placeboxue's Stars
MartinGjoreski/band_feature_extraction
yanxum/aco_feature_selection_svm_classify
Ant colony optimization (aco) algorithm is used to select the features of hyperspectral remote sensing image bands,And then use Support Vector Machines(svm) to classify pixels.
hello-sea/DeepLearning_Wavelet-LSTM
LSTM + Wavelet（长短期记忆神经网络+小波分析）：深度学习与数字信号处理的结合
regeirk/pycwt
A Python module for continuous wavelet spectral analysis. It includes a collection of routines for wavelet transform and statistical analysis via FFT algorithm. In addition, the module also includes cross-wavelet transforms, wavelet coherence tests and sample scripts.
grinsted/wavelet-coherence
A cross wavelet and wavelet coherence toolbox for MATLAB
fbcotter/pytorch_wavelets
Pytorch implementation of 2D Discrete Wavelet (DWT) and Dual Tree Complex Wavelet Transforms (DTCWT) and a DTCWT based ScatterNet
PyWavelets/pywt
PyWavelets - Wavelet Transforms in Python
Hsieh-Cheng-Hsien/Vegetation-Region-Detection-for-RSI-based-on-Deep-Learning
由於部分遙測影像在某些特定地區存在光斑或雜訊，如：建築物附近，容易在地物分類上產生問題，而本研究的目為都市地區綠地覆蓋的偵測，即找出植生與非植生區域。然而植被在這些區域附近的像元多少會受到干擾，導致人工判釋上的困擾，因此我們將利用Sentinel-2影像當作參考，以人工選取的方式挑選屬於植生和非植生的像元，作為本次訓練和測試使用的地真資料（Ground Truth），並在經輻射校正過後目標影像中，選取研究範圍作為訓練樣本和測試樣本的來源。本實驗的研究區域為「竹南頭份都市計畫地區」，並選取該地區清晰無雲的影像，能降低訓練和測試時的誤差和提升準確性。實驗採用Deep ML、Spectral DeseNet和Spectral-Spatial DenseNet三種不同的深度學習方式進行模型訓練，並隨機選取樣本作為訓練和測試資料。最後將三種深度學習方式的分類結果和根據植生指數NDVI閾值所選取的分類結果作比較，檢驗模型分類能力。In the particular situation, we might get noise from the high reflectance region e.g. Building area, and it exactly affect our classification result and accuracy in vegetation regions. Thus, the purpose of this research is to detect green space coverage in urban areas, that is, to identify Vegetation and non-Vegetation area. However, the pixels of vegetation near these areas that mentioned above will be disturbed to some extent, which will cause problems in manual interpretation. Therefore, we use the Sentinel-2 image as a reference, selecting the pixels as the ground truth data manually. The training samples and test samples were selected from the target image after radiation correction base on the ground truth. The research area of this experiment is the "Zhunan Toufen Urban Planning Area", the clear and cloudless images of this area are selected, which can reduce the error and improve the accuracy during training and testing. The experiment uses three different deep learning methods, Deep ML, Spectral DeseNet and Spectral-Spatial DenseNet for model training, and randomly selects samples as training and test data. Finally, the classification results of these three deep learning methods are compared with the classification results selected according to the NDVI and EVI thresholds of the vegetation index to test the classification ability of the model.
AhmedHamdy2b/gender-predection-py-voice
About Dataset Voice Gender Gender Recognition by Voice and Speech Analysis This database was created to identify a voice as male or female, based upon acoustic properties of the voice and speech. The dataset consists of 3,168 recorded voice samples, collected from male and female speakers. The voice samples are pre-processed by acoustic analysis in R using the seewave and tuneR packages, with an analyzed frequency range of 0hz-280hz (human vocal range). The Dataset The following acoustic properties of each voice are measured and included within the CSV: meanfreq: mean frequency (in kHz) sd: standard deviation of frequency median: median frequency (in kHz) Q25: first quantile (in kHz) Q75: third quantile (in kHz) IQR: interquantile range (in kHz) skew: skewness (see note in specprop description) kurt: kurtosis (see note in specprop description) sp.ent: spectral entropy sfm: spectral flatness mode: mode frequency centroid: frequency centroid (see specprop) peakf: peak frequency (frequency with highest energy) meanfun: average of fundamental frequency measured across acoustic signal minfun: minimum fundamental frequency measured across acoustic signal maxfun: maximum fundamental frequency measured across acoustic signal meandom: average of dominant frequency measured across acoustic signal mindom: minimum of dominant frequency measured across acoustic signal maxdom: maximum of dominant frequency measured across acoustic signal dfrange: range of dominant frequency measured across acoustic signal modindx: modulation index. Calculated as the accumulated absolute difference between adjacent measurements of fundamental frequencies divided by the frequency range label: male or female
Monisha-Balaji/Data-Mining-Clustering-Algorithms
Implemented five clustering algorithms namely K-Means, Density-Based SCAN, Gaussian Mixture Model, Spectral Model and Hierarchical using python and validated the clustering results using Rand Index and Jaccard Coefficient.
