This dataset contains the full set of experimental waveforms that were used to produce the article "Non-Linear Phase Noise Mitigation over Systems using Constellation Shaping", published in the Journal of Lightwave Technology with DOI: 10.1109/JLT.2019.2917308.

 

Instructions: 

The dataset contains two files: PartIII_NZDSF.tar and PartIV_GS.tar, corresponding to part III and part IV of the paper. Each file contains: * Transmitted one-sample-per-symbol sequence, that is loaded into the DAC. * Intradyne back-to-back results. * Propagation results over the recirculating loop. All data has been captured with a 50 GS/s Tektronix oscilloscope, and symbol rate is 16 GBaud. Back-to back results are quoted as a function of noise power, i.e. attenuation of the ASE source used for noise loading. Loop results are quoted as a function of the per-channel optical power and recirculation number. Details on the experiment are available on the paper.

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A VOR receiver based on Software-Defined Radio is presented. Experiments showed that the system indicated the radials of the VOR station of São José dos Campos with an average error rate of less than 1% and a standard deviation of less than 2.14% in relation to those calculated cartographically. The results suggest that low volume and weight SDR-based VOR receivers can be developed with processing on microcontrollers or FPGAs to equip drones that need to operate in aerodrome environments.

 

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The MATLAB program provides the performance of a sliding window based detection for a pulse radar signal.

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The MATLAB program provides the performance of a sliding window based detection for a pulse radar signal.

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High-fidelity, physics-based multichannel radar data cube provided by the DARPA KASSPER project. This data is ideal for analyzing space-time adaptive processing (STAP) algorithms since both sample data and truth data are provided.

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Speech detection systems are known as a type of audio classifier systems which are used to recognize, detect or mark parts of audio signal including human speech. Here, a novel robust feature named Long-Term Spectral Pseudo-Entropy (LTSPE) is proposed to detect speech and its purpose is to improve performance in combination with other features, increase accuracy and to have acceptable performance. Experimental results show that if LTSPE is combined with other features, performance of the detector is improved.

Instructions: 

 please download files from here

 files: 

1. Source code of the LTSPE feature in MATLAB (.m file)

2. Related paper (pdf)

3. Test.WAV file

 

 

                                                                            

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