Foreground detection is one of the major tasks in the field of computer vision and image processing whose aim is to detect changes in image sequences. Background subtraction is any technique which allows an image's foreground to be extracted for further processing (object recognition etc.). Many applications do not need to know everything about the evolution of movement in a video sequence, but only require the information of changes in the scene, because an image's regions of interest are objects (humans, cars, text etc.) in its foreground. After the stage of image preprocessing (which may include image denoising, post processing like morphology etc.) object localisation is required which may make use of this technique. Foreground detection separates foreground from background based on these changes taking place in the foreground. It is a set of techniques that typically analyze video sequences recorded in real time with a stationary camera. == Description == All detection techniques are based on modelling the background of the image, i.e., setting the background and detecting which changes occur. Defining the background can be difficult when it contains shapes, shadows, and moving objects. In defining the background, it is assumed that stationary objects may vary in color and intensity over time. Scenarios in which these techniques apply tend to be very diverse. There can be highly variable sequences, such as images with different lighting, interiors, exteriors, quality, and noise. In addition to real-time processing, systems need to adapt to these changes. A foreground detection system should be able to: Develop a background model (estimate). Be robust to lighting changes, repetitive movements (leaves, waves, shadows), and long-term changes. == Background subtraction == Background subtraction is a widely used approach for detecting moving objects in videos from static cameras. The rationale in the approach is that of detecting the moving objects from the difference between the current frame and a reference frame, often called "background image", or "background model". Background subtraction is mostly done if the image in question is a part of a video stream. Background subtraction provides important cues for numerous applications in computer vision, for example surveillance tracking or human pose estimation. Background subtraction is generally based on a static background hypothesis which is often not applicable in real environments. With indoor scenes, reflections or animated images on screens lead to background changes. Similarly, due to wind, rain or illumination changes brought by weather, static backgrounds methods have difficulties with outdoor scenes. == Temporal average filter == The temporal average filter is a method that was proposed at the Velastin. This system estimates the background model from the median of all pixels of a number of previous images. The system uses a buffer with the pixel values of the last frames to update the median for each image. To model the background, the system examines all images in a given time period called training time. At this time, we only display images and will find the median, pixel by pixel, of all the plots in the background this time. After the training period for each new frame, each pixel value is compared with the input value of funds previously calculated. If the input pixel is within a threshold, the pixel is considered to match the background model and its value is included in the pixbuf. Otherwise, if the value is outside this threshold pixel is classified as foreground, and not included in the buffer. This method cannot be considered very efficient because they do not present a rigorous statistical basis and requires a buffer that has a high computational cost. == Conventional approaches == A robust background subtraction algorithm should be able to handle lighting changes, repetitive motions from clutter and long-term scene changes. The following analyses make use of the function of V(x,y,t) as a video sequence where t is the time dimension, x and y are the pixel location variables. e.g. V(1,2,3) is the pixel intensity at (1,2) pixel location of the image at t = 3 in the video sequence. === Using frame differencing === A motion detection algorithm begins with the segmentation part where foreground or moving objects are segmented from the background. The simplest way to implement this is to take an image as background and take the frames obtained at the time t, denoted by I(t) to compare with the background image denoted by B. Here using simple arithmetic calculations, we can segment out the objects simply by using image subtraction technique of computer vision meaning for each pixels in I(t), take the pixel value denoted by P[I(t)] and subtract it with the corresponding pixels at the same position on the background image denoted as P[B]. In mathematical equation, it is written as: P [ F ( t ) ] = P [ I ( t ) ] − P [ B ] {\displaystyle P[F(t)]=P[I(t)]-P[B]} The background is assumed to be the frame at time t. This difference image would only show some intensity for the pixel locations which have changed in the two frames. Though we have seemingly removed the background, this approach will only work for cases where all foreground pixels are moving, and all background pixels are static. A threshold "Threshold" is put on this difference image to improve the subtraction (see Image thresholding): | P [ F ( t ) ] − P [ F ( t + 1 ) ] | > T h r e s h o l d {\displaystyle |P[F(t)]-P[F(t+1)]|>\mathrm {Threshold} } This means that the difference image's