Execution Time Of Algorithm Research Articles

A Bitwise Design and Implementation for Privacy-Preserving Data Mining: From Atomic Operations to Advanced Algorithms

Homomorphic encryption (HE) is considered as one of the most powerful solutions to securely protect clients’ data from malicious users and even severs in the cloud computing. However, though it is known that HE can protect the data in theory, it has not been well utilized because many operations of HE are too slow, especially multiplication. In addition, existing data mining research studies using encrypted data focus on implementing only specific algorithms without addressing the fundamental problem of HE. In this paper, we propose a fundamental design and implementation of data mining algorithm through logical gates. In order to do this, we design various logic of atomic operations in encrypted domain and finally apply these logic to well-known data mining algorithms. We also analyze the execution time of atomic and advanced algorithms.

Enhanced self‐adaptive differential evolution multi‐Objective algorithm for coordination of directional overcurrent relays contemplating maximum and minimum fault points

In this study, a parameter tune free enhanced self-adaptive differential evolution multi-objective (ESA-DEMO) approach has been proposed for coordination of directional overcurrent relays. The advantages of the proposed method are: avoid the use of conventional single-objective function, which requires tuning of weighting parameters; avoid tuning of algorithm parameters; minimisation of primary, backup and coordination time interval; zero violation of coordination constraints in large interconnected network; and low computational resource consumption leading to fast algorithm execution time. The proposed method has been implemented on the highly interconnected 6-bus, IEEE 14- and 30-bus systems, where results have shown robustness and consistency of the algorithm. Moreover, two-fault point coordination criterion considering close- and far-end (maximum and minimum) faults has been performed. ESA-DEMO has been compared with popular genetic algorithms and state-of-the-art multi-objective algorithm for protection coordination study.

Automatic Door Opening using Quantitative Gait Algorithm

Quantitative Gait algorithm provides a system to identify a person from a distance. This project identifies three types of systems : Detection, Recognition and Control to open the Door.The initial process is extracting the forefront facial (human) images are converted into ordinary binary silhouette, it is captured from each frame in burst mode. The images are converted into values and stored in the database. In the process at real time, the facial recognition is done by comparing with the database. From the results it can also be observed that the person identified by raspberry pi camera and is done mainly for security purpose. This proposed system is more efficacious, consistently good in quality, and it utilize very less amount of data and power that is compared to the other existing systems. This project indicates that very better sensitivity, specificity and also the execution time of Quantitative Gait algorithms which is implemented in ARM processor therefore the results are obtained using MATLAB software.

Improving genetic algorithm performance by population initialisation with dispatching rules

Scheduling is an important process that is present in many real world scenarios where it is essential to obtain the best possible results. The performance and execution time of algorithms that are used for solving scheduling problems are constantly improved. Although metaheuristic methods by themselves already obtain good results, many studies focus on improving their performance. One way of improvement is to generate an initial population consisting of individuals with better quality. For that purpose a variety of methods can be designed. The benefit of scheduling problems is that dispatching rules (DRs), which are simple heuristics that provide good solutions for scheduling problems in a small amount of time, can be used for that purpose. The goal of this paper is to analyse whether the performance of genetic algorithms can be improved by using such simple heuristics for initialising the starting population of the algorithm. For that purpose both manual and different kinds of automatically designed DRs were used to initialise the starting population of a genetic algorithm. In case of the manually designed DRs, all existing DRs for the unrelated machines environment were used, whereas the automatically designed DRs were generated by using genetic programming. The obtained results clearly demonstrate that using populations initialised by DRs leads to a significantly better performance of the genetic algorithm, especially when using automatically designed DRs. Furthermore, it is also evident that such a population initialisation strategy also improves the convergence speed of the algorithm, since it allows it to obtain significantly better results in the same amount of time. Additionally, the DRs have almost no influence on the execution speed of the genetic algorithm since they construct the schedule in time which is negligible when compared to the execution of the genetic algorithm. Based on the obtained results it can be concluded that initialising individuals by using DRs significantly improves both the convergence and performance of genetic algorithm, without the need of having to manually design new complicated initialisation procedures and without increasing the execution time of the genetic algorithm.

