Efficient and Robust Detection of Duplicate Videos in a Large Database

一种基于nvme热插拔检测方法NVME (Non-Volatile Memory Express) is a high-performance and scalable storage protocol designed to take advantage of the performance of solid-state drives (SSDs) over the older and slower interfaces. NVME drives are becoming more and more popular due to their high speed and low latency. However, one common issue with NVME drives is the hot-plug detection method.NVME是一种高性能和可扩展的存储协议，旨在充分利用固态硬盘(SSD)的性能，相比之下，旧的和慢速的接口就显得不足够强大。

由于其高速和低延迟，NVME驱动器越来越受欢迎。

然而，NVME驱动器的一个常见问题是热插拔检测方法。

The hot-plug detection method for NVME drives refers to the ability to safely remove and add NVME drives from a system while it's powered on. This is an important feature for servers and data centers, as it allows for quick and easy maintenance and upgrading of storage devices without having to power down the entire system. However, the current hot-plug detection methods for NVME drivescan be unreliable, leading to potential data loss or drive damage if not properly handled.NVME驱动器的热插拔检测方法指的是在系统运行时安全地移除和添加NVME驱动器的能力。

电力故障诊断方法研究的一些参考文献Research on the method of power failure diagnosis has been a crucial area of focus in the field of electrical engineering. One important reference in this area is the paper "Power Proxy: Anomaly Detection in Power Usage Data" by Zhang et al. This paper proposes a novel approach using deep learning techniques to detect anomalies in power usage data, which can aid in the diagnosis of power failures. The authors demonstrate the effectiveness of their method through experiments on real-world power usage datasets.电力故障诊断方法的研究一直是电气工程领域的一个重要研究方向。

张等人的论文《Power Proxy: Anomaly Detection in Power Usage Data》是这方面的一个重要参考文献。

这篇论文提出了一种新颖的方法，利用深度学习技术来检测电力使用数据中的异常，有助于诊断电力故障。

作者通过对真实电力使用数据集的实验验证了他们方法的有效性。

In addition to Zhang et al.'s work, another valuable reference is the paper "A Survey on Fault Diagnosis Techniques Through EE Stream Processing" by Wang et al. This survey paper provides a comprehensive overview of fault diagnosis techniques in the contextof electrical engineering stream processing. The authors discuss various methods such as Bayesian networks, neural networks, and decision trees, highlighting their applications in diagnosing power failures. This paper serves as a useful guide for researchers interested in exploring different fault diagnosis techniques.除了张等人的工作，王等人的论文《A Survey on Fault Diagnosis Techniques Through EE Stream Processing》也是一个很有价值的参考文献。

机器视觉行业的职业发展目标与技能特长In the rapidly advancing field of machine vision, professionals can pursue various career development goals and cultivate specific skill sets to stay competitive and excel in their roles. Here are some key career development objectives and sought-after skills in the machine vision industry:1. Develop expertise in image processing algorithms and computer vision techniques.Image processing involves manipulating digital images to extract useful information or enhance visual quality. Professionals can focus on learning various image filtering and enhancement techniques, segmentation algorithms, object detection, recognition, and tracking methods. These skills are essential for enabling machines to understand and interpret visual data.中文翻译：在快速发展的机器视觉领域，专业人士可以追求各种职业发展目标，并培养特定的技能集，以保持竞争力并在其岗位上取得优异成绩。

Date:
Customer Approve Auditing Admit
Provider Approve Auditing Admit
Customer: Add: Tel: Fax: Attn: E-mail:
Provider:HK NATER TECH LIMITED Add: 2F,NO.27,2 Baomin Rd.,Baoan Dist.SZ City,China Tel:0086-755-61522172/13510620050 Fax:0086-755-61522171 Attn:Lingo E-mail:hsdgood@
WLAN PHY Features IEEE 802.11n OFDM One Transmit and one Receive path (1T1R) Host Interface 20MHz and 40MHz bandwidth Complies with SDIO 1.1/ 2.0 for transmission WLAN with clock rate up to 100MHz GSPI interface for configurable endian Short Guard Interval (400ns) DSSS with DBPSK and DQPSK, CCK for WLAN modulation with long and short preamble OFDM with BPSK, QPSK, 16QAM, and Standards Supported 64QAM modulation. Convolutional IEEE 802.11b/g/n compatible WLAN Coding Rate: 1/2, 2/3, 3/4,and 5/6 IEEE 802.11e QoS Enhancement Maximum data rate 54Mbps in 802.11g (WMM) and 150Mbps in 802.11n 802.11i (WPA, WPA2). Open, shared Fast receiver Automatic Gain Control key,and pair-wise key authentication (AGC) services On-chip ADC and DAC Switch diversity for DSSS/CCK Hardware antenna diversity in per packet base Peripheral Interfaces General Purpose Input/Output (4 pins) Selectable receiver FIR filters Programmable scaling in transmitter and receiver to trade quantization noise against increased probability of clipping

互相关zncc原理Cross-correlation (互相关) and zero-normalized cross-correlation (ZNCC) are fundamental principles in the field of signal processing and image analysis. These techniques are widely used in various applications such as pattern recognition, feature matching, motion tracking, and image registration.互相关和零标准化互相关（ZNCC）是信号处理和图像分析领域的基本原理。

这些技术广泛应用于模式识别、特征匹配、运动跟踪和图像配准等各种应用中。

From a mathematical perspective, cross-correlation measures the similarity between two signals or images by sliding one over the other and computing the summation of the product of corresponding elements. It is a fundamental operation in signal processing that allows us to detect patterns and identify similarities between different sets of data. ZNCC, on the other hand, is a normalized version of cross-correlation that takes into account the mean and standard deviation of the signals to provide a more robust measure of similarity.从数学角度来看，互相关通过将一个信号或图像滑过另一个，并计算相应元素的乘积之和来衡量它们之间的相似性。

信道信源编码英文文献English:In the field of communication systems, the concepts of channel coding and source coding are fundamental. Channel coding, also known as error-correcting coding, involves adding redundancy to data before transmission to enable error detection and correction at the receiver. This technique is essential for reliable data transmission over noisy communication channels. Source coding, on the other hand, focuses on compressing data to reduce the number of bits required for representation without significant loss of information. Efficient source coding techniques are crucial for optimizing bandwidth usage and storage in various applications such as image and video compression, speech encoding, and data transmissionover networks. Understanding the principles and techniques of both channel coding and source coding is vital for designing efficient and robust communication systems.中文翻译:在通信系统领域，信道编码和信源编码是基础概念。

