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Activity recognition offers a wide range of applications in various industrial processes and healthcare. This work proposes an approach to collect data from a spherical coordinate system using smartphones, then extract the highly efficient features using advanced preprocessing. The paper also proposes an algorithm to recognize activity using various ensemble machine-learning approaches based on extracted features. These approaches are evaluated under various combinations of features to analyze the accuracy, sensitivity, specificity, and training time. Experimental results reveal that weighted KNN performs best among all models by achieving 96.2% accuracy with 12 features. On the other hand, Bagged tree ensemble classifiers perform better than subspace KNN ensemble classifiers with an accuracy of 95.3% using 12 features. [ABSTRACT FROM AUTHOR]

The paper analyzes the influence of the adaptive mechatronic system parameters on the process of stabilizing the step of moving materials during automatic assembly. Dependences of variances and mathematical expectations of the displacement step are obtained depending on the parameters of the system under a random external action. Numerical simulation of the operation of the algorithms used is carried out, recommendations are given for the optimal choice of system parameters. [ABSTRACT FROM AUTHOR]

In the present paper algorithms for constructing a finite element mesh of compound domains and minimizing the ribbon width of the system of resolving equations by the finite element method (FEM) are considered. A technology for constructing a finite element mesh by connecting elementary subdomains, a semi-automatic method for partitioning a two-dimensional multiply connected domain with a curvilinear boundary, and ordering the node numbers of a finite element mesh is described. In this case, the algorithms allow, by ordering the numbering of nodes, to determine such a sequence in which the ribbon width of non-zero coefficients of the system of resolving equations by the FEM will be locally minimal. [ABSTRACT FROM AUTHOR]

This paper describes in detail the method and algorithm for calculating the numerical integration of non-stationary two-dimensional systems of Navier-Stokes equations for a compressible gas using high-order implicit (explicit) difference schemes, i.e., the Beam-Warming difference scheme. A number of mathematical transformations, such as non-dimensionalization of coordinates and physical parameters, transforming the shape of the channel into a square one, as well as thickening the integration steps with large gradients of unknowns, which make it possible to bring problems to a model one for solving similar problems. In addition to the study, as convergence in the number of calculated points, the dependence of the number of iterations on the Courant number and comparison of the results obtained by different proposed models for calculating the effective viscosity, as well as a number of numerical studies as co-occurrence, temperature inhomogeneities, non-design, the ratio of the sizes of the inlet slots of cocurrent flows and the length of the channel to the height of the main flow and the degree of expansion of the channel to the process of mixing and distribution of flows in the channel. It was revealed at what ratios of the initial parameters of the cocurrent flows and the linear dimensions of the channel a recirculation zone is formed. For a channel of constant cross section at large ratios of velocities (72, 46) and temperature (2, 33), as well as small ratios of the channel length to the height of the inlet slot of the main flow (5, 31), even at the same jet pressures in the initial section, the recirculation zone occupies more than 55% of the input section, and the length along the longitudinal coordinate reaches 20 cm. [ABSTRACT FROM AUTHOR]

Data Mining and Analytics have transformed the way business is conducted. Prominent techniques of Data Mining include Clustering, Classification and Association Rule Mining. There are a lot of algorithms for cluster analysis. But all algorithms have some limitations especially for high dimensional dataset. In this paper modified the k-mean algorithm for better cluster accuracy and minimized the computational time processing the dataset for student academic performance. Students undergo immense stress and pressure to outscore their competitors. Higher and Technical Education has many challenges owing to voluminous data and various dimensions for acquisition and processing. Various Clustering Algorithms provide partition accuracy, computational time, lead time but lack visual assessment post partition. This paper proposes a variant of k-Means algorithm using comparative criteria of Partition accuracy (PA) parameter and computational time. [ABSTRACT FROM AUTHOR]

Multiple Cloud Compute providers can be managed together as a Federated Cloud. Such an agglomeration introduces challenges for effective resource allocation and load balancing. To address such issues, multiple papers have been written describing various algorithms for resource allocation. This paper thus compares and contrasts various algorithms proposed. A Federated Cloud environment is usually organized as a many providers having multiple datacenters (DCs), each with multiple hosts, and each host having multiple VMs. We compare the various algorithms on the basis of execution time (time-till-response), latency (time between task scheduling and VM allocation), and load (resource utilization). [ABSTRACT FROM AUTHOR]

