Nanoscale_imaging_magnetometry_with_diamond_spins_under_ambient_conditions1

Digital Image Processing and Edge DetectionDigital Image ProcessingInterest in digital image processing methods stems from two principal application areas: improvement of pictorial information for human interpretation; and processing of image data for storage, transmission, and representation for autonomous machine perception.An image may be defined as a two-dimensional function, f(x,y), where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. When x, y, and the amplitude values of f are all finite, discrete quantities, we call the image a digital image. The field of digital image processing refers to processing digital images by means of a digital computer. Note that a digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image elements, pels, and pixels. Pixel is the term most widely used to denote the elements of a digital image.Vision is the most advanced of our senses, so it is not surprising that images play the single most important role in human perception. However, unlike humans, who are limited to the visual band of the electromagnetic (EM) spectrum, imaging machines cover almost the entire EM spectrum, ranging from gamma to radio waves. They can operate on images generated by sources that humans are not accustomed to associating with images. These include ultrasound, electron microscopy, and computer generated images. Thus, digital image processing encompasses a wide and varied field of applications.There is no general agreement among authors regarding where image processing stops and other related areas, such as image analysis and computer vision, start. Sometimes a distinction is made by defining image processing as a discipline in which both the input and output of a process are images. We believe this to be a limiting and somewhat artificial boundary. For example, under this definition,even the trivial task of computing the average intensity of an image (which yields a single number) would not be considered an image processing operation. On the other hand, there are fields such as computer vision whose ultimate goal is to use computers to emulate human vision, including learning and being able to make inferences and take actions based on visual inputs. This area itself is a branch of artificial intelligence(AI) whose objective is to emulate human intelligence. The field of AI is in its earliest stages of infancy in terms of development, with progress having been much slower than originally anticipated. The area of image analysis (also called image understanding) is in between image processing and computer vision.There are no clearcut boundaries in the continuum from image processing at one end to computer vision at the other. However, one useful paradigm is to consider three types of computerized processes in this continuum: low, mid, and highlevel processes. Low-level processes involve primitive operations such as image preprocessing to reduce noise, contrast enhancement, and image sharpening. A low-level process is characterized by the fact that both its inputs and outputs are images. Mid-level processing on images involves tasks such as segmentation (partitioning an image into regions or objects), description of those objects to reduce them to a form suitable for computer processing, and classification (recognition) of individual objects. A midlevel process is characterized by the fact that its inputs generally are images, but its outputs are attributes extracted from those images (e.g., edges, contours, and the identity of individual objects). Finally, higherlevel processing involves “making sense” of an ensemble of recognize d objects, as in image analysis, and, at the far end of the continuum, performing the cognitive functions normally associated with vision.Based on the preceding comments, we see that a logical place of overlap between image processing and image analysis is the area of recognition of individual regions or objects in an image. Thus, what we call in this book digital image processing encompasses processes whose inputs and outputs are images and, in addition, encompasses processes that extract attributes from images, up to and including the recognition of individual objects. As a simple illustration to clarify these concepts, consider the area of automated analysis of text. The processes of acquiring an image of the area containing the text, preprocessing that image, extracting (segmenting) the individual characters, describing the characters in a form suitable for computer processing, and recognizing those individual characters are in the scope of what we call digital image processing in this book. Making sense of the content of the page may be viewed as being in the domain of image analysis and even computer vision, depending on the level of complexity implied by the statement “making sense.” As will become evident shortly, digital image processing, as we have defined it, is used successfully in a broad range of areas of exceptional social and economic value.The areas of application of digital image processing are so varied that some formof organization is desirable in attempting to capture the breadth of this field. One of the simplest ways to develop a basic understanding of the extent of image processing applications is to categorize images according to their source (e.g., visual, X-ray, and so on). The principal energy source for images in use today is the electromagnetic energy spectrum. Other important sources of energy include acoustic, ultrasonic, and electronic (in the form of electron beams used in electron microscopy). Synthetic images, used for modeling and visualization, are generated by computer. In this section we discuss briefly how images are generated in these various categories and the areas in which they are applied.Images based on radiation from the EM spectrum are the most familiar, especially images in the X-ray and visual bands of the spectrum. Electromagnetic waves can be conceptualized as propagating sinusoidal waves of varying wavelengths, or they can be thought of as a stream of massless particles, each traveling in a wavelike pattern and moving at the speed of light. Each massless particle contains a certain amount (or bundle) of energy. Each bundle of energy is called a photon. If spectral bands are grouped according to energy per photon, we obtain the spectrum shown in fig. below, ranging from gamma rays (highest energy) at one end to radio waves (lowest energy) at the