Towards Contactless Elevators with TinyML using CNN-based Person Detection and Keyword Spotting

This study presents a proof of concept for a contactless elevator operation system aimed at minimizing human intervention while enhancing safety, intelligence, and efficiency. A microcontroller-based edge device executing tiny Machine Learning (tinyML) inferences is developed for elevator operation. Using person detection and keyword spotting algorithms, the system offers cost-effective and robust units requiring minimal infrastructural changes. The design incorporates preprocessing steps and quantized convolutional neural networks in a multitenant framework to optimize accuracy and response time. Results show a person detection accuracy of 83.34% and keyword spotting efficacy of 80.5%, with an overall latency under 5 seconds, indicating effectiveness in real-world scenarios. Unlike current high-cost and inconsistent contactless technologies, this system leverages tinyML to provide a cost-effective, reliable, and scalable solution, enhancing user safety and operational efficiency without significant infrastructural changes. The study highlights promising results, though further exploration is needed for scalability and integration with existing systems. The demonstrated energy efficiency, simplicity, and safety benefits suggest that tinyML adoption could revolutionize elevator systems, serving as a model for future technological advancements. This technology could significantly impact public health and convenience in multi-floor buildings by reducing physical contact and improving operational efficiency, particularly relevant in the context of pandemics or hygiene concerns

Critical issues in Leveraging Blockchain in Healthcare Sector

Blockchain innovation has brought various benefits to the healthcare sector. Utilizing blockchains in clinical contexts will reduce handling time since when a patient signs up for a review, the complete collected data will be accessible at once because of accessibility on the distributed ledger. Also, specialists will not need to stress over the patients giving them a legit clinical history, because of their capacity to progressively see the correct, credible, and quality source-recorded information. It eliminates any likely clinical history mistakes. Similarly, the patients will not need to stress over having a second assessment from another specialist, because of the straightforwardness of the information. Having patient records on a blockchain organization will prompt individuals to know and associate with various others, across the globe, with similar ailments as they have, which is not only valuable for their well-being, but also make the patients feel acknowledged, upheld, and have reinforced determination to battle the ailment. Patients will have total independence regarding their information, and they will choose who to impart the information to. In this paper, we present all the challenges and critical issues associated with implementing blockchains in the healthcare sector

Smart Transportation System using IOT

In this paper a Smart Vehicle Assistance and Monitoring system (SVAMS) is presented. SVAMS is an intelligent transportation system (ITS), developed to tackle various traffic related issues. It is a traffic management, monitoring and optimization solution in which all the vehicles are interconnected through Zigbee and are monitored and assisted centrally, by a data center. The system has two parts; one part is mounted in/on the vehicle and the other part is at the data centre. Part one collects data from various sensors and transmits it to central data centre. All the data will be stored on cloud for further analysis, processing and future use. SVAMS is relatively low-cost, compact and has various functionalities such as emergency response, pollution level monitoring, automatic toll collection, traffic rule violation detection, vehicle tracking, etc. The use of SVAMS will help to build up Clean, Corruption free and Crime free (C-3) cities.</jats:p