pixels' intensities are 'thresholded' or filtered on the basis of value of Threshold. The accuracy of this approach is dependent on speed of movement in the scene. Faster movements may require higher thresholds. === Mean filter === For calculating the image containing only the background, a series of preceding images are averaged. For calculating the background image at the instant t: B ( x , y , t ) = 1 N ∑ i = 1 N V ( x , y , t − i ) {\displaystyle B(x,y,t)={1 \over N}\sum _{i=1}^{N}V(x,y,t-i)} where N is the number of preceding images taken for averaging. This averaging refers to averaging corresponding pixels in the given images. N would depend on the video speed (number of images per second in the video) and the amount of movement in the video. After calculating the background B(x,y,t) we can then subtract it from the image V(x,y,t) at time t = t and threshold it. Thus the foreground is: | V ( x , y , t ) − B ( x , y , t ) | > T h {\displaystyle |V(x,y,t)-B(x,y,t)|>\mathrm {Th} } where Th is a threshold value. Similarly, we can also use median instead of mean in the above calculation of B(x,y,t). Usage of global and time-independent thresholds (same Th value for all pixels in the image) may limit the accuracy of the above two approaches. === Running Gaussian average === For this method, Wren et al. propose fitting a Gaussian probabilistic density function (pdf) on the most recent n {\displaystyle n} frames. In order to avoid fitting the pdf from scratch at each new frame time t {\displaystyle t} , a running (or on-line cumulative) average is computed. The pdf of every pixel is characterized by mean μ t {\displaystyle \mu _{t}} and variance σ t 2 {\displaystyle \sigma _{t}^{2}} . The following is a possible initial condition (assuming that initially every pixel is background): μ 0 = I 0 {\displaystyle \mu _{0}=I_{0}} σ 0 2 = ⟨ some default value ⟩ {\displaystyle \sigma _{0}^{2}=\langle {\text{some default value}}\rangle } where I t {\displaystyle I_{t}} is the value of the pixel's intensity at time t {\displaystyle t} . In order to initialize variance, we can, for example, use the variance in x and y from a small window around each pixel. Note that background may change over time (e.g. due to illumination changes or non-static background objects). To accommodate for that change, at every frame t {\displaystyle t} , every pixel's mean and variance must be updated, as follows: μ t = ρ I t + ( 1 − ρ ) μ t − 1 {\displaystyle \mu _{t}=\rho I_{t}+(1-\rho )\mu _{t-1}} σ t 2 = d 2 ρ + ( 1 − ρ ) σ t − 1 2 {\displaystyle \sigma _{t}^{2}=d^{2}\rho +(1-\rho )\sigma _{t-1}^{2}} d = | ( I t − μ t ) | {\displaystyle d=|(I_{t}-\mu _{t})|} Where ρ {\displaystyle \rho } determines the size of the temporal window that is used to fit the pdf (usually ρ = 0.01 {\displaystyle \rho =0.01} ) and d {\displaystyle d} is the Euclidean distance between the mean and the value of the pixel. We can now classify a pixel as background if its current intensity lies within some confidence interval of its distribution's mean: | ( I t − μ t ) | σ t > k ⟶ foreground {\displaystyle {\frac {|(I_{t}-\mu _{t})|}{\sigma _{t}}}>k\longrightarrow {\text{foreground}}} | ( I t − μ t ) | σ t ≤ k ⟶ background {\displaystyle {\frac {|(I_{t}-\mu _{t})|}{\sigma _{t}}}\leq k\longrightarrow {\text{background}}} where the parameter k {\displaystyle k} is a free threshold (usuall
Document-oriented database
A document-oriented database, or document store, is a computer program and data storage system designed for storing, retrieving, and managing document-oriented information, also known as semi-structured data. Document-oriented databases are one of the main categories of NoSQL databases, and the popularity of the term "document-oriented database" has grown alongside the adoption of NoSQL itself. XML databases are a subclass of document-oriented databases optimized for XML documents. Graph databases are similar, but add another layer, the relationship, which allows them to link documents for rapid traversal. Document-oriented databases are conceptually an extension of the key–value store, another type of NoSQL database. In key-value stores, data is treated as opaque by the database, whereas document-oriented systems exploit the internal structure of documents to extract metadata and optimize storage and queries. Although in practice the distinction can be minimal due to modern tooling, document stores are designed to provide a richer programming experience with modern programming techniques. Document databases differ significantly from traditional relational databases (RDBs). Relational databases store data in predefined tables, often requiring an object to be split across multiple tables. In contrast, document databases store all information for a given object in a single document, with each document potentially having a unique structure. This design eliminates the need for object-relational mapping when loading data into the database. == Documents == The central concept of a document-oriented database is the notion of a document. Although implementations vary in their specific definitions, document-oriented databases generally treat documents as self-contained units that encapsulate and encode data in a standardized format. Common encoding formats include XML, YAML, JSON, as well as binary representations such as BSON. Documents in a document store are equivalent to the programming concept of an object. They are not required to adhere to a fixed schema, and documents within the same collection may contain different fields or structures. Fields may be optional, and