Sony Corporation

Sumitomo Electric Industries, Limited

Other

Kabushiki Kaisha Toshiba

Nippon Kokan Kabushiki Kaisha

Nippon Telegraph and Telephone Corporation

Toshiba Lighting & Technology Corporation

Fujikura Limited

Fujitsu Corporation

Sei Optifrontier Co., Ltd.

Sumitomo Electric Lightwave Corp.

Tsutomu Watanabe

Tetsujiro Kondo

Hiroto Kimura

Masaaki Hattori

Takahiro Nagano

Hideo Nakaya

Hisakazu Shiraki

Yasuhiro Suto

Kazutaka Ando

Satoshi Inoue

Masashi Uchida

Yasushi Tatehira

Nobuyuki Asakura

Takuo Morimura

Wataru Niitsuma

Naoki Fujiwara

Junichi Ishibashi

Masanori Kanemaru

Shinichiro Kaneko

Yasuhiro Fujimori

Yukihiro Yokomachi

Toshihiko Honma

Takashi Nakamura

Shinji Ishikawa

Noriaki Takahashi

Akira Katsuyama

Kazuhito Saito

Yoshikyo Tamekuni

Akihisa Inatani

Takeshi Adaniya

Gakuho Fukushi

Shuichi Nagano

Akira Kawakatsu

Kanji Mita

Masami Ishikawa

Kazumasa Ozawa

Taiji Noshitani

Haruyuki Tamura

Shuji Minami

Seiji Wada

Masahiro Shibata

James J Carrig

Sugata Ghosal

Yasuaki Takahashi

Takafumi Morifuji

Koji Ohta

Sakon Yamamoto

Kazunari Hattori

Nobuo Tomita

Akihiko Arimitsu

Tomoyuki Ohtsuki

Hiroshi Kamata

Hideki Ohtsuka

Masaru Ohmura

Kazuo Matsudo

Hisao Maki

Takashi Murakami

Kenichi Torii

Masahiro Shoji

Masahiro Abe

Takeshi Suzuki

Yoshiaki Nakamura

Kensuke Ito

Ryuichiro Sato

Hitoshi Mukai

Hisashi Honda

Shuzo Fukuda

Yasunobu Node

Shizuo Chikaoka

Keiji Osaka

Tsutomu Ichikawa

Kenji Sakai

Mitsuyoshi Shinonaga

Takumi Suwa

Tomihiro Hara

Takehisa Ide

Ariyoshi Ishizaki

Tomomi Kageyama

Izumi Sankawa

Eisuke Oki

Takayuki Hoshiya

Shunichi Mizuno

Yoji Yuge

Hiroyoshi Takanishi

Akiko Saitou

Kohji Ohta

Yutaka Ito

Yoshiya Isono

Noriaki Miyano

Makoto Bessho

Masaru Sagiyama

Nobuyuki Yamaguchi

Makoto Ohsawa

Koji Tanaka

Akira Tanabe

Ryuji Soma

Akimasa Endou

Kazuo Tarumi

Minoru Akiyama

Jun-ichi Mizusawa

Optical fiber networks, devices, and related methods are disclosed herein. In some aspects, an optical fiber network includes network devices having optical fibers for transmitting and receiving data. In the network, 100% of the optical fibers are utilized end-to-end across the plurality of network devices. Networks herein are also devoid of converters and include only a single direction connection between fibers at interconnect points between devices. Network devices may include ferrules, where at least some of the ferrules are devoid of an optical fiber. A method for providing an optical fiber network includes providing network devices having optical fibers for transmitting and receiving data and transmitting data using 100% of the optical fibers end-to-end across the network devices.

Reduced fiber count networks, devices, and related methods

According to one embodiment, a measurement system includes a wireless relay device, a moving-object-mounted terminal and a data processor. The wireless relay device forms a wireless area for performing wireless communication. The moving-object-mounted terminal is mounted to a moving object within the wireless area. The moving-object-mounted terminal includes a Global Positioning System (GPS) unit and a wireless unit. The GPS unit acquires positional data of the moving object and time data indicating the time when the positional data has been acquired by GPS. The wireless unit converts the positional data and the time data into a wireless signal, and transmits the wireless signal to the wireless relay device. The data processor receives the wireless signal from the moving-object-mounted terminal via the wireless relay device, and calculates time required for the moving object to move a predetermined distance within the wireless area based on the positional data and the time data.

