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In this paper, a proposal is made for a cryptographic algorithm designed for passive ultra-high-frequency (UHF) radio frequency identification systems. The algorithm relies on the advanced encryption standard (AES) as its fundamental encryption technique, augmented by two supplementary steps: the initial step involves generating a random key and the second is the randomization of data, this introduces an extra level of security to encryption process against attacks. The developed architecture has been optimized to minimize hardware resource consumption with faster execution speed. The algorithm has been simulated, synthesized and implemented in an xtreme digital signal processing (DSP) starter kit equipped with xilinx’s spartan-3A DSP 1800A edition and it serves the purpose of encrypting and decrypting user data on a radio frequency identification (RFID) passive tag. The main objective is to make it difficult to break the algorithm because of its multiple steps. The experimental results showed that the speed, functionality and cost of encryption and decryption make this a perfectly practical solution, providing a satisfactory level of security for today’s communications systems, or other electronic data transfer processes where security is required. [ABSTRACT FROM AUTHOR]

A comprehensive systematic study was carried out in order to identify various deep learning methods developed and used for predicting student academic performance. Predicting academic performance allows for the implementation of various preventive and supportive measures earlier in order to improve academic performance and reduce failure and dropout rates. Although machine learning schemes were once popular, deep learning algorithms are now being investigated to solve difficult predictions of student performance in larger datasets with more data attributes. Deep neural network prediction methods with clear modelling and parameter measurements formulated on publicly available and recognised datasets are the focus of the research. Widely used for academic performance prediction, backpropagation algorithms have been trained and tested with various datasets, especially those related to learning management systems (LMS) and massive open online courses (MOOC). The most widely used prediction method appears to be the standard artificial neural network approach. The long-short-term memory (LSTM) approach has been reported to achieve an accuracy of around 87 percent for temporal student performance data. The number of papers that study and improve this method shows that there is a clear rise in deep learning-based academic performance prediction over the last few years. [ABSTRACT FROM AUTHOR]

Genetic algorithm (GA) is one of popular heuristic-based optimization methods that attracts engineers and scientists for many years. With the advancement of multiand many-core technologies, GAs are transformed into more powerful tools by parallelising their core processes. This paper describes a feasibility study of implementing parallel GAs (pGAs) on a SpiNNaker. As a many-core neuromorphic platform, SpiNNaker offers a possibility to scale-up a parallelised algorithm, such as a pGA, whilst offering low power consumption on its processing and communication overhead. However, due to its small packets distribution mechanism and constrained processing resources, parallelising processes of a GA in SpiNNaker is challenging. In this paper we show how a pGA can be implemented on SpiNNaker and analyse its performance. Due to inherently numerous parameter and classification of pGAs, we evaluate only the most common aspects of a pGA and use some artificial benchmarking test functions. The experiments produced some promising results that may lead to further developments of massively parallel GAs on SpiNNaker. [ABSTRACT FROM AUTHOR]

Student academic accomplishment is the foremost focus of every educational institution. In developing student achievement in educational institutions, the researchers finally created a new research area, namely educational data mining (EDM). How the feature selection (FS) algorithm works is by removing unrelated data from educational datasets; therefore, this algorithm can improve the classification performance managed in EDM techniques. This research presents an analysis of the performance of the FS algorithm from the student dataset. The results received from other FS algorithms and classifiers will help other researchers to gain some best combination regarding FS algorithms and the classification. Selecting features that are relevant for student forecast models is a sensitive problem to stakeholders in education because they must make decisions based on the results of the prediction models. For the future, our paper seeks to play a decisive part while developing quality concerning education, as well as guiding different researchers in conducting educational interventions. [ABSTRACT FROM AUTHOR]

Data recovery is one of the tools used to obtain digital forensics from various storage media that rely entirely on the file system used to organize files in these media. In this paper, two of the latest techniques of file recovery from file system (new technology file system (NTFS)) logical data recovery, Aho- Corasick data recovery were studied, examined and a practical comparison was made between these two techniques according to the speed and accuracy factors using three global datasets. It was noted that all previous studies in this field completely ignored the time criterion despite the importance of this standard. On the other hand, algorithms developed with other algorithms were not compared. The proposed comparison of this paper aims to detect the weaknesses and strength of both algorithms to develop a new algorithm that is more accurate and faster than both algorithms. The paper concluded that the logical algorithm was superior to the Aho-Corasick algorithm according to the speed criterion, whereas the algorithms gave the same results according to the accuracy criterion. The paper leads to a set of suggestions for future research aimed at achieving a highly efficient and high-speed data recovery algorithm such as the file-carving algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we presented two developments, the first deals with generalizing the modernization of the damped Dai-Liao formula in an optimized manner. The second development is by suggesting a monotone line search formula for our new algorithms. These new algorithms with the new monotone line search yielded good numerical results when compared to the standard Dai-Liao (DL) and minimum description length (MDL) algorithms. Through several theorems, the new algorithms proved to have a faster convergence to reach the optimum point. These comparisons are drawn in the results section for the tools (Iter, Eval-F, Time) which are a comprehensive measure of the efficiency of the new algorithms with the basic algorithms that we used in the paper. [ABSTRACT FROM AUTHOR]