documents of the same logical type may differ in composition. For example, the following illustrates a document encoded in JSON: A second document might be encoded in XML as: The two example documents share some structural elements but also contain unique fields. The structure, text, and other data within each document are collectively referred to as the document's content and can be accessed or modified using retrieval or editing operations. Unlike relational databases, in which each record contains the same fields and unused fields are left empty, document-oriented databases do not require uniform fields across documents. This design allows new information to be added to some documents without affecting the structure of others. Document databases often support the storage of additional metadata alongside the document content. Such metadata may relate to organizational features, security, indexing, or other implementation-specific features. === CRUD operations === The core operations supported by a document-oriented database for manipulating documents are similar to those in other databases. Although terminology is not perfectly standardized, these operations are generally recognized as Create, Read, Update, and Delete (CRUD). Creation (C): Adds a new document to the database. Retrieval (R): Retrieves documents or fields based on queries. Update (U): Modifies the contents of existing documents. Deletion (D): Removes documents from the database. === Keys === Documents in a document-oriented database are addressed via a unique identifier. This identifier, often a string, URI, or path, can be used to retrieve the document from the database. Most document stores maintain an index on the key to optimize retrieval, and in some implementations the key is required when creating or inserting a new document. === Retrieval === In addition to key-based access, document-oriented databases typically provide an API or query language that enables retrieval based on document content or associated metadata. For example, a query may return all documents with a specific field matching a given value. The available query features, indexing options, and performance characteristics vary across implementations. Document stores differ from key-value stores in that they exploit the internal structure and metadata of stored documents. In many key-value stores, values are treated as opaque or "black-box" data, meaning the database system does not interpret their internal structure. By contrast, document-oriented databases can classify and interpret document content. This enables queries that distinguish between types of data––for example, retrieving all phone numbers containing "555" without also matching a postal code such as "55555." === Editing === Document databases typically provide mechanisms for updating or editing the content or metadata of a document. Updates may involve replacing the entire document or modifying individual elements or fields within the document. === Organization === Document database implementations support a variety of methods for organizing documents, including: Collections: Groups of documents. Depending on the implementation, a document may be required to belong to a single collection or may be allowed in multiple collections. Tags and non-visible metadata: Additional data stored outside the main document content. Directory hierarchies: Documents organized in a tree-like structure, often based on path or URI. These organizational structures may differ between logical and physical representations (e.g. on disk or in memory). == Relationship to other databases == === Relationship to key-value stores === A document-oriented database can be viewed as a specialized form of key-value store, which is itself a category of NoSQL database. In a basic key-value store, the stored value is typically treated as opaque by the database system. By contrast, a document-oriented database provides APIs or a query and update language that allows queries and modifications based on the internal structure of the document. For users who do not require advanced query, retrieval, or update capabilities, the distinction between document-oriented databases and key-value stores may be minimal. === Relationship to search engines === Some search engine and information retrieval systems, such as Apache Solr and Elasticsearch, provide document storage and support core document operations. As a result, they may meet certain functional definitions of a document-oriented database, although their primary design goals differ. === Relationship to relational databases === In a relational database, data is organized into predefined types represented as tables. Each table contains rows (records) with a fixed set of columns (fields), so all records in a table share the same structure. Administrators typically define indexes on selected fields to improve query performance. A central principle of relational database design is database normalization, in which data that might otherwise be repeated is stored in separate tables and linked using keys. When records in different tables are related, a foreign key is used to associate them. For example, an address book application may store a contact's name, image, phone numbers, mailing addresses, and email addresses. In a normalized relational design, separate tables might be created for contacts, phone numbers, and email addresses. The phone number table would include a foreign key referencing the associated contact. To reconstruct a complete contact record, the database retrieves related information