AI Video Tools

Explore the best AI Video Tools — independent reviews, comparisons, pricing and step-by-step how-to guides, curated by Aizhi.

Sentence embedding

In natural language processing, a sentence embedding is a representation of a sentence as a vector of numbers which encodes meaningful semantic information. State of the art embeddings are based on the learned hidden layer representation of dedicated sentence transformer models. BERT pioneered an approach involving the use of a dedicated [CLS] token prepended to the beginning of each sentence inputted into the model; the final hidden state vector of this token encodes information about the sentence and can be fine-tuned for use in sentence classification tasks. In practice however, BERT's sentence embedding with the [CLS] token achieves poor performance, often worse than simply averaging non-contextual word embeddings. SBERT later achieved superior sentence embedding performance by fine tuning BERT's [CLS] token embeddings through the usage of a siamese neural network architecture on the SNLI dataset. Other approaches are loosely based on the idea of distributional semantics applied to sentences. Skip-Thought trains an encoder-decoder structure for the task of neighboring sentences predictions; this has been shown to achieve worse performance than approaches such as InferSent or SBERT. An alternative direction is to aggregate word embeddings, such as those returned by Word2vec, into sentence embeddings. The most straightforward approach is to simply compute the average of word vectors, known as continuous bag-of-words (CBOW). However, more elaborate solutions based on word vector quantization have also been proposed. One such approach is the vector of locally aggregated word embeddings (VLAWE), which demonstrated performance improvements in downstream text classification tasks. == Applications == In recent years, sentence embedding has seen a growing level of interest due to its applications in natural language queryable knowledge bases through the usage of vector indexing for semantic search. LangChain for instance utilizes sentence transformers for purposes of indexing documents. In particular, an indexing is generated by generating embeddings for chunks of documents and storing (document chunk, embedding) tuples. Then given a query in natural language, the embedding for the query can be generated. A top k similarity search algorithm is then used between the query embedding and the document chunk embeddings to retrieve the most relevant document chunks as context information for question answering tasks. This approach is also known formally as retrieval-augmented generation. Though not as predominant as BERTScore, sentence embeddings are commonly used for sentence similarity evaluation which sees common use for the task of optimizing a Large language model's generation parameters is often performed via comparing candidate sentences against reference sentences. By using the cosine-similarity of the sentence embeddings of candidate and reference sentences as the evaluation function, a grid-search algorithm can be utilized to automate hyperparameter optimization. == Evaluation == A way of testing sentence encodings is to apply them on Sentences Involving Compositional Knowledge (SICK) corpus for both entailment (SICK-E) and relatedness (SICK-R). In the best results are obtained using a BiLSTM network trained on the Stanford Natural Language Inference (SNLI) Corpus. The Pearson correlation coefficient for SICK-R is 0.885 and the result for SICK-E is 86.3. A slight improvement over previous scores is presented in: SICK-R: 0.888 and SICK-E: 87.8 using a concatenation of bidirectional Gated recurrent unit.
