Digital Signal Processing

Code and dataset for Monte Carlo simulation performed in paper. Includes 2000 samples of (correlated) code factor matrices of GPS C/A code from satellite no. 17, bandwidth B = 1.023 MHz, epoch duration t = 1 ms, chip period Tc = 1/B, sampled at Ts = 1/2B (N = 2046 samples/epoch), transmitted with carrier freq. fc = 1575.42 MHz, correlator bank spaced between [-Tc,Tc] with Q = 11 taps. For model order L = 3 and L = 4.

Simulation environment MATLAB 2018a

Mfiles to generate the figures of the paper.

This code reproduces the spectrum of the unimodular sequences and the performance metrics (PSL, ripple, etc.) of beampatterns designed by PAPR-ADMM given in the study titled "Transmit beamformer design with a PAPR constraint to trade-off between beampattern shape and power efficiency" by Omer Cayir and Cagatay Candan. The suggested method introduces the peak-to-average power ratio (PAPR) constraint to the transmit beamformer design problem to establish a trade-off between the power efficiency (maximizing the average transmitted power in the mainlobe) and other metrics such as the fluctuation of power in the mainlobe, peak-sidelobe level etc.

This code reproduces the spectrum of the unimodular sequences and the performance metrics (PSL, ripple, etc.) of beampatterns designed by PAPR-ADMM given in the study titled "Transmit beamformer design with a PAPR constraint to trade-off between beampattern shape and power efficiency" by Omer Cayir and Cagatay Candan. The suggested method introduces the peak-to-average power ratio (PAPR) constraint to the transmit beamformer design problem to establish a trade-off between the power efficiency (maximizing the average transmitted power in the mainlobe) and other metrics such as the fluctuation of power in the mainlobe, peak-sidelobe level etc.

This code reproduces the spectrum of the unimodular sequences and the performance metrics (PSL, ripple, etc.) of beampatterns designed by PAPR-ADMM given in the study titled "Transmit beamformer design with a PAPR constraint to trade-off between beampattern shape and power efficiency" by Omer Cayir and Cagatay Candan. The suggested method introduces the peak-to-average power ratio (PAPR) constraint to the transmit beamformer design problem to establish a trade-off between the power efficiency (maximizing the average transmitted power in the mainlobe) and other metrics such as the fluctuation of power in the mainlobe, peak-sidelobe level etc.

A Python module to extract the envelope of signals.

This code reproduces the spectrum of the unimdular sequences and the performance metrics (PSL, ripple, etc.) of beampatterns designed by PAPR-ADMM given in the manuscript titled Transmit Beamformer Design with a PAPR Constraint to Trade-Off Between Beampattern Shape and Power Efficiency by Omer Cayir and Cagatay Candan to be submitted to Elsevier Digital Signal Processing. The suggested method introduces the peak-to-average power ratio (PAPR) constraint to the transmit beamformer design problem to establish a trade-off between the power efficiency (maximizing the average transmitted power in the mainlobe) and other metrics such as the fluctuation of power in the mainlobe, peak-sidelobe level etc.

This project provides the implementation of the algorithm proposed in the article "Multiresolution alignment for multiple unsynchronized audio sequences using sequential Monte Carlo samplers" (https://doi.org/10.1016/j.dsp.2017.10.024). In order to give the users the ability to recreate the results in article, the audio dataset is also provided. For more information, you can consult README file. You may notice that the source codes are also provided on https://github.com/ElsevierSoftwareX/SOFTX-D-17-00088. The difference between Code Ocean and GitHub is that the former provides an opportunity to run the algorithm on the server side with all dependencies are taken care of. This capsule utilizes both MATLAB and python environments, the former for the implementation of the algorithm and the latter for the evaluation of the results.

Neuro-Speech is an open source software platform designed to perform speech analysis of people with neuro-degenerative disorders. Particularly patients with Parkinson's disease. The software is designed to be used by medical examiners such as speech therapists and neurologists, but it can also be used by patients to perform the analysis, and by general population interested in the analysis of pathological speech. The software computes several measures to evaluate the communication capabilities of the patients and includes analyses of phonation, articulation, prosody, and intelligibility. The software calculates also specific bio-markers related to the dysarthria levels of the patients and perform a prediction of the Movement disorder society--Parkinson's disease rating scale, part III (MDS-UPDRS-III), which is a general evaluation of the motor capabilities of the patients. All of the results obtained are compared to reference patterns obtained with a group of 50 healthy control speakers. Finally a medical report can be generated, which describes the different speech deficits of the patients, and how the measures are deviated respect those computed with information from healthy controls.