图像科学综述 外文翻译 外文文献 英文文献
外文参考文献翻译-中文
外⽂参考⽂献翻译-中⽂基于4G LTE技术的⾼速铁路移动通信系统KS Solanki教授，Kratika ChouhanUjjain⼯程学院，印度Madhya Pradesh的Ujjain摘要：随着时间发展，⾼速铁路（HSR）要求可靠的，安全的列车运⾏和乘客通信。
为了实现这个⽬标，HSR的系统需要更⾼的带宽和更短的响应时间，⽽且HSR的旧技术需要进⾏发展，开发新技术，改进现有的架构和控制成本。
为了满⾜这⼀要求，HSR采⽤了GSM的演进GSM-R技术，但它并不能满⾜客户的需求。
因此采⽤了新技术LTE-R，它提供了更⾼的带宽，并且在⾼速下提供了更⾼的客户满意度。
本⽂介绍了LTE-R，给出GSM-R与LTE-R之间的⽐较结果，并描述了在⾼速下哪种铁路移动通信系统更好。
关键词：⾼速铁路，LTE，GSM，通信和信令系统⼀介绍⾼速铁路需要提⾼对移动通信系统的要求。
随着这种改进，其⽹络架构和硬件设备必须适应⾼达500公⾥/⼩时的列车速度。
HSR还需要快速切换功能。
因此，为了解决这些问题，HSR 需要⼀种名为LTE-R的新技术，基于LTE-R的HSR提供⾼数据传输速率，更⾼带宽和低延迟。
LTE-R能够处理⽇益增长的业务量，确保乘客安全并提供实时多媒体信息。
随着列车速度的不断提⾼，可靠的宽带通信系统对于⾼铁移动通信⾄关重要。
HSR的应⽤服务质量（QOS）测量，包括如数据速率，误码率（BER）和传输延迟。
为了实现HSR的运营需求，需要⼀个能够与 LTE保持⼀致的能⼒的新系统，提供新的业务，但仍能够与GSM-R长时间共存。
HSR系统选择合适的⽆线通信系统时，需要考虑性能，服务，属性，频段和⼯业⽀持等问题。
4G LTE系统与第三代（3G）系统相⽐，它具有简单的扁平架构，⾼数据速率和低延迟。
在LTE的性能和成熟度⽔平上，LTE- railway（LTE-R）将可能成为下⼀代HSR通信系统。
⼆ LTE-R系统描述考虑LTE-R的频率和频谱使⽤，对为⾼速铁路（HSR）通信提供更⾼效的数据传输⾮常重要。
图像识别中英文对照外文翻译文献
中英文对照外文翻译文献(文档含英文原文和中文翻译)Elastic image matchingAbstractOne fundamental problem in image recognition is to establish the resemblance of two images. This can be done by searching the best pixel to pixel mapping taking into account monotonicity and continuity constraints. We show that this problem is NP-complete by reduction from 3-SAT, thus giving evidence that the known exponential time algorithms are justified, but approximation algorithms or simplifications are necessary.Keywords: Elastic image matching; Two-dimensional warping; NP-completeness 1. IntroductionIn image recognition, a common problem is to match two given images, e.g. when comparing an observed image to given references. In that pro-cess, elastic image matching, two-dimensional (2D-)warping (Uchida and Sakoe, 1998) or similar types of invariant methods (Keysers et al., 2000) can be used. For this purpose, we can define cost functions depending on the distortion introduced in the matching andsearch for the best matching with respect to a given cost function. In this paper, we show that it is an algorithmically hard problem to decide whether a matching between two images exists with costs below a given threshold. We show that the problem image matching is NP-complete by means of a reduction from 3-SAT, which is a common method of demonstrating a problem to be intrinsically hard (Garey and Johnson, 1979). This result shows the inherent computational difficulties in this type of image comparison, while interestingly the same problem is solvable for 1D sequences in polynomial time, e.g. the dynamic time warping problem in speech recognition (see e.g. Ney et al., 1992). This has the following implications: researchers who are interested in an exact solution to this problem cannot hope to find a polynomial time algorithm, unless P=NP. Furthermore, one can conclude that exponential time algorithms as presented and extended by Uchida and Sakoe (1998, 1999a,b, 2000a,b) may be justified for some image matching applications. On the other hand this shows that those interested in faster algorithms––e.g. for pattern recognition purposes––are right in searching for sub-optimal solutions. One method to do this is the restriction to local optimizations or linear approximations of global transformations as presented in (Keysers et al., 2000). Another possibility is to use heuristic approaches like simulated annealing or genetic algorithms to find an approximate solution. Furthermore, methods like beam search are promising candidates, as these are used successfully in speech recognition, although linguistic decoding is also an NP-complete problem (Casacuberta and de la Higuera, 1999). 2. Image matchingAmong the varieties of matching algorithms,we choose the one presented by Uchida and Sakoe(1998) as a starting point to formalize the problem image matching. Let the images be given as(without loss of generality) square grids of size M×M with gray values (respectively node labels)from a finite alphabet &={1，…，G}. To define thed：&×&→N , problem, two distance functions are needed,one acting on gray valuesg measuring the match in gray values, and one acting on displacement differences :Z×Z→N , measuring the distortion introduced by t he matching. For these distance ddfunctions we assume that they are monotonous functions (computable in polynomial time) of the commonly used squared Euclid-ean distance, i.ed g (g 1,g 2)=f 1(||g 1-g 2||²)and d d (z)=f 2(||z||²) monotonously increasing. Now we call the following optimization problem the image matching problem (let µ={1，…M} ).Instance: The pair( A ; B ) of two images A and B of size M×M .Solution: A mapping function f :µ×µ→µ×µ.Measure:c （A,B,f ）=),(),(j i f ij g B Ad ∑μμ⨯∈),(j i+∑⨯-⋅⋅⋅∈+-+μ}1,{1,),()))0,1(),(())0,1(),(((M j i d j i f j i f dμ⨯-⋅⋅⋅∈}1,{1,),(M j i +∑⋅⋅⋅⨯∈+-+1}-M ,{1,),()))1,0(),(())1,0(),(((μj i d j i f j i f d 1}-M ,{1,),(⋅⋅⋅⨯∈μj iGoal:min f c(A,B,f).In other words, the problem is to find the mapping from A onto B that minimizes the distance between the mapped gray values together with a measure for the distortion introduced by the mapping. Here, the distortion is measured by the deviation from the identity mapping in the two dimensions. The identity mapping fulfills f(i,j)=(i,j),and therefore ，f((i,j)+(x,y))=f(i,j)+(x,y)The corresponding decision problem is fixed by the followingQuestion:Given an instance of image matching and a cost c′, does there exist a ma pping f such that c(A,B,f)≤c′?In the definition of the problem some care must be taken concerning the