AI Generator Zdjec Za Darmo

AI Generator Zdjec Za Darmo — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Machine vision

Machine vision is the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control, and robot guidance, usually in industry. Machine vision refers to many technologies, software and hardware products, integrated systems, actions, methods and expertise. Machine vision as a systems engineering discipline can be considered distinct from computer vision, a form of computer science. It attempts to integrate existing technologies in new ways and apply them to solve real world problems. The term is the prevalent one for these functions in industrial automation environments but is also used for these functions in other environment vehicle guidance. The overall machine vision process includes planning the details of the requirements and project, and then creating a solution. During run-time, the process starts with imaging, followed by automated analysis of the image and extraction of the required information. == Definition == Definitions of the term "Machine vision" vary, but all include the technology and methods used to extract information from an image on an automated basis, as opposed to image processing, where the output is another image. The information extracted can be a simple good-part/bad-part signal, or more a complex set of data such as the identity, position and orientation of each object in an image. The information can be used for such applications as automatic inspection and robot and process guidance in industry, for security monitoring and vehicle guidance. This field encompasses a large number of technologies, software and hardware products, integrated systems, actions, methods and expertise. Machine vision is practically the only term used for these functions in industrial automation applications; the term is less universal for these functions in other environments such as security and vehicle guidance. Machine vision as a systems engineering discipline can be considered distinct from computer vision, a form of basic computer science; machine vision attempts to integrate existing technologies in new ways and apply them to solve real world problems in a way that meets the requirements of industrial automation and similar application areas. The term is also used in a broader sense by trade shows and trade groups such as the Automated Imaging Association and the European Machine Vision Association. This broader definition also encompasses products and applications most often associated with image processing. The primary uses for machine vision are automatic inspection and industrial robot/process guidance. In more recent times the terms computer vision and machine vision have converged to a greater degree. See glossary of machine vision. == Imaging based automatic inspection and sorting == The primary uses for machine vision are imaging-based automatic inspection and sorting and robot guidance.; in this section the former is abbreviated as "automatic inspection". The overall process includes planning the details of the requirements and project, and then creating a solution. This section describes the technical process that occurs during the operation of the solution. === Methods and sequence of operation === The first step in the automatic inspection sequence of operation is acquisition of an image, typically using cameras, lenses, and lighting that has been designed to provide the differentiation required by subsequent processing. MV software packages and programs developed in them then employ various digital image processing techniques to extract the required information, and often make decisions (such as pass/fail) based on the extracted information. === Equipment === The components of an automatic inspection system usually include lighting, a camera or other imager, a processor, software, and output devices. === Imaging === The imaging device (e.g. camera) can either be separate from the main image processing unit or combined with it in which case the combination is generally called a smart camera or smart sensor. Inclusion of the full processing function into the same enclosure as the camera is often referred to as embedded processing. When separated, the connection may be made to specialized intermediate hardware, a custom processing appliance, or a frame grabber within a computer using either an analog or standardized digital interface (Camera Link, CoaXPress). MV implementations also use digital cameras capable of direct connections (without a framegrabber) to a computer via FireWire, USB or Gigabit Ethernet interfaces. While conventional (2D visible light) imaging is most commonly used in MV, alternatives include multispectral imaging, hyperspectral imaging, imaging various infrared bands, line scan imaging, 3D imaging of surfaces and X-ray imaging. Key differentiations within MV 2D visible light imaging are monochromatic vs. color, frame rate, resolution, and whether or not the imaging process is simultaneous over the entire image, making it suitable for moving processes. Though the vast majority of machine vision applications are solved using two-dimensional imaging, machine vision applications utilizing 3D imaging are a growing niche within the industry. The most commonly used method for 3D imaging is scanning based triangulation which utilizes motion of the product or image during the imaging process. A laser is projected onto the surfaces of an object. In machine vision this is accomplished with a scanning motion, either by moving the workpiece, or by moving the camera & laser imaging system. The line is viewed by a camera from a different angle; the deviation of the line represents shape variations. Lines from multiple scans are assembled into a depth map or point cloud. Stereoscopic vision is used in special cases involving unique features present in both views of a pair of cameras. Other 3D methods used for machine vision are time of flight and grid based. One method is grid array based systems using pseudorandom structured light system as employed by the Microsoft Kinect system circa 2012. === Image processing === After an image is acquired, it is processed. Central processing functions are generally done by a CPU, a GPU, a FPGA or a combination of these. Deep learning training and inference impose higher processing performance requirements. Multiple stages of processing are generally used in a sequence that ends up as a desired result. A typical sequence might start with tools such as filters which modify the image, followed by extraction of objects, then extraction (e.g. measurements, reading of codes) of data from those objects, followed by communicating that data, or comparing it against target values to create and communicate "pass/fail" results. Machine vision image processing methods include; Stitching/Registration: Combining of adjacent 2D or 3D images. Filtering (e.g. morphological filtering) Thresholding: Thresholding starts with setting or determining a gray value that will be useful for the following steps. The value is then used to separate portions of the image, and sometimes to transform each portion of the image to simply black and white based on whether it is below or above that grayscale value. Pixel counting: counts the number of light or dark pixels Segmentation: Partitioning a digital image into multiple segments to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Edge detection: finding object edges Color Analysis: Identify parts, products and items using color, assess quality from color, and isolate features using color. Blob detection and extraction: inspecting an image for discrete blobs of connected pixels (e.g. a black hole in a grey object) as image landmarks. Neural network / deep learning / machine learning processing: weighted and self-training multi-variable decision making Circa 2019 there is a large expansion of this, using deep learning and machine learning to significantly expand machine vision capabilities. The most common result of such processing is classification. Examples of classification are object identification,"pass fail" classification of identified objects and OCR. Pattern recognition including template matching. Finding, matching, and/or counting specific patterns. This may include location of an object that may be rotated, partially hidden by another object, or varying in size. Barcode, Data Matrix and "2D barcode" reading Optical character recognition: automated reading of text such as serial numbers Gauging/Metrology: measurement of object dimensions (e.g. in pixels, inches or millimeters) Comparison against target values to determine a "pass or fail" or "go/no go" result. For example, with code or bar code verification, the read value is compared to the stored target value. For gauging, a measurement is compared against the proper value and tolerances. For verification of alpha-numberic codes, the
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Microscope image processing