A Method To Improve The Time Of Computing For Detecting Community Structure In Social Network Graph

Identifying communities has always been a fundamental task in analysis of complex networks. Currently used algorithms that identify the community structures in large-scale real-world networks require a priori information such as the number and sizes of communities or are computationally expensive. Amongst them, the label propagation algorithm (LPA) brings great scaslability together with high accuracy but which is not accurate enough because of its randomness. In this paper, we study the equivalence properties of nodes on social network graphs according to the labeling criteria to shorten social network graphs and develop label propagation algorithms on shortened graphs to discover effective social networking communities without requiring optimization of the objective function as well as advanced information about communities. Test results on sample data sets show that the proposed algorithm execution time is significantly reduced compared to the published algorithms. The proposed algorithm takes an almost linear time and improves the overall quality of the identified community in complex networks with a clear community structure.

Performance Analysis of Bit Loading Algorithms for fault tolerant Submarine Communication

In this paper, performance metrics of rate adaptive and margin adaptive bit loading algorithms is simulated and analyzed to improve the data rate in undersea communication channel. In fault tolerant undersea communication, achieving higher data rate is a challenge. Bit loading algorithms have proven efficient with limited power budget. An optimal bit loading algorithm with low complexity and fast convergence for rate adaptive and margin adaptive problems with bit error rate (BER) constraints will increase the data rate and reliability of the system significantly. The simulation has been carried for submarine audio channel with a bandwidth of 0-20kHz considering ambient noise and thorps attenuation model for 16 subcarriers. Three different approaches such as Chow, Campello and Ant Colony Optimization are simulated for margin adaptive and rate adaptive criteria. Simulation results show that, for margin adaptive conditions Campello and ACO algorithms reduce energy by a factor of 71.50% and 13.05% with respect to Chow’s algorithm with an input of 90 bits. It was also observed that for rate adaptive conditions, Chow and ACO algorithms gave marginally increased bit rate compared to Campello algorithm with energy fixed at 8.35J. Campello and ACO algorithm execution time were reduced by a factor of 67% and 87.10% respectively as compared to Chow’s algorithm.

A method to obtain the near-large fruit from apple image in orchard for single-arm apple harvesting robot

It is appropriate for near-large fruit to be the first target of single-arm harvesting robot based on machine vision in each operation. The work introduced a method to obtain the near-large fruit from apple image in orchard. Firstly, the R-channel and G-channel images of orchard apple RGB (Red, Green, Blue) images were operated by the adaptive Gamma correction method to overcome the influence of natural illumination on subsequent image segmentation. After combination, the treatment process was formed by the basic image processing method such as corrosion, expansion, hole filling, and fixed threshold segmentation to obtain a complete, clean fruit area in the collected apple image. Finally, the near-large fruit was obtained from apple image of orchard based on the optimized iterative open operation. To verify the designed method, 30 apple images were selected according to strong to low light conditions from the captured images. Experimental results showed that the apple image after the adaptive Gamma correction was segmented to obtain the cleaner fruit region. The proposed method was used to obtain the correct near-large fruit with nearly real area. The optimized iterative open operation reduced the execution time of entire algorithm, with average and maximum reduction ratios of 70 and 84%, respectively.

Intelligent Rapid Adaptive Offloading Algorithm for Computational Services in Dynamic Internet of Things System.

