Granger Causality with Signal-dependent Noise
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Updated
May 1, 2018 - MATLAB
Granger Causality with Signal-dependent Noise
Allows to reproduce all figures from "Pruned DFT Spread FBMC: Low PAPR, Low Latency, High Spectral Efficiency", IEEE Transactions on Communications, 2018
LDFR model
Allows to reproduce all figures from "FBMC-OQAM in Doubly-Selective Channels: A New Perspective on MMSE Equalization", IEEE SPAWC, 2017.
Simulates an FBMC and OFDM transmission over a doubly-selective channel. Allows to reproduce all figures from "Doubly-Selective Channel Estimation in FBMC-OQAM and OFDM Systems", IEEE VTC Fall, 2018
Simulates pruned DFT spread FBMC and compares the performance to OFDM, SC-FDMA and conventional FBMC. The included classes (QAM, DoublySelectiveChannel, OFDM, FBMC) can be reused in other projects.
Proof of concept for online hybrid message passing inference for AR-HGF.
Forecasting in Non-stationary Environments with FuzzyTime Series
Code release for "Relaxed Weight Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series" (Oh, Wang, Tang, Sjoding, Wiens), MLHC 2019. https://arxiv.org/abs/1906.02898
A simulation study exploring the effects of excluding an important slow-changing variable from a network of depression.
Compares FBMC to OFDM based schemes. Reproduces all figures from “Filter bank multicarrier modulation schemes for future mobile communications”, IEEE Journal on Selected Areas in Communications, 2017.
R functions for g-estimation of structural nested cumulative failure models using (1) confounding adjustment or (2) instrumental variable analysis
Implementation of time-varying SLR using OLS estimates
Simulations of MR analyses with time-varying exposures using structural mean models
该项目为了抑制FDA波束方向图的时变特性,提出了一种基于粒子群优化算法的时间调制非线性频偏FDA。根据仿真结果可以说明,该方法可以抑制FDA的时变特性,并且相较于传统时变抑制方法—时间调制频偏和时间调制非线性频偏,得到的波束方向图聚焦性更好。
Abundance models for avian point counts that use distance and time-removal sampling to estimate detection probabilities.
Estimating Time-Varying Models in High-Dimensional EMA Data to predict psychological behavior in time series. Two Methods: Time-Varying Vector Autoregression & Kernel Splines and Time-Varying Generalized Additive Models.
A Virtual Analog Library written in SOUL
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