Microscope image processing is a broad term that covers the use of digital image processing techniques to process, analyze and present images obtained from a microscope. Such processing is now commonplace in a number of diverse fields such as medicine, biological research, cancer research, drug testing, metallurgy, etc. A number of manufacturers of microscopes now specifically design in features that allow the microscopes to interface to an image processing system. == Image acquisition == Until the early 1990s, most image acquisition in video microscopy applications was typically done with an analog video camera, often simply closed circuit TV cameras. While this required the use of a frame grabber to digitize the images, video cameras provided images at full video frame rate (25-30 frames per second) allowing live video recording and processing. While the advent of solid state detectors yielded several advantages, the real-time video camera was actually superior in many respects. Today, acquisition is usually done using a CCD camera mounted in the optical path of the microscope. The camera may be full colour or monochrome. Very often, very high resolution cameras are employed to gain as much direct information as possible. Cryogenic cooling is also common, to minimise noise. Often digital cameras used for this application provide pixel intensity data to a resolution of 12-16 bits, much higher than is used in consumer imaging products. Ironically, in recent years, much effort has been put into acquiring data at video rates, or higher (25-30 frames per second or higher). What was once easy with off-the-shelf video cameras now requires special, high speed electronics to handle the vast digital data bandwidth. Higher speed acquisition allows dynamic processes to be observed in real time, or stored for later playback and analysis. Combined with the high image resolution, this approach can generate vast quantities of raw data, which can be a challenge to deal with, even with a modern computer system. While current CCD detectors allow very high image resolution, often this involves a trade-off because, for a given chip size, as the pixel count increases, the pixel size decreases. As the pixels get smaller, their well depth decreases, reducing the number of electrons that can be stored. In turn, this results in a poorer signal-to-noise ratio. For best results, one must select an appropriate sensor for a given application. Because microscope images have an intrinsic limiting resolution, it often makes little sense to use a noisy, high resolution detector for image acquisition. A more modest detector, with larger pixels, can often produce much higher quality images because of reduced noise. This is especially important in low-light applications such as fluorescence microscopy. Moreover, one must also consider the temporal resolution requirements of the application. A lower resolution detector will often have a significantly higher acquisition rate, permitting the observation of faster events. Conversely, if the observed object is motionless, one may wish to acquire images at the highest possible spatial resolution without regard to the time required to acquire a single image. == 2D image techniques == Image processing for microscopy application begins with fundamental techniques intended to most accurately reproduce the information contained in the microscopic sample. This might include adjusting the brightness and contrast of the image, averaging images to reduce image noise and correcting for illumination non-uniformities. Such processing involves only basic arithmetic operations between images (i.e. addition, subtraction, multiplication and division). The vast majority of processing done on microscope image is of this nature. Another class of common 2D operations called image convolution are often used to reduce or enhance image details. Such "blurring" and "sharpening" algorithms in most programs work by altering a pixel's value based on a weighted sum of that and the surrounding pixels (a more detailed description of kernel based convolution deserves an entry for itself) or by altering the frequency domain function of the image using Fourier Transform. Most image processing techniques are performed in the Frequency domain. Other basic two dimensional techniques include operations such as image rotation, warping, color balancing etc. At times, advanced techniques are employed with the goal of "undoing" the distortion of the optical path of the microscope, thus eliminating distortions and blurring caused by the instrumentation. This process is called deconvolution, and a variety of algorithms have been developed, some of great mathematical complexity. The end result is an image far sharper and clearer than could be obtained in the optical domain alone. This is typically a 3-dimensional operation, that analyzes a volumetric image (i.e. images taken at a variety of focal planes through the sample) and uses this data to reconstruct a more accurate 3-dimensional image. == 3D image techniques == Another common requirement is to take a series of images at a fixed position, but at different focal depths. Since most microscopic samples are essentially transparent, and the depth of field of the focused sample is exceptionally narrow, it is possible to capture images "through" a three-dimensional object using 2D equipment like confocal microscopes. Software is then able to reconstruct a 3D model of the original sample which may be manipulated appropriately. The processing turns a 2D instrument into a 3D instrument, which would not otherwise exist. In recent times this technique has led to a number of scientific discoveries in cell biology. == Analysis == Analysis of images will vary considerably according to application. Typical analysis includes determining where the edges of an object are, counting similar objects, calculating the area, perimeter length and other useful measurements of each object. A common approach is to create an image mask which only includes pixels that match certain criteria, then perform simpler scanning operations on the resulting mask. It is also possible to label objects and track their motion over a series of frames in a video sequence.
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Conduit (company)

Conduit Ltd. is an international software company. From its founding in 2005 to 2013, its most well-known product was the Conduit toolbar, which was widely-described as malware. In 2013, it spun off its toolbar business; today, its main product is a mobile development platform that allows users to create native and web mobile applications for smartphones. == Products == From 2005 to 2013, the company's most well-known product was the Conduit toolbar, which is flagged by most antivirus software as potentially unwanted and adware. Conduit's toolbar software is often downloaded by malware packages from other publishers. The company spun off the toolbar division that manages the Conduit toolbar in 2013. Today, the company's main product is a mobile development platform that allows users to create native and web mobile applications for smartphones. App creation for its App Gallery is free, but it charges a monthly subscription fee to place apps on the App Store or Google Play. == History == Conduit was founded in 2005 by Shilo, Dror Erez, and Gaby Bilcyzk. Between years 2005 and 2013, it ran a successful but controversial toolbar platform business. Conduit was part of the so-called Download Valley companies monetizing free software and downloads by bundling adware. The toolbars were criticized by some as being very difficult to uninstall. The toolbar software was referred to as a "potentially unwanted program" by some in the computer industry because it could be used to change browser settings. The company had more than 400 employees in 2013. In September same year, Conduit spun off its entire website toolbar business division, which combined with Perion Network. After the deal, Conduit shareholders owned 81% of Perion's existing shares and both Perion and Conduit remained independent companies. The substantial size of the Conduit user base allowed Perion to immediately surpass AOL in U.S. searches. In 2015, Conduit announced it would purchase Keeprz, a mobile customer loyalty platform, for $45 million.
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Imix video cube

The Imix (also known as ImMix) Video Cube is one of the first computer non-linear editing systems that was a full broadcast quality online video finishing machine. After its release in 1994, Imix released a more advanced version, the Imix Turbo Cube, which boasted 4 channels of real time layered visual effects. It was a hardware computer system controlled by an Apple Macintosh computer.
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AltStore

AltStore is an alternative app store for the iOS and iPadOS[1] mobile operating systems, which allows users to download applications that are not available on the App Store, most commonly tweaked apps, jailbreak apps, and apps including paid apps on the app store. It was publicly announced on September 25, 2019, and launched on September 28. == History == Riley Testut is an American developer who began to work on AltStore after Apple declined to allow his Nintendo emulator Delta on the App Store. Since Xcode allowed him to temporarily install his Delta app to his iOS device for 7 days of testing, he created AltStore in 2019 to replicate this functionality, which could be extended to other .ipa files. As of 2022, AltStore had been downloaded 1.5 million times. In the following years, AltStore expanded beyond its initial sideloading functionality. The platform was founded by Testut, with Shane Gill later joining as co-founder. AltStore was initially supported through Patreon contributions from its user community, and later saw increased adoption following regulatory developments in the European Union that enabled broader third-party app distribution. The project has also been involved in notable industry collaborations, including a partnership with Epic Games. == Features == AltStore exploits a loophole in the Xcode developer platform, which allows developers to sideload their own apps which they are working on without needing to jailbreak. Sideloaded apps are signed like a developer project for testing and will expire after 7 days with a free account or one year with a paid developer account, by which they will need to be refreshed or reinstalled.
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Feed forward (control)

