressing vagueness in a parametric manner and developing an algorithm for decision-making problems. Pythagorean fuzzy soft sets are one of the best platforms to express uncertainty as it the generalization of soft sets, fuzzy soft sets including intuitionistic fuzzy soft sets. Various algorithms for decision-making problems centered on Pythagorean fuzzy soft sets have been developed recently. In this scenario, the development of parameter- reduction for Pythagorean fuzzy soft sets is inevitable because it reduces the complexity of decision-making without affecting the optimal decision. Here, two algorithms for parameter-reduction is introduced and illustrated with examples. [ABSTRACT FROM AUTHOR]

The paper presents a novel steganographic algorithm. It is based on a least significant bit method and a chaotic random system. The designed scheme is realized by the Raspberry Pi hardware. It is proved that the novel algorithm shows good statistical results. [ABSTRACT FROM AUTHOR]

In the present paper, hyperbolic type involutory partial differential equations with the Neumann condition are studied. The first and second order of accuracy difference schemes for the numerical solution of the initial boundary value problem for one dimensional hyperbolic type involutory partial differential equations are presented. Some numerical results are provided. [ABSTRACT FROM AUTHOR]

The paper presents the recursive-search based algorithm f determining the displacements of surface patch (optical flow) to be applied on GPU utilizing the parallel computations principle. OpenCL computing language is used to implement the parallel computations algorithm which is focused on application on AMD Radeon GPUs. The results of testing of the proposed algorithm show the multiple decreases the time of displacement vector field construction while maintaining the accuracy and robustness. [ABSTRACT FROM AUTHOR]

The SolarPACES Reflectance Guideline provides an essential tool to obtain comparable reflectance measurements, but because of the lack of adequate commercial instrumentation, till now the exhaustive characterization of reflectance behaviour versus incidence θi and (half) acceptance angle φ is unachieved. An expert group in Task III has been working to outline some practicable solutions. The Equivalent Model Algorithm (EMA) was found quite promising: with few input data, EMA allows to predict any reflectance feature by computation. The recent availability of reliable reflectance measurements at oblique incidence made possible the refinement of EMA by analysing a representative set of commercial solar mirrors. This paper describes the new EMA for solar mirror, named EMA4SM, and reports its validation1. [ABSTRACT FROM AUTHOR]

The paper presents a computational algorithm for solving problems formulated in terms of Mean Field Game theory with restriction on the control resource. The Mean Field equilibrium leads to a coupled system of two parabolic partial differential equations: Fokker-Planck-Kolmogorov and Hamilton-Jacobi-Bellman ones. The article is devoted to the discrete approximation of these equations. In addition, contrary to other studies, the algorithm constructed here is applicable to problem with more general control function. This algorithm is applied to the problem of heating insulation. [ABSTRACT FROM AUTHOR]

Effective optics is crucial to realize all the possibilities and fully reveal the potential of 4th generation synchrotron sources. Compound Refractive Lenses (CRLs) are commonly used for focusing X-rays at synchrotrons due to easy alignment and good focusing efficiency. Diamond CRLs have low absorption, high heat conductivity and are well reproducible, that makes such lenses a perfect candidate for a different type of experiments that require high focusing. Unfortunately, optics, made of single-crystal material, has one drawback that was recently reported - intensity modulation at certain energies in the transmission spectrum. This issue is well known in X-ray spectroscopy and is called "diffraction loss" or "glitch effect". It is especially bad for spectroscopic experiments, where a certain energy range has to be scanned. However, glitches affect not only spectroscopic experiments – they are often present in the transmission spectrum of single-crystal materials. Therefore it is necessary to understand the formation of glitches and to be able to predict glitches for any experimental conditions. In this paper, we present an algorithm that can determine the precise orientation of the lens solely from the measured spectrum of glitches and analytical approach to refine the actual orientation of the lens and cell constant. Moreover, we propose a method of "getting rid" of glitches for each energy with the help of a small tilt of the lens. [ABSTRACT FROM AUTHOR]