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Cloud Data Management Interface

ISO/IEC 17826 Information technology — Cloud Data Management Interface (CDMI) Version 2.0.0 is an international standard that specifies a protocol for self-provisioning, administering and managing access to data stored in cloud storage, object storage, storage area network and network attached storage systems. The CDMI standard is developed and maintained by the Storage Networking Industry Association, who makes a publicly accessible version of the specification available. CDMI defines new resource representations to enable standardized management of any URI-accessible data, and defines RESTful HTTP operations using these representations to discover the capabilities of the storage system, discover stored data, access and update management metadata, specify data storage protocols (such as iSCSI and NFS) through which the stored data is accessed, and provide cross-system and cross-cloud import and export in order to enable data portability. Management functions enabled by CDMI include managing data ownership, identity mapping, access controls, user-specified metadata, and to declaratively specify desired data protection, data retention, constraints on geographic placement, desired quality of service, data versioning and security requirements. CDMI also defines utility services to facilitate data management, such the ability to query data matching specific criteria, and includes extensions to perform bulk updates using CDMI Jobs. == Capabilities == Compliant implementations must provide access to a set of configuration parameters known as capabilities. These are either boolean values that represent whether or not a system supports things such as queues, export via other protocols, path-based storage and so on, or numeric values expressing system limits, such as how much metadata may be placed on an object. As a minimal compliant implementation can be quite small, with few features, clients need to check the cloud storage system for a capability before attempting to use the functionality it represents. Resource allocation assignments limited to the data management interface protocols must possess access bypass capabilities which extend beyond the layered framework. This integral function is vital to the prevention of transport layer session hijacking by unauthorized entities which may circumvent standard interfacing security parameters. == Containers == A CDMI client may access objects, including containers, by either name or object id (OID), assuming the CDMI server supports both methods. When storing objects by name, it is natural to use nested named containers; the resulting structure corresponds exactly to a traditional filesystem directory structure. == Objects == Objects are similar to files in a traditional file system, but are enhanced with an increased amount and capacity for metadata. As with containers, they may be accessed by either name or OID. When accessed by name, clients use URLs that contain the full pathname of objects to create, read, update and delete them. When accessed by OID, the URL specifies an OID string in the cdmi-objectid container; this container presents a flat name space conformant with standard object storage system semantics. Subject to system limits, objects may be of any size or type and have arbitrary user-supplied metadata attached to them. Systems that support query allow arbitrary queries to be run against the metadata. == Domains, Users and Groups == CDMI supports the concept of a domain, similar in concept to a domain in the Windows Active Directory model. Users and groups created in a domain share a common administrative database and are known to each other on a "first name" basis, i.e. without reference to any other domain or system. Domains also function as containers for usage and billing summary data. == Access Control == CDMI exactly follows the ACL and ACE model used for file authorization operations by NFSv4. This makes it also compatible with Microsoft Windows systems. == Metadata == CDMI draws much of its metadata model from the XAM specification. Objects and containers have "storage system metadata", "data system metadata" and arbitrary user specified metadata, in addition to the metadata maintained by an ordinary filesystem (atime etc.). == Queries == CDMI specifies a way for systems to support arbitrary queries against CDMI containers, with a rich set of comparison operators, including support for regular expressions. == Queues == CDMI supports the concept of persistent FIFO (first-in, first-out) queues. These are useful for job scheduling, order processing and other tasks in which lists of things must be processed in order. == Compliance == Both retention intervals and retention holds are supported by CDMI. A retention interval consists of a start time and a retention period. During this time interval, objects are preserved as immutable and may not be deleted. A retention hold is usually placed on an object because of judicial action and has the same effect: objects may not be changed nor deleted until all holds placed on them are removed. == Billing == Summary information suitable for billing clients for on-demand services can be obtained by authorized users from systems that support it. == Serialization == Serialization of objects and containers allows export of all data and metadata on a system and importation of that data into another cloud system. == Foreign protocols == CDMI supports export of containers as NFS or CIFS shares. Clients that mount these shares see the container hierarchy as an ordinary filesystem directory hierarchy, and the objects in the containers as normal files. Metadata outside of ordinary filesystem metadata may or may not be exposed. Provisioning of iSCSI LUNs is also supported. == Client SDKs == CDMI Reference Implementation Droplet libcdmi-java libcdmi-python .NET SDK
