This demonstration presents an energy and area efficient Reconfigurable Cryptographic Accelerator (RCA) SoC for secure communication in IoT devices. Built on a ZYNQ-7000 development board, the platform supports multiple block ciphers (DES, AES, SM4) and Hash functions (SHA-1, SHA-2, SM3). Users can follow prompts on the OLED screen to select the cryptographic algorithm via buttons and input data through a keyboard or choose large text files from SD card. The ARM Core and accelerator execute the cryptographic operation simultaneously, and energy efficiency is calculated based on power and computing time, showcasing the improved computing speed and energy efficiency of the proposed accelerator.

In the field of human-computer interaction, natural goals, operators, methods, and selection rules language (NGOMSL) model is one of the most popular method for modelling knowledge and cognitive processes for rapid usability evaluation. NGOMSL model is a description of the knowledge that a user must possess to operate the system represented as elementary actions for effective usability evaluations. In the last few years, mobile robots have been exhibiting a stronger presence in the commercial markets and very little work has been done with NGOMSL modelling for usability evaluations in human-robot interaction discipline. This paper focuses on applying NGOMSL modelling for usability evaluation of human-humanoid robot interaction in soccer domain. Usability evaluations were performed and adequate results were obtained. Evaluated interaction design was adopted by our Robo-Erectus Junior version of humanoid robots in the RoboCup 2007 humanoid soccer league.

A new estimation of distribution algorithm (EDA) with spline kernel function (EDA_S) is proposed to optimize biped gait for a nine-link humanoid robot. Gait synthesis of the biped locomotion is firstly formulated as a multi-constraint optimization problem with consideration of two objectives, including zero-moment point (ZMP) for dynamically stable locomotion and driving torque for energy efficiency. The parameters to be optimized are joint coordinates at transition poses between three successive phases. Rather than searching in joint angle permutation space directly, the proposed method approximates the probability distribution by Catmull-Rom (CR) cubic spline function and updates them with gradient descent learning strategy. The effectiveness of EDA_S for biped gait optimization has been successfully tested on the simulated model of a humanoid soccer robot. It is shown that the flexible kernel with the updating rule is able to remarkably accelerate the learning speed with comparison to the traditional EDA

A new biped gait generation and optimization method is proposed in the frame of estimation of distribution algorithms (EDAs) with Q-learning method. By formulating the biped gait synthesis as a constrained multi-objective optimization problem, a dynamically stable and low energy cost biped gait is generated by EDAs with Q-learning (EDA_Q), which estimate probability distributions derived from the objective function to be optimized to generate searching points in the highly-coupled and high dimensional working space of biped robots. To get the preferable permutation of the interrelated parameters, Q-learning is combined to build and modify the probability models in EDA autonomously. By making use of the global optimization capability of EDA, the proposed EDA_Q can also solve the local minima problem in traditional Q-learning. On the other hand, with the learning agent, EDA_Q can evaluate the probability distribution model regularly without pre-designed structure and updating rule. The simulation results show that faster and more accurate searching can be achieved to generate preferable biped gait. The gait has been successfully used to drive a soccer-playing humanoid robot called Robo-Erectus which is one of the foremost leading soccer-playing humanoid robots in the RoboCup Humanoid League

A stable gait generation algorithm based on T-S type fuzzy learning net is proposed in this paper. Gait generation is divided into model construction and error learning. Reference gait model and dynamic model are firstly constructed with basic gait geometric knowledge. Then reinforcement learning method is introduced into T-S type fuzzy network to learn the gain parameters for hip trajectory adjustment. Few fuzzy rules with ZMP stable knowledge are needed to formulate the nonlinear relation between the ZMP curve and hip trajectory. The problem of finding multi-variables in continuous space is also simplified to searching independent action gains simultaneously. Results of simulation on a biped robot proved the feasibility.