ivanzhovannik/MilkHeatTreatmentEvaluation
A readily available, fast, non-destructive front-face fluorescence technique is used as a tool for assessing heat treatment effects on milk. Spectral data on raw, pasteurized, UHT pasteurized and sterilized milk samples from a wide range of manufacturers are obtained, including reconstituted milk and its mixtures with pasteurized milk. The principal component analysis (PCA) is used to summarize all the data obtained, and a classification model is developed to distinguish between two classes of milk (1) raw and pasteurized milk and (2) milk that was exposed to high heat treatment (UHT pasteurization or sterilization), or contains products of such a treatment (dry milk). A validation procedure using a test set showed the model to be accurate to within less than 5%. A new spectral index for use in the present context, which uses the ratio of the vitamin A and Maillard reaction products peaks in fluorescence excitation spectra, is proposed and compared with the conventional FAST index.
Jawad-Dar/Design-and-development-of-hybrid-Optimization-enabled-Deep-Q-learning-model-for-Covid-19-detection-
The problem of respiratory sound classification has received good attention from the clinical scientists and medical researcher’s community in the last year to the diagnosis of COVID-19 disease. In this paper, the input audio samples are fed into the pre-processing module in which median filtering is done to remove the noise and artifacts from the audio samples. The feature extraction is carried out by considering features, like spectral contrast, Mel frequency cepstral coefficients (MFCC), Empirical Mode Decomposition (EMD) algorithm, spectral flux, Fast Fourier Transform (FFT), spectral roll-off, spectral centroid, Root mean square energy, zero-crossing rate, spectral bandwidth, spectral flatness, power spectral density, mobility complexity, fluctuation index and relative amplitude. Moreover, the deep Q network is applied for Covid-19 classification phase wherein the training of deep Q network is done using the proposed optimization algorithm, named Snake Jaya Honey Badger Optimization (SJHBO) algorithm. The proposed SJHBO algorithm is the hybridization of Jaya Honey Badger Optimization (JHBO) along with Snake optimization. Hence, the developed method achieved the better superior performance based on the accuracy, sensitivity and specificity .
LouisFreytag/S2CyanoGit
The following code was developed to calculate the Mean absolute percentage error (MAPE) as a texture index, based on spectral indices, to distinguish between Cyanobacteria and algal blooms.
DeepNets-US/Spectral-Normalization-GAN
Implemented a Spectral Normalization Generative Adversarial Network (SN GAN) for image generation. Achieved an impressive Structural Similarity Index (SSIM) score of 0.94, showcasing the model's capacity for realistic image synthesis.
Carlocktography/CropVigorIndex
CVI is a derived multi-spectral index for usage with the KolariVision Infrared Blue/NIR NDVI filter (and similar).