A feed forward (sometimes written feedforward) is an element or pathway within a control system that passes a controlling signal from a source in its external environment to a load elsewhere in its external environment. This is often a command signal from an external operator. In control engineering, a feedforward control system is a control system that uses sensors to detect disturbances affecting the system and then applies an additional input to minimize the effect of the disturbance. This requires a mathematical model of the system so that the effect of disturbances can be properly predicted. A control system which has only feed-forward behavior responds to its control signal in a pre-defined way without responding to the way the system reacts; it is in contrast with a system that also has feedback, which adjusts the input to take account of how it affects the system, and how the system itself may vary unpredictably. In a feed-forward system, the control variable adjustment is not error-based. Instead it is based on knowledge about the process in the form of a mathematical model of the process and knowledge about, or measurements of, the process disturbances. Some prerequisites are needed for control scheme to be reliable by pure feed-forward without feedback: the external command or controlling signal must be available, and the effect of the output of the system on the load should be known (that usually means that the load must be predictably unchanging with time). Sometimes pure feed-forward control without feedback is called 'ballistic', because once a control signal has been sent, it cannot be further adjusted; any corrective adjustment must be by way of a new control signal. In contrast, 'cruise control' adjusts the output in response to the load that it encounters, by a feedback mechanism. These systems could relate to control theory, physiology, or computing. == Overview == With feed-forward or feedforward control, the disturbances are measured and accounted for before they have time to affect the system. In the house example, a feed-forward system may measure the fact that the door is opened and automatically turn on the heater before the house can get too cold. The difficulty with feed-forward control is that the effects of the disturbances on the system must be accurately predicted, and there must not be any unmeasured disturbances. For instance, if a window was opened that was not being measured, the feed-forward-controlled thermostat might let the house cool down. The term has specific meaning within the field of CPU-based automatic control. The discipline of feedforward control as it relates to modern, CPU based automatic controls is widely discussed, but is seldom practiced due to the difficulty and expense of developing or providing for the mathematical model required to facilitate this type of control. Open-loop control and feedback control, often based on canned PID control algorithms, are much more widely used. There are three types of control systems: open-loop, feed-forward, and feedback. An example of a pure open-loop control system is manual non-power-assisted steering of a motor car; the steering system does not have access to an auxiliary power source and does not respond to varying resistance to turning of the direction wheels; the driver must make that response without help from the steering system. In comparison, power steering has access to a controlled auxiliary power source, which depends on the engine speed. When the steering wheel is turned, a valve is opened which allows fluid under pressure to turn the wheels. A sensor monitors that pressure so that the valve only opens enough to cause the correct pressure to reach the wheel turning mechanism. This is feed-forward control where the output of the system, the change in direction of travel of the vehicle, plays no part in the system. See Model predictive control. If the driver is included in the system, then they do provide a feedback path by observing the direction of travel and compensating for errors by turning the steering wheel. In that case you have a feedback system, and the block labeled System in Figure(c) is a feed-forward system. In other words, systems of different types can be nested, and the overall system regarded as a black-box. Feedforward control is distinctly different from open-loop control and teleoperator systems. Feedforward control requires a mathematical model of the plant (process and/or machine being controlled) and the plant's relationship to any inputs or feedback the system might receive. Neither open-loop control nor teleoperator systems require the sophistication of a mathematical model of the physical system or plant being controlled. Control based on operator input without integral processing and interpretation through a mathematical model of the system is a teleoperator system and is not considered feedforward control. == History == Historically, the use of the term feedforward is found in works by Harold S. Black in US patent 1686792 (invented 17 March 1923) and D. M. MacKay as early as 1956. While MacKay's work is in the field of biological control theory, he speaks only of feedforward systems. MacKay does not mention feedforward control or allude to the discipline of feedforward controls. MacKay and other early writers who use the term feedforward are generally writing about theories of how human or animal brains work. Black also has US patent 2102671 invented 2 August 1927 on the technique of feedback applied to electronic systems. The discipline of feedforward controls was largely developed by professors and graduate students at Georgia Tech, MIT, Stanford and Carnegie Mellon. Feedforward is not typically hyphenated in scholarly publications. Meckl and Seering of MIT and Book and Dickerson of Georgia Tech began the development of the concepts of Feedforward Control in the mid-1970s. The discipline of Feedforward Controls was well defined in many scholarly papers, articles and books by the late 1980s. == Benefits == The benefits of feedforward control are significant and can often justify the extra cost, time and effort required to implement the technology. Control accuracy can often be improved by as much as an order of magnitude if the mathematical model is of sufficient quality and implementation of the feedforward control law is well thought out. Energy consumption by the feedforward control system and its driver is typically substantially lower than with other controls. Stability is enhanced such that the controlled device can be built of lower cost, lighter weight, springier materials while still being highly accurate and able to operate at high speeds. Other benefits of feedforward control include reduced wear and tear on equipment, lower maintenance costs, higher reliability and a substantial reduction in hysteresis. Feedforward control is often combined with feedback control to optimize performance. == Model == The mathematical model of the plant (machine, process or organism) used by the feedforward control system may be created and input by a control engineer or it may be learned by the control system. Control systems capable of learning and/or adapting their mathematical model have become more practical as microprocessor speeds have increased. The discipline of modern feedforward control was itself made possible by the invention of microprocessors. Feedforward control requires integration of the mathematical model into the control algorithm such that it is used to determine the control actions based on what is known about the state of the system being controlled. In the case of control for a lightweight, flexible robotic arm, this could be as simple as compensating between when the robot arm is carrying a payload and when it is not. The target joint angles are adjusted to place the payload in the desired position based on knowing the deflections in the arm from the mathematical model's interpretation of the disturbance caused by the payload. Systems that plan actions and then pass the plan to a different system for execution do not satisfy the above definition of feedforward control. Unless the system includes a means to detect a disturbance or receive an input and process that input through the mathematical model to determine the required modification to the control action, it is not true feedforward control. === Open system === In control theory, an open system is a feed forward system that does not have any feedback loop to control its output. In contrast, a closed system uses on a feedback loop to control the operation of the system. In an open system, the output of the system is not fed back into the input to the system for control or operation. == Applications == === Physiological feed-forward system === In physiology, feed-forward control is exemplified by the normal anticipatory regulation of heartbeat in advance of actual physical exertion by the central autonomic network. Feed-forward
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Sub-pixel resolution

In digital image processing, sub-pixel resolution can be obtained in images constructed from sources with information exceeding the nominal pixel resolution of said images. == Example == For example, if the image of a ship of length 50 metres (160 ft), viewed side-on, is 500 pixels long, the nominal resolution (pixel size) on the side of the ship facing the camera is 0.1 metres (3.9 in). Now sub-pixel resolution of well resolved features can measure ship movements which are an order of magnitude (10×) smaller. Movement is specifically mentioned here because measuring absolute positions requires an accurate lens model and known reference points within the image to achieve sub-pixel position accuracy. Small movements can however be measured (down to 1 cm) with simple calibration procedures. Specific fit functions often suffer specific bias with respect to image pixel boundaries. Users should therefore take care to avoid these "pixel locking" (or "peak locking") effects. == Determining feasibility == Whether features in a digital image are sharp enough to achieve sub-pixel resolution can be quantified by measuring the point spread function (PSF) of an isolated point in the image. If the image does not contain isolated points, similar methods can be applied to edges in the image. It is also important when attempting sub-pixel resolution to keep image noise to a minimum. This, in the case of a stationary scene, can be measured from a time series of images. Appropriate pixel averaging, through both time (for stationary images) and space (for uniform regions of the image) is often used to prepare the image for sub-pixel resolution measurements.
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Artipic

