An open-source electronics platform that is widely embraced is Arduino, it appears to be modest for difficult AI undertakings. Yet, due to the rise of edge computing and the trimming of AI models, our researchers have developed numerous AI applications that have now become more compatible in Arduino with microcontrollers. Look for the Arduino help you need in no time whatever circumstances may be by our leading professionals.

          Under you’ll find top Arduino experts, developers and writers. Get on your project built, code reviewed, or problems solved by our trained experts. Learn from our expert guides about the latest arduino topic titles and explore more in the field of artificial intelligence for your arduino projects.

The project ideas that we have worked under arduino has been explained:

  • Voice Recognition and Control:

                    A simple voice recognition module will be used that controls LEDs, motors, or actuators by using voice commands.

  • Simple Chatbot:

              To identify from a set of predefined commands or questions and respond accordingly we will implement a basic chatbot on an Arduino. Through a serial monitor communication takes place.

  • Gesture Recognition:

             Here we perform actions based on those gestures (like tilting, shaking, etc.) by using sensors like accelerometers or gyroscopes with the Arduino.

  • Face Detection:

               Arduino is not powerful enough for real-time face detection, so we make use of a camera module with onboard processing (like the OV7670) and cross point it with the Arduino to activate actions once we detect a face.

  • Obstacle Avoidance Robot:

                  A robot with ultrasonic sensors will be developed by our developers and we will then implement algorithms to allow the robot to steer and evade obstacles in its ongoing path.

  • Anomaly Detection:

                Here, the Arduino will be set to trigger an alarm once we have collected the data for an environmental sensor so, when readings depart from a predefined range, thus it indicates potential anomalies.

  • Smart Plant Monitoring System:

                 To screen the plant health, we must use soil moisture, humidity, and light sensors. The ideal conditions for the plant will be learnt by Arduino, notified by the user or routinely water the plants when required.

  • Neural Network on Arduino:

              The area of running machine learning models on small devices is TinyML. We use libraries like TensorFlow Lite Micro, to train our model and deploy it to Arduino for elegant calculations.

  • Emotion Recognition with Music Player:

                 Even though we may consider it more advanced, we can apply a basic emotion detection module in combination with an SD card module that will be attached to an Arduino here it will play a specific selection of songs that is based on the detected emotions.

  • Automated Sorting System:

               The Arduino is designed to control the logic and actuation that we execute a system which categories objects on its colour or size by making use of sensors and actuators.

  • Predictive Maintenance:

                  A vibration or sound sensors will be attached to the machinery. As the Arduino has the skill to monitor these sensors for any number of noticeable patterns which  could show an imminent failure, and  alert the users.

  • Traffic Monitoring System:

             The Arduino can analyse patterns and predict peak traffic times so here we can set up infrared sensors to monitor traffic flow.

The following areas can be considered to work on AI with Arduino so that the constraints of microcontrollers can be reduced:

  • TensorFlow Lite Micro: A lightweight version of TensorFlow will be designed for microcontrollers.
  • Edge Impulse: It serves as a platform for creating intelligent device solutions by using embedded Machine Learning.

                For prototyping and learning, Arduino serves as a great platform while its processing capabilities are restricted don’t worry, we serve you the best for your Audrino research issues as we work on the current trend. Currently we work on more advanced AI applications, by studying its powerful boards and systems and thus using we have contributed a wide range of platforms that can be used for artificial intelligence (AI) projects. By making use of our massive resources our outcome will be 100% success.

Artificial Intelligence Arduino Topics

Artificial intelligence arduino Research Topics

                   Some of the latest and trending Artificial intelligence arduino Research Topics that we have worked with are been listed down. Follow us and stay updated for all work AI research work get your Article writing done at the best by our writers. We aid our scholars by giving proper explanation of how the algorithm works we are good enough to explain any queries regarding your final year artificial intelligence projects.

  1. Advanced synaptic transistor device towards AI application in hardware perspective
  2. Artificial Intelligence in the IoT Era: A Review of Edge AI Hardware and Software
  3. Opportunities of using artificial intelligence in hardware verification
  4. Effectiveness of Artificial Intelligence Based Recruitment process in the Employment of Indian Hardware Industry
  5. Artificial Intelligence-Based Hardware Fault Detection for Battery Balancing Circuits
  6. Hardware Acceleration of Explainable Artificial Intelligence
  7. Innovation Practices Track: Testability and Dependability of AI Hardware and Autonomous Systems
  8. Challenges and Opportunities in AI Hardware Design
  9. Acceleration of Neural Network Training on Hardware via HLS for an Edge-AI Device
  10. A Flexible Remote Laboratory Platform for Interactive AI Experiments with Hardware and Software Facilities
  11. Implementation of Secure and Privacy-aware AI Hardware using Distributed Federated Learning
  12. Hardware Design and Integration of Low-cost Edge AI Smart Power Management and Home Automation
  13. High Efficient Bandwidth Utilization Hardware Design and Implement for AI Deep Learning Accelerator
  14. Accelerating Simulation-based Inference with Emerging AI Hardware
  15. Future Computing Hardware for AI
  16. ARBiS: A Hardware-Efficient SRAM CIM CNN Accelerator With Cyclic-Shift Weight Duplication and Parasitic-Capacitance Charge Sharing for AI Edge Application
  17. SPARROW: A Low-Cost Hardware/Software Co-designed SIMD Microarchitecture for AI Operations in Space Processors
  18. AI, IoT hardware and Algorithmic Considerations for Hearing aid and Extreme Edge Applications
  19. Bringing AI to Sensors – Simulation of Hardware-Aware AI Models
  20. Bent-Pyramid: Towards A Quasi-Stochastic Data Representation for AI Hardware


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