Measurement system, moving-object-mounted terminal and data processing apparatus

A coefficient learning apparatus includes: a student-image generation section configured to generate a student image from the teacher image; a class classification section configured to sequentially set each of pixels in the teacher image as a pixel of interest and generate a class for the pixel of interest from the values of a plurality of specific pixels; a weight computation section configured to add up feature quantities; and a processing-coefficient generation section configured to generate a prediction coefficient on the basis of a determinant including said deterioration equation and a weighted constraint condition equation.

Learning of coefficients for motion deblurring by pixel classification and constraint condition weight computation

An image processing device for converting an input image into an output image whose blur is reduced. The image processing device includes a first image extracting section that extracts a plurality of pixels composed of a pixel of the input image corresponding to a pixel of interest in the output image and predetermined pixels surrounding the pixel of the input image. A first feature quantity calculating section calculates a first feature quantity from the plurality of pixels extracted by the first image extracting section. A second pixel extracting section extracts a plurality of pixels composed of the pixel of interest and predetermined pixels surrounding the pixel of interest from the input image. A predicting section then generates a pixel value of the pixel of interest.

Image processing for reducing blur

An image processing apparatus includes: a blur removing processing section configured to carry out a blur removing process for an input image using a plurality of blur removal coefficients for removing blur of a plurality of different blur amounts to produce a plurality of different blur removal result images; a feature detection section configured to detect a feature from each of the different blur removal result images; a blur amount class determination section configured to determine blur amount classes representative of classes of the blur amounts from the features; and a prediction processing section configured to carry out mathematic operation of pixel values of predetermined pixels of the input image and prediction coefficients learned in advance and corresponding to the blur amount classes to produce an output image from which the blur is removed.

Image processing apparatus, image processing method, program and learning apparatus

An optical connector, which is capable of enhancing fittability into a cabinet, or the like on account of compactification and also avoiding problems of an increase of bead loss, a breakage, etc. of an optical fiber, can be provided. In an optical connectorthat houses a fusion-spliced portion, in which a short optical fiberthat is fitted previously to an optical connector ferruleand a coated optical fiberare fusion spliced together and holds the fusion-spliced portiontherein, one end of a protection sleevemade of the thermal shrinkage material that externally fixes the fusion-spliced portionis coupled to the optical connector ferrule. An air escape holeffor escaping an air, which is confined in the protection sleevewhen the protection sleeveis thermally shrunk and adhered closely to the optical connector ferrule, to the outside is formed in the optical connector ferrule

Optical connector

An image data processing method includes selecting a motion compensating vector of a noticed region based on additional information added to student data. The image data processing method also includes classifying the noticed region into one of a plurality of classes based on the motion compensating vector selected by the selecting. In addition, the image data processing method includes constructing a prediction tap of the noticed region based on the motion compensating vector selected by the selecting and learning a prediction coefficient based on the one of the plurality of classes using teacher data corresponding to the constructed prediction tap. The image data processing method additionally includes producing prediction coefficient data for each class, based on the prediction coefficient. The image data processing method further includes storing the prediction coefficient data in a coefficient memory based on a quantization scale and a bit rate.

Image processing apparatus and associated method

An image processing apparatus for processing first image data into second image data having a higher quality includes the following elements. A determination unit determines whether broadcast image data is up-converted image data generated by up-converting different image data by increasing the number of pixels forming the different image data. A down-converter down-converts the broadcast image data into down-converted image data by decreasing the number of pixels forming the broadcast image data. A first image converter converts, if the broadcast image data is not the up-converted image data, the first image data into the second image data by using the down-converted image data and the broadcast image data as the first image data. A second image converter converts, if the broadcast image data is the up-converted image data, the first image data into the second image data by using the down-converted image data as the first image data.

Image processing apparatus, image processing method, and program

A motion-vector-setting section () sets a motion vector in units of pixel in a target image. Based on the motion vector, a target-pixel-setting section () sets a target pixel for each image in plural images to be processed. A motion-blur-amount-setting section () sets a motion blur amount in units of pixel based on the motion vector and the exposure-time ratio set in units of image in the exposure-time-ratio-setting section (). A processing-region-setting section () sets processing regions corresponding to the target pixel for each of the plural images based on the motion blur amount. A processing-coefficient-setting section () sets processing coefficients based on the motion blur amount. A pixel-value-generating section () generates motion-blur-removed pixel values that correspond to the target pixel by linear combination of pixel values corresponding to pixels in the processing region and the processing coefficients, so that they can be output from an integration section () as one pixel value. By utilizing any time-directional information significantly, motion-blur-removing processing can be accurately performed.