In this paper, two new hybrid algorithms are proposed to solve global optimization problems. The first algorithm combines the sooty tern optimization algorithm with the arithmetic optimization algorithm, and the second algorithm combines the sooty tern optimization algorithm with the conjugate gradient method. The sooty tern optimization algorithm is a metaheuristic algorithm proposed by Dhiman and Kaur in 2019 Sooty migration and attack behaviors seabirds in nature were the primary inspiration for this algorithm. In this paper, this algorithm was hybridized using the arithmetic optimization algori thm and the conjugate gradient method, and the two algorithms were measured by applying them to 10 functions, and the results of the hybrid algorithms were very good compared to the original algorithm. [ABSTRACT FROM AUTHOR]

Absolute value detectors implement a widely used spike-detection algorism called absolute value detection. 4-bit absolute value detectors are extensively used in computer storage and acquiring neural signals. As a basic logic component, there's a rising demand for low energy cost and high working frequency. This paper performed a series of optimizations to the conventional design. A traditional absolute value finder is composed of an absolute value finder using ripple-carry adder and multiplexer, and a comparator. In this paper, both parts are redesigned with static-CMOS logic and transmission gate logic (TGL) to shorten critical path. After the circuit topology is determined, two methods are used to control delay and power consumption: sizing logic gates and adjusting supply voltage. A technique called logic effort is used to derive the delay of critical path. By using a linear programming solver, three sets of parameters are found, in which 38.59-unit delay, 52.75-unit energy cost is a balanced design, with the lowest EDP (Energy delay product). [ABSTRACT FROM AUTHOR]

In order to make the auxiliary training ball picking robot pick up the ball efficiently, the path planning of the ball picking robot is needed. The traditional A-star algorithm can be applied to project the trajectory of the mobile robot under the motion model, but there are problems of general obstacle avoidance and insufficient accuracy of obstacle avoidance, based on the characteristics of iteration of the colony algorithm to improve the accuracy of the algorithm. This paper proposes an approach to make the A* algorithm iterative to improve the ability of the algorithm to perform path planning. Compared with the separate A* and colony algorithms, the algorithm in this paper is more accurate and efficient in handling complex routes. It is also paired with object recognition and grasping functions to achieve the function of identifying basketballs, grasping them and transporting them back to the players for the ball pickup robot. In the final Matlab simulation experiments, the effectiveness of target recognition, object grasping, and optimized path recognition is verified. Among them, the optimization algorithm of A* shortens the length of the path by 28% after 40 iterations. [ABSTRACT FROM AUTHOR]

The field of robotics research has been well developed with the combination of computers and machinery. With the advancement of artificial intelligence, mobile robots are also attracting more attention today, and path planning is a crucial foundational technology for mobile robots to complete transportation requirements and reach pertinent target areas. In an efficiency-conscious society, the single-goal planning of transmission is gradually failing to meet the needs of enterprises and factories for efficient operations, path planning that can simultaneously plan the optimal methods and reach many target points is increasingly replacing the conventional single-goal path planning. However, there are more factors to be considered in the real complex environment to face various complex road conditions, and for this reason, various single or hybrid algorithms are being optimized and solved for this kind of problem. This paper summarizes the main methods of path planning to simulate the scale of obstacles and environmental scene maps in various conditions, focusing on several basic algorithms and their hybrid algorithms for solving multi-objective path planning problems in global and local path planning, as well as their improvements and innovations on the basic algorithms. This paper's primary idea is to divide the multi-path planning process into various components and substitute each part into a suitable algorithm and model to solve it separately to accomplish the task of reaching multiple target points efficiently. [ABSTRACT FROM AUTHOR]

