scholarly journals Improved Method for Predicting the Performance of the Physical Links in Telecommunications Access Networks

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Ferenc Lilik ◽  
Szilvia Nagy ◽  
László T. Kóczy
Keyword(s):  
Wavelet Transformation ◽  
Fuzzy Inference ◽  
Data Transfer ◽  
Continuous Functions ◽  
Access Networks ◽  
Electrical Parameters ◽  
Inference System ◽  
Fuzzy Interpolation ◽  
Novel Approach ◽  
Rule Bases

A novel approach is presented which is able to predict the available maximal data transfer rate of SHDSL connections from measured frequency dependent electrical parameters of wire pairs. Predictions are made by a fuzzy inference system. The basis of the operable and tested method will be introduced, then an improved version is shown, in which the problems derived from sampling of continuous functions of electrical parameters are eliminated by wavelet transformation. Also possibilities for simplification of the problem and a way of reducing the dimensions of the applied rule bases are presented. As the set of the measured data leads to sparse rule bases, handling of sparseness is unavoidable. Two different ways—fuzzy interpolation and various membership functions—will be introduced. The presented methods were tested by measurements in real telecommunications access networks.

An Intuitionistic Fuzzy Based Novel Approach to CPU Scheduler

2020 ◽  
Vol 16 (4) ◽  
pp. 316-328
Author(s):  
Supriya Raheja
Keyword(s):  
Fuzzy Inference ◽  
The Other ◽  
Simulation Environment ◽  
Inference System ◽  
Intuitionistic Fuzzy ◽  
Novel Approach ◽  
Continuous Feedback ◽  
Simulation Results ◽  
Effectiveness And Efficiency ◽  
Unique Capability

Background: The extension of CPU schedulers with fuzzy has been ascertained better because of its unique capability of handling imprecise information. Though, other generalized forms of fuzzy can be used which can further extend the performance of the scheduler. Objectives: This paper introduces a novel approach to design an intuitionistic fuzzy inference system for CPU scheduler. Methods: The proposed inference system is implemented with a priority scheduler. The proposed scheduler has the ability to dynamically handle the impreciseness of both priority and estimated execution time. It also makes the system adaptive based on the continuous feedback. The proposed scheduler is also capable enough to schedule the tasks according to dynamically generated priority. To demonstrate the performance of proposed scheduler, a simulation environment has been implemented and the performance of proposed scheduler is compared with the other three baseline schedulers (conventional priority scheduler, fuzzy based priority scheduler and vague based priority scheduler). Results: Proposed scheduler is also compared with the shortest job first CPU scheduler as it is known to be an optimized solution for the schedulers. Conclusion: Simulation results prove the effectiveness and efficiency of intuitionistic fuzzy based priority scheduler. Moreover, it provides optimised results as its results are comparable to the results of shortest job first.

Multicamera Video Stitching for Multiple Human Tracking

2021 ◽  
Vol 11 (1) ◽  
pp. 17-38
Author(s):  
S. Vasuhi ◽  
A. Samydurai ◽  
Vijayakumar M.
Keyword(s):  
Fuzzy Inference ◽  
Human Tracking ◽  
Foreground Extraction ◽  
Inference System ◽  
Video Stitching ◽  
Novel Approach ◽  
Key Points ◽  
Feature Correspondences ◽  
Centroid Point ◽  
Stitching Techniques

In this paper, a novel approach is proposed to track humans for video surveillance using multiple cameras and video stitching techniques. SIFT key points are extracted from all camera inputs. Using k-d tree algorithm, all the key points are matched and random sample consensus (RANSAC) is used to identify the match correspondence among all the matched points. Homography matrix is calculated using four matched robust feature correspondences, the images are warped with respect to the other images, and the human tracking is performed on the stitched image. To identify the human in the stitched video, background modeling is performed using fuzzy inference system and perform foreground extraction. After foreground extraction, the blobs are constructed around each detected human and centroid point is calculated for each blob. Finally, tracking of multiple humans is done by Kalman filter (KF) with Hungarian algorithm.

scholarly journals Clustering Stock Performance Considering Investor Preferences Using a Fuzzy Inference System

