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《机器视觉技术》产品白皮书目录1引言........................................................................ - 3 -2产品概述.................................................................... - 4 -2.1产品体系............................................................ - 4 -2.2产品资源............................................................ - 5 -3产品介绍.................................................................... - 8 -3.1机器视觉技术........................................................ - 8 -3.1.1课程说明........................................................ - 8 -3.1.2教学大纲....................................................... - 12 -3.1.3教学指导....................................................... - 16 -4配套产品................................................................... - 19 -4.1实验设备........................................................... - 19 -4.2软件平台........................................................... - 24 -5技术支持................................................................... - 28 -5.1.1升级服务....................................................... - 28 -5.1.2师资培训....................................................... - 28 -1引言中国人工智能发展迅猛，中国政府也高度重视人工智能领域的发展。
百度大脑AI技术成果白皮书i目录目录引言.1一、百度大脑进化到5.0.2二、基础层.32.1算法.32.2算力.52.3数据10三、感知层113.1语音113.2视觉133.3增强现实/虚拟现实.17四、认知层194.1知识图谱，科技与商业发展的一个关键词就是“人工智能”。
在近一年的时间里，百度科学家和工程师们不仅在人工智能算法、核心框架、芯片、计算平台、量子计算、语音技术、计算机视觉、增强现实与虚拟现实、语言与知识、开放平台、开放数据等诸多方面取得了令人瞩目的技术成果，还将这些技术成果与行业相结合，成功应用于众多产品之中，取得了丰硕的人工智能应用成果。
2月，世界知识产权组织（WorldIntellectualPropertyOrganization，简称WIPO）发布了首份技术趋势报告，聚焦人工智能领域专利申请及发展状况。
报告显示，百度在深度学习领域的专利申请量位居全球第二，超越Alphabet、微软、IBM等企业和国外学术机构，在全球企业中居于首位。
过去的一年，百度基础技术体系、智能云事业群组和AI技术平台体系进行了重大组织机构调整，三个体系统一向集团CTO 汇报，这为技术中台建设和人工智能技术落地提供了良好的组织保障。
本报告总结了百度大脑在度取得的部分技术成果：第一章主要概述百度大脑5.0，第二至六章分别介绍百度大脑在基础层、感知层、认知层、平台层和安全方面的技术成果。
面向未来，百度将继续打造领先的AI技术能力，构建更加繁荣的人工智能生态系统，助力各行各业进入智能化的工业大生产阶段，在智能时代创造更广泛的社会经济价值。
2一、百度大脑进化到一、百度大脑进化到5.0百度大脑是百度AI集大成者。
百度大脑自起开始积累基础能力，后逐步完善。
，百度大脑1.0完成了部分基础能力和核心技术对外开放；，2.0版形成了较为完整的技术体系，开放60多项AI能力；，3.0版在“多模态深度语义理解”上取得重大突破，同时开放110多项核心AI技术能力；，百度大脑升级为5.0，核心技术再获重大突破，实现了AI算法、计算架构与应用场景的创新融合，成为软硬件一体的AI大生产平台。
SMIA 1.0 Part 5: Camera Characterisation SpecificationDISCLAIMERThe contents of this document are copyright © 2004 Nokia Corporation, ST Microelectronics NV and their licensors. All rights reserved. You may not copy, modify nor distribute this document without prior written consent by Nokia and ST. No license to any Nokia’s, ST’s or their licensor’s intellectual property rights are granted herein.YOU ACKNOWLEDGE THAT THIS SMIA SPECIFICATION IS PROVIDED "AS IS" AND NEITHER NOKIA, ST NOR THEIR LICENSORS MAKE ANY REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE OR THAT THIS SMIA SPECIFICATION OR ANY PRODUCT, SOFTWARE APPLICATION OR SERVICE IMPLEMENTING THIS SMIA SPECIFICATION WILL NOT INFRINGE ANY THIRD PARTY PATENTS, COPYRIGHTS, TRADEMARKS OR OTHER RIGHTS. THERE IS NO WARRANTY BY NOKIA, ST OR BY ANY OTHER PARTY THAT THE FUNCTIONS CONTAINED IN THIS SMIA SPECIFICATION WILL MEET YOUR REQUIREMENTS.LIMITATION OF LIABILITY. IN NO EVENT SHALL NOKIA, ST OR THEIR EMPLOYEES, LICENSORS OR AGENTS BE LIABLE FOR ANY LOST PROFITS OR COSTS OF PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES, PROPERTY DAMAGE, PERSONAL INJURY, LOSS OF PROFITS, INTERRUPTION OF BUSINESS OR FOR ANY SPECIAL, INDIRECT, INCIDENTAL, ECONOMIC, COVER, PUNITIVE, OR CONSEQUENTIAL DAMAGES, HOWEVER CAUSED AND WHETHER ARISING UNDER CONTRACT, TORT, NEGLIGENCE, OR OTHER THEORY OF LIABILITY ARISING OUT OF THIS SMIA SPECIFICATION, EVEN IF NOKIA, ST OR THEIR LICENSORS ARE ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. IN THE EVENT THAT ANY EXCLUSION CONTAINED HEREIN SHALL BE HELD TO BE INVALID FOR ANY REASON AND NOKIA, ST OR THEIR LICENSORS BECOMES LIABLE FOR LOSS OR DAMAGE THAT MAY LAWFULLY BE LIMITED, SUCH LIABILITY SHALL BE LIMITED TO U.S.