This project implements "virtual speed" from heart rate monito

Related tags

Deep Learningvspeed
Overview

ANT+ Virtual Stride Based Speed and Distance Monitor

Overview

This project implements "virtual speed" from heart rate monitor. The calculated speed is broadcasted as such on ANT+ (using python-ant). Based on vpower by Darren Hague.

Even if the receiver app runs on the same computer, you will need two ANT+ sticks, because one device can't be used by two apps simultaneously.

Supported devices:

Warning: the Cycplus ANT Stick is not compatible, even though it uses the same Vendor ID and Product ID (0fcf:1008) as the ANTUSB2 Stick.

Running on Windows

  • Download the standalone executable
  • Install the libusb-win32 driver for the ANT+ device (if not already installed), it can be easily done using Zadig
    • Options - List All Devices
    • Select ANT+ stick
    • Select libusb-win32 driver and click Replace Driver
  • Run the downloaded executable

Running from source code (Windows, Linux, macOS)

  • Install Python 3 if not already installed
    • Check "Add Python to PATH" or use the full path in the commands below
  • Clone or download this repo
  • CD to the repo directory and run pip install -r requirements.txt
    • On Linux and macOS use pip3 instead of pip
  • [Optional] Run pip install pywin32 (Windows only, to stop the ANT node on terminal window close)
  • Run python vspeed.py (or double click vspeed.py if you installed the Python Launcher)
    • On Linux and macOS use python3 instead of python
You might also like...
AdamW optimizer and cosine learning rate annealing with restarts

AdamW optimizer and cosine learning rate annealing with restarts This repository contains an implementation of AdamW optimization algorithm and cosine

Maksym Pyrozhok 133 Dec 20, 2022
Jigsaw Rate Severity of Toxic Comments

Jigsaw Rate Severity of Toxic Comments

Guanshuo Xu 66 Nov 30, 2022
Model search is a framework that implements AutoML algorithms for model architecture search at scale
Model search is a framework that implements AutoML algorithms for model architecture search at scale

Model search (MS) is a framework that implements AutoML algorithms for model architecture search at scale. It aims to help researchers speed up their exploration process for finding the right model architecture for their classification problems (i.e., DNNs with different types of layers).

Google 3.2k Dec 31, 2022
Implements Gradient Centralization and allows it to use as a Python package in TensorFlow
Implements Gradient Centralization and allows it to use as a Python package in TensorFlow

Gradient Centralization TensorFlow This Python package implements Gradient Centralization in TensorFlow, a simple and effective optimization technique

Rishit Dagli 101 Nov 1, 2022
Implements MLP-Mixer: An all-MLP Architecture for Vision.
Implements MLP-Mixer: An all-MLP Architecture for Vision.

MLP-Mixer-CIFAR10 This repository implements MLP-Mixer as proposed in MLP-Mixer: An all-MLP Architecture for Vision. The paper introduces an all MLP (

Sayak Paul 51 Jan 4, 2023
QueryFuzz implements a metamorphic testing approach to test Datalog engines.
QueryFuzz implements a metamorphic testing approach to test Datalog engines.

Datalog is a popular query language with applications in several domains. Like any complex piece of software, Datalog engines may contain bugs. The mo

null 34 Sep 10, 2022
This repository implements and evaluates convolutional networks on the Möbius strip as toy model instantiations of Coordinate Independent Convolutional Networks.
This repository implements and evaluates convolutional networks on the Möbius strip as toy model instantiations of Coordinate Independent Convolutional Networks.

Orientation independent Möbius CNNs This repository implements and evaluates convolutional networks on the Möbius strip as toy model instantiations of

Maurice Weiler 59 Dec 9, 2022
This framework implements the data poisoning method found in the paper Adversarial Examples Make Strong Poisons
This framework implements the data poisoning method found in the paper Adversarial Examples Make Strong Poisons

Adversarial poison generation and evaluation. This framework implements the data poisoning method found in the paper Adversarial Examples Make Strong

null 31 Nov 1, 2022
Implements an infinite sum of poisson-weighted convolutions

An infinite sum of Poisson-weighted convolutions Kyle Cranmer, Aug 2018 If viewing on GitHub, this looks better with nbviewer: click here Consider a v

Kyle Cranmer 26 Dec 7, 2022
Owner
GitHub Repository
Price-Prediction-For-a-Dream-Home - A machine learning based linear regression trained model for house price prediction.

