We cleveloped a high-speed information retrieval system. The system hased on the IXP 2800 is one of the dedicute device. The velocity of the information retrieval is 6.8 Gb/s. The protocol support Telnet, FTP, SMTP, P...We cleveloped a high-speed information retrieval system. The system hased on the IXP 2800 is one of the dedicute device. The velocity of the information retrieval is 6.8 Gb/s. The protocol support Telnet, FTP, SMTP, POP3 etc. various networks protocols. The information retrieval supports the key word and the natural language process. This paper explains the hardware system, software system and the index of the performance. Key words network processor - IXP2800 - information retrieval - IXA CLC number TP 309 Foundation item: Supported by the National Natural Science Foundation of China (69873016 & 69972017) and the National High Technology Development Program of China (863-301-06-1)Biography: SHI Shu-dong (1963-), male, Ph. D. candidate, research direction: network & information security.展开更多
Point spread function(PSF)engineering has been pivotal in the remarkable progress made in high-resolution imaging in the last decades.However,the diversity in PSF structures attainable through existing engineering met...Point spread function(PSF)engineering has been pivotal in the remarkable progress made in high-resolution imaging in the last decades.However,the diversity in PSF structures attainable through existing engineering methods is limited.Here,we report universal PSF engineering,demonstrating a method to synthesize an arbitrary set of spatially varying 3D PSFs between the input and output volumes of a spatially incoherent diffractive processor composed of cascaded transmissive surfaces.We rigorously analyze the PSF engineering capabilities of such diffractive processors within the diffraction limit of light and provide numerical demonstrations of unique imaging capabilities,such as snapshot 3D multispectral imaging without involving any spectral filters,axial scanning or digital reconstruction steps,which is enabled by the spatial and spectral engineering of 3D PSFs.Our framework and analysis would be important for future advancements in computational imaging,sensing,and diffractive processing of 3D optical information.展开更多
为了提高图书馆数据信息监控和分析能力,提高图书馆数据信息应用能力,将TIAM3517处理器作为主CPU处理核心,实现与600MHz ARM Cortex-A8内核集成,构建了人工鱼群优化算法(Artificial Fish Swarm,AFS)模型,进而获取图书馆数据信息中的目...为了提高图书馆数据信息监控和分析能力,提高图书馆数据信息应用能力,将TIAM3517处理器作为主CPU处理核心,实现与600MHz ARM Cortex-A8内核集成,构建了人工鱼群优化算法(Artificial Fish Swarm,AFS)模型,进而获取图书馆数据信息中的目标数据信息,在个性化推荐中引入了自适应策略,结合K-means聚类算法找到最佳聚类中心,最终实现图书馆图书的个性化推荐,提高了图书馆数据信息的目标检索能力。通过试验,该研究系统在进行系统性能的测试时,系统响应时间为3s,数据监控能力强,检索能力强,大幅提高了图书馆数据信息监控及分析。展开更多
Nonlinear computation is essential for a wide range of information processing tasks,yet implementing nonlinear functions using optical systems remains a challenge due to the weak and power-intensive nature of optical ...Nonlinear computation is essential for a wide range of information processing tasks,yet implementing nonlinear functions using optical systems remains a challenge due to the weak and power-intensive nature of optical nonlinearities.Overcoming this limitation without relying on nonlinear optical materials could unlock unprecedented opportunities for ultrafast and parallel optical computing systems.Here,we demonstrate that large-scale nonlinear computation can be performed using linear optics through optimized diffractive processors composed of passive phase-only surfaces.In this framework,the input variables of nonlinear functions are encoded into the phase of an optical wavefront—e.g.,via a spatial light modulator(SLM)—and transformed by an optimized diffractive structure with spatially varying point-spread functions to yield output intensities that approximate a large set of unique nonlinear functions–all in parallel.We provide proof establishing that this architecture serves as a universal function approximator for an arbitrary set of bandlimited nonlinear functions,also covering wavelength-multiplexed nonlinear functions as well as multi-variate and complex-valued functions that are all-optically cascadable.Our analysis also indicates the successful approximation of typical nonlinear activation functions commonly used in neural networks,including the sigmoid,tanh,ReLU(rectified linear unit),and softplus.We numerically demonstrate the parallel computation of one million distinct nonlinear functions,accurately executed at wavelength-scale spatial density at the output of a diffractive optical processor.Furthermore,we experimentally validated this framework using in situ optical learning and approximated 35 unique nonlinear functions in a single shot using a compact setup consisting of an SLM and an image sensor.These results establish diffractive optical processors as a scalable platform for massively parallel universal nonlinear function approximation,paving the way for new capabilities in analog optical computing based on linear materials.展开更多
文摘We cleveloped a high-speed information retrieval system. The system hased on the IXP 2800 is one of the dedicute device. The velocity of the information retrieval is 6.8 Gb/s. The protocol support Telnet, FTP, SMTP, POP3 etc. various networks protocols. The information retrieval supports the key word and the natural language process. This paper explains the hardware system, software system and the index of the performance. Key words network processor - IXP2800 - information retrieval - IXA CLC number TP 309 Foundation item: Supported by the National Natural Science Foundation of China (69873016 & 69972017) and the National High Technology Development Program of China (863-301-06-1)Biography: SHI Shu-dong (1963-), male, Ph. D. candidate, research direction: network & information security.
