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Paper presents comparison of parallelization effectiveness in the forward gravity problem calculation for structural boundary. The same algorithm is tested using 3 AMD (ROCm technology) and 4 nVidia (CUDA technology) graphic processing units (GPU). Results show that the AMD GPUs are more preferable for usage in terms of performance and cost efficacy. [ABSTRACT FROM AUTHOR]

The paper presents a consideration of the possibility of using a 6-axis industrial robot to build an integrated masonry system. The design, construction and programming capabilities are discussed in the context of the application of the robot to construction works, in particular, bricklaying works. The method of programming the Bricklaying Robot System (BRS) workspace along with the kinematic model is presented. A discussion of the programming procedure for bricklaying is presented based on simulation and real-world tests. [ABSTRACT FROM AUTHOR]

The object of the research is the system for management decision-making in transporting enterprises. The paper provides a functional-structural model of an expert anti-crisis centre supporting the activities carried out by the corresponding experts, analysts, and managers in the decision-making process in the event of crisis and its effective prevention. An expert crisis centre algorithm is developed for facilitating transport enterprises crisis management. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are natural networks with restricted energy and networking technologies. It is a network made up of small nodes that also function as routers. These nodes have a very restricted power supply that is non-rechargeable and non-replaceable, making energy usage a major concern. WSN nodes have minimum energy and ability, and energy conservation has always been the most significant problem in WSNs. Energy conservation is a critical concern for extending the network's lifespan. A Sink Node (SN) is a node with an abundance of processing storage, power and a mutable battery, whereas a Member Node (MN) is a node with very little energy. Because the sensor nodes also function as routers, identifying the appropriate routing approach is critical for reducing energy consumption. WSNs are utilised in various applications, including ocean monitoring, sea monitoring, environmental monitoring, weather monitoring, submerged wildlife monitoring, weather monitoring, patient monitoring, and industrial monitoring. This research paper describes WSN using various algorithms associated with an energy-efficient routing network. [ABSTRACT FROM AUTHOR]

The Internet is the source of information that first comes to mind for a large number of people today. Over the years the size of the internet has significantly increased. While the size of the surface web is estimated to be over fifty billion web pages, the deep web, on the other hand, is estimated to be more than thrice it's size. While it's impossible to list every URL that has ever existed or go through it for a single user, it is also highly inefficient to apply brute force while going through it. Search engine ranking algorithms are the tools that help the user reach the relevant content based upon their respective queries. While search engines can randomly pick the web URLs by just comparing the titles orsome random words, it's not recommended as not only is it unfair, it can also lead to performance issues, getting irrelevant data, but also a huge security threat and waste of the user's precious time. What's relevant today might just become irrelevant tomorrow. Not only that but also we can't use human employees to verify every web page either. Various search engine ranking algorithms come into play here and help the users to get the relevant information based on various factors like hyperlinks, popularity, distance between pages, relevance, compatibility, time etc. In the given paper we compare several such algorithms to find out their limitations and advantages that can help us in further research of relevant and more efficient web page ranking algorithms. [ABSTRACT FROM AUTHOR]

The Internet is the supply of statistics that first involves thoughts for a big quantity of human beings today. Over the years the dimensions of the net have considerably increased. While the dimensions of the floor internet is envisioned to be over fifty billion internet pages, the deep internet, on the alternative hand, is envisioned to be greater than three times its size. While it's not possible to listing each URL that has ever existed or undergo it for a unmarried person, it's also exceptionally inefficient to use brute pressure at the same time as going thru it. Search engine rating algorithms are the gear that assist the person attain the applicable content material primarily based totally upon their respective queries. While search can randomly select out the internet URLs through simply evaluating the titles or a few random words, it's now no longer encouraged as now no longer simplest is it unfair, it is able to additionally result in overall performance issues, getting inappropriate data, however additionally a large protection danger and waste of the user's treasured time. What's applicable nowadays may simply end up inappropriate tomorrow. Not simplest that however additionally we can't use human personnel to affirm each internet web page either. Various seek engine rating algorithms come into play right here and assist the customers to get the applicable facts primarily based totally on different factors like hyperlinks, popularity, distance among pages, relevance, compatibility, time etc. In the given paper we examine numerous such algorithms to discover their boundaries and benefits which can assist us in similarly studies of applicable and greater green internet web page rating algorithms. [ABSTRACT FROM AUTHOR]

Submarine cameras are commonly used to track and observe the seabed. They are often used in under-water robotics, marine exploration and oceanography. The under-water cameras face several faces due to under-water optics. As the light travelling through the water is absorbed and scattered due to small particles that brings noise in the captured images. Many approaches have been suggested over the past five years to solve the conventional problems of under-water imaging. This review paper deals with under-water image enhancement (UIE) methods and existing dataset. At last discussion on the identified gaps is included that will help the researchers to overcome the gaps identified. [ABSTRACT FROM AUTHOR]