$50.Specifications mentioned in this publication are subject to change without notice.This document supersedes and replaces all versions previously supplied.HistoryVersion Date Author Status Notes1.0 30-June-04 Nokia and ST ApprovedTable of contentsSCOPE (11)1.Definitions (12)1.1Arrays (12)1.2Standard Parameters (13)1.2.1Raw Bayer Image Data (13)1.2.2Green (Red) Raw Bayer (13)1.2.3Region of Interest (ROI) (13)1.3Function Descriptions (15)1.3.1AV_IMAGE(#1, ..., #F) (15)1.3.2COLUMN_AV(#) (15)1.3.3CONVOLUTION(#1,#2) (16)1.3.4Extract Colour Plane - GREENRED (16)1.3.5LOG10(x) (16)1.3.6Local Deviation (16)1.3.7MAX(#) (16)1.3.8MEAN(#) (16)1.3.9MIN(#) (16)1.3.10Regression Analysis (17)1.3.11RMS(#) (17)1.3.12ROI(a,b;x,y;#) (17)1.3.13ROW_AV(#) (17)1.3.14STDEV(#) (17)2.Pre-Processing (18)2.1Data Pre-Processing (18)2.2Image Pre-Processing (18)2.2.1Introduction (18)2.2.2Kernel Kern k (18)2.2.3Error Detection (19)2.2.4Defect Correction (19)3.Test Equipment and Environmental Requirements (20)3.1General (20)3.2Illumination Specification (20)3.3Environmental Specification (20)3.4Electrical Specification (21)3.4.1Analogue Supply (21)3.4.2Digital Supply (21)4.Default Configuration (22)4.1Default Camera Configuration (22)4.1.1Default Electrical Conditions (22)4.1.2Camera Register Settings (22)4.1.3Default Environmental Conditions (22)4.1.4Default Lighting Conditions (22)4.2Default Characterisation Configuration (23)4.2.1Darkroom Set Up (23)4.2.2Mobile Device Equivalence Model (24)5.Characterisation Test Methods (25)5.1Dynamic Range (25)5.1.1Description (25)5.1.2Test Conditions (25)5.1.3Analysis (25)5.2Vertical Fixed Pattern Noise (26)5.2.1Description (26)5.2.2Test Conditions (27)5.2.3Analysis (27)5.3Horizontal Fixed Pattern Noise (28)5.3.1Description (28)5.3.2Test Conditions (28)5.3.3Analysis (29)5.4Temporal Noise (30)5.4.1Description (30)5.4.2Test Conditions (30)5.4.3Analysis (30)5.5Column Noise (31)5.5.1Description (31)5.5.2Test Conditions (31)5.5.3Analysis (32)5.6Row Noise (33)5.6.1Description (33)5.6.2Test Conditions (33)5.6.3Analysis (34)5.7Frame to Frame Flicker (35)5.7.1Description (35)5.7.2Test Conditions (35)5.7.3Analysis (35)5.8Dark Signal (36)5.8.1Description (36)5.8.2Test Conditions (36)5.8.3Analysis (36)5.9Dark Signal Non-uniformity (37)5.9.1Description (37)5.9.2Test Conditions (37)5.9.3Analysis (37)5.10Power Supply Rejection Ratio (38)5.10.1Description (38)5.10.2Test Conditions (39)5.10.3Analysis (40)5.11Signal to Noise Ratio (41)5.11.1Description (41)5.11.2Test Conditions (41)5.11.3Analysis (42)5.12Sensitivity (43)5.12.1Description (43)5.12.2Test Conditions (43)5.12.3Analysis (44)5.13Maximum Illumination (45)5.13.1Description (45)5.13.2Analysis (45)5.14Minimum Illumination (46)5.14.1Description (46)5.14.2Test Conditions (46)5.14.3Analysis (46)5.15Module Response Non-Linearity (47)5.15.1Description (47)5.15.2Test Conditions (48)5.15.3Analysis (49)5.16Photo-Response Non-Uniformity (50)5.16.1Description (50)5.16.2Test Conditions (50)5.16.3Analysis (51)5.17Relative Illumination (52)5.17.1Description (52)5.17.2Test Conditions (52)5.17.3Analysis (52)5.18Spatial Frequency Response (53)5.18.1Description (53)5.18.2Test Conditions (54)5.18.3Analysis (54)5.19Image Sharpness Measurement (56)5.19.1Description (56)5.19.2Test conditions (58)5.19.3Analysis (59)5.20TV Distortion (61)5.20.1Description (61)5.20.2Test Conditions (62)5.20.3Analysis (62)5.21Field of View (64)5.21.1Description (64)5.21.2Test Conditions (64)5.21.3Analysis (65)5.22Colour Accuracy (66)5.22.1Description (66)5.22.2Test Conditions (66)5.22.3Conversion of Data into CIELAB (67)5.22.4Conversion from sRGB into XYZ 1931 CIE (Tristimulus) Values (69)5.22.5Conversion from XYZ 1931 CIE (Tristimulus) Values to CIELAB (70)5.22.6Calculation of Colour/Hue Accuracy from L*a*b* (CIELAB) Data (70)5.22.7Pseudo Code for the Analysis Process (71)5.23Image Lag (74)5.23.1Description (74)5.23.2Test Conditions (74)5.23.3Analysis (75)5.24Veiling Glare (76)5.24.1Description (76)5.24.2Test Conditions (77)5.24.3Analysis (78)References (79)Appendix A- Default Test Conditions (80)Appendix B- Test Charts (81)Appendix C– Possible Trial-and-Error Image Lag Test Method (82)List of tablesTable 1: Acronyms (ix)Table 2: Definitions (ix)Table 3: ECR (x)Table 4: Bayer Matrix Orientation (13)Table 5: Standard ROIs (14)Table 6: Example ROI Dimensions (14)Table 7: Illumination Specification (20)Table 8: Environmental Specification (20)Table 9: Analogue Supply Specification (21)Table 10: Digital Supply Specification (21)Table 11: Default Electrical Conditions (22)Table 12: Default Environmental Conditions (22)Table 13: Default Lighting Conditions (22)Table 14: Dynamic Range Test Conditions (25)Table 15: Vertical Fixed Pattern Noise Test Conditions (27)Table 16: Horizontal Fixed Pattern Noise Test Conditions (28)Table 17: Temporal Noise Test Conditions (30)Table 18: Column Noise Test Conditions (31)Table 19: Row