Minimum Computation Time Research Articles

Evolutionary correlated gravitational search algorithm (ECGS) with genetic optimized Hopfield neural network (GHNN) – A hybrid expert system for diagnosis of diabetes

In worldwide 415 million of peoples are affected by diabetics in the year of 2015, that is increased from the year of 2012. Based on the survey, it clearly shows the diabetics are one of the dangerous diseases because it leads to create several risk of early death. Due to the seriousness of the diabetic, it has been detected in early stage by creating expert system. During this process, the expert system has several issues such as accuracy of prediction due to the huge dimension of the diabetic feature that reduce the entire efficiency of the system. So, in this paper introduced the evolutionary correlated gravitational search algorithm (ECGS) for selecting the optimized features. The introduced method analyzes each diabetic feature according to the correlation and mutual information is selected with minimum computation time and cost. The selected features are processed by genetic optimized Hopfield neural network (GHNN) for predicting the diabetic related features effectively. Then the efficiency of the system is implemented using MATLAB tool that utilizes the Pima Indian Diabetic Dataset for analyzing the efficiency of introduced diabetic expert system. The efficiency of the system is evaluated in terms of using mean square error rate, F-measurer, accuracy, confusion matrix and ROC curve.

A Practical Traffic Control Scheme With Load Balancing Based on PCE Architecture

The path computation element (PCE) is used for the path computation for traffic engineering systems such as multi-protocol label switching (MPLS) and Generalized MPLS networks. It offers a good architecture for the centralized traffic routing and control. The classic routing policy aims to select one “best” path for the traffic, which may cause network congestion in the best link. How to adjust traffic to alleviate or avoid congestion is an important issue for operating networks. In this paper, according to the topology and the traffic model of China Telecom’s network, we propose a practical traffic control scheme with load balancing based on the PCE architecture. The objective of the traffic control scheme is twofold. First, an adaptive end-to-end path selection algorithm is proposed to select high-quality main and backup paths with the consideration of cost and path utilization. Second, a global routing optimization algorithm is proposed to adjust the path for the traffic, which considers the congestion feedback, the adjustment cost, and the expectation of load balance. The results show that our proposed scheme is able to efficiently alleviate or avoid congestion problem and achieve good performance in terms of the balance of link utilization and minimum computation time.

Numerical simulation of acoustic scattering from coaxial sound-penetrable spheres

This paper presents a generalisation of the mathematical model and numerical study of the acoustic scattering problem from multiple spheres in the case of spheres through which the wave passes and whose centers are located on the same axis (the case of sound-penetrable and coaxial spheres) under the action of spherical waves from a monopoly radiation source arbitrarily located in space. When solving the Helmholtz equations, a numerical technique based on the fast multipole method has been adapted for this task, which allows one to achieve high accuracy of the results obtained with minimal computer time. Comparison of the different approaches to truncation infinite series in the expansion showed the following: the result with a good degree of accuracy by a single calculation gives approach based on the truncation of all the rows with a fixed number in each expansion, and the result with a certain degree of accuracy gives an approach based on comparing two consecutive values of the sum of the series. A numerical parametric analysis of the pressure distribution inside and outside the spheres is carried out for various values of their radii, physical characteristics of the external and internal media, the number of spheres, the distances between the centers of the spheres, the frequency of exposure and the location of the monopole radiation source. It is shown that at certain values of the parameters, the appearance of zones of decrease or increase in pressure behind sound-penetrable spheres is possible. The obtained results will further allow to carry out test calculations to verify the general numerical algorithm for the case of a multitude of spheres arbitrarily located in space.

Human Activity Recognition based on Interaction Modelling

Human activity recognition aims at recognising and interpreting the activities of humans automatically from videos. Among the activities of humans, identifying the interactions between human within minimal computation time and reduced misclassification rate is a cumbersome task. Hence, an interaction-based human activity recognition system is proposed in this paper that utilises silhouette features to identify and classify the interactions between humans. The main issues that affect the performance of activity recognition are sudden illumination changes, detection of static human, data discrimination, data variance, crowding problem, and computational complexity. To accomplish the preceding issues, three new algorithms named weight-based updating Gaussian mixture model (wu-GMM), spatial dissemination-based contour silhouettes (SDCS), and weighted constrained dynamic time warping (WCDTW) are proposed. Experiments are conducted with the gaming dataset and Kinect interaction dataset to show that the proposed system recognises the interactions with reduced misclassification rate and minimal processing time compared to the existing system.