from each table using the foreign keys and combines it into a single record. In contrast, a document-oriented database stores all data related to an object within a single document, and stored in the database as a single entry. In the address book example,the contact's name, image, and contact information may be stored together in one document. The document is retrieved using a unique key, and all related information is returned together, without needing to look up multiple tables. A key difference between the document-oriented and relational models is that the data formats are not predefined in the document case. In most cases, any sort of document can be stored in a database, and documents can change in type and form over time. For example, a new field such as COUNTRY_FLAG can be added to new documents as they are inserted without affecting existing documents. To aid retrieval, document-oriented systems generally allow the administrator to provide hints to the database for locating certain types of information. These hints work in a similar fashion to indexes in relational databases. Many systems also allow additional metadata outside the content of the document itself
Spintronics
Spintronics (a portmanteau of spin transport electronics), also known as spin electronics, is the study of the intrinsic spin of the electron and its associated magnetic moment, in addition to its fundamental electronic charge, in solid-state devices. The field of spintronics concerns spin-charge coupling in metallic systems. The analogous effects in insulators fall into the field of multiferroics. Spintronics fundamentally differs from traditional electronics in that, in addition to charge state, electron spins are used as a further degree of freedom, with implications in the efficiency of data storage and transfer. Spintronic systems are most often realised in dilute magnetic semiconductors (DMS) and Heusler alloys and are of particular interest in the field of quantum computing, such as atomtronics computation. == History == Spintronics emerged from discoveries in the 1980s concerning spin-dependent electron transport phenomena in solid-state devices. This includes the observation of spin-polarized electron injection from a ferromagnetic metal to a normal metal by Johnson and Silsbee (1985) and the discovery of giant magnetoresistance independently by Albert Fert et al. and Peter Grünberg et al. (1988). The origin of spintronics can be traced to the ferromagnet/superconductor tunneling experiments pioneered by Meservey and Tedrow and initial experiments on magnetic tunnel junctions by Julliere in the 1970s. The use of semiconductors for spintronics began with the theoretical proposal of a spin field-effect-transistor by Datta and Das in 1990 and of the electric dipole spin resonance by Rashba in 1960. In 2012, persistent spin helices of synchronized electrons were made to persist for more than a nanosecond, a 30-fold increase over earlier efforts, and longer than the duration of a modern processor clock cycle. In 2025, at 60 K (−213.2 °C; −351.7 °F) crystalline nickel(II) iodide (NiI2) was reported to exhibit p-wave magnetism, in which the spins of nickel atoms became arranged in a spiral pattern in two orientations. The orientations can be switched via a small electrical current. Applied in digital devices, this spintronics behavior requires far less current than the conventional charge-based electronics that powers devices such as computers and phones. == Theory == The spin of the electron is an intrinsic angular momentum that is separate from the angular momentum due to its orbital motion. The magnitude of the projection of the electron's spin along an arbitrary axis is 1 2 ℏ {\displaystyle {\tfrac {1}{2}}\hbar } , implying that the electron acts as a fermion by the spin-statistics theorem. Like orbital angular momentum, the spin has an associated magnetic moment, the magnitude of which is expressed as μ = 3 2 q m e ℏ {\displaystyle \mu ={\tfrac {\sqrt {3}}{2}}{\frac {q}{m_{e}}}\hbar } . In a solid, the spins of many electrons can act together to affect the magnetic and electronic properties of a material, for example endowing it with a permanent magnetic moment as in a ferromagnet. In many materials, electron spins are equally present in both the up and the down state, and no transport properties are dependent on spin. A spintronic device requires generation or manipulation of a spin-polarized population of electrons, resulting in an excess of spin up or spin down electrons. The polarization of any spin dependent property X can be written as P X = X ↑ − X ↓ X ↑ + X ↓ {\displaystyle P_{X}={\frac {X_{\uparrow }-X_{\downarrow }}{X_{\uparrow }+X_{\downarrow }}}} . A net spin polarization can be achieved either through creating an equilibrium energy split between spin up and spin down. Methods include putting a material in a large magnetic field (Zeeman effect), the exchange energy present in a ferromagnet or forcing the system out of equilibrium. The period of time that such a non-equilibrium population can be maintained is known as the spin lifetime, τ {\displaystyle \tau } . In a diffusive conductor, a spin diffusion length λ {\displaystyle \lambda } can be defined as the distance over which a non-equilibrium spin population can propagate. Spin lifetimes of conduction electrons in metals are relatively short (typically less than 1 nanosecond). An important research area is devoted to extending this lifetime to technologically relevant timescales. The mechanisms of decay for a spin polarized population can be broadly classified