distance functions. For example, if either one of the distance functions is a constant function, the problem is clearly in P (for d g constant, the minimum is given by the identity mapping and for d d constant, the minimum can be determined by sorting all possible matching for each pixel by gray value cost and mapping to one of the pixels with minimum cost). But these special cases are not those we are concerned with in image matching in general.We choose the matching problem of Uchida and Sakoe (1998) to complete the definition of the problem. Here, the mapping functions are restricted by continuity and monotonicity constraints: the deviations from the identity mapping may locally be at most one pixel (i.e. limited to the eight-neighborhood with squared Euclidean distance less than or equal to 2). This can be formalized in this approach bychoosing the functions f1,f2as e.g.f 1=id，f2(x)=step(x):=⎩⎨⎧.2,)10(,2,0>≤⋅xGxMM3. Reduction from 3-SAT3-SAT is a very well-known NP-complete problem (Garey and Johnson, 1979), where 3-SAT is defined as follows:Instance: Collection of clauses C={C1，···,CK} on a set of variables X={x1， (x)L}such that each ckconsists of 3 literals for k=1，···K .Each literal is a variable or the negation of a variable.Question:Is there a truth assignment for X which satisfies each clause ck, k=1，···K ?The dependency graph D（Ф）corresponding to an instance Ф of 3-SAT is defined to be the bipartite graph whose independent sets are formed by the set of clauses Cand the set of variables X .Two vert ices ck and x1are adjacent iff ckinvolvesx 1or-xL.Given any 3-SAT formula U, we show how to construct in polynomial time anequivalent image matching problem l（Ф）=（A(Ф),B(Ф)）; . The two images of l （Ф）are similar according to the cost function （i.e.f:c(A(Ф),B(Ф),f）≤0) iff the formulaФ is satisfiable. We perform the reduction from 3-SAT using the following steps:• From the formula Ф we construct the dependency graph D（Ф）.• The dependency graph D（Ф）is drawn in the plane.• The drawing of D（Ф）is refined to depict the logical behaviour of Ф , yielding two images（A(Ф),B(Ф)）.For this, we use three types of components: one component to represent variables of Ф , one component to represent clauses of Ф, and components which act as interfaces between the former two types. Before we give the formal reduction, we introduce these components.3.1. Basic componentsFor the reduction from 3-SAT we need five components from which we will construct the in-stances for image matching , given a Boolean formula in 3-DNF,respectively its graph. The five components are the building blocks needed for the graph drawing and will be introduced in the following, namely the representations of connectors,crossings, variables, and clauses. The connectors represent the edges and have two varieties, straight connectors and corner connectors. Each of the components consists of two parts, one for image A and one for image B , where blank pixels are considered to be of the‘background ’color.We will depict possible mappings in the following using arrows indicating the direction of displacement (where displacements within the eight-neighborhood of a pixel are the only cases considered). Blank squares represent mapping to the respective counterpart in the second image.For example, the following displacements of neighboring pixels can be used with zero cost:On the other hand, the following displacements result in costs greater than zero:Fig. 1 shows the first component, the straight connector component, which consists of a line of two different interchanging colors,here denoted by the two symbols◇and□. Given that the outside pixels are mapped to their respe ctive counterparts and the connector is continued infinitely, there are two possible ways in which the colored pixels can be mapped, namely to the left (i.e. f(2,j)=(2,j-1)) or to the right (i.e. f(2,j)=(2,j+1)),where the background pixels have different possibilities for the mapping, not influencing the main property of the connector. This property, which justifies the name ‘connector ’, is the following: It is not possible to find a mapping, which yields zero cost where the relative displacements of the connector pixels are not equal, i.e. one always has f(2,j)-(2,j)=f(2,j')-(2,j'),which can easily be observed by induction over j'.That is, given an initial displacement of one pixel (which will be ±1 in this context), the remaining end of the connector has the same displacement if overall costs of the mapping are zero. Given this property and the direction of a connector, which we define to be directed from variable to clause, wecan define the state of the connector as carrying the‘true’truth value, if the displacement is 1 pixel in the direction of the connector and as carrying the‘false’ truth value, if the displacement is -1 pixel in the direction of the connector. This property then ensures that the truth value transmitted by the connector cannot change at mappings of zero cost.Image A image Bmapping 1 mapping 2Fig. 1. The straight connector component with two possible zero cost mappings.For drawing of arbitrary graphs, clearly one also needs corners,which are represented in Fig. 2.By considering all possible displacements which guarantee overall cost zero, one can observe that the corner component also ensures the basic connector property. For example, consider the first depicted mapping, which has zero cost. On the other hand, the second