other. The bands are shown shaded to convey the fact that bands of the EM spectrum are not distinct but rather transition smoothly from one to the other.Fig1Image acquisition is the first process. Note that acquisition could be as simple as being given an image that is already in digital form. Generally, the image acquisition stage involves preprocessing, such as scaling.Image enhancement is among the simplest and most appealing areas of digital image processing. Basically, the idea behind enhancement techniques is to bring out detail that is obscured, or simply to highlight certain features of interest in an image.A familiar example of enhancement is when we increase the contrast of an imagebecause “it looks better.” It is important to keep in mind that enhancement is a very subjective area of image processing. Image restoration is an area that also deals with improving the appearance of an image. However, unlike enhancement, which is subjective, image restoration is objective, in the sense that restoration techniques tend to be based on mathematical or probabilistic models of image degradation. Enhancement, on the other hand, is based on human subjective preferences regarding what constitutes a “good” en hancement result.Color image processing is an area that has been gaining in importance because of the significant increase in the use of digital images over the Internet. It covers a number of fundamental concepts in color models and basic color processing in a digital domain. Color is used also in later chapters as the basis for extracting features of interest in an image.Wavelets are the foundation for representing images in various degrees of resolution. In particular, this material is used in this book for image data compression and for pyramidal representation, in which images are subdivided successively into smaller regions.F ig2Compression, as the name implies, deals with techniques for reducing the storage required to save an image, or the bandwidth required to transmi it.Although storagetechnology has improved significantly over the past decade, the same cannot be said for transmission capacity. This is true particularly in uses of the Internet, which are characterized by significant pictorial content. Image compression is familiar (perhaps inadvertently) to most users of computers in the form of image file extensions, such as the jpg file extension used in the JPEG (Joint Photographic Experts Group) image compression standard.Morphological processing deals with tools for extracting image components that are useful in the representation and description of shape. The material in this chapter begins a transition from processes that output images to processes that output image attributes.Segmentation procedures partition an image into its constituent parts or objects. In general, autonomous segmentation is one of the most difficult tasks in digital image processing. A rugged segmentation procedure brings the process a long way toward successful solution of imaging problems that require objects to be identified individually. On the other hand, weak or erratic segmentation algorithms almost always guarantee eventual failure. In general, the more accurate the segmentation, the more likely recognition is to succeed.Representation and description almost always follow the output of a segmentation stage, which usually is raw pixel data, constituting either the boundary of a region (i.e., the set of pixels separating one image region from another) or all the points in the region itself. In either case, converting the data to a form suitable for computer processing is necessary. The first decision that must be made is whether the data should be represented as a boundary or as a complete region. Boundary representation is appropriate when the focus is on external shape characteristics, such as corners and inflections. Regional representation is appropriate when the focus is on internal properties, such as texture or skeletal shape. In some applications, these representations complement each other. Choosing a representation is only part of the solution for transforming raw data into a form suitable for subsequent computer processing. A method must also be specified for describing the data so that features of interest are highlighted. Description, also called feature selection, deals with extracting attributes that result in some quantitative information of interest or are basic for differentiating one class of objects from another.Recognition is the pro cess that assigns a label (e.g., “vehicle”) to an object based on its descriptors. As detailed before, we conclude our coverage of digital imageprocessing with the development of methods for recognition of individual objects.So far we have said nothing about the need for prior knowledge or about the interaction between the knowledge base and the processing modules in Fig2 above. Knowledge about a problem domain is coded into an image processing system in the form of a knowledge database. This knowledge may be as simple as detailing regions of an image where the information of interest is known to be located, thus limiting the search that has to be conducted in seeking that information. The knowledge base also can be quite complex, such as an interrelated list of all major possible defects in a materials inspection problem or an image database containing high-resolution satellite images of a region in connection with change-detection applications. In addition to guiding the operation of each processing module, the knowledge base also controls the interaction between modules. This distinction is made in Fig2 above by the use of double-headed arrows between the processing modules and the knowledge base, as opposed to single-headed arrows linking the processing modules.Edge detectionEdge detection is a terminology in image processing and computer vision, particularly in the areas of feature detection and feature extraction, to refer to algorithms which aim at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuities.Although point and line detection certainly are important in any discussion on segmentation,edge dectection is by far the most common approach for detecting meaningful discounties in gray level.Although certain literature has considered the detection of ideal step edges, the edges obtained from natural images are usually not at all ideal step edges. Instead they are normally affected by one or several of the following effects:1.focal b lur caused by a finite