C S T e c h n i c a l R e p o r t #395, A p r i l 8, 2003Institute of ScientificComputingVolumetric Collision Detectionfor Derformable ObjectsBruno Heidelberger, Matthias Teschner, Markus GrossComputerScience DepartmentETH Zurich, Switzerland e-mail: {heidelberger, teschner, grossm}@inf.ethz.chhttp://graphics.ethz.chVolumetric Collision Detection for Deformable ObjectsBruno Heidelberger Matthias Teschner Markus GrossComputer Graphics LaboratoryETH ZurichFigure1:This sequence shows two deforming objects with150k and5k faces.An explicit representation of the red intersection volume can be computed at interactive frame rate.AbstractWe present a new algorithm for the efficient detection of colli-sions of geometrically complex objects.Our approach requires nei-ther expensive setup nor sophisticated spatial data structures and is hence specifically suitable for handling deformable objects with ar-bitrarily shaped,closed surfaces.The algorithm is based on a Lay-ered Depth Image(LDI)decomposition of the intersection volume. Currently,we have implemented two types of collision queries.The first one comprises an explicit representation of the intersection vol-ume.The second one computes vertex-in-volume tests.All queries are processed on the LDI–based volume representation.Our algo-rithm is very easy to implement and can be accelerated in graphics hardware by using OpenGL.Keywords:Collision Detection,Computational Geometry,Lay-ered Depth Image,Shape Approximation,Deformable Modeling, Physically–based Modeling,Graphics Hardware1IntroductionThe detection of collisions of geometric objects constitutes a fun-damental problem in computer graphics,computational geometry, modeling,robotics,and many other areas.Most research is devoted to the design of algorithms for the efficient computation of queries, such as intersecting surfaces or penetration depth.For complex ob-jects,such queries are computationally expensive,because they re-quire the testing of many individual graphics primitives.Hence,ef-ficient collision detection algorithms are accelerated by spatial data structures including bounding–box hierarchies,distancefields,or other ways of spatial partitioning.Such object representations are built in a pre–processing stage,and once they are created,perform very well for rigid objects.Modern physics–based simulation,as for instance used in game engines or computational surgery,demand the detection of colli-sions of objects deforming over time.In the case of deformable objects,the aforementioned acceleration structures have to be up-dated to adapt to changes of the geometry.While some of these algorithms have been successfully employed in such cases,the ac-celeration structures constitute an additional overhead in memory consumption and computational efficiency.We propose a simple and efficient algorithm based on Layered Depth Images(LDI)[Shade et al.1998]as the fundamental repre-sentation to accelerate collision detection of rigid and deformable objects.While most existing approaches consider object surfaces, our method is inherently volumetric.Rather than explicitly detect-ing surface intersections,the LDI provides a discrete representation of the intersection volume and allows for volume–based collision queries as shown in Fig.1.The generation of LDIs is extremely efficient and can be accelerated by graphics hardware.Various op-timizations minimize buffer read–backs.Our algorithm proceeds in three stages.First,we calculate the intersection of axis–aligned bounding boxes of pairs of objects.If an intersection is non–empty,a second stage computes an LDI rep-resentation of each object within the bounding–box intersection.Fi-nally,we obtain the overall intersection volume by simple Boolean operations on the LDI representation.The simplicity of the algorithm,the absence of expensive setup and the ease of implementation in OpenGL makes it especially at-tractive for real–time simulation and computer games.1.1ContributionsThe contributions of this work can be summarized as follows:•We present a new algorithm for the computation of volumetric penetrations of geometrically complex,deformable objects in real time.Our method handles arbitrarily shaped,closed sur-faces of manifold geometry.•We currently process two different types of collision queries: Thefirst one includes the explicit computation of the intersec-tion volume of two objects.The second one performs vertex-in-volume tests.Our approach is also robust in cases,where one object lies completely inside another object.•Unlike most existing approaches,our method does not require an involved setup or the implementation of a sophisticatedspatial data structure.It is memory efficient,computation-ally economical and performs on deformable models of up to hundred thousands of primitives.1.2Background and Related WorkDue to the importance of the topic,the interference detection of geometrically complex objects has been investigated extensively in various areas.Collision algorithms.In graphics,efficient collision detection is an essential component in physically–based simulationtion [Baraff 2001],[Debunne et al.2001],including cloth [Bridson et al.2002].Further applications can be found in computer animation,medical simulations,and games.Collision detection algorithms based on hierarchies have proven to be very efficient and many BVs have been investigated.Among the acceleration find spheres [Hubbard 1993],[Quinlan 1994],ing boxes [Hughes et al.1996],[van den Bergen oriented bounding boxes [Gottschalk et al.1996],and oriented polytopes [Klosowski et al.1996].Initially,BV approaches were designed for rigid context,the hierarchy is computed in a pre–processing case of deforming objects,however,this hierarchy must at run time.While effort has been spent to optimize BV for deformable objects [Larsson and Akenine-Moeller still pose a substantial computational burden and storage for complex objects.As an additional limitation for cations,BV approaches typically detect intersecting computation of the intersection volume requires an As an alternative to object partitioning,other ploy discretized distance fields as volumetric object for collision detection.The presented results,however,this data structure is less suitable for real–time metrically complex objects [Hirota et al.2000].Recently,various approaches have been introduced that employ graphics hardware for collision detection.In [Baciu et al.1999],a multi–pass rendering method is proposed for collision detection.However,this algorithm is restricted to convex objects.In [Lom-bardo et al.1999],the interaction of a cylindrical tool with de-formable tissue is accelerated by graphics hardware.The method is restricted to collisions of simplified surgical tools with object surfaces.[Hoff III et al.2001]proposes a multi–pass rendering approach for collision detection of 2–D objects,while [Kim et al.2002]and [Kim et al.2003]perform closest–point queries using bounding–volume hierarchies along with a multipass–rendering ap-proach.The aforementioned approaches decompose the objects into convex polytopes.Layered Depth Images (LDI).LDIs have been introduced as an efficient image–based rendering technique.The LDI data structure essentially stores multiple depth values per pixel.Thus,an LDI can be used to approximate the volume of an object.A method for generating LDIs is presented,for instance,in the depth–peeling approach to order–independent transparency [Everitt 2001].This implementation,however,demands a complex setup including two active depth buffers and texture shaders.In contrast,our approach to LDI generation is much simpler by employing a more efficient rendering setup based on plain OpenGL 1.4.Hardware–based approaches to interactive CSG rendering,e.g.[Goldfeather et al.1986]and [Rappoport and Spitz 1997],could also be employed for LDI generation.However,since such CSG rendering implementations have to handle more complex modeling operations they feature more involved rendering setups.Further-more,these CSG rendering approaches do not explicitly compute the resulting solid.2Method This section presents an overview of our algorithm followed by a detailed description of the three stages.Input.Our approach takes 3–D closed objects of manifold geome-try as an input.Although the approach is not confined to triangular (a)Stage 1.(b)Stage 2.(c)Stage 3a.Figure 2:Algorithm overview in 2–D and 3–D.(a)AABB inter-section.(b)LDI generation within the V oI.(c)Computation of the intersection volume.Algorithm overview.Our approach proceeds in three stages.Stage 1computes the AABB intersection for a pair of objects.If the intersection is empty,the two objects do not overlap.If it is not,stages 2and 3are applied to the AABB intersection volume.We will refer to it as V olume-of-Interest (V oI).Stage 2computes two LDIs,one for each object.LDI gener-ation is restricted to the V oI.The depth values of the LDI can be interpreted as the intersections of parallel rays or 3–D scan lines entering or leaving the object.Thus,an LDI classifies the V oI into inside and outside regions with respect to an object.Likewise,we classify the corresponding intersections into entry points and leav-ing points .The concept is similar in spirit to well–known scan–conversion algorithms to fill concave polygons [Foley et al.1990],where intersections of a scan line with a polygon represent transi-tions between interior and exterior.Stage 3performs the actual collision detection.Currently,we distinguish two different types of queries:a)Both LDIs are com-bined using Boolean intersection.If the intersection of all inside regions is not empty,a collision is detected.This operation also pro-vides an explicit representation of the intersection volume.b)Indi-vidual vertices within the V oI of one object are transformed into the LDI of the second object.If such a vertex lies in an inside region,a collision is detected.This query can also be used to check atomic or point–sampled objects for intersection.Fig.2illustrates the three stages of our algorithm.For a detailed description of all stages,refer to Sections2.1–2.3.2.1AABB IntersectionAABB intersections are computed for pairs of objects.If two AABBs do not overlap,the corresponding objects cannot interfere. Otherwise,the intersection volume provides a V oI,which is con-sidered for further processing.Although AABBs do not provide an optimal,i.e.smallest,bounding volume,they can be computed very efficiently.Furthermore,the intersection of two AABBs is again an AABB which keeps subsequent stages of the algorithm simple.Alternative BVs,such as object–oriented bounding boxes [Gottschalk et al.1996]or discrete–oriented polytopes[Klosowski et al.1996],provide tighterfitting object approximations.How-ever,the computation of these representations is more involved and the resulting intersection volume can have a more complex shape making it much more difficult for further processing.In addition, such structures are impractical for deforming objects,which require a dynamic adaptation of the bounding box.In order to simplify and accelerate further computations,the V oI has to meet the following requirements:Firstly,to guarantee proper boundary conditions,we have to render the LDI for each object from the outside.We select one of the six faces of the V oI,the so–called outside face,as the near rendering plane for each object(see Fig.3).If a sorted LDI is generated with respect to this face,en-try points and leaving points alternate.For closed objects,thefirst layer is assumed to contain entry points,the second layer leaving points and so on.Secondly,outside faces for both objects should correspond to opposite sides of the V oI(see Fig.3(a)).This sim-plifies the combination of both LDIs for the computation of the intersection volume.(a)V oI for A and B.(b)Extended V oI to obtainan outside face for A. Figure3:The intersection of the AABBs of two objects A and B provides the V oI.The intersection volume of two AABBs,i.e.the V oI,is bounded by parts of their faces.Fig.3(a)shows a V oI with corresponding outside faces.In some cases,for instance when one box is en-tirely within another box,appropriate outside faces for both objects cannot be found.This problem is solved by extending the V oI.If the outside face of one object isfixed,the opposite face of the V oI can be scaled to touch the bounding box of the other object(see Fig.3(b)).If several eligible pairs of outside faces exist,we choose the ones with the smallest distance between them.This criterion is a fast heuristic to optimize depth complexity for LDI generation assum-ing that depth complexity scales with the distance between the two outside faces.and their opposite faces defining the far planes.The remaining faces define top,bottom,left,and right of the rendering volume. Orthographic projection is used for rendering.The resolution of the LDI,which defines the accuracy of the object approximation,is specified by the user.Objects are rendered n max times for LDI gen-eration,where n max denotes the depth complexity of the relevant part of the object within the V oI.Thus,the computational com-plexity of the LDI generation is O(n max).For details regarding the OpenGL implementation,refer to Sec.3.Thefirst rendering pass generates a single LDI layer and com-putes n max.Depth testing and face culling are disabled.Only the stencil test is employed to discard fragments.The stencil test con-figuration allows thefirst fragment per pixel to pass,while the cor-responding stencil value is incremented.Subsequent fragments are discarded by the stencil test,but still increment the stencil buffer. Hence,after thefirst rendering pass the depth buffer contains the first object layer per pixel and the stencil buffer contains a map rep-resenting the depth complexity per pixel.Thus,n max is found by searching the maximum stencil value.Note,that the max()com-putation constitutes a very small computational burden,given the typical LDI resolution.If n max>1,additional rendering passes2to n max generate the remaining layers.The rendering setup is similar to thefirst pass. However,during the n-th rendering pass,thefirst n fragments per pixel pass the stencil test and,as a consequence,the resulting depth buffer contains the n-th LDI layer.During these passes,the stencil buffer is not incremented,if the stencil test fails.Since fragments are rendered in arbitrary order,the algorithm generates an unsorted LDI.For further processing,the LDI is sorted per pixel.Only thefirst n p layers per pixel are considered,where n p is the depth complexity of this pixel.n p is taken from the stencil buffer as computed in thefirst rendering pass.If n p is smaller than n max,layers n p+1to n max do not contain valid LDI values for this pixel and are discarded(see Fig.5).Although the order in which the fragments of an object are ren-dered is arbitrary,our algorithm relies on consistency across the individual passes.Our LDIs store normalized values.For advanced collision queries,additional information,such as viewing direction and viewing volume are stored as well.The accuracy of the method can be adjusted.In(x,y)-direction, accuracy corresponds with the user–defined LDI resolution.The accuracy in z-direction is given by the depth–buffer resolution.Figure 5:LDI for two pixels,unsorted and sorted with respect to depth values.Compared to alternative data structures,such as bounding–volume hierarchies or distance fields,the LDI representation is very memory efficient.Both the memory consumption and the perfor-mance of the LDI generation depend on the geometric complexity and the depth complexity of the objects.However,our results pre-sented in Sec.4suggest,that the number of LDI layers does not exceed reasonable values even for complex objects.Further,col-lision queries performed on the generated LDIs are very efficient even in the case of a large number of LDI layers.2.3Collision DetectionThe computed LDIs can be utilized to process a variety of collision queries in an efficient way.Our current implementation comprises two different types of collision queries:Intersection volume.Two LDIs can be combined to compute an intersection volume.The LDIs for two colliding objects are dis-cretized with the same resolution on corresponding sampling grids,but with opposite viewing directions.Hence,pixels in bothLDIs represent corresponding volume spans.Therefore,intersection vol-umes can be computed by a pixelwise intersection of the inside re-gions of both LDIs.Ifthe resulting intersection is non–empty,a collision is detected.The sum of all pixelwise intersection regions constitutes a discrete representation of the intersection volume (see Fig.6(a)).(a)Intersection volume.(b)Vertex-in-volume.Figure 6:Two types of collision queries.Pixelwise intersection and testing of the LDIs.Vertex-in-volume.Individual vertices can also be tested against an LDI.To this end,the vertex is transformed into the local coordinate system of the LDI.If the transformed vertex intersects with an in-side region,a collision is detected (see Fig.6(b)).As demonstrated in Fig.11and Fig.12,this query can be used to compute collisionswith atomic objects,such as particles.3Implementation The implementation of V oI computation (stage 1)and collision queries (stage 3)can be easily accomplished by following the de-scriptions in Sec.2.1and Sec.2.3,respectively.This section de-scribes some details regarding the efficient implementation of the LDI generation (see Sec.2.2)using core OpenGL 1.4functionality.The LDI generation is performed in a multi–pass rendering pro-cess following the rendering setup as described in Sec.2.2.To im-prove performance,the color buffer is disabled.Each LDI layer is generated in one rendering pass.Except for the first pass,which computes the depth complexities per pixel,all passes are identical.Our actual OpenGL implementation is very similar to the following pseudo code.rendering setup;//pass 1computes n max and the first LDI layer glClear(GL DEPTH BUFFER BIT |GL STENCIL BUFFER BIT);glStencilFunc(GL GREATER,1,0xff);glStencilOp(GL INCR,GL INCR,GL INCR);render object;depth complexities ←stencil buffer;n max ←max(depth complexities);layer [1]←depth buffer;//passes 2to n max compute the remaining LDI layers n ←2;while n ≤n max begin glClear(GL DEPTH BUFFER BIT |GL STENCIL BUFFER BIT);glStencilFunc(GL GREATER,n,0xff);glStencilOp(GL KEEP,GL INCR,GL INCR);render object;layer [n ]←depth buffer;n ←n +1;end;Each rendering pass actually requires a buffer read–back.Read-ing back buffers from the GPU,however,can be expensive de-pending on the actual architecture.We propose two methods for optimizing the data transfer using OpenGL extensions.Firstly,depth and stencil values of a pixel are usually stored in the same 32–bit word in the framebuffer.This allows to read–back both buffers in a single pass using an OpenGL extension,such as GL NV packed depth stencil.Secondly,all rendering passes can be performed independently from each other except for the first pass.We exploit this fact to render individual layers into differ-ent regions of the framebuffer.Once finished,the whole frame-buffer is read back in a single pass.This optimization reduces the number of read–backs to a maximum of two,assuming that the framebuffer memory is sufficiently large.It reduces stalls in the rendering pipeline and substantially improves the performance of the algorithm.4Results All of the experiments described in this section have been per-formed on a PC with 2.8GHz Intel Pentium IV ,2GB main mem-ory,and NVidia GeForce 4Ti 4600graphics card with 128MB on–board memory.Results for the two types of collision queries are presented using five different test objects (see Fig.7).Intersection volume.In Tab.1,computing times for six cases are given.Since the performance of our algorithm depends on the ge-ometrical complexity of the object and on the depth complexities of both objects within the V oI,we have carried out experiments asshown in Fig.8and Fig.9.In the worst case,when both AABBs coincide,the V oI and the depth complexity are maximal.Hence, both LDIs are computed for the entire object.The performance of these cases can be derived from Tab.2,where timings for the LDI generation for entire objects are given.Objects Faces DepthCom-plexityStage1+2[ms]Stage3a[ms]Fig.Knot/Rabbit12k/50k3/11918 Knot/Rabbit12k/50k4/65619 Rabbit/Reptile50k/110k7/53618 Rabbit/Reptile50k/110k4/24819 Reptile/Santa110k/150k1/32518 Reptile/Santa110k/150k8/611419 Table1:Computation of the intersection volume using various ob-ject pairs as shown in Fig.8and Fig.9.The resolution of the LDI is 128x128.The depth complexities are given for both objects within the V oI.Stage1and2compute the AABBs,the V oI,and both LDIs. Stage3a generates the intersection volume.In the case of rigid ob-jects,stages1and2can be pre–computed.An additional scenario with a very complex intersection volume is illustrated in Fig.10.The Knot and the Dragon have12k faces and520k faces,respectively.Depth complexities within the V oI are 7and8.With an LDI resolution of128x128the intersection volume can be computed in256ms.puting times for the vertex-in-volume test are given in Tab.2.For each object,100,000arbitrarily positioned vertices within the AABB of an object are tested.Fig.11illustrates the experiment for one of the test objects.Object FacesDepthComplexityStage1+2[ms]Stage3b[ms]Knot12k81126Rabbit50k82927Reptile110k128328Santa150k87926Dragon520k1030727 Table2:Vertex-in-volume test for various objects.The V oI com-puted in stage1coincides with the AABB of the object.Stage2 computes the LDI representation for the entire object with a reso-lution of128x128.In stage3,100,000vertices with arbitrary posi-tions within the AABB of the object are tested.In the case of rigid objects,stages1and2can be pre–computed.Fig.11illustrates the test for one object.An application of the vertex-in-volume test is shown in Fig.12. The simulation environment includes20,000particles and the de-forming Santa with10k faces.The object deformation,the particle dynamics,the collision detection and response can be computed and rendered in real time with29fps.5Conclusions5.1DiscussionWe have presented a fast,hardware–accelerated method for volu-metric collision detection.As a central acceleration structure our algorithm computes an LDI that approximates the intersection vol-ume.Unlike many existing collision detection schemes,our method does not require expensive setup or pre–processing and is,hence, specifically suited for deformable objects and dynamic simulation. Currently,we have implemented two different types of collision queries and demonstrated that the method performs at interactive rates up to very complex geometry.As a limitation,our collision detection approach is restricted to objects with closed,watertight surfaces,since it relies on the notion of inside and outside.5.2Future WorkCurrently,collision queries are computed on the CPU.The perfor-mance of the algorithm could be improved by using the GPU.Em-ploying pixel shaders with multiple render targets for LDI genera-tion might support GPU–based queries.While we compute an explicit representation of the intersection volume,additional queries,such as penetration depth or closest point,might be needed for advanced collision response.This is also subject to future research.At this point,self–collisions are not detected.Actually,our al-gorithm could be extended towards self–collisions by treating the front and back faces separately during the rendering process.This would allow us to efficently detect an invalid ordering of front and back faces.6AcknowledgementsThis project is funded by the National Center of Competence in Research“Computational Medicine”(NCCR Co–Me). ReferencesB ACIU,G.,W ONG,W.S.-K.,AND S UN,H.1999.RECODE:an image–based col-lision detection algorithm.The Journal of Visualization and Computer Animation 10,181–192.B ARAFF,D.2001.Collision and contact.SIGGRAPH2001Course Notes.B RIDSON,R.,F EDKIW,R.,AND A NDERSON,J.2002.Robust treatment of colli-sions,contact and friction for cloth animation.In Proc.of the29th annual confer-ence on Computer graphics and interactive techniques,ACM Press,594–603.D EBUNNE,G.,D ESBRUN,M.,C ANI,M.-P.,AND B ARR,A.H.2001.Dynamicreal-time deformations using space&time adaptive sampling.In Proc.of the28th annual conf.on Computer graphics and interactive techniques,ACM Press,31–36.E VERITT, C.2001.Interactive order–independent transparency.Technical report,NVIDIA Corporation.F OLEY,J.D.,VAN D AM,A.,F EINER,S.K.,AND H UGHES,puterGraphics:Principles and Practics.Addison-Wesley Publishing Company.G OLDFEATHER,J.,H ULTQUIST,J.P.M.,AND F UCHS,H.1986.Fast constructive–solid geometry display in the pixel–powers graphics system.In Proc.of the13th annual conference on Computer graphics and interactive techniques,ACM Press, 107–116.G OTTSCHALK,S.,L IN,M.C.,AND M ANOCHA,D.1996.Obbtree:a hierarchicalstructure for rapid interference detection.In Proc.of the23rd annual conference on Computer graphics and interactive techniques,ACM Press,171–180.H IROTA,G.,F ISHER,S.,AND L IN,M.C.2000.Simulation of non–penetratingelastic bodies using distancefields.Technical Report TR00-018,University of North Carolina at Chapel Hill.H OFF III,K.E.,Z AFERAKIS,A.,L IN,M.C.,AND M ANOCHA,D.2001.Fast andsimple2d geometric proximity queries using graphics hardware.In Proc.of2001 Symposium on Interactive3D Graphics,145–148.H UBBARD,P.M.1993.Interactive collision detection.In Proc.IEEE Symposium onResearch Frontiers in Virtual Reality,24–31.H UGHES,M.,D I M ATTIA,C.,L IN,M.C.,AND M ANOCHA,D.1996.Efficient andaccurate interference detection for polynomial deformation and soft object anima-tion.In Proc.of Computer Animation,155–166.K IM ,Y.J.,O TADUY ,M.A.,L IN ,M.C.,AND M ANOCHA ,D.2002.Fast pen-etration depth computation for physically-based animation.In Proc.of the ACM SIGGRAPH symposium on Computer animation ,ACM Press,23–31.K IM ,Y.J.,H OFF III,K.E.,L IN ,M.C.,AND M ANOCHA ,D.2003.Closest point query among the union of convex polytopes using rasterization hardware.Journal of Graphics Tools ,to appear.K LOSOWSKI ,J.T.,H ELD ,M.,M ITCHELL ,J.S.B.,S OWIZRAL ,H.,AND Z IKAN ,K.1996.Efficient collision detection using bounding volume hierarchies of k–DOPs.In Proc.of the 23rd annual conference on Computer graphics and interac-tive techniques ,ACM Press,171–180.L ARSSON ,T.,AND A KENINE -M OELLER ,T.2001.Collision detection for continu-ously deforming bodies.In Proc.of Eurographics 2001,325–333.L OMBARDO ,J.C.,C ANI ,M.-P.,AND N EYRET ,F.1999.Real–time collision detec-tion for virtual surgery.In Proc.of Computer Animation ’99,33–39.Q UINLAN ,S.1994.Efficient distance computation between non–convex objects.In Proc.IEEE Int.Conference on Robotics and Automation ,IEEE,3324–3329.R APPOPORT ,A.,AND S PITZ ,S.1997.Interactive boolean operations for conceptual design of 3–D solids.In Proc.of the 24th annual conference on Computer graphics and interactive techniques ,ACM Press/Addison-Wesley Publishing Co.,269–278.S HADE ,J.,G ORTLER ,S.,WEI H E ,L.,AND S ZELISKI ,yered depth im-ages.In Proc.of the 25th annual conference on Computer graphics and interactive techniques ,ACM Press,231–242.VAN DEN B ERGEN ,G.1997.Efficient collision detection of complex deformable models using aabb trees.Journal of Graphics Tools 2,4,1–13.Figure 7:Test objects:Dragon,Santa,Rabbit,Reptile,Knot.Figure 8:Computation of the intersection volume.AABBs,V oI,and intersection volume are shown for three examples with simpleintersections.Figure 9:Computation of the intersection volume.AABBs,V oI,and intersection volume are shown for three examples with complex intersections.Figure 10:Intersection volume for Knot andDragon.Figure 11:Vertex-in-volume test.Left:The LDI representation is computed for the entire object.V oI and AABB coincide.Right:The vertex-in-volume test is processed for 100,000arbitrarily posi-tionedvertices.Figure 12:Deforming Santa and particles.Red color on the right–hand side indicates actual or recent contact.。

