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..

Homomorphic Encryption and Signatures from Vector Decomposition

Abstract: This paper introduces a new concept, distortion eigenvector space; it is a (higher dimensional) vector space in which bilinear pairings and distortion maps are available. A distortion eigenvector space can be efficiently realized on a supersingular hyperelliptic curve or a direct product of supersingular elliptic curves. We also introduce an intractable problem (with trapdoor) on distortion eigenvector spaces, the higher dimensional generalization of the vector decomposition problem (VDP). We define several computational and decisional problems regarding VDP, and clarify the relations among them. A trapdoor bijective functionwith algebraically rich properties can be obtained from the VDP on distortion eigenvector spaces. This paper presents two applications of this trapdoor bijective function; one is multivariate homomorphic encryption as well as a two-party protocol to securely evaluate 2DNF formulas in a higher dimensional manner, and the other is various types of signatures such as ordinary signatures, blind signatures, generically (selectively and universally) convertible undeniable signatures and their combination.

Contextual tagger utilizing deterministic finite state transducer

Abstract: A system for assigning part-of-speech tags to English text includes an improved contextual tagger which utilizes a deterministic finite state transducer to improve tagging speed such that large documents can have its sentences accurately tagged as to parts of speech to permit fast grammar checking, spell checking, information retrieval, text indexing and optical character recognition. The subject system performs by first acquiring a set of rules by examining a training corpus of tagged text. Then, these rules are transformed into a deterministic finite-state transducer through the utilization of non-deterministic transducers, a composer and a determiniser. In order to tag an input sentence, the sentence is initially tagged by first assigning each word in the sentence with its most likely part of speech tag regardless of the surrounding words in the sentences. The deterministic finite-state transducer is then applied on the resulting sequence of part of speech tags using the surrounding words and obtains the final part of speech tags. The Subject System requires an amount of time to compute the part-of-speech tags which is proportional to the number of words in the input sentence and which is independent of the number of rules it has applied.

Security evaluation of DPA countermeasures using dual-rail pre-charge logic style

Abstract: In recent years, some countermeasures against Differential Power Analysis (DPA) at the logic level have been proposed. At CHES 2005 conference, Popp and Mangard proposed a new countermeasure named Masked Dual-Rail Pre-Charge Logic (MDPL) which combine dual-rail circuits with random masking to improve Wave Dynamic Differential Logic (WDDL). The proposers of MDPL claim that it can implement secure circuits using a standard CMOS cell library without special constraints for the place-and-route because the difference of loading capacitance between all pairs of complementary logic gates in MDPL can be covered up by the random masking. In this paper, we especially focus the signal transition of the MDPL gate and evaluate the DPA-resistance of MDPL in detail. Our evaluation results show that the leakage occurs in the MDPL gates as well as WDDL gates when input signals have difference of delay time even if MDPL has an effectiveness on reducing the leakage caused by the difference of loading capacitance. Furthermore, we demonstrate the problem with different input signal delays by measurements of an FPGA and show the validity of our evaluation.

Using distance maps for accurate surface representation in sampled volumes

Abstract: High quality rendering and physics based modeling in volume graphics have been limited because intensity based volumetric data do not represent surfaces well. High spatial frequencies due to abrupt intensity changes at object surfaces result in jagged or terraced surfaces in rendered images. The use of a distance-to-closest-surface function to encode object surfaces is proposed. This function varies smoothly across surfaces and hence can be accurately reconstructed from sampled data. The zero value iso surface of the distance map yields the object surface and the derivative of the distance map yields the surface normal. Examples of rendered images are presented along with a new method for calculating distance maps from sampled binary data.

Robust abandoned object detection using dual foregrounds

Abstract: As an alternative to the tracking-based approaches that heavily depend on accurate detection of moving objects, which often fail for crowded scenarios, we present a pixelwise method that employs dual foregrounds to extract temporally static image regions. Depending on the application, these regions indicate objects that do not constitute the original background but were brought into the scene at a subsequent time, such as abandoned and removed items, illegally parked vehicles. We construct separate long- and short-term backgrounds that are implemented as pixelwise multivariate Gaussian models. Background parameters are adapted online using a Bayesian update mechanism imposed at different learning rates. By comparing each frame with these models, we estimate two foregrounds. We infer an evidence score at each pixel by applying a set of hypotheses on the foreground responses, and then aggregate the evidence in time to provide temporal consistency. Unlike optical flow-based approaches that smear boundaries, our method can accurately segment out objects even if they are fully occluded. It does not require on-site training to compensate for particular imaging conditions. While having a low-computational load, it readily lends itself to parallelization if further speed improvement is necessary.