Description the EEG (Electroencephalogram) signal of the consumer was calculated in this paper and the EEG signal rule was classified using the SVM (Support Vector Machine) algorithm and the signal accuracy was measured. An experiment was performed by separating males and females in order to determine the user's EEG signal, and a single channel EEG unit was used to measure the EEG signal. The effect of the user's EEG signal measurement using the EEG system was analyzed using R. Furthermore, the prediction accuracy of the recognition rate was 90.2 percent as a result of predicting the EEG measurement experiment data at a ratio of 75:25 (training data: test data) by applying a combination of unique vectors that have the best SVM classification output. Approximately 90.2% of the EEG signal of the user can be identified in this document, and the fact that it can only be done with a simple linear classification of the SVM algorithm indicates that it can be used in different ways for biometric authentication using EEG signals. [ABSTRACT FROM AUTHOR]

The growing reality today is the application of Internet of Things (IoT) in day-to-day. Though there are lot of complexities around this environment such as security, energy consumption and heterogeneity, this giant network of connecting devices with internet is sky rocketing. The mounting increase in the sharing of information made this connected network of devices is mandatory in everyday life. This powerful IoT platform suffers a lot by the security breaches as more people uses it. Authentication of IoT users using One Time Password (OTP) is one kind of resolution to solve many security issues. The OTP has added an additional coating to the traditional username-password authentication system. So, this research proposes a hybrid One-time Password generation algorithm, AroSheb_Jo, for IoT data and presents a security analysis of that algorithm. Additionally, this paper presents a comparison of its lightweight characteristics and resistance against security attacks. Experimental and performance analysis of that algorithm is also elaborated in this paper. [ABSTRACT FROM AUTHOR]

Multi-core processing can decrease the cost of many Machine Learning (ML) tasks by executing them in parallel using multiple cores of a processor. Modern computers are equipped with processors that contain multiple cores that can be leveraged to decrease the execution time of many ML tasks by multiple folds. Especially ensemble of ML algorithms like Random Forest (RF) can take advantage of the multi-core processing ability for improving their performance. In this paper five models using RF algorithm using Gaussian_Quantiles (GQ), Load_Wine (LW), Load_Iris (LI), Load_Breast_Cancer (BC) and Load_Digits (LD) datasets are developed and evaluated using k-fold cross-validation respectively. In general, the execution time of RF algorithm decreases as the number of cores increases. [ABSTRACT FROM AUTHOR]

In this digital era, the amount of sensitive data getting transferred every day is very high, the digitization of all data brought a lot of advantages along with serious security threats. The security standards exist in present scenario provides three basic services such as Confidentiality, Integrity, and Authentication (CIA), the two common types of symmetric and asymmetric algorithms are being used at different levels and capacities. The emerging of new technologies and high computing facilities makes the existing standards vulnerable to different attacks and security threats. There is a need of developing a new set of cryptographic algorithms those are quantum-safe, which means, the arrival of quantum computers can break any sized keywithin a polynomial solvable time, the quantum computers can do prime factorization in realistic time complexities. There are new and smart cryptanalysis emerging to break the existing standards such as Advanced Encryption Standard (AES) and RSA, these algorithms are ruling the cyber world for more than two decades. In this research paper, a new concept of using polynomials to encrypt and decrypt data is proposed, however, the power of polynomials are not fully explored in cryptography, we use Newton Raphson method and its advancements to implement a new cryptography algorithm that is more accurate and faster than existing standards. The proposed algorithm doesn't use any fixed size to avoid cryptanalysis and attacks based on key sizes, the highly intensive computing can break any length of key in realistic time delays, so variable sized key solves this problem in our approach. [ABSTRACT FROM AUTHOR]

Cloud computing is an evolving concept which has now come out with a radical shift in storage and hosting services. The huge requirement for cloud computing services desired various technologies and different approaches for the effective and timely delivery of consistent quality to the users. With the increasing growth in the network and arising with the latest technologies including cloud and big data, to control and manage the traditional networks is quite tedious. Therefore, it's mandatory to transform conventional network architecture. To resolve this issue, Software Defined Networking (SDN) came into existence which makes the management of the network more conformable. To satisfy the fulfilments of Quality of Service (QoS) and with the limited network availability, one of the keynotes that have been taken into consideration is the load balancing issue. It is quite a tedious job to handle the massive amount of load for a single server. This paper proposed the minimal loaded load balancing approach and compare it with the different load balancing techniques based on the response time. [ABSTRACT FROM AUTHOR]

In today's world Cloud computing is perhaps the most utilized technology. Distributed computing offers web administrations, information stockpiling and computing assets over the web effortlessly. Cloud computing permits clients to get to the IT assets anyplace and whenever on a compensation for every utilization premise. As the interest for distributed computing develops quickly, the traffic additionally increases. There are two answers for this issue, one is to upgrade a single server to a high-performance server but upgraded server may also overload soon and second is multi server (gathering of workers). Multi server arrangement is adaptable and practical. While making a group of servers, the issue is Load adjusting. Load balancing is one of the critical issues in cloud computing. Load balancing is an interaction of parting the unique responsibility and dispersing the responsibility among every one of the hubs with the end goal that no hub in the cloud climate is overloaded/under loaded or inactive. Load balancing in Cloud computing is utilized to improve the proficiency and utilizing resources effectively. This journal paper objective is on investigation on cloud load balancing, Load balancing methods, Load balancing algorithms. [ABSTRACT FROM AUTHOR]