mapping shows, that it is not possible to construct a zero cost mapping with both connectors‘leaving’the component. In that case, the pixel at the position marked‘? ’either has a conflict (that i s, introduces a cost greater than zero in the criterion function because of mapping mismatch) with the pixel above or to the right of it,if the same color is to be met and otherwise, a cost in the gray value mismatch term is introduced.image A image Bmapping 1 mapping 2Fig. 2. The corner connector component and two example mappings.Fig. 3 shows the variable component, in this case with two positive (to the left) and one negated output (to the right) leaving the component as connectors. Here, a fourth color is used, denoted by ·.This component has two possible mappings for thecolored pixels with zero cost, which map the vertical component of the source image to the left or the right vertical component in the target image, respectively. (In both cases the second vertical element in the target image is not a target of the mapping.) This ensures±1 pixel relative displacements at the entry to the connectors. This property again can be deducted by regarding all possible mappings of the two images.The property that follows (which is necessary for the use as variable) is that all zero cost mappings ensure that all positive connectors carry the same truth value,which is the opposite of the truth value for all the negated connectors. It is easy to see from this example how variable components for arbitrary numbers of positive and negated outputs can be constructed.image A image BImage C image DFig. 3. The variable component with two positive and one negated output and two possible mappings (for true and false truth value).Fig. 4 shows the most complex of the components, the clause component. This component consists of two parts. The first part is the horizontal connector with a 'bend' in it to the right.This part has the property that cost zero mappings are possible for all truth values of x and y with the exception of two 'false' values. This two input disjunction，can be extended to a three input dis-junction using the part in the lower left. If the z connector carries a 'false' truth value, this part can only be mapped one pixel downwards at zero cost.In that case the junction pixel (the fourth pixel in the third row) cannot be mapped upwards at zero cost and the 'two input clause' behaves as de-scribed above. On the other hand, if the z connector carries a 'true' truth value, this part can only be mapped one pixel upwards at zero cost,and the junction pixel can be mapped upwards,thus allowing both x and y to carry a 'false' truth value in a zero cost mapping. Thus there exists a zero cost mapping of the clause component iff at least one of the input connectors carries a truth value.image Aimage B mapping 1(true,true,false)mapping 2 (false,false,true,)Fig. 4. The clause component with three incoming connectors x, y , z and zero cost mappings forthe two cases(true,true,false)and (false, false, true).The described components are already sufficient to prove NP-completeness by reduction from planar 3-SAT (which is an NP-complete sub-problem of 3-SAT where the additional constraints on the instances is that the dependency graph is planar),but in order to derive a reduction from 3-SAT, we also include the possibility of crossing connectors.Fig. 5 shows the connector crossing, whose basic property is to allow zero cost mappings if the truth–values are consistently propagated. This is assured by a color change of the vertical connector and a 'flexible' middle part, which can be mapped to four different positions depending on the truth value distribution.image Aimage Bzero cost mappingFig. 5. The connector crossing component and one zero cost mapping.3.2. ReductionUsing the previously introduced components, we can now perform the reduction from 3-SAT to image matching .Proof of the claim that the image matching problem is NP-complete:Clearly, the image matching problem is in NP since, given a mapping f and two images A and B ,the computation of c（A,B,f）can be done in polynomial time. To prove NP-hardness, we construct a reduction from the 3-SAT problem. Given an instance of 3-SAT we construct two images A and B , for which a mapping of cost zero exists iff all the clauses can be satisfied.Given the dependency graph D ,we construct an embedding of the graph into a 2D pixel grid, placing the vertices on a large enough distance from each other (say100（K+L）² ).This can be done using well-known methods from graph drawing (see e.g.di Battista et al.,1999).From this image of the graph D we construct the two images A and B , using the components described above.Each vertex belonging to a variable is replaced with the respective parts of the variable component, having a number of leaving connectors equal to the number of incident edges under consideration of the positive or negative use in the respective clause. Each vertex belonging to a clause is replaced by the respective clause component,and each crossing of edges is replaced by the respective crossing component. Finally, all the edges are replaced with connectors and corner connectors, and the remaining pixels inside the rectangular hull of the construction are set to the background gray value. Clearly, the placement of the components can be done in such a way that all the components are at a large enough distance from each other, where