Real-Time Multi-Modal Embedded Vision Framework for Object Detection Facial Emotion Recognition and Biometric Identification on Low-Power Edge Platforms

Intelligent surveillance systems often handle perceptual tasks such as object detection, facial recognition, and emotion analysis independently, but they lack a unified, adaptive runtime scheduler that dynamically allocates computational resources based on contextual triggers. This limits their holistic understanding and efficiency on low-power edge devices. To address this, we present a real-time multi-modal vision framework that integrates object detection, owner-specific face recognition, and emotion detection into a unified pipeline deployed on a Raspberry Pi 5 edge platform. The core of our system is an adaptive scheduling mechanism that reduces computational load by 65\% compared to continuous processing by selectively activating modules such as, YOLOv8n for object detection, a custom FaceNet-based embedding system for facial recognition, and DeepFace's CNN for emotion classification. Experimental results demonstrate the system's efficacy, with the object detection module achieving an Average Precision (AP) of 0.861, facial recognition attaining 88\% accuracy, and emotion detection showing strong discriminatory power (AUC up to 0.97 for specific emotions), while operating at 5.6 frames per second. Our work demonstrates that context-aware scheduling is the key to unlocking complex multi-modal AI on cost-effective edge hardware, making intelligent perception more accessible and privacy-preserving.

Design and Development of a Low-Cost Scalable GSM-IoT Smart Pet Feeder with a Remote Mobile Application

Pet ownership is increasingly common in modern households, yet maintaining a consistent feeding schedule remains challenging for the owners particularly those who live in cities and have busy lifestyles. This paper presents the design, development, and validation of a low-cost, scalable GSM-IoT smart pet feeder that enables remote monitoring and control through cellular communication. The device combines with an Arduino microcontroller, a SIM800L GSM module for communication, an ultrasonic sensor for real-time food-level assessment, and a servo mechanism for accurate portion dispensing. A dedicated mobile application was developed using MIT App Inventor which allows owners to send feeding commands and receive real-time status updates. Experimental results demonstrate a 98\% SMS command success rate, consistent portion dispensing with $\pm 2.67$\% variance, and reliable autonomous operation. Its modular, energy-efficient design makes it easy to use in a wide range of households, including those with limited resources. This work pushes forward the field of accessible pet care technology by providing a practical, scalable, and completely internet-independent solution for personalized pet feeding. In doing so, it sets a new benchmark for low-cost, GSM-powered automation in smart pet products.