Mitsubishi Electric

About: Mitsubishi Electric is a company organization based out in Ratingen, Germany. It is known for research contribution in the topics: Signal & Voltage. The organization has 23024 authors who have published 27591 publications receiving 255671 citations. The organization is also known as: Mitsubishi Electric Corporation & Mitsubishi Denki K.K..

A method for measuring amplitude and phase of each radiating element of a phased array antenna

Abstract: In the phased array system, excitation amplitudes and phases based on the design are specified for each antenna element in order to synthesize desired beam scannings and radiation patterns. However, due to fluctuations of antenna and feed network characteristics, the amplitude and phase of each antenna element deviate from the desired values. To correct these deviations, the amplitude and phase of each antenna element must be accurately measured under specific operating conditions. In this paper, we employ variable phase shifters connected to the antenna elements and measure only the amplitude variation of the composite electric field of the entire array when the phase of each element is modified. The rotating element electric field vector method in which the measured amplitude variation is numerically processed for obtaining the amplitude and phase of the particular element is theoretically discussed and experimentally tested for its usefulness. The present method can be easily attained by simply adding software to the computer-controlled phased array system.

Zero-Phonon Lines and Phonon Coupling in ZnS:Mn

Abstract: The optical absorption and emission spectra of several ZnS:Mn crystals (0.07 to 1.0 mole%) were obtained at 4.2 and 77\ifmmode^\circ\else\textdegree\fi{}K. Fine structure was observed in all bands. The predominant structure was identified in terms of phonon emission coupled to one electronic transition in each band. Zero-phonon lines were found at 17 891, 19 683, 21 237, 22 638, and 25 297 ${\mathrm{cm}}^{\ensuremath{-}1}$. They should correspond to transitions between some levels of the cubic crystalline field\char22{}terms such as $^{6}A_{1}\ensuremath{\rightarrow}^{4}T_{1}$, $^{6}A_{1}\ensuremath{\rightarrow}^{4}T_{2}$, $^{6}A_{1}\ensuremath{\rightarrow}^{4}E$, $^{6}A_{1}\ensuremath{\rightarrow}^{4}A_{1}$, etc. Principal phonons participating are of energies of about 86, (183,263), 298, and 340 ${\mathrm{cm}}^{\ensuremath{-}1}$. Possible mode assignments are pointed out. In conclusion, we suggest that the usually observed widths and shape of manganese bands are due predominantly to phonon coupling.

Adaptive video coding method

Abstract: In accordance with the present invention, a method is described which achieves a constant bit rate output when coding multiple video objects. This implementation makes use of a quadratic rate-distortion model. Each object is described by its own set of parameters. With these parameters, an initial target bit estimate is made for each object after a first frame is encoded. Based on output buffer fullness, the total target is adjusted and then distributed proportional to a parameter set representative of the activity of the objects in the frame. Activity is determined by reference to weighted ratios derived from motion, size and variance parameters associated with each object. A shape rate control parameter is also invoked. Based on the new individual targets and second order model parameters, appropriate quantization parameters can be calculated for each video object. This method assures that the target bit rate is achieved for low latency video coding. In order to provide a suitable bit rate control system based on a quadratic rate-distortion model, it has been found that control information may be applied jointly with respect to video objects (VO's), rather than entire frames.

New structure of block ciphers with provable security against differential and linear cryptanalysis

Abstract: We introduce a methodology for designing block ciphers with provable security against differential and linear cryptanalysis. It is based on three new principles: change of the location of round functions, round functions with recursive structure, and substitution boxes of different sizes. The first realizes parallel computation of the round functions without losing provable security, and the second reduces the size of substitution boxes; moreover, the last is expected to make algebraic attacks difficult. We also give specific examples of practical block ciphers that are provably secure under an independent subkey assumption and are reasonably fast in hardware as well as in software implementation.

Deep Active Learning for Civil Infrastructure Defect Detection and Classification

Abstract: Automatic detection and classification of defects in infrastructure surface images can largely boost its maintenance efficiency. Given enough labeled images, various supervised learning methods have been investigated for this task, including decision trees and support vector machines in previous studies, and deep neural networks more recently. However, in real world applications, labels are harder to obtain than images, due to the limited labeling resources (i.e., experts). Thus we propose a deep active learning system to maximize the performance. A deep residual network is firstly designed for defect detection and classification in an image. Following our active learning strategy, this network is trained as soon as an initial batch of labeled images becomes available. It is then used to select a most informative subset of new images and query labels from experts to retrain the network. Experiments demonstrate more efficient performance improvements of our method than baselines, achieving 87.5% detection accuracy. International Workshop on Computing in Civil Engineering (IWCCE) This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c © Mitsubishi Electric Research Laboratories, Inc., 2017 201 Broadway, Cambridge, Massachusetts 02139 Deep Active Learning for Civil Infrastructure Defect Detection and Classification Chen Feng1, Ming-Yu Liu1, Chieh-Chi Kao2, and Teng-Yok Lee1 1Mitsubishi Electric Research Laboratories (MERL), 201 Broadway, Cambridge, MA 02139; email: {cfeng, mliu, tlee}@merl.com 2University of California, Santa Barbara; email: chiehchi.kao@gmail.com ABSTRACT Automatic detection and classification of defects in infrastructure surface images can largely boost its maintenance efficiency. Given enough labeled images, various supervised learning methods have been investigated for this task, including decision trees and support vector machines in previous studies, and deep neural networks more recently. However, in real world applications, labels are harder to obtain than images, due to the limited labeling resources (i.e., experts). Thus we propose a deep active learning system to maximize the performance. A deep residual network is firstly designed for defect detection and classification in an image. Following our active learning strategy, this network is trained as soon as an initial batch of labeled images becomes available. It is then used to select a most informative subset of new images and query labels from experts to retrain the network. Experiments demonstrate more efficient performance improvements of our method than baselines, achieving 87.5% detection accuracy.Automatic detection and classification of defects in infrastructure surface images can largely boost its maintenance efficiency. Given enough labeled images, various supervised learning methods have been investigated for this task, including decision trees and support vector machines in previous studies, and deep neural networks more recently. However, in real world applications, labels are harder to obtain than images, due to the limited labeling resources (i.e., experts). Thus we propose a deep active learning system to maximize the performance. A deep residual network is firstly designed for defect detection and classification in an image. Following our active learning strategy, this network is trained as soon as an initial batch of labeled images becomes available. It is then used to select a most informative subset of new images and query labels from experts to retrain the network. Experiments demonstrate more efficient performance improvements of our method than baselines, achieving 87.5% detection accuracy.