KDD CUP 2009英文关键词：Customer Relationship Management (CRM), marketing databases, French Telecom company Orange, propensity of customers,中文关键词：客户关系管理(CRM)、营销数据库，法国电信公司橙、倾向的客户，数据格式：TEXT数据介绍：Customer Relationship Management (CRM) is a key element of modern marketing strategies. The KDD Cup 2009 offers the opportunity to work on large marketing databases from the French Telecom company Orange to predict the propensity of customers to switch provider (churn), buy new products or services (appetency), or buy upgrades or add-ons proposed to them to make the sale more profitable (up-selling).The most practical way, in a CRM system, to build knowledge on customer is to produce scores. A score (the output of a model) is anevaluation for all instances of a target variable to explain (i.e. churn, appetency or up-selling). Tools which produce scores allow to project, on a given population, quantifiable information. The score is computed using input variables which describe instances. Scores are then used by the information system (IS), for example, to personalize the customer relationship. An industrial customer analysis platform able to build prediction models with a very large number of input variables has been developed by Orange Labs. This platform implements several processing methods for instances and variables selection, prediction and indexation based on an efficient model combined with variable selection regularization and model averaging method. The main characteristic of this platform is its ability to scale on very large datasets with hundreds of thousands of instances and thousands of variables. The rapid and robust detection of the variables that have most contributed to the output prediction can be a key factor in a marketing application.The challenge is to beat the in-house system developed by Orange Labs. It is an opportunity to prove that you can deal with a very large database, including heterogeneous noisy data (numerical and categorical variables), and unbalanced class distributions. Time efficiency is often a crucial point. Therefore part of the competition will be time-constrained to test the ability of the participants to deliver solutions quickly.[以下内容来由机器自动翻译]客户关系管理(CRM) 是现代营销战略的一个关键要素。

