Python interface for SmartRF Sniffer 2 Firmware

Overview

#TI SmartRF Packet Sniffer 2 Python Interface

TI Makes available a nice packet sniffer firmware, which interfaces to Wireshark. You can see this project here: https://www.ti.com/tool/download/PACKET-SNIFFER-2

Unfortunately sometimes you want to do stuff like scan channels, which is a hassle to do via the GUI. Luckily they document the interface.

Documentation from TI

The Documentation is installed when you install the Tool, but you can find it online.

Link to documentation: https://dev.ti.com/tirex/explore/node?node=AJ1gMrg0O1AMxi9KUZmTiQ__FUz-xrs__LATEST

Direct link to firmware interface codes: https://software-dl.ti.com/lprf/packet_sniffer_2/docs/user_guide/html/sniffer_fw/firmware/command_interface.html

Errata from TI Docs

The TI documentation claims the serial interface is at 921.6kbit baud, but on my board it was at 3Mbit baud. I'm not sure if all firmware does this or just some of the boards?

Setup

You'll need to flash the firmware onto your board. Basically ensure the GUI tool works first before trying this interface, as you'll get less feedback with this Python tool.

Scanning

For example, on my CC1352R1 board I can scan all 900 MHz channels + PHYs:

test = TICommand('COM90')

for phy in range(0, 9):
    for ch in range(0, 129):
        print("Phy = %x, Channel %d"%(phy, ch))
        test.sniff_stop()    
        test.cmd_set_frequencymhz(902.2 + 0.2*ch)
        test.cmd_set_phy(phy)
        test.sniff_start()

        rxd = 0

        for i in range(0, 3):
            time.sleep(1)

            resp = test.rx()
            if resp:
                print(resp)
                rxd += 1

Note the PHY index is specified in the reference docs from TI, as each board/chip uses different indexes. Also some of them are using the wrong frequency for the given phy etc, this is a quick-n-dirty way of doing it.

Owner
Colin O'Flynn
Colin is a huge nerd.
Colin O'Flynn
Analysis of Smiles through reservoir sampling & RDkit

Analysis of Smiles through reservoir sampling and machine learning (under development). This is a simple project that includes two Jupyter files for t

Aurimas A. Nausėdas 6 Aug 30, 2022
Adabelief-Optimizer - Repository for NeurIPS 2020 Spotlight "AdaBelief Optimizer: Adapting stepsizes by the belief in observed gradients"

AdaBelief Optimizer NeurIPS 2020 Spotlight, trains fast as Adam, generalizes well as SGD, and is stable to train GANs. Release of package We have rele

Juntang Zhuang 998 Dec 29, 2022
A python library for highly configurable transformers - easing model architecture search and experimentation.

A python library for highly configurable transformers - easing model architecture search and experimentation.

Anthony Fuller 51 Nov 20, 2022
Pydantic models for pywttr and aiopywttr.

Pydantic models for pywttr and aiopywttr.

Almaz 2 Dec 08, 2022
“袋鼯麻麻——智能购物平台”能够精准地定位识别每一个商品

“袋鼯麻麻——智能购物平台”能够精准地定位识别每一个商品,并且能够返回完整地购物清单及顾客应付的实际商品总价格,极大地降低零售行业实际运营过程中巨大的人力成本,提升零售行业无人化、自动化、智能化水平。

thomas-yanxin 192 Jan 05, 2023
Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation.

Understanding Minimum Bayes Risk Decoding This repo provides code and documentation for the following paper: Müller and Sennrich (2021): Understanding

ZurichNLP 13 May 01, 2022
Mercer Gaussian Process (MGP) and Fourier Gaussian Process (FGP) Regression

Mercer Gaussian Process (MGP) and Fourier Gaussian Process (FGP) Regression We provide the code used in our paper "How Good are Low-Rank Approximation

Aristeidis (Ares) Panos 0 Dec 13, 2021
Out-of-Domain Human Mesh Reconstruction via Dynamic Bilevel Online Adaptation

DynaBOA Code repositoty for the paper: Out-of-Domain Human Mesh Reconstruction via Dynamic Bilevel Online Adaptation Shanyan Guan, Jingwei Xu, Michell

198 Dec 29, 2022
SMCA replication There are no extra compiled components in SMCA DETR and package dependencies are minimal

Usage There are no extra compiled components in SMCA DETR and package dependencies are minimal, so the code is very simple to use. We provide instruct

22 May 06, 2022
Creating predictive checklists from data using integer programming.

Learning Optimal Predictive Checklists A Python package to learn simple predictive checklists from data subject to customizable constraints. For more

Healthy ML 5 Apr 19, 2022
The official implementation of A Unified Game-Theoretic Interpretation of Adversarial Robustness.

This repository is the official implementation of A Unified Game-Theoretic Interpretation of Adversarial Robustness. Requirements pip install -r requi

Jie Ren 17 Dec 12, 2022
Official PyTorch implementation of Learning Intra-Batch Connections for Deep Metric Learning (ICML 2021) published at International Conference on Machine Learning

About This repository the official PyTorch implementation of Learning Intra-Batch Connections for Deep Metric Learning. The config files contain the s

Dynamic Vision and Learning Group 41 Dec 10, 2022
TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks

TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks [Paper] [Project Website] This repository holds the source code, pretra

Humam Alwassel 83 Dec 21, 2022
Code to reproduce the experiments in the paper "Transformer Based Multi-Source Domain Adaptation" (EMNLP 2020)

Transformer Based Multi-Source Domain Adaptation Dustin Wright and Isabelle Augenstein To appear in EMNLP 2020. Read the preprint: https://arxiv.org/a

CopeNLU 36 Dec 05, 2022
Official implementation of FCL-taco2: Fast, Controllable and Lightweight version of Tacotron2 @ ICASSP 2021

FCL-Taco2: Towards Fast, Controllable and Lightweight Text-to-Speech synthesis (ICASSP 2021) Paper | Demo Block diagram of FCL-taco2, where the decode

Disong Wang 39 Sep 28, 2022
VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.

What's New Below we share, in reverse chronological order, the updates and new releases in VISSL. All VISSL releases are available here. [Oct 2021]: V

Meta Research 2.9k Jan 07, 2023
Official code of CVPR 2021's PLOP: Learning without Forgetting for Continual Semantic Segmentation

PLOP: Learning without Forgetting for Continual Semantic Segmentation This repository contains all of our code. It is a modified version of Cermelli e

Arthur Douillard 116 Dec 14, 2022
Meta Self-learning for Multi-Source Domain Adaptation: A Benchmark

Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark Project | Arxiv | YouTube | | Abstract In recent years, deep learning-based methods

CVSM Group - email: <a href=[email protected]"> 188 Dec 12, 2022
Hybrid Neural Fusion for Full-frame Video Stabilization

FuSta: Hybrid Neural Fusion for Full-frame Video Stabilization Project Page | Video | Paper | Google Colab Setup Setup environment for [Yu and Ramamoo

Yu-Lun Liu 430 Jan 04, 2023
PyTorch implementation of DreamerV2 model-based RL algorithm

PyDreamer Reimplementation of DreamerV2 model-based RL algorithm in PyTorch. The official DreamerV2 implementation can be found here. Features ... Run

118 Dec 15, 2022