as spin-flip scattering and spin dephasing. Spin-flip scattering is a process inside a solid that does not conserve spin, and can therefore switch an incoming spin up state into an outgoing spin down state. Spin dephasing is the process wherein a population of electrons with a common spin state becomes less polarized over time due to different rates of electron spin precession. In confined structures, spin dephasing can be suppressed, leading to spin lifetimes of milliseconds in semiconductor quantum dots at low temperatures. Superconductors can enhance central effects in spintronics such as magnetoresistance effects, spin lifetimes and dissipationless spin-currents. The simplest method of generating a spin-polarised current in a metal is to pass the current through a ferromagnetic material. The most common applications of this effect involve giant magnetoresistance (GMR) devices. A typical GMR device consists of at least two layers of ferromagnetic materials separated by a spacer layer. When the two magnetization vectors of the ferromagnetic layers are aligned, the electrical resistance will be lower (so a higher current flows at constant voltage) than if the ferromagnetic layers are anti-aligned. This constitutes a magnetic field sensor. Two variants of GMR have been applied in devices: Current-in-plane (CIP), where the electric current flows parallel to the layers and, Current-perpendicular-to-plane (CPP), where the electric current flows in a direction perpendicular to the layers. Other metal-based spintronics devices: Tunnel magnetoresistance (TMR), where CPP transport is achieved by using quantum-mechanical tunneling of electrons through a thin insulator separating ferromagnetic layers. Spin-transfer torque, where a current of spin-polarized electrons is used to control the magnetization direction of ferromagnetic electrodes in the device. Spin-wave logic devices carry information in the phase. Interference and spin-wave scattering can perform logic operations. == Device types == === Spintronic-logic === Non-volatile spin-logic devices to enable scaling are being extensively studied. Spin-transfer, torque-based logic devices that use spins and magnets for information processing have been proposed. These devices are part of the ITRS exploratory road map. Logic-in memory applications are already in the development stage. A 2017 review article can be found in Materials Today. A generalized circuit theory for spintronic integrated circuits has been proposed so that the physics of spin transport can be utilized by SPICE developers and subsequently by circuit and system designers for the exploration of spintronics for "beyond CMOS computing". === Semiconductor === Doped semiconductor materials display dilute ferromagnetism. In recent years, dilute magnetic oxides (DMOs) including ZnO based DMOs and TiO2-based DMOs have been the subject of numerous experimental and computational investigations. N`0 sources (like manganese-doped gallium arsenide (Ga,Mn)As), increase the interface resistance with a tunnel barrier, or using hot-electron injection. Spin detection in semiconductors has been addressed with multiple techniques: Faraday/Kerr rotation of transmitted/reflected photons Circular polarization analysis of electroluminescence Nonlocal spin valve (adapted from Johnson and Silsbee's work with metals) Ballistic spin filtering The latter technique was used to overcome the lack of spin-orbit interaction and materials issues to achieve spin transport in silicon. Because external magnetic fields (and stray fields from magnetic contacts) can cause large Hall effects and magnetoresistance in semiconductors (which mimic spin-valve effects), the only conclusive evidence of spin transport in semiconductors is demonstration of spin precession and dephasing in a magnetic field non-collinear to the injected spin orientation, called the Hanle effect. === Storage media === Antiferromagnetic storage media have been studied as an alternative to ferromagnetism, especially since with antiferromagnetic material the bits can be stored as well as with ferromagnetic material. Instead of the usual definition 0 ↔ 'magnetisation upwards', 1 ↔ 'magnetisation downwards', the states can be, e.g., 0 ↔ 'vertically alternating spin configuration' and 1 ↔ 'horizontally-alternating spin configuration'.). The main advantages of antiferromagnetic material are: insensitivity to data-damaging perturbations by stray fields due to zero net external magnetization; no effect on near particles, implying that antiferromagnetic device elements wo
List of video games using NFC
This is a list of video games that use near field communication (NFC) technology. Currently, games have leveraged NFC in unlocking additional features through payment. This takes the form of a direct transaction over NFC or by purchasing a physical item, which signals to the platform that a certain set of features has been purchased (e.g. Skylanders). This list catalogues gaming NFC platforms by device. == Mobile == === Android === Gun Bros. Near Field Ninja NFC Cards Skylanders, with an NFC base. The Haunted House: Soul Fighters, with an NFC base. === iOS === ==== As item-triggered game enhancement ==== Skylanders, with an NFC base. ==== As payment ==== In-App Purchases Here, games that leverage Apple's In-App Purchase framework use information stored in the NFC Secure Element to process the purchase through Apple Pay. While an NFC radio is not used here, the NFC protocol is used nonetheless. == Console == === Nintendo Wii, Wii U, Switch, Switch 2, 3DS and 2DS === ==== As