leejunghan

leejunghan's Stars

• davidscmx/radar-target-generation-and-detection

  Configures the FMCW waveform based on the system requirements. Then defines the range and velocity of a target and simulates its displacement. For the same simulation loop process, the transmit and receive signals are computed to determine the *beat* signal. Then it performs a Range FFT on the received signal to determine the Range Towards the end, perform the CFAR processing on the output of 2nd FFT to display the target.

  Language:MATLAB10729

• rowg/hfrprogs

  Programs for analysis and visualization of oceanographic HF radar data

  Language:MATLAB188

• brianemery/hfr_cs_processing

  Tools for processing cross spectra from oceanographic HF radar

  Language:MATLAB124

• LohitSubodh/AI-Tool-for-Landmine-detection-using-Gpr

  Development of AI tool for Landmine detection from GPR using gprMax software.

  Language:Jupyter Notebook2

• LuckySting/gprMax_plus

  gprMax with some fetures

  Language:Python2

• ccccmagicboy2020/fmcw-3

  Library for fmcw radar

  11

• Ttl/fmcw3

  Two RX-channel 6 GHz FMCW radar design files

  Language:VHDL22788

• TonyStark2007/GPR_FMCW_SubGHz

  This is an attempt to develop a ground penetrating FMCW radar. The operating range is 100MHz to 1000MHz, central frequency 435MHz. This is work in progress and not yet concrete.

  Language:C94

• LavanyaGovindan/gpr

  Language:Jupyter Notebook2

• geoscixyz/gpgLabs

  Tutorials and examples for applied geophysics

  Language:Jupyter Notebook4539

• pwu01/ALADDIN-BiGAN-anomaly-detection

  Language:Python122

• aldente0630/sound-anomaly-detection-with-autoencoders

  MIMII Sound Anomaly Detection with AutoEncoders

  Language:Jupyter Notebook3410

• rajathkmp/LandmineLocalizationGPR

  An algorithm that performs localization of landmines and estimates the material of the same.

  Language:Jupyter Notebook62

• CVxTz/EEG_classification

  EEG Sleep stage classification using CNN with Keras

  Language:Python15039

• ambitious-octopus/MI-EEG-1D-CNN

  A new approach based on a 10-layer one-dimensional convolution neural network (1D-CNN) to classify five brain states (four MI classes plus a 'baseline' class) using a data augmentation algorithm and a limited number of EEG channels. Paper: https://doi.org/10.1088/1741-2552/ac4430

  Language:Python15518

• chickenbestlover/RNN-Time-series-Anomaly-Detection

  RNN based Time-series Anomaly detector model implemented in Pytorch.

  Language:Python1.2k316

• ngoclesydney/Anomaly-Detection-with-Swat-Dataset

  Develope novel security metric using Deep-Learning to detect anomaly attacks into the critical infrastructure systems. This metric will be tested by Secure Water Treatment (SWaT) Dataset.

  Language:Jupyter Notebook327

• chen0040/keras-anomaly-detection

  Anomaly detection implemented in Keras

  Language:Python375155

• abishek-as/Audio-Classification-Deep-Learning

  We'll look into audio categorization using deep learning principles like Artificial Neural Networks (ANN), 1D Convolutional Neural Networks (CNN1D), and CNN2D in this repository. We undertake some basic data preprocessing and feature extraction on audio sources before developing models. As a result, the accuracy, training time, and prediction time of each model are compared. This is explained by model deployment, which allows users to load the desired sound output for each model that is successfully deployed, as will be addressed in more depth later.

  Language:Jupyter Notebook3210

• gvoulgaris0/WaveRIC

  Wave Radar Inversion Code (WaveRIC) as described in Alattabi, Cahl and Voulgaris 2019, doi:10.1175/JTECH-D-18-0166.1

  Language:HTML3

• elisejiuqizhang/USAD-on-WADI-and-SWaT

  Unofficial implementation of the KDD2020 paper "USAD: UnSupervised Anomaly Detection on multivariate time series" on two datasets cited in the papers, "SWaT" (Secure Water Treatment) and "WADI" (Water Distribution)

  Language:Jupyter Notebook511

• finloop/usad-torchlightning

  Implementation of USAD (UnSupervised Anomaly Detection on multivariate time series) in PyTorch Lightning

  Language:Python191

• finloop/usad-on-ucr-data

  USAD model on UCR Time Series Anomaly Archive

  Language:Jupyter Notebook113

• cauchyturing/UCR_Time_Series_Classification_Deep_Learning_Baseline

  Fully Convlutional Neural Networks for state-of-the-art time series classification

  Language:Python677203

• hfawaz/dl-4-tsc

  Deep Learning for Time Series Classification

  Language:Python1.6k571

• rakibhhridoy/AnomalyDetectionInTimeSeriesData-Keras

  Statistics, signal processing, finance, econometrics, manufacturing, networking[disambiguation needed] and data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.

  Language:Jupyter Notebook162

• cruiser101/Magnetic-anomaly-detection

  Submit for COMPUTERS & GEOSCIENCES

  Language:MATLAB52

• justinflesch/magnetic-flux-anomaly-detection

  Machine learning applied to magnetic flux datasets (.tdms files) to detect anomalies within the data.

  Language:Jupyter Notebook54

• dongdongou/MAD

  magnetic anomaly detection

  Language:MATLAB51

• amusi/awesome-object-detection

  Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

  7.4k1.9k