A dynamic connectome mapping module in python.

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

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.

A Virtual Analog Library written in SOUL

Allows to reproduce all figures from "Pruned DFT Spread FBMC: Low PAPR, Low Latency, High Spectral Efficiency", IEEE Transactions on Communications, 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.

This repository includes the source code of the CNN-based channel estimators proposed in "CNN Aided Weighted Interpolation for Channel Estimation in Vehicular Communications" paper [1] that is published in the IEEE Transactions on Vehicular Technology, 2021.

Corresponding code guide to the tutorial paper "Introducing longitudinal modified treatment policies: a unified framework for studying complex exposures" (Hoffman et al., 2023)

Allows to reproduce all figures from "FBMC-OQAM in Doubly-Selective Channels: A New Perspective on MMSE Equalization", IEEE SPAWC, 2017.

Forecasting in Non-stationary Environments with FuzzyTime Series

Dynamic Models for Survival Data

R functions for g-estimation of structural nested cumulative failure models using (1) confounding adjustment or (2) instrumental variable analysis

Granger Causality with Signal-dependent Noise

Matching Methods for Time-Varying Observational Studies, in R

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。

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

Simulations of MR analyses with time-varying exposures using structural mean models

A simulation study exploring the effects of excluding an important slow-changing variable from a network of depression.

LDFR model