In this article, we present implementations for Addition, Rotation, and eXclusive-or (ARX)-based block ciphers, including LEA and HIGHT, on IoT devices, including 8-bit AVR, 16-bit MSP, 32-bit ARM, and 32-bit ARM-NEON processors. We optimized 32-/8-bitwise ARX operations for LEA and HIGHT block ciphers by considering variations in word size, the number of general purpose registers, and the instruction set of the target IoT devices. Finally, we achieved the most compact implementations of LEA and HIGHT block ciphers. The implementations were fairly evaluated through the Fair Evaluation of Lightweight Cryptographic Systems framework, and implementations won the competitions in the first and the second rounds.
-The aim of this study is to design and implement a biped humanoid robot which can interact with humans. The developed robot consists of a self-contained body, head, two arms, with a two legged (biped) mechanism. Its control hardware includes vision and speech capabilities and various control boards such as motion controllers, with a signal processing board for several types of sensors. Using the developed robot, biped locomotion study and social interaction research were concurrently carried out. The developed robot can perform various movements with its two arms, and can track an object and a face using its active vision system. It is also able to perform emotional communications with human while it is walking.
SUMMARYDetecting emergency situation is very important to a surveillance system for people like elderly live alone. A vision-based emergency response system with a paramedic mobile robot is presented in this paper. The proposed system is consisted of a vision-based emergency detection system and a mobile robot as a paramedic. A vision-based emergency detection system detects emergency by tracking people and detecting their actions from image sequences acquired by single surveillance camera. In order to recognize human actions, interest regions are segmented from the background using blob extraction method and tracked continuously using generic model. Then a MHI (Motion History Image) for a tracked person is constructed by silhouette information of region blobs and model actions. Emergency situation is finally detected by applying these information to neural network. When an emergency is detected, a mobile robot can help to diagnose the status of the person in the situation. To send the mobile robot to the proper position, we implement mobile robot navigation algorithm based on the distance between the person and a mobile robot. We validate our system by showing emergency detection rate and emergency response demonstration using the mobile robot.
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