depth-of-field and finite point spread function; 2.penumbral blur caused by shadows created by light sources of non-zero radius; 3.shading at a smooth object edge; 4.local specularities or interreflections in the vicinity of object edges.A typical edge might for instance be the border between a block of red color and a block of yellow. In contrast a line (as can be extracted by a ridge detector) can be a small number of pixels of a different color on an otherwise unchanging background. For a line, there may therefore usually be one edge on each side of the line.To illustrate why edge detection is not a trivial task, let us consider the problemof detecting edges in the following one-dimensional signal. Here, we may intuitively say that there should be an edge between the 4th and 5th pixels.If the intensity difference were smaller between the 4th and the 5th pixels and if the intensity differences between the adjacent neighbouring pixels were higher, it would not be as easy to say that there should be an edge in the corresponding region. Moreover, one could argue that this case is one in which there are several edges.Hence, to firmly state a specific threshold on how large the intensity change between two neighbouring pixels must be for us to say that there should be an edge between these pixels is not always a simple problem. Indeed, this is one of the reasons why edge detection may be a non-trivial problem unless the objects in the scene are particularly simple and the illumination conditions can be well controlled.There are many methods for edge detection, but most of them can be grouped into two categories,search-based and zero-crossing based. The search-based methods detect edges by first computing a measure of edge strength, usually a first-order derivative expression such as the gradient magnitude, and then searching for local directional maxima of the gradient magnitude using a computed estimate of the local orientation of the edge, usually the gradient direction. The zero-crossing based methods search for zero crossings in a second-order derivative expression computed from the image in order to find edges, usually the zero-crossings of the Laplacian or the zero-crossings of a non-linear differential expression, as will be described in the section on differential edge detection following below. As a pre-processing step to edge detection, a smoothing stage, typically Gaussian smoothing, is almost always applied (see also noise reduction).The edge detection methods that have been published mainly differ in the types of smoothing filters that are applied and the way the measures of edge strength are computed. As many edge detection methods rely on the computation of image gradients, they also differ in the types of filters used for computing gradient estimates in the x- and y-directions.Once we have computed a measure of edge strength (typically the gradient magnitude), the next stage is to apply a threshold, to decide whether edges are present or not at an image point. The lower the threshold, the more edges will be detected, and the result will be increasingly susceptible to noise, and also to picking outirrelevant features from the image. Conversely a high threshold may miss subtle edges, or result in fragmented edges.If the edge thresholding is applied to just the gradient magnitude image, the resulting edges will in general be thick and some type of edge thinning post-processing is necessary. For edges detected with non-maximum suppression however, the edge curves are thin by definition and the edge pixels can be linked into edge polygon by an edge linking (edge tracking) procedure. On a discrete grid, the non-maximum suppression stage can be implemented by estimating the gradient direction using first-order derivatives, then rounding off the gradient direction to multiples of 45 degrees, and finally comparing the values of the gradient magnitude in the estimated gradient direction.A commonly used approach to handle the problem of appropriate thresholds for thresholding is by using thresholding with hysteresis. This method uses multiple thresholds to find edges. We begin by using the upper threshold to find the start of an edge. Once we have a start point, we then trace the path of the edge through the image pixel by pixel, marking an edge whenever we are above the lower threshold. We stop marking our edge only when the value falls below our lower threshold. This approach makes the assumption that edges are likely to be in continuous curves, and allows us to follow a faint section of an edge we have previously seen, without meaning that every noisy pixel in the image is marked down as an edge. Still, however, we have the problem of choosing appropriate thresholding parameters, and suitable thresholding values may vary over the image.Some edge-detection operators are instead based upon second-order derivatives of the intensity. This essentially captures the rate of change in the intensity gradient. Thus, in the ideal continuous case, detection of zero-crossings in the second derivative captures local maxima in the gradient.We can come to a conclusion that,to be classified as a meaningful edge point,the transition in gray level associated with that point has to be significantly stronger than the background at that point.Since we are dealing with local computations,the method of choice to determine whether a value is “significant” or not id to use a threshold.Thus we define a point in an image as being as being an edge point if its two-dimensional first-order derivative is greater than a specified criterion of connectedness is by definition an edge.The term edge segment generally is used if the edge is short in relation to the dimensions of the image.A key problem insegmentation is to assemble edge segments into longer edges.An alternate definition if we elect to use the second-derivative is simply to define the edge ponits in an image as the zero crossings of its second derivative.The definition of an edge in this case is the same as above.It is important to note that these definitions do not guarantee success in finding edge in an image.They simply give us a formalism to look for them.First-order derivatives in an image are computed using the gradient.Second-order derivatives are obtained using the Laplacian.数字图像处理与边缘检测数字图像处理数字图像处理方法的研究源于两个主要应用领域：其一是改进图像信息以便于人们分析；其二是为使机器自动理解而对图像数据进行存储、传输及显示。