Noise Test Conditions (33)Table 20: Frame to Frame Flicker Test Conditions (35)Table 21: Dark Signal Test Conditions (36)Table 22: Dark Signal Non-Uniformity Test Conditions (37)Table 23: Power Supply Rejection Ratio Test Conditions (39)Table 24: Electrical Test Conditions (40)Table 25: Signal to Noise Ratio Test Conditions (41)Table 26: Sensitivity Test Conditions (43)Table 27: Minimum Illumination Test Conditions (46)Table 28: Module Response Non-Linearity Test Conditions (48)Table 29: Photo-Response Non-Uniformity Test Conditions (50)Table 30: Relative Illumination Test Conditions (52)Table 31: SFR Test Conditions (54)Table 32: Image Sharpness ROIs (57)Table 33: Image Sharpness Test Kernel (58)Table 34: Image Sharpness Test Conditions (58)Table 35: TV Distortion Test Conditions (62)Table 36: FOV Test Conditions (64)Table 37: Colour Accuracy Test Conditions (66)Table 38: Image Lag Test Conditions (74)Table 39: Veiling Glare Test Conditions (77)Table 40 - Default Test Conditions (80)List of figuresFigure 1: Taking a sub-matrix (12)Figure 2 - Measurement ROIs (15)Figure 3: Default Darkroom Set Up (23)Figure 4: Electrical Schematic of Mobile Device Equivalence Model (24)Figure 5: Image Sharpness ROIs For Example Chart (57)Figure 6: Distorted image of a square, showing pincushion distortion (61)Figure 7: Colour Test Image Capture Set Up (67)Figure 8: Colour Accuracy Process (68)Figure 9: Bayer Data to sRGB Conversion (68)Figure 10: Veiling Glare Measurement Set-up schematic (77)Figure 11: Image Lag Frame Acceptability (82)Acronyms Abbreviations and Definitions:Functional descriptions can be found in section 1.3.CCP Compact Camera PortCCI Camera Control InterfaceEMC Electro Magnetic CompatibilityEMI Electro Magnetic InterferenceFE Frame EndFps Frames per secondFS Frame StartFSD Full Scale DeflectionI2C Inter ICbusIF InterfaceIO Input/OutputLSB Least Significant ByteLVDS Low Voltage Differential SignallingMbps Megabits per secondMSB Most Significant ByteOECF Opto-Electronic Conversion FunctionPSRR Power Supply Rejection RatioRH Relative HumidityRO Read OnlyROI Region of InterestRW Read/WriteSCK System ClockSFR Spatial Frequency ResponseSMIA Standard Mobile Imaging ArchitectureSubLVDS Sub-Low Voltage Differential SignallingSVGA Super Video Graphics Array (800x600)VGA Video Graphics Array (640x480)Table 1: AcronymsFull scale deflection Taken to be the maximum pixel output minus the minimum pixeloutput (pedestal). Note that the maximum pixel output might notbe 2n-1 and the minimum pixel output is unlikely to be 0. Integration time Integration is the time in seconds between pixel reset and read. Optical axis Line through the centres of curvature of the surfaces of the opticalsystem.Pedestal Fixed offset used to compensate for black level of the cameramodule. The pedestal value is the offset from 0 codes to therequired black level.Table 2: DefinitionsPREFACESpecification Supersedes Earlier DocumentsThis document contains the SMIA Characterisation specification.Following publication of the SMIA Standard, there may be future approved errata and/or approved changes to the standard prior to the issuance of another formal revision.Incorporation of Engineering Change Requests (ECRs)The following ECRs have been incorporated into this version of the specification:ECR DESCRIPTIONTable 3: ECRSCOPEThis document describes the tests which are used to characterise the performance of a SMIA camera. In general, a test plan will be used to define the number of samples to be used for each test, and any deviations from the test methods and test conditions described in this specification.The document is arranged as follows:-• Chapter 1 Definitions. This provides standard definitions which are used throughout the document. These include array nomenclature, image data formats and function descriptions.• Chapter 2 Pre-Processing. This includes descriptions of data manipulation steps that are used prior to calculations on captured image data.• Chapter 3 Test Equipment and Environmental Requirements. This defines the capabilities of the equipment and environment required to make the measurements.• Chapter 4 Default Configuration. This describes the default camera configuration & physical darkroom set up for tests, and electrical schematic. Specific settings which deviate from the defaults are defined in each test method description.