In their highest grade, brain tumors are the most widespread and dangerous diseases with a very short life span. Therefore, early automatic brain tumor detection is required to lower the fatality rate. Due to this, MRI is a commonly used imaging technology for diagnosis; however, it is practically impossible to do manual segmentation of the volume of data generated by MRI promptly. This paper is intended to analyze the suitable tools, techniques and language for automatic detection of Brain Tumor. From the nature of the problem, it is quite evident that it requires high precision of accuracy in detecting such a deadly disease in a very short period and if possible, in real-time, for a large number of datasets will be required not only to train the algorithm but also for its testing. Spark is an open-source platform to deal with lots of data. Spark's API, PySpark is coupled with Python language to enable the developers to develop a python script for the Spark processing engine. Deep learning algorithms are the most useful and appropriate for such types of tasks where a large amount of data is involved and requires high precision training and testing of the models and the algorithms. In this paper, we have discussed the selection of the tools, techniques and language for the implementation of the model for detecting the brain tumor. Tools like Google Colab and PySpark have been explored in this paper. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to improve the accuracy for automatic classification of meal waste from innovative trash data with the help of image processing. There are 2572 images for the classification of meal waste were used for this paper. The images are labeled as "Cardboard", "Plastic", "Paper", "Metal", "Glass", "Trash" and there are 20 number of images have been used for RF classifier taken as first set of machine learning algorithm and is compared with SVM algorithm taken as second set of machine learning algorithm With a g-power value of 80%, the revolutionary garbage data images, a threshold of 0.05%, a confidence interval of 95%, and a standard deviation, these photographs were gathered from various web sources. When compared to the SVM method, which had an accuracy of 61.45%, the proposed system's accuracy was enhanced to 84.81%, with a significant value of 0.001 (p 0.05) with a 95% confidence interval. This study found the meal waste from trash using the RF is significantly better than SVM algorithm. [ABSTRACT FROM AUTHOR]

With the spread of the Internet and a large number of network users, the problem of congestion on the network has arisen. This paper presents a strategy to reduce network congestion using clustering algorithms, K-means, and shortest path algorithms. The paper aims to solve the problems of congestion control, access to quality of service, reduce time wasted when sending and receiving network users, take advantage of network devices, and provide quality of service without data loss. The results of the proposed system simulation showed its effectiveness and accuracy through the simulation of the system. [ABSTRACT FROM AUTHOR]

One of the profound things that human being has ventured is to demystify the natural process that is happening in nature. Nature inspires technology to solve real-time problems. There are various nature-inspired techniques based on insects that are used in population-based or ecology-based nature-inspired algorithms. In this paper, the nature-inspired Bat Algorithm, which is based on the echolocation behavior of bats, is discussed in detail. The Bat Algorithm has various hybrids which are used in numerous applications; all these hybrids are reviewed and discussed in this paper. Systematic analysis is conducted using Scopus and WoS databases to analyze the research trend for these hybrid algorithms, study its variants and application domains, and predict the future for these nature-inspired algorithms. Numerous experiments based upon year-wise analysis, correlation of related terms, article growth, etc. are conducted and described in this paper. [ABSTRACT FROM AUTHOR]

Abnormality location frameworks conveyed for checking in oil and gas ventures are generally WSN based frameworks or SCADA frameworks which all experience the ill effects of significant impediments. WSN based frameworks are not homogenous or contradictory frameworks. They need facilitated correspondence and straightforwardness among districts and cycles. Then again, SCADA frameworks are costly, unyielding, not versatile, and furnish information with a long postponement. In this paper, a novel IoT based engineering is proposed for Oil and gas businesses to make information assortment from associated objects as straightforward, secure, hearty, solid and speedy. This paper audits and examinations recurrence and outcomes of disappointment rates for inward and outside consumption, human activity and normal powers are dissected and the normal disappointment rate for every disappointment instrument in oil pipeline transport frameworks. Verifiable information on results of the unplanned loss of regulation of inland pipelines are evaluated. To stay away from this event, designs for the most part do pressure transient investigation in the water powered plan period of pipeline network frameworks. Demonstrating and reproduction of homeless people in pipelines is an adequate and savvy technique for evaluating this issue and discovering specialized arrangements. Qualities and restrictions of different discovery and control procedures with some down to earth models are examined. [ABSTRACT FROM AUTHOR]

The paper is devoted to the modernization of the previously developed algorithm for detecting pulses in geoacoustic signals against a background of noise. The algorithm is implemented as a computer program. Geoacoustic signals are recorded in Mikizha Lake in Kamchatka using a combined receiver. The receiver simultaneously records acoustic pressure and three orthogonal components of its gradient at a point in the medium. From the registered signals, an input data series is formed in various ways for further processing by the program. The paper proposes a new way of forming the input series based on the Umov–Poynting vector. The improved algorithm detects hundreds of times more pulses in highly noisy signals. This increases the accuracy of determining the direction to their sources. Detailed histograms of the spatial location of geoacoustic emission sources are constructed. Note that the algorithm can be applied to pulsed acoustic signals only if they are registered by a combined receiver, i.e, the values of pressure and its gradient at the point of the medium under study must be known simultaneously. [ABSTRACT FROM AUTHOR]