The land registry system is one of the very important department in any governance system that stores the records of land ownership. There are various issues and loopholes in the existing system that give rise to corruption and disputes. This requires a significant chunk of valuable government resources from judiciary and law enforcement agencies in settling these issues. Blockchain technology has the potential to counter these loopholes and sort out the issues related with land registry system like tempering of records, trading of the same piece of land to more than one buyer. In this paper, a secure and reliable framework for land registry system using Blockchain has been proposed. The proposed framework uses the concept of smart contract at various stages of the land registry and gives an algorithm for pre-agreement. First, we describe the conventional land registry system and reviews the issues in it. Then, we outline the potential benefits of employing Blockchain technology in the land registry system and presented a framework. Finally, a number of case studies are presented. [ABSTRACT FROM AUTHOR]

Radiological imaging generates large data volumes for diagnosing. These data volumes still constitute an immense challenge for individual radiology practices, either when complying with the archive, hacker manipulation or swiftly distributing images among specialists as part of the diagnostic process. Analyzing images through illegal distortions may lead to inaccurate medical decisions. Watermarking techniques can be used to authenticate images, detect, and recover illegal changes made to teleradiology images as well. In this paper, a new blind medical image watermarking joint cryptocompression system is proposed to simultaneously achieve watermarking and compression. A discrete wavelet transform is applied to the medical image to locate different frequency sub-bands. The watermark is scrambled using one-dimensional chaotic map generator with uniform distribution modulo-one transformations for generating highly independent and uniformly distributed random chaotic sequences. A particle cryptocompression algorithm is applied for the insertion step: the scrambled watermark is inserted into the highest singular value derived from the compressed low-frequency sub-band, ensuring a delicate balance between imperceptibility and robustness. From the conducted tests and comparison of the obtained results with other techniques, we concluded that the robustness of the algorithm under various attacks was improved by the excellent value of the NC parameter, especially in the case of compression and geometric attacks. [ABSTRACT FROM AUTHOR]

The generalized new Mersenne number transforms (GNMNTs) have proved to be significant number theoretic transforms (NTTs) used to calculate convolutions and correlations accurately. In this paper, by applying the principles of the decimation-in-frequency (DIF) approach with appropriate relations in finite field modulo Mersenne primes, two new fast algorithms for computing odd NMNT (ONMNT) and odd-squared NMNT (O²NMNT) are introduced. Moreover, by formulating a unified index mapping scheme for data sequence, a close relationship between the structures of the developed algorithms has been established. As a result, it has been shown that only a single universal butterfly structure is adequate to execute both algorithms. Consequently, a unified implementation platform can be used to compute the ONMNT as well as the O²NMNT. The validity of the development has been checked via an example for fast calculations of different types of convolutions, using both the GNMNTs and the proposed algorithms. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present an algorithm to determine the shortest path for quadrotor to be able to navigate in an unknown area. The problem in robot navigation is that a robot has incapability of finding the shortest path while moving to the goal position and avoiding obstacles. Hence, a modification of several algorithms is proposed to enable the robot to reach the goal position through the shortest path. The algorithms used are fuzzy logic and cell decomposition algorithms, in which the fuzzy algorithm which is an artificial intelligence algorithm is used for robot path planning and cell decomposition algorithm is used to create a map for the robot path, but the merger of these two algorithms is still incapable of finding the shortest path. Therefore, this paper describes a modification of the both algorithms by adding potential field algorithm that is used to provide weight values on the map in order for the quadrotor to move to its goal position and find the shortest path. The modification of the algorithms has shown that quadrotor is able to avoid various obstacles and find the shortest path so that the time required to get to the goal position is shorter. [ABSTRACT FROM AUTHOR]

Image fusion is a comprehensive information processing technique and its purpose is to enhance the reliability of the image via the processing of the redundant data among multiple images, improve the image definition and information content through fusion of the complementary information of multiple images so as to obtain the information of the objective or the scene in a more accurate, reliable and comprehensive manner. This paper uses the sparse representation method of compressive sensing theory, proposes a multi-source and multi-feature image information fusion method based on compressive sensing in accordance with the features of image fusion, performs sparsification processing on the source image with K-SVD algorithm and OMP algorithm to transfer from spatial domain to frequency domain and decomposes into low-frequency part and high-frequency park. Then it fuses with different fusion rules and the experimental results prove that the method of this paper is better than the traditional methods and it can obtain better fusion effects. [ABSTRACT FROM AUTHOR]