Quantitation of proliferating cells with the EVOS FL Auto Imaging SystemIntroductionThe Invitrogen™ EVOS™ FL Auto Imaging Systemis a fully automated, digital, inverted multi-channelfluorescence and transmitted-light imaging systemwith outstanding workflow efficiency. Designed tomeet demanding requirements over a broad range ofapplications, it supports high-resolution mosaic tiling,multiple-position well scanning, cell counting withthresholding, and time-lapse studies.Among the versatile software features is cell counting that can be done automatically with a captured or live image. A powerful watershed algorithm has enhanced the precision of counting cells stainedwith Invitrogen™ Molecular Probes™ NucBlue™ Fixed Cell ReadyProbes™ Reagent. Other nuclear stains/fluorescent proteins, or general cytoplasmic fluorescent dyes/proteins, permit the easy determination of total cell numbers and/or percentage of cells stained for a functional readout.In this application note, the number of replicating cells was identified as a percentage of the total population of cells. The percentage of proliferating cells was easily determined by labeling cells with EdU-Alexa Fluor™ 594 to identify proliferating cells and NucBlue Fixed Cell ReadyProbes Reagent to identify total cells. Materials• I nvitrogen™ Molecular Probes™ Image-iT™ Fixation/Permeabilization Kit (Cat. No. R37602)• Invitrogen™ Molecular Probes™ Click-iT™ EdU Alexa Fluor™ 594 Imaging Kit (Cat. No. C10339)• NucBlue Fixed Cell ReadyProbes Reagent (Cat. No. R37606)• Invitrogen™ EVOS™ Light Cube, DAPI (Cat. No. AMEP4650)• Invitrogen™ EVOS™ Light Cube, Texas Red™ (Cat. No. AMEP4655)APPLICATION NOTE EVOS FL Auto Imaging SystemFigure 1. Overlay of 10x image used in the quantitative analysis of proliferating HeLa cells.Figure 2. Screen capture of the quantitation of the totalpopulation of cells (blue) within the image. 373 total cells werecounted in this field of view.Figure 3. Screen capture of the quantitation of the total population of proliferating cells (red) within the image. 138 proliferating cells were counted in this field of view.MethodsHeLa cells grown in a 96-well plate were pulsed with EdU for 1 hour. Cells were fixed, permeabilized and blocked using the Image-iT Fixation/Permeabilization Kit. Following fixation and permeabilization, nuclei were labeled with 2 drops/mL of NucBlue Fixed Cell ReadyProbes Reagent and the incorporated EdU was detected with Alexa Fluor 594 azide (Component B from Cat. No. C10339). Cells with incorporated EdU are shown in pink while all cells have nuclei labeled in blue (Figure 1). Following sample preparation, cells were imaged on the the EVOS FL Auto Imaging System using the EVOS DAPI light cube to visualize NucBlue Fixed Cell ReadyProbes Reagent and the EVOS Texas Red light cube to visualize Click-iT EdU using a 10x objective. Cells were quantitated using the Cell Counting tab on the EVOS FL Auto Imaging System.Results and discussionTotal cell population was determined to be 373 by counting the number of cells in the DAPI channel corresponding to NucBlue Fixed staining (Figure 2). Within this population, the number of proliferating cells was determined to be 138 by counting the number of cells in the Texas Red channel corresponding to Click-iT EdU-positive cells. Based on these results, 37% (138/373) of the cells within this population were shown to be actively proliferating (Figure 3). Using the EVOS FL Auto Imaging System allows researchers to combine qualitative images with quantitative measurements to provide rich data for research applications.Find out more at /evosflautoFor Research Use Only. Not for use in diagnostic procedures. © 2015 Thermo Fisher Scientific Inc. All rights reserved. All trademarks are the property of Thermo Fisher Scientific and its subsidiaries unless otherwise specified. CO017255 0815。

引言监控视频是犯罪案件调查中一项重要的信息来源与破案依据，但犯罪者极有可能通过对摄像机进行人为干预甚至破坏来掩盖其可疑活动，因此，如何有效地检测监控摄像机干扰事件具有重要的应用价值。

在目前的监控摄相机干扰检测方法中，特殊场景误检测依旧是巨大的挑战，如：照明变化、天气变化、人群流动、大型物体通过等。

对于一些纹理较少或无纹理背景的、黑暗或低质量的监控视频来说，大多数检测方法都会将其误识别为散焦干扰；对于使用带纹理物体进行镜头遮挡的情况，当遮挡物的灰度和亮度与图像背景非常相似时，无法将遮挡物与图像背景区分。

除此之外，物体缓慢地遮挡镜头也是一个具有挑战性的检测问题。

本文使用深度神经网络建立检测模型，利用改进的ConvGRU （Convolutional Gated Recurrent Unit ）提取视频的时序特征和图像的空间全局依赖关系，结合Siamese 架构，提出了SCG 模型。

一种监控摄像机干扰检测模型刘小楠，邵培南（中国电子科技集团公司第三十二研究所，上海201808）摘要为减少监控干扰检测中因特殊场景引起的误检测，文中提出一种基于Siamese 架构的SCG（Siamese with Convolutional Gated Recurrent Unit ）模型，利用视频片段间的潜在相似性来区分特殊场景与干扰事件。

通过在Siamese 架构中融合改进ConvGRU 网络，使模型充分利用监控视频的帧间时序相关性，在GRU 单元间嵌入的非局部操作可以使网络建立图像空间依赖响应。

与使用传统的GRU 模块的干扰检测模型相比，使用改进的ConvGRU 模块的模型准确率提升了4.22%。

除此之外，文中还引入残差注意力模块来提高特征提取网络对图像前景变化的感知能力，与未加入注意力模块的模型相比，改进后模型的准确率再次提高了2.49%。

关键词Siamese ；ConvGRU ；Non-local block ；相机干扰；干扰检测中图分类号TP391文献标识码A文章编号1009-2552（2021）01-0090-07DOI 10.13274/ki.hdzj.2021.01.016A surveillance camera tampering detection modelLIU Xiao -nan,SHAO Pei -nan（The 32nd Research Institute of China Electronics Technology Group Corporation ，Shanghai 201808，China ）Abstract ：This paper proposes an SCG model based on Siamese network to reduce the detection error caused by some special scenes in camera tampering detection.The model can use the potential similarity be⁃tween video clips to distinguish special scenes from tampering events.The improved ConvGRU network is in⁃tegrated to capture the temporal correlation between the frames of surveillance video.We embed non -local block s between GRU cells simultaneously,so the model can establish the spatial dependence of the image.The improved ConvGRU network improves model performance by 4.22%.We also add residual attention mod⁃ule to improve the perception ability of the model to the change of image foreground,this again improves the accuracy of the model by 2.49%.Key words ：Siamese ；ConvGRU ；Non-local block ；camera tampering ；tampering detection作者简介：刘小楠（1994-），女，硕士研究生，研究方向为计算机视觉、深度学习。