the background pixels act as an 'insulation' against mapping of pixels, which do not belong to the same component. It can be easily seen, that the size of the constructed images is polynomial with respect to the number of vertices and edges of D and thus polynomial in the size of the instance of 3-SAT, at most in the order （K+L）².Furthermore, it can obviously be constructed in polynomial time, as the corresponding graph drawing algorithms are polynomial.Let there exist a truth assignment to the variables x1，…，xL, which satisfies allthe clauses c1，…，cK. We construct a mapping f , that satisfies c（f,A,B）=0 asfollows.For all pixels (i, j ) belonging to variable component l with A（i，j）not of the background color,set f（i，j）=（i，j-1）if xlis assigned the truth value 'true' , set f（i，j）=（i，j+1）, otherwise. For the remaining pixels of the variable component set A（i，j）=B（i，j），if f（i，j）=（i，j）, otherwise choose f（i，j）from{(i,j+1),(i+1,j+1),(i-1,j+1)}for xl'false' respectively from {(i,j-1),(i+1,j-1),(i-1,j-1)}for xl'true ',such that A（i，j）=B（f（i，j））. This assignment is always possible and has zero cost, as can be easily verified.For the pixels（i，j）belonging to (corner) connector components,the mapping function can only be extended in one way without the introduction of nonzero cost,starting from the connection with the variable component. This is ensured by thebasic connector property. By choosing f （i ，j ）=（i,j ）for all pixels of background color, we obtain a valid extension for the connectors. For the connector crossing components the extension is straight forward, although here ––as in the variable mapping ––some care must be taken with the assign ment of the background value pixels, but a zero cost assignment is always possible using the same scheme as presented for the variable mapping.It remains to be shown that the clause components can be mapped at zero cost, if at least one of the input connectors x , y , z carries a ' true' truth value.For a proof we regard alls even possibilities and construct a mapping for each case. In thedescription of the clause component it was already argued that this is possible,and due to space limitations we omit the formalization of the argument here.Finally, for all the pixels （i ，j ）not belonging to any of the components, we set f （i ，j ）=（i ，j ）thus arriving at a mapping function which has c （f ，A ，B ）=0。
关于机器视觉的外文文献
关于机器视觉的外文文献Title: Overview of Machine Vision: Technologies and ApplicationsAbstract:Machine vision is an important technology that enables computers to interpret visual information from images or videos. This paper provides an overview of the technologies behind machine vision and their various applications. Thefirst section covers the basics of machine vision, including image acquisition, processing, and analysis. The secondsection discusses some common machine vision techniques suchas pattern recognition, object detection and classification. The third section gives examples of machine vision applications, including manufacturing, medical diagnosis, and surveillance.Introduction:Machine vision has become increasingly important in many fields, from manufacturing to medical diagnosis to robotics.It is a technology that allows computers to interpret visual information from images or videos, enabling them to make automated decisions based on that information. This paperwill provide an overview of machine vision technologies and their applications.Section 1: Basics of Machine Vision1.1 Image Acquisition: This section discusses the different ways in which images or videos can be captured and stored for machine vision applications. It covers topics such as cameras, sensors, and storage devices.1.2 Image Processing: This section explains the different methods used to clean up and enhance images before analysis. Techniques such as filtering and image segmentation are discussed.1.3 Image Analysis: This section discusses the different algorithms used to analyze images and extract important features such as edges, corners, and textures.Section 2: Machine Vision Techniques2.1 Pattern Recognition: This section covers the basics of pattern recognition and its importance in machine vision. Methods such as template matching and machine learning are discussed.2.2 Object Detection: This section explains the different ways in which objects can be detected in a scene, including feature-based methods and deep learning.2.3 Object Classification: This section covers the different algorithms used to classify objects based on their features or attributes. Methods such as decision trees and support vector machines are discussed.Section 3: Applications of Machine Vision3.1 Manufacturing: This section discusses the various applications of machine vision in manufacturing, including quality control, inspection, and assembly.3.2 Medical Diagnosis: This section covers the different ways in which machine vision can be used for medical diagnosis, including pathology, radiology, and ophthalmology.3.3 Surveillance: This section explains the different ways in which machine vision can be used for surveillance, including face recognition and crowd monitoring.Conclusion:Machine vision is a powerful technology that is becomingincreasingly important in many fields. With its ability to interpret visual information and make automated decisions based on that data, it has the potential to revolutionize the way we live and work. As the technology continues to evolve and improve, we can expect to see even more exciting applications of machine vision in the future.。