The new results presented in this paper are based on the long-term research conducted recently over several years. Creation and development of multi-disciplinary knowledge resources and implementation of practical computational and numerical algorithms and solutions require flexible and long-term approaches to structures. The goal of this research is to create a flexible fundament of structures for arbitrary long-term knowledge and references, which allow an efficient deployment for knowledge creation, development, and mining. The knowledge-centric structural solution is consequently based on complements of knowledge and a coherent system of conceptual knowledge integrated in the fundamental methodology of superordinate knowledge. The results allow to sustainably master structures and algorithms for long-term computational and numerical scenarios. The solution is targeting knowledge resources and scenarios and has been developed and used for many practical implementations over a time span of more than three decades. The paper presents an overview of new, previously unpublished results on architecture complements, object development architecture, resulting knowledge resources' structures, and the new archaeological Rhodos case results. [ABSTRACT FROM AUTHOR]

The primary objective of this paper is to verify individuals as indicated by their finger surfaces. We propose to remove Finger Texture (FT) highlights of the two finger pictures (center, ring) from a low goal contactless hand picture utilizing LBP strategy. The utilization of Inner-Knuckle-Print (IKP) in biometric recognition is the most broadly proposed validation work. The unique characteristics of the IKP give us the requirement for recognizable proof. During the IKP filtering process, the image created by the scanner might be partially unique. This paper proposes artificial neural networks for effectively coordinating procedures to IKP validation. By utilizing the Back-Propagation method, the algorithm coordinates IKP and relates them to a novel accomplished client. After grouping, the procedure restores to the best counterpart for the given finger impression variables. [ABSTRACT FROM AUTHOR]

This paper attempts to compare the two socio-inspired based metaheuristics algorithms which were introduced recently. These metaheuristics optimizations and search techniques "Election algorithm, and Election Campaign Optimization Algorithm" were inspired by socio-political processes of human ideologies. This paper gives an overview of these algorithms with respect to their control parameters, components, intensification and diversification as well as their applications. It also makes a simple comparison between the two metaheuristics algorithms in terms of quality of solution, ease of finding correct control parameters and further explored their strengths and limitations as well as their superiority over other metaheuristics algorithms. [ABSTRACT FROM AUTHOR]

This paper provides the explanation of the concepts of point tracking technique to be implemented in mitral valve locating in video frames. Object tracking has been used for many applications in motion-based recognition and monitoring. This paper discussed about the implementation of Kanade-Lucas-Tomasi (KLT) algorithm for automatic detection of the mitral valve in video frames. An experiment is carried out which covers the patient scanning who suffers from mitral valve disease. The performance of the method is validated by comparing the value of point track per frames. It is found that the point tracker systems can track the mitral valve up to 0.3s. [ABSTRACT FROM AUTHOR]

Fatigue life assessment for random loading can be divided into two groups, defined in time or frequency domain. In this paper we are within the range of calculations in the frequency domain. The paper deals with the problem of fatigue life assessment of materials subjected to wind loading spectrum. The paper analyses three types of standardized spectrums that is: Wisper, Wisperx and New Wisper. The spectrums have been processed to the use for fatigue life assessment algorithms. All the analyzed spectrums have a non zero mean stress characteristic and have a non Gaussian distribution. The proposed solution to the correction of these signals is widely explained. The paper deals with simulation based on fatigue material constants obtained from the literature. The results of assessment are compared to results obtained with the use of rainflow cycle counting method described in the time domain. [ABSTRACT FROM AUTHOR]

The aim of the paper is to summarize and review methods for modeling and estimating time-varying quantiles and to make several notes on their properties. The paper is focused on conditional autoregressive value-at-risk via regression quantiles (CAViaR) models, models based on signal extraction and state space algorithm. Several notes are made on changing copula models for dynamic distributions and models based on kernel. A simple local linear trend model and a simple CAViaR model are estimated for illustration purposes and some related practical issues are highlighted. [ABSTRACT FROM AUTHOR]

To minimize makespan for scheduling independent tasks in cloud computing, an improved estimation of distribution algorithm (IEDA) is proposed to tackle the investigated problem in this paper. Considering that the problem is concerned with multi-dimensional discrete problems, an improved population-based incremental learning (PBIL) algorithm is applied, which the parameter for each component is independent with other components in PBIL. In order to improve the performance of PBIL, on the one hand, the integer encoding scheme is used and the method of probability calculation of PBIL is improved by using the task average processing time; on the other hand, an effective adaptive learning rate function that related to the number of iterations is constructed to trade off the exploration and exploitation of IEDA. In addition, both enhanced Max-Min and Min-Min algorithms are properly introduced to form two initial individuals. In the proposed IEDA, an improved genetic algorithm (IGA) is applied to generate partial initial population by evolving two initial individuals and the rest of initial individuals are generated at random. Finally, the sampling process is divided into two parts including sampling by probabilistic model and IGA respectively. The experiment results show that the proposed IEDA not only gets better solution, but also has faster convergence speed. [ABSTRACT FROM AUTHOR]