Artipic is a graphics editor developed for Microsoft Windows. An older version for macOS is still available but unsupported. Artipic features drawing, editing, retouching, transforming and composing images including color corrections, effects and layer-based operations. It converts all common image formats and imports camera raw formats. In the global image editing ecosystem Artipic can be positioned somewhere in the middle. It differs from simple free photo editors by more advanced capabilities, however it does not cover the complete professional-level functionality pack provided by industry leaders like Adobe Photoshop. == History == Artipic developed by Swedish company Artipic AB. Artipic 1.0 was released in March 2014 as a free version. The first commercial version on Microsoft Windows was released in November 2014, on macOS – in October 2015. == Features == Supports Microsoft Windows and macOS Standard tools: select, crop, move, rotate, transform, stamp, color picking, text Advanced tools: custom brushes, gradients, shapes, paths, layers and masks Special tools: healing brush, red-eye effect reduction, dodge and burn brushes Adjustments: Brightness & Contrast, Hue & Saturation, Curves, Levels, Color Balance, Gamma Correction, Exposure, Color Temperature, Tint, Color Enhancer, Photo Filter Simulation, Posterization, Thresholding Filters: Smoothen, Sharpen, Vignetting, High-pass, Diffuse Glow, Shadow, Gaussian Blur Reversible (non-destructive) stylization presets Batch processing White balance RAW-converter including Gray Card Adobe Photoshop images supported == Version history ==
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Web application

A web application (or web app) is application software that is created with web technologies and runs via a web browser. Web applications emerged during the late 1990s and allowed for the server to dynamically build a response to the request, in contrast to static web pages. Web applications are commonly distributed via a web server. There are several different tier systems that web applications use to communicate between the web browsers, the client interface, and server data. Each system has its own uses as they function in different ways. However, there are many security risks that developers must be aware of during development; proper measures to protect user data are vital. Web applications are often constructed with the use of a web application framework. Single-page applications (SPAs) and progressive web apps (PWAs) are two architectural approaches to creating web applications that provide a user experience similar to native apps, including features such as smooth navigation, offline support, and faster interactions. Web applications are often fully hosted on remote cloud services, can require a constant connection to them, and can replace conventional desktop applications for operating systems such as Microsoft Windows, thus facilitating the operation of software as a service as it grants the developer the power to tightly control billing based on use of the remote services as well as vendor lock-in by hosting data remotely. Modern browsers such as Chrome offer sandboxing for every browser tab which improves security and restricts access to local resources. No software installation is required as the app runs within the browser which reduces the need for managing software installations. With the use of remote cloud services, customers do not need to manage servers as that can be left to the developer and the cloud service and can use the software with a relatively low power, low-resource PC such as a thin client. The source code of the application can stay the same across operating systems and devices of users with the use of responsive web design, since it only needs to be compatible with web browsers which adhere to web standards, making the code highly portable and saving on development time. Numerous JavaScript frameworks and CSS frameworks facilitate development. == History == The concept of a "web application" was first introduced in the Java language in the Servlet Specification version 2.2, which was released in 1999. At that time, both JavaScript and XML had already been developed, but the XMLHttpRequest object had only been recently introduced on Internet Explorer 5 as an ActiveX object. Beginning around the early 2000s, applications such as "Myspace (2003), Gmail (2004), Digg (2004), [and] Google Maps (2005)," started to make their client sides more and more interactive. A web page script is able to contact the server for storing/retrieving data without downloading an entire web page. The practice became known as Ajax in 2005. Eventually this was replaced by web APIs using JSON, accessed via JavaScript asynchronously on the client side. In earlier computing models like client-server, the processing load for the application was shared between code on the server and code installed on each client locally. In other words, an application had its own pre-compiled client program which served as its user interface and had to be separately installed on each user's personal computer. An upgrade to the server-side code of the application would typically also require an upgrade to the client-side code installed on each user workstation, adding to the support cost and decreasing productivity. Additionally, both the client and server components of the application were bound tightly to a particular computer architecture and operating system, which made porting them to other systems prohibitively expensive for all but the largest applications. Later, in 1995, Netscape introduced the client-side scripting language called JavaScript, which allowed programmers to add dynamic elements to the user interface that ran on the client side. Essentially, instead of sending data to the server in order to generate an entire web page, the embedded scripts of the downloaded page can perform various tasks such as input validation or showing/hiding parts of the page. "Progressive web apps", the term coined by designer Frances Berriman and Google Chrome engineer Alex Russell in 2015, refers to apps taking advantage of new features supported by modern browsers, which initially run inside a web browser tab but later can run completely offline and can be launched without entering the app URL in the browser. == Structure == Traditional PC applications are typically single-tiered, residing solely on the client machine. In contrast, web applications inherently facilitate a multi-tiered architecture. Though many variations are possible, the most common structure is the three-tiered application. In its most common form, the three tiers are called presentation, application and storage. The first tier, presentation, refers to a web browser itself. The second tier refers to any engine using dynamic web content technology (such as ASP, CGI, ColdFusion, Dart, JSP/Java, Node.js, PHP, Python or Ruby on Rails). The third tier refers to a database that stores data and determines the structure of a user interface. Essentially, when using the three-tiered system, the web browser sends requests to the engine, which then services them by making queries and updates against the database and generates a user interface. The 3-tier solution may fall short when dealing with more complex applications, and may need to be replaced with the n-tiered approach; the greatest benefit of which is how business logic (which resides on the application tier) is broken down into a more fine-grained model. Another benefit would be to add an integration tier, which separates the data tier and provides an easy-to-use interface to access the data. For example, the client data would be accessed by calling a "list_clients()" function instead of making an SQL query directly against the client table on the database. This allows the underlying database to be replaced without making any change to the other tiers. There are some who view a web application as a two-tier architecture. This can be a "smart" client that performs all the work and queries a "dumb" server, or a "dumb" client that relies on a "smart" server. The client would handle the presentation tier, the server would have the database (storage tier), and the business logic (application tier) would be on one of them or on both. While this increases the scalability of the applications and separates the display and the database, it still does not allow for true specialization of layers, so most applications will outgrow this model. == Security == Security breaches on these kinds of applications are a major concern because it can involve both enterprise information and private customer data. Protecting these assets is an important part of any web application, and there are some key operational areas that must be included in the development process. This includes processes for authentication, authorization, asset handling, input, and logging and auditing. Building security into the applications from the beginning is sometimes more effective and less disruptive in the long run. == Development == Writing web applications is simplified with the use of web application frameworks. These frameworks facilitate rapid application development by allowing a development team to focus on the parts of their application which are unique to their goals without having to resolve common development issues such as user management. In addition, there is potential for the development of applications on Internet operating systems, although currently there are not many viable platforms that fit this model.
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Semi-automation