• Chapter 5 Characterisation Test Methods. This includes descriptions of each of the individual test methods. A standard template is used for each method, with the following parts:- o Overview of the test objective.o Formal description of the calculations required and the physical set up.o Table to define the test conditions (illumination, environmental, electrical, camer settings, capture method and pre-processing).o Pseudo code to describe the analysis required, using functions defined in the “Definitions” chapter.• Appendix A describes the default environmental, electrical supply and analogue gain test conditions at which each characterisation test is conducted. These should be used where a specific test plan has not been provided.• Appendix B provides information on suitable test charts for various tests, and references to the electronic versions.• Additional appendices are used for supplementary information .This specification includes descriptions for 24 characterisation tests. Additional tests for depth of focus, flare, ghosting, out of scene image artifacts, infra red response, blemish and EMC will be added to a future release of this specification.Nokia & ST Confidential Page 11 of 83Nokia & ST ConfidentialPage 12 of 831. DefinitionsCare should be taken to avoid loss of precision, for example due to rounding errors when performing calculations or by reducing the bit depth of the data.1.1 ArraysIn this document a generic array is a collection of values ordered in a 2-dimensional matrix. AThe size of is written where m is the number of columns and n is the number of rows. When the number of elements in the array is needed as a quantity, size may also be used as a function: . A n m ×mn A size =)(An individual value in may be referred to as an element, entry, item, member, pixel, position, value, etc. The value occurring on the i A th row and in the j th column of is written . The top left value is while the bottom right value is A ),(j i A )0,0(A )1,1(−−n m A . The sum of a matrix is the sum of all its elements: .∑∑−=−==101),()(m i n j j i A A sum A sub-array can be described using sets of values, e.g. )2,2(j i A B = where andrepresents an array one quarter the size of and taking it’s values from the 2m i <≤20n j <≤20A nd , 4th , 6thetc rows and 2nd , 4th , 6th etc columns. This is illustrated in Figure 1.a b c d e f …g h i j k la c e … m n o p q r …m o qs t u v w xy aa ac … y z aa ab acadB=: : ae af ag ah ai aj … A= :::Figure 1: Taking a sub-matrixFor convenience we also define the mean of an array as )()()()(A size A sum A A A mean ===µ, thevariance of an array as ∑∑−=−=−−=1012)),((1)(1)var(m i n j A j i A A size A . and the standard deviation of an array as )var()()(A A A std ==σ.Nokia & ST ConfidentialPage 13 of 831.2 Standard Parameters1.2.1 Raw Bayer Image DataThe precise data format of an SMIA compatible image is already given in the SMIA Functional Specification and consists of a single bit depth Bayer pixel array, with size where m and n are both even numbers. A n m ×Using this image data the following types of image arrays are required for the Optical Characterisation measurements.1.2.2 Green (Red) Raw BayerGreen (Red) Raw Bayer contains the visible Green pixels data from each row containing Green and Red Bayer pixels. This array can be written )2,2()(q j p i A A GR G ++= where m i <≤20,, and n j <≤20p and depend upon the alignment of the Bayer matrix as shown in the tablebelow.qFirst Bayer column contains blue pixelsFirst Bayer column contains red pixelsFirst Bayer row contains red pixelsp = 0, q = 0 p = 0, q = 1 First Bayer row contains blue pixelsp = 0, q = 1p = 1, q = 1Table 4: Bayer Matrix Orientation1.2.3 Region of Interest (ROI)A Region Of Interest (ROI) is a continuous sub-array of the form where and. For a given process one or more ROIs may be defined with algorithms being run just onthe ROI sub-arrays instead of on the whole data set. ),(j i A 21x i x ≤≤21y j y ≤≤Some standard ROIs are defined in Table 5.Nokia & ST ConfidentialPage 14 of 83Description of location Area relative to )(GR G A Sub-array of with size )(GR G A n m ×Range for columnsRange for rowsROI (1)Geometric centre 5⅓% *),()(j i A GR Gi k m ≤−12/ 12/1−+≤k m **j k n ≤−12/ 12/1−+≤k n **ROI (2)Geometric centre 1% ),()(j i A GR G 120/1120/9−≤≤m i m 120/1120/9−≤≤n j nROI (3)Upper left 1% ),()(j i A GR G 110/0−≤≤m i 110/0−≤≤n j