In this paper, a new multi-agent genetic algorithm based on orthogonal experiment is proposed, which is based on multi-agent system, genetic algorithm and orthogonal experimental design. The design of neighborhood competition operator, orthogonal crossover operator, Son and self-learning operator. The new algorithm is applied to mobile tower defense game, according to the characteristics of the game, the establishment of mathematical models, and finally increases the value of the game’s monster. [ABSTRACT FROM AUTHOR]

In order to solve the problem of how to reconstruct the original image under the condition of unknown sparse basis, this paper proposes an image reconstruction method based on blind compressed sensing model. In this model, the image signal is regarded as the product of a sparse coefficient matrix and a dictionary matrix. Based on the existing blind compressed sensing theory, the optimal solution is solved by the alternative minimization method. The proposed method solves the problem that the sparse basis in compressed sensing is difficult to represent, which restrains the noise and improves the quality of reconstructed image. This method ensures that the blind compressed sensing theory has a unique solution and can recover the reconstructed original image signal from a complex environment with a stronger self-adaptability. The experimental results show that the image reconstruction algorithm based on blind compressed sensing proposed in this paper can recover high quality image signals under the condition of under-sampling. [ABSTRACT FROM AUTHOR]

Graph Isomorphism is the illustration of same graph in more than one pattern. In order to check that the graphs are isomorphic it should follow some properties. Graphs have many real life applications. Here we are considering Intuitionistic Fuzzy Graphs. As it is extended from crisp graph, Intuitionistic fuzzy graphs have all the properties of crisp graphs. Intuitionistic Fuzzy graph theory is tracking down an expanding number of uses in demonstrating genuine frameworks and circumstances where the level of data is uncertain. In the field of networks, one has to take into account whether two networks are similar or not. In this context studying isomorphism of Intuitionistic Fuzzy graphs become important. In this paper we propose algorithms to check weak isomorphism and co-weak isomorphism between Intuitionistic Fuzzy Graphs. We have applied this algorithms to detect Intuitionistic Fuzzy Graph isomorphism with the suitable examples. [ABSTRACT FROM AUTHOR]

Two-wheeler vehicles are a major form of personal transport in the world and specially India, a substantial number of 2 wheelers on roads have made it difficult to monitor them correctly. This has led to carelessness from the drivers and increase in the number of accidents that can be prevented and minimized. One of the protective gears-helmet is not always worn by riders and it is tough for the traffic police to catch these violators, due to sheer size. Our project involves using the YOLO (You Only Look Once) algorithm to detect whether the riders as well as pillions are wearing the helmets so that they can be caught, and their vehicle details be sent for further processing to the traffic police. Along with the 2 wheelers, helmets are also required at construction sites, and we plan to apply the same. This paper proposes a framework for identification of motorcyclists without helmets. The application, with the help of YOLOv5 (You Only Look Once) and rigorous training gives us an accuracy of 87% true positives detection. [ABSTRACT FROM AUTHOR]

The development of aberrant cell proliferation in the lungs is a problematic condition that has the potential to result in death. On the list of diseases that most frequently result in mortality, lung cancer takes first place. The early stages of lung cancer are notoriously difficult to diagnose due to the fact that cancer cells with dimensions less than very small are notoriously difficult to spot by imaging. If the cell abnormalities are discovered in the early stages, it will be possible to begin therapy sooner, which will result in an improved chance of the patient surviving the illness. Several different image processing strategies can be utilized in the diagnostic phase of patient care to help spot signs of disease. In this paper, classification of Lung Cancer from chest X-ray images has been done using optimized Convolutional Neural Network (OCNN) and Ant Lion Optimization (ALO) algorithm. In pre-processing step, the contrast of all images are enhanced using Histogram Equalization (HE) method and the noises are removed from all images using Median Filtering. After the pre-processing step, feature extraction is performed using Gray Level Spatial Dependence (GLSD) to extract the statistical features. The feature vector is then trained and classified using OCNN-ALO algorithm. The ALO algorithm is used to optimize the hyper parameters of CNN layers. It classifies the lung images into normal and lung tumor affected. Performance results have indicated that OCNN-ALO attains the superior performance with 95.15% accuracy, 85.43% sensitivity, 93.4% specificity and 76.43% F1-score. [ABSTRACT FROM AUTHOR]

This research paper addresses the complexity of understanding the critical concept of process scheduling in operating systems and proposes an innovative approach to teaching it using an AR-based game. We have developed a game that leverages AR technology to virtualize the process scheduling concept, using practical examples of different users with varying arrival, and burst times. To simplify the explanation, we have utilizedthe First Come First Serve (FCFS) scheduling algorithm in the game. The game development was carried out using the Unity game engine and Vuforia software development kit, which provides robust support for AR game development. The AR-based game offers an immersive and engaging learning experience, providing aneffective means of making complex concepts like process scheduling more accessible to a wider audience. [ABSTRACT FROM AUTHOR]