Multi-objective evolutionary algorithm is a powerful tool in resolving multi-objective optimization problems. This algorithm inherits the advantages of parallel random search, strong global searching capability and the ability to solve highly-complicated non-linear problems of evolutionary algorithm and it is usually used in the optimization problems with multiple mutual conflicts. However, such algorithms are slow in convergence and easy to be trapped in local optimal solution. This paper proposes a multi-objective dual population genetic algorithm (MODPGA) and explores the improvement strategies of multi-objective genetic algorithm. The adoption of self-adaption and dual population strategy can guarantee that the algorithm of this paper can converge to Pareto solution set in a reliable and quick manner and it can perform more extensive search on the objective function space and conduct more samples on multi-objective functions so as to be closer to the approximate optimal solution set of global optimal solutions. This solution set also includes more optimal feasible points and provides reliable basis for the decision making. [ABSTRACT FROM AUTHOR]

The Euclidean geometry (EG) based low-density parity check (LDPC) codes are enciphered and deciphered in various modes. These algorithms have the back-and-forth between decoding delay, and power usage, device unpredictability resources, and error rate efficacy are all available with these methods. As a result, the goal of this paper is to develop a comprehensive method to describe both soft and burst error bits for optimal data transfer. As a result, for EG-LDPC codes, a hybrid soft bit flipping (HSBF) decoder is suggested, which decreases decoding complications while improving message data transfer. A simulation model is formed using Xilinx synthesis report to study decoding latency, hardware usage, and power usage. A HSBF decoder is used in this paper, which accepts a 64-bit coding sequence and assigns 64 Adjustable nodes to it. It checks all customizable cluster connections and quantifies adjustable node values and actions. As a consequence of the data collected, our simulation model demonstrates that the HSBF technique outperforms soft bit flipping (SBF) algorithms. As a result, the techniques are ideal for usage in intermediate applications and as well as in cyber security processing technologies, medical applications. [ABSTRACT FROM AUTHOR]

Pertinent to the problems that wind turbine blades operate in complicated conditions, frequent failures and low replacement rate as well as rational inventory need, this paper, we build a fault tree model based on in-depth analysis of the failure causes. As the mechanical vibration of the wind turbine takes place first on the blades, the paper gives a detailed analysis to the Failure mechanism of blade vibration. Therefore the paper puts forward a dynamic prediction model of wind turbine blade failure number based on the grey theory. The relative error between its prediction and the field investigation data is less than 5%, meeting the actual needs of engineering and verifying the effectiveness and applicability of the proposed algorithm. It is of important engineering significance for it to provide a theoretical foundation for the failure analysis, failure research and inventory level of wind turbine blades. [ABSTRACT FROM AUTHOR]

The history recorded moon as the most inspiring object in the sky, but it combined with visibility issues to study the phases. This research paper proposes a novel algorithm named Sunfa Ata Zuyan (SAZ), which is meant to extend the shape detection algorithms to aim for lunar phase deceleration and overcome the difficulties encountered by the previous methods to find the moon and determine its phase. The paper sets to investigate two aims. First, propose the add-on algorithm SAZ to determine the lunar phase's data faster. Secondly, evaluate the Raspberry Pi as the main CPU due to its compact size and power as the primary processor based on the idea of a portable designed system. Then to examine the ability of the SAZ algorithm, it's combined with famous algorithms like hue, saturation and value (HSV), Canny, erosion, shape detection, and binarization has been tested on both personal computers (PC) and Raspberry Pi with the same images being compared. The results show that SAZ will help the shape detection algorithm to find the object and disclose the moon phases. Furthermore, the Raspberry Pi, functioning as a CPU, can perform as a hand-to-hand system to determine the lunar phase as a compact portable remote sensing structure. [ABSTRACT FROM AUTHOR]

Digital transformation (DT) is one of the key technologies with effective impacts on many traditional processes towards a digital world. DT influences the way other digital services behave. Hence, there is a need to consider DTrelated processes carefully specifically while designing phase. DT contributes to many services. It can, for example, contribute to implement security tasks applied to digital contents and therefore can be applied to change contents being secured. One of the transformation ways applied in security is to consider the way those digital contents are being stored or transferred. This paper proposes a DT algorithm (DTA) for energy internet-of-things (EIOT) contents. DTA consists of two steps, to convert original contents to another digital form and to transfer that form utilizing IOT. This paper utilizes DT in term of security. EIOT contents are converted to increase security. It is aimed to transfer EIOT contents to destination safely and efficiently. Thus, EIOT contents are transformed first to hide original contents. To make sure that the transferring process is done safely, DTA is evaluated in terms of efficiency, accuracy, and robustness. Results confirm that DTA is efficient, accurate, and robust against loss of bits caused by transferring. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a matrix-free double-search direction based on the updated parameter file of the double-search direction with a new mathematical formula for the gamma parameter. When comparing the numerical results of this algorithm with the standard (HWY) algorithm which given by Halilu, Waziri and Yusuf in 2020. We get very robust numerical results. The proposed algorithm is devoid of derivatives to solve large-scale non-linear problems by combining two search directions in one search direction. We demonstrated the overall convergence of the proposed algorithm under certain conditions. The numerical results presented in this paper show that the new search direction is useful for solving widespread non-linear test problems. [ABSTRACT FROM AUTHOR]

