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projects

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publications

Autonomous Mobility Vehicle for Hospital Sterile Instrument Logistics and Waste Collection during Surgeries Permalink

Published in Design Patent, 2024

This design patent presents an autonomous mobility vehicle specifically engineered for hospital environments. The vehicle is capable of transporting sterile surgical instruments and collecting waste during operations, optimizing workflow and hygiene. It addresses the growing need for automation in surgical logistics to reduce human intervention, prevent contamination, and improve operational efficiency. This innovation reflects the confluence of robotics and healthcare design, underscoring its practical significance in clinical settings.

CycleGAN for Flash-to-Ambient Image Conversion: A Style Transfer Approach Permalink

Published in IEEE Access, 2024

With the increasing prominence of mobile photography, capturing high-quality images in low-light conditions, especially with flash, remains a significant challenge. This study introduces innovative deep learning techniques to convert flash images into ambient images, with a particular focus on style transfer methods. A novel approach employing CycleGAN for flash-to-ambient image conversion achieves a mean PSNR of 16.667 on a diverse dataset. Comparative analysis against other models highlights CycleGAN’s superior performance, both in terms of objective metrics and subjective visual quality. This approach showcases promising potential in overcoming the limitations of current techniques, significantly enhancing the realism and quality of images captured in challenging lighting conditions.

Improving the Responsiveness of Fall Detection using Spiking Neural Networks Permalink

Published in CoMoRea, PerCom, 2025

This paper presents a novel approach to improving the responsiveness of fall detection systems by leveraging Spiking Neural Networks (SNNs). The proposed system significantly reduces latency in detection while maintaining high accuracy, making it suitable for real-time health monitoring applications. The method was evaluated using benchmark datasets and demonstrated superior performance in comparison to traditional ANN-based approaches.

The Promise of Spiking Neural Networks for Ubiquitous Computing: A Survey and New Perspectives Permalink

Published in ArXiv, 2025

Spiking neural networks (SNNs) have emerged as a class of bio-inspired networks that leverage sparse, event-driven signaling to achieve low-power computation while inherently modeling temporal dynamics. Such characteristics align closely with the demands of ubiquitous computing systems, which often operate on resource-constrained devices while continuously monitoring and processing time-series sensor data. Despite their unique and promising features, SNNs have received limited attention and remain underexplored (or at least, under-adopted) within the ubiquitous computing community. To address this gap, this paper first introduces the core components of SNNs, both in terms of models and training mechanisms. It then presents a systematic survey of 76 SNN-based studies focused on time-series data analysis, categorizing them into six key application domains. For each domain, we summarize relevant works and subsequent advancements, distill core insights, and highlight key takeaways for researchers and practitioners. To facilitate hands-on experimentation, we also provide a comprehensive review of current software frameworks and neuromorphic hardware platforms, detailing their capabilities and specifications, and then offering tailored recommendations for selecting development tools based on specific application needs. Finally, we identify prevailing challenges within each application domain and propose future research directions that need be explored in ubiquitous community. Our survey highlights the transformative potential of SNNs in enabling energy-efficient ubiquitous sensing across diverse application domains, while also serving as an essential introduction for researchers looking to enter this emerging field.

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015