A Computation-Efficient Approach for Segment Routing Traffic Engineering

The unprecedented growth of network traffic has brought excessive challenges to network operators. To prevent network congestion, network operators conduct traffic engineering (TE) for their routing optimization. In recent years, segment routing traffic engineering (SRTE) has emerged as one of the promising approaches for its high scalability and low control overheads. However, conventional SRTE approaches in large-scale networks are computationally prohibitive, which may lead to delayed system operations and unsatisfactory service qualities. In this paper, we formulate a bi-objective mixed-integer nonlinear program (BOMINLP) to investigate the trade-off between link utilization and computation time in SRTE. Due to the difficulty in solving the original problem directly, we decompose it into two sequential sub-problems. The first sub-problem is to minimize computation time through node selection, and the second one is to minimize maximum link utilization via flow assignment. To this end, we first employ randomized sampling based on stretch bounding to obtain a reduced solution space and then solve a linear program (LP) using existing software tools for the sub-problems. To evaluate our proposed solution, we employ network topologies and traffic matrices from publicly available datasets. Our simulation results show that our proposed solution can effectively reduce computation time while retaining comparable maximum link utilization as compared with several comparison approaches.

Heterogeneous Implementation of a Novel Indirect Visual Odometry System

In this paper, a novel parallel indirect visual odometry (VO) system is proposed based on a newly designed map management method, key-frame selection, and a camera pose correction model, where the speeded-up robust features (SURF) algorithm is used to extract features from an image, and a linear exhaustive search (LES) algorithm is introduced to match features. To minimize computation time, a key-frame selection mechanism is proposed to distinguish key frames among the input images. Moreover, map management is proposed to filter out unstable landmarks and add features for a reliable estimation of the relative camera pose. In addition, estimation accuracy is improved by the camera pose correction model. To enhance computational efficiency of the VO system, the proposed approach is implemented on a TX2 embedded system with multiple graphics processing units (GPUs), taking advantage of a heterogeneous parallel computing architecture. To validate the performances of the proposed system, several experiments are conducted using an ASUS Xtion 3D camera and a laptop. Average errors of pose estimations are compared with those via the conventional VO to show the effectiveness of the proposed VO system. Thanks to the proposed approach, a real-time and reliable VO system can be established with low cost, low power consumption, high processing efficiency, and portability. The experimental results show that based on heterogeneous computing, the required computation time of the overall system with GPUs is approximately 80 times faster than that with only a CPU, when dealing with 80 features in the environment. To the best of our knowledge, this is the first released paper that implements an indirect VO on a CPU/GPU heterogeneous computing platform.

A predictive model to estimate ice accumulation on ship and offshore rig

Ice accumulation on ships and offshore rigs creates unsafe working conditions and may damage critical equipment. Several approaches have been developed in the past to predict ice accumulation; these include analytical models, experimental investigations, computational fluid dynamics simulations, empirical and statistical models. This work proposes a probabilistic causal relationship-based model to predict ice accumulation on ships or offshore rigs. The model uses a Bayesian probabilistic approach to establish the relationships among the factors affecting icing. The model is successfully tested on an experimental set-up designed to simulate the spray icing condition observed on a seagoing vessel in the subzero environment. The results of the experimental tests were compared with the outputs from the predictive model. It was observed that the predicted values gave a reasonably good match with the observed values.The proposed model considered a range of environmental and process parameters that affect ice accumulation. The model has the flexibility to include more parameters affecting icing, based on location and system. The model can be used for dynamically changing conditions with minimal computational load and time.

Efficiently Simulating Personal Vehicle Energy Consumption in Mesoscopic Transport Models

Mesoscopic transport models can efficiently simulate complex travel behavior and traffic patterns over large networks, but simulating energy consumption in these models is difficult with traditional methods. As mesoscopic transport models rely on a simplified handling of traffic flow, they cannot provide the second-by-second measurement of vehicle speeds and accelerations that are required for accurately estimating energy consumption. Here we present extensions to the TripEnergy model that fill in the gaps of low-resolution trajectories with realistic, contextual driving behavior. TripEnergy also includes a vehicle energy model capable of simulating the impact of traffic conditions on energy consumption and CO2 emissions, with inputs in the form of widely available calibration data, allowing it to simulate thousands of different real-world vehicle makes and models. This design allows TripEnergy to integrate with mesoscopic transport models and to be fast enough to run on a large network with minimal additional computation time. We expect it to benefit from and enable advances in transport simulation, including optimizing traffic network controls to minimize energy, evaluating the performance of different vehicle technologies under wide-scale adoption, and better understanding the energy and climate impacts of new infrastructure and policies.

