Multiple satellite imaging is the process of using multiple satellites to gather more information than a single satellite so that a better estimate of the desired source is possible. Something that cannot be resolved with one telescope might be visible with two or more telescopes. == Background == Interferometry is the process of combining waves in such a way that they constructively interfere. When two or more independent sources detect a signal at the same given frequency those signals can be combined and the result is better than each one individually. An overview of Astronomical interferometers and a History of astronomical interferometry can be referenced from their respective pages. The NASA Origins Program was created in the 1990s to ultimately search for the origin of the universe. The theory that the Origins Program is based on is: since light travels at a constant speed until it is absorbed by something; there is still light that was part of the first light ever created traveling about the universe and ultimately some of that light is coming in the general direction of Earth. So a satellite system capable of collecting light from the beginning of the universe would be able to tell us more about where we came from. There is also the constant search for life in other worlds. A satellite system using the interferometric technologies mentioned above would be able to have a much higher resolution than any of the current deep space imaging systems. == Future == NASA is currently focused on the Vision for Space Exploration and has reduced current funding for scientific unmanned space exploration in favor of human exploration. These budget cuts have slowed the multiple satellite imaging development and relevant scientific missions as Project Prometheus and Terrestrial Planet Finder have ended as well but research continues.
Point-set registration
In computer vision, pattern recognition, and robotics, point-set registration, also known as point-cloud registration or scan matching, is the process of finding a spatial transformation (e.g., scaling, rotation and translation) that aligns two point clouds. The purpose of finding such a transformation includes merging multiple data sets into a globally consistent model (or coordinate frame), and mapping a new measurement to a known data set to identify features or to estimate its pose. Raw 3D point cloud data are typically obtained from Lidars and RGB-D cameras. 3D point clouds can also be generated from computer vision algorithms such as triangulation, bundle adjustment, and more recently, monocular image depth estimation using deep learning. For 2D point set registration used in image processing and feature-based image registration, a point set may be 2D pixel coordinates obtained by feature extraction from an image, for example corner detection. Point cloud registration has extensive applications in autonomous driving, motion estimation and 3D reconstruction, object detection and pose estimation, robotic manipulation, simultaneous localization and mapping (SLAM), panorama stitching, virtual and augmented reality, and medical imaging. As a special case, registration of two point sets that only differ by a 3D rotation (i.e., there is no scaling and translation), is called the Wahba Problem and also related to the orthogonal procrustes problem. == Formulation == The problem may be summarized as follows: Let { M , S } {\displaystyle \lbrace {\mathcal {M}},{\mathcal {S}}\rbrace } be two finite size point sets in a finite-dimensional real vector space R d {\displaystyle \mathbb {R} ^{d}} , which contain M {\displaystyle M} and N {\displaystyle N} points respectively (e.g., d = 3 {\displaystyle d=3} recovers the typical case of when M {\displaystyle {\mathcal {M}}} and S {\displaystyle {\mathcal {S}}} are 3D point sets). The problem is to find a transformation to be applied to the moving "model" point set M {\displaystyle {\mathcal {M}}} such that the difference (typically defined in the sense of point-wise Euclidean distance) between M {\displaystyle {\mathcal {M}}} and the static "scene" set S {\displaystyle {\mathcal {S}}} is minimized. In other words, a mapping from R d {\displaystyle \mathbb {R} ^{d}} to R d {\displaystyle \mathbb {R} ^{d}} is desired which yields the best alignment between the transformed "model" set and the "scene" set. The mapping may consist of a rigid or non-rigid transformation. The transformation model may be written as T {\displaystyle T} , using which the transformed, registered model point set is: The output of a point set registration algorithm is therefore the optimal transformation T ⋆ {\displaystyle T^{\star }} such that M {\displaystyle {\mathcal {M}}} is best aligned to S {\displaystyle {\mathcal {S}}} , according to some defined notion of distance function dist ( ⋅ , ⋅ ) {\displaystyle \operatorname {dist} (\cdot ,\cdot )} : where T {\displaystyle {\mathcal {T}}} is used to denote the set of all possible transformations that the optimization tries to search for. The most popular choice of the distance function is to take the square of the Euclidean distance for every pair of points: where ‖ ⋅ ‖ 2 {\displaystyle \|\cdot \|_{2}} denotes the vector 2-norm, s m {\displaystyle s_{m}} is the corresponding point in set S {\displaystyle {\mathcal {S}}} that attains the shortest distance to a given point m {\displaystyle m} in set M {\displaystyle {\mathcal {M}}} after transformation. Minimizing such a function in rigid registration is equivalent to solving a least squares problem. == Types of algorithms == When the correspondences (i.e., s m ↔ m {\displaystyle s_{m}\leftrightarrow m} ) are given before the optimization, for example, using feature matching techniques, then the optimization only needs to estimate the transformation. This type of registration is called correspondence-based registration. On the other hand, if the correspondences are unknown, then the optimization is required to jointly find out the correspondences and transformation together. This type of registration is called simultaneous pose and correspondence registration. === Rigid registration === Given two point sets, rigid registration yields a rigid transformation which maps one point set to the other. A rigid transformation is defined as a transformation that does not change the distance between any two points. Typically such a transformation consists of translation and rotation. In rare cases, the point set may also be mirrored. In robotics and computer vision, rigid registration has the most applications. === Non-rigid registration === Given two point sets, non-rigid registration yields a non-rigid transformation which maps one point set to the other. Non-rigid transformations include affine transformations such as scaling and shear mapping. However, in the context of point set registration, non-rigid registration typically involves nonlinear transformation. If the eigenmodes of variation of the point set are known, the nonlinear transformation may be parametrized by the eigenvalues. A nonlinear transformation may also be parametrized as a thin plate spline. === Other types === Some approaches to point set registration use algorithms that solve the more general graph matching problem. However, the computational complexity of such methods tend to be high and they are limited to rigid registrations. In this article, we will only consider algorithms for rigid registration, where the transformation is assumed to contain 3D rotations and translations (possibly also including a uniform scaling). The PCL (Point Cloud Library) is an open-source framework for n-dimensional point cloud and 3D geometry processing. It includes several point registration algorithms. == Correspondence-based registration == Correspondence-based methods assume the putative correspondences m ↔ s m {\displaystyle m\leftrightarrow s_{m}} are given for every point m ∈ M {\displaystyle m\in {\mathcal {M}}} . Therefore, we arrive at a setting where both point sets M {\displaystyle {\mathcal {M}}} and S {\displaystyle {\mathcal {S}}} have N {\displaystyle N} points and the correspondences m i ↔ s i , i = 1 , … , N {\displaystyle m_{i}\leftrightarrow s_{i},i=1,\dots ,N} are given. === Outlier-free registration === In the simplest case, one can assume that all the correspondences are correct, meaning that the points m i , s i ∈ R 3 {\displaystyle m_{i},s_{i}\in \mathbb {R} ^{3}} are generated as follows:where l > 0 {\displaystyle l>0} is a uniform scaling factor (in many cases l = 1 {\displaystyle l=1} is assumed), R ∈ SO ( 3 ) {\displaystyle R\in {\text{SO}}(3)} is a proper 3D rotation matrix ( SO ( d ) {\displaystyle {\text{SO}}(d)} is the special orthogonal group of degree d {\displaystyle d} ), t ∈ R 3 {\displaystyle t\in \mathbb {R} ^{3}} is a 3D translation vector and ϵ i ∈ R 3 {\displaystyle \epsilon _{i}\in \mathbb {R} ^{3}} models the unknown additive noise (e.g., Gaussian noise). Specifically, if the noise ϵ i {\displaystyle \epsilon _{i}} is assumed to follow a zero-mean isotropic Gaussian distribution with standard deviation σ i {\displaystyle \sigma _{i}} , i.e., ϵ i ∼ N ( 0 , σ i 2 I 3 ) {\displaystyle \epsilon _{i}\sim {\mathcal {N}}(0,\sigma _{i}^{2}I_{3})} , then the following optimization can be shown to yield the maximum likelihood estimate for the unknown scale, rotation and translation:Note that when the scaling factor is 1 and the translation vector is zero, then the optimization recovers the formulation of the Wahba problem. Despite the non-convexity of the optimization (cb.2) due to non-convexity of the set SO ( 3 ) {\displaystyle {\text{SO}}(3)} , seminal work by Berthold K.P. Horn showed that (cb.2) actually admits a closed-form solution, by decoupling the estimation of scale, rotation and translation. Similar results were discovered by Arun et al. In addition, in order to find a unique transformation ( l , R , t ) {\displaystyle (l,R,t)} , at least N = 3 {\displaystyle N=3} non-collinear points in each point set are required. More recently, Briales and Gonzalez-Jimenez have developed a semidefinite relaxation using Lagrangian duality, for the case where the model set M {\displaystyle {\mathcal {M}}} contains different 3D primitives such as points, lines and planes (which is the case when the model M {\displaystyle {\mathcal {M}}} is a 3D mesh). Interestingly, the semidefinite relaxation is empirically tight, i.e., a certifiably globally optimal solution can be extracted from the solution of the semidefinite relaxation. === Robust registration === The least squares formulation (cb.2) is known to perform arbitrarily badly in the presence of outliers. An outlier correspondence is a pair of measurements s i ↔ m i {\displaystyle s_{i}\leftrightarrow m_{i}} that departs from the generative model (cb.1). In this case, one can consider a differen
WebCL
