ericphilbaz
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An ultra-lightweight(ROM<1.6K, RAM<0.3k), high-performance C/C++ log library. | 一款超轻量级(ROM<1.6K, RAM<0.3k)、高性能的 C/C++ 日志库
An up5k board to manage pulse-echo ultrasound acquisition.
Acoustic camera system for measuring ultrasound communication in rodents
STM32Cube MCU Full Package for the STM32G0 series - (HAL + LL Drivers, CMSIS Core, CMSIS Device, MW libraries plus a set of Projects running on all boards provided by ST (Nucleo, Evaluation and Dis…
Ready To Install Packs For STM32CubeMX and STM32CubeIDE
C-Library for SCL-3300 sensor interfaced with STM32H743 using SPI
Awesome Incremental Learning
An additional, very in progress, high-level library as an add-on to https://github.com/NURobotics/PS4-esp32 to aid in ease of use.
适用于嵌入式单片机的差分升级库，通用所有单片机，如stm32、华大、复旦微、瑞萨等。适合嵌入式的差分升级又叫增量升级，顾名思义就是通过差分算法将源版本与目标版本之间差异的部分提取出来制作成差分包，然后在设备通过还原算法将差异部分在源版本上进行还原从而升级成目标版本的过程。 差分升级方案不仅可以节省MCU内部的资源空间、还可以节省下载流程及下载和升级过程中的功耗。技术支持vx 18219255930
世博同学轮足机器人的Arduino工程转换而成的PlatformIO工程，解决了Arduino编译速度巨慢的问题
Official code for First-Spike (FS) coding of spiking neural networks
Haptic input knob with software-defined endstops and virtual detents
Official implementation of Fully Spiking Denoising Diffusion Implicit Models
Temporal backpropagation for spiking neural networks with one spike per neuron, by S. R. Kheradpisheh and T. Masquelier, International Journal of Neural Systems (2020), doi: 10.1142/S0129065720500276
Transformer在CV和NLP领域的变体模型的从零解读：Transformer；VIT；Swin Transformer
T-FFTRadNet: Object Detection with Swin Vision Transformers from Raw ADC Radar Signals
Transformer是谷歌在17年发表的Attention Is All You Need 中使用的模型，经过这些年的大量的工业使用和论文验证，在深度学习领域已经占据重要地位。Bert就是从Transformer中衍生出来的语言模型。我会以中文翻译英文为例，来解释Transformer输入到输出整个流程。
i. A practical application of Transformer (ViT) on 2-D physiological signal (EEG) classification tasks. Also could be tried with EMG, EOG, ECG, etc. ii. Including the attention of spatial dimension…
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
keras implement of transformers for humans