Hardware Trojans (HTs) are regarded as a security threat in the information society. HTs are unintentionally injected into LSI circuits by untrusted entities before the chip fabrication. HTs trigger malicious operations such as information leakage without designers noticing their operations. This paper proposes fine-grained power domain partitioning, which is a circuit design technique for detecting HT activities. This paper assumes a scenario where a fab injects HTs into the taped-out layout data and that circuit designers test the presence of HTs by using side-channel information (power consumption) of fabricated chips. Fine-grained power domain partitioning decomposes the power domain of target circuit into multiple power domains, enabling to effectively measure the small power consumption introduced by activities of tiny HTs. The measurement result using an HT-injected Advanced Encryption Standard (AES) circuit with fine-grained power domain partitioning shows that HTs can be detected.

The problem of real-time scheduling based on the Directed Acyclic Graph (DAG) task model has been extensively studied in the literature. Most of the studies are aimed at the development of efficient scheduling algorithms to reduce deadline misses and/or improve schedulability bounds on the given task system. In order to guarantee real-time performance of the DAG task model in practice, the latency imposed on the communication between the DAG nodes must be systematically taken into account. This paper aims at demystifying the latency of the Robot Operating System (ROS) as a practical DAG task model, which leverages the publish/subscribe mechanism to send and receive data between the nodes. To this end we present the ROS-Aware Publish/Subscribe Latency Evaluation Tool (RAPLET) which is designed to measure and visualize the details of the publish/subscribe latency in ROS. RAPLET consists of (i) the LD PRELOAD scheme that inserts function hooks in user-land and (ii) the extended Berkeley Packet Filter (eBPF) scheme that monitors the run-queue level and the network states in kernel-land. The performance analysis on ROS applications, including a real-world autonomous driving software, is performed using RAPLET to demonstrate that the publish/subscribe latency imposed on inter-node communication can be demystified and reasoned with respect to system issues including the message size and network bandwidth consumption.

In this paper, we introduce ChainerRL, an open-source deep reinforcement learning (DRL) library built using Python and the Chainer deep learning framework. ChainerRL implements a comprehensive set of DRL algorithms and techniques drawn from state-of-the-art research in the field. To foster reproducible research, and for instructional purposes, ChainerRL provides scripts that closely replicate the original papers' experimental settings and reproduce published benchmark results for several algorithms. Lastly, ChainerRL offers a visualization tool that enables the qualitative inspection of trained agents. The ChainerRL source code can be found on GitHub: https://github.com/chainer/chainerrl.

The ambulatory gait monitoring systems (AGMS) using wearable devices have a great attention for health monitoring of individuals. Foot clearance is one of gait parameters and an indicator of gait quality and safety. Conventionally, foot clearance is calculated by post-processing using several methods such as extended Kalman filter (EKF), a weighted Fourier linear combiner (WFLC), a simple biomechanical foot model, zero velocity update (ZVU), and optimally filtered direct and reverse integration (OFDRI). However, real-time foot clearance (FC) estimation is required to apply to control of neural prostheses and assistive devices to prevent falling down. In addition, since humans adapt to several environments during gait motion, walking patterns are different among leveled walk, ramp walk, and stair walk. Therefore, environmental recognition also has an important role for the devices. Aim of this study is an implication of a system which performs real-time foot clearance and environment estimation in several situations. Different configurations of infrared (IR) distance sensors are utilized with a foot-mounted inertia measurement unit (IMU: an acceleration sensor, a gyro sensor, a magnetometer, and an air pressure sensor) sensor. Two IR sensors are attached to both sides of a shoe with different orientation. Developed system is tested in case of leveled walk, ramp walk, and stair walk. As the result, the developed system can estimate foot clearance and find characteristics of walking in different gait motion.

Machine automation is getting significant attention from the society due to the declining labor population in Japan. This paper considers Motion Copying System (MCS) that is known as the machine automation method. MCS is able to play back the saved motion if environmental conditions between the saving and loading phases are the same. The variation of environmental condition, however, degrades the motion reproduction performance because the tactile information needs the bilateral communication between the master and the slave, which can also be said of the sender and the receiver, unlike the visual or audio information. This paper proposes the scaling adjustment method for compensating the environmental impedance variation between the saving and loading phases. The effectiveness of the proposal was verified through simulation.

This paper aims to develop the method for identifying human and environmental impedances with information in the frequency domain by using bilateral control system. In the conventional method, a sinusoidal wave is added to the common mode of bilateral control system, however, this method cannot extract haptic information in frequency domain. Human impedances are assumed to change every moment, and the identification method of impedance is strongly desired to develop. In this paper, low-pass filtered M-sequence signal is added to the bilateral control system for identifying both human and environmental impedances with information in the frequency domain. Experiments were conducted for verifying the effectiveness of the proposed method.

This paper describes a new measuring model of human body motion using 3D gyro sensors. Proposed model is applied to measure forehand swing motion of tennis players, and swing behaviors are captured. With proposed model, body motion and flow of energy between tennis players are compared. This method can be applied to various motion research with small cost.

This study explains that a method utilizing the eigen spaces obtained by the KL transform for automatic recognition by camera of the speed on a speed limit sign has the following advantages: it is robust in response to changes in intensity patterns caused by the direction the sign is facing and by the amount of light striking the sign, and it is able to reduce the recognition processing time by reducing the number of feature vector dimensions during analysis. The method for recognition of traffic signs previously proposed by the authors of this study was a method for recognition based on extracting geometric shapes from the sign and recognizing them based on their aspect ratios. As such, this method was not able to identify the numbers on a speed limit sign, all of which have identical aspect ratios. It will be shown that the method in this study is able to recognize nearly all speed limits indicated on traffic signs within several 100s of ms after image acquisition. This method was applied to still images and its effectiveness was verified from the perspective of the following requirements for providing accurate information concerning the vehicle surroundings to the driver: high processing speed, high recognition accuracy, detection of all detectable objects without omission, and robustness in response to changes in the surrounding environment and to geometric changes in the sign image as the vehicle approaches it.

Template tracking dates back to the 1981 Lucas-Kanade algorithm. One question that has received very little attention, however, is how to update the template so that it remains a good model of the tracked object. We propose a template update algorithm that avoids the "drifting" inherent in the naive algorithm.

Template tracking dates back to the 1981 Lucas-Kanade algorithm. One question that has received very little attention, however, is how to update the template so that it remains a good model of the tracked object. We propose a template update algorithm that avoids the "drifting" inherent in the naive algorithm.

Muscle based face image synthesis is one of the most realistic approaches to realizing life-like agents in a computer. A facial muscle model is composed of facial tissue elements and muscles. In this model, forces are calculated effecting facial tissue elements by contraction of each muscle strength, so the combination of each muscle parameter decides a specific facial expression. Each muscle parameter is decided based on a trial and error procedure comparing the sample photograph and generated image using our Muscle-Editor to generate a specific face image. We propose a strategy of automatic estimation of facial muscle parameters from 2D marker movements using a neural network. We can also carry out 3D motion estimation from 2D point or flow information in a captured image under restriction of a physics based face model.