Modern-day, global, and clever array systems have several distinguished approaches with regard to a wireless device. In this paper, a comparative analysis has been carried out amongst nine direction of arrival (DOA) algorithms: Capon, multiple signal classification (MUSIC), Bartlett, Pisarenko, linear prediction, maximum entropies, and min-norm, root- MUSIC, and estimation of signal parameters via rotational invariance technique (ESPRIT). in this paper reduces the number of antennas and snaps necessary to observe six target swithout interference for several DOA estimation methods. After comparing and analyzing all the algorithms, it has been shown that the music algorithm is the best algorithm that reduces interference and detects the desired sources. MATLAB R2019b was used in the simulation process. [ABSTRACT FROM AUTHOR]

Telemedicine applications allow the patient and doctor to communicate with each other through network services. Several medical image compression techniques have been suggested by researchers in the past years. This review paper offers a comparison of the algorithms and the performance by analysing three factors that influence the choice of compression algorithm, which are image quality, compression ratio, and compression speed. The results of previous research have shown that there is a need for effective algorithms for medical imaging without data loss, which is why the lossless compression process is used to compress medical records. Lossless compression, however, has minimal compression ratio efficiency. The way to get the optimum compression ratio is by segmentation of the image into region of interest (ROI) and non-ROI zones, where the power and time needed can be minimised due to the smaller scale. Recently, several researchers have been attempting to create hybrid compression algorithms by integrating different compression techniques to increase the efficiency of compression algorithms. [ABSTRACT FROM AUTHOR]

Aiming at the current problems of community detection algorithm in which user's properties are not used; the community structure is not stable and the efficiency of the algorithm is low, this paper proposes a community detection algorithm based on the user influence. In terms of the concept of user influence in the subject communication and the PageRank algorithm, this paper uses the properties of nodes of users in social networks to form the user influence factor. Then, the user with the biggest influence is set as the initial node of new community and the local modularity method is introduced into detecting the community structure. Experiments show that the improved algorithm can efficiently detect the community structure with large scale users and the results are stable. Therefore, this algorithm will have a wide applied prospect. [ABSTRACT FROM AUTHOR]

At present, a good deal of methods for the Maximum Power Point Tracking (MPPT) has been used in engineering applications. However, Matlab simulation proved that they were difficult to harmonize the stability and speed ability of system. In addition, in order to maximize the use of PV panels 'power, the paper focused on aneonatalalgorithm for Maximum Power Point Tracking (MPPT). Based on the algorithm, this paper designed an improved and feasible variable step perturbation and observation method which well alleviated the conflict that the maximum power point tracking could not take into account the stability and speed of response efficiently. [ABSTRACT FROM AUTHOR]

Clustering refers to the method grouping the large data into the smaller groups based on the similarity measure. Clustering techniques have been applied on numerical, categorical and mix data. One of the categorical data clustering technique based on the soft set theory is Maximum Attribute Relation (MAR). The MAR technique allows calculating all of pair multi soft set made. However, the computational complexity is still an issue of the technique. To overcome the drawback, the paper proposes the alternative algorithm to decrease the complexity so get the faster response time. In this paper, to get the similar results as MAR without calculating all pair of soft set is proved. The alternative algorithm is implemented in MATLAB Software, and then experimental is run on the 10 benchmark datasets. The results show that the alternative algorithm improves the computational complexity in term of response time up to 36.46%. [ABSTRACT FROM AUTHOR]

Today, the wireless sensor network (WSN) plays an important role in our daily life. In addition, it is used in many applications such as military, medical, greenhouse, and transport. Due to the sending data between its nodes or to the base station requires a connection link, the sensor nodes can be exposed to the many attacks that exploit the weaknesses of the network. One of the most important types of these attacks is the denial of service (DoS). DoS attack exhausts the system's resources that lead the system to be out of service. In this paper, a cyber-security algorithm is proposed for physical level of WSN that adopts message queuing telemetry transport (MQTT) protocol for data transmission and networking. This algorithm predicts the DoS attacks at the first time of happening to be isolated from the WSN. It includes three stages of detecting the attack, predicting the effects of this attack and preventing the attacks by excluding the predicted nodes from the WSN. We applied a type of DoS attack that is a DoS injection attack (DoSIA) on the network protocol. The proposed algorithm is tested by adopting three case studies to cover the most common cases of attacks. The experiment results show the superior of the proposed algorithm in detecting and solving the cyber-attacks. [ABSTRACT FROM AUTHOR]