Conventionally, a six-step inverter used in Active Power Filter (APF) to mitigate the harmonics in power system because of its simplicity and easy to control. However, six-step inverter only operates in low voltage rating for APF and additional transformer is essential to operate in high voltage rating of APF. There are three types of Multilevel Inverter (MLI) namely Cascaded H-Bridge, Neutral Point Diode Clamped (NPC) and Flying Capacitor (FC). Among the types of Multilevel Inverter (MLI), only the Cascaded H-Bridge (CHB MLI) can be used to replace the conventional six-step inverter due to its capability to work in high voltage rating, less used power device and easy to control. This paper evaluates a three-phase three-wire shunt APF based on six-step conventional inverter and Nine Level CHB MLI using Indirect Current Control (ICC) schemes. MATLAB/Simulink tools are used to model and simulate both of algorithms, consisting of three main parts namely harmonic extraction algorithm, DC link algorithm and switching algorithm. In MATLAB/Simulink, the model was created and validated the Total Harmonic Distortion (THD) line current with different frequency modulation (mf) values. According to the findings, the increasing of frequency modulation will produce a good result of THD line current. The THD line current produced by both algorithms is less than 5%, which is the permitted value as stated in IEEE 519. [ABSTRACT FROM AUTHOR]

This paper discusses the application of the Content-Based Image Retrieval (CBIR) system in image classification for various types of batik in the region. The aim is to detect and recognize certain types of batik with high accuracy, as well as develop a digital image-based security system for various types of batik with various motifs, patterns, and colors. In this research, a typical South Sulawesi batik type, namely Lontara motif batik, was used as a sample. The methods used to perform feature extraction and classification are the Gray Level Co-Occurrence Matrix (GLCM) and Multilayer Perceptron (MLP). The use of GLCM is equipped with the Angular Second Moment (ASM) feature with contrast, correlation, energy, and homogeneity parameters, so that the image data extraction results will be more accurate. Apart from that, the combination with the Multi-Layer Perceptron (MLP) algorithm will make it easier for the system to carry out classification because MLP uses three training functions, namely Levenberg-Marquardt (LM), Variable Learning Rate Backpropagation (GDX), and Scaled Conjugate Gradient (SCG), which produce high accuracy values at smaller epochs. The best accuracy value obtained in the test results was 88.89%. The combination of the GLCM-MLP method can be used as a solution to accurately detect various batik motifs. [ABSTRACT FROM AUTHOR]

Removing the specular reflection of an image by using a dark channel prior algorithm is analyzed in this paper. This method is gives to provide solution to the Eproblem in specular highlight images of information loss. It involves mainly three steps. First, the image is passed through a dark channel prior algorithm, which will give the atmospheric light component. Second, with the help of the atmospheric light component by using the L1 norm function remove the reflection component from the original image, then adjust the halo artifacts in the image by comparing adjacent pixels. Third, the image is passed through the Contrast Limited Adaptive Histogram Equalization (CLAHE) method to improve brightness, color, sharpness. This method is most effective when compared to the existing methods. [ABSTRACT FROM AUTHOR]

This paper examines the use of mobile phone sensor data to identify transportation mode detection using the K-nearest Neighbor algorithm. The model tries to recognize the walking, still walking, Bus, Train, and Car transportation modes. One of the normalization methods, such as the Min-Max normalization or the Z-Score Normalization, is implemented to pre-process the data. It uses four distance methods such as Euclidian, Manhattan, Chebyshev, and Minkowski as distance calculation mechanisms. Based on the highest accuracy result, the model is selected. The analysis also concluded that the optimal model has the highest accuracy, which has validated the results achieved through extensive normalization methods and the choosing of the most appropriate distance functions. As a result, the outcomes of the research have further indicated the importance of selecting the appropriate normalization techniques and distance functions on the accuracy of the model used in transportation detection. Additionally, the results have also provided critical additional knowledge in the development and formation of Intelligent Transportation Systems (ITS). [ABSTRACT FROM AUTHOR]

In this paper, reinforcement learning (RL) algorithms Proximal Policy Optimization (PPO) and Deep Q Networks (DQN) are applied into the setting of autonomous driving research. A highway environment is formalized into a Markov Decision Process, and a simulated highway environment is constructed in compatible with Gymnasium. In this simulated environment, comparisons of PPO and DQN algorithm performance are investigated under different traffic load and vehicle starting position - main road and onramp - in terms of total return gained during an episode of highway driving after training all algorithms for the same number of timesteps. The total return takes driving speed and safety into account to connect with real world driving convention. According to the result, PPO algorithms achieve higher average return and lower standard deviation, indicating their ability to achieve more stable high-quality performance. All trained algorithms are able to drive quickly on the road and avoid other vehicles, demonstrating the potential of reinforcement learning in the application of complete driving automation. [ABSTRACT FROM AUTHOR]