hiltiHilti: A Leader in Construction Technology and SolutionsIntroductionIn the realm of construction and engineering, quality tools and equipment are vital for both efficiency and safety. Hilti is a well-known global brand that specializes in providing innovative solutions and technologies for the construction industry. Established in 1941, Hilti has become synonymous with quality, reliability, and excellence. With a strong focus on customer satisfaction and advanced engineering, Hilti continues to be a leader in the construction field.History and BackgroundHilti was founded in the small town of Schaan, Liechtenstein, by brothers Eugen and Martin Hilti. Initially, the company operated as a workshop, producing tools and machines primarily for the local construction industry. However, with a keen eye for innovation, the Hilti brothers quickly realized the potential of their products and expanded their operations.In the 1950s, Hilti introduced the world's first electric hammer drill, revolutionizing the construction industry. This breakthrough technology made drilling faster and more efficient than ever before. As word spread about the superior performance and durability of Hilti tools, the company's reputation grew exponentially.Product Range and SolutionsHilti offers a wide range of products and solutions tailored to the various needs of the construction industry. Their product portfolio includes power tools, anchoring systems, measuring and detection tools, cutting and grinding equipment, and much more.One of Hilti's flagship products is the Hilti TE 1000-AVR Breaker. This powerful and robust tool is highly valued by professionals for its exceptional performance in concrete demolition and renovation tasks. With its vibration reduction system and innovative design, the TE 1000-AVR Breaker ensures both operator comfort and efficiency.Hilti also provides specialized anchoring systems that ensure secure and reliable fastening in concrete, masonry, and steel structures. Their patented Hilti SafeSet™ technology offers aunique combination of speed, precision, and safety in anchor installation. These systems have been used in landmark projects around the world, enabling safe and efficient construction.Another notable product from Hilti is their advanced line of cordless tools, including drills, impact drivers, and saws. These tools combine high performance with long battery life, allowing professionals to work without the limitations of cords and power outlets. With their exceptional durability and ergonomic design, Hilti cordless tools are trusted by construction professionals worldwide.Innovation and ResearchContinuous innovation is at the heart of Hilti's success. The company invests heavily in research and development to provide its customers with cutting-edge solutions. Hilti's engineers and scientists work closely with construction professionals to understand their challenges and develop products that can overcome them.One notable example of Hilti's dedication to innovation is the ON!Track asset management system. This cloud-based software allows construction companies to track and managetheir tools and equipment, leading to improved productivity and cost efficiency. With real-time information on tool locations, maintenance schedules, and usage history, construction managers can optimize their workflows and reduce downtime.Sustainability and Corporate Social ResponsibilityHilti recognizes the importance of sustainability and the need to minimize the environmental impact of construction activities. The company designs its tools and equipment with energy efficiency and long-lasting durability in mind. Hilti also actively promotes recycling initiatives, ensuring that their products can be disposed of responsibly at the end of their lifecycle.In addition, Hilti is committed to supporting the communities in which it operates through various corporate social responsibility initiatives. These include vocational training programs, charitable donations, and volunteering efforts. By empowering individuals and investing in education, Hilti aims to make a positive impact on society.ConclusionHilti's long-standing commitment to innovation, quality, and customer satisfaction has solidified its position as a global leader in the construction industry. With a diverse range of products and solutions, Hilti continues to drive progress and efficiency in construction projects around the world. As the demand for sustainable and innovative technologies increases, Hilti is well-positioned to meet and exceed the expectations of its customers in the years to come.。

Robust Control and Estimation Robust control and estimation are critical aspects of engineering and technology, playing a pivotal role in ensuring the stability and performance of complex systems. These techniques are employed in a wide range of applications, including aerospace, automotive, robotics, and industrial automation. The primary goal of robust control is to design systems that can effectively operate in the presence of uncertainties and variations, while robust estimation focuses on accurately inferring the state of a system despite disturbances and measurement errors. From a technical perspective, robust control involves the design of control systems that can accommodate variations and uncertainties in the system dynamics and external disturbances. This is particularly important in real-world applications where the exact mathematical model of the system may not be known with certainty. Robust control techniques, such as H-infinity control and mu-synthesis, aim to guarantee system stability and performance under a wide range of operating conditions. These methods often involve the use of advanced mathematical tools and optimization algorithms to synthesize controllers that are resilient to uncertainties. In the realm of robust estimation, the focus is on accurately determining the state of a system based on noisy and imperfect measurements. This is crucial for feedback control systems, where the state information is used to compute control actions. Robust estimation techniques, such as Kalman filtering and robust observers, are employed to mitigate the effects of measurement noise and disturbances, providing an accurate estimate of the system state. These methods are essential for applications such as autonomous navigation, sensor fusion, and fault detection, where reliable state estimation is critical for decision-making. Beyond the technical aspects, the significance of robust control and estimation extends to the broader societal and economic impact. In the field of transportation, robust control plays a crucial role in ensuring the safety and efficiency of vehicles, ranging from airplanes to autonomous cars. By enabling systems to operate reliably under varying conditions, robust control contributes to reducing the risk of accidents and improving overall transportation infrastructure. Similarly, in industrial automation, robust estimation techniques enhance the reliability and performance of manufacturing processes, leading tohigher product quality and increased productivity. Moreover, the development of robust control and estimation techniques requires a multidisciplinary approach, encompassing aspects of mathematics, control theory, signal processing, and computer science. This fosters collaboration and knowledge exchange among experts from diverse backgrounds, driving innovation and advancements in the field. Furthermore, the application of these techniques often involves the integration of cutting-edge technologies, such as artificial intelligence, machine learning, and data-driven modeling, leading to the development of more sophisticated and adaptive control systems. From a personal standpoint, the pursuit of robust control and estimation represents a fascinating and intellectually stimulating endeavor. The challenges inherent in dealing with uncertainties and disturbances in real-world systems demand creative and innovative solutions, pushing the boundaries of knowledge and technology. The ability to design control systems that exhibit resilience and adaptability to unforeseen circumstances is not only intellectually rewarding but also holds the potential to make a tangible impact on society by enhancing the safety, reliability, and efficiency of critical systems. In conclusion, robust control and estimation stand as indispensable pillars in the realm of engineering and technology, addressing the complexities and uncertainties inherent in modern systems. From a technical standpoint, these techniques enable the design of control systems that can operate effectively under varying conditions and provide accurate state estimation despite disturbances. Moreover, their impact extends to diverse domains, including transportation, manufacturing, and robotics, where they contribute to safety, reliability, and performance. The multidisciplinary nature of robust control and estimation fosters collaboration and innovation, while the personal and societal implications underscore their significance in shaping the future of technology and engineering.。

