xtag_v1.2
Xtag monitors multiple components of the pump installation which includes the pump, its motor and connection bearing frame. Parameters are sensed and recorded, including point temperatures, motion, vibration, and pump pressure values, stored with report on request. Xtag is part of a larger tracking system that collects pump status and information using the xCloud, connected over a secure extranet connected through a gateway. Control Solution: nordic nRF52840 WiFi Solution: Espressif ESP32-S0WD
Hakkımda
7 years in Full Stack Development 5 years of experience in PCB design and firmware development of various kinds of IoT devices 5 years of Computer Vision and Machine Learning 2 years of NLP and Data Science My goal is to meet your needs and deadline. I always pay attention to detail, speed and quality. Making complex workflows simple is my thing. Python and JavaScript always empower me to implement these complex things simply. so, I love them. __________________ Full Stack Development __________________ - Back End: Django, Flask, [login to view URL], Laravel, CI, Celery/RabbitMq, Redis. - Front End: React.js(Redux/React Hook/Context API/[login to view URL]), Angular(1.x, 2/4/6, 7), Vue.js and jQuery. - Database: MySql, MariaDB, PostgreSql, MongoDB. - Clouds Platform: AWS, GCP, Firebase, Godaddy - Web Scraping: Scrapy/Selenium/Bs4, Cheerio/Puppeteer. Scrapy and Puppeteer is preferred now. Development Tools and Environtment: - Linux (Ubuntu/Cent OS) - PyCharm - WebStorm - PhpStorm __________________ IoT | PCB Design __________________ Skills: - Solution proposal, Rapid Prototyping and Testing of various kinds of IoT devices using Arduino, Raspberry Pi, ESP8266 WiFi module, ESP32 WiFi /BLE module, nRF52830, nRF52840, LoRa module, STM32 and several kinds of sensors - Schematic and PCB layout design for validated prototypes - Xilinx Vertex series/Spartan series FPGA design - AC/DC adapter, ADC/DAC module design - Solar Panel Charge Circuit design - High-Speed signal processing device/ USB hub design Preferred Development IDE: - Altium Designer2018/Eagle5.2/KiCAD5.1 - Arduino IDE - Visual Studio Code(PlatformIO) - Zerynth Studio IDE __________________ Machine Learning and Computer Vision ___ - Computer Vision: Experiences: People detection, face detection, vehicle detection, industrial products detection and counts, and other specification objects. Image classification, object tracking, segmentation, Image style transformation The machine or deep learning approaches with CNN/DNNs: YOLO(v3, v3 tiny, darknet), SSD (SSD-Mobile, SSD-ResNet, SSD-Inception), VGG (VGG-COCO, VGG-PASCAL) RCNN (Faster-RCNN-ResNet, Faster-RCNN-Inception, Faster-RCNN-NasNet) Mask RCNN, DeepLap - Typical image processing: Image filtering, enhancement, calibration, stitching, color correction, geometric transformation, keypoint feature extraction, lens calibration, stabilization - Data Science: Pandas, Scipy, Numpy, Tensorflow, Keras, Torch, Matplotlib - Natural Language Processing: NLTK, Gensim, Spacy - AI chatbot developement: Google DialogFlow, Rasa Chatbot, Microsoft Bot, [login to view URL] - OpenCV, Dlib, PIL, Scikit-imag, Darkflow If you need any help to implement your interesting ideas, I will always be available for you. Thanks