Within the domain of robotics and related fields, path planning algorithms have perennially posed a profoundly challenging problem, marked by an ongoing quest for refinement without attaining a universally perfect solution. Among the spectrum of path planning algorithms, A* algorithm stands out as a relatively stable and efficient approach. This paper presents improvements to the conventional A* algorithm by introducing several key modifications. Firstly, it adopts the Euclidean distance metric instead of the Manhattan distance, thereby mitigating the proclivity of the algorithm for diagonal movements. Secondly, the dynamic weight parameterization is introduced, markedly amplifying computational efficiency. In exemplar map scenarios, this augmentation yields a reduction of approximately 50% in program execution time compared to the conventional A* algorithm. Lastly, the integration of Bezier curves serves to optimize path inflections, imparting a smoother trajectory. This approach substitutes discrete grid-based path planning inflection points with continuous curves, obviating the need for abrupt halts and turns in the trajectory of the robot during actual execution. Incorporating the stability and optimality inherited from the traditional A* algorithm, this algorithm enhances program execution speed while reducing inflection points in the route, substituting them with smooth curves. The results identify an improved A* method as an important improvement in path planning, which greatly improves the computational and operational efficiency related to robot motion in production practice. [ABSTRACT FROM AUTHOR]

Path planning plays a key role in autonomous robot research. This paper studies in detail three of the various path planning algorithms for autonomous robots, including the A-Star path planning algorithm, unit decomposition, and rapid exploration of random trees (RRT). The A-Star algorithm determines the shortest route between two points in a graph. The principle is to calculate the distance between the starting point and the end point, select the shortest point, and then recalculate the distance and select the shortest route until reaching the target point. RRT can be used for path planning in multi-dimensional, complex spaces. The main principle is to randomly generate points in the map and connect them to the nearest nodes to form Cell decomposition is to divide the plane space into units of different sizes, turn the complex space into a simple space, and use the A-Star path planning algorithm to calculate the shortest path from the starting point to the target point. This article chooses experiments with different formers to analyze the application of various algorithms. Finally, this article concludes that cell decomposition divides simple geometric maps and uses the A-Star algorithm to plan paths, which can make the route on the geometric map more accurate. Different extension algorithms of RRT can be used in scenarios that require high accuracy, high quality, and reasonable computational costs. A-Star is also a suitable choice for quick route planning. This study conducts a comprehensive study of autonomous robot path planning algorithms, which will help future research select the most appropriate algorithm based on specific application requirements. [ABSTRACT FROM AUTHOR]

Path planning is a key problem that needs to be studied in the field of automated guided vehicles. Its ultimate goal is to accomplish the movement of the AGV from the initial target point to the final target point. Although many mature algorithms can achieve this goal. In practical applications, problems of dynamic obstacle avoidance and global movement combination still exist due to the complexity and dynamics of the application scenarios. The map needs real-time control of the position, posture, and kinematic parameters of Automated Guided Vehicles to solve this problem. Based on these issues, this paper combines global path planning with local path planning algorithms to achieve algorithm fusion. Then, it constructs a warehouse scene in Gazebo and simulates the operation of the AGV. Finally, it compares the accuracy of the fusion algorithms, obstacle avoidance techniques, and search speed. Choose the fusion algorithm of the A* hybrid algorithm and the TEB algorithm as the optimal solution for the shortest and smoothest path in practice. Compared to traditional algorithms, there is a 220%-615.8% improvement in time. With the help of this algorithm, intelligent warehouse operations may operate more effectively and the risk of accidents can be decreased. [ABSTRACT FROM AUTHOR]

Presently, path-planning algorithms play an important role in unmanned robots. In coverage of these algorithms, scientists try to seek some ways to improve those traditional algorithms. For example, A* was invented to solve Dijkstra's problem, of searching for targets slowly, using a heuristic function. Although these algorithms have been significantly improved, these changes still cannot adapt to special environments, such as amphibious environments and complex traffic environments. In this paper, four conventional algorithms are analyzed to test them in a similar traffic map to find their weakness and merits. First, we propose to design a map with different extents of traffic congestion on different roads. Then, four algorithms, A*, Probabilistic Roadmap Method, Artificial Potential Field, and Rapidly Exploring Random Tree series, are applied in this map to search for a road from the initial point to the goal point. Finally, all results are recorded and compared with other algorithms to investigate their effectiveness and disadvantages. Experiments with randomly generated paths in the map are conducted to demonstrate the proposed approach. The experimental results are concluded in the end. The A* algorithm is a suitable choice when solving the shortest path problems. The RRT series can search for a road frequently. For the APF, it is confirmed that it has a significant usage in selecting a path with less congestion and the PRM method can complete the same task as well with a certain possibility of failure in path planning. This study is of great importance for discovering the directions of future research. [ABSTRACT FROM AUTHOR]

