General Electric

About: General Electric is a company organization based out in Boston, Massachusetts, United States. It is known for research contribution in the topics: Turbine & Rotor (electric). The organization has 76365 authors who have published 110557 publications receiving 1885108 citations. The organization is also known as: General Electric Company & GE.

Fully embedded board-level guided-wave optoelectronic interconnects

Abstract: A fully embedded board-level guided-wave optical interconnection is presented to solve the packaging compatibility problem. All elements involved in providing high-speed optical communications within one board are demonstrated. Experimental results on a 12-channel linear array of thin-film polyimide waveguides, vertical-cavity surface-emitting lasers (VCSELs) (42 /spl mu/m), and silicon MSM photodetectors (10 /spl mu/m) suitable for a fully embedded implementation are provided. Two types of waveguide couplers, titled gratings and 45/spl deg/ total internal reflection mirrors, are fabricated within the polyimide waveguides. Thirty-five to near 100% coupling efficiencies are experimentally confirmed. By doing so, all the real estate of the PC board surface are occupied by electronics, and therefore one only observes the performance enhancement due to the employment of optical interconnection but does not worry about the interface problem between electronic and optoelectronic components unlike conventional approaches. A high speed 1-48 optical clock signal distribution network for Cray T-90 super computer is demonstrated. A waveguide propagation loss of 0.21 dB/cm at 850 nm was experimentally confirmed for the 1-48 clock signal distribution and for point-to-point interconnects. The feasibility of using polyimide as the interlayer dielectric material to form hybrid three-dimensional interconnects is also demonstrated. Finally, a waveguide bus architecture is presented, which provides a realistic bidirectional broadcasting transmission of optical signals. Such a structure is equivalent to such IEEE standard bus protocols as VME bus and FutureBus.

Deep learning for tomographic image reconstruction

Abstract: Deep-learning-based tomographic imaging is an important application of artificial intelligence and a new frontier of machine learning. Deep learning has been widely used in computer vision and image analysis, which deal with existing images, improve these images, and produce features from them. Since 2016, deep learning techniques have been actively researched for tomographic imaging, especially in the context of biomedicine, with impressive results and great potential. Tomographic reconstruction produces images of multi-dimensional structures from externally measured ‘encoded’ data in the form of various tomographic transforms (integrals, harmonics, echoes and so on). In this Review, we provide a general background, highlight representative results with an emphasis on medical imaging, and discuss key issues that need to be addressed in this emerging field. In particular, tomographic imaging is an integral part of modern medicine, and will play a key role in personalized, preventive and precision medicine and make it intelligent, inexpensive and indiscriminate. The popularity of deep learning is leading to new areas in biomedical applications. Wang and colleagues summarize in this Review the recent development and future directions of deep neural networks for superior image quality in the tomographic imaging field.

Digital signal encoding and decoding apparatus

Abstract: Digital data is conveyed along with the analog signal by selectively quantizing the analog signal in response to the level of each of the digital bits to be sent. By determining which quantization function was used, a decoder may recover the embedded digital data.

Extracting company names from text

Abstract: A detailed description is given of an implemented algorithm that extracts company names automatically from financial news. Extracting company names from text is one problem; recognizing subsequent references to a company is another. The author addresses both problems in an implemented, well-tested module that operates as a detachable process from a set of natural language processing tools. She implements a good algorithm by combining heuristics, exception lists and extensive corpus analysis. The algorithm generates the most likely variations that those names may go by, for use in subsequent retrieval. Tested on over one million words of naturally occurring financial news, the system has extracted thousands of company names with over 95% accuracy (precision) compared to a human, and succeeded in extracting 25% more companies than were indexed by a human. >