In this paper, we propose a high-order positivity-preserving simple corner balance (SCB) scheme for the two-dimensional radiative transfer equation on arbitrary quadrilateral meshes. This method is based on the construction of a nonlinear two-point flux, and the M-matrix is deduced, so that the resulting algorithm can preserve positivity as well as conservation at each step of nonlinear iteration. Moreover, it is fairly easy to be implemented into existing SCB codes. Numerical results are presented to demonstrate that the accuracy of our scheme preserving positivity with conservation is higher than the original SCB scheme. [ABSTRACT FROM AUTHOR]

An application of the differential transformation is proposed in this paper which is convenient for finding approximate solutions to boundary value problems for functional differential equations. We focus on two-point boundary value problem for equations with constant delays. Delayed differential equation is turned into an ordinary differential equation using the method of steps. The ordinary differential equation is transformed into recurrence relation in one variable. Based on the structure of the studied boundary value problem, the solution to the recurrence relation depends on one real parameter. Using the boundary conditions leads to an equation with the unknown parameter as the single variable which occurs generally in infinitely many terms. Approximate solution has the form of a Taylor polynomial. Coefficients of the polynomial are determined by solving the recurrence relation and a truncated equation with respect to the unknown parameter. Particular steps of the algorithm are demonstrated in an example of two-point boundary value problem for a differential equation with one constant delay. [ABSTRACT FROM AUTHOR]

A growing complexity of modern engineering problems emphasises the need to methods working with discrete mathematical structures, such as methods of discrete potential theory and discrete function theory, are gaining more and more popularity nowadays. However, solution procedure of boundary value problems by these methods requires at first a construction of the discretisation of a given domain. Thus, in this paper, we present a general discretisation algorithm for discrete potential and function theories. The algorithm allows a consistent discretisation of a domain for interior and exterior boundary value problems based only on a definition of a given continuous domain; and the complete discretisation procedure is based on the application of well-known set-theoretic operations originating from the characterisation of mesh points by help of star-Laplacian. Thus, the algorithm results in a rectangular lattice established over an arbitrary two-dimensional domain and its exterior. [ABSTRACT FROM AUTHOR]

In this paper, the algorithm of the automatic Ziegler-Nichols sustained oscillation method is described. The algorithm is used as the essential way of PID setting in the Control System Designer for JSBSim. The algorithm is tested during a control system design of the wing leveler, and its sufficient functionality is proved. The first section introduces the Ziegler-Nichols method itself, and the description of the Control System Designer for JSBSim follows. The main section shows the algorithm which can perform the method automatically. The last section illustrates the experiment with the successful PID adjustment. [ABSTRACT FROM AUTHOR]

In this paper, we propose an optimal diagonal algorithm under a generalized interval arithmetic for solving the problem of complete interval integer transport without converting the crisp equivalent problem. The proposed algorithm is simple to implement and it offers a numerical illustration to demonstrate the efficacy of the proposed process. [ABSTRACT FROM AUTHOR]

Fractional linear programming models are widely used in real life complications. The fractional transportation problem (FTP) in particular is wide spread where the goal is the optimization of the ratio between 2 cost functions or damage functions or demand functions. But in view of the uncertainty involved in the parameters, fuzzy techniques become more essential. In the paper, we discuss a solution for a fully fuzzy multi objective FTP with the use of triangular fuzzy numbers without transforming it to a crisp equivalent to the existing model. The algorithm is explained with a numerical example with a better solution. [ABSTRACT FROM AUTHOR]