As restricted resources have seriously limited the computational performance of massive Internet of things (IoT) devices, better processing capability is urgently required. As an innovative technology, multi-access edge computing can provide cloudlet capabilities by offloading computation-intensive services from devices to a nearby edge server. This paper proposes an intelligent rapid adaptive offloading (IRAO) algorithm for a dynamic IoT system to increase overall computational performance and simultaneously keep the fairness of multiple participants, which can achieve agile centralized control and solve the joint optimization problems related to offloading policy and resource allocation. For reducing algorithm execution time, we apply machine learning methods and construct an adaptive learning-based framework consisting of offloading decision-making, radio resource slicing and algorithm parameters updating. In particular, the offloading policy can be rapidly derived from an estimation algorithm based on a deep neural network, which uses an experience replay training method to improve model accuracy and adopts an asynchronous sampling trick to enhance training convergence performance. Extensive simulations with different parameters are conducted to maintain the trade-off between accuracy and efficiency of the IRAO algorithm. Compared with other candidates, the results illustrate that the IRAO algorithm can achieve superior performance in terms of scalability, effectiveness and efficiency.

Drug Discovery Acceleration Using Digital Microfluidic Biochip Architecture and Computer-aided-design Flow

A Digital Microfluidic Biochip (DMFB) offers a promising platform for medical diagnostics, DNA sequencing, Polymerase Chain Reaction (PCR), and drug discovery and development. Conventional Drug discovery procedures require timely and costly manned experiments with a high degree of human errors with no guarantee of success. On the other hand, DMFB can be a great solution for miniaturization, integration, automation, and cost reduction of drug discovery. DMFB can improve the parallelism of drug discovery procedures; since most procedures in drug discovery are concurrent and parallel, DMB can reduce the execution time of these bioassays. Therefore, there is a critical need to develop DMFBs to speed up the drug discovery applications and improve cost and error of these reactions. In this paper, a new architecture is used for drug discovery applications. The architecture is evaluated and compared with FPPC architecture. The experimental results prove that the new architecture is faster and cheaper than FPPC; it reduces all the important parameters such as total execution time, number of controlling pins, CAD algorithm execution time, and the area usage and its costs. There is an urgent need for collaboration between experts of drug discovery, microfluidic platform architecture and also machine learning to design a data-driven microfluidic architecture which improves the CAD algorithms by learning from prior knowledge.

An Optimized Breadth-First Search Algorithm for Routing in Optical Access Networks

This work consists in the application of an optimized breadth-first search (BFS) algorithm to select a couple of link-and-node-disjoint shortest-path between the two most remote users within an optical access network. Our results showed that while the average execution time of the original BFS algorithm was 12.23 seconds that of the optimized BFS was 10.80 seconds, which means a reduction of 11.69 percent of the average computing time. In future works we intend to apply the modified BFS algorithm to a generic optical network and evaluate its performance for different mesh size, node concentration and random link arrangement.

Development of a map-matching algorithm for dynamic-sampling-rate GPS signals to determine vehicle routes on a MATSim network

The rapid development and proliferation of global positioning system (GPS)-enabled systems and devices have led to a significant increase in the availability of transport data, more specifically GPS trajectories, that can be used in researching vehicle activities. In order to save data storage- and handling costs many vehicle tracking systems only store low-frequency trajectories for vehicles. A number of existing methods used to map GPS trajectories to a digital road network were analysed and such an algorithm was implemented in Multi-Agent Transport Simulation (MATSim), an open source collaborative simulation package for Java. The map-matching algorithm was tested on a simple grid network and a real and extensive network of the City of Cape Town, South Africa. Experimentation showed the network size has the biggest influence on algorithm execution time and that a network must be reduced to include only the links that the vehicle most likely traversed. The algorithm is not suited for trajectories with sampling rates less than 5 seconds as it can result in unrealistic paths chosen, but it manages to obtain accuracies of around 80% up until sampling sizes of around 50 seconds whereafter the accuracy decreases. Further experimentation also revealed optimal algorithm parameters for matching trajectories on the Cape Town network. The use case for the implementation was to infer basic vehicle travel information, such as route travelled and speed of travel, for municipal waste collection vehicles in the City of Cape Town, South Africa.

