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AI Development Board

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Pantech’s AI development board that can help you to learn and practice on Artificial Intelligence (AI) by using 200+ hands-on experiments.

Pantech’s AI development board comes with Raspberry Pi with SD card booted with OS having pre-installed Deep learning, & OpenCV libraries

Human can see & Recognize – Computer Vision
Human can Listen & Speak – Natural Language Processing
Human can learn – Machine Learning & Deep Learning
Human can solve problems – Analytics & Data driven decision

What you can do with AI development Board?

  1. Pantech’s AI development board helps you to learn and work with Deep Learning & Machine Learning from the provided lessons
  2. You will learn about computer vision with deep learning as well as complete image processing using OpenCV based on several algorithms
  3. AI kit helps you to work with real-time sensors to apply a Machine learning algorithm for decision making
  4. This AI development board also helps you to work with embedded systems using  IoT and Cloud-based applications
  5. It helps you to do research on ML and DL by training your own AI program to recognize the object, Person, Place, expressions, etc.
  6. This AI development board will teach you to design your own AI voice assistants like Jarvis and your own Chabot using Natural Language Processing(NLP)

AI Development Board Package Includes

AI Development Board Package

AI Development Board Specifications

Specification of AI Development board
  1. Raspberry Pi  
  2. Intel Movidius Neural compute stick2 
  3. 8 LED
  4. 4×4 Keypad
  5. Buzzer
  6. Respeaker 4 Mic array
  7. Mini Breadboard
  8. Barometric sensor
  9. Traffic light interface 
  10. Additional Sensor (Opt.) 
  11. Additional Sensor (Opt.) 
  12. Additional Sensor (Opt.) 
  13. Additional Sensor (Opt.) 
  14. ZigBee/GPS/LORA interface  
  15. RFID
  16. Level sensor
  17. DHT11 sensor
  18. Accelerometer
  19. Joystick
  20. PIR sensor
  21. Additional Sensor (Opt.) 
  22. Additional Sensor (Opt.) 
  23. LDR 
  24. Ultrasonic  
  25. Gas Sensor
  26. Moisture sensor 
  27. Load cell
  28. MEMS sensor
  29. Servo  
  30. DC Motor  
  31. Stepper 
  32. 8×8 Dot matrix 
  33. Relay-2 
  34. 8 Slide switch 
  35. Power supply  
  36. LCD display
  37. CAN Interface 
  38. 4 Digit 7-segment 

AI development board will introduce you to

AI applications on AI development Kit

Experiments & Lessons based on Category

  1. Deep Learning
  2. Computer Vision Lessons with Experiments
  3. Natural Language Processing (NLP) Projects
  4. Machine learning
  5. Internet of Things(IoT) Hands-on experiments
  6. Programming Embedded system using python programming
  7. Basic Sensor Interface
Artificial Intelligence Development Kit

List of Deep Learning Experiments -Source code and Video tutorials -AI Development board

Yolo Object detection

In this example, you’ll learn how to use the YOLO object detector to detect objects in both images and video streams using Deep Learning, OpenCV, and Python.

Dog and Cat classification using CNN

Cat vs Dog Image Classifier using CNN implemented using Keras. This project aims to classify the input image as either a dog or a cat image.

Semantic Segmentation using RCNN

In this tutorial, we’ll discuss the difference between image classification, object detection, instance segmentation, and semantic segmentation. From there we’ll briefly review the Mask R-CNN architecture and its connections to Faster R-CNN.In this example, you will know how to apply Mask R-CNN with OpenCV to both images and video streams.

Lane detection and Tracking

Robust lane-detection and tracking framework is an essential component of an advanced driver assistant system, for autonomous vehicle applications. The problem of lane detection and tracking includes challenges such as varying clarity of lane markings, change in visibility conditions like illumination, reflection, shadows, etc. In this example, a robust and real-time vision-based lane detection and tracking framework are proposed. The example uses the lane boundary candidate generation based on extended hough transform and CNN based lane classification model for detection. Additionally, a Kalman filter is used for lane tracking. 

Handwritten Text Recognition System using TensorFlow

Offline Handwritten Text Recognition (HTR) systems transcribe text contained in scanned images into digital text, We build a Neural Network (NN) which is trained on word-images from the IAM dataset. As the input layer (and therefore also all the other layers) can be kept small for word-images, NN-training is feasible on the CPU. Implementation was done using Python 3, TensorFlow, NumPy and OpenCV

OpenCV  OCR and text recognition with Tesseract

In order to perform OpenCV OCR text recognition, we’ll first need to install Tesseract v4 which includes a highly accurate deep learning-based model for text recognition.