Palm oil is an important commodity for Indonesia. Therefore, oil palm cultivation is a supporter of the success of oil palm commodities in producing products. The harvesting process is an activity that must be considered because it affects the production and quality of oil palm fresh fruit bunches. For this reason, the sorting of oil palm fresh fruit bunches is very important. This paper tries to classify the maturity level of oil palm fresh fruit bunches through image management using the Linear Discriminant Analysis (LDA) algorithm. LDA algorithm is an approach used to classify data into several classes. The steps taken in this paper are starting with the transformation of the color space from RGB images to L*a*b images, then image segmentation with thresholding and feature extraction with HSV. Image transformation to HSV aims to make it easier to classify image characteristics based on hue and saturation. Furthermore, the LDA algorithm will perform a dispersion matrix analysis in order to obtain optimal projections to enter into spaces to look for patterns that can be separated. This paper finds that the classification of oil palm fresh fruit bunches image processing using the LDA algorithm produces an accuracy rate of 85%. [ABSTRACT FROM AUTHOR]

This paper introduce the comprehensive overview of the Arithmetic Optimizer Algorithm (AOA). Laith Abualigah et.al introduced novel meta-heuristic approached of AOA by arithmetically modelled and executed to implement the optimization developments in an extensive array of spaces. The review included the application and variants of Arithmetic Optimization Algorithm to solve complicated engineering problems. To showcase its applicability the performance of AOA is checked by the Laith Abualigah et.al on 29 benchmark functions and actual world engineering design problems. This review paper also given the idea how AOA has been evaluated by the analysis of enactment, convergence performances and the computational involvedness. This paper also review all the experimental results of AOA which are tested on the different uni-modal and multimodal benchmarks functions and also review the usefulness of AOA for solving the challenging engineering optimization problems compared with others optimization algorithms. This paper also covers all the parameters values that has been used for the comparative algorithms. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) has become an easy target. As there are numerous devices, management is hard and security problems arise. There are various encryption algorithms to provide the security of data on an IoT device. One of the encryption algorithms that is used for IoT devices is the AES algorithm. Although the AES algorithm is not a lightweight algorithm, it can effectively be used to protect the data of IoT devices. In this paper, a Bluetooth Low Energy (BLE) module is used as an IoT device. The AES algorithm is used to encrypt the data that is generated on the BLE device. There are two options for encryption: hardware-based encryption and software-based one. The aim of the paper is to show the hardware implementation efficiency of the AES algorithm on an IoT device by comparing it with software implementation. As a result, the hardware implementation of the AES algorithm is efficient when using the proper hardware infrastructure. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) has become an easy target. As there are numerous devices, management is hard and security problems arise. There are various encryption algorithms to provide the security of data on an IoT device. One of the encryption algorithms that is used for IoT devices is the AES algorithm. Although the AES algorithm is not a lightweight algorithm, it can effectively be used to protect the data of IoT devices. In this paper, a Bluetooth Low Energy (BLE) module is used as an IoT device. The AES algorithm is used to encrypt the data that is generated on the BLE device. There are two options for encryption: hardware-based encryption and software-based one. The aim of the paper is to show the hardware implementation efficiency of the AES algorithm on an IoT device by comparing it with software implementation. As a result, the hardware implementation of the AES algorithm is efficient when using the proper hardware infrastructure. [ABSTRACT FROM AUTHOR]

This paper analyzed the feature selection for reducing the number of input variables when developing a predictive model. Boruta Algorithm is using in this paper as a wrapper around a Random Forest classification algorithm. Boruta algorithm is one of the algorithms used to determine the significant variables (feature selection) in a classification model in the machine learning approach, as supervised learning. Our results show that on the German Credit Data from the UCI Machine Learning with 20 variables, feature selection using Boruta Algorithm with Python Programming obtains 4 significant features. [ABSTRACT FROM AUTHOR]