Semi-automation is a process or procedure that is performed by the combined activities of man and machine with both human and machine steps typically orchestrated by a centralized computer controller. Within manufacturing, production processes may be fully manual, semi-automated, or fully automated. In this case, semi-automation may vary in its degree of manual and automated steps. Semi-automated manufacturing processes are typically orchestrated by a computer controller which sends messages to the worker at the time in which he/she should perform a step. The controller typically waits for feedback that the human performed step has been completed via either a human-machine interface or via electronic sensors distributed within the process. Controllers within semi-automated processes may either directly control machinery or send signals to machinery distributed within the process. Centralized computer controllers within semi-automated processes orchestrate processes by instructing the worker, providing electronic communication and control to process equipment, tools, or machines, as well as perform data management to record and ensure that the process meets established process criteria. Many manufacturers choose not to fully automate a process, and instead implement semi-automation due to the complexity of the task, or the number of products produced is too low to justify the investment in full automation. Other processes may not be fully automated because it may reduce the flexibility to easily adapt the processes to reflect production needs.
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Rapid prototyping

Rapid prototyping is a group of techniques used to quickly fabricate a scale model of a physical part or assembly using three-dimensional computer aided design (CAD) data. Construction of the part or assembly is usually done using 3D printing or "additive layer manufacturing" technology. The first methods for rapid prototyping became available in mid 1987 and were used to produce models and prototype parts. Today, they are used for a wide range of applications and are used to manufacture production-quality parts in relatively small numbers if desired without the typical unfavorable short-run economics. This economy has encouraged online service bureaus. Historical surveys of RP technology start with discussions of simulacra production techniques used by 19th-century sculptors. Some modern sculptors use the progeny technology to produce exhibitions and various objects. The ability to reproduce designs from a dataset has given rise to issues of rights, as it is now possible to interpolate volumetric data from 2D images. As with CNC subtractive methods, the computer-aided-design – computer-aided manufacturing CAD -CAM workflow in the traditional rapid prototyping process starts with the creation of geometric data, either as a 3D solid using a CAD workstation, or 2D slices using a scanning device. For rapid prototyping this data must represent a valid geometric model; namely, one whose boundary surfaces enclose a finite volume, contain no holes exposing the interior, and do not fold back on themselves. In other words, the object must have an "inside". The model is valid if for each point in 3D space the computer can determine uniquely whether that point lies inside, on, or outside the boundary surface of the model. CAD post-processors will approximate the application vendors' internal CAD geometric forms (e.g., B-splines) with a simplified mathematical form, which in turn is expressed in a specified data format which is a common feature in additive manufacturing: STL file format, a de facto standard for transferring solid geometric models to SFF machines. To obtain the necessary motion control trajectories to drive the actual SFF, rapid prototyping, 3D printing or additive manufacturing mechanism, the prepared geometric model is typically sliced into layers, and the slices are scanned into lines (producing a "2D drawing" used to generate trajectory as in CNC's toolpath), mimicking in reverse the layer-to-layer physical building process. == Application areas == Rapid prototyping is also commonly applied in software engineering to try out new business models and application architectures such as Aerospace, Automotive, Financial Services, Product development, and Healthcare. Aerospace design and industrial teams rely on prototyping in order to create new AM methodologies in the industry. Using SLA they can quickly make multiple versions of their projects in a few days and begin testing quicker. Rapid Prototyping allows designers/developers to provide an accurate idea of how the finished product will turn out before putting too much time and money into the prototype. 3D printing being used for Rapid Prototyping allows for Industrial 3D printing to take place. With this, you could have large-scale moulds to spare parts being pumped out quickly within a short period of time. == Types of Rapid Prototyping == Stereolithography (SLA) → a laser-cured photopolymer for materials such as thermoplastic-like photopolymers. Selective Laser Sintering (SLS) → a laser-sintered powder for materials such as Nylon or TPU. Direct Metal Laser Sintering (DMLS) → laser-sintered metal powder for materials like stainless steel, titanium, chrome, and aluminum. Fused Deposition Modeling (FDM) → fused extrusions of filaments like ABS, PC, and PPCU. Multi Jet Fusion (MJF) → it is an inkjet array selective fusing across bed of nylon powder for Black Nylon 12. PolyJet (PJET) → it is a uv-cured jetted photopolymer to work with acrylic-based and elastomeric photopolymers. Computer Numerical Controlled Machine (CNC) → it is used for manipulating engineering-grade thermoplastics and metals. Injection Molding (IM) → the injection is done using aluminum molds and it is used for thermoplastics, metals and liquid silicone rubber. Vacuum Casting→ is a manufacturing process used to create high-quality prototypes and small batches of parts. == History == In the 1970s, Joseph Henry Condon and others at Bell Labs developed the Unix Circuit Design System (UCDS), automating the laborious and error-prone task of manually converting drawings to fabricate circuit boards for the purposes of research and development. By the 1980s, U.S. policy makers and industrial managers were forced to take note that America's dominance in the field of machine tool manufacturing evaporated, in what was named the machine tool crisis. Numerous projects sought to counter these trends in the traditional CNC CAM area, which had begun in the US. Later when Rapid Prototyping Systems moved out of labs to be commercialized, it was recognized that developments were already international and U.S. rapid prototyping companies would not have the luxury of letting a lead slip away. The National Science Foundation was an umbrella for the National Aeronautics and Space Administration (NASA), the US Department of Energy, the US Department of Commerce NIST, the US Department of Defense, Defense Advanced Research Projects Agency (DARPA), and the Office of Naval Research coordinated studies to inform strategic planners in their deliberations. One such report was the 1997 Rapid Prototyping in Europe and Japan Panel Report in which Joseph J. Beaman founder of DTM Corporation [DTM RapidTool pictured] provides a historical perspective: The roots of rapid prototyping technology can be traced to practices in topography and photosculpture. Within TOPOGRAPHY Blanther (1892) suggested a layered method for making a mold for raised relief paper topographical maps .The process involved cutting the contour lines on a series of plates which were then stacked. Matsubara (1974) of Mitsubishi proposed a topographical process with a photo-hardening photopolymer resin to form thin layers stacked to make a casting mold. PHOTOSCULPTURE was a 19th-century technique to create exact three-dimensional replicas of objects. Most famously Francois Willeme (1860) placed 24 cameras in a circular array and simultaneously photographed an object. The silhouette of each photograph was then used to carve a replica. Morioka (1935, 1944) developed a hybrid photo sculpture and topographic process using structured light to photographically create contour lines of an object. The lines could then be developed into sheets and cut and stacked, or projected onto stock material for carving. The Munz (1956) Process reproduced a three-dimensional image of an object by selectively exposing, layer by layer, a photo emulsion on a lowering piston. After fixing, a solid transparent cylinder contains an image of the object. "The Origins of Rapid Prototyping - RP stems from the ever-growing CAD industry, more specifically, the solid modeling side of CAD. Before solid modeling was introduced in the late 1980's, three-dimensional models were created with wire frames and surfaces. But not until the development of true solid modeling could innovative processes such as RP be developed. Charles Hull, who helped found 3D Systems in 1986, developed the first RP process. This process, called stereolithography, builds objects by curing thin consecutive layers of certain ultraviolet light-sensitive liquid resins with a low-power laser. With the introduction of RP, CAD solid models could suddenly come to life". The technologies referred to as Solid Freeform Fabrication are what we recognize today as rapid prototyping, 3D printing or additive manufacturing: Swainson (1977), Schwerzel (1984) worked on polymerization of a photosensitive polymer at the intersection of two computer controlled laser beams. Ciraud (1972) considered magnetostatic or electrostatic deposition with electron beam, laser or plasma for sintered surface cladding. These were all proposed but it is unknown if working machines were built. Hideo Kodama of Nagoya Municipal Industrial Research Institute was the first to publish an account of a solid model fabricated using a photopolymer rapid prototyping system (1981). The first 3D rapid prototyping system relying on Fused Deposition Modeling (FDM) was made in April 1992 by Stratasys but the patent did not issue until June 9, 1992. Sanders Prototype, Inc introduced the first desktop inkjet 3D Printer (3DP) using an invention from August 4, 1992 (Helinski), Modelmaker 6Pro in late 1993 and then the larger industrial 3D printer, Modelmaker 2, in 1997. Z-Corp using the MIT 3DP powder binding for Direct Shell Casting (DSP) invented 1993 was introduced to the market in 1995. Even at that early date the technology was seen as having a place in manufacturing practice. A low resol
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Adobe InDesign