MPU-6881 Product Specification Revision 1.0TABLE OF CONTENTSTABLE OF FIGURES (4)TABLE OF TABLES (5)1DOCUMENT INFORMATION (6)1.1R EVISION H ISTORY (6)1.2P URPOSE AND S COPE (7)1.3P RODUCT O VERVIEW (7)1.4A PPLICATIONS (7)2FEATURES (8)2.1G YROSCOPE F EATURES (8)2.2A CCELEROMETER F EATURES (8)2.3A DDITIONAL F EATURES (8)3ELECTRICAL CHARACTERISTICS (9)3.1G YROSCOPE S PECIFICATIONS (9)3.2A CCELEROMETER S PECIFICATIONS (10)3.3E LECTRICAL S PECIFICATIONS (11)3.4I2C T IMING C HARACTERIZATION (15)3.5SPI T IMING C HARACTERIZATION (16)3.6A BSOLUTE M AXIMUM R ATINGS (18)4APPLICATIONS INFORMATION (19)4.1P IN O UT D IAGRAM AND S IGNAL D ESCRIPTION (19)4.2T YPICAL O PERATING C IRCUIT (20)4.3B ILL OF M ATERIALS FOR E XTERNAL C OMPONENTS (20)4.4B LOCK D IAGRAM (21)4.5O VERVIEW (21)4.6T HREE-A XIS MEMS G YROSCOPE WITH 16-BIT ADC S AND S IGNAL C ONDITIONING (22)4.7T HREE-A XIS MEMS A CCELEROMETER WITH 16-BIT ADC S AND S IGNAL C ONDITIONING (22)4.8I2C AND SPI S ERIAL C OMMUNICATIONS I NTERFACES (22)4.9S ELF-T EST (24)4.10C LOCKING (25)4.11S ENSOR D ATA R EGISTERS (25)4.12FIFO (25)4.13I NTERRUPTS (25)4.14D IGITAL-O UTPUT T EMPERATURE S ENSOR (25)4.15B IAS AND LDO S (26)4.16C HARGE P UMP (26)4.17S TANDARD P OWER M ODES (26)5PROGRAMMABLE INTERRUPTS (27)6DIGITAL INTERFACE (28)6.1I2C AND SPI S ERIAL I NTERFACES (28)6.2I2C I NTERFACE (28)6.3I2C C OMMUNICATIONS P ROTOCOL (28)6.4I2C T ERMS (31)6.5SPI I NTERFACE (32)7SERIAL INTERFACE CONSIDERATIONS (32)7.1MPU-6881S UPPORTED I NTERFACES (33)8ASSEMBLY (34)8.1O RIENTATION OF A XES (34)8.2P ACKAGE D IMENSIONS (35)9PART NUMBER PACKAGE MARKING (36)10RELIABILITY (37)10.1Q UALIFICATION T EST P OLICY (37)10.2Q UALIFICATION T EST P LAN (37)11REFERENCE (38)Table of FiguresFigure 1 I2C Bus Timing Diagram (15)Figure 2 SPI Bus Timing Diagram (16)Figure 3 Pin out Diagram for MPU-6881 3.0x3.0x0.9mm QFN (19)Figure 4 MPU-6881 QFN Application Schematic. (a) I2C operation, (b) SPI operation. (20)Figure 5 MPU-6881 Block Diagram (21)Figure 6 MPU-6881 Solution Using I2C Interface (23)Figure 7 MPU-6881 Solution Using SPI Interface (24)Figure 8 START and STOP Conditions (29)Figure 9 Acknowledge on the I2C Bus (29)Figure 10 Complete I2C Data Transfer (30)Figure 11 Typical SPI Master / Slave Configuration (32)Figure 12 I/O Levels and Connections (33)Figure 13 Orientation of Axes Sensitivity and Polarity of Rotation (34)Table of TablesTable 1 Gyroscope Specifications (9)Table 2 Accelerometer Specifications (10)Table 3 D.C. Electrical Characteristics (11)Table 4 A.C. Electrical Characteristics (13)Table 5 Other Electrical Specifications (14)Table 6 I2C Timing Characteristics (15)Table 7 SPI Timing Characteristics (16)Table 8 fCLK = 20MHz (17)Table 9 Absolute Maximum Ratings (18)Table 10 Signal Descriptions (19)Table 11 Bill of Materials (20)Table 12 Standard Power Modes for MPU-6881 (26)Table 13 Table of Interrupt Sources (27)Table 14 Serial Interface (28)Table 15 I2C Terms (31)1 Document Information1.2 Purpose and ScopeThis document is a preliminary product specification, providing a description, specifications, and design related information on the MPU-6881™ MotionTracking device. The device is housed in a small 3x3x0.9mm 24-pin QFN package.Specifications are subject to change without notice. Final specifications will be updated based upon characterization of production silicon. For references to register map and descriptions of individual registers, please refer to the MPU-6881 Register Map and Register Descriptions document.1.3 Product OverviewThe MPU-6881 is a 6-axis MotionTracking device that combines a 3-axis gyroscope, and a 3-axis accelerometer in a small 3x3x0.9mm (24-pin QFN) package. It also features a 4096-byte FIFO that can lower the traffic on the serial bus interface, and reduce power consumption by allowing the system processor to burst read sensor data and then go into a low-power mode. With its dedicated I2C sensor bus, the MPU-6881 directly accepts inputs from external I2C devices. MPU-6881, with its 6-axis integration, enables manufacturers to eliminate the costly and complex selection, qualification, and system level integration of discrete devices, guaranteeing optimal motion performance for consumers. MPU-6881 is also designed to interface with multiple non-inertial digital sensors, such as pressure sensors, on its auxiliary I2C port.The gyroscope has a programmable full-scale range of ±250, ±500, ±1000, and ±2000 degrees/sec. The accelerometer has a user-programmable accelerometer full-scale range of ±2g, ±4g, ±8g, and ±16g. Factory-calibrated initial sensitivity of both sensors reduces production-line calibration requirements.Other industry-leading features include on-chip 16-bit ADCs, programmable digital filters, a precision clock with 1% drift from -40°C to 85°C, an embedded temperature sensor, and programmable interrupts. The device features I2C and SPI serial interfaces, a VDD operating range of 1.71 to 3.45V, and a separate digital IO supply, VDDIO from 1.71V to 3.45V.Communication with all registers of the device is performed using either I2C at 400kHz or SPI at 1MHz. For applications requiring faster communications, the sensor and interrupt registers may be read using SPI at 20MHz.By leveraging its patented and volume-proven CMOS-MEMS Fabrication platform, which integrates MEMS wafers with companion CMOS electronics through wafer-level bonding, InvenSense has driven the package size down to a footprint and thickness of 3x3x0.9mm (24-pin QFN), to provide a very small yet high performance low cost package. The device provides high robustness by supporting 10,000g shock reliability.1.4 Applications∙TouchAnywhere™ technology (for “no touch” UI Application Control/Navigation)∙MotionCommand™ technology (for Gesture S hort-cuts)∙Motion-enabled game and application framework∙Location based services, points of interest, and dead reckoning∙Handset and portable gaming∙Motion-based game controllers∙3D remote controls for Internet connected DTVs and set top boxes, 3D mice∙Wearable sensors for health, fitness and sports2 Features2.1 Gyroscope FeaturesThe triple-axis MEMS gyroscope in the MPU-6881 includes a wide range of features:∙Digital-output X-, Y-, and Z-axis angular rate sensors (gyroscopes) with a user-programmable full-scale range of ±250, ±500, ±1000, and ±2000°/sec and integrated 16-bit ADCs ∙Digitally-programmable low-pass filter∙Gyroscope operating current: 3.2mA∙Factory calibrated sensitivity scale factor∙Self-test2.2 Accelerometer FeaturesThe triple-axis MEMS accelerometer in MPU-6881 includes a wide range of features:∙Digital-output X-, Y-, and Z-axis accelerometer with a programmable full scale range of ±2g, ±4g, ±8g and ±16g and integrated 16-bit ADCs∙Accelerometer normal operating current: 450µA∙Low power accelerometer mode current: 7.27µA at 0.98Hz, 18.65µA at 31.25Hz∙User-programmable interrupts∙Wake-on-motion interrupt for low power operation of applications processor∙Self-test2.3 Additional FeaturesThe MPU-6881 includes the following additional features:∙Auxiliary master I2C bus for reading data from external sensors (e.g. magnetometer)∙ 3.4mA operating current when all 6 motion sensing axes are active∙VDD supply voltage range of 1.8 – 3.3V ± 5%∙VDDIO reference voltage of 1.8 – 3.3V ± 5% for auxiliary I2C devices∙Smallest and thinnest QFN package for portable devices: 3x3x0.9mm (24-pin