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Blinding (cryptography)

In cryptography, blinding first became known in the context of blind signatures, where the message author blinds the message with a random blinding factor, the signer then signs it and the message author "unblinds" it; signer and message author are different parties. Since the late 1990s, blinding mostly refers to countermeasures against side-channel attacks on encryption devices, where the random blinding and the "unblinding" happen on the encryption devices. The techniques used for blinding signatures were adapted to prevent attackers from knowing the input to the modular exponentiation function for Diffie-Hellman or RSA. Blinding must be applied with care, for example Rabin–Williams signatures. If blinding is applied to the formatted message but the random value does not honor Jacobi requirements on p and q, then it could lead to private key recovery. A demonstration of the recovery can be seen in CVE-2015-2141 discovered by Evgeny Sidorov. Side-channel attacks allow an adversary to recover information about the input to a cryptographic operation within an asymmetric encryption scheme, by measuring something other than the algorithm's result, e.g., power consumption, computation time, or radio-frequency emanations by a device. Typically these attacks depend on the attacker knowing the characteristics of the algorithm, as well as (some) inputs. In this setting, blinding serves to alter the algorithm's input into some unpredictable state. Depending on the characteristics of the blinding function, this can prevent some or all leakage of useful information. Note that security depends also on the resistance of the blinding functions themselves to side-channel attacks. == Examples == In RSA blinding involves computing the blinding operation E(x) = (xr)e mod N, where r is a random integer between 1 and N and relatively prime to N (i.e. gcd(r, N) = 1), x is the plaintext, e is the public RSA exponent and N is the RSA modulus. As usual, the decryption function f(z) = zd mod N is applied thus giving f(E(x)) = (xr)ed mod N = xr mod N. Finally it is unblinded using the function D(z) = zr−1 mod N. Multiplying xr mod N by r−1 mod N yields x, as desired. When decrypting in this manner, an adversary who is able to measure time taken by this operation would not be able to make use of this information (by applying timing attacks RSA is known to be vulnerable to) as they does not know the constant r and hence has no knowledge of the real input fed to the RSA primitives. Blinding in GPG 1.x
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Electronic lab notebook

An electronic lab notebook or electronic laboratory notebook (ELN) is a computer program designed to replace paper laboratory notebooks. Lab notebooks in general are used by scientists, engineers, and technicians to document research, experiments, and procedures performed in a laboratory. A lab notebook is often maintained to be a legal document and may be used in a court of law as evidence. Similar to an inventor's notebook, the lab notebook is also often referred to in patent prosecution and intellectual property litigation. Electronic lab notebooks offer many benefits to the user as well as organizations; they are easier to search upon, simplify data copying and backups, and support collaboration amongst many users. ELNs can have fine-grained access controls, and can be more secure than their paper counterparts. They also allow the direct incorporation of data from instruments, replacing the practice of printing out data to be stapled into a paper notebook. == Types == ELNs can be divided into two categories: "Specific ELNs" contain features designed to work with specific applications, scientific instrumentation or data types. "Cross-disciplinary ELNs" or "Generic ELNs" are designed to support access to all data and information that needs to be recorded in a lab notebook. Lab Platforms that combine an ELN, LIMS, and scientific data management together, all-in-one configurable software environment. Solutions range from specialized programs designed from the ground up for use as an ELN, to modifications or direct use of more general programs. Examples of using more general software as an ELN include using OpenWetWare, a MediaWiki install (running the same software that Wikipedia uses), WordPress, or the use of general note taking software such as OneNote as an ELN. ELN's come in many different forms. They can be standalone programs, use a client-server model, or be entirely web-based. Some use a lab-notebook approach, others resemble a blog. ELNs are embracing artificial intelligence and LLM technology to provide scientific AI chat assistants. A good many variations on the "ELN" acronym have appeared. Differences between systems with different names are often subtle, with considerable functional overlap between them. Examples include "ERN" (Electronic Research Notebook), "ERMS" (Electronic Resource (or Research or Records) Management System (or Software) and SDMS (Scientific Data (or Document) Management System (or Software). Ultimately, these types of systems all strive to do the same thing: Capture, record, centralize and