Electrochemical machining (ECM) process has a wide capability to generate complex shapes on different materials which are occasionally difficult to cut. Its ability to machine a variety of materials makes it an extensively accepted non-traditional machining process in modern day manufacturing sector. Thus, selection of the optimal input parameters for an ECM process is crucial for its efficient utilization. In this paper, a comparative analysis is made among four metaheuristics, i.e. firefly algorithm (FA), differential evolution (DE), ant colony optimization (ACO) algorithm and teaching-learning-based optimization (TLBO) algorithm to discover the optimal values of various control parameters for an ECM process. Dimensional inaccuracy, tool life and material removal rate are the three responses considered which are subjected to temperature, choking and passivity constraints. The TLBO algorithm shows the best performance among the others without violating any of the constraints. The paired t-test is also performed to prove the efficacy of TLBO algorithm over the other optimization techniques. The results derived from these algorithms are finally compared with those obtained by the past researchers using other optimization methods for both single and multi-objective optimization problems. [ABSTRACT FROM AUTHOR]

A new evolutionary algorithm called colliding bodies optimization (CBO) considered in this paper to solve optimal power flow (OPF) problem in a power system network. CBO is based on natural phenomenon of one dimensional collision between two objectives. It does not have any control parameters and simple in structure. IEEE 30-bus system with various objective functions like, minimization of total fuel cost with valve point loading effect, minimization of emission profile are considered to test the feasibility and effectiveness of the CBO algorithm. In comparison with the other existed methods presented in the literature, the proposed CBO algorithm gives better optimal solutions for single objective OPF problems. [ABSTRACT FROM AUTHOR]

2 Satisfiability (2SAT) is a language that bridges real life application to neural network optimization. 2SAT is an interesting paradigm because the outcome of this logical rule is always positive. Hopfield Neural Network (HNN) is a type of neural network that finds the solution based on energy minimization. Interesting intelligent behavior has been observed when logical rule is embedded to HNN. Increasing the storage capacity during learning phase of HNN has been a challenging problem for most neural network researchers. Ant Colony Optimization (ACO) is a population-based and nature-inspired algorithm to decipher several combinatorial optimization problems. In this paper, metaheuristic algorithm ACO will be proposed in learning 2SAT programming. To this end, all the learning model will be tested in a new restricted learning environment. In circumstances of where HNN model was not given unlimited iteration until all 2SAT clauses were fully satisfied, we would like to determine the better performing search method between exhaustive search and ACO. Results acquired from the computer simulation showed that ACO outperformed exhaustive search in restricted learning environment. [ABSTRACT FROM AUTHOR]

Hopfield Neural Network (HNN) is a sort of neural network that is strongly dependent to energy minimization of solution. Although HNN managed to solve various optimization problem, the output of HNN suffered from a lack of interpretability and variation. This has severely limited the practical usability of HNN in doing logic programming. Inspired by random neuron perturbation, Estimation of Distribution Algorithm (EDA) has been proposed to explore various optimal neuron state. EDAs employs a probabilistic model to sample the neuron state in order to move toward the various optimal location of global minimum energy. In this paper, a new Mutation Hopfield Neural Network (MHNN) will be proposed to do k Satisfiability programming. Based on the experimental result, the proposed MHNN has outperformed conventional HNN in various performance metric. [ABSTRACT FROM AUTHOR]

In this paper micro-electro-mechanical systems (MEMS) deformable mirror (DM) for its use in adaptive optics in Indus-2 is studied. DMs are used to correct optical aberrations by precisely adjusting the deflection of mirror surface. A piezo based micro-actuator array of 19 keystone shape elements in diameter of 10 mm on silicon of 20 µm thickness is considered for the study. The finite element method (FEM) simulations were carried out using COMSOL Multiphysics 5.4 to estimate the displacement profile. In order to evaluate the applicability of this MEMS based DM in adaptive optics system, piezo response function matrix was determined by applying dc voltage of 1volt to each actuator one by one while keeping the others at zero voltage. The optimum voltage of each actuator to produce a target profile was determined using TLBO algorithm in MATLAB. In this way the obtained voltages are used to generated the low order Zernike mode shapes. [ABSTRACT FROM AUTHOR]