基于边缘显著区域和结构相似度的图像视觉效果评价刘伟;薄华【摘要】图像的大部分结构信息都集中在了边缘,在进行边缘检测时滤除一些与图像计算不相关的信息,可减少计算中的数据量,使得计算更加便捷;在结构属性上也得到了很好的保留,因此边缘检测方法在图像视觉效果评估上是可行的.人眼对于一幅图像的视觉并不是每一个图像区域都具有同等的视觉重要性.可以建立一种数学方法,提取图像中的视觉重要区域,对这些区域进行视觉效果评价.选取基于结构相似度方法作为最终评价方法,得到了一种全参考图像质量评价算法.最后将实验结果与3个图像评价库的参考结果进行拟合,得到的结果与其他算法相比表明,该算法更加符合人眼的视觉效果特性.【期刊名称】《微型机与应用》【年(卷),期】2016(035)009【总页数】5页(P48-51,54)【关键词】边缘检测;Canny算子;局部方差;结构相似度【作者】刘伟;薄华【作者单位】上海海事大学信息工程学院,上海 201306;上海海事大学信息工程学院,上海 201306【正文语种】中文【中图分类】TP391.4随着现代多媒体信息技术的快速发展，数字图像作为多媒体技术之一在当今生活中的作用越来越受到大家的关注，图像在获取、压缩、编码、传输、处理等过程中产生的各种失真和误差是无法回避的，这些不同程度的误差和失真会直接影响到图像的质量，从而影响了人眼对图像观赏效果，因此需要一种更加方便且精确的方法来评价图像的质量。

当下图像视觉效果评估方法主要分为两大类：主观评估和客观评估。

主观评估法昂贵、耗时又不方便实时应用，不仅受到人体本身条件的影响而且还受到环境因素的影响，因此评估稳定性较差。

客观评估方法是利用计算机通过算法来算出失真图片视觉效果值作为评估的依据，客观评估方法可分为全参考、部分参考和无参考三种类型。

本文是基于全参考下的图像视觉效果评价方法。

全参考图像视觉效果评估方法是指拥有该失真图像的原始图像，运用某种数学算法将图像某一特征进行提取，与待评价图像进行对比分析，通过差异的大小判断失真图像的质量的好坏，这种模式的好处是充分运用了数学模型的稳定性，准确性较好。

Detection Verification: A Comprehensive OverviewIntroductionDetection verification is a crucial process in various fields, including security, technology, and healthcare. It involves the identification and confirmation of detected objects or events to ensure accuracy and reliability. This article provides a comprehensive overview of detection verification, including its definition, applications, techniques, challenges, and future prospects.DefinitionDetection verification refers to the process of confirming the presence or absence of an object or event identified by a detection system. It aims to minimize false positives and false negatives by validating the detection results through additional analysis or human intervention. The ultimate goal is to enhance the reliability and trustworthiness of detection systems.ApplicationsSecurityDetection verification plays a vital role in security systems such as surveillance cameras, access control systems, and intrusion detection systems. By verifying detected events like potential threats or unauthorized access attempts, security personnel can quickly respond to real incidents while minimizing false alarms.TechnologyIn the field of technology, detection verification is used in various areas such as computer vision, natural language processing (NLP), and machine learning. For example, in computer vision applications like object recognition or facial recognition systems, verification techniques are employed to confirm the accuracy of detected objects or individuals.HealthcareIn healthcare settings, detection verification is essential for medical imaging techniques such as X-rays and MRIs. Radiologists useverification methods to ensure accurate identification of abnormalities or diseases in patients’ scans. This helps in providing precise diagnoses and appropriate treatments.Techniques for Detection Verification1.Manual Verification: Human experts manually review the detectedobjects or events to confirm their presence or absence. Thistechnique ensures high accuracy but can be time-consuming andsubjective.2.Rule-based Verification: Predefined rules are applied to verifythe detected objects or events based on specific criteria. Forexample, an intrusion detection system may verify an alarm bychecking if multiple sensors have triggered simultaneously.3.Machine Learning Verification: Machine learning algorithms aretrained to verify the detection results based on a labeled dataset.The algorithms learn patterns and characteristics of truepositives and negatives, improving the accuracy of verification. 4.Statistical Analysis: Statistical methods are used to analyze thedetection results and calculate probabilities or confidence levels.By setting appropriate thresholds, verification can be performedbased on statistical significance.Challenges in Detection Verificationplex Environments: Detection verification becomes challengingin complex environments with high levels of noise, occlusions, oroverlapping objects. These factors can lead to false detectionsand require sophisticated techniques for accurate verification.2.Real-time Verification: In applications where real-time responseis critical, such as security systems, performing efficientverification within tight time constraints can be challenging.Fast and reliable algorithms are required to ensure timelyresponses.3.Adversarial Attacks: Malicious actors may attempt to deceivedetection systems by introducing adversarial inputs thatmanipulate the detection results. Detecting and verifying suchattacks pose significant challenges for detection verificationtechniques.4.Scalability: As the volume of data increases, scalability becomescrucial for efficient detection verification. Handling largedatasets in real-time while maintaining high accuracy is achallenge that needs to be addressed.Future ProspectsDetection verification is an evolving field with promising future prospects. Advances in machine learning, deep learning, and artificial intelligence will contribute to more robust and accurate verification techniques. Additionally, the integration of multiple sensors and data fusion techniques will enhance the reliability of detection systems.Furthermore, research efforts should focus on addressing the challenges posed by complex environments and adversarial attacks through innovative algorithms and robust architectures. Collaborative studies between academia, industry, and government entities can accelerate advancements in detection verification technology.In conclusion, detection verification plays a critical role in ensuring the accuracy and reliability of detected objects or events across various domains such as security, technology, and healthcare. By employing different techniques and overcoming challenges, detection verification contributes to enhanced decision-making processes and improved overall system performance.Note: This article provides a comprehensive overview of detection verification. However, due to the requirement of avoiding sensitive topics or vocabulary in China, some specific examples or discussions related to these topics have been omitted.。

卫生检验与检疫技术英语作文As crucial components of public health and safety measures, sanitary inspection and quarantine technology is essential in preventing the spread of diseases and ensuring the quality of imported and exported goods. 作为公共卫生和安全措施的重要组成部分，卫生检验与检疫技术对于防止疾病传播、确保进出口商品的质量至关重要。

Sanitary inspection involves the evaluation of food, water, and environmental conditions to ensure they meet established health and safety standards. 卫生检验涉及对食品、水以及环境条件进行评估，以确保它们符合既定的健康和安全标准。

In the context of global trade and travel, the role of quarantine technology becomes increasingly important in detecting and preventing the spread of infectious diseases across borders. 在全球贸易和旅行的背景下，检疫技术的作用变得日益重要，可以检测和预防跨境传播的传染病。

The use of advanced technologies such as DNA sequencing, rapid diagnostic tests, and artificial intelligence has revolutionized the fieldof sanitary inspection and quarantine, allowing for more accurateand efficient detection of pathogens and contaminants. 使用DNA测序、快速诊断测试以及人工智能等先进技术已经彻底改变了卫生检验和检疫领域，使得可以更准确和高效地检测病原体和污染物。

介绍自己未来社区医院的英语作文In the heart of our bustling community lies a beacon of health and wellness: our future community hospital. This state-of-the-art facility is designed with the patient experience at its core, offering a comprehensive range of healthcare services that cater to the diverse needs of our local population.Upon entering the hospital, one is greeted by a spacious lobby with natural light streaming in through large windows, creating a warm and welcoming atmosphere. The check-in process is streamlined, thanks to our advanced electronic health record system, which ensures a smooth and efficient patient flow.Our community hospital boasts a team of dedicated healthcare professionals, including doctors, nurses, and specialists, all committed to providing the highest standard of care. We have invested in cutting-edge medical technology, such as telemedicine capabilities, which allow our patients to consult with specialists remotely, especially beneficial for those living in more rural areas of our community.The hospital is equipped with various departments, including emergency care, pediatrics, obstetrics and gynecology, and geriatric care, ensuring that every member of the family has access to specialized treatment. We also have a robust mental health and wellness program, recognizing theimportance of mental well-being in overall health.Our commitment to the community extends beyond the hospital walls. We actively engage in health education and preventive care initiatives, aiming to educate the public on the importance of a healthy lifestyle and early detection of illnesses.Sustainability is a key aspect of our hospital's design and operations. We have implemented energy-efficient systems and utilize renewable energy sources whenever possible. Our grounds are landscaped with native plants that require minimal water and maintenance, contributing to our goal of reducing our environmental footprint.As we look to the future, our community hospital will continue to evolve, adapting to the changing healthcare landscape and the needs of our community. We are dedicated to remaining a trusted partner in health, providing exceptional care and support to all who walk through our doors.。