liangsizhuang/Demonstration-DFT-PS-PSD
This is a demonstration to show how to calculate power spectra and power spectral densities in real time. We calculate power spectra directly using DFT (or FFT). There are many conventions for DFT. We use the convention is the paper “Analysis of Relationship between Continuous Time Fourier Transform (CTFT), Discrete Time Fourier Transform (DTFT), Fourier Series (FS), and Discrete Fourier Transform (DFT)”. We calculate power spectral and power spectral densities using the MATLAB function periodogram. We could use pwelch to replace periodogram. The only difference between periodogram and pwelch is that pwelch supports segmentation and averaging, whereas periodogram does not. For the sake of simplicity, we only use periodogram in this demonstration. One will see that the power spectrum is equal to the square of the absolute value of DFT. When manually calculating a power spectrum, the hard job is to calculate the argument vector, or the independent variable vector, which is a frequency vector in this case. The frequency vector depends on the representation of the power spectrum. In general, there are three ways to represent a power spectrum for a real valued signal. One way is called “two-sided”. This is the default way to represent a power spectrum with DFT. However, this representation is not intuitive. The frequency vector is calculated by f = (0:N-1)/T, where T is the time period (or duration) of the input signal. When using the MATLAB function, periodogram, one can specify this representation using “onesided”. A more natural way is to use a centered representation. In this case, the frequency 0 is centered in the spectrum. If the number of spectral lines (equal to the number of input points) is odd, then we have a unique centered representation. If the number of spectral lines is even, then we have a problem. Let us assume that we use a zero-based index for spectral lines. The spectral line 0 is the DC component, and it is put in the f = 0 location. However, the spectral line N/2 can be placed on the positive side or the negative side. Different conventions may have different placements. In order to obtain this representation, one has to shift the FFT result. One way is to use the MATLAB function fftshift. This MATLAB function always places the N/2 spectral line on the negative side. When using the MATLAB function, periodogram, one can specify this representation using “centered”. It should be noted that the MATLAB function, periodogram, usually puts the N/2 spectral line on the positive side. The last way to represent a power spectrum is the one-sided representation. For this representation, we need to combine negative frequency components and positive components together, and we only show the positive half as well as the DC component. The combination process depends the evenness or oddness of the number of spectral lines. If the number of spectral lines is odd, we can simply combine spectral lines 1 to (N-1)/2 with spectral lines (N+1)/2 to N-1. The spectral line 0 is left untouched. If the number of spectral lines is even, we need to combine spectral lines 1 to N/2-1 with lines N/2+1 to N-1. The spectral lines 0 and N/2 are left untouched. In order to obtain this representation, one has to manually carry out the combination process. The combination process is different depending on the evenness or oddness of the number of spectral lines. When using the MATLAB function, periodogram, one can specify this representation using “onesided”. In this demonstration, we only use the centered representation. Hence, there is no need to do combination. One can see that the sum of all power spectral lines in a power spectrum is equal to the power of the input signal. One can alternatively calculate the PSD with the periodogram function by specifying “psd” instead of “power”. In fact, the PSD obtained by periodogram is an equivalent noise power spectral density. One can see that ENPSD is related to PS by a factor of 1/T. It should be noted that a power spectrum is a discrete sequence, or a discrete continuous-argument function, whereas an ENPSD is a non-discrete continuous argument function. For emphasize this, I used stem for power spectra and plot for ENPSD. In this demonstration, we start with a sinusoidal signal with various parameters. We then proceed with an actual audio signal.
TiagoDaFonseca/vegetation-index-api
API that apply vegetation indexes onto hyper spectral images
mattpeck00/python-final-project
spectral index calculation and change analysis
BrunoSlaus/Spectral_Index_Bias_Plots
Plotting the bias in spectral indices
rifatSDAS/py_spectral_index
This a repository of numbers of spectral index for remote sensing studies. All of the available spectral indexes are coded in python.
mariodamore/spectral_indexes_generation
Practical S6 activity during the Geology & Planetary Mapping Winter School Program: Exploiting spectral data to generate compositional indexes.
JBalanza/QGIS_Sentinel2IndexExtractor
QGIS module for calculating Vegetation Indexes on Sentinel-2 multispectral images. There are two branches: "Master" which supports photographs downloaded from scihub and "landviewer" which stands for photographs downloaded from eos-landviewer.
landmanbester/spimple
Spectral index fitting made simple
Shybert-AI/Prediction-of-stock-price-based-on-BP-neural-network
基于BP神经网络的股票价格预测
ChuckWangCx/bpnetworkdemo
用BP算法实现神经网络
yuhy7/-handwriting-recognition
MATLAB自编程实现BP神经网络手写数字识别。
meton-robean/SPO_BPNN_PID
基于粒子群优化的神经网络PID控制
xdjcl/BP_network
BP神经网络的MATLAB实现
tjaume/BPNeuralNetworks
利用Python实现三层BP神经网络
stxupengyu/BP-RBF-Prediction
使用BP神经网络、RBF神经网络以及PSO优化的RBF神经网络进行数据的预测