Minimum Computation Time Research Articles

Lifetime maximization of wireless sensor networks using particle swarm optimization

Wireless sensor networks have multiple applications in intelligent environment and structural monitoring. The major challenge in wireless sensor networks is the power constraint. This paper deals with minimizing the energy utilization of wireless sensor nodes and maximizing their overall life span. The objective of our proposed scheme is to find a method for grouping sensors into the maximum number of distinct sensor cover sets to totally monitor the required area. This problem can be solved by using the disjoint cover set problem. Present optimization techniques take much time and deliver unsatisfactory results in large-scale networks. This paper proposes a technique that delivers optimal results in minimal computation time. The results of the proposed technique are superior to existing techniques ranging from four to more than eighteen times better in various cases.

Determination of Band Structure of Gallium-Arsenide and Aluminium-Arsenide Using Density Functional Theory

This research paper is on Density Functional Theory (DFT) within Local Density Approximation. The calculation was performed using Fritz Haber Institute Ab-initio Molecular Simulations (FHIAIMS) code based on numerical atomic-centered orbital basis sets. The electronic band structure, total density of state (DOS) and band gap energy were calculated for Gallium-Arsenide and Aluminium-Arsenide in diamond structures. The result of minimum total energy and computational time obtained from the experimental lattice constant 5.63 A for both Gallium Arsenide and Aluminium Arsenide is -114,915.7903 eV and 64.989 s, respectively. The electronic band structure analysis shows that Aluminium-Arsenide is an indirect band gap semiconductor while Gallium-Arsenide is a direct band gap semiconductor. The energy gap results obtained for GaAs is 0.37 eV and AlAs is 1.42 eV. The band gap in GaAs observed is very small when compared to AlAs. This indicates that GaAs can exhibit high transport property of the electron in the semiconductor which makes it suitable for optoelectronics devices while the wider band gap of AlAs indicates their potentials can be used in high temperature and strong electric fields device applications. The results reveal a good agreement within reasonable acceptable errors when compared with the theoretical and experimental values obtained in the work of Federico and Yin wang [1] [2].

Computational study of nanoscale Si-CMOS transport phenomena for a promising source of renewable energy

Recent Si-CMOS transport models in Nanoscale paradigm have been analyzed and compared in this work. Modeling scenarios for each of these models are presents and compared with the others. Modeling of some nanoscale parameters such asmobility (μ), temperature (T), injection velocity (Vinj), backscattering coefficient (RB), and effective electric field (Eeff) are presented in some or all of these models forshort channel effect (SCE) condition. A new proposed model for carrier transport low field mobility is introduced and compared with these models based on elastic and inelastic scattering mechanisms and channel potential profile (Vx). Observations and recommendations results taken from the evaluated comparison are: determining the simultaneous injection and temperature dependence of the sum of the majority and minority carrier mobilitiesin silicon wafers is an important issue in modeling mobility. Insertion of partial mono-layers of oxygen during silicon epitaxial of the channel layer is the best in modeling the effective electric field effect in nanoscale CMOS transport models. Furthermore, the proposed model conducts minimum computational time when compared with BSIM6 model and the other voted models with more than 46.23% saving of BSIM6 running time while BSIM6 model got the second low computational cost model. This will pave the road into efficient and modified modeling approach that optimize CMOS functionality for scales lower than 22nm in addition to leakage current density mitigation.