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1148 ◽  
Author(s):  
Siti Nazifah Zainol Abidin ◽  
Saiful Hafizah Jaaman ◽  
Munira Ismail ◽  
Ahmad Syafadhli Abu Bakar
Keyword(s):  
Cluster Analysis ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Extreme Values ◽  
Stock Selection ◽  
Actual Performance ◽  
Inference System ◽  
Stock Performance ◽  
Fuzzy Clustering Method ◽  
Rule Bases

The fact that many stocks are traded in the marketplace makes the selection process of choosing the right stocks for investment crucial and challenging. In the literature on stock selection, cluster analysis-based methods have usually been used to group and to determine the best stock for investment. Many established cluster analysis-based methods often cluster stocks under consideration using the average of the variables, where stocks with similar scores are concluded as having the same performances. Nevertheless, the performance results obtained do not reflect the actual performance of the stocks. Depending only on the average score of each variable is inefficient, as market situations usually involve uncertain extreme values. Moreover, when grouping stock performance, the established clustering methods assume that investors’ selection preferences are single and unclear, when actually, in reality, investors’ selection preferences vary; some investors are pessimistic, while others may be more optimistic. Due to this issue, this paper presents a novel fuzzy clustering method using a fuzzy inference system to flexibly assess the consistent evaluations given to stock performance that differentiate between pessimistic and optimistic investors that are symmetrical in nature. All variables considered in this study were defined in terms of linguistic inputs, where the consensus among them was aggregated using rule bases. These rule bases provide assistance in obtaining the linguistic output, which is the actual performance of the stock. Next, each stock under consideration was ranked using the proposed novel stock selection strategy based on investors’ confidence levels and preferences. The proposed method was then applied to a case study of 30 Syariah stocks listed on the Malaysian stock exchange, where the results obtained were empirically validated with established cluster analysis-based methods.

A Novel Approach of Restoration of Digital Images Degraded by Impulse Noise

2014 ◽  
Vol 4 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Rashmi Kumari ◽  
Anupriya Asthana ◽  
Vikas Kumar
Keyword(s):  
Impulse Noise ◽  
Fuzzy Inference ◽  
Digital Images ◽  
Window Size ◽  
Qualitative And Quantitative Analysis ◽  
Inference System ◽  
Qualitative And Quantitative ◽  
Novel Approach ◽  
Neuro Fuzzy ◽  
Restoration Method

Restoration of digital images degraded by impulse noise is still a challenge for researchers. Various methods proposed in the literature suffer from common drawbacks: such as introduction of artifacts and blurring of the images. A novel idea is proposed in this paper where presence of impulsive pixels are detected by ANFIS (Adaptive Neuro-Fuzzy Inference System) and mean of the median of suitable window size of noisy image is taken for the removal of the detected corrupted pixels. Experimental results show the effectiveness of the proposed restoration method both by qualitative and quantitative analysis.

A New Approach to Realizing the Soft-and Operation in Cloud Model-Based Control

2019 ◽  
Vol 27 (05) ◽  
pp. 755-771
Author(s):  
Jing Wang ◽  
Alessandro Ferrero ◽  
Qi Zhang ◽  
Marco Prioli
Keyword(s):  
Traditional Method ◽  
Fuzzy Inference ◽  
Cloud Model ◽  
Inference System ◽  
Model Based Control ◽  
Model Based ◽  
Novel Approach ◽  
Numeric Simulation ◽  
Cloud Model Theory ◽  
Cloud Control System

Considering fuzziness, randomness, and the association between them, cloud model-based control is a new way to address uncertainty in the inference system. Similar to fuzzy control theory, this method includes an important step of dealing with the logic concept “and”, which is defined as the operation of soft-and between several antecedents and has not been scientifically solved in the current literatures. The traditional method of realizing soft-and is to use multi-dimensional cloud model theory, which lacks a theoretical basis. Based on the fuzzy and random theory, this paper proposes a novel approach using numeric simulation to calculate the soft-and in the cloud control system. In this method, the theory to determine the distribution of the minimum value between two random variables is applied. Compared with the traditional method, the considered approach is more reliable and reasonable, and its result is also in accordance with the standard fuzzy inference system.

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