DQL Integration in Arduino for Power Management
Budget: ₹600 – ₹1,500 INR
Freelancer Job Post: Arduino + MATLAB + Proteus Expert for Deep Q Learning Integration
Project Title:
Upgrade Power Management System from Fuzzy Logic to Deep Q Learning on Arduino Mega 2560
Project Overview:
We have completed a minor project on a Simulation-Based Smart Power Management System in Wireless Sensor Nodes using an Arduino Mega 2560 and Fuzzy Logic Controller (FLC). Now, for our major project, we're enhancing the system by replacing FLC with a Deep Q Learning (DQL) approach for improved adaptability and efficiency.
The Deep Q Learning code is ready — we need help integrating it into the Arduino system and simulating the updated setup.
Requirements:
Modify the Arduino Code:
Replace the existing Fuzzy Logic code with the provided Deep Q Learning implementation.
Ensure proper interfacing with sensors, relays, and MOSFETs.
Optimize the integration for Arduino Mega’s constraints.
MATLAB Output Representation:
Extract and log relevant data from the Arduino.
Plot performance graphs and output visualizations in MATLAB.
Proteus Simulation Adjustments (if required):
Update the Proteus simulation circuits to reflect control changes based on Deep Q Learning.
Validate power flow, battery SOC management, and source switching in simulation.
Required Skills:
Arduino Programming (C/C++)
Proteus 8.x Simulation
MATLAB data visualization and plotting
Deep Q Learning concepts (for integration, not for developing models)
Understanding of Hybrid Renewable Energy and Power Management Systems
Additional Information:
We are a group of final-year students undertaking this as a major academic project. Hence, we are seeking a freelancer who is:
Cooperative, patient, and supportive toward student projects.
Comfortable with assisting and clarifying during integration and testing.
Flexible with minor adjustments or queries during implementation.
Deliverables:
Modified and functional Arduino sketch (.ino)
Updated Proteus simulation file (.pdsprj)
MATLAB result plots and analysis (.m files or figure files)
A brief documentation summarizing changes and final observations
Budget:
Open for discussion based on work scope and mutual understanding.
How to Apply:
If you have expertise in these areas and are available for a collaborative, short-term project, please submit your proposal with details of your experience in Arduino, Deep Q Learning integration, Proteus, and MATLAB.
Project Title:
Upgrade Power Management System from Fuzzy Logic to Deep Q Learning on Arduino Mega 2560
Project Overview:
We have completed a minor project on a Simulation-Based Smart Power Management System in Wireless Sensor Nodes using an Arduino Mega 2560 and Fuzzy Logic Controller (FLC). Now, for our major project, we're enhancing the system by replacing FLC with a Deep Q Learning (DQL) approach for improved adaptability and efficiency.
The Deep Q Learning code is ready — we need help integrating it into the Arduino system and simulating the updated setup.
Requirements:
Modify the Arduino Code:
Replace the existing Fuzzy Logic code with the provided Deep Q Learning implementation.
Ensure proper interfacing with sensors, relays, and MOSFETs.
Optimize the integration for Arduino Mega’s constraints.
MATLAB Output Representation:
Extract and log relevant data from the Arduino.
Plot performance graphs and output visualizations in MATLAB.
Proteus Simulation Adjustments (if required):
Update the Proteus simulation circuits to reflect control changes based on Deep Q Learning.
Validate power flow, battery SOC management, and source switching in simulation.
Required Skills:
Arduino Programming (C/C++)
Proteus 8.x Simulation
MATLAB data visualization and plotting
Deep Q Learning concepts (for integration, not for developing models)
Understanding of Hybrid Renewable Energy and Power Management Systems
Additional Information:
We are a group of final-year students undertaking this as a major academic project. Hence, we are seeking a freelancer who is:
Cooperative, patient, and supportive toward student projects.
Comfortable with assisting and clarifying during integration and testing.
Flexible with minor adjustments or queries during implementation.
Deliverables:
Modified and functional Arduino sketch (.ino)
Updated Proteus simulation file (.pdsprj)
MATLAB result plots and analysis (.m files or figure files)
A brief documentation summarizing changes and final observations
Budget:
Open for discussion based on work scope and mutual understanding.
How to Apply:
If you have expertise in these areas and are available for a collaborative, short-term project, please submit your proposal with details of your experience in Arduino, Deep Q Learning integration, Proteus, and MATLAB.