Image processing device, image processing method, and image processing program

A motion vector detecting apparatus includes a first generation unit that generates a first evaluation value table from an image signal of a first level, a second generation unit that generates a second evaluation value table from an image signal of a second level higher than the first level, a correction unit that corrects the first evaluation value table on the basis of the second evaluation value table, a first extraction unit that extracts first candidate vectors using the first evaluation value table, a second extraction unit that extracts second candidate vectors using the corrected first evaluation value table, and a detection unit that detects a motion vector from the first and second candidate vectors.

Apparatus and method for detecting motion vector, program, and recording medium

An optical connector in which the housing property into a cabinet or the like can be enhanced because of the compactification, and which can solve problems of the increase of the bending loss of an optical fiber, the breakage, and the like is obtained. In an optical connector which houses and holds a fusion spliced portion where a short optical fiber previously fitted to an optical connector ferrule is fusion-spliced with a coated optical fiber, one end of a protection sleeve which armors the fusion spliced portion is coupled to the optical connector ferrule. As a result, the length of the protection sleeve which covers the fusion spliced portion so that the fusion spliced portion is positioned at the middle can be set with reference to an end portion of the optical connector ferrule. Therefore, the protection sleeve can be shortened, and compactification of the optical connector can be realized.

The present invention relates to an apparatus for processing informational signal etc. that are preferably applied to a digital broadcast receiver etc. A class classification portion () generates a class code CL for indicating a class to which pixel data y of a target position in an image signal Vb belongs. From an accumulation table (), based on this class code CL, difference data DF (correction data of encoding noise) that corresponds to the target position is read. Pixel data (pixel value or DCT coefficient) x that corresponds to the target position in the image signal Vb is supplied to an addition portion (). The addition portionadds the difference data DF read out of the accumulation table () to this pixel data x, to obtain pixel data y of the target position in the image signal Vb. Encoding noise (encoding distortion) of this pixel data y is reduced.

Information signal processing device, information signal processing method, image signal processing device, image displaying device, device and method for producing correction data used in them, device and method for producing coefficient data, programs for executing these methods, and computer-readable medium for storing the program

A signal processing system, a signal processing apparatus and method, a recording medium, and a program in which high frequency components of a narrow-range signal having suppressed high frequency components can be restored with high accuracy. A narrow-range audio signal having low frequency components is divided into frames by a frame-unit dividing unit, and is spectral-transformed into a signal on a frequency domain by a DCT transform unit. A learning-produced-data table extracts, at an address corresponding to a class code supplied from a classifying unit, high-frequency-component DCT spectrum data. In a spectrum combining unit, the extracted high-frequency-component DCT spectrum data is combined with low-frequency-component DCT spectrum data output from the DCT transform unit. Subsequently, a broad-range audio signal having combined high frequency components and low frequency components is output.

Signal processing system, signal processing apparatus and method, recording medium, and program

An image data processing apparatus is provided and includes a variable length decoding section to decode and extract quantized DCT coefficients from an encoded signal input thereto and extract encoded information from side information added to the encoded signal. Classification adaptation processing sections use the input signal supplied from a decoding section and a creation signal supplied from a signal storage section to determine the reliability of each of motion compensating vectors of the encoded signal supplied from a coded information storage section, selects that one of the motion compensating vectors which has the highest reliability as a selected vector, and produces a creation signal based on the selection vector.

Image processing apparatus and associated methodology of creating motion compensating vectors

A motion-setting section () sets a motion amount and a motion direction for obtaining processing coefficients. A student-image-generating section () generates student images obtained by adding a motion blur to a teacher image not only based on the set motion amount and the set motion direction but also by changing at least one of the motion amount and motion direction in a specific ratio and student images obtained by adding no motion blur to the teacher image. A prediction-tap-extracting section () extracts, in order to extract a main term that mainly contains component of the target pixel, at least a pixel value of pixel in the student image whose space position roughly agrees with space position of the target pixel in the teacher image. A processing-coefficient-generating section () generates processing coefficients for predicting the target pixels in the teacher images from the pixel values of extracted pixels based on a relationship between the pixels thus extracted and the target pixels in the teacher images. The processing coefficients that are suitable for any motion blur removing which is robust against any shift of the motion vector can be generated through learning.

Learning device, learning method, and learning program

Growing community of inventors

Kanagawa, Japan

Tsutomu Watanabe