Software defined network suggests centralizing network knowledge in one network portion by separating the routing (control plane) mechanism from the transmission network packet operation (data plane). The control plane is composed of one, two or more controllers which are considered as softwaredefined networking (SDN) network brain where the real intelligence is incorporated. The process of separating the control unit from the data unit led to a problem related to poor security of data sent in the network, so solutions to these problems had to be found. In this paper, address this problem by implementing robust algorithms to encrypt information, based on advanced encryption standard (AES), Rivest-Shamir-Adleman (RSA), and hybrid encryption algorithms to guarantee data protection and authenticity. The results showed that the hybrid coding method is better in terms of security and improved time (faster than RSA alone) by applying several scenarios in the SDN network to a set of encrypted files. [ABSTRACT FROM AUTHOR]

The depletion of conventional energy sources and global warming has raised worldwide awareness on the usage of renewable energy sources particularly solar photovoltaic (PV). Renewable energy sources are non-polluting sources which can meet energy demands without causing any environmental issues. For standalone PV systems, a low conversion efficiency of the solar panel and high installation cost due to storage elements are the two primary constraints that limit the widespread use of this system. As the size of the system increases, the demand for a highly efficient tracking and charging system is very crucial. Direct charging of battery with PV module will results in loss of capacity or premature battery degradation. Furthermore, most of the available energy generated by the PV module or array will be wasted if proper tracking technique is not employed. As a result, more PV panels need to be installed to provide the same output power capacity. This paper presents selection, design and simulation of maximum power point tracker (MPPT) and battery charge controller for standalone Photovoltaic (PV) system. Contributions are made in several aspects of the whole system, including selection of suitable converter, converter design, system simulation, and MPPT algorithm. The proposed system utilizes direct duty cycle technique thus simplifying its control structure. MPPT algorithm based on scanning approach has been applied by sweeping the duty cycle throughout the I-V curve to ensure continuous tracking of the maximum power irrespective of any environmental circumstances. For energy storage, lead acid battery is employed in this work. MATLAB/Simulink® was utilized for simulation studies. Results show that the propose strategy can track the MPPs and charge the battery effectively. [ABSTRACT FROM AUTHOR]

Digital images are suffering from the stair-step effect because they are built from small pixels. This effect termes aliasing and the method uses to decrease so-called anti-aliasing. This paper offers a hardware accelerator of an antialiasing algorithm using HLS (high level synthesis) along straight-line segments or edges. These straight-line segments are smoothed by modifying the intensity of the pixel. The hardware implementation of two different architectures which is based on Zynq FPGA are presented in this work. The first architecture is built from one core while the second architecture is built from multi-core and uses a parallel technique to speed up the algorithm by dividing line segments into sub-segments and drawing them after smoothing instantaneously to formulate the main line. This parallel usage leads to a very fast execution of Wu's algorithm which is represented one-tenth hardware runtime for one core only. Also, the optimized resource utilization and power consumption for different cores have been compared, through single-core design which utilizes 8% and consumes 1.6 W, while utilized resources using 10 cores are 77% with a power consumption of 2 W. [ABSTRACT FROM AUTHOR]

This paper proposes, a novel hybrid color component (HCC) issued from amounts number of color space with iterative manner, in fact traditional images obtained by RGB sensor weren't the effective way in image processing applications, for this purpose we have propose a supervised algorithm to substitute RGB level by hybrid and suitable color space at the aim to make well representation of the handled amounts of data, this step is extremely important because the obtained results it will be injected in many future studies like tracking, classification, steganography and cryptography. The second part of this paper consists to segment image coded in hybrid color space already selected, the used algorithm is inspired from kernel function where statistical distribution was used to model background and Bayes rule to make decision of the membership of each pixel, in this research topics we have extended this algorithm in the aim to improve compactness of these distribution. Cauchy background modeling and subtraction is used, and shows the high accuracy of automatic player detection. [ABSTRACT FROM AUTHOR]

Personal identification has become one of the most important terms in our society regarding access control, crime and forensic identification, banking and also computer system. The fingerprint is the most used biometric feature caused by its unique, universality and stability. The fingerprint is widely used as a security feature for forensic recognition, building access, automatic teller machine (ATM) authentication or payment. Fingerprint recognition could be grouped in two various forms, verification and identification. Verification compares one on one fingerprint data. Identification is matching input fingerprint with data that saved in the database. In this paper, we measure the performance of the memetic algorithm to process the image fingerprints dataset. Before we run this algorithm, we divide our fingerprints into four groups according to its characteristics and make 15 specimens of data, do four partial tests and at the last of work we measure all computation time. [ABSTRACT FROM AUTHOR]

