Throughput analysis and protocol design for CSMA and BTMA protocols under noisy environments

1992 ◽  
Vol 139 (3) ◽  
pp. 289 ◽  
Author(s):  
J.-H. Huang ◽  
L. Kleinrock
Keyword(s):  
Protocol Design ◽  
Noisy Environments ◽  
Throughput Analysis

Protocol design and throughput analysis for multi-user cognitive cooperative systems

2009 ◽  
Vol 8 (9) ◽  
pp. 4740-4751 ◽  
Author(s):  
Ioannis Krikidis ◽  
J. Laneman ◽  
John Thompson ◽  
Steve Mclaughlin
Keyword(s):  
Cooperative Systems ◽  
Protocol Design ◽  
Throughput Analysis

Generalized CSMA/CA for OFDMA systems: protocol design, throughput analysis, and implementation issues

2009 ◽  
Vol 8 (8) ◽  
pp. 4176-4187 ◽  
Author(s):  
Hojoong Kwon ◽  
Hanbyul Seo ◽  
Seonwook Kim ◽  
Byeong Gi Lee
Keyword(s):  
Protocol Design ◽  
Throughput Analysis

Degree-of-Freedom Based Transmission Protocol Design with Optimization for Multi-Cell Multi-User Green Systems

2014 ◽  
Vol E97.B (12) ◽  
pp. 2784-2789
Author(s):  
Chunguo LI ◽  
Yongping ZHANG ◽  
John M. CIOFFI ◽  
Luxi YANG
Keyword(s):  
Protocol Design ◽  
Degree Of Freedom ◽  
Transmission Protocol

SMART IDENTIFICATION OF POWER QUALITY EVENTS USING NEW STOCKWELL TRANSFORM AND MACHINE LEARNING ALGORITHM

2019 ◽  
Vol XVI (4) ◽  
pp. 95-113
Author(s):  
Muhammad Tariq ◽  
Tahir Mehmood
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Noisy Environments ◽  
Satisfactory Performance ◽  
Feature Values ◽  
Learning Machine ◽  
Stockwell Transform ◽  
Electrical Utilities

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.

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