item-triggered game enhancement ==== Pokémon Rumble U NFC Figure Amiibo, built into Nintendo consoles since 2014. Works with Wii U, New Nintendo 3DS/3DS XL, New Nintendo 2DS XL, Nintendo Switch, Nintendo Switch 2 and older Nintendo 3DS/Nintendo 2DS systems via a peripheral device. Disney Infinity, with an NFC base. Works with Wii, Nintendo 3DS, Nintendo 2DS and Wii U. Lego Dimensions, with an NFC base. Works with Wii U. Skylanders, with an NFC base. Works with Wii, Nintendo 3DS, Nintendo 2DS and Wii U. The Nintendo Switch version of Skylanders: Imaginators uses the NFC built into the game controller, it is also has full backward compatibility with Nintendo Switch 2. Some functionalities are missing compared to the other versions. ==== As payment ==== The Wii U GamePad controller, Joy-Con R, Joy-Con 2 R, Nintendo Switch Pro Controller and Nintendo Switch 2 Pro Controller can read information from an NFC data source. === PlayStation === Disney Infinity, with an NFC base. Works with PlayStation 3, PlayStation Vita, PlayStation 4 and PlayStation 5. Lego Dimensions, with an NFC base. Works with PlayStation 3, PlayStation 4 and PlayStation 5. Skylanders, with an NFC base. Works with PlayStation 3, PlayStation 4 and PlayStation 5. === Xbox === While NFC bases are normally interoperable between all platforms, the Xbox 360, Xbox One and Xbox Series X require specific bases that are compatible only with the respective platform. Disney Infinity, with an NFC base. Lego Dimensions, with an NFC base. Skylanders, with an NFC base.
AMiner (database)
AMiner (formerly ArnetMiner) is a free online service used to index, search, and mine big scientific data. == Overview == AMiner (ArnetMiner) is designed to search and perform data mining operations against academic publications on the Internet, using social network analysis to identify connections between researchers, conferences, and publications. This allows it to provide services such as expert finding, geographic search, trend analysis, reviewer recommendation, association search, course search, academic performance evaluation, and topic modeling. AMiner was created as a research project in social influence analysis, social network ranking, and social network extraction. A number of peer-reviewed papers have been published arising from the development of the system. It has been in operation for more than three years, and has indexed 130,000,000 researchers and more than 265 million publications. The research was funded by the Chinese National High-tech R&D Program and the National Science Foundation of China. AMiner is commonly used in academia to identify relationships between and draw statistical correlations about research and researchers. It has attracted more than 10 million independent IP accesses from 220 countries and regions. The product has been used in Elsevier's SciVerse platform, and academic conferences such as SIGKDD, ICDM, PKDD, WSDM. == Operation == AMiner automatically extracts the researcher profile from the web. It collects and identifies the relevant pages, then uses a unified approach to extract data from the identified documents. It also extracts publications from online digital libraries using heuristic rules. It integrates the extracted researchers’ profiles and the extracted publications. It employs the researcher name as the identifier. A probabilistic framework has been proposed to deal with the name ambiguity problem in the integration. The integrated data is stored into a researcher network knowledge base (RNKB). The principal other product in the area are Google Scholar, Elsevier's Scirus, and the open source project CiteSeer. == History == It was initiated and created by professor Jie Tang from Tsinghua University, China. It was first launched in March 2006. The following provide a list of updates in the past years: March 2006, Version 0.1, Functions include researcher profiling, expert search, conference search, and publication search. The system was developed in Perl; August 2006, Version 1.0, The system was re-implemented in Java; July 2007, Version 2.0, New functions include researcher interest mining, association search, survey paper finding (unavailable now); April 2008, Version 3.0, New functions include query understanding, new GUI, and search log analysis; November 2008, Version 4.0, New functions include graph search, topic modeling, NSF/NSFC funding information extraction; April 2009, Version 5.0, New functions include Profile edition, open API service, Bole search, course search (unavailable now); December 2009, Version 6.0, New functions include academic performance evaluation, user feedback, conference analysis; May 2010, Version 7.0, New functions include name disambiguation, paper-reviewer recommendation, ArnetPage creation; March 2012, Version II, renamed as AMiner, rewrote all the codes and redesign the GUI. New functions include: geographic search, ArnetAPP platform. June 2014, Version II, renamed as AMiner, rewrote all the codes and redesign the GUI. New functions include: geographic search, ArnetAPP platform. December 2015, a completely new version got online. May 2017, professional version got online. April 2018, New functions include Trend Analysis, a deep learning based Name Disambiguation == Resources == AMiner published several datasets for academic research purpose, including Open Academic Graph, DBLP+citation (a data set augmenting citations into the DBLP data from Digital Bibliography & Library Project), Name Disambiguation, Social Tie Analysis. For more available datasets and source codes for research, please refer to.