The smooth variable structure filter (ASVSF) has been relatively considered as a new robust predictor-corrector method for estimating the state. In order to effectively utilize it, an SVSF requires the accurate system model, and exact prior knowledge includes both the process and measurement noise statistic. Unfortunately, the system model is always inaccurate because of some considerations avoided at the beginning. Moreover, the small addictive noises are partially known or even unknown. Of course, this limitation can degrade the performance of SVSF or also lead to divergence condition. For this reason, it is proposed through this paper an adaptive smooth variable structure filter (ASVSF) by conditioning the probability density function of a measurement to the unknown parameters at one iteration. This proposed method is assumed to accomplish the localization and direct point-based observation task of a wheeled mobile robot, TurtleBot2. Finally, by realistically simulating it and comparing to a conventional method, the proposed method has been showing a better accuracy and stability in term of root mean square error (RMSE) of the estimated map coordinate (EMC) and estimated path coordinate (EPC). [ABSTRACT FROM AUTHOR]

During software maintenance, software systems need to be modified by adding or modifying source code. These changes are required to fix errors or adopt new requirements raised by stakeholders or market place. Identifying the targeted piece of code for refactoring purposes is considered a real challenge for software developers. The whole process of refactoring mainly relies on software developers’ skills and intuition. In this paper, a deep learning algorithm is used to develop a refactoring prediction model for highlighting the classes that require refactoring. More specifically, the gated recurrent unit algorithm is used with proposed pre-processing steps for refactoring prediction at the class level. The effectiveness of the proposed model is evaluated using a very common dataset of 7 open source java projects. The experiments are conducted before and after balancing the dataset to investigate the influence of data sampling on the performance of the prediction model. The experimental analysis reveals a promising result in the field of code refactoring prediction. [ABSTRACT FROM AUTHOR]

Dynamic spectrum access is a paradigm used to access the spectrum dynamically. A hidden Markov model (HMM) is one in which you observe a sequence of emissions, but do not know the sequence of states the model went through to generate the emissions. Analysis of hidden Markov models seeks to recover the sequence of states from the observed data. In this paper, we estimate the occupancy state of channels using hidden Markov process. Using Viterbi algorithm, we generate the most likely states and compare it with the channel states. We generated two HMMs, one slowly changing and another more dynamic and compare their performance. Using the Baum-Welch algorithm and maximum likelihood algorithm we calculated the estimated transition and emission matrix, and then we compare the estimated states prediction performance of both the methods using stationary distribution of average estimated transition matrix calculated by both the methods. [ABSTRACT FROM AUTHOR]

Antennas are generally contructed from metallic materials; therefore it is prone to corrosion when installed outdoors. Radome is an important part of an outdoor antenna that serves to protect the antenna from environmental conditions. Radome structure is not expected to have a significant influence on the characteristics of the antenna. Parametric study is generaly applied in finding the optimum antenna dimenssion included radome. A method for guiding a parametric study proses in finding the optimum antenna dimensions has investigated and proposed in this paper. In this study, a method for determining the optimum radome dimension for planar antenna by applying the algorithm Least Mean Error (LME) has investigated. LME algorithm is used to find the optimum dimensions of the radome. The simulation results show that the proposed method can be applied to determine the dimensions of a planar antenna. [ABSTRACT FROM AUTHOR]

An integrity monitoring system is an inseparable part of aviation navigation system for global navigation satellite system (GNSS). Failures or faults due to malfunctions in the systems should be detected and isolated to keep the integrity of the GNSS intact. Because the pseudorange residual noise of navigation satellites does not follow the Gaussian distribution perfectly, and the performance of traditional filtering algorithms, such as the Kalman filter, might degrades for non-Gaussian noise. The particle filter algorithm has great advantage to handle the nonlinear and non-Gaussian system. Therefore, in this paper, the particle filter algorithm is employed to GNSS receiver autonomous integrity monitoring(RAIM) to detect the fault of navigation satellite. The log-likelihood ratio(LLR) test is established. By checking the consistency between the state estimate of the main particle filter and that of the auxiliary particle filters, the fault of navigation satellite will be effectively detected. And then, the novel RAIM algorithm is undertaken by field programmable gate array(FPGA). Moreover, the modules of the proposed RAIM algorithm is implemented. The effectiveness of the proposed approach is illustrated in a problem of GPS (Global Positioning System) autonomous integrity monitoring system. In addition, The algorithm and its implementation can be embeded in GNSS receiver. [ABSTRACT FROM AUTHOR]

Copyright of Telkomnika is the property of Department of Electrical Engineering, Ahmad Dahlan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Stereo vision is a popular method for an artificial vision-based environment perception system used in various applications such as intelligent transportation. With two cameras, the disparity map is calculated to find the distance and depth of objects in front of a moving vehicle. The key element of the stereoscopic system is based on the sum of absolute differences (SAD) algorithm, which is the most repeated operation in the stereo matching subsystem; however, this algorithm requires a very intensive processing time, statistical analysis show that the SAD block can consume more than 80% of the overall processing time of the algorithm. In this paper we propose a highly efficient hardware architecture of the SAD algorithm for real time stereo matching, the proposed architecture is established by a hierarchical parallel architecture of the SAD block, and verified by simulation and successfully implemented in Cyclone IV field programmable gate array (FPGA), it provides a significant reduction of processing time and the performance of the stereo imaging system is able to achieve 30 frames per second of 640x480 resolution color images. [ABSTRACT FROM AUTHOR]

