GROBO: 2019

2019年11月25日星期一

ESP32-CAM MJPEG Stream Decoder and Control Library

半年前在做自己的Computer Vision Board採買材料時，無意間知道ESP32-CAM這東西，買來放一段時間後，上週末有空拿出來測試這東西挺讓我驚艷，相見恨晚，主要是150NTD的價格加上已經有現成的Arduino相容範例，直接搞定SCCB設定跟DCMI Data還有一個4M PSRAM作為Frame Buffer，收到的MJPEG Stream在 UXGA 1600x1200解析度可以約10FPS每個JPEG 100Kb左右。
就整體來說這東西在Wi-Fi環境感覺上非常具有淺力，一些多Camera Computer Vision的應用可以配合Raspberry Pi運算，或者簡單的Robot需要Computer Vision然後輔以別的控制器以UART通訊控制（GPIO扣掉SCCB+DCMI和SDIO只剩UART...）都是不錯的選擇。
我把wxRovio的軟體改一改後主要是抽出C/C++ MJPEG Stream decode，還有示範POST參數給ESP32-CAM修改解析度，基本上相依OpenCV和libcurl，在Github有用OpenCV GUI和wxWidgets GUI的範例，後者順便放YOLO V3和OpenPose的DNN應用，Library在MAC和Win10+VS2017編譯x64版本都可以通過。

Github

2019年11月9日星期六

Atrial Fibrillation Detection Blood Pressure Monitor Oscillometric Method

This project is electronic blood pressure monitor research platform with Oscillometric method, include SBP and DBP estimate algorithm, Atrial Fibrillation detection algorithm, calibration curve, GUI tool for fine-tune BP algorithm...etc, ofcourse, this is research use only.

Github

OSHWA CERTIFIED

2019年9月29日星期日

Enable STM32F4 FPU

1. ./Libraries/CMSIS/Device/ST/STM32F4xx/Include/stm32f4.h -> add
#include <arm_math.h>
2. ./system_stm32f4xx.c -> in "void SystemInit(void)" add
#if (__FPU_PRESENT == 1) && (__FPU_USED == 1)
SCB->CPACR |= ((3UL <<10*2)|(3UL <<11*2));/* set CP10 and CP11 Full Access */
#endif
3.main.c add
#define __FPU_PRESENT
#define __FPU_USED

wxECGAnalyzer - cross platform ECG signal process tool

Detection of abnormal rhythm morphologies is more difficult than normal beat, therefore, we need to collect abnormal rhythm signals in clinical practice to improve the detection of QRS-complex.

This project is for Electrocardiogram(ECG) signal algorithms design and validation, include preprocessing, QRS-Complex detection, embedded system validation, ECG segmentation, label your machine learning dataset, and clinical trial...etc.

For algorithm performance, in ANSI/AAMI EC38,it is required that the detected QRS shall in the 150ms range of the signed point from annotation by human exper.

目前僅用MIT-BIH等標準心律不整資料庫或是生理訊號挑戰賽提供的標註資料，理論上很難得到超越其標注的學習模型，實務上還是需要配合其他臨床實驗搜集更多案例強化模型，下個To-Do會用標準資料庫訓練分類模型增加基本的自動化標註，目前僅針對臨床實時運行的特殊案例配合人工選擇QRS-Complex演算法自動切片。