QFN)∙Minimal cross-axis sensitivity between the accelerometer and gyroscope axes∙4096 byte FIFO buffer enables the applications processor to read the data in bursts∙Digital-output temperature sensor∙User-programmable digital filters for gyroscope, accelerometer, and temp sensor∙10,000 g shock tolerant∙400kHz Fast Mode I2C for communicating with all registers∙1MHz SPI serial interface for communicating with all registers∙20MHz SPI serial interface for reading sensor and interrupt registers∙MEMS structure hermetically sealed and bonded at wafer level∙RoHS and Green compliant3 Electrical Characteristics3.1 Gyroscope SpecificationsTypical Operating Circuit of section 4.2, VDD = 1.8V, VDDIO = 1.8V, T A=25°C, unless otherwise noted.Please refer to the following document for information on Self-Test: MPU-6500 Accelerometer and Gyroscope Self-Test Implementation; AN-MPU-6500A-02.Table 1 Gyroscope SpecificationsNotes:1. Derived from validation or characterization of parts, not guaranteed in production.3.2 Accelerometer SpecificationsTypical Operating Circuit of section 4.2, VDD = 1.8V, VDDIO = 1.8V, T A=25°C, unless otherwise noted.Please refer to the following document for information on Self-Test: MPU-6500 Accelerometer and Gyroscope Self-Test Implementation; AN-MPU-6500A-02.Table 2 Accelerometer SpecificationsNotes:1. Derived from validation or characterization of parts, not guaranteed in production.3.3 Electrical Specifications3.3.1 D.C. Electrical CharacteristicsTypical Operating Circuit of section 4.2, VDD = 1.8V, VDDIO = 1.8V, T A=25°C, unless otherwise noted.Table 3 D.C. Electrical CharacteristicsNotes:1. Derived from validation or characterization of parts, not guaranteed in production.2. Accelerometer Low Power Mode supports the following output data rates (ODRs): 0.24, 0.49, 0.98,1.95, 3.91, 7.81, 15.63, 31.25, 62.50, 125, 250, 500Hz. Supply current for any update rate can becalculated as:a. Supply Current in µA = 6.9 + Update Rate * 0.3763.3.2 A.C. Electrical CharacteristicsTypical Operating Circuit of section 4.2, VDD = 1.8V, VDDIO = 1.8V, T A=25°C, unless otherwise noted.Table 4 A.C. Electrical CharacteristicsNotes:1. Derived from validation or characterization of parts, not guaranteed in production.3.3.3 Other Electrical SpecificationsTypical Operating Circuit of section 4.2, VDD = 1.8V, VDDIO = 1.8V, T A=25°C, unless otherwise noted.Table 5 Other Electrical SpecificationsNotes:1. Derived from validation or characterization of parts, not guaranteed in production.3.4 I2C Timing CharacterizationTypical Operating Circuit of section 4.2, VDD = 1.8V, VDDIO = 1.8V, T A=25°C, unless otherwise noted.Table 6 I2C Timing CharacteristicsNotes:1.Timing Characteristics apply to both Primary and Auxiliary I2C Bus2.Based on characterization of 5 parts over temperature and voltage as mounted on evaluation board or in socketsFigure 1 I2C Bus Timing Diagram3.5 SPI Timing CharacterizationTypical Operating Circuit of section 4.2, VDD = 1.8V, VDDIO = 1.8V, T A=25°C, unless otherwise noted.Table 7 SPI Timing CharacteristicsNotes:3.Based on characterization of 5 parts over temperature and voltage as mounted on evaluation board or in socketsFigure 2 SPI Bus Timing Diagram3.5.1 fSCLK = 20MHzTable 8 fCLK = 20MHzNotes:1.Based on characterization of 5 parts over temperature and voltage as mounted on evaluation board or in sockets3.6 Absolute Maximum RatingsStress above those listed as “Absolute Maximum Ratings” may cause permanent damage to the device. These are stress ratings only and functional operation of the device at these conditions is not implied. Exposure to the absolute maximum ratings conditions for extended periods may affect device reliability.Table 9 Absolute Maximum Ratings4 Applications Information4.1Pin Out Diagram and Signal DescriptionTable 10 Signal DescriptionsA U X _C LV D D I OS D O / A D 0R E G O U TF S Y N CI N TS C L / S C L Kn C SR E S V S D A / S D IA U X _D AR E S VNC NC NC NC NC NCFigure 3 Pin out Diagram for MPU-6881 3.0x3.0x0.9mm QFN4.2Typical Operating Circuit– 3.3VDC m F1.8 –AD0– 3.3VDC m F1.8 –SD0(a)(b)Figure 4 MPU-6881 QFN Application Schematic. (a) I2C operation, (b) SPI operation.4.3Bill of Materials for External Components Table 11 Bill of Materials4.4 Block DiagramVDD GND PLLFILTFigure 5 MPU-6881 Block Diagram4.5 OverviewThe MPU-6881 is comprised of the following key blocks and functions:∙Three-axis MEMS rate gyroscope sensor with 16-bit ADCs and signal conditioning ∙Three-axis MEMS accelerometer sensor with 16-bit ADCs and signal conditioning ∙Primary I2C and SPI serial communications interfaces∙Auxiliary I2C serial interface∙Self-Test∙Clocking∙Sensor Data Registers∙FIFO∙Interrupts∙Digital-Output Temperature Sensor∙Bias and LDOs∙Charge Pump∙Standard Power Modes4.6 Three-Axis MEMS Gyroscope with 16-bit ADCs and Signal ConditioningThe MPU-6881 consists of three independent vibratory MEMS rate gyroscopes, which detect rotation about the X-, Y-, and Z- Axes. When the gyros are rotated about any of the sense axes, the Coriolis Effect causes a vibration that is detected by a capacitive pickoff. The resulting signal is amplified, demodulated, and filtered to produce a voltage that is proportional to the angular rate. This voltage is digitized using individual on-chip 16-bit Analog-to-Digital Converters (ADCs) to sample each axis. The full-scale range of the gyro sensors may be digitally programmed to ±250, ±500, ±1000, or ±2000 degrees per second (dps). The ADC sample rate is programmable from 8,000 samples per second, down to 3.9 samples per second, and user-selectable low-pass filters enable a wide range of cut-off frequencies.4.7 Three-Axis MEMS Accelerometer with 16-bit ADCs and Signal ConditioningThe MPU-6881’s 3-Axis accelerometer uses separate proof masses for each axis. Acceleration along a particular axis induces displacement on the corresponding proof mass, and capacitive sensors detect the displacement differentially. The MPU-6881’s architecture reduces the accelerometers’ susceptibility to fabrication variations as well as to thermal drift. When the device is placed on a flat surface, it will measure 0g on the X- and Y-axes and +1g on the Z-axis. The accelerometer s’ scale factor is calibrated at the factory and is nominally independent of supply voltage. Each sensor has a dedicated sigma-delta ADC for providing digital outputs. The full scale range of the digital output can be adjusted to ±2g, ±4g, ±8g, or ±16g.4.8 I2C and SPI Serial Communications InterfacesThe MPU-6881 communicates to a system