ROI (4)Lower left 1% ),()(j i A GR G 110/9−≤≤m i m 110/0−≤≤n j ROI (5)Upper right 1% ),()(j i A GR G 110/0−≤≤m i 110/9−≤≤n j n ROI (6)Lower right1%),()(j i A GR G110/9−≤≤m i m110/9−≤≤n j nTable 5: Standard ROIs1.2.3.1 Example ROI DimensionsExample ROI dimensions are shown in Table 6.Module type Green-Red Bayer pixel dimensionsROI () dimensions)1(ROI ROI () dimensions)6,5,4,3,2(ROI SMIA VGA(640 x 480) 320 x 240 64 x 6432 x 24 SMIA SVGA(800 x 600)400 x 300 80 x 8040 x 30Table 6: Example ROI Dimensions*If this is less then 64x64 pixels then area is defined as 64 x 64 pixels**where 5/)3/(1mn k =Nokia & ST ConfidentialPage 15 of 831.2.3.2 Measurement Locations)(n iFigure 2 - Measurement ROIs1.3 Function Descriptions1.3.1 AV_IMAGE(#1, ..., #F)Takes a number of frames, F, to produce a composite image containing the average values for each pixel.Thus if A = AV_IMAGE(A 1, A 2, …, A F ) then ∑==Fk kj i A Fj i A 1),(1),( for each i,j where 0 ≤ i < m ,0 ≤ j < n .1.3.2 COLUMN_AV(#)Takes the column averages for a frame and outputs a row vector.Thus if C = COLUMN_AV(A) then ∑−==1),(1)(N j j i A n i C for each i where 0 ≤ i < m .Nokia & ST ConfidentialPage 16 of 831.3.3 CONVOLUTION(#1,#2)Convolves two arrays to produce a fresh array with the output being placed at the position in array #1 coincident with centre entry of the array #2.Thus if A is an m x n matrix, K is a r x s matrix (where r = 2u+1 and s = 2v+1), and B = CONVOLUTION(A, K) then for each i,j where 0 ≤ i < m , 0 ≤ j < n .∑∑−=−=++++=u u g vvh h v g u K h j g i A j i B ),(),(),(In cases when i + u < 0 or i + u ≥ m , and/or j + v < 0 or j + v ≥ n the kernel array K overhangs the edge of the array A and so A(i + g, j + h) is undefined for some values of g and h . In such cases define a sub-array C of A asC = A(e, f) where max(0, i - u) ≤ e ≤ min(m - 1, i + u) and max(0, j - v) ≤ f ≤ min(n - 1, j + v)and use A(i + g, j + h) = mean(C) when i + g < 0 or i + g ≥ m , and/or j + h < 0 or j + h ≥ n . 1.3.4 Extract Colour Plane - GREENREDThus GREENRED(A) = A G(GR) as defined in section 1.2.2. 1.3.5 LOG10(x)Logarithm to the base 10.1.3.6 Local DeviationThis takes the deviation of a point from the average of its locality.The local standard deviation is defined by∑∑−=−=⋅−⋅=1012,11M i N j j i local local N M δσ ,where()⎟⎟⎠⎞⎜⎜⎝⎛−⎟⎟⎠⎞⎜⎜⎝⎛⋅−+⋅−=∑∑+−=+−=j i K i K i n K j K j m m n j i ji local p p K p ,,2,,1121δ given p i,j is the pixel value at (i,j) and K is the locality parameter. For most cameras the localityparameter can be set to K = 5, which yields an average over 120 pixels for the locality. When dealing with pixels at the edge of the array, for calculation purposes the pixels outside the array assume the value of the average of those inside the array and the locality. 1.3.7 MAX(#)Finds the maximum entry value in an array.1.3.8 MEAN(#)Finds the mean of the entry values in an array, i.e. MEAN(A) = mean(A) as defined in section 1.1. 1.3.9 MIN(#)Finds the minimum entry value in an array.Nokia & ST ConfidentialPage 17 of 831.3.10 Regression AnalysisSome methods give rise to a set of pairs of measured values (x 1,y 1), (x 2,y 2), …, (x n ,y n ). We can draw a best fit straight line y=mx+c through these points using the Gaussian method of least squares by setting m and c as follows.Let∑==n i i x n x 11 and ∑==n i iy n y 11. Also let∑=−−−=n i i i xy y y x x n s 1))((11 and ∑=−−=n i i x x n s 1221)(11. Then21s s m xy= and x m y c −=.1.3.11 RMS(#)Finds the root mean squared of array. Thus ∑∑−=−==1012),()(1)(m i n j j i A A size A RMS , where size(A) is defined in section 1.1.1.3.12 ROI(a,b;x,y;#)ROI extracts a region of interest of size (a,b) with top left coordinates (x,y) from an array and outputs the ROI as a new arrayThus ROI(a,b;x,y;A) = A(x + i, y + j) for each i,j where 0 ≤ i < a , 0 ≤ j < b . 1.3.13 ROW_AV(#)Takes the row averages for a frame and outputs a column vector.Thus if R = ROW_AV(A) then ∑−==1),(1)(m i j i A m j R for each j where 0 ≤ j < n . 