Parallel Fractal Compression Method for Big Video Data

Liu, Shuai; Bai, Weiling; Liu, Gaocheng; Li, Wenhui; Srivástava, H. M.

doi:10.1155/2018/2016976

Cited by 73 publications

(20 citation statements)

References 30 publications

(26 reference statements)

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“…The priority level of big data scheduling is

. During execution of data scheduling tasks, there is a constraint on the amount of data sent by data nodes [ 22 ] and calculation method of data volume is as shown in Equation ( 11 ).…”

Section: Materials Methodsmentioning

confidence: 99%

Optimization of Big Data Scheduling in Social Networks

Liu

Srivastava

2019

Entropy

Self Cite

224

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In social network big data scheduling, it is easy for target data to conflict in the same data node. Of the different kinds of entropy measures, this paper focuses on the optimization of target entropy. Therefore, this paper presents an optimized method for the scheduling of big data in social networks and also takes into account each task’s amount of data communication during target data transmission to construct a big data scheduling model. Firstly, the task scheduling model is constructed to solve the problem of conflicting target data in the same data node. Next, the necessary conditions for the scheduling of tasks are analyzed. Then, the a periodic task distribution function is calculated. Finally, tasks are scheduled based on the minimum product of the corresponding resource level and the minimum execution time of each task is calculated. Experimental results show that our optimized scheduling model quickly optimizes the scheduling of social network data and solves the problem of strong data collision.

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“…The priority level of big data scheduling is

. During execution of data scheduling tasks, there is a constraint on the amount of data sent by data nodes [ 22 ] and calculation method of data volume is as shown in Equation ( 11 ).…”

Section: Materials Methodsmentioning

confidence: 99%

Optimization of Big Data Scheduling in Social Networks

Liu

Srivastava

2019

Entropy

Self Cite

224

View full text Add to dashboard Cite

show abstract

“…However, this fusion strategy reduces the contrast of results. It is known from the optical system imaging principle that the high frequency component of sharp image is much larger than blurred image [19]. If gradient, variance, and mean value of a certain area are larger, the more high-frequency information is included in the area and image information is richer.…”

Section: Rules Of Fusion Decisionmentioning

confidence: 99%

Regularized super-resolution restoration algorithm for single medical image based on fuzzy similarity fusion

2019

J Image Video Proc.

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Medical images are blurred and noised due to various reasons in the acquirement, transmission and storage. In order to improve the restoration quality of medical images, a regular super-resolution restoration algorithm based on fuzzy similarity fusion is proposed. Based on maintained similarity in multiple scales, the fused similarity of the medical images is computed by fuzzy similarity fusion. First, fuzzy similarity is determined by the regional features. The images with certain similarity are obtained according to the maximum value, and the fused image is obtained by all obvious regional features. Then, an adaptive regularized restoration algorithm is employed. In order to ensure the objective function has a global optimal solution, regularized parameters of the global minimum solution of nonlinear function are solved iteratively. Finally, experimental results show that mean square error (MSE) and peak signal-to-noise ratio (PSNR) of the restored image are visibly improved. The restored image also has an obvious improvement in the burr of local edge. Moreover, the algorithm has good stability with significantly enhanced PSNR.

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“…On the basis of these technologies, the anti-lock braking system (ABS), traction control system (TCS) and vehicle stability control (VSC) system have greater potential for development [2]. At present, research directions are mainly divided into (a) electronic differential technology based on rotation speed control and (b) electronic differential technology based on torque control [3,4].…”

Section: Introductionmentioning

confidence: 99%

Adaptive electronic differential control of vehicle by torque balance

Hong

Zhu²,

Wang

2019

Mobile Netw Appl

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Current adaptive torque balancing control of electric wheel-driven vehicle has shortcomings in electronic differential control of drive motor by using rotation speed mode. In order to solve this problem, an adaptive electronic differential control method of electric wheel-driven vehicle is proposed in this paper by torque balance. Firstly, by starting from the kinematics and dynamics of vehicle steering, the speed and force of each drived wheel in the steering are analyzed to explain the auxiliary role of electronic differential control to adaptive torque balancing, as well as influence of steering radius in vehicle. Then, electronic differential distribution by torque of wheel is used to control the abnormal jump interference and calculate the optimal combination of parameters in electronic differential control system. Finally, based on the optimal combination of these parameters, an adaptive electronic differential control of electric wheel-driven vehicle by torque balance is realized with fuzzy control in active and the reactive power. Experimental results show that the proposed method suppresses the abnormal jump interference factors of electronic differential control, as well as realizes the differential functions in control system. It has far-reaching significance by provideing a basic guarantee to realize adaptive electronic differential control system in electronic wheel-driven vehicle.

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Parallel Fractal Compression Method for Big Video Data

Cited by 73 publications

References 30 publications

Optimization of Big Data Scheduling in Social Networks

Optimization of Big Data Scheduling in Social Networks

Regularized super-resolution restoration algorithm for single medical image based on fuzzy similarity fusion

Adaptive electronic differential control of vehicle by torque balance

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