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spatial pattern recognition for near-surface high temperature increases in mountain areas using MODIS and SRTM DEM

Journal of Mountain Science 2024年第6期21卷 2025-2042页

作者： WANG Yanxia YANG Lisha HUANG Xiaoyuan ZHOU Ruliang School of Soil and Water Conservation Southwest Forestry UniversityKunming 650224China School of Economics and Management Southwest Forestry UniversityKunming 650224China

Abrupt near-surface temperature changes in mountainous areas are a special component of the mountain climate *** and accurate measurements of the locations,intensity,and width of the near-surface changes are necessary but highly difficult due to the complicated environmental conditions and instrumental *** paper develops a spatial pattern recognition method to measure the near-surface high temperature increase(NSHTI),one of the lesser-attended ***,raster window measurement was proposed to calculate the temperature lapse rate using MODIS land surface temperature and SRTM DEM *** fully considers the terrain heights of two neighboring cells on opposite or adjacent slopes with a moving window of 3×3 cell ***,a threshold selection was performed to identify the NSHTI cells using a threshold of-0.65℃/100 ***,the NSHTI strips were parameterized through raster vectorization and spatial *** Yunnan,a mountainous province in southwestern China,as the study area,the results indicate that the NSHTI cells concentrate in a strip-like pattern along the mountains and valleys,and the strips are almost parallel to the altitude contours with a slight northward ***,they are located mostly at a 3/5 height of high mountains or within 400 m from the valley floors,where the controlling topographic index is the altitude of the terrain trend surface but not the absolute elevation and the topographic uplift height and cutting ***,the NSHTI intensity varies with the geographic locations and the proportions increase with an exponential trend,and the horizontal width has a mean of about 1000 m and a maximum of over 5000 *** result demonstrates that the proposed method can effectively recognize NSHTI boundaries over mountains,providing support for the modeling of weather and climate systems and the development of mountain resources.

关键词： High temperature increase Mountain areas MODIS spatial pattern recognition Raster window measurement Threshold selection

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Automatic Speaker recognition Using Mel-Frequency Cepstral Coefficients Through Machine Learning

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Computers, Materials & Continua 2022年第6期71卷 5511-5521页

作者： U˘gur Ayvaz Hüseyin Gürüler Faheem Khan Naveed Ahmed Taegkeun Whangbo Abdusalomov Akmalbek Bobomirzaevich Department of Computer Engineering Istanbul Technical UniversityIstanbul34485Turkey Department of Information Systems Engineering Mugla Sitki Kocman UniversityMugla48000Turkey Artificial Intelligence Lab Department of Computer EngineeringGachon UniversitySeongnam13557Korea Department of Computer Science College of Computing and InformaticsUniversity of SharjahSharjah27272UAE

Automatic speaker recognition(ASR)systems are the field of Human-machine interaction and scientists have been using feature extraction and feature matching methods to analyze and synthesize these *** of the most commonly used methods for feature extraction is Mel Frequency Cepstral Coefficients(MFCCs).Recent researches show that MFCCs are successful in processing the voice signal with high *** represents a sequence of voice signal-specific *** experimental analysis is proposed to distinguish Turkish speakers by extracting the MFCCs from the speech *** the human perception of sound is not linear,after the filterbank step in theMFCC method,we converted the obtained log filterbanks into decibel(dB)features-based spectrograms without applying the Discrete Cosine Transform(DCT).A new dataset was created with converted spectrogram into a 2-D *** learning algorithms were implementedwith a 10-fold cross-validationmethod to detect the *** highest accuracy of 90.2%was achieved using Multi-layer Perceptron(MLP)with tanh activation *** most important output of this study is the inclusion of human voice as a new feature set.

关键词： Automatic speaker recognition human voice recognition spatial pattern recognition MFCCs spectrogram machine learning artificial intelligence

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