Adobe InDesign is a desktop publishing and page layout designing software application produced by Adobe and first released in 1999. It can be used to create works such as posters, flyers, brochures, magazines, newspapers, presentations, books and ebooks. InDesign can also publish content suitable for tablet devices in conjunction with Adobe Digital Publishing Suite. Graphic designers and production artists are the principal users. InDesign is the successor to PageMaker, which Adobe acquired by buying Aldus Corporation in late 1994. (Freehand, Aldus's competitor to Adobe Illustrator, was licensed from Altsys, the maker of Fontographer.) By 1998, PageMaker had lost much of the professional market to the comparatively feature-rich QuarkXPress version 3.3, released in 1992, and version 4.0, released in 1996. In 1999, Quark announced its offer to buy Adobe and to divest the combined company of PageMaker to avoid problems under United States antitrust law. Adobe declined Quark's offer and continued to develop a new desktop publishing application. Aldus had begun developing a successor to PageMaker, code-named "Shuksan". Later, Adobe code-named the project "K2", and Adobe released InDesign 1.0 in 1999. InDesign exports documents in Adobe's Portable Document Format (PDF) and supports multiple languages. It was the first DTP application to support Unicode character sets, advanced typography with OpenType fonts, advanced transparency features, layout styles, optical margin alignment, and cross-platform scripting with JavaScript. Later versions of the software introduced new file formats. To support the new features, especially typography, introduced with InDesign CS, the program and its document format are not backward-compatible. Instead, InDesign CS2 introduced the INX (.inx) format, an XML-based document representation, to allow backward compatibility with future versions. InDesign CS versions updated with the 3.1 April 2005 update can read InDesign CS2-saved files exported to the .inx format. The InDesign Interchange format does not support versions earlier than InDesign CS. With InDesign CS4, Adobe replaced INX with InDesign Markup Language (IDML), another XML-based document representation. InDesign was the first native Mac OS X publishing software. With the third major version, InDesign CS, Adobe increased InDesign's distribution by bundling it with Adobe Photoshop, Adobe Illustrator, and Adobe Acrobat in Adobe Creative Suite. Adobe developed InDesign CS3 (and Creative Suite 3) as universal binary software compatible with native Intel and PowerPC Macs in 2007, two years after the announced 2005 schedule, inconveniencing early adopters of Intel-based Macs. Adobe CEO Bruce Chizen said, "Adobe will be first with a complete line of universal applications." == File format == The MIME type is not official File Open formats: indd, indl, indt, indb, inx, idml, pmd, xqx New File formats: indd, indl, indb File Save As formats: indd, indt Save file format for InCopy: icma (Assignment file) icml (Content file, Exported file) icap (Package for InCopy) idap (Package for InDesign) File Export formats: pdf, idml, icml, eps, jpg, txt, XML, rtf == Versions == Newer versions can, as a rule, open files created by older versions, but the reverse is not true. Current versions can export the InDesign file as an IDML file (InDesign Markup Language), which can be opened by InDesign versions from CS4 upwards; older versions from CS4 down can export to an INX file (InDesign Interchange format). === Server version === In October 2005, Adobe released InDesign Server CS2, a modified version of InDesign (without a user interface) for Windows and Macintosh server platforms. It does not provide any editing client; rather, it is for use by developers in creating client-server solutions with the InDesign plug-in technology. In March 2007 Adobe officially announced Adobe InDesign CS3 Server as part of the Adobe InDesign family. == Features == Paragraph styles are an essential tool for designers when working with text in Adobe InDesign. Despite their menacing appearance, they are straightforward to operate. Other features that make InDesign a good tool for working with text and paragraphs include: Creating frames and shapes Aligning objects with grids and guides Manipulating objects Organizing objects Importing text Formatting text Spell checking Importing images Parent pages (formerly master pages) Paragraph styles == Internationalization and localization == InDesign Middle Eastern editions have unique settings for laying out Arabic or Hebrew text. They feature: Text settings: Special settings for laying out Arabic or Hebrew text, such as: Ability to use Arabic, Persian or Hindi digits; Use kashidas for letter spacing and full justification; Ligature option; Adjust the position of diacritics, such as vowels of the Arabic script; Justify text in three possible ways: Standard, Arabic, Naskh; Option to insert special characters, including Geresh, Gershayim, Maqaf for Hebrew and Kashida for Arabic texts; Apply standard, Arabic, or Hebrew styles for page, paragraph, and footnote numbering. Bi-directional text flow: Right-to-left behavior applies to several objects: Story, paragraph, character, and table. It allows mixing right-to-left and left-to-right words, paragraphs, and stories in a document. Changing the direction of neutral characters (e.g., / or ?) is possible according to the user's keyboard language. Table of contents: Provides a table of contents titles, one for each supported language. This table is sorted according to the chosen language. InDesign CS4 Middle Eastern versions allow users to select the language of the index title and cross-references. Indices: This allows the creation of a simple keyword index or a somewhat more detailed index of the information in the text using embedded indexing codes. Unlike more sophisticated programs, InDesign cannot insert character style information as part of an index entry (e.g., when indexing book, journal, or movie titles). Indices are limited to four levels (the top level and three sub-levels). Like tables of contents, indices can be sorted according to the selected language. Importing and exporting: Can import QuarkXPress files up to version 4.1 (1999), even using Arabic XT, Arabic Phonyx, or Hebrew XPressWay fonts, retaining the layout and content. Includes 50 import/export filters, including a Microsoft Word 97-98-2000 import filter and a plain text import filter. Exports IDML files can be read by QuarkXPress 2017. Reverse layout: Include a reverse layout feature to reverse the layout of a document when converting a left-to-right document to a right-to-left one or vice versa. Complex script rendering: InDesign supports Unicode character encoding, and Middle Eastern editions support complex text layouts for Arabic and Hebrew complex scripts. The underlying Arabic and Hebrew support is present in the Western editions of InDesign CS4, CS5, CS5.5, and CS6, but the user interface is not exposed, making it difficult to access.
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Kernel density estimation

