scholarly journals A Novel Method for Online Detection of Faults Affecting Execution-Time in Multicore-Based Systems

2017 ◽  
Vol 16 (4) ◽  
pp. 1-19 ◽  
Author(s):  
Stefano Esposito ◽  
Massimo Violante ◽  
Marco Sozzi ◽  
Marco Terrone ◽  
Massimo Traversone
Keyword(s):  
Execution Time ◽  
Online Detection ◽  
Novel Method

scholarly journals An Efficient and Fast Partial Template Matching Technique – Enhancement in Normalized Cross Correlation

2019 ◽  
Vol 9 (1) ◽  
pp. 7232-7237
Keyword(s):  
Image Processing ◽  
Execution Time ◽  
Template Matching ◽  
Absolute Difference ◽  
Processing Efficiency ◽  
Memory Space ◽  
Matching Technique ◽  
Speed Up ◽  
Novel Method ◽  
Window Approach

Template matching forms the basis of many image processing algorithms and hence the computer vision algorithms. There are many existing template matching algorithms like Sum of Absolute Difference (SAD), Normalized SAD (NSAD), Correlation methods (CORR), Normalized CORR(NCORR), Sum of Squared Difference (SSD), and Normalized SSD(NSSD). In general, as image requires more memory space for storage and much time for processing. The above said methods involves much computation. In any processing, efficiency constraints include many factors, especially accuracy of the results and speed of processing. An approach to reduce the execution time is always most appreciated. As a result of this, a novel method of partial NCC (PNCC) template matching technique is proposed in this paper. A block window approach is used to reduce the number of operations and hence to speed up the processing. A comparative study between existing NCC algorithm and the proposed partial NCC, PNCC algorithm is done. It is experimented and results proves that the execution time is reduced by 8 - 47 times approximately based on the various template images for different main images in PNCC. The accuracy of the result obtained is 100%. This proposed algorithm works for various types of images. The experiment is repeated for various sizes of templates and different sizes of main image. Further improvement in the speed of execution can be achieved by implementation of the proposed algorithm using parallel processors. It may find its importance in the real time image processing

scholarly journals Implementation of Extended Kalman Filter with Optimized Execution Time for Sensorless Control of a PMSM Using ARM Cortex-M3 Microcontroller

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3491
Author(s):  
Justas Dilys ◽  
Voitech Stankevič ◽  
Krzysztof Łuksza
Keyword(s):  
Kalman Filter ◽  
Permanent Magnet ◽  
Extended Kalman Filter ◽  
Execution Time ◽  
Sensorless Control ◽  
Permanent Magnet Synchronous Motor ◽  
Computation Time ◽  
Covariance Matrices ◽  
Kalman Gain ◽  
Novel Method

This paper addresses the implementation and optimization of an Extended Kalman Filter (EKF) for the Permanent Magnet Synchronous Motor (PMSM) sensorless control using an ARM Cortex-M3 microcontroller. A various optimization levels based on arithmetic calculation reduction was implemented in ARM Cortex-M3 microcontroller. The execution time of EKF estimator was reduced from 260.4 μs to 37.7 μs without loss of accuracy. To further reduce EKF execution time, the separation of a Kalman gain and covariance matrices calculation from prediction and measurement state update, a novel method was proposed, and the performance of it an EKF estimator with separation of a Kalman gain and covariance matrices calculation from prediction and measurement state update was analyzed. Simulation and experiments results validate that the proposed technique could provide the same accuracy with less computation time. A tendency of minimum Kalman gain and covariance matrices calculation frequency from rotor electrical frequency was analyzed and are presented in the paper.

Planning trigonometric frequency central pattern generator trajectory for cyclic tasks of robot manipulators

2018 ◽  
Vol 233 (11) ◽  
pp. 4014-4031 ◽  
Author(s):  
Yi Fang ◽  
Jie Hu ◽  
Jin Qi ◽  
Wenhai Liu ◽  
Weiming Wang ◽  
...  
Keyword(s):  
Central Pattern Generator ◽  
Execution Time ◽  
Neural Circuit ◽  
A Priori ◽  
Pattern Generator ◽  
Time Optimal ◽  
Novel Method ◽  
Point To Point ◽  
Central Pattern Generator Model ◽  
Simple Network

Planning efficient trajectories is an essential task in most automated robotic applications. The execution time and smoothness are usually important considerations for economic and safety reasons. A novel method to generate a trigonometric frequency central pattern generator trajectory is presented in this paper for cyclic point-to-point tasks of industrial robotic manipulators. The proposed method is biologically motivated by the concept of central pattern generator, which is a special neural circuit underlying most rhythmic activities in living beings. A modified central pattern generator model with simple network structure is developed for yielding the desired joint trajectories of robots. An important property of this technique lies in the fact that stable online trajectory transition between different paths is enabled by simply adjusting the central pattern generator control parameters. Moreover, kinematic constraints of the robot can be taken into account for optimizing the robot motion instead of setting a priori the execution time. Two examples of the pick-and-place operation, which is a typical cyclic point-to-point task, are used to illustrate the validity of the method. The results of simulation indicate that the proposed method is capable of producing smooth and time-optimal trajectories, which have also been compared with those yielded by other trajectory planning approaches found in the scientific literature.