【图像复原技术研究文献综述2000字】
图像复原技术研究国内外文献综述作为日常生活中广泛使用的技术，图像修复技术汇集了国内外许多重要技术。
实际上，图像复原分为三种标准：首先是搭建其劣化图像的图像模型；其次去研究和筛选最佳的图像复原方法；最后进行图像复原。
所有类型的成像模型和优化规则都会导致应用于不同领域的不同图像恢复算法。
我们对现有的图像复原方法大致做了总结，如利用线性代数知识的线性代数复原技术、搭建图像退化模型的去卷积图像恢复技术以及不考虑PSF的图像盲解卷积算法等。
其中，去卷积方法主要包括功率谱均衡、Wiener滤波和几何平均滤波等，然而这些方法需要许多预信息和噪声稳定假设，这在现实当中我们不可能利用计算机去做到的的事情，因此它们只适用于线性空间不变的理想系统，仅当噪声与信号无关时才能达到很好的效果。
但是在一些条件恶化的情况下，就不能满足图像修复的效果了。
在图像恢复领域当中，另一个重要且常见的方法是盲去卷积复原法。
它的优势是在没有预先了解退化函数和实际信号的知识前提下，可以根据劣化图像直接估计劣化函数和初始信号。
实际上，现在有几个算法通过不充分的预测信息来恢复劣化图像。
由于我们需要对图像和点扩展函数的一些相关信息进行假设和推导，所以这就导致图像恢复的解通常并不存在唯一性，并且我们已知的初始条件和对附加图像假设的选择也会对解的优劣产生很大的关系。
与此同时，由于信道中不可避免的加性噪声的影响，会进一步导致盲图像的复原变差，给图像复原造成许多困难。
也就是说，如果我们直接利用点扩展函数进行去卷积再现初始图像，则一般会导致高频噪声放大，导致算法的性能恶化，恢复不出清晰的图像。
因此，我们要尽可能的提高图像的信噪比，从而提高图像复原的效果。
基于已知的降质算子和加性噪声的某些统计性质从而恢复清晰的图像，我们将这种方法叫做线性代数恢复方法，并且这种线性代数恢复方法在一定程度上提出了用于恢复滤波器的数值计算从而使得模糊图像恢复到与清晰图像一致的新的设计思想。
医学影像学英文文献
医学影像学英文文献英文回答：Within the realm of medical imaging, sophisticated imaging techniques empower healthcare professionals with the ability to visualize and comprehend anatomical structures and physiological processes in the human body. These techniques are instrumental in diagnosing diseases, guiding therapeutic interventions, and monitoring treatment outcomes.Computed tomography (CT) and magnetic resonance imaging (MRI) are two cornerstone imaging modalities widely employed in medical practice. CT utilizes X-rays and advanced computational algorithms to generate cross-sectional images of the body, providing detailed depictions of bones, soft tissues, and blood vessels. MRI, on the other hand, harnesses the power of powerful magnets and radiofrequency waves to create intricate images that excel in showcasing soft tissue structures, including the brain,spinal cord, and internal organs.Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) are nuclear medicine imaging techniques that involve the administration of radioactive tracers into the body. These tracers accumulate in specific organs or tissues, enabling the visualization and assessment of metabolic processes and disease activity. PET is particularly valuable in oncology, as it can detect the presence and extent of cancerous lesions.Ultrasound, also known as sonography, utilizes high-frequency sound waves to produce images of internal structures. It is a versatile technique commonly employed in obstetrics, cardiology, and abdominal imaging. Ultrasound offers real-time visualization, making it ideal for guiding procedures such as biopsies and injections.Interventional radiology is a specialized field that combines imaging guidance with minimally invasive procedures. Interventional radiologists utilize imaging techniques to precisely navigate catheters and otherinstruments through the body, enabling the diagnosis and treatment of conditions without the need for open surgery. This approach offers reduced invasiveness and faster recovery times compared to traditional surgical interventions.Medical imaging has revolutionized healthcare by providing invaluable insights into the human body. The ability to visualize anatomical structures andphysiological processes in exquisite detail has transformed the practice of medicine, leading to more accurate diagnoses, targeted treatments, and improved patient outcomes.中文回答：医学影像学是现代医学不可或缺的一部分，它利用各种成像技术对人体的解剖结构和生理过程进行可视化和理解，在疾病诊断、治疗方案制定和治疗效果评估中发挥着至关重要的作用。
电气工程及其自动化专业 外文文献 英文文献 外文翻译 plc方面
1、外文原文（复印件）A: Fundamentals of Single-chip MicrocomputerTh e si ng le-ch i p mi cr oc om pu ter is t he c ul mi nat i on o f bo th t h e d ev el op me nt o f th e d ig it al com p ut er an d t he int e gr at ed ci rc ui ta r gu ab ly th e t ow m os t s i gn if ic ant i nv en ti on s o f t h e 20t h c en tu ry[1].Th es e to w typ e s of a rc hi te ctu r e ar e fo un d i n s in gl e-ch ip m i cr oc om pu te r. So m e em pl oy t he sp l it p ro gr am/d ata me mo ry o f th e H a rv ar d ar ch it ect u re, sh ow n i n -5A, ot he rs fo ll ow th e ph i lo so ph y, w i de ly a da pt ed fo r g en er al-p ur pos e c om pu te rs an d m i cr op ro ce ss or s, o f m a ki ng no lo gi c al di st in ct io n b e tw ee n p ro gr am a n d da t a m em ory a s i n th e Pr in cet o n ar ch it ec tu re,sh ow n in-5A.In g en er al te r ms a s in gl e-chi p m ic ro co mp ut er i sc h ar ac te ri zed b y the i nc or po ra tio n of al l t he uni t s o f a co mp ut er i n to a s in gl e dev i ce, as s ho wn in Fi g3-5A-3.-5A-1 A Harvard type-5A. A conventional Princeton computerFig3-5A-3. Principal features of a microcomputerRead only memory (ROM).R OM i s u su al ly f or th e p er ma ne nt, n o n-vo la ti le s tor a ge o f an a pp lic a ti on s pr og ra m .M an ym i cr oc om pu te rs an d mi cr oc on tr ol le r s a re in t en de d fo r h ig h-v ol ume a p pl ic at io ns a nd h en ce t he e co nom i ca l ma nu fa ct ure of t he d ev ic es r e qu ir es t ha t the co nt en ts o f the pr og ra m me mo ry b e co mm it te dp e rm an en tl y d ur in g th e m an uf ac tu re o f c hi ps . Cl ear l y, th is im pl ie sa ri g or ou s a pp roa c h t o R OM co de d e ve lo pm en t s in ce c ha ng es ca nn otb e m ad e af te r man u fa ct ur e .T hi s d e ve lo pm en t pr oce s s ma y in vo lv e e m ul at io n us in g a s op hi st ic at ed deve lo pm en t sy st em w i th a ha rd wa re e m ul at io n ca pa bil i ty a s we ll a s th e u se of po we rf ul so ft wa re t oo ls.So me m an uf act u re