MRI Brain Tumour Segmentation Using Hybrid Clustering and Classification by Back Propagation Algorithm

Generally the segmentation refers, the partitioning of an image into smaller regions to identify or locate the region of abnormality. Even though image segmentation is the challenging task in medical applications, due to contrary image, local observations of an image, noise image, non uniform texture of the images and so on. Many techniques are available for image segmentation, but still it requires to introduce an efficient, fast medical image segmentation methods. This research article introduces an efficient image segmentation method based on K means clustering integrated with a spatial Fuzzy C means clustering algorithms. The suggested technique combines the advantages of the two methods. K means segmentation requires minimum computation time, but spatial Fuzzy C means provides high accuracy for image segmentation. The performance of the proposed method is evaluated in terms of accuracy, PSNR and processing time. It also provides good implementation results for MRI brain image segmentation with high accuracy and minimal execution time. After completing the segmentation the of abnormal part of the input MRI brain image, it is compulsory to classify the image is normal or abnormal. There are many classifiers like a self organizing map, Back propagation algorithm, support vector machine etc., The algorithm helps to classify the abnormalities like benign or malignant brain tumour in case of MRI brain image. The abnormality is detected based on the extracted features from an input image. Discrete wavelet transform helps to find the hidden information from the MRI brain image. The extracted features are trained by Back Propagation Algorithm to classify the abnormalities of MRI brain image.

Nonlinear reduced order model of high aspect ratio rectangular wing plate

This paper presents a Combined Modal Finite Element (CMFE) approach to develop a Nonlinear Reduced Order Model (NROM) in order to characterize the nonlinear properties of the wing plate model. The wing plate model is subjected to three types of loading cases. The first case considers a uniformly distributed loading on the whole wing plate model for describing the bending deflection; the second case considers a uniformly distributed loading on both leading and trailing edges with one of them of an opposite direction for describing the twisting deflection; the third case considers the loading on the leading edge for describing a combination of bending-twisting deflection. The accuracy of the results is represented in the form of mean error, the standard deviation of the error and the percentage of error. From the findings, the NROMs are able to predict the nonlinear deformations of the wing plate with a minimal computational time and reasonably good accuracy. The results also indicate the importance of the selection modes when conducting the analysis.

Secret sharing scheme based on binary trees and Boolean operation

Conventional visual secret sharing schemes hide a secret into a number of shares, which are either printed or in digital form. Noise-like pixels appear due to hiding a secret in shares, but during the transmission of shares, it increases the interception risk. Hence, visual secret sharing schemes suffer from a transmission risk problem. To solve this problem, we propose a secret sharing scheme which will allow the sender to divide the secret into $$2^h$$ shares using a full binary tree structure and Boolean XOR, where h is the height of the tree. A man-in-the-middle attack is only possible if the attacker knows the number of shares the sender has generated. The attacker cannot decipher the secret using less than $$2^h$$ number of shares. This scheme is secure and takes minimum computation time for sharing as well as recovery of a secret. The proposed scheme works good on binary, grayscale, and colored images, and recovery of this scheme is lossless.

Hybrid Stand-alone Photovoltaic Systems Sizing Optimization Based on Load Profile

This paper presents a sizing optimization technique for Hybrid Stand-Alone Photovoltaic (HSAPV). In this research, three optimization techniques have been developed, namely Dolphin Echolocation Algorithm (DEA), Fast Evolutionary Programming (FEP), and Classical Evolutionary Programming (CEP). These techniques have been incorporated into the sizing process to maximize the technical performance of the SAPV system. The components of PV modules, charge controllers, inverters, and batteries are used to determine the optimum value. These components are used as the control parameters to maximize the expected performance ratio (PR) of the SAPV system. The Iterative Sizing Algorithm (ISA) is the benchmarking technique to conduct the optimization technique achieving maximum PR value and minimal computation time. Results obtained from the research show that DE overcomes FEP and CEP. In addition, the optimization techniques also demonstrated comparatively fast with respect to ISA as the benchmark technique."

Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification

Convolutional neural networks (CNNs) excel in a wide variety of computer vision applications, but their high performance also comes at a high computational cost. Despite efforts to increase efficiency both algorithmically and with specialized hardware, it remains difficult to deploy CNNs in embedded systems due to tight power budgets. Here we explore a complementary strategy that incorporates a layer of optical computing prior to electronic computing, improving performance on image classification tasks while adding minimal electronic computational cost or processing time. We propose a design for an optical convolutional layer based on an optimized diffractive optical element and test our design in two simulations: a learned optical correlator and an optoelectronic two-layer CNN. We demonstrate in simulation and with an optical prototype that the classification accuracies of our optical systems rival those of the analogous electronic implementations, while providing substantial savings on computational cost.