Chromatic Dispersion (CD) compensation in optical fiber Offset Quadrature Amplitude Modulation based Filter Bank Multicarrier (FBMC-OQAM) systems is the most used today, especially on terrestrial Long-haul optical fiber type. As any other related system, FBMC-OQAM have a lot of strength and weakness points that should take in account, therefore, focus on studying data transmission over optical cables with FBMC-OQAM modulation for long distance and improve data access by reducing the noise on phase and have less dispersion on the transmitted light. To get a high spectral efficiency keep the subcarriers number sufficiently high, so will work on 256 subcarriers with cable length up to 3000 km. To get less complexity on an algorithm chose to use, and therefore compensate some of the dispersion at the receiver, must not set the subcarriers number at too high level. In parallel, know that the fiber dispersion or the propagation distance increases or the number of subcarriers decreases, the CD tolerance of single-tap equalization is reduced, so in this paper, propose to use the frequency sampling multi-tap equalizer for improving the CD compensation. Furthermore, will derive a new simple phase noise (PN) estimation algorithm which can be seen as an adaptive maximum likelihood (AML) algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we present a way how to automatically find and set an optimal configuration for a linear system solver during a transient analysis in order to reduce computation time. Our approach is based on biologically inspired algorithms which are able to find a reasonable configuration in tens of trials, i.e., tens of timesteps in terminology of the transient analysis. This work shows a measurement within which we compared several optimization algorithms and explored their time overhead. We focus on parallel multiphysical solvers for engineering simulations developed for high-performance computing. Our experiment includes one such solver based on Finite Element Tearing and Interconnection and 4 test cases, but our solution is modular, thus, it can be connected to other similar solvers. [ABSTRACT FROM AUTHOR]

This paper deals with an unfavorable phenomenon called the ponding effect. This phenomenon can cause an increase in local deformations on a tensile surface and therefore endanger the integrity of the structure in case of overcoming the tensile strength of the membrane material. A searching algorithm has been developed in order to analyze and prevent occurrence of this phenomenon. An improvement of the algorithm was made so that only an expected amount of precipitation would be considered during the calculation. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of image deblurring. A suitable discretization scheme for a particular nonlinear time-dependent partial differential equation of parabolic type is experimented. The method is implemented by reversing the arrow of time in order to damp diffusion. Only one step is enough to reconstruct the edges of a corrupted picture affected by average blur. Thus, the procedure turns out to be extremely efficient. [ABSTRACT FROM AUTHOR]

In our research, we are focusing on the development of iterative algorithms for solving contact problems emerging in the analysis of steel structures bolt connections. Despite the project is focusing mainly on the development of efficient numerical algorithms, several supplementary problems have to be solved. In our research, we decided to utilize widely-used popular commercial software Ansys to provide the data for our algorithms implemented in Matlab as well as for the comparison of results. This paper presents experiences with our newly developed data interface. [ABSTRACT FROM AUTHOR]

In the paper, the BFGS algorithm is applied to minimization of the functional in the problem of approximating the gravity anomalies by the fields of material segments and the fields of point sources. The selected fields were compared at different heights. Efficiency of the algorithm is demonstrated on model examples. Fields from the three-dimensional sources were selected as selected ones. [ABSTRACT FROM AUTHOR]

In Handwritten Character Recognition (HCR), interest in feature extraction has been on the increase with the abundance of algorithms derived to increase the accuracy of classification. In this paper, a metaheuristic approach for feature extraction technique in HCR based on Crow Search Algorithm (CSA) was proposed. Freeman Chain Code (FCC) was used as data representation. The main problem in representing a character using FCC is that the results of the extractions depend on the starting points that affected the route length of chain code. To solve this problem, the metaheuristic approach via CSA was proposed to find the shortest route length and minimum computational time for HCR. The performance measurements of the proposed CS-FCC extraction algorithm are the route lengths and computation times. The experiments on the algorithms are performed based on the chain code representation derived from established previous works of Center of Excellence for Document Analysis and Recognition (CEDAR) dataset which consists of 126 upper-case letter characters. Based on the result, the proposed CS-FCC obtained 1880.28 in term of route length and only needs 1.10 second to solve the whole character images. [ABSTRACT FROM AUTHOR]

In this paper, we proposed the Low light image enhancement using a single scale retinex algorithm. The main aim is to improve the clarity of low light images. By considering the real scenes, the dynamic range of digital camera is small, hence contrast correction is required to reproduce the information of images in darker regions. The proposed method, by using single scale retinex algorithm RGB color components are extorted and modified by applying logarithm and converted into HSV. For enhancing this component, the gaussian surround function is convoluted to it and then scaled version of components and the convoluted one is added to the original image. Then contrast stretching is performed to increase quality of image. This is implemented with MATLAB. [ABSTRACT FROM AUTHOR]

A Blockchain is a decentralized database that stores data that all network participants can access. The central authority is no longer in charge of maintaining this public database by running a cryptographic protocol with designated nodes. Bitcoin is presently the latest object within the Blockchain, and the Bitcoin node can confirm the transaction content and package deal it into the block. Through the underlying consensus agreement, the blockchain ensures the consistency of the information. These consensus algorithms are unique because the set of rules protection assumptions differs from the actual requirements. This paper provides information and compares various blockchain consensus algorithms, classifies advancement based entirely on blockchain consensus, and highlights the benefits. [ABSTRACT FROM AUTHOR]