Gesture Recognition

The hand gesture is a natural way for humans to interact with computers to perform a variety of applications. Using Deep learning which is efficient for image recognition system is used to find the hand gesture which is captured dynamically. In particular, the Convolutional neural network is used for better performance. The model is trained with static hand gesture images. The Convolutional neural network is created without using a Pre-trained model.

Emotion recognition

Emotion recognition systems based on facial gestures enable real-time analysis, tagging, and inference of cognitive-affective states from a video recording of the face. 

Leaf Disease Detection and Classification

This experiment uses transfer learning by using Resnet a pre-trained model to use different types of leaf disease in the dataset to train which helps to recognize the type of disease from the real-time video.

Landmark recognition

Our machine learning models can be trained to recognize famous landmarks such as Big Ben, Statue of Liberty, Eiffel tower, the pyramids of Egypt and more. It utilizes a similar method to object recognition, where a machine learning model is built exclusively to recognize famous landmarks.

List of Natural Language Processing (NLP) Experiments -Source code and Video tutorials

Audio data analysis using Deep Learning

Audio analyzing experiments will teach you about analyzing the audio with different features which helps to recognize the audio based on the features

Audio Classification using Deep Learning

Audio Classification experiment will show you how to train the audio dataset to recognize the different sound which is heard from the environment using Transfer learning which is done by using any other pre-trained network.

Audio Fingerprinting

Audio Fingerprint experiment allows you to process the audio input to recognize the audio, for example: Asking google about the songs as an audio input to recognize the audio song.

Audio Music Tagging

Audio music tagging experiment will show you how to do train the audio based on genres, which is trained using MusicTaggerCNN to tags the music audio based on the training network.

Audio Segmentation

Audio signal segmentation helps you to segment the audio signal which is required from the complete audio sound.

Audio source separation

Audio source separation experiment allows you to learn about separating the certain audio part from the mixture of audio signal like extracting lyrics from the song

Beat tracking

Beat tracking is used to identify the location of the certain Beat from the entire audio signal, which can be used for audio editing applications.

Music Recommendation

The music recommendation experiment will show you how to classify the music based on users like google play music, Spotify, etc. using Convolutional Neural Network.

Google Assistance

Google assistant experiment allows you to work with Google API, with that it also helps you to work with 4-mic Respeaker mic array which can be used for long-range voice input to perform some hardware operation such as turning ON the light and also with other Google services.

Chatbot using Dialogflow

This program allows you to design your own chatbot which is an intelligent conversational program that can be built from scratch. Conversation can be text or voice commands like Google assistance and Alexa.

Chatter Baby

This chatter baby experiment allows you to analyze the crying sound of the baby and to predict the reason for the crying like high pitched cry occurred by any pain or any other low pitch cry for different reasons using deep learning.

Speech-based Emotion recognition using RNN

This Speech-based Emotion recognition will help you to study analyzing the speech to recognize the current emotion of the speaker, using RNN which is most suitable for analyzing time series of the audio signal.

Text Classifiers using Natural Language Processing

Text classifiers experiment is working by separating the complete sentence and recognize the keywords in the name of tags and then classifies the appropriate categories of the tagged word like Travel, Sports, technology, etc.

Machine Translation

Machine Translation experiment allows you to train your own program to translate one language text to another language text using Keras Model.

Named Entity Recognition

Named Entity recognition experiment allows you to analyze the whole text or article and recognize the main parts like Names, Places, Organization, and location.

Paraphrase Detection

A paraphrase detection experiment will help you to analyze the text and helps to detect the paraphrase. For example, In Grammarly, it will automatically give the suggestion of the phrase by analyzing the whole sentence.

Natural Language Generation (NLG) using Deep learning

Natural Language Generation experiment can be used to study converting the other form of representation into the human-readable text. In other words, by analyzing the incomplete sentence to predict the possible words suitable to complete that sentence. For example: I Like to ______. Possible words are study, swim, travel, etc.

Spell checking and correction using NLP

Spell check and correction experiment are used to design model which can analyze the whole text compares with the dictionary to predict or correct the mistaken words automatically using Natural Language Processing.