rs p ro vi de ad d it io na l RO M opt i on s byi n cl ud in g i n th ei r ra ng e de vi ce s wi th (or i nt en de d fo r us e wi th) u s er pr og ra mm ab le m em or y. Th e s im p le st of th es e i s us ua ll y d ev ice w h ic h ca n op er ate in a m ic ro pr oce s so r mo de b y usi n g so me o f th e i n pu t/ou tp ut li ne s as a n ad dr es s an d da ta b us f or acc e ss in g e xt er na l m e mo ry. T hi s t ype o f d ev ic e c an b e ha ve fu nc ti on al l y a s t he si ng le c h ip mi cr oc om pu te r fr om wh ic h i t i s de ri ve d a lb eit w it h r es tr ic ted I/O an d a mo di fie d e xt er na l ci rcu i t. T he u se o f t h es e RO Ml es sd e vi ce s is c om mo n e ve n in p ro du ct io n c ir cu it s wh er e t he v ol um e do es n o t ju st if y th e d e ve lo pm en t co sts of c us to m on-ch i p RO M[2];t he re c a n st il l b e a si g ni fi ca nt s a vi ng in I/O a nd ot he r c hi ps co mp ar ed t o a c on ve nt io nal mi cr op ro ce ss or b as ed c ir cu it. M o re e xa ctr e pl ac em en t fo r RO M d ev ic es c an b e o bt ai ne d in t he f o rm o f va ri an ts w i th 'pi gg y-ba ck'EP RO M(Er as ab le p ro gr am ma bl e ROM)s oc ke ts o rd e vi ce s w it h EP ROM i ns te ad o f R OM 。
自动化专业-外文文献-英文文献-外文翻译-plc方面
1、外文原文（复印件)A: Fundamentals of Single-chip MicrocomputerTh e si ng le-ch i p mi cr oc om pu ter is t he c ul mi nat i on o f bo th t h e d ev el op me nt o f th e d ig it al com p ut er an d t he int e gr at ed ci rc ui ta r gu ab ly th e t ow m os t s i gn if ic ant i nv en ti on s o f t h e 20t h c en tu ry[1].Th es e to w t ype s o f a rc hi te ct ur e a re fo un d i n s i ng le—ch ip m i cr oc om pu te r。
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图像融合技术外文翻译-中英对照
***大学毕业设计（英文翻译）原文题目：Automatic Panoramic Image Stitching using Invariant Features 译文题目：使用不变特征的全景图像自动拼接学院：电子与信息工程学院专业： ********姓名： ******学号：**********使用不变特征的全景图像自动拼接马修·布朗和戴维•洛{mbrown|lowe}@cs.ubc.ca计算机科学系英国哥伦比亚大学加拿大温哥华摘要本文研究全自动全景图像的拼接问题，尽管一维问题（单一旋转轴）很好研究，但二维或多行拼接却比较困难。
以前的方法使用人工输入或限制图像序列，以建立匹配的图像，在这篇文章中，我们假定拼接是一个多图像匹配问题，并使用不变的局部特征来找到所有图像的匹配特征。
由于以上这些，该方法对输入图像的顺序、方向、尺度和亮度变化都不敏感；它也对不属于全景图一部分的噪声图像不敏感，并可以在一个无序的图像数据集中识别多个全景图。
此外，为了提供更多有关的细节，本文通过引入增益补偿和自动校直步骤延伸了我们以前在该领域的工作。
1. 简介全景图像拼接已经有了大量的研究文献和一些商业应用。
这个问题的基本几何学很好理解，对于每个图像由一个估计的3×3的摄像机矩阵或对应矩阵组成。
估计处理通常由用户输入近似的校直图像或者一个固定的图像序列来初始化，例如，佳能数码相机内的图像拼接软件需要水平或垂直扫描，或图像的方阵。
在自动定位进行前，第4版的REALVIZ拼接软件有一个用户界面，用鼠标在图像大致定位，而我们的研究是有新意的，因为不需要提供这样的初始化。
根据研究文献，图像自动对齐和拼接的方法大致可分为两类——直接的和基于特征的。
直接的方法有这样的优点，它们使用所有可利用的图像数据，因此可以提供非常准确的定位，但是需要一个只有细微差别的初始化处理。
基于特征的配准不需要初始化，但是缺少不变性的传统的特征匹配方法（例如，Harris角点图像修补的相关性）需要实现任意全景图像序列的可靠匹配。
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附录图像科学综述近几年来，图像处理与识别技术得到了迅速的发展，现在人们己充分认识到图像处理和识别技术是认识世界、改造世界的重要手段。
目前它己应用于许多领域，成为2l世纪信息时代的一门重要的高新科学技术。
1.图像处理与识别技术概述图像就是用各种观测系统以不同形式和手段观测客观世界而获得的，可以直接或间接作用于人眼而产生视知觉的实体。
科学研究和统计表明，人类从外界获得的信息约有75%来自于视觉系统，也就是说，人类的大部分信息都是从图像中获得的。
图像处理是人类视觉延伸的重要手段，可以便人们看到任意波长上所测得的图像。
例如，借助伽马相机、x光机，人们可以看到红外和超声图像：借助CT可看到物体内部的断层图像；借助相应工具可看到立体图像和剖视图像。
1964年，美国在太空探索中拍回了大量月球照片，但是由于种种环境因素的影响，这些照片是非常不清晰的，为此，美国喷射推进实验室(JPL)使用计算机对图像进行处理，使照片中的重要信息得以清晰再现。
这是这门技术发展的重要里程碑。
此后，图像处理技术在空间研究方面得到广泛的应用。
总体来说，图像处理技术的发展大致经历了初创期、发展期、普及期和实用化期4个阶段。
初创期开始于20世纪60年代，当时的图像采用像素型光栅进行扫描显示，大多采用巾、大型机对其进行处理。
在这一时期，由于图像存储成本高，处理设备造价高，因而其应用面很窄。
20世纪70年代进入了发展期，开始大量采用中、小型机进行处理，图像处理也逐渐改用光栅扫描显示方式，特别是出现了CT和卫星遥感图像，对图像处理技术的发展起到了很好的促进作用。
到了20世纪80年代，图像处理技术进入普及期，此时购微机已经能够担当起图形图像处理的任务。
VLSL的出现更使得处理速度大大提高，其造价也进一步降低，极大地促进了图形图像系统的普及和应用。
20世纪90年代是图像技术的实用化时期，图像处理的信息量巨大，对处理速度的要求极高。
21世纪的图像技术要向高质量化方面发展，主要体现在以下几点：①高分辨率、高速度，图像处理技术发展的最终目标是要实现图像的实时处理，这在移动目标的生成、识别和跟踪上有着重要意义：②立体化，立体化所包括的信息最为完整和丰富，数字全息技术将有利于达到这个目的；②智能化，其目的是实现图像的智能生成、处理、识别和理解。
2.图像处理与识别技术的应用领域目前，图像处理与识别技术的主要应用领域有生物医学、文件处理、工业检测、机器人视觉、货物检测、邮政编码、金融、公安、银行、机械、交通、电子商务和多媒体网络通信等领域。
3.图像处理与识别数字图像处理和识别学科所涉及的知识非常广泛，具体的方法种类繁多，应用也极为普遍，但从学科研究内容上可以分为以下几个方面：图像数字化、图像变换、图像增强、图像分割、图像分析。
4.图像识别技术图像识别是近20年来发展起来的一门新型技术科学，它以研究某些对象或过程（统称图像）的分类与描述为主要内容。
图像识别所研究的领域十分广泛，它可以是医学图像中的癌细胞识别；机械加工中零部件的识别、分类；可以是认遥感图片中辨别农作物、森林、湖泊和军事设施，以及判断农作物的长势，预测收获量等；可以是自导引小车中的路径识别；邮政系统中自动分拣信函；交通管制、识别违章行驶的汽车牌照；银行的支票识别、身份证识别等。
上述都是图像识别研究的课题。
总体来说所研究的问题，主要是分类问题。
5.图像处理在研究图像时，首先要对获得的图像信息进行预处理（前处理）以滤去干扰、噪声，作几何、彩色校正等。
这样可提高信噪比；有时由于信息微弱，无法辨识，还得进行增强处理。
增强的作用，在于提供一个满足一定要求的图像，或对图像进行变换，以便人或计算机分析。
并且为了从图像中找到需要识别的东西，还得对图像进行分割，也就是进行定位和分离，以分出不同的物体。
为了给观察者以清晰的图像，还要对图像进行政善，即进行复原处理，它是把已经退化了图像加以重建或恢复的过程，以便改进图像的保真度。
在实际处理中，由于图像信息量非常大，在存储及传送时，还要对图像信息进行压缩。
上述工作必须用计算机进行，因而要进行编码等工作。
编码的作用，是用最少数量的编码位(亦称比特)，表示单色和彩色图像，以便更有效地传输和存储。
以上所述都属图像处理的范畴。
因此，图像处理包括图像编码、图像增强、图像压缩、图像复原、图像分割等。
对图像处理环节来说，输入是图像，输出也是图像。
由图像处理的内容可见，图像处理的目的主要在于解决两个问题：一是判断图像中有无需要的信息：另一是确定这些信息是什么。
6.图像理解所谓图像理解是一个总称。
上述图像处理及图像识别的最终目的，就在于对图像作描述和解释，以便最终理解它是什么图像。
所以它是在图像处理及图像识别的基础上，再根据分类作结构句法分析，去描述图像和解释图像。
因而图像理解包括图像处理、图像识别和结构分析。
对理解部分米说，输入是图像，输出则是图像的描述与解释。
7.图像识别与图像处理及图像理解的关系上面说过，图像理解是一个总称。
图像识别是一个系统。
其中每一部分和其前面的一部分都有一定的关系，也可以说有一种反馈作用，例如分割可以在预处理中进行。
并且，该系统不是孤立的，为了发挥其功能，它时时刻刻需要来自外界的必要信息，以便使每个部分能有效地工作。
这些外界信息是指处理问题及解决问题的看法、设想、方法等。
例如，根据实际图像，在处理部分需要采用什么样的预处理，在识别部分需要怎样分割，抽取什么样的特征及怎样抽取特征，怎样进行分类，要分多少类，以及最后提供结构分析所需的结构信息等。
在该系统中，图像预处理、图像分割为图像处理；图像特征提取和图像分类属图像识别；而结构句法分析涉及的内容则是从图像分割到图像结构分析这一过程；整个系统所得到的结果是图像的描述及解释。
当某个新的对象（图像）送进系统时，就可以进行解释，说明它是什么。
8.Matlab技术简介Mat lab是Math works公司与1982年推出的一套高性能的数值计算和可视化数学软件。
被誉为“巨人肩上的工具”。
由于使用Mat lab编程运算与人进行科学计算的思路和表达方式完全一致，所以不像学习其它高级语言，如Basic和C等那样难于掌握，用Mat lab编写程序犹如在演算纸上排列出公式与求解问题，所以又被称为演算科学算法语言。
一般数值分析、矩阵运算、数字信号处理、建模和系统控制和优化等应用程序，并集应用程序和图形于一个便于使用的集成环境中。
在这个环境下，对所要求解的问题，用户只需简单地列出数学表达式，其结果便以数值或图形方式显示出来。
Mat lab的含义是矩阵实验室(MATRIXLABORATORY)，主要用于方便矩阵的存取，其基本元素是无须定义维数的矩阵。
Mat lab自问世以来，就是以数值计算称雄。