processor using either a SPI or an I2C serial interface. The MPU-6881 always acts as a slave when communicating to the system processor. The LSB of the of the I2C slave address is set by pin 4 (AD0).4.8.1 MPU-6881 Solution Using I2C InterfaceIn the figure below, the system processor is an I2C master to the MPU-6881.Figure 6 MPU-6881 Solution Using I2C Interface4.8.2 MPU-6881 Solution Using SPI InterfaceIn the figure below, the system processor is an SPI master to the MPU-6881. Pins 2, 3, 4, and 5 are used to support the SCLK, SDI, SDO, and CS signals for SPI communications.Figure 7 MPU-6881 Solution Using SPI Interface4.9 Self-TestPlease refer to the register map document for more details on self-test.Self-test allows for the testing of the mechanical and electrical portions of the sensors. The self-test for each measurement axis can be activated by means of the gyroscope and accelerometer self-test registers (registers 13 to 16).When the self-test is activated, the electronics cause the sensors to be actuated and produce an output signal. The output signal is used to observe the self-test response.The self-test response is defined as follows:Self-test response = Sensor output with self-test enabled – Sensor output without self-test enabled The self-test response for each gyroscope axis is defined in the gyroscope specification table, while that for each accelerometer axis is defined in the accelerometer specification table.When the value of the self-test response is within the specified min/max limits of the product specification, the part has passed self-test. When the self-test response exceeds the min/max values, the part is deemed to have failed self-test. It is recommended to use InvenSense MotionApps software for executing self-test.4.10 ClockingThe MPU-6881 has a flexible clocking scheme, allowing a variety of internal clock sources to be used for the internal synchronous circuitry. This synchronous circuitry includes the signal conditioning and ADCs, and various control circuits and registers. An on-chip PLL provides flexibility in the allowable inputs for generating this clock.Allowable internal sources for generating the internal clock are:∙An internal relaxation oscillator∙Any of the X, Y, or Z gyros (MEMS oscillators with a variation of ±1% over temperature)Selection of the source for generating the internal synchronous clock depends on the requirements for power consumption and clock accuracy. These requirements will most likely vary by mode of operation.There are also start-up conditions to consider. When the MPU-6881 first starts up, the device uses its internal clock until programmed to operate from another source. This allows the user, for example, to wait for the MEMS oscillators to stabilize before they are selected as the clock source.4.11 Sensor Data RegistersThe sensor data registers contain the latest gyro, accelerometer, auxiliary sensor, and temperature measurement data. They are read-only registers, and are accessed via the serial interface. Data from these registers may be read anytime.4.12 FIFOThe MPU-6881 contains a 4096-byte FIFO register that is accessible via the Serial Interface. The FIFO configuration register determines which data is written into the FIFO. Possible choices include gyro data, accelerometer data, temperature readings, auxiliary sensor readings, and SYNC input. A FIFO counter keeps track of how many bytes of valid data are contained in the FIFO. The FIFO register supports burst reads. The interrupt function may be used to determine when new data is available.For further information regarding the FIFO, please refer to the MPU-6881 Register Map and Register Descriptions document.4.13 InterruptsInterrupt functionality is configured via the Interrupt Configuration register. Items that are configurable include the INT pin configuration, the interrupt latching and clearing method, and triggers for the interrupt. Items that can trigger an interrupt are (1) Clock generator locked to new reference oscillator (used when switching clock sources); (2) new data is available to be read (from the FIFO and Data registers); (3) accelerometer event interrupts; and (4) the MPU-6881 did not receive an acknowledge from an auxiliary sensor on the secondary I2C bus. The interrupt status can be read from the Interrupt Status register.For further information regarding interrupts, please refer to the MPU-6881 Register Map and Register Descriptions document.4.14 Digital-Output Temperature SensorAn on-chip temperature sensor and ADC are used to measure the MPU-6881 die temperature. The readings from the ADC can be read from the FIFO or the Sensor Data registers.4.15 Bias and LDOsThe bias and LDO section generates the internal supply and the reference voltages and currents required by the MPU-6881. Its two inputs are an unregulated VDD and a VDDIO logic reference supply voltage. The LDO output is bypassed by a capacitor at PLLFILT. For further details on the capacitor, please refer to the Bill of Materials for External Components.4.16 Charge PumpAn on-chip charge pump generates the high voltage required for the MEMS oscillators.4.17 Standard Power ModesThe following table lists the user-accessible power modes for MPU-6881.Table 12 Standard Power Modes for MPU-6881Notes:1. Power consumption for individual modes can be found in section 3.3.1.5 Programmable InterruptsThe MPU-6881 has a programmable interrupt system which can generate an interrupt signal on the INT pin. Status flags indicate the source of an interrupt. Interrupt sources may be enabled and disabled individually.Table 13 Table of Interrupt SourcesFor information regarding the interrupt enable/disable registers and flag registers, please refer to the MPU-6881 Register Map and Register Descriptions document. Some interrupt sources are explained below.6 Digital Interface6.1 I2C and SPI Serial InterfacesThe internal registers and memory of the MPU-6881 can be accessed using either I2C at 400 kHz or SPI at 1MHz. SPI operates in four-wire mode.Table 14 Serial InterfaceNote:To prevent switching into I2C mode when using SPI, the I2C interface should be disabled by setting the I2C_IF_DIS configuration bit. Setting this bit should be performed immediately after waiting for the time specified by the “Start-Up Time for Reg ister Read/Write” in Section 6.3.For further information regarding the I2C_IF_DIS bit, please refer to the MPU-6881 Register Map and Register Descriptions document.6.2 I2C InterfaceI2C is a two-wire interface comprised of the