Flat-field correction
Flat-field correction (FFC) is a digital imaging technique to mitigate pixel-to-pixel differences in the photodetector sensitivity and distortions in the optical path. It is a standard calibration procedure in everything from personal digital cameras to large telescopes. == Overview == Flat fielding refers to the process of compensating for different gains and dark currents in a detector. Once a detector has been appropriately flat-fielded, a uniform signal will create a uniform output (hence flat-field). This then means any further signal is due to the phenomenon being detected and not a systematic error. A flat-field image is acquired by imaging a uniformly-illuminated screen, thus producing an image of uniform color and brightness across the frame. For handheld cameras, the screen could be a piece of paper at arm's length, but a telescope will frequently image a clear patch of sky at twilight, when the illumination is uniform and there are few, if any, stars visible. Once the images are acquired, processing can begin. A flat-field consists of two numbers for each pixel, the pixel's gain and its dark current (or dark frame). The pixel's gain is how the amount of signal given by the detector varies as a function of the amount of light (or equivalent). The gain is almost always a linear variable, as such the gain is given simply as the ratio of the input and output signals. The dark-current is the amount of signal given out by the detector when there is no incident light (hence dark frame). In many detectors this can also be a function of time, for example in astronomical telescopes it is common to take a dark-frame of the same time as the planned light exposure. The gain and dark-frame for optical systems can also be established by using a series of neutral density filters to give input/output signal information and applying a least squares fit to obtain the values for the dark current and gain. C = ( R − D ) × m ( F − D ) = ( R − D ) × G {\displaystyle C={\frac {(R-D)\times m}{(F-D)}}=(R-D)\times G} where: C = corrected image R = raw image F = flat field image D = dark frame image m = image-averaged value of (F−D) G = Gain = m ( F − D ) {\displaystyle m \over (F-D)} In this equation, capital letters are 2D matrices, and lowercase letters are scalars. All matrix operations are performed element-by-element. In order for an astrophotographer to capture a light frame, they must place a light source over the imaging instrument's objective lens such that the light source emanates evenly through the users optics. The photographer must then adjust the exposure of their imaging device (charge-coupled device (CCD) or digital single-lens reflex camera (DSLR) ) so that when the histogram of the image is viewed, a peak reaching about 40–70% of the dynamic range (maximum range of pixel values) of the imaging device is seen. The photographer typically takes 15–20 light frames and performs median stacking. Once the desired light frames are acquired, the objective lens is covered so that no light is allowed in, then 15–20 dark frames are taken, each of equal exposure time as a light frame. These are called Dark-Flat frames. == In X-ray imaging == In X-ray imaging, the acquired projection images generally suffer from fixed-pattern noise, which is one of the limiting factors of image quality. It may stem from beam inhomogeneity, gain variations of the detector response due to inhomogeneities in the photon conversion yield, losses in charge transport, charge trapping, or variations in the performance of the readout. Also, the scintillator screen may accumulate dust and/or scratches on its surface, resulting in systematic patterns in every acquired X-ray projection image. In X-ray computed tomography (CT), fixed-pattern noise is known to significantly degrade the achievable spatial resolution and generally leads to ring or band artifacts in the reconstructed images. Fixed pattern noise can be easily removed using flat field correction. In conventional flat field correction, projection images without sample are acquired with and without the X-ray beam turned on, which are referred to as flat fields (F) and dark fields (D). Based on the acquired flat and dark fields, the measured projection images (P) with sample are then normalized to new images (N) according to: N = ( P − D ) ( F − D ) {\displaystyle N={\frac {(P-D)}{(F-D)}}} == Dynamic flat field correction == While conventional flat field correction is an elegant and easy procedure that largely reduces fixed-pattern noise, it heavily relies on the stationarity of the X-ray beam, scintillator response and CCD sensitivity. In practice, however, this assumption is only approximately met. Indeed, detector elements are characterized by intensity dependent, nonlinear response functions and the incident beam often shows time dependent non-uniformities, which render conventional FFC inadequate. In synchrotron X-ray tomography, many factors may cause flat field variations: instability of the bending magnets of the synchrotron, temperature variations due to the water cooling in mirrors and the monochromator, or vibrations of the scintillator and other beamline components. The latter is responsible for the biggest variations in the flat