Price-Prediction-For-a-Dream-Home ROADMAP TO THIS LINEAR REGRESSION BASED HOUSE PRICE PREDICTION PREDICTION MODEL Import all the dependencies of the p

DIKSHA DESWAL 1 Dec 29, 2021
A library for hidden semi-Markov models with explicit durations

hsmmlearn hsmmlearn is a library for unsupervised learning of hidden semi-Markov models with explicit durations. It is a port of the hsmm package for

Joris Vankerschaver 69 Dec 20, 2022
Shuffle Attention for MobileNetV3

SA-MobileNetV3 Shuffle Attention for MobileNetV3 Train Run the following command for train model on your own dataset: python train.py --dataset mnist

Sajjad Aemmi 36 Dec 28, 2022
Residual Dense Net De-Interlace Filter (RDNDIF)

Residual Dense Net De-Interlace Filter (RDNDIF) Work in progress deep de-interlacer filter. It is based on the architecture proposed by Bernasconi et

Louis 7 Feb 15, 2022
MRI reconstruction (e.g., QSM) using deep learning methods

deepMRI: Deep learning methods for MRI Authors: Yang Gao, Hongfu Sun This repo is devloped based on Pytorch (1.8 or later) and matlab (R2019a or later

Hongfu Sun 17 Dec 18, 2022
Mail classification with tensorflow and MS Exchange Server (ham or spam).

Mail classification with tensorflow and MS Exchange Server (ham or spam).

Metin Karatas 1 Sep 11, 2021
Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation

SimplePose Code and pre-trained models for our paper, “Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation”, a

Jia Li 256 Dec 24, 2022
Optimus: the first large-scale pre-trained VAE language model

Optimus: the first pre-trained Big VAE language model This repository contains source code necessary to reproduce the results presented in the EMNLP 2

314 Dec 19, 2022
Blind Video Temporal Consistency via Deep Video Prior

deep-video-prior (DVP) Code for NeurIPS 2020 paper: Blind Video Temporal Consistency via Deep Video Prior PyTorch implementation | paper | project web

Chenyang LEI 272 Dec 21, 2022
An Active Automata Learning Library Written in Python

AALpy An Active Automata Learning Library AALpy is a light-weight active automata learning library written in pure Python. You can start learning auto

TU Graz - SAL Dependable Embedded Systems Lab (DES Lab) 78 Dec 30, 2022
DeepAL: Deep Active Learning in Python

DeepAL: Deep Active Learning in Python Python implementations of the following active learning algorithms: Random Sampling Least Confidence [1] Margin

Kuan-Hao Huang 583 Jan 03, 2023
DCSL - Generalizable Crowd Counting via Diverse Context Style Learning

DCSL Generalizable Crowd Counting via Diverse Context Style Learning Requirement

3 Jun 13, 2022
Train emoji embeddings based on emoji descriptions.

emoji2vec This is my attempt to train, visualize and evaluate emoji embeddings as presented by Ben Eisner, Tim Rocktäschel, Isabelle Augenstein, Matko

Miruna Pislar 17 Sep 03, 2022
PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations

SDEdit: Image Synthesis and Editing with Stochastic Differential Equations Project | Paper | Colab PyTorch implementation of SDEdit: Image Synthesis a

536 Jan 05, 2023
GestureSSD CBAM - A gesture recognition web system based on SSD and CBAM, using pytorch, flask and node.js

GestureSSD_CBAM A gesture recognition web system based on SSD and CBAM, using pytorch, flask and node.js SSD implementation is based on https://github

xue_senhua1999 2 Jan 06, 2022
A spatial genome aligner for analyzing multiplexed DNA-FISH imaging data.

jie jie is a spatial genome aligner. This package parses true chromatin imaging signal from noise by aligning signals to a reference DNA polymer model

Bojing Jia 9 Sep 29, 2022
Code for models used in Bashiri et al., "A Flow-based latent state generative model of neural population responses to natural images".

A Flow-based latent state generative model of neural population responses to natural images Code for "A Flow-based latent state generative model of ne

Sinz Lab 5 Aug 26, 2022
The implementation of PEMP in paper "Prior-Enhanced Few-Shot Segmentation with Meta-Prototypes"

Prior-Enhanced network with Meta-Prototypes (PEMP) This is the PyTorch implementation of PEMP. Overview of PEMP Meta-Prototypes & Adaptive Prototypes

Jianwei ZHANG 8 Oct 14, 2021
Implementation of Monocular Direct Sparse Localization in a Prior 3D Surfel Map (DSL)

DSL Project page: https://sites.google.com/view/dsl-ram-lab/ Monocular Direct Sparse Localization in a Prior 3D Surfel Map Authors: Haoyang Ye, Huaiya

Haoyang Ye 93 Nov 30, 2022
Paaster is a secure by default end-to-end encrypted pastebin built with the objective of simplicity.

Follow the development of our desktop client here Paaster Paaster is a secure by default end-to-end encrypted pastebin built with the objective of sim

Ward 211 Dec 25, 2022