Copyright of Telkomnika is the property of Department of Electrical Engineering, Ahmad Dahlan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telkomnika is the property of Department of Electrical Engineering, Ahmad Dahlan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telkomnika is the property of Department of Electrical Engineering, Ahmad Dahlan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Blockchain Technology is exclusively used to make online transactions secure by maintaining a distributed and decentralized ledger of records across multiple computers. Tendermint is a general-purpose blockchain engine that is composed of two parts; Tendermint Core and the blockchain application interface. The application interface makes Tendermint suitable for a wide range of applications. In this paper, we analyze and improve Practical Byzantine Fault Tolerant (PBFT), a consensus-based Tendermint blockchain algorithm. In order to avoid negative issues of locks, we first propose a lock-free algorithm for blockchain in which the proposal and voting phases are concurrent whereas the commit phase is sequential. This consideration in the algorithm allows parallelism. Secondly, a new methodology is used to decide the size of the voter set which is a subset of blockchain nodes, further investigating the block sensitivity and trustworthiness of nodes. Thirdly, to fairly select the voter set nodes, we employ the random walk algorithm. Fourthly, we imply the wait-freedom property by using a timeout due to which all blocks are eventually committed or aborted. In addition, we have discussed voting conflicts and consensuses issues that are used as a correctness property, and provide some supportive techniques. [ABSTRACT FROM AUTHOR]

Quantum algorithms are well-known for their quadratic if not exponential speedup over their classical counterparts. The two widely-known quantum algorithms are Shor's quantum factoring algorithm and Grover's quantum search algorithm. Shor's quantum factoring algorithm could perform integer factorization in O(logN). Grover's quantum search algorithm could solve the unsorted search problem in O(√N). However, both algorithms are introduced as theoretical concepts in the original papers due to the limitations of quantum technology at that time. In this paper, an improved way is presented to realize the two algorithms into a web application using state-of-the-art quantum technology. The web-app is designed and built considering the uses of a quantum simulator and libraries provided by ProjectQ and Rigetti Forest. The result shows that both algorithms are realizable into web-applications. [ABSTRACT FROM AUTHOR]

Stereo vision is one of the interesting research topics in the computer vision field. Two cameras are used to generate a disparity map, resulting in the depth estimation. Camera calibration is the most important step in stereo vision. The calibration step is used to generate an intrinsic parameter of each camera to get a better disparity map. In general, the calibration process is done manually by using a chessboard pattern, but this process is an exhausting task. Self-calibration is an important ability required to overcome this problem. Self-calibration required a robust and good matching algorithm to find the key feature between images as reference. The purpose of this paper is to analyze the performance of three matching algorithms for the autocalibration process. The matching algorithms used in this research are SIFT, SURF, and ORB. The result shows that SIFT performs better than other methods. [ABSTRACT FROM AUTHOR]

Blowfish is a block cypher algorithm used in many applications to enhance security, but it includes several drawbacks. For example, the mix between the key and data is limited. This paper presents a new modification to the Blowfish algorithm to overcome such problems realised through a multi-state operation instead of an XOR. Our proposed algorithm uses three keys in the encryption and decryption processes instead of one for controlling the variable block bits sizes (1, 2, 4, and 8) bits and for determining the state table numbers. These tables are formed from the addition in a Galois field GF (2n) based on block bit size to increase the complexity of the proposed algorithm. Results are evaluated based on the criteria of complexity, time encryption, throughout, and histogram, and show that the original Blowfish, those modified by other scholars, and our proposed algorithm are similar in time computation. Our algorithm is demonstrated to be the most complex compared with other well-known and modified algorithms. This increased complexity score for our proposed Blowfish makes it more resistant against attempts to break the keys. [ABSTRACT FROM AUTHOR]

In this context, this paper proposes a combination of parameterised decision mining and relation sequences to detect wrong indirect relationship in the non-free choice. The existing decision mining without parameter can only detect the direction, but not the correctness. This paper aims to identify the direction and correctness with decision mining with parameter. This paper discovers a graph process model based on the event log. Then, it analyses the graph process model for obtaining decision points. Each decision point is processed by using parameterised decision mining, so that decision rules are formed. The derived decision rules are used as parameters of checking wrong indirect relationship in the non-free choice. The evaluation shows that the checking wrong indirect relationships in non-free choice with parameterised decision mining have 100% accuracy, whereas the existing decision mining has 90.7% accuracy. [ABSTRACT FROM AUTHOR]

Various researches have been conducted to discover the machinery that led to the evolvement of non-symmetric formation of groups by uncountable marine animals. The huge of tasks comes per unit of time brought obstacles to assign each to particular server, while task assignment have needed a fast strategy to make decision. Artificial fish affect the environment through their behavior and the behavior of their peers. Creating a synthetic fish model has two parts: variables and functions which could be used for task assignment. This paper present improved fish swarm algorithm (IFSA) for task assignment to reduce the latency in cloud computing that could achieve one green computing goals. The research trying to reduce the pending job numbers compared with exist research. [ABSTRACT FROM AUTHOR]