1.3.14 STDEV(#)Finds the standard deviation of an array's entry values. Note that this is always the sample standard deviation σn-1 and this is the quantity referred to as standard deviation in the text, i.e. STDEV(A) = std(A) as defined in section 1.1.Nokia & ST ConfidentialPage 18 of 832. Pre-Processing2.1 Data Pre-ProcessingThe SMIA Functional Specification describes the format of the data output by SMIA cameras, and should be used to correctly unpack the captured image data.Additionally, it describes Data Pedestal. Each characterisation test method in section 5 states whether the data pedestal should be subtracted from the unpacked data. Data pedestal subtraction is performed by subtracting the pedestal from each pixel value, clipping to zero if the original pixel value is less than the pedestal.Thus if A = Pedestal_Offset_Subtraction(F) then A(i, j) = F(i, j ) - p if F(i, j ) >p otherwise A(i, j) = 0, for each i,j where 0 ≤ i < m , 0 ≤ j < n , and where p is the pedestal value in codes.2.2 Image Pre-Processing2.2.1 IntroductionIn normal use, a camera module can be expected to operate with a large amount of digital signal processing to remove errors and enhance the overall image quality. As a SMIA camera module is characterised using raw Bayer data a certain amount of low-level error detection and defect correction is necessary for the majority of tests.We use a measurement Kernel of size (see Section 2.2.2), which traverses each image pixel in the image array from top left through to bottom right. The default value of k is 1. The result of this convolution creates a corrected image data array (see Sections 2.2.3, 2.2.4) based on the value of the central pixel of the Kernel compared to its surrounding pixels. If a pixel is close to the edge of the array its Kernel may extend past the array boundaries. In such cases the value of the corresponding pixel in the corrected array is set to the value of the pixel in the original array. This new corrected array is then used for later analysis. k Kern k 2.2.2 Kernel Kern kThe kernel of size used in section 2.2 is a square array of size with equal weightings on for each entry (i.e. all entries in the kernel parameter of the convolution are set to 1). k Kern k 1212+×+k kOther kernels are also used elsewhere in the text and are described explicitly when required.Given an array , the kernel of the pixel is the sub-array whereand A ),(y x A ),()),((j i A y x A Kern k =k x i k x +≤≤−k y j k y +≤≤−.Nokia & ST ConfidentialPage 19 of 832.2.3 Error DetectionThe purpose of this routine is to detect pixels defects on the image by recording the difference between the local pixel value and the kernel mean. This may be used at a later date for blemish test method, but is currently not used.k K For each pixel let , let ),(y x A )),((y x A Kern Kern k =1)(),()(−−=Kern size y x A Kern sum aThen create a new array with ected errors A det _a y x A y x A ected errors −=),(),(det _.2.2.4 Defect CorrectionThe purpose of this correction routine is to remove the effects of large pixels defects on the images by setting any pixel with a value that is +/-15% of FSD deviation from the Kernel mean (Kern ).For each pixel let , let ),(y x A )),((y x A Kern Kern k =1)(),()(−−=Kern size y x A Kern sum aand create a new array where if corrected defect A _15.0)),((×<−FSD a y x A abs thenotherwise ),(),(_y x A y x A corrected defect =a y x A corrected defect =),(_.3. Test Equipment and Environmental Requirements3.1 GeneralAll test results should state the measurement accuracy achieved with the measurement equipment used. The following sections specify the capability of the equipment required to make the measurements, not the actual measurement conditions.3.2 Illumination SpecificationIllumination type TungstenHalogenTungstenD65 D75 DiffuseColour temperature 3200-3400K2500-3000K6500K 7500K2500-3400KType TungstenhalogenTungstenDaylightFluorescentFluorescentTungsten ortungstenhalogenElectrical SupplyFrequency DC DC 20 – 100kHz 20 – 100kHzIntensity range atchart10 - 2000 Lux1 - 2000 Cd/m2> 100 Lux> 100 Lux > 100 Lux> 50 Lux atdiffuserAngle of incidence of eachlight source (with respect to chart) 45º 45º 45º 45ºDiffuseUniformity ofillumination atchart± 5% ± 5% ± 5% ± 5% ± 2%Table 7: Illumination SpecificationAdditionally, the “Dark” condition is defined as one in which no detectable light (< 1mLux) can reach the camera. It is recommended that a double shielding approach is taken. For instance, the camera is covered by a black cap and blackout cloth in a darkroom.3.3 Environmental SpecificationParameter Value Tolerance UnitsTemperature rangeMinimum Maximum -30+70± 1%± 1%°C°CHumidity <70 ± 5% % RHTable 8: Environmental SpecificationNokia & ST Confidential Page 20 of 833.4 Electrical Specification3.4.1 Analogue SupplyParameter