protect scientific data in a way that is highly searchable, historically accurate, and legally stringent, and which also promotes secure collaboration, greater efficiency, reduced mistakes and lowered total research costs. == Objectives == A good electronic laboratory notebook should offer a secure environment to protect the integrity of both data and process, whilst also affording the flexibility to adopt new processes or changes to existing processes without recourse to further software development. The package architecture should be a modular design, so as to offer the benefit of minimizing validation costs of any subsequent changes that you may wish to make in the future as your needs change. A good electronic laboratory notebook should be an "out of the box" solution that, as standard, has fully configurable forms to comply with the requirements of regulated analytical groups through to a sophisticated ELN for inclusion of structures, spectra, chromatograms, pictures, text, etc. where a preconfigured form is less appropriate. All data within the system may be stored in a database (e.g. MySQL, MS-SQL, Oracle) and be fully searchable. The system should enable data to be collected, stored and retrieved through any combination of forms or ELN that best meets the requirements of the user. The application should enable secure forms to be generated that accept laboratory data input via PCs and/or laptops / palmtops, and should be directly linked to electronic devices such as laboratory balances, pH meters, etc. Networked or wireless communications should be accommodated for by the package which will allow data to be interrogated, tabulated, checked, approved, stored and archived to comply with the latest regulatory guidance and legislation. A system should also include a scheduling option for routine procedures such as equipment qualification and study related timelines. It should include configurable qualification requirements to automatically verify that instruments have been cleaned and calibrated within a specified time period, that reagents have been quality-checked and have not expired, and that workers are trained and authorized to use the equipment and perform the procedures. == Regulatory and legal aspects == The laboratory accreditation criteria found in the ISO 17025 standard needs to be considered for the protection and computer backup of electronic records. These criteria can be found specifically in clause 4.13.1.4 of the standard. Electronic lab notebooks used for development or research in regulated industries, such as medical devices or pharmaceuticals, are expected to comply with FDA regulations related to software validation. The purpose of the regulations is to ensure the integrity of the entries in terms of time, authorship, and content. Unlike ELNs for patent protection, FDA is not concerned with patent interference proceedings, but is concerned with avoidance of falsification. Typical provisions related to software validation are included in the medical device regulations at 21 CFR 820 (et seq.) and Title 21 CFR Part 11. Essentially, the requirements are that the software has been designed and implemented to be suitable for its intended purposes. Evidence to show that this is the case is often provided by a Software Requirements Specification (SRS) setting forth the intended uses and the needs that the ELN will meet; one or more testing protocols that, when followed, demonstrate that the ELN meets the requirements of the specification and that the requirements are satisfied under worst-case conditions. Security, audit trails, prevention of unauthorized changes without substantial collusion of otherwise independent personnel (i.e., those having no interest in the content of the ELN such as independent quality unit personnel) and similar tests are fundamental. Finally, one or more reports demonstrating the results of the testing in accordance with the predefined protocols are required prior to release of the ELN software for use. If the reports show that the software failed to satisfy any of the SRS requirements, then corrective and preventive action ("CAPA") must be undertaken and documented. Such CAPA may extend to minor software revisions, or changes in architecture or major revisions. CAPA activities need to be documented as well. Aside from the requirements to follow such steps for regulated industry, such an approach is generally a good practice in terms of development and release of any software to assure its quality and fitness for use. There are standards related to software development and testing that can be applied (see ref.).
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Cooliris (plugin)

Cooliris (for Desktop), formerly known as PicLens, was a web browser extension developed by Cooliris, Inc, and later acquired by Yahoo. The plugin provides an interactive 3D-like experience for viewing digital images and videos from the web and from desktop applications. The software places a small icon atop image thumbnails that appear on a webpage. Clicking on the icon loads the Cooliris 3D Wall, a browsing environment that gives the user the effect of flying through a three-dimensional space. Released to the public in January 2008, The New York Times described Cooliris as the "new immersive approach to Web navigation". Cooliris went out to win the 2008 Crunchies Award for Best Design. The plugin has received over 50 million downloads. As of May 2014 browser plugins are unavailable from the official website. There are only links to tablet apps - for iOS and Android.