Caption Generation using NLP

Customer service chatbot allows you to learn about building your own chatbot either by Rule-based approach or Self Learning bot which replies to the customer based on their requirement using Natural Language Toolkit (NLTK).

Customer Service Chatbot using NLTK

Customer service chatbot allows you to learn about building your own chatbot either by Rule-based approach or Self Learning bot which replies to the customer based on their requirement using Natural Language Toolkit (NLTK).

Topic Tagging and Word Association

This experiment allows you to design the system to analyze the whole document to grouping the similar topics together instead of using every topic as a different operation in the document using the LDA algorithm (Latent Dirichlet Allocation).

Internet of Things  (IoT) Experiments -AI Development board

Cloud-based Temperature and Humidity monitoring system

This system, allows you to be familiar with basic monitoring cloud that can be accessed through API. In this experiment, the Thingspeak cloud is used to monitor the Temperature and Humidity used for industry or monitoring system.

Weather Reporting system using weather cloud

This cloud-based weather reporting system allows you to work with reporting cloud to read the data from the various websites using their API in the format of JSON. So you will learn about the cloud which sends the data logs in the format of JSON. Here “Wunderground” is used for both current conditioning as well as forecasting weather of the specific region.

Intelligent Traffic Management system

This system helps you to learn about the SMTP protocol which is used to send the notification to the Police station about the vehicle which disobeys the traffic rules, detected using the IR sensor or camera. The intimation is sent using SMTP Protocol to send Mail.

Smart Parking System

This IoT enabled the Parking system to help you to work with RFID which is used to recognize the vehicle and generates bill amount based on the parking time of the specific vehicle. That ensures the payment completes by getting the payment information by using the webserver with username and password for users.

Smart Irrigation system

This Smart irrigation system allows you to work with a number of nodes which monitors the current weather condition using weather cloud as well as soil condition using sensors and reports to take action based on the reports like watering. Else it can be configured for scheduled watering throughout the field.

Waste management system

This system contains nodes that communicate with each other using MQTT protocol, information sharing is based on the current condition of the garbage container placed in various places. The decision has been made by the server to take action on garbage container which is worse than other.

Forest Fire detection

A forest fire can be detected, by placing a number of detecting nodes in various places in the forest. Nodes that contain temperature sensors and also camera used to detect the temperature changes and to alert the server that Fire is detected in that specific node. So that fire extinguisher can be sent to a specific node easily. Else some extinguisher can be placed in someplace to keep control over fire. It also is visualized using the camera remotely.

Baby Monitoring System

This system helps you to work with a sound sensor to detect the sound of the baby and to play music remotely after getting alert when the baby cries, using SMS/Mail services. And you can perform web server-based operation to swing the cradle remotely.

Family Monitoring system

The main objective of this experiment is to visualize the camera stream remotely using a web server, and also you will learn about using a mobile camera instead of using USB or another type of camera to monitor the different places of the home.

Smart Door Bell system

This experiment allows you to use OpenCV technology for face recognition and based on a face detected the alert message is sent to the house owner with OTP, only after guest entered the OTP they got authenticated to get inside the room. Lots of wrong attempts will lead to sending warning messages to users.

MQTT based Coalmine monitoring system

This experiment allows you to work with the Lightweight protocol, MQTT (Message Queuing Telemetry Transport) a protocol that also works in low network coverage area like mines. This helps to implement IoT in mine to send alert messages or reports about the current condition of each nodes using sensors like Temperature, Atmospheric Pressure, and toxic gas.


Smart Mirror has a feature to recognize the user with welcome greetings using speaker connected to it. Recognizing can be done using “HaarcascadeFrontalface.xml”. With the welcome greetings, it also displays the date, time and current weather report.

Data Logger

In this experiment you will learn about creating data logger for different values of sensors which can be later used for applying any graphical application as well as any machine learning application to that data which is already in the format of .csv, by using the Google sheet as data logger, It turns out into the IoT based data logger.

Transmission Line monitoring system

There are many power grid lines passing throughout every place, It should be maintained carefully, when any disaster happens it is essential to shut down the complete transmission which is passing by the same line. To overcome those difficulties, this system helps to make each and every node in communication to the previous nodes, so that if any fault occurs in any node which gets identified by the previous node, and only shutdowns that specific node using IoT.

The web server-based Home automation system

From this experiment you will learn about the web servers, here Apache web servers are used to connect with the server remotely using the localhost. The webserver is designed based on a number of appliances that needed to be controlled. You will also learn to assign authentication before controlling the appliances.