WebCL (Web Computing Language) is a JavaScript binding to OpenCL for heterogeneous parallel computing within any compatible web browser without the use of plug-ins, first announced in March 2011. It is developed on similar grounds as OpenCL and is considered as a browser version of the latter. Primarily, WebCL allows web applications to actualize speed with multi-core CPUs and GPUs. With the growing popularity of applications that need parallel processing like image editing, augmented reality applications and sophisticated gaming, it has become more important to improve the computational speed. With these background reasons, a non-profit Khronos Group designed and developed WebCL, which is a Javascript binding to OpenCL with a portable kernel programming, enabling parallel computing on web browsers, across a wide range of devices. In short, WebCL consists of two parts, one being Kernel programming, which runs on the processors (devices) and the other being JavaScript, which binds the web application to OpenCL. The completed and ratified specification for WebCL 1.0 was released on March 19, 2014. == Implementation == Currently, no browsers natively support WebCL. However, non-native add-ons are used to implement WebCL. For example, Nokia developed a WebCL extension. Mozilla does not plan to implement WebCL in favor of WebGL Compute Shaders, which were in turn scrapped in favor of WebGPU. Mozilla (Firefox) - hg.mozilla.org/projects/webcl/ === WebCL working draft === Samsung (WebKit) - github.com/SRA-SiliconValley/webkit-webcl (unavailable) Nokia (Firefox) - github.com/toaarnio/webcl-firefox (down since Nov 2014, Last Version for FF 34) Intel (Crosswalk) - www.crosswalk-project.org === Example C code === The basic unit of a parallel program is kernel. A kernel is any parallelizable task used to perform a specific job. More often functions can be realized as kernels. A program can be composed of one or more kernels. In order to realize a kernel, it is essential that a task is parallelizable. Data dependencies and order of execution play a vital role in producing efficient parallelized algorithms. A simple example can be thought of the case of loop unrolling performed by C compilers, where a statement like:can be unrolled into:Above statements can be parallelized and can be made to run simultaneously. A kernel follows a similar approach where only the snapshot of the ith iteration is captured inside kernel. Rewriting the above code using a kernel:Running a WebCL application involves the following steps: Allow access to devices and provide context Hand over the kernel to a device Cause the device to execute the kernel Retrieve results from the device Use the data inside JavaScript Further details about the same can be found at == Exceptions List == WebCL, being a JavaScript based implementation, doesn't return an error code when errors occur. Instead, it throws an exception such as OUT_OF_RESOURCES, OUT_OF_HOST_MEMORY, or the WebCL-specific WEBCL_IMPLEMENTATION_FAILURE. The exception object describes the machine-readable name and human-readable message describing the error. The syntax is as follows: From the code above, it can be observed that the message field can be a NULL value. Other exceptions include: INVALID_OPERATION – if the blocking form of this function is called from a WebCLCallback INVALID_VALUE – if eventWaitList is empty INVALID_CONTEXT – if events specified in eventWaitList do not belong to the same context INVALID_DEVICE_TYPE – if deviceType is given, but is not one of the valid enumerated values DEVICE_NOT_FOUND – if there is no WebCLDevice available that matches the given deviceType More information on exceptions can be found in the specs document. There is another exception that is raised upon trying to call an object that is ‘released’. On using the release method, the object doesn't get deleted permanently but it frees the resources associated with that object. In order to avoid this exception, releaseAll method can be used, which not only frees the resources but also deletes all the associated objects created. == Security == WebCL, being an open-ended software developed for web applications, has lots of scope for vulnerabilities in the design and development fields too. This forced the developers working on WebCL to give security the utmost importance. Few concerns that were addressed are: Out-of-bounds Memory Access: This occurs by accessing the memory locations, outside the allocated space. An attacker can rewrite or erase all the important data stored in those memory locations. Whenever there arises such a case, an error must be generated at the compile time, and zero must be returned at run-time, not letting the program override the memory. A project WebCL Validator, was initiated by the Khronos Group (developers) on handling this vulnerability. Memory Initialization: This is done to prevent the applications to access the memory locations of previous applications. WebCL ensures that this doesn't happen by initializing all the buffers, variables used to zero before it runs the current application. OpenCL 1.2 has an extension ‘cl_khr_initialize_memory’, which enables this. Denial of Service: The most common attack on web applications cannot be eliminated by WebCL or the browser. OpenCL can be provided with watchdog timers and pre-emptive multitasking, which can be used by WebCL in order to detect and terminate the contexts that are taking too long or consume lot of resources. There is an extension of OpenCL 1.2 ‘cl_khr_terminate_context’ like for the previous one, which enables to terminate the process that might cause a denial of service attack. == Related browser bugs == Bug 664147 - [WebCL] add openCL in gecko, Mozilla Bug 115457: [Meta] WebCL support for WebKit, WebKit Bugzilla
Coupling (electronics)