fields. To deal with such variations, a dynamic flat field correction procedure can be employed that estimates a flat field for each individual projection. Through principal component analysis of a set of flat fields, which are acquired prior and/or posterior to the actual scan, eigen flat fields can be computed. A linear combination of the most important eigen flat fields can then be used to individually normalize each X-ray projection: N j = P j − D ¯ F ¯ + ∑ k w j k u k − D ¯ {\displaystyle N_{j}={\frac {P_{j}-{\bar {D}}}{{\bar {F}}+\sum _{k}w_{jk}u_{k}-{\bar {D}}}}} where N j {\displaystyle N_{j}} = intensity normalized X-ray projection P j {\displaystyle P_{j}} = raw X-ray projection F ¯ {\displaystyle {\bar {F}}} = mean flat field image (average of flat fields) u k {\displaystyle u_{k}} = k-th eigen flat field w j k {\displaystyle w_{jk}} = weight of the eigen flat field u k {\displaystyle u_{k}} D ¯ {\displaystyle {\bar {D}}} = mean dark field (average of dark fields)
Temporal resolution
Temporal resolution (TR) refers to the discrete resolution of a measurement with respect to time. It is defined as the amount of time needed to revisit and acquire data for the same location. When applied to remote sensing, this amount of time is influenced by the sensor platform's orbital characteristics and the features of the sensor itself. The temporal resolution is low when the revisiting delay is high and vice versa. Temporal resolution is typically expressed in days. == Physics == Often there is a trade-off between the temporal resolution of a measurement and its spatial resolution, due to Heisenberg's uncertainty principle. In some contexts, such as particle physics, this trade-off can be attributed to the finite speed of light and the fact that it takes a certain period of time for the photons carrying information to reach the observer. In this time, the system might have undergone changes itself. Thus, the longer the light has to travel, the lower the temporal resolution. == Technology == === Computing === In another context, there is often a tradeoff between temporal resolution and computer storage. A transducer may be able to record data every millisecond, but available storage may not allow this, and in the case of 4D PET imaging the resolution may be limited to several minutes. === Electronic displays === In some applications, temporal resolution may instead be equated to the sampling period, or its inverse, the refresh rate, or update frequency in Hertz, of a TV, for example. The temporal resolution is distinct from temporal uncertainty. This would be analogous to conflating image resolution with optical resolution. One is discrete, the other, continuous. The temporal resolution is a resolution somewhat the 'time' dual to the 'space' resolution of an image. In a similar way, the sample rate is equivalent to the pixel pitch on a display screen, whereas the optical resolution of a display screen is equivalent to temporal uncertainty. Note that both this form of image space and time resolutions are orthogonal to measurement resolution, even though space and time are also orthogonal to each other. Both an image or an oscilloscope capture can have a signal-to-noise ratio, since both also have measurement resolution. === Oscilloscopy === An oscilloscope is the temporal equivalent of a microscope, and it is limited by temporal uncertainty the same way a microscope is limited by optical resolution. A digital sampling oscilloscope has also a limitation analogous to image resolution, which is the sample rate. A non-digital non-sampling oscilloscope is still limited by temporal uncertainty. The temporal uncertainty can be related to the maximum frequency of continuous signal the oscilloscope could respond to, called the bandwidth and given in Hertz. But for oscilloscopes, this figure is not the temporal resolution. To reduce confusion, oscilloscope manufacturers use 'Sa/s' instead of 'Hz' to specify the temporal resolution. Two cases for oscilloscopes exist: either the probe settling time is much shorter than the real time sampling rate, or it is much larger. The case where the settling time is the same as the sampling time is usually undesirable in an oscilloscope. It is more typical to prefer a larger ratio either way, or if not, to be somewhat longer than two sample periods. In the case where it is much longer, the most typical case, it dominates the temporal resolution. The shape of the response during the settling time also has as strong effect on the temporal resolution. For this reason probe leads usually offer an arrangement to 'compensate' the leads to alter the trade off between minimal settling time, and minimal overshoot. If it is much shorter, the oscilloscope may be prone to aliasing from radio frequency interference, but this can be removed by repeatedly sampling a repetitive signal and averaging the results together. If the relationship between the 'trigger' time and the sample clock can be controlled with greater accuracy than the sampling time, then it is possible to make a measurement of a repetitive waveform with much higher temporal resolution than the sample period by upsampling each record before averaging. In this case the temporal uncertainty may be limited by clock jitter.