An evolutionary approach based design automation of low power CMOS Two-Stage Comparator and Folded Cascode OTA

This paper presents an evolutionary approach to design CMOS Two-Stage Comparator (TSC) and CMOS Folded Cascode Operational Trans-conductance Amplifier (FCOTA) using simplex particle swarm optimization (Simplex-PSO) method. The simplex particle swarm optimization (Simplex-PSO) is a swarm intelligent based evolutionary computation method. Simplex-PSO is the hybridization of Nelder–Mead Simplex method (NMSM) and Particle Swarm Optimization (PSO) without the velocity term. The Simplex-PSO has fast optimizing capability and high computational precision for high-dimensionality functions. This work has focused on the optimization of the area, power and has improved all other performance parameters of the CMOS TSC and CMOS FCOTA with minimum computational time. The proposed Simplex-PSO based circuit optimization technique is relieved from the inherent drawbacks of premature convergence and stagnation, unlike Differential Evolution (DE), Harmony Search (HS). The simulation results prove that Simplex-PSO yields the optimized result with improved functionality. Simulation results obtained for CMOS TSC and CMOS FCOTA prove the effectiveness of the proposed Simplex-PSO method based approach over the reported DE, HS, and PSO in terms of convergence speed, design specifications and design objectives. The optimally designed TSC and FCOTA circuits, individually, occupy the least MOS areas and dissipate the least powers. The simulation plots and results are shown to have the improved performance parameters compared with those of other reported literature. Validation of the design is carried out by Cadence version 5 (IC 5.10.41) of TSMC 0.35μm technology.

An Improved Crossover Genetic Algorithm with Distributed Generator (DG) Installation for DNR Restoration in Reducing Power Losses

Most common problems with distribution network are having stable, flexible and reliable restoration system after experiencing fault occurrences to ensure the continuity of power supply. This paper proposes a new algorithm for distribution network reconfiguration (DNR) with the simultaneous operation of DG for fault restoration on 132/33 kV of the IEEE 69 buses system. The proposed algorithm is conducted in three (3) cases; maximum, moderate and minimum fault conditions on the tested system. There are four (4) DGs that were located at the test system with optimal sizing based on the maximum total connected load at the particular area. An immediate isolation of faulted areas and the best radial number of switches configuration for unfaulted areas during the restoration process with minimum power losses are effectively obtained using the proposed algorithm. The effectiveness of the proposed algorithm was verified by comparing with the conventional Genetic Algorithm. From the analysis, the proposed algorithm gives best solutions for fitness function and minimum computational time compared to the Genetic algorithm.

Volunteer-based surveys offer enhanced opportunities for biodiversity monitoring across broad spatial extent

The growing public interest in biodiversity projects provides great opportunities to monitor biodiversity across broad geographic areas at low cost. Such volunteer-based surveys should however need careful consideration during statistical analysis since the presence of residual spatial autocorrelation and over-heterogeneity can lead to misguided inference. The recent development of new statistical tools allows accounting for these problems in all steps of the statistical analysis. Especially, the spatial leave-one-out method allows accounting for spatial autocorrelation in the variable selection step while the R-INLA tool box provides a useful way to estimate complex spatial hierarchical models in a minimum computation time. We applied such tools on a dataset collected by volunteers between 2000 and 2013 giving the relative abundance of 12 raptors breeding in France. We then estimated their spatial distribution, population sizes and trends with a particular emphasis in quantifying the uncertainty of our estimations. Our results suggest that broad-scale volunteer-based surveys offer enhanced opportunities for monitoring widespread species but may fail in giving accurate information for less common species, especially when sampling is too scattered. Providing uncertainty of estimations helps in identifying species and areas from which estimations are the more reliable and thus gives more powerful information for conservation practitioners.

Optimizing Workflow Scheduling using Max-Min Algorithm in Cloud Environment

With the up rise of fourth paradigm, that is discovery of science over a prolonged period of time, scientific workflows commence to amend their status amongst innumerous science subject areas including physics, astronomy, biology, chemistry, earthquake science and many more. In Scientific workflows, a heavy volume of data processing is required and workflows with up to a few million tasks are not unusual. With the advent of Cloud Computing as a new model of service provisioning in distributed systems, a new direction comes in light for executing scientific applications such as Workflows by deploying resources of Cloud. The scheduling of millions of tasks of workflows, while processing with Cloud resources, in a most profitable manner i.e. minimum computation time is still an attractive research area. The existing scheduling algorithms are brushing off the individual dependent and independent tasks. In this research paper, MaxMin algorithm is implemented for scheduling of workflow tasks that is focalized on the consideration of dependent and independent tasks and process independent tasks in parallel that directly gives profit in minimizing computation time. General Terms Cloud Computing, Scheduling Algorithms.

