Search Results for "packet data decoder"
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Showing 177 open source projects for "packet data decoder"
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ChirpStack Gateway Bridge
ChirpStack Gateway Bridge abstracts Packet Forwarder protocols
ChirpStack Gateway Bridge is a service that converts LoRa® Packet Forwarder protocols into a ChirpStack common data format (JSON and Protobuf). This component is part of the ChirpStack open-source LoRaWAN® Network Server project. -
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Brim
Application to efficiently search and analyze super-structured data
Desktop application to efficiently search and analyze super-structured data. Powered by Zed. Zed is a system that makes data easier by utilizing our new super-structured data model. Brim is a desktop app to explore, query, and shape the data in your super-structured data lake. Brim is an open source desktop application for security and network specialists. Brim makes it easy to search and analyze data from packet captures, like those created by Wireshark, and structured logs, especially from the Zeek network analysis framework. ... -
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Arkime
A full packet capturing, indexing, and database system
Arkime is an open source, large-scale, full packet capturing, indexing, and database system designed to augment existing security infrastructure by storing and indexing network traffic in standard PCAP format. It offers full network visibility, facilitating the swift identification and resolution of security and network issues. Security teams gain access to the necessary network visibility data essential for responding to and investigating incidents to expose the full attack scope. ... -
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simplejson
simplejson is a simple, fast, extensible JSON encoder/decoder
simplejson is a simple, fast, complete, correct and extensible JSON <http://json.org> encoder and decoder for Python 3.3+ with legacy support for Python 2.5+. It is pure Python code with no dependencies but includes an optional C extension for a serious speed boost. simplejson is the externally maintained development version of the json library included with Python (since 2.6). This version is tested with the latest Python 3.8 and maintains backward compatibility with Python 3.3+ and the... -
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UltraJSON
Ultra fast JSON decoder and encoder written in C with Python bindings
UltraJSON is an ultra-fast JSON encoder and decoder written in pure C with bindings for Python 3.7+. May be used as a drop-in replacement for most other JSON parsers for Python. Used to enable special encoding of "unsafe" HTML characters into safer Unicode sequences. Limits output to ASCII and escapes all extended characters above 127. Default is True. If your end format supports UTF-8, setting this option to false is highly recommended to save space. By default, debugging symbols are... -
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go-json
Fast JSON encoder/decoder compatible with encoding/json for Go
Fast JSON encoder/decoder compatible with encoding/json for Go. -
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Zstandard
Zstandard - Fast real-time compression algorithm
Zstandard is a fast compression algorithm, providing high compression ratios. It also offers a special mode for small data, called dictionary compression. The reference library offers a very wide range of speed / compression trade-off, and is backed by an extremely fast decoder (see benchmarks below). Zstandard library is provided as open source software using a BSD license. Its format is stable and published as IETF RFC 8478. The negative compression levels, specified with --fast=#, offer faster compression and decompression speed in exchange for some loss in compression ratio compared to level 1, as seen in the table above. ... -
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Typia
Super-fast/easy runtime validations and serializations
Super-fast/easy runtime validations and serializations through transformation. -
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LiteNetLib 1.0 indev
Lite reliable UDP library for Mono and .NET
Lite reliable UDP library for .NET Framework 3.5, Mono, .NET Core 2.0, .NET Standard 2.0. Small packet size overhead (1 byte for unreliable, 3 bytes for reliable packets). Helper classes for sending and reading messages. Always use library sources instead of precompiled DLL files (because there are platform-specific #ifdefs and workarounds for unity bugs). Fast packet serializer (Usage manual). Automatic small packets merging. Automatic fragmentation of reliable packets. Automatic MTU... -
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ruroco
A tool that lets you execute commands on a server
Ruroco is a tool that lets you execute commands on a server by sending UDP packets. The commands are configured on the server side, so the client does not define what is going to be executed, it only picks from existing commands. -
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JSONLab
JSONLab: compact, portable, robust JSON/binary-JSON encoder
JSONLab is a free and open-source JSON/UBJSON/MessagePack encoder and decoder written in the native MATLAB language. It can be used to convert a MATLAB data structure (array, struct, cell, struct array, cell array, and objects) into JSON/UBJSON/MessagePack formatted strings and files, or to parse a JSON/UBJSON/MessagePack file into MATLAB data structure. JSONLab supports nearly all versions of MATLAB and GNU Octave (a free MATLAB clone). -
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Wavelets.jl
A Julia package for fast discrete wavelet transforms and utilities
A Julia package for fast wavelet transforms (1-D, 2-D, 3-D, by filtering or lifting). The package includes discrete wavelet transforms, column-wise discrete wavelet transforms, and wavelet packet transforms. 1st generation wavelets using filter banks (periodic and orthogonal). Filters are included for the following types: Haar, Daubechies, Coiflet, Symmlet, Battle-Lemarie, Beylkin, Vaidyanathan. 2nd generation wavelets by lifting (periodic and general type including orthogonal and... -
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fq
jq for binary formats
Tool, language, and decoders for working with binary data. fq is inspired by the well-known jq tool and language and allows you to work with binary formats the same way you would using jq. In addition, it can also present data similar to a hex viewer, transform, slice, and concatenate binary data, supports nested formats, and has an interactive REPL with auto-completion. It was originally designed to query, inspect and debug codecs and metadata in media files and containers like mp4, FLAC, mp3, and jpeg. ... -
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Generic Image Decoder
A standalone, portable generic Ada package for decoding images