An efficient texture classification algorithm using integrated Discrete Wavelet Transform and local binary pattern features

This manuscript is keen to the Texture Classification problem. Texture is mainly defined as measuring the variation in the surface intensity such as regularity, smoothness, coarseness, etc. Texture classification is one of the most important issues in image processing and computer vision. Orientation, scale, image transitions or singularities such as edges, and the other visual appearance are the major problems in texture classification. Already works have done in texture classification by using Discrete Wavelet Transforms (DWT) and Local Binary Pattern (LBP) separately. The above techniques give minimum classification Accuracy. LBP is considered as an effective method but its performance is lower if the image has poor quality. We propose a technique to characterize the texture properties based on DWT using Local Binary Pattern. In this proposed work, input texture images are decomposed using single level Discrete Wavelet Transform. Then LBP features are extracted from all sub bands. The extracted LBP features for sub bands are combined to form main feature (1024 features). Image classification is done by using k-Nearest Neighbour (kNN) Classifier. The experiments validation are achieved by using four standard data sets (KTH-TIPS, KTH-TIPS-2a, Brodatz and Curet). The results are compared with Dense Micro block Difference (DMD) feature descriptors. The experimental result shows that the proposed method outperforms than the existing techniques. Also reduce the computational complexity and minimum computational time than the existing classification techniques.

Using the TSP Solution Strategy for Cloudlet Scheduling in Cloud Computing

Cloudlet scheduling in cloud computing is one of the most issues that face the cloud computing environment. This paper presents a new efficient approach, called Traveling Salesman Approach for Cloudlet Scheduling (TSACS), to solve the cloudlet-scheduling problem. The main idea is to convert the cloudlet-scheduling problem into an instance of the Traveling Salesman Problem (TSP) and then apply one of the TSP solution strategies to solve the problem. The proposed approach consists of three phases: clustering phase, converting phase, and assignment phase. In the clustering phase, the proposed approach converts the large size cloudlet-scheduling problem into a small size cluster-scheduling problem to minimize computation time complexity of the proposed approach. In the converting phase, the approach forms the cluster-scheduling problem as an instance of the TSP. In the assignment phase, the approach schedules the clusters into the available virtual machines by using the nearest neighbor algorithm. The proposed approach is evaluated by using the CloudSim and the results are compared with that obtained by the most recent algorithms. The results show that the proposed approach enhances the overall system performance in terms of schedule length, balancing degree, and time complexity. In addition, the proposed TSACS overcomes the oscillation problem of the existing cloudlet-scheduling algorithms.

Finite-element analysis of microwave scattering from a three-dimensional human head model for brain stroke detection.

In this paper, a detailed analysis of microwave (MW) scattering from a three-dimensional (3D) anthropomorphic human head model is presented. It is the first time that the finite-element method (FEM) has been deployed to study the MW scattering phenomenon of a 3D realistic head model for brain stroke detection. A major contribution of this paper is to add anatomically more realistic details to the human head model compared with the literature available to date. Using the MRI database, a 3D numerical head model was developed and segmented into 21 different types through a novel tissue-mapping scheme and a mixed-model approach. The heterogeneous and frequency-dispersive dielectric properties were assigned to brain tissues using the same mapping technique. To mimic the simulation set-up, an eight-elements antenna array around the head model was designed using dipole antennae. Two types of brain stroke (haemorrhagic and ischaemic) at various locations inside the head model were then analysed for possible detection and classification. The transmitted and backscattered signals were calculated by finding out the solution of the Helmholtz wave equation in the frequency domain using the FEM. FE mesh convergence analysis for electric field values and comparison between different types of iterative solver were also performed to obtain error-free results in minimal computational time. At the end, specific absorption rate analysis was conducted to examine the ionization effects of MW signals to a 3D human head model. Through computer simulations, it is foreseen that MW imaging may efficiently be exploited to locate and differentiate two types of brain stroke by detecting abnormal tissues’ dielectric properties. A significant contrast between electric field values of the normal and stroke-affected brain tissues was observed at the stroke location. This is a step towards generating MW scattering information for the development of an efficient image reconstruction algorithm.

