RFID Camera Lock Smart Mailbox

IEEE Spectrum Robotics

A self-locking mailbox could someday flag down delivery drones and intelligently screen your driveway for intruders. Columbus State University computer scientist Lydia Ray presented the technology, called the ADDSMART project, during a 20 October session at the annual IEEE Ubiquitous Computing, Electronics, and Mobile Communication Conference in New York City. The project aims to achieve two goals: clearly marking addresses for autonomous vehicles, and reducing the energy and data storage costs of home surveillance systems. An early prototype mailbox attachment suggests that the trick, in both cases, may be radio-frequency identification. Powered by an Arduino Yun processor, one component of the ADDSMART device controls a high-frequency 13.56-MHz RFID reader, USB camera, passive-infrared motion sensor, solenoid lock, and an onboard Wi-Fi module.


How Can Finance Catch Up With Other Intelligent Real-Time Systems?

International Business Times

When it comes to bringing intelligence to real-time engineering systems, the world of finance has been hindered by its legacy. Compared to things like self-driving cars, incumbent financial infrastructure takes a very long time to update, and is siloed into systems that cannot really talk to each other. Paul Bilokon, founder of Thalesians, an organisation to promote deeper thinking and philosophy within finance, points out that many non-financial systems are using software techniques that are far ahead. But he also sees this changing thanks to improved infrastructure tools and advancements in machine learning within finance. Paul will be speaking about new infrastructure and showing off some machine learning libraries at the forthcoming IBT data science and capital markets event.


Deep learning based object classification model for Autonomous vehicles and Advanced Driver Assist System

#artificialintelligence

Special purpose object detection systems need to be fast, accurate and dedicated to classifying a handful but relevant number of objects. Our aim was to integrate a system which utilizes the Inception's vast heuristically mapped image pre-diction tree along-with a real time system accurate and robust enough to work at various processing powers and give the user enough confidence of identifying and detecting object with a single frame. As this property can be utilized at huge number of places relying on real time detection, it might not be limited to only driving assistance or autonomous driving systems, but are beyond the scope of this project. To come up with a novel dataset, which would have an image tree with enough weights and variety so as to predict the objects being identified with high accuracy and precision, was taken up to set up the softmax layer of Inception, which was earlier weighted by the existing ImageNet dataset. The results were a convincing recognition accuracy and prediction confidence with real time test frames of a video.