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 half-pel precision motion estimation processor for NTSC-resolution video

Abstract: A motion estimation processor compatible with CCITT H.261 and MPEG (Moving Pictures Experts Group) standards is described. A half-pel precision processing unit with an exhaustive block matching unit for integer-pel precision search is introduced. The necessary processing power for the exhaustive block matching is implemented with a one-dimensional array structure using a subsampling technique. The problem of communication bandwidth to the frame memory, which is a bottleneck of half-pel precision motion estimation, is solved by introducing a candidate pixel buffer in the interprocessor data transfer. It is noted that this is the first single-chip solution proposed that is capable of processing NTSC-resolution video in real time in 40-MHz operation.

Surface light source device and liquid crystal display device

Abstract: An angular intensity distribution shaping member includes a first surface for receiving a light beam emitted from a light source, the first surface having a length in a width direction that is longer than that in a thickness direction; second surfaces forming a plate-shaped light path on which the light beam incident from the first surface propagates by total reflection, the second surfaces including at least one adjustment surface for spreading a full angle of an angular intensity distribution, in the width direction, of the light beam incident from the first surface so that the full angle becomes wider than that of the angular intensity distribution of the light beam just after emission from the light source; and a third surface through which the light beam exits, the full angle of its angular intensity distribution in the width direction having been widened by the at least one adjustment surface.

The sound dimension

Abstract: Although the spotlight of virtual reality research has been on providing views of simulated scenes and objects, some researchers have chosen to study how to fool other senses: hearing, touch, and even smell, into perceiving what is not there. They have good reason: the virtual environments that are best at stimulating multiple senses are also best at evoking a feeling of presence and immersion. Next to sight, hearing is the sense on which people rely the most. So sounds, too, can play an extremely critical role in a distributed virtual environment (DVE). The virtual reality (VR) experience is more satisfying when sound adds to or reinforces other DVE information. The paper discusses the variety of sound in VR systems and considers the selection of software and hardware for these uses of audio in DVE systems.

The MERL/MELCO/TUM system for the REVERB Challenge using Deep Recurrent Neural Network Feature Enhancement

Abstract: This paper describes our joint submission to the REVERB Challenge, which calls for automatic speech recognition systems which are robust against varying room acoustics. Our approach uses deep recurrent neural network (DRNN) based feature enhancement in the log spectral domain as a single-channel front-end. The system is generalized to multi-channel audio by performing single-channel feature enhancement on the output of a sum-and-delay beamformer with direction of arrival estimation. On the back-end side, we employ a state-of-the-art speech recognizer using feature transformations, utterance based adaptation, and discriminative training. Results on the REVERB data indicate that the proposed front-end provides acceptable results already with a simple clean trained recognizer while being complementary to the improved back-end. The proposed ASR system with eight-channel input and feature enhancement achieves average word error rates (WERs) of 7.75 % and 20.09 % on the simulated and real evaluation sets, which is a drastic improvement over the Challenge baseline (25.26 and 49.16 %). Further improvements can be obtained by system combination with a DRNN tandem recognizer, reaching 7.02 % and 19.61 % WER.