Minimum Typical Maximum UnitsVoltsVoltage 0.0 2.8 5.0 DCCurrent(Resistive) +/-50 mATable 9: Analogue Supply Specification3.4.2 Digital SupplyParameter Minimum Typical Maximum UnitsVoltsVoltage 0.0 1.8 5.0 DCCurrent(Resistive) +/-50 mATable 10: Digital Supply SpecificationNokia & ST Confidential Page 21 of 834. Default Configuration4.1 Default Camera Configuration4.1.1 Default Electrical ConditionsParameter Value Tolerance UnitsReference Analogue supply (VANA) 2.8 ± 0.1 DC Volts SMIA Functional SpecificationModulation OFFDigital supply (VDIG) 1.8 ± 0.1 DC Volts SMIA Functional SpecificationModulation OFFExternal Clock (EXTCLK) SMIA Functional SpecificationFrequency 13.0 ±0.1 MHzLevel V DIG- VoltsTable 11: Default Electrical Conditions4.1.2 Camera Register SettingsThe SMIA camera shall be reset before each Characterisation test is performed so that the camera registers contain the default data defined in the SMIA Functional Specification.Additionally, the camera registers should be configured for• 13MHz External Clock, unless specified in the test plan• Analogue gain specified for the test• Digital gain specified for the test• Integration time required for the test• Frame Rate required for the test• Other camera-specific registersThe default frame rate is the lesser of 15fps or the maximum achievable frame rate.Information on the register settings used for each test should be supplied with the test results.4.1.3 Default Environmental ConditionsParameter Value Tolerance UnitsTemperature23 ±2 °CHumidity <70 %RHTable 12: Default Environmental Conditions4.1.4 Default Lighting ConditionsParameter ValueTolerance UnitsIlluminationType Intensity at chartUniformity Tungsten Halogen300± 5%----Lux-Table 13: Default Lighting ConditionsNokia & ST Confidential Page 22 of 83。
深信服超融合架构技术白皮书深信服科技有限公司修订记录深信服超融合架构技术白皮书文档密级：内部第1章、前言 (8)1.1IT时代的变革 (8)1.2白皮书总览 (9)第2章、深信服超融合技术架构 (11)1.1超融合架构概述 (11)1.1.1超融合架构的定义 (11)1.2深信服超融合架构组成模块 (11)1.2.1.1系统总体架构 (11)1.2.1.2aSV计算虚拟化平台 (12)1.2.1.2.1概述 (12)1.2.1.2.2aSV技术原理 (13)1.2.1.2.2.1aSV的Hypervisor架构 (14)1.2.1.2.2.2Hypervisor虚拟化实现 (17)1.2.1.2.3aSV的技术特性 (25)1.2.1.2.3.1内存NUMA技术 (25)1.2.1.2.3.2SR-IOV (26)1.2.1.2.3.3Faik-raid (27)1.2.1.2.3.4虚拟机生命周期管理 (28)1.2.1.2.3.5虚拟交换机 (29)1.2.1.2.3.6动态资源调度 (30)1.2.1.2.4aSV的特色技术 (30)1.2.1.2.4.1快虚 (30)1.2.1.2.4.2虚拟机热迁移 (31)1.2.1.2.4.3虚拟磁盘加密 (32)1.2.1.2.4.4虚拟机的HA (33)1.2.1.2.4.5多USB映射 (33)1.2.1.3aSAN存储虚拟化 (35)1.2.1.3.1存储虚拟化概述 (35)1.2.1.3.1.1虚拟后对存储带来的挑战 (35)1.2.1.3.1.2分布式存储技术的发展 (35)1.2.1.3.1.3深信服aSAN概述 (36)1.2.1.3.2aSAN技术原理 (36)1.2.1.3.2.1主机管理 (36)1.2.1.3.2.2文件副本 (37)1.2.1.3.2.3磁盘管理 (38)1.2.1.3.2.4SSD读缓存原理 (39)1.2.1.3.2.5SSD写缓存原理 (45)1.2.1.3.2.6磁盘故障处理机制 (49)1.2.1.3.3深信服aSAN功能特性 (60)1.2.1.3.3.1存储精简配置 (60)1.2.1.3.3.2aSAN私网链路聚合 (61)1.2.1.3.3.3数据一致性检查 (61)1.2.1.4aNet网络虚拟化 (61)1.2.1.4.1网络虚拟化概述 (61)1.2.1.4.2aNET网络虚拟化技术原理 (62)1.2.1.4.2.1SDN (62)1.2.1.4.2.2NFV (63)1.2.1.4.2.3aNet底层的实现 (64)1.2.1.4.3功能特性 (68)1.2.1.4.3.1aSW分布式虚拟交换机 (68)1.2.1.4.3.2aRouter (68)1.2.1.4.3.3vAF (69)1.2.1.4.3.4vAD (69)1.2.1.4.4深信服aNet的特色技术 (69)1.2.1.4.4.1网络探测功能 (69)1.2.1.4.4.2全网流量可视 (70)1.2.1.4.4.3所画即所得业务逻辑拓扑 (70)1.2.2深信服超融合架构产品介绍 (71)1.2.2.1产品概述 (71)1.2.2.2产品定位 (71)第3章、深信服超融合架构带来的核心价值 (73)1.1可靠性： (73)1.2安全性 (73)1.3灵活弹性 (73)1.4易操作性 (73)第4章、超融合架构最佳实践 (74)第1章、前言1.1 IT时代的变革20 世纪90 年代，随着Windows 的广泛使用及Linux 服务器操作系统的出现奠定了x86服务器的行业标准地位，然而x86 服务器部署的增长带来了新的IT 基础架构和运作难题，包括：基础架构利用率低、物理基础架构成本日益攀升、IT 管理成本不断提高以及对关键应用故障和灾难保护不足等问题。
目录1 产业背景 (1)1.1机器智能社会将全面到来 (1)1.2网络将出现云、边、端三级算力架构 (2)1.3实现云、边、端算力的高效需要算力网络 (2)1.4运营商的可持续发展需要算力网络 (4)2 算力网络的概念和架构 (7)2.1算力网络是云化网络发展演进的下一个阶段 (7)2.2算力网络的关键技术元素 (8)2.2.1联网元素：打造无损和确定性的网络联接 (9)2.2.2云网元素：智能网络与网络云化的持续推进 (10)2.2.3算网元素：为计算服务的可信、高效、随需网络 (10)2.3算力网络的典型应用场景 (16)2.3.1运营商ToB的“5G园区+AI”场景 (16)2.3.2运营商ToC的“5G+Cloud X”场景 (17)2.3.3算力开放，运营商提供可交易的算力通证 (18)3 算力网络的标准与生态 (19)4 总结与展望 (20)5 缩略语 (22)1产业背景1.1机器智能社会将全面到来人类将步入智能社会，智能是知识和智力的总和，翻译到数字世界就是“数据+算力+算法”，其中算法需要通过科学家研究实现，海量数据来自于各行各业的人和物，数据的处理需要大量算力，算力是智能的基础平台，由大量计算设备组成。
图1-1 智能的三要素：算法、算力、数据人脑的算力相当于约300亿颗晶体管，人类的历史和文明都是由无数人脑算力所创造，但人脑算力正面临老龄化的挑战，2020年超高龄国家（65岁以上人口超过总人口20%）将达到13个，2030年将上升到34个，而且主要集中在亚太、欧美等较发达国家。
考虑到儿童占比约15%，实际这些国家的劳动适龄人口只占不到60%。
全球2020年人口约77亿，较发达国家人口约30亿，所以这些国家实际处于劳动适龄段的人脑只有不到20亿。
现阶段电子工艺可以做到的机器算力已经接近人脑算力，如麒麟980基于7nm工艺集成了69亿晶体管，AMD Radeon VII GPU将采用7nm工艺，晶体管数量约132亿，未来5年，基于5nm工艺，芯片集成度据信可以做到300亿晶体管，此时处理器的信息分析处理能力已经与人脑相当，并且相比于人脑，处理器更聚焦于专业领域的数据处理，不知疲劳，所以在具体的数据处理领域，高端CPU的算力已经事实上相当于甚至于超过了人脑。
智能投顾，是IT科技和金融领域相结合的前沿应用领域。