Cognitive null steering in frequency diverse array radars

Null steering has been a challenge in radar communications for the past few decades. In this paper, a novel cognitive null steering technique in frequency diverse array radars using frequency offset selection is presented. The proposed system is a complete implementable framework that provides precise and deep null placement in the range and angle locations of the interference source. The proposed system is cognitive such that the transmitter and receiver are connected via a feedback loop. System extracts interference source location parameters from the radar scene using Multiple Signal Classification, a super resolution direction of arrival estimation technique. Neural networks known for minimum computation time, and good non-linear and non-parametric approximation have been utilized for prediction of next location of the interference source. Simulation results validate the proposed frequency offset selection by demonstrating precise and deep nulls at the desired locations.

The jigsaw puzzle of sequence phenotype inference: Piecing together Shannon entropy, importance sampling, and Empirical Bayes

A nucleotide sequence 35 base pairs long can take 1,180,591,620,717,411,303,424 possible values. An example of systems biology datasets, protein binding microarrays, contain activity data from about 40,000 such sequences. The discrepancy between the number of possible configurations and the available activities is enormous. Thus, albeit that systems biology datasets are large in absolute terms, they oftentimes require methods developed for rare events due to the combinatorial increase in the number of possible configurations of biological systems. A plethora of techniques for handling large datasets, such as Empirical Bayes, or rare events, such as importance sampling, have been developed in the literature, but these cannot always be simultaneously utilized. Here we introduce a principled approach to Empirical Bayes based on importance sampling, information theory, and theoretical physics in the general context of sequence phenotype model induction. We present the analytical calculations that underlie our approach. We demonstrate the computational efficiency of the approach on concrete examples, and demonstrate its efficacy by applying the theory to publicly available protein binding microarray transcription factor datasets and to data on synthetic cAMP-regulated enhancer sequences. As further demonstrations, we find transcription factor binding motifs, predict the activity of new sequences and extract the locations of transcription factor binding sites. In summary, we present a novel method that is efficient (requiring minimal computational time and reasonable amounts of memory), has high predictive power that is comparable with that of models with hundreds of parameters, and has a limited number of optimized parameters, proportional to the sequence length.

On the use of randomization tests following adaptive designs

ABSTRACTRandomization tests (sometimes referred to as “re-randomization” tests) are used in clinical trials, either as an assumption-free confirmation of parametric analyses, or as an independent analysis based on the principle of randomization-based inference. In the context of adaptive randomization, either restricted or response-adaptive procedures, it is unclear how accurate such Monte Carlo approximations are, or how many Monte Carlo sequences to generate. In this paper, we describe several randomization procedures for which there is a known exact or asymptotic distribution of the randomization test. For a special class of procedures, called , and binary responses, the exact test statistic has a simple closed form. For the limited subset of existing procedures with known exact and asymptotic distributions, we can use these as a benchmark for the accuracy of Monte Carlo randomization techniques. We conclude that Monte Carlo tests are very accurate, and require minimal computation time. For simple tests with binary response in the class of procedures, the exact distribution provides the best test, but Monte Carlo approximations can be used when the exact distribution is difficult to compute.