A Method for the Rapid and Efficient Elution of NativeAffinity-Purified Protein A Tagged ComplexesCaterina Strambio-de-Castillia,†Jaclyn Tetenbaum-Novatt,†Brian S.Imai,‡Brian T.Chait,§andMichael P.Rout*,†The Rockefeller University,1230York Avenue,New York New York 10021-6399Received May 24,2005A problem faced in proteomics studies is the recovery of tagged protein complexes in their native and active form.Here we describe a peptide,Bio-Ox,that mimics the immunoglobulin G (IgG)binding interface of Staphylococcus aureus Protein A,and competitively displaces affinity-purified Protein A fusion proteins and protein complexes from IgG-Sepharose.We show that Bio-Ox elution is a robust method for the efficient and rapid recovery of native tagged proteins,and can be applied to a variety of structural genomics and proteomics studies.Keywords:Staphylococcus aureus •Protein A •affinity purification •proteomics •fusion proteinIntroductionProtein -protein interactions are central to the maintenance and control of cellular processes.The study of such protein -protein interactions has been greatly enhanced by fusion protein technology,wherein specific peptide or protein domain “tags”are fused to the protein of interest (generally at either its carboxyl-terminus or amino-terminus).These tags can facilitate the detection,increase the yield,and enhance the solubility of their associated proteins.1-3Most importantly,these fusion domains have been exploited to allow the single-step purification of the test protein either alone or in complexes with its in vivo binding partners.4-6The yield of these purifica-tion methods is often high enough to allow the identification of such binding partners by mass spectrometry.A commonly used affinity tagging method generates ge-nomically expressed Protein A (PrA)fusion proteins by modify-ing the coding sequence of the protein under study via PCR-directed approaches.7-9This method takes advantage of the ∼10nM binding affinity of PrA from S taphylococcus aureus for the constant region (Fc)of immunoglobulin G (IgG).10After purification on IgG-conjugated resins,PrA-tagged proteins or protein complexes are most commonly eluted from the resin using high or low pH conditions.These elution methods typically lead to the denaturation of the isolated proteins,the dissociation of complexes,and concomitant loss of activity.However,it is often desirable to recover soluble native protein or protein complexes.One method by which this can be achieved is by constructing a cleavable tag.Such tags carry a specific cleavage site for a protease placed proximal to the tagged protein,allowing the tag to be removed from the fusion protein.Proteases that are widely used for this purpose includeblood coagulation factors X (factor Xa),enteropeptidase (en-terokinase),alpha-thrombin,and the tobacco etch virus (TEV)protease.Nevertheless,this method has drawbacks.First,the literature is replete with reports of fusion proteins that were cleaved by these proteases at sites other than the canonical cleaving site.11-14Second,the removal of the tag destroys the ability to detect or further purify the protein of interest,necessitating the encumbrance of a second,tandem tag.15Here,we describe a rapid single step method for the efficient recovery of native and active PrA fusion proteins and protein complexes from IgG-Sepharose.This technique avoids the complications of having to use a protease and in addition has the advantage of retaining the original tag on the target protein after elution,permitting further purification steps and detection of the fusion protein in subsequent experiments.Our method takes advantage of a previously described peptide,termed FcIII,16which mimics the protein -protein binding interface of PrA for the hinge region on the Fc domain of human IgG.We modified FcIII by the addition of a biotin moiety to its amino-terminus to increase the peptide’s solubility while leaving its affinity for Fc intact s making it a more effective elution reagent.We termed this modified peptide,Bio-Ox.To investigate the properties of Bio-Ox,PrA-tagged proteins were isolated in their native state from yeast on an IgG-conjugated Sepharose resin,either alone or in combination with their in vivo interacting partners;the Bio-Ox peptide was then used to competitively displace the tagged proteins and elute them from the resin.The efficiency of elution was monitored by quantitatively comparing the amounts of proteins eluted to the amounts remaining on the resin under a variety of test conditions.We show that Bio-Ox elution is a robust method for the efficient and rapid recovery of native tagged proteins that can be applied to a variety of structural genomics and proteomics studies.*To whom correspondence should be addressed.Tel:+1(212)327-8135.E-mail:rout@.†Laboratory of Cellular and Structural Biology,Box 213.‡Proteomics Resource Center,Box 105.§Laboratory of Mass Spectrometry and Gaseous Ion Chemistry,Box 170.2250Journal of Proteome Research 2005,4,2250-225610.1021/pr0501517CCC:$30.25©2005American Chemical SocietyPublished on Web10/08/2005Experimental SectionPeptide Synthesis,Oxidation and Cyclization.Peptides were synthesized using standard Fmoc protocols.Typical deprotec-tion times with20%piperidine were2times10min and typical coupling times with4-10-fold excess of amino acids over resin were2to6h.Small batches of peptides were made on a Symphony synthesizer(Protein Technologies,Inc.),while larger batches were made manually.Peptides were cleaved from the resin using94.5%trifluoroacetic acid, 2.5%water, 2.5% ethanedithiol and1%triisopropylsilane for3h at25°C.The solubilized peptides were precipitated with10volumes of cold tert-butyl methyl ether and the precipitated peptide was washed several times with ether prior to air-drying.The air-dried peptide was dissolved in20%acetonitrile in water to approximately0.5mg/mL,the pH was adjusted to8.5using sodium bicarbonate and the peptide was allowed to air oxidize overnight to promote cyclization.The progress of cyclization was monitored by mass spectrometry.The cyclized crude peptide was purified using standard preparative reversed phase HPLC using a Vydac218TP1022C18column.Peptide Solubility.Eluting peptides were suspended at a concentration of440µM(0.77mg/mL for BioOx;0.67mg/mL for FcIII),in peptide buffer by extensive vortexing.The peptide concentration was verified by measuring the OD280nm of each solution(extinction coefficient:1OD280nm)0.13mg/mL).The peptide solutions/suspensions were then combined with equal amounts of a100mM buffer to obtain∼220µM peptide at a range of pH values(buffers:Na-Acetate pH4.8,Na-Citrate pH 5.4,Na-Succinate pH5.8,Na-MES pH6.2,BisTris-Cl pH6.5, Na-HEPES pH7.4,Na-TES pH7.5,Tris-Cl pH8.3,Na-CAPSO pH9.6).Samples were incubated at room temperature with gentle agitation for20min,and then insoluble material was removed by centrifugation at21000×g max for20min at25°C.The concentration of peptide in each remaining superna-tant was determined by measuring its OD280nm.To determine the maximum solubility of each peptide,the peptides were dissolved to saturation in peptide buffer by extensive vortexing and incubation with stirring at25°C overnight.Insoluble material was removed by centrifugation at15000×g for15min at25°C and the amount of dissolved peptide was measured directly by amino acid analysis.Peptide Competitive Displacement of Bound Recombinant PrA from IgG-Sepharose.Recombinant PrA(280µg;6.7nmol) from S.aureus(Pierce)was dissolved in1mL TB-T[20mM HEPES-KOH pH7.4,110mM KOAc,2mM MgCl2,0.1%Tween-20(vol/vol)]and added to280µL of packed pre-equilibrated Sepharose4B(Amersham Biosciences)conjugated with affinity-purified rabbit IgG(ICN/Cappel; 1.87nmoles IgG).After incubation on a rotating wheel overnight at4°C,the resin was washed twice with1mL TB-T,twice with1mL TB-T containing 200mM MgCl2,and twice with1mL TB-T.After the final wash, the resin was divided evenly into14equal aliquots.The peptide was dissolved in peptide buffer at concentrations ranging between0and440µM peptide.Aliquots of400µL of the appropriate peptide solution was added to each PrA-IgG-Sepharose containing tube,and the tubes were then incubated on a rotating platform for3h at4°C followed by1h at25°C. After displacement of bound PrA from the IgG-Sepharose,the resin was recovered by centrifugation on a Bio-Spin column (BioRad),and resuspended in one-bed volume of sample buffer.Samples were separated by SDS-PAGE.Yeast Strains.Strains are isogenic to DH5alpha unless otherwise specified.All yeast strains were constructed using standard genetic techniques.C-terminal genomically tagged strains were generated using the PCR method previously described.7,17Affinity Purification of Proteins and Protein Complexes on IgG-Sepharose.The protocol for the purification of PrA-containing complexes was modified from published methods.18-20For the purification of Kap95p-PrA,yeast cytosol was prepared essentially as previously described.21,22Kap95p-PrA cytosol was diluted with3.75volumes of extraction buffer 1[EB1:20mM Hepes/KOH,pH7.4,0.1%(vol/vol)Tween-20, 1mM EDTA,1mM DTT,4µg/mL pepstatin,0.2mg/mL PMSF]. The diluted cytosol was cleared by centrifugation at2000×g av for10min in a Sorvall T6000D tabletop centrifuge and at 181000×g max for1h in a Type80Ti Beckman rotor at4°C.10µL bed volume of IgG-Sepharose pre-equilibrated in EB1was added per0.5mL of cytosol and the binding reaction was incubated overnight at4°C on a rotating wheel.The resin was recovered by centrifugation at2000×g av for1min in a Sorvall T6000D tabletop centrifuge,transferred to1.5mL snap-cap tubes(Eppendorf),and washed6times with EB1without DTT. For the purification of Nup82p-PrA,cells were grown in Whickerham’s medium21to a concentration of4×107cells/ mL,washed with water and with20mM Hepes/KOH pH7.4, 1.2%PVP(weight/vol),4µg/mL pepstatin,0.2mg/mL PMSF, and frozen in liquid N2before being ground with a motorized grinder(Retsch).Ground cell powder(1g)was thawed into10 mL of extraction buffer2[EB2;20mM Na-HEPES,pH7.4,0.5% TritonX-100(vol/vol),75mM NaCl,1mM DTT,4µg/mL pepstatin,0.2mg/mL PMSF].Cell lysates were homogenized by extensive vortexing at25°C followed by the use of a Polytron for25s(PT10/35;Brinkman Instruments)at4°C.Clearing of the homogenate,binding to IgG-Sepharose,resin recovery and washing was done as above except that10µL of IgG-Sepharose bed volume was used per1g of cell powder and EB2without DTT was used for all the washes.Elution of the PrA tagged complexes was performed as described below.Peptide Elution of Test Proteins and Protein Complexes and Removal of Peptide by Size Exclusion.Kap95p-PrA or Nup82p-PrA bound IgG-Sepharose resin was recovered over a pre-equilibrated Bio-Spin column(BioRad)by centrifugation for1min at1000×g max.Three bed-volumes of440µM(unless otherwise indicated in the text)of eluting peptide in peptide buffer were added per volume of packed IgG Sepharose resin. The elution was carried out for various times(as indicated in the text)at either4°C or at25°C.When elution was complete, the eluate was recovered over a Bio-Spin column.Finally,the resin was washed with one bed-volume of elution buffer to displace more eluted material from the resin and the wash was pooled with the initial eluate.The peptide was removed by filtration of the eluate over a micro spin G25column(Amer-sham Biosciences)as described by the manufacturer.Kap95p-Nup2p in Vitro Binding Experiments.To demon-strate in vitro binding of proteins after elution from the resin, Kap95p-PrA from0.3mL of yeast cytosol was affinity-purified on17.5µL of packed IgG-Sepharose and eluted with52.5µL of440µM Bio-Ox for2.5h at4°C followed by1h at25°C. The resulting sample(total volume88µL)was mixed with0.1µL of E.coli total cell lysate containing Nup2p-GST(generous gift from David Dilworth and John Aitchison23)and brought to a total volume of500µL with TB-T,1mM DTT,4µg/mL pepstatin,0.2mg/mL PMSF.Controls were set up in the absence of either Kap95p-PrA or Nup2p-GST.The samples were incubated at25°C for30min after which40µL of packed,pre-Native Elution of PrA-Tagged Proteins research articlesJournal of Proteome Research•Vol.4,No.6,20052251equilibrated glutathione-Sepharose 4B resin (Amersham Bio-sciences)was added per sample and the incubation was continued at 4°C for 1h.After nine washes with 1mL of TB-T,1mM DTT,4µg/mL pepstatin,0.2mg/mL PMSF,at 25°C,the resin was recovered on Bio-Spin columns as described above and bound material was eluted with 40µL of sample buffer.The samples were resolved on SDS-PAGE alongside an aliquot of input peptide-eluted Kap95p-PrA.To demonstrate the recovery of in vitro reconstituted protein complexes from the resin,Kap95p-PrA from 0.3mL of yeast cytosol was affinity-purified on 10µL of packed IgG-Sepharose and the washed resin was equilibrated in TB-T,1mM DTT,4µg/mL pepstatin,0.2mg/mL PMSF.This pre-bound Kap95p-PrA was mixed with 50µL of E.coli total cell lysate containing Nup2p-GST in a total volume of 1mL of TB-T,1mM DTT,4µg/mL pepstatin,0.2mg/mL PMSF.A mock control experiment was set up in the absence of Nup2p-GST.The binding reaction was carried out for 1h at 4°C and the resin was washed 2times with 1mL of TB-T,2times with 1mL of TB-T containing 100µM ATP and 3times with peptide buffer (all washed were without DTT).Bound material was eluted with 30µL of 440µM Bio-Ox in peptide buffer at 4°C for 2.5h at 4°C followed by 1h at 25°C.Samples were resolved by SDS-PAGE.Figure 1.Addition of a Biotin moiety to the FcIII peptide does not alter the ability of the peptide to competitively displace bound PrA from IgG-Sepharose.(a)Primary sequence and chemical structure of the biotinylated FcIII peptide,Bio-Ox.(b)220µM suspensions of peptides were prepared in buffers of different pHs,and allowed to solubilize.The material remaining in the buffer after centrifugation is plotted for Bio-Ox (closed triangles,black trend line )and FcIII (open circles,gray trend line ;dashed horizontal line represents the starting 220µM level .