This paper presents methods for software licenses & patents issuance & validation using the peer-to-peer block chain technology and leveraging the properties of non-fungible token in order to state proof of ownership of the software licenses and patents. Further, the paper illustrates the implementation of different hashing algorithms to secure the confidential and non-public facing data associated with software licenses and patents and a mechanism to do validation of the ownership and authenticity of software licenses and patents on public block chain. By tokenization of these license and patent documents the intellectual property creators will have the capability to only reveal the data to the owners of their choice or whom they can trust to reveal the original data of these documents and can provide only ownership to the other holders of the non-fungible token. The paper will also illustrate brief comparisons between the gas requirements of different on-chain hashing algorithms and then using them while minting the non-fungible tokens for the licenses and patents. [ABSTRACT FROM AUTHOR]

The term optimization refers to find the best possible solution. Gradient computation and Nature evolution rule are the two main bases of almost all optimization algorithms. Single objective optimization problems deal with optimization of one objective function whereas multi-objective problems deal with two or more objective functions to be optimized. Many algorithms have been developed till now for the optimization of multi-objective problems. In this paper, we will review and compare two multi-objective evolutionary algorithms SPEA2 (Strength Pareto Evolutionary algorithm) and PAES (Pareto Archived Evolution strategy). Evolutionary algorithms use fitness function, selection, crossover, and mutation procedures to generate a new population. SPEA2 algorithm uses the population set as well as an external set known as an archive to select and generate the next population members. PAES uses a local search approach to generate new solutions and maintains an archive to refer to previously generated solutions and thereby find the dominance ranking of current and newly generated solutions. Four sets of problems (DTLZ1, DTLZ2, ZDT1, ZDT2) will be solved using these algorithms and the results of these algorithms on these test problems will be compared in this paper. [ABSTRACT FROM AUTHOR]

Feature selection is an important technique in building prediction systems. In various searches, the judgment of the relevancy of a feature is often calculated using all the instances of the considered sample. However, when the dataset size grows, some of the instances are not useful for weighting the features. In this paper, we rank features according to a fitness function that relies on the relevancy using all instances and on the relevancy using a maximum number of significant instances (which really contribute to the feature positive relevancy with the target variable). The relevancy is based on preordonnances theory where the instances are expressed in pairs. The proposed algorithm can be mainly divided into three steps, namely, (a) eliminating all features that are in disagree with the target feature, (b) Finding the best subset of instances, to each feature, that maximize the relevancy and which the cardinal tends to the cardinal of instances in the original dataset, and (c) Ranking features. The second step is defined by dividing the dataset (instances of each feature) into several consistent regions by fuzzy clustering. Then, performing GA-based instances selection independently within each cluster. Finally, aggregating of the partial results by the ensemble voting. Experimental results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Uncertainties, parameters variations and external disturbances may affect highly the task of lane-keeping, lane-changing and path-tacking autonomous vehicles. This paper presents a novel variable gains second-order sliding mode algorithm for en-hanced path tracking performance of autonomous vehicles subject to uncertainties and disturbances. The used algorithm is a modified super-twisting with both nonlinear and linear correction terms. Hence, the proposed method doesn't suffer from the chattering phenomenon, ensures high order precision and guarantees both the robustness against perturbations and the convergence in finite-time during the reaching phase. To evaluate the efficiency of the developed approach, a double lane-changing scenario is considered in simulation for an uncertain autonomous vehicle and the results are compared with the first-order sliding mode. [ABSTRACT FROM AUTHOR]

The concept of web of things (WoT) is to connect anything in the physical world and then display them in the World Wide Web, that is the major objective, but that mean information storage or transfer that led to thinking how can protected this information from attacks, this can happen by protected information by encryption it. Cryptography and chaos inextricably linked because of many features. In this paper uses a novel model for the encryption of information via WoT, we suggest HIGHT and Simon as a block cipher algorithm using for lightweight encryption by make a new hybrid between them. Also use chaotic system to take key from it; moreover, we use other operations to arrive the best result. [ABSTRACT FROM AUTHOR]