In electronics, electric power and telecommunication, coupling is the transfer of electrical energy from one circuit to another, or between parts of a circuit. Coupling can be deliberate as part of the function of the circuit, or it may be undesirable, for instance due to coupling to stray fields. For example, energy is transferred from a power source to an electrical load by means of conductive coupling, which may be either resistive or direct coupling. An AC potential may be transferred from one circuit segment to another having a DC potential by use of a capacitor. Electrical energy may be transferred from one circuit segment to another segment with different impedance by use of a transformer; this is known as impedance matching. These are examples of electrostatic and electrodynamic inductive coupling. == Types == Electrical conduction: Direct coupling, also called conductive coupling and galvanic coupling Resistive conduction Atmospheric plasma channel coupling Electromagnetic induction: Electrodynamic induction — commonly called inductive coupling, also magnetic coupling Capacitive coupling Evanescent wave coupling Electromagnetic radiation: Radio waves — Wireless telecommunications. Electromagnetic interference (EMI) — Sometimes called radio frequency interference (RFI), is unwanted coupling. Electromagnetic compatibility (EMC) requires techniques to avoid such unwanted coupling, such as electromagnetic shielding. Microwave power transmission Other kinds of energy coupling: Acoustic coupler
Mixvoip
Mixvoip S.A. is a Luxembourg-based telecommunications service provider founded in 2008. The company offers IP telephony, high-speed Internet connectivity, and IT solutions to businesses and individuals. == Company history == In November 2017, Mixvoip expanded its operations to Belgium and Germany. At the beginning of 2019, the company acquired the telecommunications provider Voipgate. In December 2019, Mixvoip was named Telecom Company of the Year at the Luxembourg ICT Awards 2019 organized by Farvest and IT One. A 2024 article in Duke described the company's transition during the 2010s from traditional telephony services to cloud-based communication platforms. In the end of 2024, the ILR published the statistics about electronic communications in Luxembourg, including Mixvoip in the fix telephony section. In July 2025, Mixvoip acquired Crossing Telecom. In 2026, Mixvoip acquired Nomado's portfolio.
Conversational user interface
A conversational user interface (CUI) is a user interface for computers that emulates a conversation with a human. Historically, computers have relied on text-based user interfaces and graphical user interfaces (GUIs) (such as the user pressing a "back" button) to translate the user's desired action into commands the computer understands. While an effective mechanism of completing computing actions, there is a learning curve for the user associated with GUI. Instead, CUIs provide opportunity for the user to communicate with the computer in their natural language rather than in a syntax specific commands.
Computer aided transceiver
Computer aided transceiver (CAT) is a non-generic serial protocol used by radio amateurs for (remotely) controlling a transceiver radio receiver equipment using a computer. Conventional transmitters are manually controlled and used to transmit voice using buttons, dials, etc. However, advances in electronics have come to market devices that can be controlled by a computer and allow digital modes such as packet radio and also the use of satellite tracking, because it can continuously change the device's frequency according to the Doppler effect. This is done by connecting a Radio receiver and a PC using a CAT interface and a CAT Program Additionally, CAT interfaces can also be used to position tracking antennas, in controllers. As a satellite moves overhead. A CAT interface is a piece of hardware that connects the PC and radio that provides a connection to allows the radio and the PC to communicate with each other. The CAT interface provides the signals to and fro via correct voltage levels and in the case of a Universal Serial Bus (USB) CAT interface it requires a "protocol" for communication but communication itself is down to the radio and the software on the PC. Software that may be called a CAT program allows a radio to be controlled through the PC. Changes made on the radio through user interactions on the CAT Program are (generally) shown on the PC's screen. The functionality of CAT equipment (software & interface) depends on the radio and what features the software writers included in the CAT software. Modern radio systems do have more CAT functionality If you run a logging program that supports CAT, then that software may take advantage of the CAT system by retrieving information from the radio to help fill in log details, such as the frequency that the contact was made. CAT is also useful on many radios where there are many sub-menus in the radios menu system, and many of the sub-menu items can be easily changed via the PC. On many HF radios, the CAT system is also used to program the memories on the radio, but you would need to use appropriate programming software. A CAT interface does not receive or transmit any DATA mode, that is the purpose of a DATA interface. Although, both may be used at the same time with the correct CAT Equipment. DATA modes, and getting audio to and from the PC is the function of a DATA interface. A completely different thing but it is easier and more useful when CAT and DATA are used at the same time. Wouldn't it be nice to have an interface that could operate Frequency-shift keying (FSK), Audio FSK (AFSK), (real) Morse Code (CW), with a CAT interface and its own sound card..... (eg. The DigiMaster Pro3).