Network Security Using Python: An Empirical Analysis of Advance Encryption Standard (AES)

Nowadays, security in network environment is becoming a challenging problem, providing prevention of confidential records arises as a critical task. Modern creation in network can reduce the rate of uncertainty as-well-as at the same time growth of threats increases rapidly in past few decades i.e. vulnerabilities, and potential risks. Confidentiality of crucial informationis one of the main objectives, to protect integrity when its traveling from one node to another, ensure it can’t divulge by malicious insider, anonymous, trust, and malicious attacks.In this research paper, the proposed solution is to design interface where communication can be done in a secure manner, behind it has a mechanism which hide essential information, symmetric key cryptographic algorithm named advance encryption standard (AES) using python with additional library called pycrypto and tries to formulate algorithm efficiency, accuracy, execution time plus system capability.

Unscented Kalman filter state estimation for manipulating unmanned aerial vehicles

Manipulating unmanned aerial vehicles (MUAVs) are aerial robots equipped with a mechanism to physically interact with the environment. State estimation of such robots is a challenging problem due to inherent couplings, nonlinearities and uncertainties of MUAV complex dynamics and, therefore, popular algorithms such as extended Kalman filter may not be applicable. With the above considerations, this paper formulates two variants of the unscented Kalman filter using (i) general and (ii) spherical unscented transform to address state estimation problem in MUAVs. In order to examine the effect of estimation quality on overall control performance, first the coupled dynamics of a quadcopter endowed with a robotic manipulator is presented. Next, a linear–quadratic–Gaussian (LQG) control is designed to achieve simultaneous control of the quadcopter and its manipulator. Then, the performance of each unscented Kalman filter algorithm is compared with that of extended Kalman filter in the context of estimation accuracy, overall control performance, and algorithm execution time. Additionally, sensitivity of the proposed approaches to increasing noise levels and total loss of sensory data are examined.

파이썬을 활용한 탐색 알고리즘 수행시간 분석이 초등학생의 논리성에 미치는 효과

파이썬을 활용한 탐색 알고리즘 수행시간 분석이 초등학생의 논리성에 미치는 효과

Initial Rotor Position Detection Method of SPMSM Based on New High Frequency Voltage Injection Method

A novel initial position detecting strategy of sensorless control for the surface-mounted permanent magnet synchronous motor is proposed, the scheme combines the virtual high frequency (HF) voltage pulsating injection method with the carrier frequency component method. In order to improve the sensorless control performance, this method adds the virtual HF rotation frame based on the traditional HF pulsating injection method. At the same time, the carrier frequency component signals are utilized to detect a magnetic pole. The simulation and experimental results show that the proposed method does not require proportion-integration (PI) regulation. The novel magnetic pole judgment method can effectively improve the detecting performance of sensorless control in the long algorithm execution time, complex process, etc. The experimental results verified the correctness of the strategy.

Effectiveness of Segment Routing Technology in Reducing the Bandwidth and Cloud Resources Provisioning Times in Network Function Virtualization Architectures

Network Function Virtualization is a new technology allowing for a elastic cloud and bandwidth resource allocation. The technology requires an orchestrator whose role is the service and resource orchestration. It receives service requests, each one characterized by a Service Function Chain, which is a set of service functions to be executed according to a given order. It implements an algorithm for deciding where both to allocate the cloud and bandwidth resources and to route the SFCs. In a traditional orchestration algorithm, the orchestrator has a detailed knowledge of the cloud and network infrastructures and that can lead to high computational complexity of the SFC Routing and Cloud and Bandwidth resource Allocation (SRCBA) algorithm. In this paper, we propose and evaluate the effectiveness of a scalable orchestration architecture inherited by the one proposed within the European Telecommunications Standards Institute (ETSI) and based on the functional separation of an NFV orchestrator in Resource Orchestrator (RO) and Network Service Orchestrator (NSO). Each cloud domain is equipped with an RO whose task is to provide a simple and abstract representation of the cloud infrastructure. These representations are notified of the NSO that can apply a simplified and less complex SRCBA algorithm. In addition, we show how the segment routing technology can help to simplify the SFC routing by means of an effective addressing of the service functions. The scalable orchestration solution has been investigated and compared to the one of a traditional orchestrator in some network scenarios and varying the number of cloud domains. We have verified that the execution time of the SRCBA algorithm can be drastically reduced without degrading the performance in terms of cloud and bandwidth resource costs.