Path planning algorithms is the most significant area in the robotics field. Path Planning (PP) can be defined as the process of determining the most appropriate navigation path before a mobile robot moves. The goal of optimization of PP is to find a path that is close to or optimal path. Multi-objective optimization (MOO) is concerned with finding the best solution values that satisfy multiple objectives, such as shortness, smoothness, and safety. MOO present the challenge of making decisions while balancing these contradictory issues through compromise (tradeoff). As a result, there is no single solution appropriate for all purposes in MOO, but rather a range of solutions. This paper the problem of PP is investigated with path length, security and smoothness objectives for autonomous mobile robots in dynamic environment with moving target. The proposed approach based on Bat algorithm (BA). The proposed solution is tested in a number of simulations with varying scenarios. The simulation results show that multi objective can be achieved efficiently in a dynamic environment which proves its effectiveness. [ABSTRACT FROM AUTHOR]

During pandemic circumstance world, the size of data is exploded like rocket speed. At the same time, there is a need to process it and investigate the enormous volumes of information. The company and research tool is Hadoop and is most broadly utilized in information serious applications. The Hadoop default processing nature is homogeneous. The limit of enormous measure of data in Hadoop is finished utilizing Hadoop Distributed File System. The performance is degraded when it applied to heterogeneous environment. Hadoop mainly uses 2 components viz HDFS used for storage purpose and MapReduce used for programming. In this paper we introduce Optimal Data Placement Algorithm (ODPA). [ABSTRACT FROM AUTHOR]

The real world has been changed into a virtual one as a result of the pandemic scenario. Cloud computing plays a major role for storing and computing the data using remote computing infrastructure for solving the day to day activities. The LB Techniques are used in the cloud computing process. LB techniques distributing the system's load among its various nodes in order to maximise resource efficiency and job response time. The major issues in load balancing are Geographical Distributed system, Single point of failure (SPoF), live Migration of instance, Heterogeneous device, Storage device Management, Scale-out/scale-in and Complexity of Algorithm. Load balancing ensures that each node in the network shortens reaction times, reduces expenses, and boosts performance. This paper address the various cloud computing balancing algorithm survey. [ABSTRACT FROM AUTHOR]

Today's modern world is being driven towards automation and as well gathering any type of information from every nook and corner of the world which is possible through one of the technology named Internet of Things(IoT). IoT is not just a single technology but it is a combination of various technologies like embedded systems, wireless sensor networks, cloud computing etc. Basic IoT network consists of various layers; one among them is the device layer which is the base layer responsible for gathering the data from physical world through the sensors. Various sensors can be deployed into an area depending upon the type of application it is used for. Various homogenous sensors can be made into clusters due to which it would be beneficiary in terms of bandwidth and power consumption, number of internet connections required, fault tolerance, longevity of devices etc. This paper presents a comparison between various algorithms used to cluster the sensors at device level. [ABSTRACT FROM AUTHOR]

By using thoracic electrical bioimpedance (TEB) analysis stroke volume calculated for sudden cardiac arrests. In this paper proposed robust normalized diffusion least power algorithm (RNDLPA) is proposed to get high resolution components. In clinical scenarios, TEB components are contaminated with various artifact components, which interrupts the identification of several features in stroke intensity. The number of multiplications is also one of the key elements for monitoring in health care applications. Hence by proposed RNDLPA algorithm it reduces computational complexity, stability is improved and also steady state error rate is reduced. To eliminate noise components various adaptive cancelers are presented and they are compared with proposed algorithm. Further to show performance improvement signal to noise ratio (SNR) improvement is taken into consideration it gets better values 14.3594 dB and 12.3551 dB for respiratory noise and muscle noise respectively when compared to existed techniques. [ABSTRACT FROM AUTHOR]

Xilinx System Generator (XSG) integrates with MATLAB Simulink to build a complex system through Xilinx and Simulink blocks. This paper presents a hardware implementation of various image processing techniques to modify and extract the pixel information from various input images in run time. Complex coding strategies are eradicated with integrated system. The intricacy in structural design is reduced to a huge extent using Xilinx System generator. The reconfigurable attribute of Field Programmable Gate Array (FPGA) bags the massive advantages in image processing like co simulating with selective pixel and verified on FPGA. Image processing algorithms explored in methodical manner by performing diverse operations like edge detection, and color enhancement, complement, contrast stretching etc. The proposed image processing techniques (IPT) intended for various applications. Dissimilar image processing techniques are processed by FPGA and displayed on Video Graphics Array (VGA). Image processing system is designed in XSG, hardware description language (HDL) like VHDL & Verilog code is generated by processing system by system generator token. Design is implemented on Artix-7 and targeted on Basys3 reconfigurable hardware (XC7A35T-1-CPG236C). [ABSTRACT FROM AUTHOR]