This paper analyses the characteristics of flexible job shop scheduling problem (FJSP), and proposes a genetic simulated annealing algorithm to solve the scheduling problem. Considering the balance of the utilization rate of each machine, the maximum utilization rate of machine, the maximum completion time and other performance indicators are more reasonable, a genetic simulated annealing algorithm combining global search and local search is designed. Through the design of reasonable crossover operator, the global search ability of the genetic simulated annealing algorithm is improved. In addition, the precocity is improved by the simulated annealing algorithm, and the quality of understanding is improved. And finally, the effectiveness and feasibility of the algorithm is verified by simulation experiments. [ABSTRACT FROM AUTHOR]

To solve many complex scientific problems, grid computing is used by many researchers for utilizing and sharing large-scale resources which is increasing expeditiously, in the distributed heterogeneous system. Computational grid, Data grid and Service grid are the three categories in which grid can be classified. Grid computing is also known as a type of parallel and distributed computing system, which allows sharing, collecting and selection of geographically distributed resources depending on their component such as performance, availability, capability, user QoS requirement, and cost during run time. Job scheduling plays very important role in grid computing system, which is one of the key research area in grid computing. This paper proposed a new scheduling algorithm called Feed forward neural network in grid computing system (FFNNGC), which is used to train the network. The training of the network is done until the network converges to a minimum error solution. To verify the proposed scheduling algorithm performance, a grid computing network is used which is based on Globus Toolkit 4. The proposed method is applied in real life problems such as vowels classification or voice recognition problems. Experiments were performed under these real life problems and the results indicated that the proposed method is helpful in such types of problems. [ABSTRACT FROM AUTHOR]

The heart muscle activity monitoring and possibility of timely detection of its pathologies are considered as important tasks of modern medicine since cardiovascular deceases represent the most common cause of human death. This paper is devoted to the development of neural network based algorithm for the binary classification of the ECG cardiac beats waveforms into the following two categories: background beats and the beats with considerably deviated forms. The vector transformation of the signal was used as a method of the ECG representation where synchronous values of ECG signal from different leads were interpreted as the vector coordinates. Three variants of the vector transformation were examined in order to check the robustness of the waveform classification procedure to the possible loss of some signal features: with the use of one, two and three ECG leads. Before the classification procedure, the ECG signals were preprocessed using digital filtering and spline interpolation. The feedforward neural network structure with one hidden layer was used for the waveforms classification. Some standard methods to avoid the network overfitting were applied. The results of the proposed algorithm testing showed rather good values of its accuracy estimates. As it was expected, the best results were obtained when all three synchronous ECG leads were used. Thus, the presented in this paper approach can be used as a base for the further development of a noise robust cardiac arrhythmia detection algorithm. [ABSTRACT FROM AUTHOR]

Methods of processing quantum data become more important as quantum computing devices improve their quality towards fault tolerant universal quantum computers. These methods include discrimination and filtering of quantum states given as an input to the device that may find numerous applications in quantum information technologies. In the present paper, we address a scheme of a classification of input states, which is nondestructive and deterministic for certain inputs, while probabilistic, in general case. This can be achieved by incorporating phase estimation algorithm into the hybrid quantum-classical computation scheme, where quantum block is trained classically. We perform proof-of-principle implementation of this idea using superconducting quantum processor of IBM Quantum Experience. Another aspect we are interested in is a mitigation of errors occurring due to the quantum device imperfections. We apply a series of heuristic tricks at the stage of classical postprocessing in order to improve raw experimental data and to recognize patterns in them. These ideas may find applications in other realization of hybrid quantum-classical computations with noisy quantum machines. [ABSTRACT FROM AUTHOR]

