
The rapid growth of electric vehicles (EVs) has introduced new challenges for urban parking management, mainly in enforcing EV-designated parking spaces without intrusive infrastructure. This paper presents a deep-learning-based vision system for the automated classification of electric and gasoline vehicles in urban parking environments, using convolutional neural networks trained on real-world data from Berlin, Germany. A YOLO-based object detection model is employed to identify visually distinctive EV-specific features in rear-view vehicle images while preserving privacy by anonymizing license plates. The proposed approach relies solely on visual cues, eliminating the need for vehicle metadata, sensors, or network connectivity. Experimental results demonstrate robust classification performance, achieving high detection accuracy and consistent results across desktop and edge computing platforms. To validate real-world applicability, the trained model is deployed on both a mobile device and a low-cost Raspberry Pi-based edge system, enabling fully offline operation. These results indicate that deep learning-based visual classification can provide a scalable, privacy-aware solution for smart parking systems and urban mobility applications. This supports the effective management of EV infrastructure in modern cities.

We present a head-mounted holographic display system for thermographic image overlay, biometric sensing, and wireless telemetry. The system is lightweight and reconfigurable for multiple field applications, including object contour detection and enhancement, breathing rate detection, and telemetry over a mobile phone for peer-to-peer communication and incident commanding dashboard. Due to the constraints of the limited computing power of an embedded system, we developed a lightweight image processing algorithm for edge detection and breath rate detection, as well as an image compression codec. The system can be integrated into a helmet or personal protection equipment such as a face shield or goggles. It can be applied to firefighting, medical emergency response, and other first-response operations. Finally, we present a case study of "Cold Trailing" for forest fire prevention in the wild.