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Merit Network

Merit Network, Inc., is a nonprofit member-governed organization providing high-performance computer networking and related services to educational, government, health care, and nonprofit organizations, primarily in Michigan. Created in 1966, Merit operates the longest running regional computer network in the United States. == Organization == Created in 1966 as the Michigan Educational Research Information Triad by Michigan State University (MSU), the University of Michigan (U-M), and Wayne State University (WSU), Merit was created to investigate resource sharing by connecting the mainframe computers at these three Michigan public research universities. Merit's initial three node packet-switched computer network was operational in October 1972 using custom hardware based on DEC PDP-11 minicomputers and software developed by the Merit staff and the staffs at the three universities. Over the next dozen years the initial network grew as new services such as dial-in terminal support, remote job submission, remote printing, and file transfer were added; as gateways to the national and international Tymnet, Telenet, and Datapac networks were established, as support for the X.25 and TCP/IP protocols was added; as additional computers such as WSU's MVS system and the UM's electrical engineering's VAX running UNIX were attached; and as new universities became Merit members. Merit's involvement in national networking activities started in the mid-1980s with connections to the national supercomputing centers and work on the 56 kbit/s National Science Foundation Network (NSFNET), the forerunner of today's Internet. From 1987 until April 1995, Merit re-engineered and managed the NSFNET backbone service. MichNet, Merit's regional network in Michigan was attached to NSFNET and in the early 1990s Merit began extending "the Internet" throughout Michigan, offering both direct connect and dial-in services, and upgrading the statewide network from 56 kbit/s to 1.5 Mbit/s, and on to 45, 155, 622 Mbit/s, and eventually 1 and 10 Gbit/s. In 2003 Merit began its transition to a facilities based network, using fiber optic facilities that it shares with its members, that it purchases or leases under long-term agreements, or that it builds. In addition to network connectivity services, Merit offers a number of related services within Michigan and beyond, including: Internet2 connectivity, VPN, Network monitoring, Voice over IP (VOIP), Cloud storage, E-mail, Domain Name, Network Time, VMware and Zimbra software licensing, Colocation, and professional development seminars, workshops, classes, conferences, and meetings. == History == === Creating the network: 1966 to 1973 === The Michigan Educational Research Information Triad (MERIT) was formed in the fall of 1966 by Michigan State University (MSU), University of Michigan (U-M), and Wayne State University (WSU). More often known as the Merit Computer Network or simply Merit, it was created to design and implement a computer network connecting the mainframe computers at the universities. In the fall of 1969, after funding for the initial development of the network had been secured, Bertram Herzog was named director for MERIT. Eric Aupperle was hired as senior engineer, and was charged with finding hardware to make the network operational. The National Science Foundation (NSF) and the State of Michigan provided the initial funding for the network. In June 1970, the Applied Dynamics Division of Reliance Electric in Saline, Michigan was contracted to build three Communication Computers or CCs. Each would consist of a Digital Equipment Corporation (DEC) PDP-11 computer, dataphone interfaces, and interfaces that would attach them directly to the mainframe computers. The cost was to be slightly less than the $300,000 ($2,487,100, adjusted for inflation) originally budgeted. Merit staff wrote the software that ran on the CCs, while staff at each of the universities wrote the mainframe software to interface to the CCs. The first completed connection linked the IBM S/360-67 mainframe computers running the Michigan Terminal System at WSU and U-M, and was publicly demonstrated on December 14, 1971. The MSU node was completed in October 1972, adding a CDC 6500 mainframe running Scope/Hustler. The network was officially dedicated on May 15, 1973. === Expanding the network: 1974 to 1985 === In 1974, Herzog returned to teaching in the University of Michigan's Industrial Engineering Department, and Aupperle was appointed as director. Use of the all uppercase name "MERIT" was abandoned in favor of the mixed case "Merit". The first network connections were host to host interactive connections which allowed person to remote computer or local computer to remote computer interactions. To this, terminal to host connections, batch