The advancement of an autonomous Unmanned Aerial Vehicle (UAV) has permeated throughout various aspect of our lives, from military to home entertainment. Autonomous UAV has bought a new level of sensation which enables the operator of the autonomous UAV to fly the UAV to the predetermined location just by selecting the location on the map projected on the screen as this can be done by implementing the GPS module on the drone. However, a slight interference will cause a tremendous effect on the GPS signal and it is impossible to operate the autonomous UAV indoor due to the weak signal. Therefore, the objective of this research paper is to develop an alternative for UAV to detect its own location up to 25cm of accuracy without the use of GPS module in a closed environment or under the tree canopy. It is also to perform a highly accurate and precise Simultaneous Localization and Mapping (SLAM) algorithm on a lowpower processing unit yet robust for UAV to map and navigate. In order to have further understand on how the SLAM algorithm works, the offline simulation was carried out on a ground computer. On top of that, a monocular offline data was downloaded and simulation of monocular SLAM was carried out with the data. Once the offline data simulation was completed, Robot Operating System (ROS) was then installed in the ground computer to perform real time monocular SLAM using a webcam. From the real time monocular SLAM, the webcam was used to capture the images and pinpoint the feature points of each image. This process will slowly generating a 3D map by each key frame. The real time SLAM was then performed with a low-powered processing unit on top of the UAV for mapping and navigation. In a nutshell, the expected outcome of this research paper is to develop a low-powered yet robust processing unit for SLAM algorithm in autonomous UAV to determine its own location up to the accuracy of 25cm for navigation and mapping purpose. [ABSTRACT FROM AUTHOR]

The Consultative Committee for Space Data Systems (CCSDS) recommends AES algorithm as a cryptographic algorithm to maintain confidentiality and data security between spacecraft and ground systems. In implementation, there are several AES algorithm operating modes including Cipher-Block Chaining (CBC), Electronic Codebook (EBC), Cipher Feedback (CFB), Output Feedback (OFB), and counter mode (CTR). One of the operating modes of AES algorithm recommended by CCSDS is CTR because it has advantages in terms of efficiency of implementation and offers a high level of data security. LAPAN-A4 as the next generation of LAPAN satellites also uses CCSDS standard in terms of data exchange between satellites and ground stations including telemetry and telecommand (TTC) data and satellite payload data. In order to protect LAPAN-A4 satellite data, especially TTC data, in this paper AES algorithm was implemented in CTR operating mode on the FPGA which will then be implemented on LAPAN-A4 satellite onboard data handling (OBDH) as one of LAPAN-A4 satellite data security systems. To overcome the limited resource of FPGA, optimization is also done using basic iterative AES hardware reused method, which is to reuse hardware that has been designed n times for same operation for AES algorithm. From the results of implementation and optimization, efficiency of logic gates used is increasing almost a quarter less than the previous research. [ABSTRACT FROM AUTHOR]

Recent years have observed an immense refinement in the domain of object localization, especially over remote sensing images. Object localization aims to predict the objects within an image as well as its boundaries. Achieving accurate object localization over remote sensing images is always challenging due to the complex context information. The paper mainly concentrates on solving the problem of accurate object localization using a three-stage localization pipeline. The introductory stage in the pipeline focus on generating regions of interest (RoI) called candidate regions using selective search algorithm. Then, each of these candidate features is extracted by passing it through a hybrid convolutional neural network (CNN) model. Finally, to improve the localization accuracy we propose an optimal object localization technique called Unsupervised Score Based Bounding Box Regression using Feature Descriptors (USBBBR-FD) algorithm integrated with non-maximum suppression (NMS) algorithm to optimize the bounding boxes of regions which are detected as objects. Analysis shows that the detection precision of the hybrid CNN model is higher than that of any other single CNN model and the dimension-reduction CNN model performs far better than retrained CNN models. Experiments show that the proposed USBBBR-FD algorithm can more accurately locate objects within remote sensing images and also shows robustness in complex background as compared with traditional features extraction methods. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid firefly algorithm is used to identify dynamic systems considering nonlinearities in the delta domain. In the hybrid method, the search space is diversified globally by the firefly algorithm (FA), while the gray wolf optimizer (GWO) intensifies the solutions through local search. For system identification in the delta domain, a test model with polynomial nonlinearities was considered. Modelling in the delta domain unifies continuous-time systems at higher sampling frequencies with discrete-time results. Pseudo-random binary sequence poisoned with noise with a signal-to-noise ratio of 50 dB, was used as the input signal for the estimation of unknown model parameters and the static nonlinear coefficients. Not only does the hybrid algorithm outperform the parent algorithms of which it is made up, but also better than some standard and latest heuristic approaches published in the literature. [ABSTRACT FROM AUTHOR]