The Generic Image Decoder is a package for decoding a broad variety of image formats, from any data stream, to any kind of medium. Unconditionally portable code: OS-, CPU-, compiler- independent code. More information on... http://gen-img-dec.sf.net Alire crate: https://alire.ada.dev/crates/gid Mirror: https://github.com/zertovitch/gid -
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TorchAudio
Data manipulation and transformation for audio signal processing
The aim of torchaudio is to apply PyTorch to the audio domain. By supporting PyTorch, torchaudio follows the same philosophy of providing strong GPU acceleration, having a focus on trainable features through the autograd system, and having consistent style (tensor names and dimension names). Therefore, it is primarily a machine learning library and not a general signal processing library. The benefits of PyTorch can be seen in torchaudio through having all the computations be through PyTorch... -
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ESPnet
End-to-end speech processing toolkit
...The toolkit also hosts numerous pretrained models and example configs, ranging from Transformer and Conformer architectures to various attention-based encoder-decoder models. -
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RSC Explorer
A tool for people curious about the React Server Components protocol
...In practice, it functions like a “packet analyzer” for RSC, turning a normally opaque stream into something you can study and explain. -
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fwknop
Single Packet Authorization, Port Knocking
fwknop stands for the "FireWall KNock OPerator", and implements an authorization scheme called Single Packet Authorization (SPA). This method of authorization is based around a default-drop packet filter (fwknop supports iptables and firewalld on Linux, ipfw on FreeBSD and Mac OS X, and PF on OpenBSD) and libpcap. SPA is essentially next-generation port knocking (more on this below). The design decisions that guide the development of fwknop can be found in the blog post "Single Packet... -
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Hoplite
Boilerplate-free Kotlin config library for loading configuration files
Hoplite is a Kotlin library for loading configuration files into typesafe classes in a boilerplate-free way. Define your config using Kotlin data classes, and at startup Hoplite will read from one or more config files, mapping the values in those files into your config classes. Any missing values, or values that cannot be converted into the required type will cause the config to fail with detailed error messages. -
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Percona Toolkit
A collection of advanced open source command-line tools
Percona Toolkit is a collection of battle-tested command-line tools for MySQL and MariaDB that help diagnose performance, verify integrity, and perform online maintenance safely. Utilities such as pt-query-digest analyze slow logs and packet captures to surface hotspots and regressions, while pt-online-schema-change applies ALTERs with minimal blocking by copying and swapping tables. Consistency tools like pt-table-checksum and pt-table-sync detect and reconcile replication drift across replicas or shards. Other helpers find duplicate indexes, unused keys, skewed data distributions, and configuration issues that quietly erode performance. ... -
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Poison
An incredibly fast, pure Elixir JSON library
Poison is a fast and lightweight JSON library for Elixir focused on performance and idiomatic APIs. It provides straightforward encode and decode functions, along with a protocol-based encoder that lets you customize how your structs become JSON. Developers can derive or implement Poison.Encoder for domain types, control which fields are included, and map complex values into JSON-friendly forms. On the decoding side, it supports options for key handling and flexible parsing of JSON into... -
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LLM Foundry
LLM training code for MosaicML foundation models
Introducing MPT-7B, the first entry in our MosaicML Foundation Series. MPT-7B is a transformer trained from scratch on 1T tokens of text and code. It is open source, available for commercial use, and matches the quality of LLaMA-7B. MPT-7B was trained on the MosaicML platform in 9.5 days with zero human intervention at a cost of ~$200k. Large language models (LLMs) are changing the world, but for those outside well-resourced industry labs, it can be extremely difficult to train and deploy... -
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TimesFM
Pretrained time-series foundation model developed by Google Research
TimesFM is a pretrained time-series foundation model from Google Research built for forecasting tasks, designed to generalize across many domains without requiring extensive per-dataset retraining. It provides a decoder-only model approach to forecasting, aiming for strong performance even in zero-shot or low-data settings where traditional models often struggle. The project includes code and an inference API intended to make it practical to run forecasts programmatically, with options to use different backends such as Torch or Flax depending on your environment and performance needs. ... -
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Segmentation Models
Segmentation models with pretrained backbones. PyTorch
...High-level API (just two lines to create a neural network) 9 models architectures for binary and multi class segmentation (including legendary Unet) 124 available encoders (and 500+ encoders from timm) All encoders have pre-trained weights for faster and better convergence. Popular metrics and losses for training routines. All encoders have pretrained weights. Preparing your data the same way as during weights pre-training may give you better results (higher metric score and faster convergence). It is not necessary in case you train the whole model, not only the decoder. Pytorch Image Models (a.k.a. timm) has a lot of pretrained models and interface which allows using these models as encoders in smp, however, not all models are supported. ... -
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OpenAI DALL·E AsyncImage SwiftUI
OpenAI swift async text to image for SwiftUI app using OpenAI
...You need to have Xcode 13 installed in order to have access to Documentation Compiler (DocC) OpenAI's text-to-image model DALL-E 2 is a recent example of diffusion models. It uses diffusion models for both the model's prior (which produces an image embedding given a text caption) and the decoder that generates the final image. In machine learning, diffusion models, also known as diffusion probabilistic models, are a class of latent variable models. They are Markov chains trained using variational inference. The goal of diffusion models is to learn the latent structure of a dataset by modeling the way in which data points diffuse through the latent space.
