Ho Joong Kim | Searcharxiv

arXiv2025

Nano Machine Intelligence: From a Communication Perspective

We present an AI-integrated molecular communication link validated on a benchtop nanomachine testbed representative of subdermal implants. The system employs an indium-gallium-zinc-oxide electrolyte-gated FET (IGZO-EGFET) functionalized with glucose oxidase as a biocompatible receiver, a microfluidic channel with a syringe-pump transmitter using on-off keying (OOK), and a machine-intelligence pipeline that addresses model mismatch and hardware non-idealities. The pipeline integrates: (i) a modular universal decoder robust to vibration-induced noise, chemical delay, and single-tap intersymbol interference; (ii) a lightweight pilot-only synchronizer that estimates symbol intervals; and (iii) a virtual-response generator that augments data and scales symbol duration. Experiments across multiple chips and sessions demonstrate end-to-end chemical text transmission with consistent error-rate reductions compared to naive thresholding and standard neural baselines. By coupling biocompatible hardware with learning-based detection and generative augmentation, this work establishes a practical route toward AI-native nanomachine networks and higher rate molecular links, while providing a system blueprint adaptable to other biochemical modalities.

eess.SY↗

arXiv2022

Towards the Human Global Context: Does the Vision-Language Model Really Judge Like a Human Being?

As computer vision and NLP make progress, Vision-Language(VL) is becoming an important area of research. Despite the importance, evaluation metrics of the research domain is still at a preliminary stage of development. In this paper, we propose a quantitative metric "Equivariance Score" and evaluation dataset "Human Puzzle" to assess whether a VL model is understanding an image like a human. We observed that the VL model does not interpret the overall context of an input image but instead shows biases toward a specific object or shape that forms the local context. We aim to quantitatively measure a model's performance in understanding context. To verify the current existing VL model's capability, we sliced the original input image into pieces and randomly placed them, distorting the global context of the image. Our paper discusses each VL model's level of interpretation on global context and addresses how the structural characteristics influenced the results.

cs.CV↗