Non-negative matrix factorization with sparseness constraints (SNMF) has become a widely used tool for keeping the main features of the original data as well as reducing the storage space. Most proposed SNMF problems are commonly solved using the multiplicative update rules. However, the popular multiplicative update rules have been shown to give poor convergence. In this paper, a comparative study of two gradient-based algorithms with strong optimization property and its implementation on SNMF are described. The objective of this study is to investigate whether these two gradient-based algorithms will provide an improvement over the multiplicative update rules in terms of convergence and sparseness. We conduct several experiments on the ORL face database. Through the experimental results, we can see that these two gradient-based algorithms do actually achieve stronger optimization property and higher sparseness of the factor matrices. [ABSTRACT FROM AUTHOR]

The paper aims to develop an algorithm to solve the inverse problem of identifying the volatility with given point measurements in a regime-switching economy. This value is of a particular importance since it is one of the main ingredients forming the option premia. After a review on the existing results for the classical Black–Scholes framework (equivalent to one regime), the direct and the inverse problems considering two regimes are defined. The forward problem is solved by applying the Simpson’s rule to an existing exact formula, while the inverse problem is tackled using a root-finding algorithm, namely Levenberg–Marquardt. What is more, a closed-form approximation formula for computing the implied volatility is proposed when at-the-money case is assumed. The numerical performance of the algorithms is studied and they are compared with a FDS approach. The paper is closed with a discussion and final remarks outlining the usefulness and applicability of the results. [ABSTRACT FROM AUTHOR]

The emerging growth of edge devices and the expansion of IoT systems produced the demand to enhance their security algorithms to run on diverse applications and appliances. Lightweight cipher algorithms are mainly designed for IoT-constrained devices. Thus, due to the new services and applications of IoT that require extensive networks and devices, the need for efficient lightweight cipher algorithm implementations is a critical issue. In this paper, an enhanced cipher LED-128 algorithm based on Galois Fields (GF) operations is presented. The mathematical model of the LED algorithm simply requires a slight execution time for encryption-decryption processing. The Enhanced LED algorithm is applied with 64 bits of block plaintext and 128 bits of the key size which turn out 64 bits block size of ciphertext. The proposed algorithm is presented to advance the randomness of cipher text by enhancing the key schedule based on GF operations. The developed key schedule is enhanced to generate a robust key for the proposed algorithm. The NIST statistical tests have been examined and experimented for the proposed enhanced algorithm by utilizing diverse keys with distinct cipher text, which passed the randomness for each test. The proposed algorithm demonstrated a high confusion and diffusion based on the correlation coefficient analysis. The executed time for the encryption and decryption processes of the proposed enhanced LED algorithm is concentrated at minimum compared to the original LED-128 cipher algorithm. Hence, the proposed enhanced algorithm can be implemented for diverse IoT systems and can reach the trade-off between cost and performance as an efficient lightweight algorithm. The results of the enhanced algorithm are benchmarked with the current LED 64/128 algorithms and presented significant improvements in the performance compared to original design of the LED cipher algorithm. [ABSTRACT FROM AUTHOR]

This paper aims to apply two variants of the hybrid optimization algorithm, namely Chaotic Honey Badger Algorithm (CHBA) and Group Teaching Optimization Algorithm (GTOA) to solve the Uncapacitated Facility Location Problem (UFLP). We enhance using chaotic maps on the HBA to strengthen the search process and hybridization with GTOA leads to a fast exploration process in finding the best solution. There are two variants of hybrid CHBA and GTOA applied. In the first variant, GTOA is applied to determine the initial population of CHBA, then for the second variant, CHBA is used to determine the initial population of GTOA. The performance of these two hybrid variants is used to solve the Uncapacitated Facility Location Problem (UFLP). There were three types of data for UFLP such as small data, medium data, and large data. Furthermore, by comparing the best results it was concluded that hybrid variant GTOA-CHBA has better solution than CHBA-GTOA. The results of the hybrid were compared with other algorithms such as Cuckoo Search (CS), Flower Pollination Algorithm (FPA), and Teaching Learning Based Optimization (TLBO). It was concluded that the average of hybrid GTOA-CHBA better than each algorithm. In terms of best and PRDmin, the hybrid GTOA-CHBA on several data has good results from the other algorithms. [ABSTRACT FROM AUTHOR]