signals serial data (SDA) and serial clock (SCL). In general, the lines are open-drain and bi-directional. In a generalized I2C interface implementation, attached devices can be a master or a slave. The master device puts the slave address on the bus, and the slave device with the matching address acknowledges the master.The MPU-6881 always operates as a slave device when communicating to the system processor, which thus acts as the master. SDA and SCL lines typically need pull-up resistors to VDD. The maximum bus speed is 400 kHz.The slave address of the MPU-6881 is b110100X which is 7 bits long. The LSB bit of the 7 bit address is determined by the logic level on pin AD0. This allows two MPU-6881s to be connected to the same I2C bus. When used in this configuration, the address of the one of the devices should be b1101000 (pin AD0 is logic low) and the address of the other should be b1101001 (pin AD0 is logic high).6.3 I2C Communications ProtocolSTART (S) and STOP (P) ConditionsCommunication on the I2C bus starts when the master puts the START condition (S) on the bus, which is defined as a HIGH-to-LOW transition of the SDA line while SCL line is HIGH (see figure below). The bus is considered to be busy until the master puts a STOP condition (P) on the bus, which is defined as a LOW to HIGH transition on the SDA line while SCL is HIGH (see figure below).Additionally, the bus remains busy if a repeated START (Sr) is generated instead of a STOP condition.SDASCLPSSTART condition STOP conditionFigure 8 START and STOP ConditionsData Format / AcknowledgeI2C data bytes are defined to be 8-bits long. There is no restriction to the number of bytes transmitted per data transfer. Each byte transferred must be followed by an acknowledge (ACK) signal. The clock for the acknowledge signal is generated by the master, while the receiver generates the actual acknowledge signal by pulling down SDA and holding it low during the HIGH portion of the acknowledge clock pulse.If a slave is busy and cannot transmit or receive another byte of data until some other task has been performed, it can hold SCL LOW, thus forcing the master into a wait state. Normal data transfer resumes when the slave is ready, and releases the clock line (refer to the following figure).DATA OUTPUT BYTRANSMITTER (SDA)DATA OUTPUT BYRECEIVER (SDA)SCL FROMMASTERSTARTacknowledgementconditionFigure 9 Acknowledge on the I2C BusCommunicationsAfter beginning communications with the START condition (S), the master sends a 7-bit slave addressfollowed by an 8thbit, the read/write bit. The read/write bit indicates whether the master is receiving data from or is writing to the slave device. Then, the master releases the SDA line and waits for the acknowledge signal (ACK) from the slave device. Each byte transferred must be followed by an acknowledge bit. To acknowledge, the slave device pulls the SDA line LOW and keeps it LOW for the high period of the SCL line. Data transmission is always terminated by the master with a STOP condition (P), thus freeing the communications line. However, the master can generate a repeated START condition (Sr), and address another slave without first generating a STOP condition (P). A LOW to HIGH transition on the SDA line while SCL is HIGH defines the stop condition. All SDA changes should take place when SCL is low, with the exception of start and stop conditions.SDASTART conditionSCLADDRESS R/W ACK DATAACK DATA ACKSTOP conditionSP1 – 789 1 – 789 1 – 789Figure 10 Complete I 2C Data TransferTo write the internal MPU-6881 registers, the master transmits the start condition (S), followed by the I 2Caddress and the write bit (0). At the 9thclock cycle (when the clock is high), the MPU-6881 acknowledges the transfer. Then the master puts the register address (RA) on the bus. After the MPU-6881 acknowledges the reception of the register address, the master puts the register data onto the bus. This is followed by the ACK signal, and data transfer may be concluded by the stop condition (P). To write multiple bytes after the last ACK signal, the master can continue outputting data rather than transmitting a stop signal. In this case, the MPU-6881 automatically increments the register address and loads the data to the appropriate register. The following figures show single and two-byte write sequences.Single-Byte Write SequenceBurst Write SequenceTo read the internal MPU-6881 registers, the master sends a start condition, followed by the I2C address and a write bit, and then the register address that is going to be read. Upon receiving the ACK signal from the MPU-6881, the master transmits a start signal followed by the slave address and read bit. As a result, the MPU-6881 sends an ACK signal and the data. The communication ends with a not acknowledge (NACK) signal and a stop bit from master. The NACK condition is defined such that the SDA line remains high at the 9th clock cycle. The following figures show single and two-byte read sequences.Single-Byte Read SequenceBurst Read Sequence6.4 I2C TermsTable 15 I2C Terms6.5 SPI InterfaceSPI is a 4-wire synchronous serial interface that uses two control lines and two data lines. The MPU-6881 always operates as a Slave device during standard Master-Slave SPI operation.With respect to the Master, the Serial Clock output (SCLK), the Serial Data Output (SDO) and the Serial Data Input (SDI) are shared among the Slave devices. Each SPI slave device requires its own Chip Select (CS) line from the master.CS goes low (active) at the start of transmission and goes back high (inactive) at the end. Only one CS line is active at a time, ensuring that only one slave is selected at any given time. The CS lines of the non-selected slave devices are held high, causing their SDO lines to remain in a high-impedance (high-z) state so that they do not interfere with any active devices.SPI Operational Features1. Data is delivered MSB first and LSB last2. Data is latched on the rising edge of SCLK3. Data should be transitioned on the falling edge of SCLK4. The maximum frequency of SCLK is 1MHz5. SPI read and write operations are completed in 16 or more clock cycles (two or more bytes). Thefirst byte contains the SPI Address, and the following byte(s) contain(s) the SPI data. The firstbit of the first byte contains the Read/Write bit and indicates the Read (1) or Write (0) operation.The following 7 bits contain the Register Address. In cases of multiple-byte Read/Writes, data istwo or more bytes:6. Supports Single or Burst Read/Writes.。