Wavelet analysis for seasonal precipitation variations of Yuanmou dry-hot valley in recent 50 years

The dry-hot valley is a special kind of degradation ecosystem region in Hengduan Mountains. Variations of seasonal precipitation have important influnces on its landscape patterns and agricultural activities. Based on the monthly and annual precipitation data from 1956 to 2006, the multi-time scales characteristics of seasonal and annual variations of precipitation in the past 50a in the Yuanmou County had been analyzed using Meyer wavelet analysis in this paper. The periodic oscillation of precipitation variation and the points of abrupt change at different time scales along the time series are discovered and the main periods of every serial are confirmed. It was showed that the periodic oscillation of 8-12a and 4-6a for the seasonal and annual precipitation variation are obvious. The time-frequency local change characteristic of Meyer wavelet analysis can demonstrate the fine structures of precipitation and the method provides a new way in analyzing climate multi-time scales characteristics and forecasting short-term climate. The localization characteristics of time -frequency for wavelet analysis can demonstrate the detailed structures of rainfall. The wavelet analysis can be an alternative approach to analyze climate multi-time scales characteristics and forecast short-term climate variations. The research on the regularity of seasonal precipitation variation in the dry-hot valley region has a great guidance meaning to the agriculture production and resilience in flood prevention.

Adaptive Meyer wavelet filters for machinery fault diagnosis based on harmonic infinite-taxicab norm and grasshopper optimization algorithm

Meyer wavelet filters are the key building blocks of empirical wavelet transform. In mechanical fault diagnosis, however, the boundaries of Meyer wavelet filters are usually defined empirically. In order to solve the problems, this paper proposes a new index called harmonic infinite-taxicab norm to guide grasshopper optimization algorithm to primarily optimize a band-pass filter and thus, concurrently and secondarily optimize a low-pass filter and a high-pass filter of Meyer wavelet. The proposed index is inspired by spectral Lp/Lq norm and it is closely related to fault characteristic frequency of rotating machinery. In addition, only three Meyer wavelet filters are demanded in each iteration of optimization. The effectiveness of the proposed method is validated by comparing with fast kurtogram method on analyzing faulty bearing data and gearbox data.

Feature Extraction of ECG signal using Meyer Wavelet Transform

Humans suffered with heart related issues in this century due to the poor and improper regular routines which causes a major damage to their entire life. This paper deals with cardiovascular arrhythmias prevention and control by the usage of Electrocardiogram. Cloud storage is utilized for storing the voluminous data of Electrocardiogram details of patients. The collected raw data is pre-processed using the Meyer wavelet transform. It is a kind of a continuous wavelet, which is applied in several cases especially in adaptive filters multi-fault classification. The features extracted are amplitude, age, sex,RR speed and Medicine.These are considered as the information of each data packets that are stored in cloud and later it is transmitted to healthcare centres and physicians for diagnosis and appropriate treatment

Uncertainty Handling using Improvised Intuitionistic Fuzzy ANN based Voice Disorder Detection

The voice pathology detection is one of the essential process which has to be determined in the earlier stages because it is a sign for raising health related problems. The aim of this paper is to handle the uncertainty in voice dataset due to inconsistency in extracting potential features and vagueness in dealing voice signals. The raw voice signals are preprocessed by feature extraction using meyer wavelet and potential features involved in voice disorder detection are done using sequential forward feature selection methods as voice preprocessing. This research work introduced an improvised intuitionistic fuzzy artificial neural network which enhances the process of voice disorder detection is SVD database by using analytical hierarchical processing for assigning weights and thus the complete neural network performance was fine tuned instead of assigning the weights randomly. The simulation results proved the performance of the proposed model as best by producing more promising result while comparing with ANN, PANN and Fuzzy ANN models.