Multi-Label Markov Random Fields as an Efficient and Effective Tool for Image Segmentation, Total Variations and Regularization

One of the classical optimization models for image segmentation is the well known Markov Random Fields (MRF) model. This model is a discrete optimization problem, which is shown here to formulate many continuous models used in image segmentation. In spite of the presence of MRF in the literature, the dominant perception has been that the model is not effective for image segmentation. We show here that the reason for the non-effectiveness is due to the lack of access to the optimal solution. Instead of solving optimally, heuristics have been engaged. Those heuristic methods cannot guarantee the quality of the solution nor the running time of the algorithm. Worse still, heuristics do not link directly the input functions and parameters to the output thus obscuring what would be ideal choices of parameters and functions which are to be selected by users in each particular application context. We describe here how MRF can model and solve efficiently several known continuous models for image segmentation and describe briefly a very efficient polynomial time algorithm, which is provably fastest possible, to solve optimally the MRF problem. The MRF algorithm is enhanced here compared to the algorithm in Hochbaum (2001) by allowing the set of assigned labels to be any discrete set. Other enhancements include dynamic features that permit adjustments to the input parameters and solves optimally for these changes with minimal computation time. Several new theoretical results on the properties of the algorithm are proved here and are demonstrated for images in the context of medical and biological imaging. An interactive implementation tool for MRF is described, and its performance and flexibility in practice are demonstrated via computational experiments. We conclude that many continuous models common in image segmentation have discrete analogs to various special cases of MRF and as such are solved optimally and efficiently, rather than with the use of continuous techniques, such as PDE methods, that restrict the type of functions used and furthermore, can only guarantee convergence to a local minimum. AMS subject classifications: 05C21, 05C85, 68W40, 68U10

Efficient image compression techniques for compressing multimodal medical images using neural network radial basis function approach

ABSTRACTImage compression technique is used to reduce the number of bits required in representing image, which helps to reduce the storage space and transmission cost. Image compression techniques are widely used in many applications especially, medical field. Large amount of medical image sequences are available in various hospitals and medical organizations. Large images can be compressed into smaller size images, so that the memory occupation of the image is considerably reduced. Image compression techniques are used to reduce the number of pixels in the input image, which is also used to reduce the broadcast and transmission cost in efficient form. This is capable by compressing different types of medical images giving better compression ratio (CR), low mean square error (MSE), bits per pixel (BPP), high peak signal to noise ratio (PSNR), input image memory size and size of the compressed image, minimum memory requirement and computational time. The pixels and the other contents of the images are less variant during the compression process. This work outlines the different compression methods such as Huffman, fractal, neural network back propagation (NNBP) and neural network radial basis function (NNRBF) applied to medical images such as MR and CT images. Experimental results show that the NNRBF technique achieves a higher CR, BPP and PSNR, with less MSE on CT and MR images when compared with Huffman, fractal and NNBP techniques.

High Resolution Identification of Wind Turbine Faults Based on Optimized ESPRIT Algorithm

Many researchers employ ESPRIT method as robust detection tool to identify fault frequency and amplitude in induction machines. However, this algorithm presents some limitation in terms of computational time and required data memory size. This drawback makes this technology unusable in real time diagnosis application. In the fact that wind turbine machine necessitates an on-line regular maintenance to guarantee an acceptable lifetime and to maximize its productivity. Thus, an improved version of ESPRIT-TLS method has been proposed and simulated to extract accurately fault frequencies and their magnitudes from the wind stator current with minimum computation time and less memory cost. The proposed approach has been evaluated by computer simulations under many fault kinds. Study outcomes prove the benefits and the performance of Fast-ESPRIT.

ESPRIT Method Enhancement for Real-time Wind Turbine Fault Recognition

Early fault diagnosis plays a very important role in the modern energy production systems. The wind turbine machine requires a regular maintenance to guarantee an acceptable lifetime and to minimize production loss. In order to implement a fast, proactive condition monitoring, ESPRIT method seems the correct choice due to its robustness in improving the frequency and amplitude detection. Nevertheless, it has a very complex computation to implement in real time. To avoid this problem, a Fast-ESPRIT algorithm that combined the IIR band-pass filtering technique, the decimation technique and the original ESPRIT method were employed to enhance extracting accurately frequencies and their magnitudes from the wind stator current. The proposed algorithm has been evaluated by computer simulations with many fault scenarios. Study results demonstrate the performance of Fast-ESPRIT allowing fast and high resolution harmonics identification with minimum computation time and less memory cost.