Three contributions are presented in this paper. Designing two cascade controllers to control the position of two brushless DC motors (BLDC) working together at the same time is the first contribution. The second is using a sliding mode speed synchronization controller to connect these motors and fine-tune the precise synchronization of their movement on the axes. In the third contribution, the best parameter values for the two cascade controllers and the synchronization controller are extracted using the salp swarm algorithm (SSA) and the objective function Integral Time Absolute Error (ITAE) to produce the most accurate results. the results were compared by using this algorithm with manual tuning. The results of the simulation revealed that the tuning that was done by the salp swarm algorithm (SSA) was superior to the manual tuning in terms of performance and overshoot as well as any deviation in tracking the path to reach the intended position. note that the drop in velocity (0.11 rad/sec) at a time of 15 seconds because of the load causes deviations (0.343 rad for BLDC 1 and 0.3496 rad for BLDC 2) For manual tuning. while the drop in velocity is very small (0.0037 rad/sec) at the same time causes deviations very small (0.003 rad for BLDC 1 and 0.0052 rad for BLDC 2) for second tuning. [ABSTRACT FROM AUTHOR]

An algorithm for modeling of diffusion, thermal conductivity and viscosity is presented in the paper. Its purpose is demonstration of the transport phenomena in fluids and some basic results in statistical physics in the physics education. The realization of the algorithm as computer program is simple permitting its application by students with only basic knowledge in programming. At the same time the results from the model describe correctly the studied phenomena. [ABSTRACT FROM AUTHOR]

The paper describes our program implementation of Global Sequence alignments of DNA sequences using the Needleman-Wunsch algorithm. This modified Neeedlenan-Wunsch algorithm is designed to align multiple sequences in parallel with OpenMP. The algorithm supports different sequence sizes by scanning and equalizing them simultaneously. The results of our experiment are obtained by comparing Sars-CoV-2 fasta files in a versatile, speedy and convenient manner. The program is adapted for optimal performance when aligning Sars-CoV-2 sequences. We analyze program hotspots, multithreading execution and potential bottlenecks using the Intel vTune Profiler. [ABSTRACT FROM AUTHOR]

The mosaic image is an essential process for merging several images, especially for very high-resolution satellite images, as its width swath is small. The main issue of mosaic images is the geometric accuracy between the images. This paper proposed a sub-pixel geometric evaluation to assess the geometric shift between the images. It is an essential step for image registration. Image registration is a process to determine a geometric shift involving two or more images with the same object but different acquisition dates, viewpoints, and sensors. The basic idea of image registration in the frequency domain is estimating the shift between the test image and the reference image. In this study, we used the phase cross-correlation function to determine the geometric shift between two different acquisition Pleiades images from each polygon. Phase correlation is based on the translation property of the Fourier Transform. It transforms the displacement of two correlated images in the spatial domain into a phase difference in the frequency domain. The phase correlation algorithm calculates a phase difference map containing a single peak. The peak location is proportional to the value of the shift between the two images. Several Pleiades images were used in the experiment. The image processing level is an ortho-image, which is already pan-sharped. As a result, the highest geometric shift is on Polygon 2 based on the R-value, and the lowest error value is in polygon 1. The experiments also showed that the most significant error value is in the blue band, and the lowest is in the red band. The average geometric shift between the two Pleiades data is 1.187 pixels, with the most shift located in the blue band with a 1.282 pixels shift, and the Root Mean Square Error (RMSE) is 1.8. [ABSTRACT FROM AUTHOR]