scholarly journals Newly Proposed k-NN Method for More Efficient Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 5417-5424
Keyword(s):  
Execution Time ◽  
Input Data ◽  
Nearest Neighbors ◽  
New Classification ◽  
K Nearest Neighbors ◽  
Final Model ◽  
Original Dataset ◽  
Input Dataset ◽  
Accuracy Result ◽  
Novel Method

The purpose of this paper is to examine a new classification algorithm based on the well-known k nearest neighbors technique that achieves better efficiency in terms of accuracy, precision and time when classifying test observations in comparison to classic k nearest neighbors.The proposed methodology splits the input dataset into n folds containing all observations. Each record is allocated to one of the folds. One of the folds is saved for testing purposes and the rest of the folds are used for training. The process is executed n times. The pair of train/test subsets which produces the highest accuracy result is selected as final model for the respective input data.18 different datasets are used for experiments. For each dataset, the classic kNN is compared to the proposed method (Mk-NN) using accuracy, F1 score and execution time as metrics. The proposed approach achieves better results than classic k-NN according to all used metrics.Based on experiments with validation subsets, evidence of overfitting was not found.This paper suggests a novel method for improvement in accuracy, precision, recall and time when classifying test observations from a dataset. The approach is based on the concept of k nearest neighbors. However, what separates it from classic k nearest neighbors is that it tries to find train and test subsets of the original dataset that best represent the input dataset using the k-fold method

ONLINE DETECTION OF THE MODALITY OF COMPLEX-VALUED REAL WORLD SIGNALS

2008 ◽  
Vol 18 (02) ◽  
pp. 67-74 ◽  
Author(s):  
DANILO P. MANDIC ◽  
PHEBE VAYANOS ◽  
MO CHEN ◽  
SU LEE GOH
Keyword(s):  
Real World ◽  
Vector Fields ◽  
Adaptive Filters ◽  
Convex Combination ◽  
Nonlinear Modeling ◽  
Complex Nature ◽  
Online Detection ◽  
Online Mode ◽  
Novel Method ◽  
Complex Valued

A novel method for the online detection of the modality of complex-valued nonlinear and nonstationary signals is introduced. This is achieved using a convex combination of complex nonlinear adaptive filters with different transient characteristics. To facilitate the online mode of operation, the convex mixing parameter λ within the proposed architecture is made gradient adaptive. Our focus is on the most important aspect of complex nonlinear modeling, that is, the identification of the split-complex and fully-complex nature of the signal in hand. The algorithms derived are robust and capable of tracking the changes in the modality of both benchmark and real world radar and wind complex vector fields.

The removal and morphology of intact biofilms from implanted venous access devices

1995 ◽  
Vol 53 ◽  
pp. 1020-1021
Author(s):  
M.A. Gregory ◽  
G.P. Hadley
Keyword(s):  
Patient Selection ◽  
Surgical Oncology ◽  
Venous Access ◽  
Common Procedure ◽  
Indwelling Catheters ◽  
Careful Patient ◽  
Novel Method ◽  
The Subject ◽  
Vascular Catheters ◽  
Venous Access Devices

The insertion of implanted venous access systems for children undergoing prolonged courses of chemotherapy has become a common procedure in pediatric surgical oncology. While not permanently implanted, the devices are expected to remain functional until cure of the primary disease is assured. Despite careful patient selection and standardised insertion and access techniques, some devices fail. The most commonly encountered problems are colonisation of the device with bacteria and catheter occlusion. Both of these difficulties relate to the development of a biofilm within the port and catheter. The morphology and evolution of biofilms in indwelling vascular catheters is the subject of ongoing investigation. To date, however, such investigations have been confined to the examination of fragments of biofilm scraped or sonicated from sections of catheter. This report describes a novel method for the extraction of intact biofilms from indwelling catheters.15 children with Wilm’s tumour and who had received venous implants were studied. Catheters were removed because of infection (n=6) or electively at the end of chemotherapy.

Longitudinal Changes in Individual BrainAGE in Healthy Aging, Mild Cognitive Impairment, and Alzheimer’s Disease

GeroPsych ◽  
2012 ◽  
Vol 25 (4) ◽  
pp. 235-245 ◽  
Author(s):  
Katja Franke ◽  
Christian Gaser
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Healthy Aging ◽  
Brain Aging ◽  
Elderly Subjects ◽  
Additional Increase ◽  
Longitudinal Changes ◽  
Novel Method ◽  
The Brain

We recently proposed a novel method that aggregates the multidimensional aging pattern across the brain to a single value. This method proved to provide stable and reliable estimates of brain aging – even across different scanners. While investigating longitudinal changes in BrainAGE in about 400 elderly subjects, we discovered that patients with Alzheimer’s disease and subjects who had converted to AD within 3 years showed accelerated brain atrophy by +6 years at baseline. An additional increase in BrainAGE accumulated to a score of about +9 years during follow-up. Accelerated brain aging was related to prospective cognitive decline and disease severity. In conclusion, the BrainAGE framework indicates discrepancies in brain aging and could thus serve as an indicator for cognitive functioning in the future.

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