(c)Increasing amounts of Bio-Ox (closed triangles )and FcIII (open diamonds )were used to competitively displace recombinant PrA from IgG-Sepharose.The amounts of PrA left on the resin after elution were resolved by SDS-PAGE alongside known amounts of PrA standards.The data are displayed on logarithmic scale on both axes.Data are displayed as a %recovery relative to the input PrA amount (i.e.,PrA amount remaining bound in the absence of eluting peptide).Linear regression for both data sets was used to calculate the IC50.research articlesStrambio-de-Castillia et al.2252Journal of Proteome Research •Vol.4,No.6,2005Quantitation and Image Analyses.Band intensities were quantified with the Openlab software (Improvision),and the data was plotted using Excel (Microsoft).Results and DiscussionDesign of the PrA Mimicking Peptide.The hinge region on the Fc fragment of immunoglobulin G (IgG)interacts with Staphylococcus aureus Protein A (PrA).This region was also found to be the preferred binding site for peptides selected by bacteriophage display from a random library.16The specific Fc binding interactions of a selected 13amino acid peptide (termed FcIII),were shown to closely mimic those of natural Fc binding partners.We reasoned that this peptide could be used to efficiently displace PrA tagged proteins from IgG-conjugated affinity resins.Initial trials with FcIII determined that,although it functioned as an eluant,it exhibited a strong tendency to aggregate and its solubility under physiological conditions was not sufficient for many practical purposes,leading to low yields and nonreproducible results.As the high peptide concentrations needed for elution are outside the conditions for which the FcIII peptide was designed,we synthesized several modified peptides based on FcIII,with the specific aim of increasing their solubility and decreasing their degree of aggregation under conditions that would be useful for the isolation of proteins and protein complexes.Among the different alternatives,the most efficient in the displacement of bound PrA-tagged Kap95p from IgG-Sepharose was a peptide in which the amino-terminus of the original FcIII peptide wasFigure 2.Bio-Ox can be used to efficiently compete bound PrA-tagged proteins and protein complexes from IgG-Sepharose in a temperature-dependent fashion.(a )Kap95p-PrA/Kap60p was affinity-purified on IgG-Sepharose from logarithmically growing yeast cells.440µM Bio-Ox was used to competitively displace the bound tagged proteins from the IgG-Sepharose resin.The elution reaction was carried out for the times indicated.At the end of the incubation time eluted proteins (E )and proteins remaining bound to the resin (B )were resolved on SDS-PAGE.(b )Kap95p-PrA (closed squares)and Nup82p-PrA (open squares )were affinity-purified on IgG-Sepharose from logarithmically growing yeast cells and eluted as described above.The amounts of eluted versus resin-bound protein was quantified using the OpenLab software and the elution efficiency for each time point is presented as the percentage of eluted material over the total amount of bound plus eluted material (%eluted).(c )440µM Bio-Ox was used to elute Kap95p-PrA or Nup82p-PrA for 1h at 4°C or 25°C as indicated.Native Elution of PrA-Tagged Proteinsresearch articlesJournal of Proteome Research •Vol.4,No.6,20052253modified by the addition of a Biotin moiety (data not shown).We termed this peptide Bio-Ox (Figure 1,panel a).The solubility of Bio-Ox was measured directly by amino acid analysis and was shown to be ∼3-fold greater than the solubility of FcIII at pH 7.4.In addition,comparison of the solubility of both peptides over a range of pHs indicated that the Bio-Ox was considerably more soluble than FcIII at all but the most extreme pHs tested;importantly,Bio-Ox is very soluble across the full physiological range of pHs (Figure 1,panel b).To determine whether the addition of the Biotin moiety could have altered the inhibiting ability of the peptide,we measured the inhibition constant for Bio-Ox and found it to be comparable with the reported K i for FcIII (∼11nM;data not shown).We then measured the IC 50for competitive displacement for FcIII and Bio-Ox,under conditions in which both were soluble.For this test,commercially available recom-binant PrA from S.aureus was first bound to IgG-Sepharose and then increasing concentrations of the peptide were used to displace the bound PrA from the immobilized IgG (Figure 1,panel c).The apparent IC 50was found to be 10.4(3.2µM for FcIII and 9.8(2.6µM for Bio-Ox (mean value of four independent trials (standard deviation of the mean).Taken together,Bio-Ox appears to be as efficient as FcIII at binding to the F c portion of antibodies and competing for this site with Protein A,but is far more soluble in physiologically compatible buffers,a key requirement for an efficient elution peptide (Figure 3).Experimental Design of the Competitive Elution Procedure.The principle of the method is as follows;genomically PrA-tagged proteins of interest are expressed in yeast and affinity isolated on IgG-conjugated Sepharose resin.Depending on the conditions used for lysis and extraction,the test protein can be recovered in native form either in isolation or in complexes with protein partners.After binding,the resin is recovered by centrifugation and washed extensively to remove unbound material.The bound material is competitively displaced from the IgG-Sepharose resin by incubation with 440µM Bio-Ox peptide in peptide buffer for 2h at 4°C.Finally,the peptide is rapidly (<1min)removed from the eluted sample by fraction-ation over a size exclusion spin column.Given a typical protein of average abundance,1-10µg of pure protein can be recovered from 1g of cells using this method.Figure 3.Elution of Kap95-PrA/Kap60p is dose dependent.(a )Kap95p-PrA was affinity-purified on IgG-Sepharose from loga-rithmically growing yeast cells and eluted using increasing concentrations of Bio-Ox peptide as indicated.(b )The elution efficiency measured as described in Figure 2was plotted versus the peptide concentration in logarithmic scale as indicated.Figure 4.Eluted Kap95p-PrA/Kap60complex retains its biological activity.(a )Kap95p-PrA was prepared by affinity purification followed by Bio-Ox peptide elution (Kap95-PrA eluate ).Three binding reactions were then set up containing eluted Kap95p-PrA and Nup2p-GST bacterial lysate,Kap95p-PrA alone or Nup2p-GST alone.At the end of the incubation,Nup2p-GST was affinity-purified on glutathione-Sepharose and the immobilized material was eluted from the resin with sample buffer and resolved on SDS -PAGE (GST bound ).(b )Kap95p-PrA was immobilized on IgG-Sepharose and incubated with (+)or without (-)bacterial lysate containing Nup2p-GST.The resulting material was eluted using Bio-Ox.Eluate (E )and resin bound (B )material was resolved on SDS-PAGE.*,indicates a Nup2p breakdown product.Table 1.Elution Efficiency for PrA Tagged Nupsname of nup%yieldNup53p 56Nup59p 81Nup84p 88Nup85p 81Nic96p 76Nsp1p 99Nup1p 99Nup120p 69Nup157p 82Nup159p 53Nup170p 80Nup192p 76Gle2p 90research articlesStrambio-de-Castillia et al.2254Journal of Proteome Research •Vol.4,No.6,2005To explore the characteristics of Bio-Ox elution under conditions that preserve native protein complexes,we chose to work with the yeast karyopherin Kap95p-PrA/Kap60p com-plex,24and with the yeast nucleoporin Nup82p-PrA/Nsp1p/ Nup159p complex.25,26This choice was dictated by our interest in the structure and function of the yeast nuclear pore complex (NPC).17,27Optimization of the Elution Conditions.An elution time course for Kap95p-PrA/Kap60p and Nup82p-PrA from IgG-Sepharose at4°C is shown in Figure2,panels a and b.In both cases,the elution was virtually complete after2h at4°C.The largest difference in elution efficiency between the two test proteins was found at the earlier time points.Thus,more than 50%of initially bound Kap95p-PrA was displaced by10min, while it took∼1h to obtain the same result with Nup82p-PrA. We also determined the temperature dependence of the elution process(Figure2,panel c).Elutions of Kap95p-PrA and Nup82p-PrA with Bio-Ox,for1h were compared at4°C and 25°C(Figure2,panel c),showing that elution was improved at25°C over4°C for both test proteins.These various factors underscore the need to conduct appropriate test experiments to determine the optimal conditions for any given application. For example,elution for shorter periods and at4°C is preferable when the proteins under study are sensitive to denaturation,dissociation or proteolytic degradation.We also tested the dependence of elution efficiency upon Bio-Ox concentration.For this test,Kap95p-PrA bound to IgG-Sepharose was competitively displaced using increasing amounts of Bio-Ox peptide for4h at4°C.(Figure3).Bio-Ox peptide displaced IgG-Sepharose bound PrA tagged Kap95p with an apparent IC50of60.8µM.For practical purposes,the protocol we use in most cases takes advantage of the high solubility of Bio-Ox to obtain maximally efficient elutions,utilizing a concentration of440µM of Bio-Ox peptide for2h at4°C.To test the general applicability of the method,we performed peptide elution experiments using a series of PrA tagged proteins that were available in our laboratories.17The yield for these proteins was in all cases>50%and in most cases was >80%(average yield78%(14%;Table1).Eluted Proteins Retain their Biological Activity.The trans-location of macromolecules between the nucleus and cytosol of eukaryotic cells occurs through the NPC and is facilitated by soluble transport factors termed karyopherins(reviewed in ref28).Nucleoporins that contain FG peptide repeats(FG Nups)function as binding sites for karyopherins within the NPC.One example of an FG Nup-karyopherin interaction is represented by the binding of the Kap95p/Kap60p complex to Nup2p,29an interaction that requires both karyopherins to be natively folded.30,31We took advantage of this interaction to demonstrate that the Bio-Ox eluted Kap95p-PrA/Kap60p com-plex retains its biological activity and is able to bind Nup2p in vitro(Figure4,panel a).In this test,Kap95p-PrA was affinity-purified and eluted from IgG-Sepharose as described above. The eluate was incubated with whole cell lysate from E.coli expressing Nup2p-GST,23and GST-tagged Nup2p was isolated over gluthatione-Sepharose resin.As a control,the same experiment was performed either in the absence of Nup2p-GST containing bacterial lysate or in the absence of Kap95p-PrA eluate.As shown,Nup2p-GST binds specifically and directly to the peptide-eluted Kap95p-PrA/Kap60p complex. This result is consistent with reported data and demonstrates that elution with Bio-Ox does not alter the native state and biological activity of Kap95p-PrA.Moreover,the apparent equimolar stoichiometry of the Nup2-GST/Kap95p-PrA/Kap60p complex indicates that essentially all of the peptide eluted karyopherins were in their native,active conformation.This result underscores the usefulness of this method for the preparation of native protein samples.The method can also be used for in vitro reconstitution experiments of biologically relevant protein-protein interac-tions of interest.For this test,Kap95p-PrA was affinity isolated on IgG-Sepharose,Nup2p-GST was bound to the immobilized Kap95p-PrA and then the reconstituted complex was competi-tively displaced from the resin by Bio-Ox peptide elution(Figure 4,panel b).This shows that the method can be used in vitro to study protein-protein interactions using purified compo-nents.ConclusionWe have used the Bio-Ox technology extensively in our laboratories for a wide variety of applications including:(1) the semipreparative purification of∼30PrA-tagged natively folded Nups for the determination of their sedimentation coefficient over a sucrose velocity gradient(S.Dokudovskaya, L.Veenhoff,personal communication);(2)the isolation of yeast cyclins and cyclin-Cdk associated proteins;32(3)the semi-preparative purification of enzymatically active Dpb4p-PrA chromatin remodeling/histone complexes;33and(4)the study of the in vitro binding property of proteins of interest using blot and resin binding experiments.34Thus,this method should be generally applicable to the native purification of most other proteins and protein complexes.Acknowledgment.We are very grateful to David Dil-worth and John Aitchison for the generous gift of bacterially expressed Nup2p-GST.We are deeply indebted to Rosemary Williams for her skilled technical assistance throughout the course of this study and to all members of the Rout and Chait laboratories and of the Proteomic Research Center,past and present,for their continual help and unwavering support.We are particularly grateful to Markus Kalkum,Bhaskar Chan-drasekhar,Svetlana Dokudovskaya and Liesbeth Veenhoff.This work was supported by grants from the American Cancer Society(RSG-0404251)and the NIH(GM062427,RR00862,and CA89810).References(1)Uhlen,M.;Forsberg,G.;Moks,T.;Hartmanis,M.;Nilsson,B.Fusion proteins in biotechnology.Curr.Opin.Biotechnol.1992, 3(4),363-369.(2)Nygren,P.A.;Stahl,S.;Uhlen,M.Engineering proteins to facilitatebioprocessing.Trends Biotechnol.1994,12(5),184-188.(3)Baneyx,F.Recombinant protein expression in Escherichia coli.Curr.Opin.Biotechnol.1999,10(5),411-421.(4)LaVallie,E.R.;McCoy,J.M.Gene fusion expression systems inEscherichia coli.Curr.Opin.Biotechnol.1995,6(5),501-506.(5)Nilsson,J.;Stahl,S.;Lundeberg,J.;Uhlen,M.;Nygren,P.A.Affinity fusion strategies for detection,purification,and im-mobilization of recombinant proteins.Protein Expr.Purif.1997, 11(1),1-16.(6)Einhauer,A.;Jungbauer,A.The FLAG peptide,a versatile fusiontag for the purification of recombinant proteins.J.Biochem.Biophys.Methods2001,49(1-3),455-465.(7)Aitchison,J. D.;Blobel,G.;Rout,M.P.Nup120p:a yeastnucleoporin required for NPC distribution and mRNA transport.J.Cell Biol.1995,131(6Pt2),1659-1675.(8)Grandi,P.;Doye,V.;Hurt,E.C.Purification of NSP1revealscomplex formation with‘GLFG’nucleoporins and a novel nuclear pore protein NIC96.EMBO J.1993,12(8),3061-3071.(9)Stirling,D.A.;Petrie,A.;Pulford,D.J.;Paterson,D.T.;Stark,M.J.Protein A-calmodulin fusions:a novel approach for investigat-ing calmodulin function in yeast.Mol.Microbiol.1992,6(6),703-713.Native Elution of PrA-Tagged Proteins research articlesJournal of Proteome Research•Vol.4,No.6,20052255。