它能够基于对投资者的精准画像，通过将现代金融理论融入人工智能算法，从而为投资者提供基于多元化资产的个性化、智能化、自动化和高速化的投资服务。
自2008年金融危机后，美国首家智能投顾公司Betterment于当年成立，随后Wealthfront，Personal Capital，Future Advisor，Motif Investing等创新型公司相继成立。
目前, 先锋集团（VanguardGroup）推出了VPAS，嘉信理财推出了SIP，富达基金推出了Fidelity Go，美林证券推出了Merrill Edge，摩根士丹利推出了Access Investing，“华尔街之狼- Kensho”推出了Warren。
智能投顾作为金融科技（FinTech）应用的最前沿领域，正席卷美国传统金融界。
发源于美国的智能投顾科技理论和技术西行东渐，我国智能投顾于2015年开始起步，虽然起步较晚，但是发展迅速。
招商银行推出了“摩羯智投”，工商银行推出了“AI投”，中国银行推出了“中银慧投”，平安银行推出了“平安智投”，兴业银行推出了“兴业智投”，广发证券推出了“贝塔牛”，平安证券推出了“AI慧炒股”，长江证券推出了“阿凡达”，京东集团推出了“京东智投”，羽时金融推出了“AI股”和“AI投顾”。
代表IT最新最前沿的人工智能技术在融入了金融行业后，有力地推进了传统金融行业的变革，有力地践行了普惠金融的理念。
虽然国内智能投顾的发展势头兴旺，但是商业模式不清晰，行业内鱼龙混杂，很多打着智能投顾概念的传统公司混杂其中，让人难以明辨。
什么是智能投顾？智能投顾的国内外发展现状如何？国内智能投顾业务的发展面临哪些问题和挑战？作为新生事物，智能投顾的IT技术路线，智能投顾的商业模式，智能投顾的风险控制，智能投顾业务的国内外监管政策比较，如何界定智能投顾公司的业务边界，采用哪些方向的标准评价智能投顾公司，智能投顾未来的发展趋势，针对这些大家关心的焦点问题，《2018智能投顾行业白皮书》希望能为大家作出一些抛砖引玉的探讨。
2023.9目录一、智能驾驶计算芯片产业现状 (3)1、国内外智能驾驶计算芯片发展情况 (3)2、智能驾驶计算芯片应用需求 (7)3、智能驾驶计算芯片标准需求 (8)二、智能驾驶计算芯片标准与评测 (9)1、智能驾驶计算芯片国内外政策和标准现状 (9)2、智能驾驶计算芯片性能评测标准 (11)3、智能驾驶计算芯片标准典型应用案例 (16)三、总结与展望 (28)四、参考文献 (29)一、智能驾驶计算芯片产业现状汽车产业正在被人工智能技术重构。
如同蒸汽机之于工业革命的意义，智能驾驶已经成为人类社会自发明汽车以来的一大颠覆性创新，持续推动汽车产品、整车市场格局和产业链变革，而数据和算力是驱动汽车智能化加速的两大动力。
关于智能驾驶发展的趋势，业内普遍认同的观点是：智能驾驶汽车将在2025年前后开始一轮爆发式增长。
智能驾驶汽车在传统驾驶的电子电气架构基础上，引入基于智能驾驶芯片的智能驾驶模块，搭载各类车载传感器、控制器、执行器等装置，并融合了现代通信、网络和计算技术，使得车辆具备复杂环境感知、智能决策、协同控制等功能，从而大大提升驾驶的自动化和智能化。
未来，汽车将从最常用的交通工具变成最大的智能终端，具备高度电动化、网联化、智能化、共享化的特征，传统汽车企业势必将重新定义和塑造自身的商业模式，传统汽车行业的市场也将向芯片厂商、互联网科技公司、造车新势力等逐步打开，生态格局不断走向开放。
1、国内外智能驾驶计算芯片发展情况——车载计算芯片成为行业竞争热点，国内外企业竞相发力随着智能驾驶技术的不断发展和汽车市场的逐渐开放，作为汽车智能化的核心，智能驾驶芯片的发展在全球范围内日益瞩目，市场也呈现出井喷式的增长态势。
除了传统的汽车制造商，科技公司也开始在智能驾驶芯片市场布局。
例如，英伟达的智能驾驶芯片“Drive”已经被包括奔驰、特斯拉和沃尔沃在内的多家汽车制造商采用。
此外，谷歌旗下的Waymo、苹果、百度和滴滴也都在智能驾驶芯片领域进行了大量尝试。
三本白皮书的内容(一)三本白皮书的内容简介•本文将介绍三本重要的白皮书的内容，涵盖了多个领域和行业的创新和发展。
1. 人工智能白皮书人工智能的概述和应用领域•人工智能白皮书详细阐述了人工智能的概念、发展历程以及当前的应用领域。
•通过深度学习、机器学习和自然语言处理等技术，人工智能已经广泛应用于语音识别、图像识别、智能机器人等领域。
人工智能的挑战和未来发展方向•人工智能白皮书详细探讨了人工智能面临的挑战，如数据安全、伦理和道德问题等。
•白皮书提出了未来人工智能的发展方向，包括加强研究合作、推进人才培养和加强政策支持等。
2. 区块链白皮书区块链的基本原理和应用场景•区块链白皮书详细介绍了区块链的基本原理和分布式记账技术。
•通过去中心化的特点，区块链已经应用于数字货币、供应链管理和智能合约等领域。
区块链的发展趋势和挑战•区块链白皮书讨论了区块链的发展趋势，如跨界合作、隐私保护和公共治理等。
•同时白皮书也提到了区块链面临的挑战，如扩容性、可扩展性和能源消耗等。
3. 生物技术白皮书生物技术的研究领域和应用方向•生物技术白皮书详细介绍了生物技术的研究领域，包括基因编辑、合成生物学和生物材料等。
•生物技术已经广泛应用于医药、农业和环境保护等领域，具有重要的应用前景。
生物技术的伦理和安全问题•白皮书提到了生物技术涉及的伦理和安全问题，如基因编辑的道德问题和生物安全风险的管理等。
•生物技术白皮书强调了加强监管和推进科学研究的重要性，以确保生物技术的安全和可持续发展。
结论•以上是三本重要的白皮书的内容概述，涵盖了人工智能、区块链和生物技术等领域的创新和发展。
•这些白皮书为相关行业提供了方向和政策支持，推动了科技创新和社会进步。
2019-2020年度⼈⼯智能-⼈⼯智能单选题。
? 17.根据本讲，（）通过交互接⼝，与外界智能产品、智能物流、智能交通和智A.增材制造、D.IT 技术 4.0A.可及性、0B.安全性、0C.观点C.条件D.意义A.⽆为D.⾃然⽂信息处理、语⾳合成与识别、语义理解、⽣物特征识别等.错，⼈⼯智能皇A.数据智能B .读写智能C.3.领英数据显⽰，截⽌2017中美国占85万，排名。
A.第四、B .第三、C.5.｝，相应地艾滋病风险就下降30%D.40% 居，打造了Apll 和DueS 两⼤⾏业开放⽣态，加速推动⽆⼈驾驶汽车和智能家居迈向世界先进⽔平。
A.阿⾥巴巴B .蚂蚁⾦服C.9.2014年7⽉，（）开发机构(NED)公布了《机器⼈⽩⽪书》。
A.韩国、B .英国、C.10.A.观察性教育、B .效仿性教育、C.11.领域的是()。
A.制造、B .⾦融 13.本讲提到，（。
A.⾼校、B .⼈⼯智能中⼼()最早报道艾滋病，近年来艾滋病患者发病⼈数逐渐增加。
年C.1987年D.1988年，AlphaG Ze 版本的特点是()。
A.没有利⽤任何历史棋谱、B .仅输⼊基本规则、C.采⽤80层卷积神经⽹络D.．．以上都对．．．．16.本讲提到，旧时的私()。
A.阅读性教育B.创造性教育。
18.（）。
A.科⼤讯飞、B .搜狗、C.19.法的是（）。
A.上层应⽤层D.弱⼈⼯智能 1.正常的⼈体CD4细胞数由于病毒的破坏陷到20个以下，免疫功能就开始崩渍，患者会出现各种机会感染或肿瘤，这⼀阶段我们称为艾滋病发病期，A.720个B .730个C.740个、12.根据本讲，智能制）为核⼼，由智能产品、智能⽣产和智能服务等三个⽅⾯构成。
A.现代⼯⼚B.智能发展C.）创新缺乏持续刺激。
.技术开发、c.联合创办经济实体、D.科技资源共享 3.德国对⼈⼯智能、智能机器⼈的⽀持，主要集中在（）当中，涉及到的机器感知、规划、决策以及⼈机交互等领域，都是⼈⼯智能技术的重点研究⽅向。
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1 开发技术：语言与平台 (6)JavaBean 组件模型特点 (6)Enterprise JavaBean （EJB）组件模型特点： (6)JSP 胜过servlet 的关键的优点： (6)J2EE 的重要组成部分： (6)RMI 和RPC 的区别： (7)XML 和HTML 的主要区别： (7)XML 技术和JSP技术集成的方案： (7)XML 与JSP 技术联合的优越性： (7)XML 的特点： (7)SAX (Simple API for XML) 和DOM (Document Object Model) (7)什么DOM? (7)什么SAX? (8)什么类型的SAX 事件被SAX解析器抛出了哪? (9)什么时候使用DOM？ (9)什么时候使用SAX？ (9)HTML 的缺点： (10)经验结论 (10)用ASP、JSP、PHP 开发的缺陷： (10)XML 的优缺点： (10)XML 主要有三个要素：模式、XSL、XLL。
(10)2 Web Service 相关技术 (10)Web Service (10)创建简单的Web Service 的方法： (11)Web Service 主要目标是跨平台和可互操作性，其特点： (11)Web Service 应该使用的情况： (11)UDDI （统一描述、发现和集成Universal Description,Discovery,andIntegration） (11)SOAP (12)Web Service 技术（SOAP、UDDI、WSDL、EBXML） (12)3 软件工程、软件架构及软件体系结构 (12)3.1 面向对象技术 (12)一组概念 (12)OOA 的主要优点： (12)OOA 过程包括以下主要活动： (12)3.2 UML： (12)UML 包含了3 个方面的内容 (13)UML 提供了3类基本的标准模型建筑块 (13)UML 规定四种事物表示法 (13)UML 提供的建筑块之间的基本联系有四种 (13)UML 图形提供了9 种图形 (13)UML 规定了语言的四种公共机制 (13)UML 的特点： (13)USE CASE： (13)对象类图： (13)交互图： (14)状态图： (14)组件图和配置图： (15)UML 开发工具：ilogix Rhapsody (15)Rational Rose家族成员有： (15)3.3 OMT 方法： (15)OMT 方法有三种模型：对象模型、动态模型、功能模型。