IoT based sensor logger using Amazon Web Service

From this experiment you will learn about a well-known cloud AWS for IoT to monitor the sensor values obtained from the system and also alerting system is done in AWS to the user based on the sensor excessing the threshold limit.

IoT based voice assistance using voice control mobile application

This experiment allows you to use Bluetooth communication for voice-controlled applications by using the android application for sending voice commands to the system. An application like capturing a photo by saying cheese, taking notes, pinging the mail by having notes as the body of the mail, talking tom, etc.

IoT based Health Monitoring system

This experiment helps to learn about some medical applications like heartbeat, temperature and pressure measurement and uploading to the cloud, which sends alert while detecting any abnormality to the caretaker or to the clinic with the patient’s location.

IoT based smart Umbrella

From this experiment, you will learn about the various weather cloud integration with bot current weather and Forecasting weather which sends alert automatically as a voice command like “It’s better to have me with you today since the possibility of rain is 90 percent”. By interfacing Speaker with the system.

IoT based Water quality management system

From this experiment, you will learn about using sensors which can be used in real-time water quality measurement, by monitoring the PH value with its turbidity, so that you can find the quality of the water.

IoT based voice assistance using voice control mobile application

This experiment helps you to work with GPS by latitude and longitude decode to identify the location and to give the voice commands continuously from the source location to the destination location using Google Map API.

IoT based attendance system using RFID

From this experiment, you will learn to use the security system by using RFID which has a unique ID of each card, which is implemented in the project of the Attendance system. Every individual gets identified by their RF ID cards.

E-Notice Board

This experiment will show you to design webserver to send the Notice message, which is displayed in the display device like LCD using IoT with the localhost by only with administrator’s authentication.

Vehicle locator using GPS co-ordinates

From this experiment, you will learn to use GPS coordinates to find the real-time location in google map using Google map API. The webpage will be automatically open after getting the latitude and longitude.

Sending WhatsApp messages using Twilio cloud

From this experiment, you will learn about the cloud which is used to send WhatsApp messages by using the python program, which is used to send alert messages using the Twilio cloud. The same cloud can be used to send Text messages as well.

List of Computer Vision experiments using AI Development Board

Edge Detection

This experiment allows you to apply edge detection techniques like Sobel, canny edge and Prewitt to the image as well as in the real-time video.

Erosion and Dilation

This experiment will show you to apply morphological operations like erosion and dilation to the image using OpenCV.

Median filter

This experiment allows you to work with applying a filter to remove noise like Salt and Pepper noise which is the Median filter applied to the noisy image.


From this experiment, you will get the knowledge about processing the image into segments to represent the image in another meaningful way.

Image Reading, Resizing and Writing

This basic experiment will show you how to read an image with a different format, to resize the image and to write an image to the specific location using OpenCV.


The Thresholding topic will teach you about applying the method of segmentation to the grayscale image to covert into binary using Thresholding.

Color space Conversion

This Color space conversion experiment allows you to convert from one color space to another like from the above example RGB is color space is converted into YCbCr color space.

Video processing

This experiment feeds the knowledge on capturing the video from the camera, load video from the directory and to save the video to the directory using OpenCV.


Steganography experiment helps to learn about hiding the information into the image using the technique called Steganography using OpenCV


This experiment projects the knowledge on creating watermarking on images using OpenCV which will come along with every picture when creating duplicates.


Denoising experiment delivers the knowledge on working with reserving the details of an image by removing the Noise of both Hard and Soft Noise using OpenCV

Image compression

This experiment teaches about compressing the image into less memory occupation with or without losses in the quality of image using python programming

Blob counting

This experiment allows you to work with either making the threshold to count the object or else by making blob without any parameter to detect light and dark pixels to make the count

Background subtraction

This experiment helps you to works with basic foreground detection techniques which is done by subtracting the background especially in moving object detection in videos.

Hough Transform for Line and Circle detection

Hough Transform experiment helps to work with one of this feature extraction algorithm based on a shape like a line or a circle using OpenCV

Face recognition

A well-known application Face recognition experiment delivers the knowledge about working with models like Haarcascade.xml to recognize the face in pictures and also in Real-time video with from the database created for different faces.

Watershed segmentation

This experiment allows you to program one of the segmentation algorithms, Watershed which denotes the grayscale images with low intensity as valleys and high intensity as peaks using some methods like marker-based watershed algorithm for certain image.