Cloud infrastructure is the "backbone" technology of the internet era. Ever-growing scale of cloud network concerns the sustainability of centralized cloud. Fog computing aims to decentralizes cloud computing distributing computational processes across network devices, offers a potential solution. If application are distributed effectively in available nodes, network resource usage can be reduce while reducing latency by existing closer to data sources and end points. However, the lack of standardization of fog networks, researcher must list its evaluation setups to contextualize placement algorithm's potential abilities. Thus, selecting an evaluation environment is crucial in researching fog application placement problem. There are several reviews about evaluation environment, but there is a lack of information regarding important aspect placement problem such as algorithm type, placement objective and performance metric available in each evaluation environment. In this paper we discuss the capabilities of several evaluation environments such as iFogSim, YAFS, MATLAB, Python, real world testbed, solver, and other evaluation environments. We surveyed usage trend per year, algorithm type usage, and performance metric that can be produced by each evaluation environment. This survey aims to help future researchers on fog application placement problems to identify evaluation environment based on selected algorithm type and objective metrics. [ABSTRACT FROM AUTHOR]

Widespread use of high-resolution satellite images may be found in many fields and applications. Its ability to record the earth's surface in more detail benefits regional spatial management, natural resources and disaster monitoring, and several other fields. The smaller coverage is unfortunate if areas are not visible due to cloud contamination. The presence of clouds can reduce or even eliminate the information in the image. Cloud detection on high-resolution images is a challenge because this image only has four channels: blue, green, red, and NIR. While the thermal channel, which is often used to detect the presence of clouds, is not owned by this image. This study proposes a method to identify clouds in high-resolution satellite imagery based on this limitation. The cloud detection software in this paper uses U-Net version 1.0. This software can be executed on a server or personal computer (PC). The model applied to the Dice coefficient and IoU to know how the segmentation model performs. The results of this cloud detection process are cloud detection raster data. This software generates the percentage of clouds in an image as a.txt file. The dice model is recommended for the cloud detection method based on the accuracy assessment. Users can utilize these results, especially in overcoming cloud constraints on high-resolution satellite imagery. This software is expected to fulfill the needs of remote-sensing data users. [ABSTRACT FROM AUTHOR]

The quick development of technology not only makes life easier but also raises several security concerns, so cyber security has become very important and vital research area, rather an inevitable part of computer system. Still, various research being done on the development of effective intrusion detection system (IDS). An IDS is one of the suspicious network activities. An IDS is used to identify many types of malicious actions that can undermine a computer system's protection and confidence. Recently, ensemble algorithms are applied in IDS in order to identify and classify the security threats. In this paper author intends to do a brief review of various Ensemble learning Algorithms in ML, which are most frequently used in IDS for several applications; with specific interest in dataset and metric. This work provides broad study and investigation on current literature, the gap for improving and creating efficient IDS can be determined. [ABSTRACT FROM AUTHOR]

Algorithms are increasingly being used and recognized for their ability to improve the performance of diagnostic tools such as contemporary electrocardiogram (ECG). For instance, evidence from previous studies reveals that QRS enhancement and detection algorithms have enabled the ECG device to measure and classify heartbeat more accurately. Based on the review of the previous works on QRS detection in ECG, this paper examines the key components of the ECG, QRS detection features, the different techniques used for developing QRS enhancement and detection algorithms as well as the criteria for evaluating their performance. [ABSTRACT FROM AUTHOR]

As the Internet of Things (IoT) becomes more widely used, the danger of information leakage, the danger of information leakage, data leakage and theft of the internet of things is continuous increasing, as the communication channel for data transfer is open to the public. A considerable portion of this data includes sensitive and personal information for the user and the company as well. Because of the openness, distributed nature, and lack of control over the entire IoT environment, the number of attack vectors for bad users is significant. End-users must trust the system for the IoT to become a viable service platform. Consequently, in critical infrastructures like smart home, smart city, smart healthcare, and smart industry, information security and privacy are the major concern in the Internet of Things. In this paper, we present an information concealment strategy using steganography to protect communication in critical IoT infrastructure. RGB photographs are employed as the information carrier. Deep layers of low-distortion image channels in the least significant bit (LSB) are used to hide the data to be used as a data marker. Both mathematically and experimentally, we evaluate our method. We display mathematically that the striker's analysis cannot expect the current information. The proposed strategy outperformed previous strategies in terms of imperceptibility and capacity, as well as resilience to steganalysis attacks like histogram analysis, as demonstrated experimentally. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive analysis of safe data from novel HMAC cloud administration with respect to low time consumption in Database Management. In order to foresee the information in time consumption of the Database Management in cloud administration, we tested the HMAC encryption Algorithm with a test size of =20 and the IDEA encryption calculation (IEA) with a test size of =20. HMAC encryption was used to reduce the time needed to recover data from more secure distributed storage. When comparing HMAC and IDEA encryption calculations, HMAC has a higher utilisation rate (66.80 percent) (62.97 percent). In general, HMAC encryption calculation outperforms IDEA encryption calculation (p 0.05, 2-tailed). In data set administration frameworks, a novel HMAC encryption computation helps reduce the possibility of data recovery. [ABSTRACT FROM AUTHOR]