connections (remote job submission, remote printing, batch file transfer), and interactive file copy were added. And, in addition to connecting to host computers over custom hardware interfaces, the ability to connect to hosts or other networks over groups of asynchronous ports and via X.25 were added. Merit interconnected with Telenet (later SprintNet) in 1976 to give Merit users dial-in access from locations around the United States. Dial-in access within the U.S. and internationally was further expanded via Merit's interconnections to Tymnet, ADP's Autonet, and later still the IBM Global Network as well as Merit's own expanding network of dial-in sites in Michigan, New York City, and Washington, D.C. In 1978, Western Michigan University (WMU) became the fourth member of Merit (prompting a name change, as the acronym Merit no longer made sense as the group was no longer a triad). To expand the network, the Merit staff developed new hardware interfaces for the Digital PDP-11 based on printed circuit technology. The new system became known as the Primary Communications Processor (PCP), with the earliest PCPs connecting a PDP-10 located at WMU and a DEC VAX running UNIX at U-M's Electrical Engineering department. A second hardware technology initiative in 1983 produced the smaller Secondary Communication Processors (SCP) based on DEC LSI-11 processors. The first SCP was installed at the Michigan Union in Ann Arbor, creating UMnet, which extended Merit's network connectivity deeply into the U-M campus. In 1983 Merit's PCP and SCP software was enhanced to support TCP/IP and Merit interconnected with the ARPANET. === National networking, NSFNET, and the Internet: 1986 to 1995 === In 1986 Merit engineered and operated leased lines and satellite links that allowed the University of Michigan to access the supercomputing facilities at Pittsburgh, San Diego, and NCAR. In 1987, Merit, IBM and MCI submitted a winning proposal to NSF to implement a new NSFNET backbone network. The new NSFNET backbone network service began July 1, 1988. It interconnected supercomputing centers around the country at 1.5 megabits per second (T1), 24 times faster than the 56 kilobits-per-second speed of the previous network. The NSFNET backbone grew to link scientists and educators on university campuses nationwide and connect them to their counterparts around the world. The NSFNET project caused substantial growth at Merit, nearly tripling the staff and leading to the establishment of a new 24-hour Network Operations Center at the U-M Computer Center. In September 1990 in anticipation of the NSFNET T3 upgrade and the approaching end of the 5-year NSFNET cooperative agreement, Merit, IBM, and MCI formed Advanced Network and Services (ANS), a new non-profit corporation with a more broadly based Board of Directors than the Michigan-based Merit Network. Under its cooperative agreement with NSF, Merit remained ultimately responsible for the operation of NSFNET, but subcontracted much of the engineering and operations work to ANS. In 1991 the NSFNET backbone service was expanded to additional sites and upgraded to a more robust 45 Mbit/s (T3) based network. The new T3 backbone was named ANSNet and provided the physical infrastructure used by Merit to deliver the NSFNET Backbone Service. On April 30, 1995, the NSFNET project came to an end, when the NSFNET backbone service was decommissioned and replaced by a new Internet architecture with commercial Internet service providers (ISPs) interconnected at Network Access Points provided by multiple providers across the country. === Bringing the Internet to Michigan: 1985 to 2001 === During the 1980s, Merit Network grew to serve eight member universities, with Oakland University joining in 1985 and Central Michigan University, Eastern Michigan University, and Michigan Technological University joining in 1987. In 1990, Merit's board of directors formally changed the organization's name to Merit Network, Inc., and created the name MichNet to refer to Merit's statewide network. The board also approved a staff proposal to allow organizations other than publicly supported universities, referred to as aff
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Comparison of OLAP servers

The following tables compare general and technical information for a number of online analytical processing (OLAP) servers. Please see the individual products articles for further information. == General information == == Data storage modes == == APIs and query languages == APIs and query languages OLAP servers support. == OLAP distinctive features == A list of OLAP features that are not supported by all vendors. All vendors support features such as parent-child, multilevel hierarchy, drilldown. == System limits == == Security == == Operating systems == The OLAP servers can run on the following operating systems: Note (1):The server availability depends on Java Virtual Machine not on the operating system == Support information ==