Video Tracking

Video Tracking experiment allows you to program the application to detect the moving object even when the camera is also in motion rather than using the static camera using computer vision.

Motion Detection

Motion detection delivers the programming concept to detect the moving objects while the camera is placed statically using background subtraction.

Object Detection

In this experiment, you will learn to use simple image processing techniques to detect the object and to find the number of an object present in the frame without deep learning.

Image Retrieval

In this lesson, you will learn about image retrieval techniques based on shape, color or texture using simple computer vision program without deep learning

Image Fusion

Image fusion experiment allows you to program the OpenCV program to extract the multiple features from multiple images and bind all image feature into less number of the image with high quality of more features.

List of Machine Learning experiments using AI Development Board

Handwritten digit recognition using KNN

From this experiment, you will learn to train the model which is capable of recognizing the Handwritten text like numbers or alphabets using K-Nearest Neighbours (KNN) algorithm.

Road sign recognition using SVM

This experiment delivers the knowledge of creating the machine learning application which performs the recognition of road sign from the images using Support Vector Machine 

Snake Game

This experiment allows you to design an AI model to give intelligence to the snake to capture the eggs automatically without collision with maximum possibilities

Sentimental Analysis

This experiment helps to develop a machine learning application to classify the sentiment based on the sentence with datasets provided to train the model. It recognizes and results in the sentence in either positive or negative.

Pedestrian tracking

This experiment explains designing the model to detect the pedestrian with a boundary box to represent the detection in real-time video or video captured from the camera using deep learning.

Deep Learning with Computer Vision

Face detection and Tracking

This experiment allows you to work with the “HaarcascadeFrontalFace” algorithm to detect the face and plot boundary box in the face and also to track the face while the person in motion using real-time stream obtained from the camera or by the video feed.

Face Recognition

This experiment delivers the knowledge about creating the databased and to train the database of multiple person face image and also to recognize the image using “Haarcascade” algorithm using Computer Vision

Sign Language Recognition

Sign language recognition is one type of gesture recognition with different methods with more accuracy to detect the sign language which is pre-assigned for every gesture.

Vehicle Detection

Vehicle detection experiment uses cars.xml a pre-trained model for detecting the vehicles using computer vision

Object detection using YOLO

YOLO –  “You Only Look Once” algorithm-based object detection delivers more knowledge about the algorithm which we can apply for the image as well as in the real-time video to detect multiple objects.

Drowsy Detection

Realtime Drowsiness detection experiment works by placing 68Landmarks on the face to detect the eye, after that it will measure the eye enclosure diameter. Based on the value of eye enclosure value drowsiness is detected

License plate Recognition

This experiment shows you to detect and recognize the License plate from the real-time video or video captured using the camera

Fingerprint Recognition

This experiment delivers the computer vision program to recognize the fingerprint from the image already present in the database

Pedestrian detection and Tracking

This experiment uses deep learning to use a pre-trained model to detect and track the pedestrian from the real-time video with Computer vision

Food Classification

Food classification experiment helps you to work with Transfer learning which is done by using a pre-trained model with our own dataset for recognizing a different kind of food

Optical character recognition

Optical character recognition used to recognize the alphabet or character from the printed image using OCR

Smile Detection

Smile detection experiment helps to work with a trained model to recognize the smile from the image as well as in the real-time video.

Text detection

Text detection experiment helps to detect the text part in the complete image

Book reader using Tesseract OCR

The book reader is the concept of using Tesseract OCR to recognize the complete text and to output, the voice read of certain text using speak or Festival.

Business card reader

Business card reader experiment is having the capability to detect the text present in the Business card, another type of character recognizing the technique.

Age and Gender classification

This lesson teaches about the model which is already trained to recognize the age with gender, the application present in the mobile phone. Now we can study the complete working of the model to recognize gender using deep learning.

Smart Selfie camera

This is another kind of face application which can be used to automatically capturing and saving the image only if it detects the happy expression in your face.

Lane detection

Lane detection, one of the applications in the autonomous car system which will teach you the concept of programming the AI to detect the Lane.

Digit recognition

Other than the complete text, this model is trained to recognize the Digit which reduces the complexity of recognizing the character I and the number 1. So the model is completely trained to recognize the digits for some application like digital Energy meter reader.

Handwriting recognition

Handwriting recognition helps to train our own handwriting to generate model capable of recognizing our handwriting in real-time.

