Sittigorn, Jirasak

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Sittigorn, Jirasak
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jirasak.si@kmitl.ac.th

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    Item type:Publication,
    Adaptive pixel-selection using chaotic map lattices for image cryptography
    (2014-02-24)
    Sittigorn, Jirasak
    ;
    Paithoonwattanakij, Kitti
    ;
    Surawatpunya, Charray
    Chaotic theory has been used in cryptography application for generating a sequence of data that is close to pseudorandom number based on an adjusted initial condition and a parameter. However, data recovery becomes a crucial problem due to the precision of the parameters. This difficulty leads to limited usage of Chaotic-based cryptography especially for error sensitive applications such as voice cryptography. In order to enhance the encryption security and overcome this limitation, an Adaptive Pixel-Selection using Chaotic Map Lattices (APCML) is proposed. In APCML, the encryption sequence has been adaptively selected based on chaos generator. Moreover, the chaotic transformation and normalization boundary have been revised to alleviate the rounding error and inappropriate normalization boundary problems. In the experiments, the measurement indices of originality preservation, visual inspection, and statistical analysis are used to evaluate the performance of the proposed APCML compared to that of the original CML. Consequently, the APCML algorithm offers greater performance with full recovery of the original message. © 2014 Copyright SPIE.
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    Item type:Publication,
    Better Learning to Drive Autonomously with Proximal-Policy Reinforcement Learning and Visual Perception Representations
    (2025-01-01)
    Sittigorn, Jirasak
    ;
    Tungtrakool, Racha
    ;
    Petchhan, Jirayu
    This study presents an approach based on a deep reinforcement learning framework for vision-based autonomous driving in the CARLA environment, focusing on urban driving tasks. Our study implements a sub-policy Proximal Policy Optimization (PPO) algorithm, demonstrating its effectiveness in navigating complex scenarios including lane following, straight driving, and left/right turns, and outperforming a single-policy approach for intersection maneuvers. To enhance learning efficiency, our representation learning leverages a deep mobile network for state representation, which significantly reduces image feature complexity. Furthermore, the integration of a single-shot multi-box detector enables the agent to perform realistic tasks such as responding to traffic lights and maintaining safe distances from leading vehicles, without compromising training speed. While the system demonstrates stable driving in various scenarios, current limitations include handling highly complex decisions and adapting to diverse speed limits due to environmental constraints. Future work will focus on expanding training environments and exploring more advanced network architectures to improve real-world applicability and learning efficiency.