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..

Hand gesture control system

Abstract: A system is provided for rapidly recognizing hand gestures for the control of computer graphics, in which image moment calculations are utilized to determine an overall equivalent rectangle corresponding to hand position, orientation and size, with size in one embodiment correlating to the width of the hand. In a further embodiment, a hole generated through the utilization of the touching of the forefinger with the thumb provides a special trigger gesture recognized through the corresponding hole in the binary representation of the hand. In a further embodiment, image moments of images of other objects are detected for controlling or directing onscreen images.

Hierarchical Predicate Encryption for Inner-Products

Abstract: This paper presents a hierarchical predicate encryption (HPE) scheme for inner-product predicates that is secure (selectively attribute-hiding) in the standard model under new assumptions These assumptions are non-interactive and of fixed size in the number of adversary's queries (ie, not "q -type"), and are proven to hold in the generic model To the best of our knowledge, this is the first HPE (or delegatable PE) scheme for inner-product predicates that is secure in the standard model The underlying techniques of our result are based on a new approach on bilinear pairings, which is extended from bilinear pairing groups over linear spaces They are quite different from the existing techniques and may be of independent interest

Portable and adjustable contra-angle dental instrument

Abstract: 1,201,927. Dental handpieces. MITSUBISHI DENKI K.K. 2 Oct., 1967 [1 Oct., 1966 (6)], No. 44686/67. Heading A5R. A portable dental handpiece contains a D.C. electric motor 22 and a storage battery 28, the handpiece being contra-angled and including a rotatable shaft driving an interchangeable tool (e.g. a burr 50) from the motor, the shaft, motor, and storage battery being in alignment and the handpiece being provided with a socket 52 for use in charging the storage battery. As shown, the motor drive shaft 24 is connected by a cross-pin and slot connection 46, 78 to the rotatable shaft 42, the motor having a governor 26 (see also Figure 6, not shown) which limits the motor speed and an on-off switch 63. The head 38 of the handpiece may be rotated relative to the handpiece body when pulled away from the body against a spring 72 and a projection 68 on the head may then be fitted into a chosen one of a number of notches 64, 66, &c. to re-secure the head and handpiece together. The handpiece housing is of a plastics material and a suitable charging circuit for the battery is described (Figure 7, not shown).

Single-Channel Multi-Speaker Separation using Deep Clustering

Abstract: Deep clustering is a recently introduced deep learning architecture that uses discriminatively trained embeddings as the basis for clustering. It was recently applied to spectrogram segmentation, resulting in impressive results on speaker-independent multi-speaker separation. In this paper we extend the baseline system with an end-to-end signal approximation objective that greatly improves performance on a challenging speech separation. We first significantly improve upon the baseline system performance by incorporating better regularization, larger temporal context, and a deeper architecture, culminating in an overall improvement in signal to distortion ratio (SDR) of 10.3 dB compared to the baseline of 6.0 dB for two-speaker separation, as well as a 7.1 dB SDR improvement for three-speaker separation. We then extend the model to incorporate an enhancement layer to refine the signal estimates, and perform end-to-end training through both the clustering and enhancement stages to maximize signal fidelity. We evaluate the results using automatic speech recognition. The new signal approximation objective, combined with end-to-end training, produces unprecedented performance, reducing the word error rate (WER) from 89.1% down to 30.8%. This represents a major advancement towards solving the cocktail party problem.