Attendance marking

Attendance marking is one of the cloud-based application, which helps to work with the cloud to recognize the n-number of face present in the database and make count within and out time using cloud-based deep learning

Diabetic retinopathy

This lesson will make you work with the medical image to detect the Diabetic retinopathy which causes vision problem, which can be recognized from the medical image.

Leaf Nodule detection

This experiment uses deep learning to design the model with datasets of having Leaf images with and without nodule, and by using either transfer learning or from the scratch model is developed to recognize the leaf nodule.

Fruit recognition

Fruit recognition lesson will give you the pre-trained model which already trained to recognize the different kinds of fruit using deep learning with computer vision

Glaucoma detection

Glaucoma is one type of vision problem, which can be analyzed and classified using deep learning by training medical images.

Template matching

This Template matching experiment helps to work with a computer vision program to recognize the hidden pattern or object in the collapsed image, using template matching. Which the application is implemented in detective games.

Breast cancer detection

Breast cancer detection is done previously using image processing; Now this experiment gives more accuracy to detect breast cancer by using deep learning.

Traffic sign detection

One of the application in the autonomous car to detect the Traffic sign using deep learning, this lesson will show you to create number classes with a number of different road signs which can be recognized by loading that trained model.

Saliency detection

Saliency the feature extraction experiment allows to extract feature of an image of the certain interesting region of the image using static saliency with computer vision.

Barcode Recognition

Barcode recognition using Computer vision helps to recognize the object from its bar code using the cloud. You can also find the object or information from Box code using the cloud with computer vision.

Food calorie Detection

Food calorie detection is one of the concepts like food recognition which takes the calorie value from the dataset to calculate its calories based on the food recognized using Machine learning

Brain tumour segmentation

This lesson also uses medical images to segment the brain tumour using computer vison from an image.

OMR sheet analyzer

This lesson uses a computer vision algorithm to compare the original Sheet with the worked sheet finds the shades done in answered sheet compares answers to projects the results.

Shadow detection

Shadow detection experiment is one of the practice experiment to detect the shadows in an image using computer vision.

Embedded Basic Experiments


AI Board consist of 8 LED which helps to work with GPIO pins of the Main Board for Some Indications

8 Switch

It consists of 8 slide switches used for the toggling operations to turn On/Off any appliances

4×4 Matrix

4×4 Matrix keypad allows you to work with some applications for any authentication purpose like entering OTP for the projects like Smart door Bell


AI board has Analog Joystick which has 5 operations like forwarding, Left Right, Backward and Button can be used for any controlling applications like Robots

DOT Matrix

8×8 Dot-matrix allows you to work with display devices to display some animated logos or icons based on applications


Ultrasonic sensor interface allows you to design an application for distance measurement or level measurement using Ultrasonic waves


This interface makes you interface any UART component like ZigBee, GPS, LORA to the AI board


PIR sensor interface can be used for motion detection application using Infrared radiations

Relay / Stepper / Servo

This Interface area in AI board contains 2 Relay with Stepper of 2 Type pinout DC motor as well as servo motor interface used to work with all those motor for various applications like robotics and also some embedded applications

Traffic Light

Traffic light interface helps you to work with Real-time logic operations with some applications for traffic management using Artificial Intelligence


Respeaker is one of the main application interfaces which help to work with all voice-based applications like NLP which has 4 mic array enables to deliver voice commands from long distance to the AI Kit

16×2 LCD

Liquid Crystal Display which helps to work with some display based applications like warnings or request to enter OTP, show your face to the camera.

RF Reader

RF Reader helps to work with authentication based application like RF cards read by the RF readers.

8 Digital Sensors Interfaces

This Digital sensor interface experiment allows you to work with digital IO reads and also to work with the Digital Interrupts using Python programming with GPIO Pins.

Analog Sensor Interfaces

Analog sensors interface helps to work with SPI protocol, since pins you are gonna use are GPIO pins so by using MCP3008 ADC which works on SPI, so by using python programming to write SPI protocol to read values from the sensors.


AI board contains 4 digits 7 segment display can be used to display Clock or counting for various applications

This Post Has 3 Comments

  1. Ghulam Mohyudin

    This was really interesting to read! I love the range of post, there is really something for everyone.
    Thank you for sharing your suggestions too, great post!

  2. Christoph Haring

    When is it available?

  3. Christoph Haring

    This is just amazing!!

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