We live in times where technology is advancing at an unprecedent rate. From online communication, shopping, data sharing, cashless payments to transmitting sensitive information through internet, everything is being done online. These online methods of data transfer are always vulnerable to online phishing and frauds. Therefore, it becomes very imperative to devise ways for safe and secure transmission of data. To keep up this, new and secure cryptographic schemes are the need of the modern age. In this paper, the author presents a new improvised encryption technique which makes modifications in the traditional approach of the Caesar cipher by combining it with extended Hill cipher. Additionally, it makes use of the Linear congruential method to generate the randomized secret key. [ABSTRACT FROM AUTHOR]

The paper considers the main methods of testing a software product at the development stage. The methods are compared with typical architectures of software products. The optimization problem of the testing process is solved on the basis of the algorithm development that presents the process under consideration at a more detailed level. The optimization of the algorithm is presented on business processes by BPMN 2.0 notation. [ABSTRACT FROM AUTHOR]

The article presents the software implementation of the bioinspired ant colony algorithm, which is used to form the composition of the multiversion software modules. It is shown that the reliability of programs and performance indicators of multiversion software significantly depend on the results of the design stage, at which a decision is made on the formation of the structure of the program complex and the choice of the composition of multiversion software modules. The solution of these problems is supported by the developed software application. The application is focused on the user, who sets all the parameters of the multiversions. Within the framework of the bioinspired approach, the paper presents the main functions implemented in a software package using ant colony algorithms. [ABSTRACT FROM AUTHOR]

Active Contour Model is the most original algorithm among the class of Contour Finding methods. Its capability is tested against wound parameter detection given image data. The performance is less satisfying, in general, few perimeters are found correctly, but the rest are far from ideal. However, through some tuning by adding an extra pre-processing step, the result can be improved. Image Interpolation is used to achieve this improvement. This paper reports how significant the improvement is. [ABSTRACT FROM AUTHOR]

One of the measurements for evaluating time series forecasting performances is the mean square error (MSE). This paper proposes an algorithm to find the smallest MSE. To capture the four components of the time series data (namely, seasonal variation, trend variation, cyclical variation, and random variation), the exponential smoothing method was used. This method uses three factors for smoothing where the data are the smoothing factor, 0 < α < 1, the trend smoothing factor, 0 < γ < 1, and the seasonal change smoothing factor, 0 < β < 1. First, all possible combination values of smoothing factors will be generated to one decimal digit. After that, the smallest MSE of those combinations will be determined by using an algorithm and observing their convergence pattern. [ABSTRACT FROM AUTHOR]

In this paper, we propose a modified mean-variance portfolio selection procedure using the proximal gradient method. This novel selection procedure has the advantage of encouraging sparsity in the entire portfolio usingl0−norm while requiring only O(n) storage. The proximal gradient method (PSG) introduces a multiple damping gradient (MDG) method to reduce the computation time, with Lipschitz constant serving as the step size, and iterative thresholding (IHT) as the proximal method to induce the sparsity of the portfolio. To find the optimal portfolio, an efficient algorithm is developed. The performance of the proposed selection procedure is illustrated by its application to real-life data set. It is shown that the proposed portfolio performs consistently better in comparison with other methods. [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]

The numerical performance of an algorithm for the inversion of a generalization of the regularized incomplete beta function (the noncentral beta distribution, Bp,q(x, y)), is discussed in this paper. The algorithm uses as starting point the asymptotic approximations obtained in [1] for the inversion problem: find y in the equation Bp,q(x, y) = z, where p, q, x, z are given. The asymptotic approximations are combined with Newton iterations in the resulting algorithm. [ABSTRACT FROM AUTHOR]

In this paper we build a constructive algorithm that returns a rectifiable curve that connects two points in a weakly convex set in a Hilbert space. We have proven that this algorithm converges and obtained an estimate on the curve's length and compare the length of the curve obtained to known results. [ABSTRACT FROM AUTHOR]