SPLMax: Exploiting the Simple Path Introduced Locality for Maximum Flow Acceleration

As a basic problem in graph theory, the maximum flow (max-flow) problem has important applications in networking and communication related areas. The simple path introduced locality is implicit in classic max-flow algorithms, i.e., only the vertices in simple paths between source and sink are involved in max-flow calculation. However, this kind of locality is completely ignored in existing acceleration methods, which leads to a lot of useless calculations and seriously degrades the acceleration effect. We propose simple-path locality based max-flow acceleration algorithm (SPLMax) to address the problem, where an overlay graph is built and used to accelerate calculation by only including necessary vertices. Random graph based simulations show that with SPLMax, at best only 0.001% vertices (i.e., 1/71193) in the graph need to be involved in max-flow calculation. For the comparison using real-world graphs, SPLMax has the minimal pre-processing time (at most 109 times faster than other methods) and minimal average max-flow computation time (at most 4.3 times faster than other methods).

Orthogonal Collocation on Finite Element Method for Lid-Driven Cavity Flow

Among the candidates for the Electrical Energy Storage (EES), Redox Flow Batteries (RFBs) are drawing attention towards a feasible solution. They are advantageous in flexibility and long-term stability of capacity and are therefore expected to play a critical role in the renewable energy and microgrid system. Meanwhile, the performance of the RFBs greatly depends on the motion of the electrolyte. And the problem becomes more complicated when we consider the edge effect in 2-dimensional modeling. Up to date, there is no analytical solution for the fluid dynamics of electrolyte flow in RFBs. To maximize the performance and enable optimum real-time battery management, fast and robust algorithm for the flow model and numerical simulation in multi-dimension is essential. In this presentation, we introduce Orthogonal Collocation on Finite Element (OCFE) approach in the successive domain for the classical 2-D lid-driven cavity flow problem. The Navier-Stokes equation is mathematically reformulated as Partial Differential Equations (PDE) of stream function and vorticity. To get the vorticity boundary condition, imaginary points, which are located at the outside of the cavity boundary, are introduced. The result shows good agreements with precedential values while using minimum computational time and memory.

Wireless and real-time structural damage detection: A novel decentralized method for wireless sensor networks

Being an alternative to conventional wired sensors, wireless sensor networks (WSNs) are extensively used in Structural Health Monitoring (SHM) applications. Most of the Structural Damage Detection (SDD) approaches available in the SHM literature are centralized as they require transferring data from all sensors within the network to a single processing unit to evaluate the structural condition. These methods are found predominantly feasible for wired SHM systems; however, transmission and synchronization of huge data sets in WSNs has been found to be arduous. As such, the application of centralized methods with WSNs has been a challenge for engineers. In this paper, the authors are presenting a novel application of 1D Convolutional Neural Networks (1D CNNs) on WSNs for SDD purposes. The SDD is successfully performed completely wireless and real-time under ambient conditions. As a result of this, a decentralized damage detection method suitable for wireless SHM systems is proposed. The proposed method is based on 1D CNNs and it involves training an individual 1D CNN for each wireless sensor in the network in a format where each CNN is assigned to process the locally-available data only, eliminating the need for data transmission and synchronization. The proposed damage detection method operates directly on the raw ambient vibration condition signals without any filtering or preprocessing. Moreover, the proposed approach requires minimal computational time and power since 1D CNNs merge both feature extraction and classification tasks into a single learning block. This ability is prevailingly cost-effective and evidently practical in WSNs considering the hardware systems have been occasionally reported to suffer from limited power supply in these networks. To display the capability and verify the success of the proposed method, large-scale experiments conducted on a laboratory structure equipped with a state-of-the-art WSN are reported.

A comparative study of several metaheuristic algorithms for optimizing complex 3-D well-path designs

Considering the importance of cost reduction in the petroleum industry, especially in drilling operations, this study focused on the minimization of the well-path length, for complex well designs, compares the performance of several metaheuristic evolutionary algorithms. Genetic, ant colony, artificial bee colony and harmony search algorithms are evaluated to seek the best performance among them with respect to minimizing well-path length and also minimizing computation time taken to converge toward global optima for two horizontal wellbore cases: (1) a real well offshore Iran; (2) a well-studied complex trajectory with several build and hold sections. A primary aim of the study is to derive less time-consuming algorithms that can be deployed to solve a range of complex well-path design challenges. This has been achieved by identifying flexible control parameters that can be successfully adjusted to tune each algorithm, leading to the most efficient performance (i.e., rapidly locating global optima while consuming minimum computational time), when applied to each well-path case evaluated. The comparative analysis of the results obtained for the two case studies suggests that genetic, artificial bee colony and harmony search algorithms can each be successively tuned with control parameters to achieve those objectives, whereas the ant colony algorithm cannot.