Search Results for author: Arsalan Heydarian

Found 7 papers, 3 papers with code

Real-Time Roadway Obstacle Detection for Electric Scooters Using Deep Learning and Multi-Sensor Fusion

1 code implementation4 Apr 2025 Zeyang Zheng, Arman Hosseini, Dong Chen, Omid Shoghli, Arsalan Heydarian

This study introduces a novel ground obstacle detection system for e-scooters, integrating an RGB camera, and a depth camera to enhance real-time road hazard detection.

object-detection Object Detection +1

Detecting Plant VOC Traces Using Indoor Air Quality Sensors

no code implementations3 Apr 2025 Seyed Hamidreza Nabaei, Ryan Lenfant, Viswajith Govinda Rajan, Dong Chen, Michael P. Timko, Bradford Campbell, Arsalan Heydarian

In the era of growing interest in healthy buildings and smart homes, the importance of sustainable, health conscious indoor environments is paramount.

Multimodal Data Integration for Sustainable Indoor Gardening: Tracking Anyplant with Time Series Foundation Model

no code implementations27 Mar 2025 Seyed Hamidreza Nabaei, Zeyang Zheng, Dong Chen, Arsalan Heydarian

Unlike previous approaches, this framework combines RGB imagery, plant phenotyping data, and environmental factors such as temperature and humidity, to predict plant water stress in a controlled growth environment.

Data Integration Plant Phenotyping +1

Performance Evaluation of Real-Time Object Detection for Electric Scooters

1 code implementation5 May 2024 Dong Chen, Arman Hosseini, Arik Smith, Amir Farzin Nikkhah, Arsalan Heydarian, Omid Shoghli, Bradford Campbell

Electric scooters (e-scooters) have rapidly emerged as a popular mode of transportation in urban areas, yet they pose significant safety challenges.

Autonomous Vehicles Benchmarking +4

WcDT: World-centric Diffusion Transformer for Traffic Scene Generation

1 code implementation2 Apr 2024 Chen Yang, Yangfan He, Aaron Xuxiang Tian, Dong Chen, Jianhui Wang, Tianyu Shi, Arsalan Heydarian

To enhance the scene diversity and stochasticity, the historical trajectory data is first preprocessed into "Agent Move Statement" and encoded into latent space using Denoising Diffusion Probabilistic Models (DDPM) enhanced with Diffusion with Transformer (DiT) blocks.

Autonomous Driving Decoder +3

Building Performance Simulations Can Inform IoT Privacy Leaks in Buildings

no code implementations26 Mar 2023 Alan Wang, Bradford Campbell, Arsalan Heydarian

Specifically, in this proof-of-concept exploration, we demonstrate the potential of physics-based simulation models to quantify the minimal number of positions necessary to capture sensitive inferences.

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