Neural network is one of the branches with the most active research, development and application in computational intelligence and machine study. Radial basis function neural network (RBFNN) has achieved some success in more than one application field, especially in pattern recognition and functional approximation. Due to its simple structure, fast training speed and excellent generalization ability, it has been widely used. Artificial fish swarm algorithm (AFSA) is a new swarm intelligent optimization algorithm derived from the study on the preying behavior of fish swarm. This algorithm is not sensitive to the initial value and the parameter selection, but strong in robustness and simple and easy to realize and it also has parallel processing capability and global searching ability. This paper mainly researches the weight and threshold of AFSA in optimizing RBFNN. The simulation experiment proves that AFSA-RBFNN is significantly advantageous in global optimization capability and that it has outstanding global optimization ability and stability. [ABSTRACT FROM AUTHOR]

Stemming has been commonly used by some researchers in natural language processing area such as text mining, text classification, and information retrieval. In information retrieval, stemming may help to raise retrieval performance. However, there is an indication that stemming does not hand over significant influence toward the accuracy in text classification. Therefore, this paper analyzes further research about the influence of stemming on tweet classification in Bahasa Indonesia. This work examines about the accuracy result between two conditions by involving stemming and without involving stemming in pre-processing task for tweet classification. The contribution of this research is to find out a better preprocessing task in order to obtain good accuracy in text classification. According to the experiments, it is observed that all accuracy results in tweet classification tend to decrease. Stemming task does not raise the accuracy either using SVM or Naive Bayes algorithm. Therefore, this work summarized that stemming process does not affect significantly towards the accuracy performance. [ABSTRACT FROM AUTHOR]

In order to improve the performance of machine learning in big data, online multiple kernel learning algorithms are proposed in this paper. First, a supervised online multiple kernel learning algorithm for big data (SOMK_bd) is proposed to reduce the computational workload during kernel modification. In SOMK_bd, the traditional kernel learning algorithm is improved and kernel integration is only carried out in the constructed kernel subset. Next, an unsupervised online multiple kernel learning algorithm for big data (UOMK_bd) is proposed. In UOMK_bd, the traditional kernel learning algorithm is improved to adapt to the online environment and data replacement strategy is used to modify the kernel function in unsupervised manner. Then, a semi-supervised online multiple kernel learning algorithm for big data (SSOMK_bd) is proposed. Based on incremental learning, SSOMK_bd makes full use of the abundant information of large scale incomplete labeled data, and uses SOMK_bd and UOMK_bd to update the current reading data. Finally, experiments are conducted on UCI data set and the results show that the proposed algorithms are effective. [ABSTRACT FROM AUTHOR]

In this paper, we present a new direct access strategy for speaker identification system. DAMClass is a method for direct access strategy that speeds up the identification process without decreasing the identification rate drastically. This proposed method uses speaker classification strategy based on human voice's original characteristics, such as pitch, flatness, brightness, and roll off. DAMClass decomposes available dataset into smaller sub-datasets in the form of classes or buckets based on the similarity of speaker's original characteristics. DAMClass builds speaker dataset index based on rangebased indexing of direct access facility and uses nearest neighbor search, range-based searching and multiclass-SVM mapping as its access method. Experiments show that the direct accessstrategy with multiclass-SVM algorithm outperforms the indexing accuracy of range-based indexing and nearest neighbor for one to nine percent. DAMClass is shown to speed up the identification process 16 times faster than sequential access method with 91.05% indexing accuracy. [ABSTRACT FROM AUTHOR]

Square root is one of the mathematical operations which are widely used in digital signal processing. Its implementation on hardware such as FPGA will provide several advantages compare to the performance offered in software. There are several algorithms which can be utilized for this calculation, but they are difficult to be implemented in FPGA. This paper presents a model of FPGA based square root calculator, which requires very low resources usage, thus occupying very low area of FPGA. The model is designed to suit the needs of medium-speed and low-speed applications which don't need very high processing speed, while optimizing the number of resources utilized. The modified non-restoring algorithm is used in this design to compute the square root. The design is coded in RTL VHDL, and implemented in Altera DE2-board for hardware validation. The implementation produced very precise square root calculation, with low latency computation and low area consumption, for various input data width tested. [ABSTRACT FROM AUTHOR]

This paper investigates quantized feedback control problems for network empowerment ammunition, where the sensors and the controller are connected by a digital communication network with data-rate limitations. Different from the existing ones, a new bit-allocation algorithm on the basis of the singular values of the plant matrix is proposed to encode the plant states. A lower bound on the data rate is presented to ensure stabilization of the unstable plant. It is shown in our results that, the algorithm can be employed for the more general case. An illustrative example is given to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]