Retrieval of temperature from a multiple-channel Rayleigh-scatter lidar using an optimal estimation method.

The measurement of temperature in the middle atmosphere with Rayleigh-scatter lidars is an important technique for assessing atmospheric change. Current retrieval schemes for this temperature have several shortcomings, which can be overcome by using an optimal estimation method (OEM). Forward models are presented that completely characterize the measurement and allow the simultaneous retrieval of temperature, dead time, and background. The method allows a full uncertainty budget to be obtained on a per profile basis that includes, in addition to the statistical uncertainties, the smoothing error and uncertainties due to Rayleigh extinction, ozone absorption, lidar constant, nonlinearity in the counting system, variation of the Rayleigh-scatter cross section with altitude, pressure, acceleration due to gravity, and the variation of mean molecular mass with altitude. The vertical resolution of the temperature profile is found at each height, and a quantitative determination is made of the maximum height to which the retrieval is valid. A single temperature profile can be retrieved from measurements with multiple channels that cover different height ranges, vertical resolutions, and even different detection methods. The OEM employed is shown to give robust estimates of temperature, which are consistent with previous methods, while requiring minimal computational time. This demonstrated success of lidar temperature retrievals using an OEM opens new possibilities in atmospheric science for measurement integration between active and passive remote sensing instruments.

Experimental Testing of an Analytical Model for Membrane Strains in Single Point Incremental Forming

Single Point Incremental Forming (SPIF) is a method of forming sheet metal components that requires only minimal tooling and a standard 3 axis milling machine. The low tooling and setup costs of SPIF make it an ideal method for prototyping and low-volume manufacturing. One of the challenges of SPIF is the development of tool paths that will form parts successfully, without encountering failure modes such as fracture due to wall thinning. To progress beyond a trial and error approach to tool path creation, an accurate and fast method of predicting failure must be developed. Forming limits in SPIF are often characterised by a maximum wall angle, corresponding to thinning limits according to the sine law [1]. While inexpensive computationally, the sine law does not account for secondary strains due to part curvature, and is not applicable to multi-pass forming [2]. A more general method of rapidly predicting wall thinning and strain state of a post-formed part is necessary. One such method is a kinematic model proposed by Bambach (2010) [3] which simulates the displacement of the model during each pass of the tool relying only on geometrical data.In this paper, the kinematic model mentioned above is extended to be applied to multi-pass forming and experimentally tested by comparing model predictions of major and minor strains to experimental measurement. The model is found to accurately predict minor strains during multi-pass forming, while over-predicting major strains, likely due to material property and friction affects unaccounted for in the model. By properly understanding the accuracy and limitations of this model as applied to real forming conditions, toolpath strategies can be generated in future with confidence and with minimal computation time.

Brain tumor segmentation based on a hybrid clustering technique

Image segmentation refers to the process of partitioning an image into mutually exclusive regions. It can be considered as the most essential and crucial process for facilitating the delineation, characterization, and visualization of regions of interest in any medical image. Despite intensive research, segmentation remains a challenging problem due to the diverse image content, cluttered objects, occlusion, image noise, non-uniform object texture, and other factors. There are many algorithms and techniques available for image segmentation but still there needs to develop an efficient, fast technique of medical image segmentation.This paper presents an efficient image segmentation approach using K-means clustering technique integrated with Fuzzy C-means algorithm. It is followed by thresholding and level set segmentation stages to provide an accurate brain tumor detection. The proposed technique can get benefits of the K-means clustering for image segmentation in the aspects of minimal computation time. In addition, it can get advantages of the Fuzzy C-means in the aspects of accuracy. The performance of the proposed image segmentation approach was evaluated by comparing it with some state of the art segmentation algorithms in case of accuracy, processing time, and performance. The accuracy was evaluated by comparing the results with the ground truth of each processed image. The experimental results clarify the effectiveness of our proposed approach to deal with a higher number of segmentation problems via improving the segmentation quality and accuracy in minimal execution time.