文摘Point spread function(PSF)engineering has been pivotal in the remarkable progress made in high-resolution imaging in the last decades.However,the diversity in PSF structures attainable through existing engineering methods is limited.Here,we report universal PSF engineering,demonstrating a method to synthesize an arbitrary set of spatially varying 3D PSFs between the input and output volumes of a spatially incoherent diffractive processor composed of cascaded transmissive surfaces.We rigorously analyze the PSF engineering capabilities of such diffractive processors within the diffraction limit of light and provide numerical demonstrations of unique imaging capabilities,such as snapshot 3D multispectral imaging without involving any spectral filters,axial scanning or digital reconstruction steps,which is enabled by the spatial and spectral engineering of 3D PSFs.Our framework and analysis would be important for future advancements in computational imaging,sensing,and diffractive processing of 3D optical information.
文摘为了提高图书馆数据信息监控和分析能力,提高图书馆数据信息应用能力,将TIAM3517处理器作为主CPU处理核心,实现与600MHz ARM Cortex-A8内核集成,构建了人工鱼群优化算法(Artificial Fish Swarm,AFS)模型,进而获取图书馆数据信息中的目标数据信息,在个性化推荐中引入了自适应策略,结合K-means聚类算法找到最佳聚类中心,最终实现图书馆图书的个性化推荐,提高了图书馆数据信息的目标检索能力。通过试验,该研究系统在进行系统性能的测试时,系统响应时间为3s,数据监控能力强,检索能力强,大幅提高了图书馆数据信息监控及分析。
文摘Nonlinear computation is essential for a wide range of information processing tasks,yet implementing nonlinear functions using optical systems remains a challenge due to the weak and power-intensive nature of optical nonlinearities.Overcoming this limitation without relying on nonlinear optical materials could unlock unprecedented opportunities for ultrafast and parallel optical computing systems.Here,we demonstrate that large-scale nonlinear computation can be performed using linear optics through optimized diffractive processors composed of passive phase-only surfaces.In this framework,the input variables of nonlinear functions are encoded into the phase of an optical wavefront—e.g.,via a spatial light modulator(SLM)—and transformed by an optimized diffractive structure with spatially varying point-spread functions to yield output intensities that approximate a large set of unique nonlinear functions–all in parallel.We provide proof establishing that this architecture serves as a universal function approximator for an arbitrary set of bandlimited nonlinear functions,also covering wavelength-multiplexed nonlinear functions as well as multi-variate and complex-valued functions that are all-optically cascadable.Our analysis also indicates the successful approximation of typical nonlinear activation functions commonly used in neural networks,including the sigmoid,tanh,ReLU(rectified linear unit),and softplus.We numerically demonstrate the parallel computation of one million distinct nonlinear functions,accurately executed at wavelength-scale spatial density at the output of a diffractive optical processor.Furthermore,we experimentally validated this framework using in situ optical learning and approximated 35 unique nonlinear functions in a single shot using a compact setup consisting of an SLM and an image sensor.These results establish diffractive optical processors as a scalable platform for massively parallel universal nonlinear function approximation,paving the way for new capabilities in analog optical computing based on linear materials.
