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Institution

Toshiba

CompanyTokyo, Japan
About: Toshiba is a company organization based out in Tokyo, Japan. It is known for research contribution in the topics: Signal & Layer (electronics). The organization has 54573 authors who have published 83689 publications receiving 1081004 citations. The organization is also known as: Toshiba Corporation & Tokyo Shibaura Electric K.K..


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Book
15 Jun 1999
TL;DR: In this article, the authors present a review of the properties of a single Nucleus to a magnetic field and its properties in the context of MR imaging, which includes the following: Magnetic Field Inhomogeneity effects and T-2 Dephasing.
Abstract: Magnetic Resonance Imaging: A Preview. Classical of a Single Nucleus to a Magnetic Field. Rotating Reference Frames and Resonance. Magnetization, Relaxation and the Bloch Equation. The Quantum Mechanical Basis of Precession and Excitation. The Quantum Mechanical Basis of Thermal Equilibrium and Longitudinal Relaxation. Signal Detection Concepts. Introductory Signal Acquisition Methods: Free Induction Decay, Spin Echoes, Inversion Recovery and Spectroscopy. One-Dimensional Fourier Imaging, k-Space and Gradient Echoes. Multi-Dimensional Fourier Imaging and Slice Excitation. The Continuous and Discrete Fourier Transforms. Sampling and Aliasing in Image Reconstruction. Filtering and Resolution in Fourier Transform Image Reconstruction. Projection Reconstruction of Images. Signal, Contrast and Noise. A Closer Look at Radiofrequency Pulses. Water/Fat Separation Techniques. Fast Imaging in the Steady State. Segmented k-Space and Echo Planar Imaging. Magnetic Field Inhomogeneity Effects and T-2 Dephasing. Random Walks, Relaxation and Diffusion. Spin Density, T-1 and T-2 Quantification Methods in MR Imaging. Motion Artifacts and Flow Compensation. MR Angiography and Flow Quantification. Magnetic Properties of Tissues: Theory and Measurement. Sequence Design, Artifacts and Nomenclature. Introduction to MRI Coils and Magnets. Appendices. Index.

2,140 citations

Journal ArticleDOI
TL;DR: In this paper, the inversion layer mobility in n-and p-channel Si MOSFETs with a wide range of substrate impurity concentrations (10/sup 15/ to 10/sup 18/ cm/sup -3/) was examined.
Abstract: This paper reports the studies of the inversion layer mobility in n- and p-channel Si MOSFET's with a wide range of substrate impurity concentrations (10/sup 15/ to 10/sup 18/ cm/sup -3/). The validity and limitations of the universal relationship between the inversion layer mobility and the effective normal field (E/sub eff/) are examined. It is found that the universality of both the electron and hole mobilities does hold up to 10/sup 18/ cm/sup -3/. The E/sub eff/ dependences of the universal curves are observed to differ between electrons and holes, particularly at lower temperatures. This result means a different influence of surface roughness scattering on the electron and hole transports. On substrates with higher impurity concentrations, the electron and hole mobilities significantly deviate from the universal curves at lower surface carrier concentrations because of Coulomb scattering by the substrate impurity. Also, the deviation caused by the charged centers at the Si/SiO/sub 2/ interface is observed in the mobility of MOSFET's degraded by Fowler-Nordheim electron injection. >

1,389 citations

Journal ArticleDOI
TL;DR: The design goals and the techniques, which different LPWA technologies exploit to offer wide-area coverage to low-power devices at the expense of low data rates are presented.
Abstract: Low power wide area (LPWA) networks are attracting a lot of attention primarily because of their ability to offer affordable connectivity to the low-power devices distributed over very large geographical areas. In realizing the vision of the Internet of Things, LPWA technologies complement and sometimes supersede the conventional cellular and short range wireless technologies in performance for various emerging smart city and machine-to-machine applications. This review paper presents the design goals and the techniques, which different LPWA technologies exploit to offer wide-area coverage to low-power devices at the expense of low data rates. We survey several emerging LPWA technologies and the standardization activities carried out by different standards development organizations (e.g., IEEE, IETF, 3GPP, ETSI) as well as the industrial consortia built around individual LPWA technologies (e.g., LoRa Alliance, Weightless-SIG, and Dash7 alliance). We further note that LPWA technologies adopt similar approaches, thus sharing similar limitations and challenges. This paper expands on these research challenges and identifies potential directions to address them. While the proprietary LPWA technologies are already hitting the market with large nationwide roll-outs, this paper encourages an active engagement of the research community in solving problems that will shape the connectivity of tens of billions of devices in the next decade.

1,362 citations

Proceedings ArticleDOI
23 Jun 2008
TL;DR: The proposed semantic texton forests are ensembles of decision trees that act directly on image pixels, and therefore do not need the expensive computation of filter-bank responses or local descriptors, and give at least a five-fold increase in execution speed.
Abstract: We propose semantic texton forests, efficient and powerful new low-level features. These are ensembles of decision trees that act directly on image pixels, and therefore do not need the expensive computation of filter-bank responses or local descriptors. They are extremely fast to both train and test, especially compared with k-means clustering and nearest-neighbor assignment of feature descriptors. The nodes in the trees provide (i) an implicit hierarchical clustering into semantic textons, and (ii) an explicit local classification estimate. Our second contribution, the bag of semantic textons, combines a histogram of semantic textons over an image region with a region prior category distribution. The bag of semantic textons is computed over the whole image for categorization, and over local rectangular regions for segmentation. Including both histogram and region prior allows our segmentation algorithm to exploit both textural and semantic context. Our third contribution is an image-level prior for segmentation that emphasizes those categories that the automatic categorization believes to be present. We evaluate on two datasets including the very challenging VOC 2007 segmentation dataset. Our results significantly advance the state-of-the-art in segmentation accuracy, and furthermore, our use of efficient decision forests gives at least a five-fold increase in execution speed.

1,162 citations

Journal ArticleDOI
04 Jan 2002-Science
TL;DR: Electroluminescence from a single quantum dot within the intrinsic region of a p-i-n junction is shown to act as an electrically driven single-photon source for applications in quantum information technology.
Abstract: Electroluminescence from a single quantum dot within the intrinsic region of a p-i-n junction is shown to act as an electrically driven single-photon source. At low injection currents, the dot electroluminescence spectrum reveals a single sharp line due to exciton recombination, while another line due to the biexciton emerges at higher currents. The second-order correlation function of the diode displays anti-bunching under a continuous drive current. Single-photon emission is stimulated by subnanosecond voltage pulses. These results suggest that semiconductor technology can be used to mass-produce a single-photon source for applications in quantum information technology.

1,110 citations


Authors

Showing all 54588 results

NameH-indexPapersCitations
Chao Zhang127311984711
Andrew Blake10544357213
Simon Jones92101239886
Robert J. Young9064031962
Kenji Hashimoto8682032020
Roberto Cipolla8649040945
Gehan A. J. Amaratunga8272530988
Michikazu Hara7832822858
Akira Nishiyama7561922487
Hiroshi Yamazaki7495327216
S. Wright7442222177
Elad I. Levy7361331272
Andrew J. Shields7356621744
Jean-Claude Latombe7121130113
Michael Pepper7084430213
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20234
202223
2021517
20201,250
20191,674
20181,709