Application of Post-stack migration to seismicdata associated with fault structures

Abstract In hydrocarbon exploration, wave-equation migrationtechniques play an important role in imaging thecomplex geological structures. Usually, post-stack migrationscheme is applied to the seismic data to improve theresolution with restoration of dipping reflectors to theirtrue position. As a result, the migrated time sections areinterpretable in terms of subsurface features. As a numericalstudy, three fault models are considered for the presentstudy. First of all, synthetic time sections are generatedcorresponding to three models. Later, post stack migrationschemes such as Gazdag(PS), Phase-shift with turningrays and reverse time migration (T-K) domain techniquesare applied in order to judge the imaging accuracy, preservationof true amplitude and computational speed. All thethree post stack time migrated sections delineate the structurewith their throw.However, the reverse time migrations(T-K) clearly delineate the reflectors in restoring the throwproperly with minimum computational time. In order totest the validity the numerical results, similar exercise hasbeen undertaken using field seismic data of KG basin, India.The results indicates that the field migrated sectionsare imaged. But, the reverse time migration (T-K ) providesthe best subsurface image with restoration of reflectorsand collapse of diffracted events with least computationaltime. Gazdag (PS) and Phase-Shift with turning migratedsection shows the reduction of amplitude whereas, the reversetime migration preserved the amplitude fully.

Efficient Scheduling of Scientific Workflows with Energy Reduction Using Novel Discrete Particle Swarm Optimization and Dynamic Voltage Scaling for Computational Grids.

One of the most significant and the topmost parameters in the real world computing environment is energy. Minimizing energy imposes benefits like reduction in power consumption, decrease in cooling rates of the computing processors, provision of a green environment, and so forth. In fact, computation time and energy are directly proportional to each other and the minimization of computation time may yield a cost effective energy consumption. Proficient scheduling of Bag-of-Tasks in the grid environment ravages in minimum computation time. In this paper, a novel discrete particle swarm optimization (DPSO) algorithm based on the particle's best position (pbDPSO) and global best position (gbDPSO) is adopted to find the global optimal solution for higher dimensions. This novel DPSO yields better schedule with minimum computation time compared to Earliest Deadline First (EDF) and First Come First Serve (FCFS) algorithms which comparably reduces energy. Other scheduling parameters, such as job completion ratio and lateness, are also calculated and compared with EDF and FCFS. An energy improvement of up to 28% was obtained when Makespan Conservative Energy Reduction (MCER) and Dynamic Voltage Scaling (DVS) were used in the proposed DPSO algorithm.

Constrained Fuzzy Predictive Control Using Particle Swarm Optimization

A fuzzy predictive controller using particle swarm optimization (PSO) approach is proposed. The aim is to develop an efficient algorithm that is able to handle the relatively complex optimization problem with minimal computational time. This can be achieved using reduced population size and small number of iterations. In this algorithm, instead of using the uniform distribution as in the conventional PSO algorithm, the initial particles positions are distributed according to the normal distribution law, within the area around the best position. The radius limiting this area is adaptively changed according to the tracking error values. Moreover, the choice of the initial best position is based on prior knowledge about the search space landscape and the fact that in most practical applications the dynamic optimization problem changes are gradual. The efficiency of the proposed control algorithm is evaluated by considering the control of the model of a 4 × 4 Multi-Input Multi-Output industrial boiler. This model is characterized by being nonlinear with high interactions between its inputs and outputs, having a nonminimum phase behaviour, and containing instabilities and time delays. The obtained results are compared to those of the control algorithms based on the conventional PSO and the linear approach.