Mat lab进行数值计算的基本单位是复数数组(或称阵列)，这使得Mat lab高度“向量化”。
经过十几年的完善和扩充，它现已发展成为线性代数课程的标准工具。
由于它不需定义数组的维数，并给出矩阵函数、特殊矩阵专门的库函数，使之在求解诸如信号处理、建模、系统识别、控制、优化等领域的问题时，显得大为简捷、高效、方便，这是其它高级语言所不能比拟的。
美国许多大学的实验室都安装有Mat lab供学习和研究之用。
在那里，Mat lab是攻读学位的大学生、硕士生、博士生必须掌握的基本工具。
Mat lab中包括了被称作工具箱(TOOU玉OX)的各类应用问题的求解工具。
工具箱实际上是对Mat lab进行扩展应用的一系列Mat lab 函数(称为M文件)，它可用来求解各类学科的问题，包括信号处理、图象处理、控制系统辨识、神经网络等。
随着Mat lab版本的不断升级，其所含的工具箱的功能也越来越丰富，因此，应用范围也越来越广泛，成为涉及数值分析的各类工程师不可不用的工具。
Image Science ReviewIn recent years, image processing and recognition technology has developed rapidly, now people have been fully aware of image processing and recognition technology is to understand the world and an important means of transforming the world. Currently, it has been used in many fields, 21 century information age an important high-tech science and technology.1. Image Processing and Recognition TechnologyImage is to use all kinds of observing systems in various forms and means of observation and access to the objective world, directly or indirectly, in the role of a human eye and visual perception of the entity. Scientific research and statistics show that the human race from the outside world the information obtained about 75 percent comes from the visual system, that is, human beings are the most of the information obtained from the image.Image processing is an important extension of human vision means that people can be arbitrary wavelength measured by the image. With CT can see objects within the fault image with the corresponding tools can be seen as three-dimensional images and post-image. 1964, the United States in space exploration shoot the moon to a large number of photos, but because of various environmental factors, these photos is not very clear, to that end, the U.S. Jet Propulsion Laboratory (JPL) on the use of computer image processing, Photos of important information to clear reproduction. This is the door an important milestone in the development of technology.Generally speaking, the development of image processing technologies generally experienced a start-up phase, the period of development, popularization and applicationof view of four stages. Start-up phase began in the 1960s, the image grating used to scan revealed that most of a towel, its mainframe processing. During this period, due to the high cost of image storage, processing equipment costs, thus its use of very narrow. In the 1970s entered a period of development, began using large, image processing is gradually switching to raster scan display, especially in the CT and satellite remote sensing image, the image processing technology development played a very good Facilitating role. By the 1980s, image processing technology into the popular view, this purchase has been able to take on computer graphics image processing tasks. VLSL makes the emergence of more processing speed greatly improved, further reducing its cost, greatly promote the popularity of graphics imaging systems and applications. 20 images in the 1990s is the period of practical technology, image processing of the huge amount of information on the requirements of high processing speed.21st century technology to high-quality images of the area of development, embodied in the following points: ①high-resolution, high speed, image processing technology development of the ultimate goal is to achieve real-time image processing, which in the formation of a moving target, identification And on the track is of great significance: ②three-dimensional, three-dimensional information covered by the most complete and rich, digital holographic technology will help to achieve this objective;②intelligent, and its purpose is to achieve a smart image formation, processing, recognition and understanding .2. Image processing and recognition technology applicationsAt present, image processing and recognition technology the main areas of biomedical applications, document processing, industrial inspection, robot vision, cargo inspection, zip code, finance, public security, banking, machinery, transport, e-commerce and multimedia communication networks, and other fields.3.Image processing and recognitionDigital image processing and identifying the knowledge disciplines involved in a very wide variety of specific ways, the application also very common, but research on the subject can be divided into the following areas: digital image, image transformation, image enhancement, image segmentation.4. Image Recognition TechnologyRecognition is nearly 20 years to develop a new type of science and technology, to study certain object or process (collectively images) and the classification described as the main content.Image Recognition by the study a wide variety of areas, it can be a medical image of the cancer cell recognition; machining parts and components in the identification, classification can be identified in remote sensing images to identify crops, forests, lakes and military facilities, and the judge of crops Growth is forecast harvest levels; can be a self-guided car in the path recognition; postal system of automatic letter sorting; traffic control, identification of regulations on vehicle, bank identification, ID cards and other identification. These images recognition of are the subject. Overall, the study's main problem is classification issues.5. Image ProcessingIn examining images, the first to receive the image information on the pretreatment (before processing) filtered to