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	Design of a Compact Superconducting RSFQ Register File 期刊论文 OAI收割 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 2023, 页码: 8 作者: Zhang, Kuozhong; Zhang, Zhimin; Tang, Guangming; Ye, Xiaochun \| 收藏 \| 浏览/下载：25/0 \| 提交时间：2023/12/04 Registers Clocks Josephson junctions Power transmission lines Logic gates Decoding Program processors RSFQ register file superconducting digital circuit
	Can wavefront coding infrared imaging system achieve decoded images approximating to in-focus infrared images? 会议论文 OAI收割 Photoelectronic Technology Committee Conferences, Hefei, China, June 14, 2015 作者: Feng B(冯斌); Zhang CS(张程硕); Xu BS(徐保树); Shi ZL(史泽林) 收藏 \| 浏览/下载：50/0 \| 提交时间：2015/12/16 Wavefront coding optical coding digital decoding infrared imaging athermalization
	Design and image restoration research of a cubic-phase-plate system (EI CONFERENCE) 会议论文 OAI收割 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Advanced Optical Manufacturing Technologies, April 26, 2010 - April 29, 2010, Dalian, China 作者: Zhang J.; Zhang J.; Zhang J.; Shi G.; Zhang X. 收藏 \| 浏览/下载：35/0 \| 提交时间：2013/03/25 Wave-front coding technology is a novel jointly optical and digital imaging technology which can greatly extend the depth of focus of optical systems. The image restoration process is an important part of wave-front coding technology. Using wave-front coding makes the modulation transfer function(MTF) values of the optical systems change little over a range of several times the depth of focus which means the system MTF is quite insensitive to defocus and there is no zero in the passband. So we can design a single filter for the restoration of images in different defocus positions. However it's hard to avoid noise during image acquisition and transmission processes. These noises will be amplified in the image restoration especially in the high frequency part when the MTF drop is relatively low. The restoration process significantly reduces the system signal to noise ratio this way. Aimed at the problem of noise amplification a new algorithm was proposed which incorporated wavelet denoising into the iterative steps of Lucy-Richardson algorithm. Better restoration results were obtained through the new algorithm effectively solving the noise amplification problem of original LR algorithm. Two sets of identical triplet imaging systems were designed in one of which the cubic-phase-plate was added. Imaging experiments of the manufactured systems were carried and the images of a traditional system and a wave-front coded system before and after decoding were compared. The results show that the designed wave-front coded system can extend the depth of focus by 40 times compared with the traditional system while maintaining the light flux and the image plane resolution. 2010 Copyright SPIE - The International Society for Optical Engineering.
	Level 0 and level 1 data processing for a type of hyper-spectral imager (EI CONFERENCE) 会议论文 OAI收割 2009 International Conference on Optical Instruments and Technology, OIT 2009, October 19, 2009 - October 21, 2009, Shanghai, China Li X.; Yan C. 收藏 \| 浏览/下载：85/0 \| 提交时间：2013/03/25 Hyper-spectral imaging (HSI) is a kind of optical remote sensor that can simultaneously obtain spatial and spectral information of ground targets. We are now designing a data processing system for a type of space-borne push-broom HSI then it performs radiometric and spectral calibration based on the ground calibration results and onboard calibration collection. The detailed algorithms for bad pixel replacement which has 128 spectral channels covering the spectral range from 400nm to 2500nm. With its large amount of spectral channels radiometric and spectral calibration were presented. After processing the HSI collects large volume of spectral imaging data need to be efficiently and accurately processed and calibrated. In this paper the digital numbers downlinked from the spacecraft can be converted into at-sensor absolute spectral radiance of ground targets the detailed Level 0 and Level 1 data processing steps for the HSI were presented. The Level 0 processing refers to a set of tasks performed on the data downlinked from the spacecraft thus providing accurate quantified spectral imaging data for various applications. 2009 SPIE. including decoding to extract science data separating the science data into files corresponding to different tasks (e.g. ground imaging dark imaging and onboard calibration) checking data integrity and instrument settings data format conversion and Level 0 files creation. The Level 1 processing performs several steps on Level 0 data. Firstly it corrects the image artifacts (mostly the SWIR smear effect) subtracts the dark background and performs the bad pixel replacement according to the prelaunch measurement
	An open system design of high speed image transmission (EI CONFERENCE) 会议论文 OAI收割 1st International Symposium on Computer Network and Multimedia Technology, CNMT 2009, December 18, 2009 - December 20, 2009, Wuhan, China 作者: Li S. 收藏 \| 浏览/下载：27/0 \| 提交时间：2013/03/25 A high speed image transmission system is designed to compress the aerial image improve the speed of image transmission. This paper introduces how PTP (Picture Transfer Protocol) can be extended to work over generic USB interfaces the multi-threaded transfer mode of file stream and the JPEG decoding algorithm self-developed an open system of aerial digital camera image transmission. Experiments show that image transmission with PTP not only guarantees a good image quality and prevents data loss but also fulfill the requirement of aerial camera large image transmission that ensure the advantage of high frame rate. 2009 IEEE.
	Lossless wavelet compression on medical image (EI CONFERENCE) 会议论文 OAI收割 4th International Conference on Photonics and Imaging in Biology and Medicine, September 3, 2005 - September 6, 2005, Tianjin, China 作者: Liu H.; Liu H.; Liu H. 收藏 \| 浏览/下载：60/0 \| 提交时间：2013/03/25 An increasing number of medical imagery is created directly in digital form. Such as Clinical image Archiving and Communication Systems (PACS). as well as telemedicine networks require the storage and transmission of this huge amount of medical image data. Efficient compression of these data is crucial. Several lossless and lossy techniques for the compression of the data have been proposed. Lossless techniques allow exact reconstruction of the original imagery while lossy techniques aim to achieve high compression ratios by allowing some acceptable degradation in the image. Lossless compression does not degrade the image thus facilitating accurate diagnosis of course at the expense of higher bit rates i.e. lower compression ratios. Various methods both for lossy (irreversible) and lossless (reversible) image compression are proposed in the literature. The recent advances in the lossy compression techniques include different methods such as vector quantization wavelet coding neural networks and fractal coding. Although these methods can achieve high compression ratios (of the order 50:1 or even more) they do not allow reconstructing exactly the original version of the input data. Lossless compression techniques permit the perfect reconstruction of the original image but the achievable compression ratios are only of the order 2:1 up to 4:1. In our paper we use a kind of lifting scheme to generate truly loss-less non-linear integer-to-integer wavelet transforms. At the same time we exploit the coding algorithm producing an embedded code has the property that the bits in the bit stream are generated in order of importance so that all the low rate codes are included at the beginning of the bit stream. Typically the encoding process stops when the target bit rate is met. Similarly the decoder can interrupt the decoding process at any point in the bil stream and still reconstruct the image. Therefore a compression scheme generating an embedded code can start sending over the network the coarser version of the image first and continues with the progressive transmission of the refinement details. Experimental results show that our method can get a perfect performance in compression ratio and reconstructive image.