Parallel Implementation of Model Predictive Control for Multilevel Cascaded H-Bridge STATCOM With Linear Complexity

The algorithm complexity of model predictive control (MPC) for cascaded H-bridge (CHB) static synchronous compensators (STATCOMs) was optimized to a polynomial level in previous studies. However, implementing MPC with conventional approaches still suffers from long execution time, where a low cost digital signal processor (DSP) is used to execute optimization algorithms and a small-sized field-programmable gate array (FPGA) is used to extend gate signals and sampling functions. This paper presents a parallel implementation approach to reduce the execution time of MPC, where the FPGA is configured to be used for accelerating the high-complexity sorting tasks. By running the DSP and FPGA in parallel, the algorithm execution time is only determined by the DSP, and the resource consumption is only determined by the FPGA. To minimize the resource consumption and requirement in FPGA, a parallel comparison-based sorter with multiple comparison and accumulators is proposed. The proposed implementation method can reduce the time and space complexity of the MPC from the polynomial level to a linear level without additional hardware cost, which means the MPC for medium-voltage CHB STATCOMs (e.g., 20 stages) can be implemented online with existing control platforms. Experimental results for various CHB STATCOMs are presented to validate the proposed configuration and algorithm.

언플러그드 방식을 활용한 알고리즘 수행시간 분석이 초등학교 3학년 학생의 컴퓨팅 사고력에 미치는 효과

언플러그드 방식을 활용한 알고리즘 수행시간 분석이 초등학교 3학년 학생의 컴퓨팅 사고력에 미치는 효과

A Modified Event Grouping Based Algorithm for The University Course Timetabling Problem

This paper presents a study of a modified event grouping based algorithm (MEGB) for university course timetabling problem (UCTP). Multiple models to describe the problem and multiple approaches to solving it are pointed out. The main idea of the modification is that through reduction on the generated solutions the execution time of the standard event grouping based algorithm (EGB) will be reduced, too. Also, an implementation of the modified algorithm based on the described approach is presented. The methodology, conditions and the aims of the experiment are described. The experimental results are analyzed and conclusions are made. When increasing the number of the groups, the execution time of the MEGB algorithm increases and equates with the execution time of the EGB algorithm. The best results are obtained with the first 30% of the groups formed. In these groupings, the execution time of the MEGB algorithm is much less than the execution time of the EGB algorithm. This is because, in the EGB algorithm, every change in the event ordinance creates a new timetable, and all events are repositioned on it. This process is optimized by creating a partial timetable, whereby the ordinance of events in groups before the current does not change. In addition, a comparative analysis between the MEGB algorithm and two other algorithms for UCTP, respectively a genetic algorithm with the local search method (GALS) and a local search algorithm based on chromatic classes (CCLS) is made as well. The obtained results show that the MEGB algorithm and the CCLS algorithm generate better solutions for smaller input data sets, while the GALS algorithm generates better solutions for larger input data sets. However, in terms of the execution time, it was ascertained that the GALS algorithm runs slowest among the others.

Execution time enhancement of DOCR coordination algorithms for on-line application

This article presents an enhanced bio-inspired algorithm to reduce the execution time of the coordination of directional overcurrent relays (DOCRs) for on-line applications. Usually, the off-line coordination is degraded under the highly dynamic operation of electric networks such as minimal load conditions, topological changes, and unregulated source operation. The solution of coordination obtained every few minutes can both reduce the loss of sensitivity and the increase in the operation times of the relays. For this purpose, it is important to determine the execution time of the coordination algorithm using complex interconnected systems for a better evaluation. An improved Grey Wolf Optimizer (GWO) is proposed to obtain better results when compared to similar heuristic algorithms. The results using the test network systems show that this methodology can be advantageous and useful to obtain optimal relay settings in complex electrical systems with a reduced execution time of the coordination algorithm.