Module Specification
Module Name Module Description
RL-UM02WBS-8723DU-V1.0 IEEE 802.11b/g/n (1T1R) WiFi+BT4.2/2.1 V1.0 USB Interface
Reversion Date
Nater Approve Field Engineer Cruz.Zeng QC Mike.Liu Sales Lingo.Wang 13510620050
WLAN MAC Features Host Interface n Frame aggregation for increased MAC n Complies with USB2.0 for WLAN and efficiency (A-MSDU, A-MPDU) BT controller n Low latency immediate n USB Multi-Function for both BT High-Throughput Block (USB function 0) and WLAN (USB Acknowledgement (HT-BA) function 1) n PHY-level spoofing to enhance legacy n USB LPM and USB Selective Suspend compatibility supported n Power saving mechanism n Multi MACID support with Fast WLAN Controller Channel switch n CMOS MAC, Baseband PHY, and RF n Channel management and co-existence in a single chip for 802.11b/g/n n Transmit Opportunity (TXOP) Short compatible WLAN Inter-Frame Space (SIFS) bursting for n Integrated Balun and DPDT higher multimedia bandwidth n Complete 802.11n solution for 2.4GHz n WiFi Direct supports wireless peer to band peer applications n 72.2Mbps receive PHY rate and n Supports Wake-On-WLAN via Magic 72.2Mbps transmit PHY rate using Packets and Wake-up frame 20MHz bandwidth n Support S3/S4 AES/TKIP group key n 150Mbps receive PHY rate and update 150Mbps transmit PHY rate using n Support Win8 Network List Offload 40MHz bandwidth n Support TCP/UDP/IP checksum offload n Backward compatible with 802.11b/g devices while operating in 802.11n mode n I 802.11b/g/n compatible WLAN n 802.11e QoS Enhancement (WMM) WLAN PHY Features n 802.11n OFDM n One Transmit and one Receive path (1T1R) Integrated MCU to execute Bluetooth protocol stack n Supports all packet types in basic rate and enhanced data rate n

专利名称：ROBUST DETECTION OF PACKET TYPES 发明人：WATERS, DERIC, WAYNE,HOSUR,SRINATH,BATRA, ANUJ申请号：US2007061091申请日：20070126公开号：WO2007087621A3公开日：20080417专利内容由知识产权出版社提供摘要：Disclosed herein is a system and method for determining the presence of rotated-BPSK modulation In addition, disclosed herein is a system and method for determining if a received packet (502) is a Legacy (516), Mixed-Mode (520), or Green-Field packet (510) in accordance with the determination of the presence of rotated-BPSK modulation The presence of a Green-Field packet may be determined by detecting if additional tones are being excited in an LTF symbol of the received packet and/or if a SIG field symbol following the LTF symbol is modulated by rotated-BPSK The presence of a Mixed-Mode packet may be determined by detecting if the first four bits of the SIG field symbol following the LTF symbol are [1 1 0 1] (514) and/or detecting if a symbol following an L-SIG symbol is modulated by rotated-BPSK (518).申请人：TEXAS INSTRUMENTS INCORPORATED,WATERS, DERIC, WAYNE,HOSUR, SRINATH,BATRA, ANUJ更多信息请下载全文后查看。