In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights. KDE answers a fundamental data smoothing problem where inferences about the population are made based on a finite data sample. In some fields such as signal processing and econometrics it is also termed the Parzen–Rosenblatt window method, after Emanuel Parzen and Murray Rosenblatt, who are usually credited with independently creating it in its current form. One of the famous applications of kernel density estimation is in estimating the class-conditional marginal densities of data when using a naive Bayes classifier, which can improve its prediction accuracy. == Definition == Let x = ( x 1 , x 2 , x 3 , . . . ) {\displaystyle \mathbf {x} =\left(x_{1},x_{2},x_{3},...\right)} be independent and identically distributed samples drawn from some univariate distribution with an unknown density f at any given point x. We are interested in estimating the shape of this function f. Its kernel density estimator is f ^ h ( x ) = 1 n ∑ i = 1 n K h ( x − x i ) = 1 n h ∑ i = 1 n K ( x − x i h ) , {\displaystyle {\hat {f}}_{h}(x)={\frac {1}{n}}\sum _{i=1}^{n}K_{h}(x-x_{i})={\frac {1}{nh}}\sum _{i=1}^{n}K{\left({\frac {x-x_{i}}{h}}\right)},} where K is the kernel — a non-negative function — and h > 0 is a smoothing parameter called the bandwidth or simply width. A kernel with subscript h is called the scaled kernel and defined as Kh(x) = ⁠1/h⁠ K(⁠x/h⁠). Intuitively one wants to choose h as small as the data will allow; however, there is always a trade-off between the bias of the estimator and its variance. The choice of bandwidth is discussed in more detail below. A range of kernel functions are commonly used: uniform, triangular, biweight, triweight, Epanechnikov (parabolic), normal, and others. The Epanechnikov kernel is optimal in a mean square error sense, though the loss of efficiency is small for the kernels listed previously. Due to its convenient mathematical properties, the normal kernel is often used, which means K(x) = ϕ(x), where ϕ is the standard normal density function. The kernel density estimator then becomes f ^ h ( x ) = 1 n ∑ i = 1 n 1 h 2 π exp ⁡ ( − ( x − x i ) 2 2 h 2 ) , {\displaystyle {\hat {f}}_{h}(x)={\frac {1}{n}}\sum _{i=1}^{n}{\frac {1}{h{\sqrt {2\pi }}}}\exp \left({\frac {-(x-x_{i})^{2}}{2h^{2}}}\right),} where h {\displaystyle h} is the standard deviation of the sample x {\displaystyle \mathbf {x} } . The construction of a kernel density estimate finds interpretations in fields outside of density estimation. For example, in thermodynamics, this is equivalent to the amount of heat generated when heat kernels (the fundamental solution to the heat equation) are placed at each data point locations xi. Similar methods are used to construct discrete Laplace operators on point clouds for manifold learning (e.g. diffusion map). == Example == Kernel density estimates are closely related to histograms, but can be endowed with properties such as smoothness or continuity by using a suitable kernel. The diagram below based on these 6 data points illustrates this relationship: For the histogram, first, the horizontal axis is divided into sub-intervals or bins which cover the range of the data: In this case, six bins each of width 2. Whenever a data point falls inside this interval, a box of height 1/12 is placed there. If more than one data point falls inside the same bin, the boxes are stacked on top of each other. For the kernel density estimate, normal kernels with a standard deviation of 1.5 (indicated by the red dashed lines) are placed on each of the data points xi. The kernels are summed to make the kernel density estimate (solid blue curve). The smoothness of the kernel density estimate (compared to the discreteness of the histogram) illustrates how kernel density estimates converge faster to the true underlying density for continuous random variables. == Bandwidth selection == The bandwidth of the kernel is a free parameter which exhibits a strong influence on the resulting estimate. To illustrate its effect, we take a simulated random sample from the standard normal distribution (plotted at the blue spikes in the rug plot on the horizontal axis). The grey curve is the true density (a normal density with mean 0 and variance 1). In comparison, the red curve is undersmoothed since it contains too many spurious data artifacts arising from using a bandwidth h = 0.05, which is too small. The green curve is oversmoothed since using the bandwidth h = 2 obscures much of the underlying structure. The black curve with a bandwidth of h = 0.337 is considered to be optimally smoothed since its density estimate is close to the true density. An extreme situation is encountered in the limit h → 0 {\displaystyle h\to 0} (no smoothing), where the estimate is a sum of n delta functions centered at the coordinates of analyzed samples. In the other extreme limit h → ∞ {\displaystyle h\to \infty } the estimate retains the shape of the used kernel, centered on the mean of the samples (completely smooth). The most common optimality criterion used to select this parameter is the expected L2 risk function, also termed the mean integrated squared error: MISE ⁡ ( h ) = E [ ∫ ( f ^ h ( x ) − f ( x ) ) 2 d x ] {\displaystyle \operatorname {MISE} (h)=\operatorname {E} \!\left[\int \!{\left({\hat {f}}\!_{h}(x)-f(x)\right)}^{2}dx\right]} Under weak assumptions on f and K, (f is the, generally unknown, real density function), MISE ⁡ ( h ) = AMISE ⁡ ( h ) + o ( ( n h ) − 1 + h 4 ) {\displaystyle \operatorname {MISE} (h)=\operatorname {AMISE} (h)+{\mathcal {o}}{\left((nh)^{-1}+h^{4}\right)}} where o is the little o notation, and n the sample size (as above). The AMISE is the asymptotic MISE, i. e. the two leading terms, AMISE ⁡ ( h ) = R ( K ) n h + 1 4 m 2 ( K ) 2 h 4 R ( f ″ ) {\displaystyle \operatorname {AMISE} (h)={\frac {R(K)}{nh}}+{\frac {1}{4}}m_{2}(K)^{2}h^{4}R(f'')} where R ( g ) = ∫ g ( x ) 2 d x {\textstyle R(g)=\int g(x)^{2}\,dx} for a function g, m 2 ( K ) = ∫ x 2 K ( x ) d x {\textstyle m_{2}(K)=\int x^{2}K(x)\,dx} and f ″ {\displaystyle f''} is the second derivative of f {\displaystyle f} and K {\displaystyle K} is the kernel. The minimum of this AMISE is the solution to this differential equation ∂ ∂ h AMISE ⁡ ( h ) = − R ( K ) n h 2 + m 2 ( K ) 2 h 3 R ( f ″ ) = 0 {\displaystyle {\frac {\partial }{\partial h}}\operatorname {AMISE} (h)=-{\frac {R(K)}{nh^{2}}}+m_{2}(K)^{2}h^{3}R(f'')=0} or h AMISE = R ( K ) 1 / 5 m 2 ( K ) 2 / 5 R ( f ″ ) 1 / 5 n − 1 / 5 = C n − 1 / 5 {\displaystyle h_{\operatorname {AMISE} }={\frac {R(K)^{1/5}}{m_{2}(K)^{2/5}R(f'')^{1/5}}}n^{-1/5}=Cn^{-1/5}} Neither the AMISE nor the hAMISE formulas can be used directly since they involve the unknown density function f {\displaystyle f} or its second derivative f ″ {\displaystyle f''} . To overcome that difficulty, a variety of automatic, data-based methods have been developed to select the bandwidth. Several review studies have been undertaken to compare their efficacies, with the general consensus that the plug-in selectors and cross validation selectors are the most useful over a wide range of data sets. Substituting any bandwidth h which has the same asymptotic order n−1/5 as hAMISE into the AMISE gives that AMISE(h) = O(n−4/5), where O is the big O notation. It can be shown that, under weak assumptions, there cannot exist a non-parametric estimator that converges at a faster rate than the kernel estimator. Note that the n−4/5 rate is slower than the typical n−1 convergence rate of parametric methods. If the bandwidth is not held fixed, but is varied depending upon the location of either the estimate (balloon estimator) or the samples (pointwise estimator), this produces a particularly powerful method termed adaptive or variable bandwidth kernel density estimation. Bandwidth selection for kernel density estimation of heavy-tailed distributions is relatively difficult. === A rule-of-thumb bandwidth estimator === If Gaussian basis functions are used to approximate univariate data, and the underlying density being estimated is Gaussian, the optimal choice for h (that is, the bandwidth that minimises the mean integrated squared error) is: h = ( 4 σ ^ 5 3 n ) 1 / 5 ≈ 1.06 σ ^ n − 1 / 5 , {\displaystyle h={\left({\frac {4{\hat {\sigma }}^{5}}{3n}}\right)}^{1/5}\approx 1.06\,{\hat {\sigma }}\,n^{-1/5},} An h {\displaystyle h} value is considered more robust when it improves the fit for long-tailed and skewed distributions or for bimodal mixture distributions. This is often done empirically by replacing the standard deviation σ ^ {\displaystyle {\hat {\sigma }}} by the parameter A {\displaystyle A} below: A = min ( σ ^ , I Q R 1.34 ) {\displaystyle A=\min \left({\hat {\sigma }},{\frac {\mathrm {IQR} }{1.34}}\right)} where IQR is the
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Autonomous things

