Hongjin Kim, Jai-Eun Kim, Harksoo Kim. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

In this paper, we analyze the applicability of color-space-based, color-independent visual-MIMO for V2X. We aim to achieve a visual-MIMO scheme that can maintain the original color and brightness while performing seamless communication. We consider two scenarios of GCM based visual-MIMO for V2X. One is a multipath transmission using visual-MIMO networking and the other is multi-node V2X communication. In the scenario of multipath transmission, we analyze the channel capacity numerically and we illustrate the significance of networking information such as distance, reference color (symbol), and multiplexing-diversity mode transitions. In addition, in the V2X scenario of multiple access, we may achieve the simultaneous multiple access communication without node interferences by dividing the communication area using image processing. Finally, through numerical simulation, we show the superior SER performance of the visual-MIMO scheme compared with LED-PD communication and show the numerical result of the GCM based visual-MIMO channel capacity versus distance.

Background: Large language models (LLMs) have shown promise in reducing clinical documentation burden, yet their real-world implementation remains rare. Especially in South Korea, hospitals face several unique challenges, such as strict data sovereignty requirements and operating in environments where English is not the primary language for documentation. Therefore, we initiated the Your-Knowledgeable Navigator of Treatment (Y-KNOT) project, aimed at developing an on-premises bilingual LLM-based artificial intelligence (AI) agent system integrated with electronic health records (EHRs) for automated clinical drafting. Objective: We present the Y-KNOT project and provide insights into implementing AI-assisted clinical drafting tools within constraints of health care system. Methods: This project involved multiple stakeholders and encompassed three simultaneous processes: LLM development, clinical co-development, and EHR integration. We developed a foundation LLM by pretraining Llama3-8B with Korean and English medical corpora. During the clinical co-development phase, the LLM was instruction-tuned for specific documentation tasks through iterative cycles that aligned physicians’ clinical requirements, hospital data availability, documentation standards, and technical feasibility. The EHR integration phase focused on seamless AI agent incorporation into clinical workflows, involving document standardization, trigger points definition, and user interaction optimization. Implementation (Results): The resulting system processes emergency department discharge summaries and preanesthetic assessments while maintaining existing clinical workflows. The drafting process is automatically triggered by specific events, such as scheduled batch jobs, with medical records automatically fed into the LLM as input. The agent is built on premises, locating all the architecture inside the hospital. Conclusions: The Y-KNOT project demonstrates the first seamless integration of an AI agent into an EHR system for clinical drafting. In collaboration with various clinical and administrative teams, we could promptly implement an LLM while addressing key challenges of data security, bilingual requirements, and workflow integration. Our experience highlights a practical and scalable approach to utilizing LLM-based AI agents for other health care institutions, paving the way for broader adoption of LLM-based solutions.

Communication performance in the color-independent visual-multiple input multiple output (visual-MIMO) technique is deteriorated by light emitting array (LEA) detection and tracking errors in the received image because the image sensor included in the camera must be used as the receiver in the visual-MIMO system. In this paper, in order to improve detection reliability, we first set up the color-space-based region of interest (ROI) in which an LEA is likely to be placed, and then use the Harris corner detection method. Next, we use Kalman filtering for robust tracking by predicting the most probable location of the LEA when the relative position between the camera and the LEA varies. In the last step of our proposed method, the perspective projection is used to correct the distorted image, which can improve the symbol decision accuracy. Finally, through numerical simulation, we show the possibility of robust detection and tracking of the LEA, which results in a symbol error rate (SER) performance improvement.

In the field of communication, synchronization is always an important issue. The communication between a light-emitting diode (LED) array (LEA) and a camera is known as visual multiple-input multiple-output (MIMO), for which the data transmitter and receiver must be synchronized for seamless communication. In visual-MIMO, LEDs generally have a faster data rate than the camera. Hence, we propose an effective time-sharing-based synchronization technique with its color-independent characteristics providing the key to overcome this synchronization problem in visual-MIMO communication. We also evaluated the performance of our synchronization technique by varying the distance between the LEA and camera. A graphical analysis is also presented to compare the symbol error rate (SER) at different distances.

Color independency is an important factor in visible light communication (VLC) systems. This paper aims to review and summarize recent achievements in color-independent visible light communication based on color space, with the main focus being on color-space-based modulation (CSBM), termed as generalized color modulation (GCM), which allows VLC to adapt to any target color. The main advantages of GCM are its color independency, reasonable bit error rate (BER) performance during color variation, and dimming control. We also address our past research works that aimed to achieve a color-independent visual MIMO system by incorporating the advantages of GCM, which can lead to higher data rates over longer distances and improved performance, using image processing in addition to color independency. Finally, two case studies are introduced to demonstrate the potential applicability of a color-independent visual-MIMO system using color-space-based modulation techniques.