Multispectral optical remote sensing images are often contaminated by haze and cirrus, especially for tropical regions such as Indonesia. This will affect the image quality and reduce the accuracy of the interpretation of the existing data. A number of methods for performing haze detection and haze removal have been developed by experts. However, haze detection and removal still remain one of the most important challenges for the correction of multispectral optical data. Haze detection and removal are also needed, especially in producing cloud-free mosaic images. A method to perform haze removal on SPOT 6/7 images using the haze index algorithm is presented in this paper. The haze removal method is developed based on the reflectance values of the blue and the red bands of SPOT 6/7 imagery. A new algorithm with new coefficient is proposed to remove the haze on SPOT 6/7 imagery. Haze removal is done by using haze index algorithm and haze threshold limit value. The experimental result demonstrate that the new coefficient used in the haze index algorithm give better result in performing haze removal on SPOT 6/7 imagery. [ABSTRACT FROM AUTHOR]

Football is a famous sport played around the world that involves various actions from defending to attacking the ball to score goals. Various rules like half-side, No hands, Throw-ins Direct and Indirect kicks, etc. are followed. In a game, players might get yellow-cards and red-cards which serves as warning and elimination from that particular game. There are-many factors like shots, possession, pass accuracy, yellow and red cards, etc. that decides which team wins in a match. In the data that we worked on,the Rating is calculated from the parameters which are mentioned. Machine learning regression algorithms have been applied to predict the Rating feature. The goal of this paper is to know which regression algorithm performs better to predict the Rating variable. Evaluation metrics like Root mean square error (RMSE), Mean absolute error (MAE), and R square (R2) are used to identify the best algorithm among the applied algorithms. [ABSTRACT FROM AUTHOR]

Cloud computing has always been an area of interest for the researchers and the industry oriented professionals. The cloud computing has its primary concern to deliver better performance with low energy consumption and least SLA violations. To rescue the rising challenges in the cloud computing, virtualization is an important process when the Physical Machines (PMs) are expected to work at a higher speed. This paper represents two co-operative algorithms: Cosine Elastic Clustered Scheduling Algorithm (CESCA) and Threshold-Modified Best Fit Algorithm (T-MBFD) for the VM selection and reallocation. CESCA clusters the VMs to be migrated based on three attributes namely CPU-Utilization (CPU), Associated RAM (AsRAM) and Bandwidth Utilization (BU) and the co-relation between them using cosine similarity. CESCA also prioritize the created clusters for the allocation. T-MBFD uses a logarithmic scale to neutralize the priority and the density of the created cluster to finally set the VM to its best suitable PM. The evaluation of the proposed algorithm is done using power consumption and total number of migrations. [ABSTRACT FROM AUTHOR]

Micro-expressions are unintended, impulsive, and fleeting facial expressions that have potential of disclosing the true feelings that people frequently strive to conceal. Experts have been paying increasingly close attention in recent years to the significance of the micro-expression as it contains abundant psychological information whose recognition has potential application value in the fields of lie detection, criminal investigation, neuromarketing, psychotherapy, teaching and learning. This paper analyzes the prominent applications of Micro-expression recognition and basic differences between two forms of facial expression (Macro-expression and Micro-expression) are also highlighted. A thorough analysis of mainstream recognition algorithms is also provided by elucidating each of their benefits and limitations. The results of this survey present readers with knowledge of Micro-expression recognition and potential application as well as related algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we studied the cyclicity parameter of dihedral group D2n and we introduced algorithm to compute these parameters. [ABSTRACT FROM AUTHOR]

In this paper, a new method of conjugate gradient methods is proposed that is used in minimizing functions to solve nonlinear fuzzy equations. This method is called hybrid conjugate gradient method, where it is hybridized using convex structures, The properties of global convergence and descent have been proven using some mathematical hypotheses, The numerical results and the figures showed a clear superiority of the proposed algorithm compared to the FR and KH algorithms for solving nonlinear fuzzy equations. [ABSTRACT FROM AUTHOR]