Autonomous things, abbreviated AuT, or the Internet of autonomous things, abbreviated as IoAT, is an emerging term for the technological developments that are expected to bring computers into the physical environment as autonomous entities without human direction, freely moving and interacting with humans and other objects. Self-navigating drones are the first AuT technology in (limited) deployment. It is expected that the first mass-deployment of AuT technologies will be the autonomous car, generally expected to be available around 2020. Other currently expected AuT technologies include home robotics (e.g., machines that provide care for the elderly, infirm or young), and military robots (air, land or sea autonomous machines with information-collection or target-attack capabilities). AuT technologies share many common traits, which justify the common notation. They are all based on recent breakthroughs in the domains of (deep) machine learning and artificial intelligence. They all require extensive and prompt regulatory developments to specify the requirements from them and to license and manage their deployment (see the further reading below). And they all require unprecedented levels of safety (e.g., automobile safety) and security, to overcome concerns about the potential negative impact of the new technology. As an example, the autonomous car both addresses the main existing safety issues and creates new issues. It is expected to be much safer than existing vehicles, by eliminating the single most dangerous element – the driver. The US's National Highway Traffic Safety Administration estimates 94 percent of US accidents were the result of human error and poor decision-making, including speeding and impaired driving, and the Center for Internet and Society at Stanford Law School claims that "Some ninety percent of motor vehicle crashes are caused at least in part by human error". So while safety standards like the ISO 26262 specify the required safety, there is still a burden on the industry to demonstrate acceptable safety. While car accidents claim every year 35,000 lives in the US, and 1.25 million worldwide, some believe that even "a car that's 10 times as safe, which means 3,500 people die on the roads each year [in the US alone]" would not be accepted by the public. The acceptable level may be closer to the current figures on aviation accidents and incidents, with under a thousand worldwide deaths in most years – three orders of magnitude lower than cars. This underscores the unprecedented nature of the safety requirements that will need to be met for cars, with similar levels of safety expected for other Autonomous Things.
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Control system

A control system manages, commands, directs, or regulates the behavior of other devices or systems using control loops. It can range from a single home heating controller using a thermostat controlling a domestic boiler to large industrial control systems which are used for controlling processes or machines. The control systems are designed via control engineering process. For continuously modulated control, a feedback controller is used to automatically control a process or operation. The control system compares the value or status of the process variable (PV) being controlled with the desired value or setpoint (SP), and applies the difference as a control signal to bring the process variable output of the plant to the same value as the setpoint. For sequential and combinational logic, software logic, such as in a programmable logic controller, is used. == Open-loop and closed-loop control == == Feedback control systems == == Logic control == Logic control systems for industrial and commercial machinery were historically implemented by interconnected electrical relays and cam timers using ladder logic. Today, most such systems are constructed with microcontrollers or more specialized programmable logic controllers (PLCs). The notation of ladder logic is still in use as a programming method for PLCs. Logic controllers may respond to switches and sensors and can cause the machinery to start and stop various operations through the use of actuators. Logic controllers are used to sequence mechanical operations in many applications. Examples include elevators, washing machines and other systems with interrelated operations. An automatic sequential control system may trigger a series of mechanical actuators in the correct sequence to perform a task. For example, various electric and pneumatic transducers may fold and glue a cardboard box, fill it with the product and then seal it in an automatic packaging machine. PLC software can be written in many different ways – ladder diagrams, SFC (sequential function charts) or statement lists. == On–off control == On–off control uses a feedback controller that switches abruptly between two states. A simple bi-metallic domestic thermostat can be described as an on-off controller. When the temperature in the room (PV) goes below the user setting (SP), the heater is switched on. Another example is a pressure switch on an air compressor. When the pressure (PV) drops below the setpoint (SP) the compressor is powered. Refrigerators and vacuum pumps contain similar mechanisms. Simple on–off control systems like these can be cheap and effective. == Linear control == == Fuzzy logic == Fuzzy logic is an attempt to apply the easy design of logic controllers to the control of complex continuously varying systems. Basically, a measurement in a fuzzy logic system can be partly true. The rules of the system are written in natural language and translated into fuzzy logic. For example, the design for a furnace would start with: "If the temperature is too high, reduce the fuel to the furnace. If the temperature is too low, increase the fuel to the furnace." Measurements from the real world (such as the temperature of a furnace) are fuzzified and logic is calculated arithmetic, as opposed to Boolean logic, and the outputs are de-fuzzified to control equipment. When a robust fuzzy design is reduced to a single, quick calculation, it begins to resemble a conventional feedback loop solution and it might appear that the fuzzy design was unnecessary. However, the fuzzy logic paradigm may provide scalability for large control systems where conventional methods become unwieldy or costly to derive. Fuzzy electronics is an electronic technology that uses fuzzy logic instead of the two-value logic more commonly used in digital electronics. == Physical implementation == The range of control system implementation is from compact controllers often with dedicated software for a particular machine or device, to distributed control systems for industrial process control for a large physical plant. Logic systems and feedback controllers are usually implemented with programmable logic controllers. The Broadly Reconfigurable and Expandable Automation Device (BREAD) is a recent framework that provides many open-source hardware devices which can be connected to create more complex data acquisition and control systems.
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