interference, noise, for geometry, color correction, and so on. This will improve the signal to noise ratio; sometimes because of weak information, is not recognized, had to deal with enhancements. Enhance the role is to provide a meet certain requirements of the image, or images transform, or to computer analysis. And in order to find images from the need to identify things have to split the image, that is, positioning and separation, separation of different objects. To give observers a clear image, but also the image of good governance, that is, rehabilitation treatment, it is already degraded the image to rebuild or restore the process in order to improve the image fidelity. In the actual processing, because image is very large amount of information in storage and transmission, but also the image data compression.The work must be carried out by computer, thus to encode and so on. The role is the least number of digital coding (also known as bits), said monochrome and color image for a more effective transmission and storage.The above are considered image processing areas. Therefore, image processing, including image coding, image enhancement, image compression, image restoration, image segmentation, and so on. On the part of image processing, image processing, themain purpose is to solve two problems: First, determine whether the image needed: another is to determine what information is.6. Image UnderstandingThe so-called image understanding is a general term. The image processing and image recognition of the ultimate goal is to make the image described and explained that in order to understand what it is the final image. So it is in image processing and image recognition on the basis of re-classification under the syntactic structure for analysis, and interpretation of images to describe images. Thus image understanding, including image processing, image recognition and analysis.7. The relationship of Image recognition and image processing and image understandingAbove that image is a general term for understanding. Recognition is a system. Each of these parts and the front part of a certain relationship can also say there is a feedback effect, for example, can be divided in the pretreatment of conduct. In addition, it always needs the necessary information from the outside world, so that each part can work effectively. For example, according to the actual images, some need to adopt in dealing with what kind of preprocessing, in recognition of the need to separate what, from what kind of features and characteristics of how to take and how to classify, to the number of sub-category, and, finally, to provide the necessary structural analysis The structure of information.In this system, image pre-processing, image segmentation for image processing, image feature extraction and image classification of image recognition and the syntactic structure of the content is separated from image to image the structure of this process; system as a whole received The result is the description and image interpretation. Whena new object (images) into the system, you can interpret that what it is.8. Mat lab Technical OverviewMat lab is Math works with the 1982 launch of a high-performance numerical computing and visualization of mathematical software. As the use of computing and programming Mat lab conducted scientific computing ideas and expression exactly the same, so unlike other high-level language learning, such as Basic and C, and so on, asdifficult to grasp, with Mat lab procedures for the preparation of the calculus is like a piece of paper with formula And solving problems, is also known as calculus scientific.General numerical analysis, matrix computing, digital signal processing, modeling and system control and optimization applications, and collect applications and graphics in a user-friendly integrated environment. In this environment, the solution of the problem required, users simply set out mathematical expression, the result will be numerical or graphic to show.Mat lab is the meaning of Matrix Laboratories (MATRIXLABORATORY), the basic element is not defined dimension of the matrix. Mat lab has since come out. Mat lab numerical calculation is the basic unit complex array (or array), which makes high Mat lab "to quantify." After more than 10 years of refinement and expansion, it has become the standard linear algebra course tools. Because no definition of an array of dimension, and gives matrix function, special matrix specialized library function, so that in solving such as signal processing, modeling, system identification, control and optimize the areas of the issue, it Greatly simple, efficient, convenient and other senior This is unprecedented language. Many American universities have installed a laboratory Mat lab for study and research purposes. There, doctoral students must master the basic tools. Mat lab, known as included in the toolbox application of the various tools to solve the problem. Kit is actually Mat lab to expand the application of a series of Mat lab function it can be used to solve various disciplines, including signal processing, image processing, the control system identification, neural networks. With Mat lab version of the continually escalating, the functions contained in the toolbox and extensive Therefore, the application of more and more extensive, involving a numerical analysis of the types of engineers do not have to be a tool.。