Preetham Rajkumar

Austin, Texas, United States
2K followers 500+ connections

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About

Meticulous. Tenacious. Hardworking.

Interests: Deep/Machine Learning, Computer…

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Activity

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Experience & Education

  • Cortal Insight

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Licenses & Certifications

Volunteer Experience

Patents

  • Fraud and theft detection and prevention systems for automatic retail and point of sale transactions

    WO2022082029A1

Projects

  • Electromagnetic Braking system

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    - Modelled a smart braking system which utilises electro-magnetism to apply brakes to a moving vehicle while optimising its performance based on existing conditions.
    - It also had an auxiliary function which optimized the braking according to obstacles present.
    - Exhibited more efficiency, reliability & durability compared to its conventional counterparts

  • Neural Image Captioning using Transfer Learning

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    Based on a convolution neural network that encodes an image into a compact representation, followed by a recurrent neural network that generates a corresponding sentence. Utilized cross-functional models to implement the same.

  • Real Time task Scheduler with DVFS settings

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    - Built a real time task scheduler with DVFS settings. The program processes an input text file that includes a list of tasks to be executed, their deadlines, and generates an output file listing a sequence of scheduling decisions.

    - Implemented the same on four different scheduling policies: (Earliest Deadline First) EDF, (Rate Monotonic) RM, Energy Efficient (EE) EDF and EE RM.

  • Vehicular Assistance System

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    The project was based on the integration autonomous car technology with Vehicle to Vehicle communication. The prototype used a 1.2 GHz ARMv8 processor as the core with a real-time operating system.

    With a combination of odometry and communication ( HC-SR04 & NRF24L01 ) , the vehicle constantly screens its path for any obstacles. In case of any obstruction to its path, it halts/slows down to automatically deviate away from the obstacle. It transmits an alert signal only when the obstacle…

    The project was based on the integration autonomous car technology with Vehicle to Vehicle communication. The prototype used a 1.2 GHz ARMv8 processor as the core with a real-time operating system.

    With a combination of odometry and communication ( HC-SR04 & NRF24L01 ) , the vehicle constantly screens its path for any obstacles. In case of any obstruction to its path, it halts/slows down to automatically deviate away from the obstacle. It transmits an alert signal only when the obstacle is unavoidable or collision is imminent. This could potentially reduce the fatalities of autonomous driving to a bare minimum.

  • Vision based tracking

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    - Designed individual models to implement Object detection (HOG & Haar Classifier), Lane tracking and Speed estimation (Optical flow analysis - LK & Farneback) with input from a monocular camera.
    - Utilized efficiency & robustness criterion, for integrated modules.
    - Analyzed the efficiency and compatibility for each scheme implemented

  • VoxelNet

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    Perform end-to-end learning in 3D point cloud generated from LIDAR to perform object detection based on a Region Proposal Network. Achieved an accuracy (AP) of 79.2%.

Languages

  • English

    Professional working proficiency

  • French

    Elementary proficiency

  • Tamil

    Native or bilingual proficiency

  • Hindi

    Native or bilingual proficiency

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