Toyota

About: Toyota is a company organization based out in Safenwil, Switzerland. It is known for research contribution in the topics: Internal combustion engine & Battery (electricity). The organization has 40032 authors who have published 55003 publications receiving 735317 citations. The organization is also known as: Toyota Motor Corporation & Toyota Jidosha KK.

Mild Traumatic Brain Injury Predictors Based on Angular Accelerations During Impacts

Abstract: Although Head Injury Criterion (HIC) is an effective criterion for head injuries caused by linear acceleration such as skull fractures, no criteria for head injuries caused by rotational kinematics has been accepted as effective so far. This study proposed two criteria based on angular accelerations for Traumatic Brain Injury (TBI), which we call Rotational Injury Criterion (RIC) and Power Rotational Head Injury Criterion (PRHIC). Concussive and non-concussive head acceleration data obtained from football head impacts were utilized to develop new injury criteria. A well-validated human brain Finite Element (FE) model was employed to find out effective injury criteria for TBI. Correlation analyses were performed between the proposed criteria and FE-based brain injury predictors such as Cumulative Strain Damage Measure (CSDM), which is defined as the percent volume of the brain that exceeds a specified first principal strain threshold, proposed to predict Diffuse Axonal Injury (DAI) which is one of TBI. The RIC was significantly correlated with the CSDMs with the strain thresholds of less than 15% (R > 0.89), which might predict mild TBI. In addition, PRHIC was also strongly correlated with the CSDMs with the strain thresholds equal to or greater than 20% (R > 0.90), which might predict more severe TBI.

Wireless Charging System Using Secure Wireless Charging Protocols

Abstract: The disclosure includes a system and method for charging a target object wirelessly. The system includes a processor and a memory storing instructions that when executed cause the system to: receive data describing a charging request from a target object; generate a challenge responsive to the charging request; send the challenge to the target object; receive a response from the target object; verify the response to determine that the response matches the challenge and a first set of secret data shared with a tagging device; determine that a location associated with the target object satisfies a safe charging range responsive to the verification of the response; and instruct a power transmitter associated with the target object to transmit power wirelessly to a power receiver associated with the target object responsive to the verification of the response and the determination that the location satisfies the safe charging range.

Fast running experiments involving a humanoid robot

Abstract: The present paper describes an implementation of fast running motions involving a humanoid robot. Two important technologies are described: a motion generation and a balance control. The motion generation is a unified way to design both walking and running and can generate the trajectory with the vertical conditions of the Center Of Mass (COM) in short calculation time. The balance control enables a robot to maintain balance by changing the positions of the contact foot dynamically when the robot is disturbed. This control consists of 1) compliance control without force sensors, in which the joints are made compliant by feed-forward torques and adjustment of gains of position control, and 2) feedback control, which uses the measured orientation of the robot's torso used in the motion generation as an initial condition to decide the foot positions. Finally, a human-sized humanoid robot that can run forward at 7.0 [km/h] is presented.

Learning Categories From Few Examples With Multi Model Knowledge Transfer

Abstract: Learning a visual object category from few samples is a compelling and challenging problem. In several real-world applications collecting many annotated data is costly and not always possible. However, a small training set does not allow to cover the high intraclass variability typical of visual objects. In this condition, machine learning methods provide very few guarantees. This paper presents a discriminative model adaptation algorithm able to proficiently learn a target object with few examples by relying on other previously learned source categories. The proposed method autonomously chooses from where and how much to transfer information by solving a convex optimization problem which ensures to have the minimal leave-one-out error on the available training set. We analyze several properties of the described approach and perform an extensive experimental comparison with other existing transfer solutions, consistently showing the value of our algorithm.

A 100-m Range 10-Frame/s 340 $\,\times\, $ 96-Pixel Time-of-Flight Depth Sensor in 0.18- $\mu\hbox{m}$ CMOS

Abstract: This paper introduces a single-photon detection technique for time-of-flight distance ranging based on the temporal and spatial correlation of photons. A proof-of-concept prototype achieving depth imaging up to 100 meters with a resolution of 340 × 96 pixels at 10 frames/s was implemented. At the core of the system, a sensor chip comprising 32 macro-pixels based on an array of single-photon avalanche diodes featuring an optical fill factor of 70% was fabricated in a 0.18-μm CMOS. The chip also comprises an array of 32 circuits capable of generating precise triggers upon correlation events as well as of sampling the number of photons involved in each correlation event, and an array of 32 12-b time-to-digital converters. Characterization of the TDC array led to -0.52 LSB and 0.73 LSB of differential and integral nonlinearities, respectively. Quantitative evaluation of the TOF sensor under strong solar background light, i.e., 80 klux, revealed a repeatability error better than 10 cm throughout the distance range of 100 m, thus leading to a relative precision of 0.1%. In the same condition, the relative nonlinearity error was 0.37%. In order to show the suitability of our approach in a real-world situation, experimental results in which the depth sensor was operated in a typical traffic scenario are also reported.