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Tumblr

Tumblr ( TUM-blər) is a microblogging and social media platform founded by David Karp in 2007 and operated by American company Tumblr, Inc., a subsidiary of Automattic. The service allows users to post multimedia and other content to a short-form blog. It has attracted significant attention and controversy for hosting a wide range of progressive user-generated content. == History == === Beginnings (2006–2012) === Development of Tumblr began in 2006 during a two-week gap between contracts at David Karp's software consulting company, Davidville. Karp had been interested in tumblelogs (short-form blogs, hence the name Tumblr) for some time and was waiting for one of the established blogging platforms to introduce their own tumblelogging platform. As none had done so after a year of waiting, Karp and developer Marco Arment began working on their own platform. Tumblr was launched in February 2007, and within two weeks had gained 75,000 users. Arment left the company in September 2010 to work on Instapaper. In June 2012, Tumblr featured its first major brand advertising campaign in collaboration with Adidas, who launched an official soccer Tumblr blog and bought ad placements on the user dashboard. This launch came only two months after Tumblr announced it would be moving towards paid advertising on its site. === Ownership by Yahoo! (2013–2018) === On May 20, 2013, it was announced that Yahoo and Tumblr had reached an agreement for Yahoo! Inc. to acquire Tumblr for $1.1 billion in cash. Many of Tumblr's users were unhappy with the news, causing some to start a petition, achieving nearly 170,000 signatures. David Karp remained CEO and the deal was finalized on June 20, 2013. Advertising sales goals were not met and in 2016 Yahoo wrote down $712 million of Tumblr's value. Verizon Communications acquired Yahoo in June 2017, and placed Yahoo and Tumblr under its Oath subsidiary. Karp announced in November 2017 that he would be leaving Tumblr by the end of the year. Jeff D'Onofrio, Tumblr's president and COO, took over leading the company. The site, along with the rest of the Oath division (renamed Verizon Media Group in 2019), continued to struggle under Verizon. In March 2019, Similarweb estimated Tumblr had lost 30% of its user traffic since December 2018, when the site had introduced a stricter content policy with heavier restrictions on adult content (which had been a notable draw to the service). In May 2019, it was reported that Verizon was considering selling the site due to its continued struggles since the purchase (as it had done with another Yahoo property, Flickr, via its sale to SmugMug). Following this news, Pornhub's vice president publicly expressed interest in purchasing Tumblr, with a promise to reinstate the previous adult content policies. === Automattic (2019–present) === On August 12, 2019, Verizon Media announced that it would sell Tumblr to Automattic, the operator of blog service WordPress.com and corporate backer of the open source blog software of the same name. The sale was for an undisclosed amount, but Axios reported that the sale price was less than $3 million, less than 0.3% of Yahoo's original purchase price. Automattic CEO Matt Mullenweg stated that the site will operate as a complementary service to WordPress.com, and that there were no plans to reverse the content policy decisions made during Verizon ownership. In November 2022, Mullenweg stated that Tumblr will add support for the decentralized social networking protocol ActivityPub. In November 2023, most of Tumblr's product development and marketing teams were transferred to other groups within Automattic. Mullenweg stated that focus would shift to core functionality and streamlining existing features. In February 2024, Automattic announced that it would begin selling user data from Tumblr and WordPress.com to Midjourney and OpenAI. Tumblr users are opted-in by default, with an option to opt out. In August 2024, Automattic announced that it would migrate Tumblr's backend to an architecture derived from WordPress, in order to ease development and code sharing between the platforms. The company stated that this migration would not impact the service's user experience and content, and that users "won't even notice a difference from the outside". In January 2025, Mullenweg stated that the migration, once completed, would also "unlock" ActivityPub access for Tumblr, including native support for the company's official ActivityPub plugin for WordPress. In April 2025, Automattic announced layoffs for 16% of its workforce, reducing a large portion of Tumblr staff. On March 16, 2026, Tumblr implemented a change to how notes were assigned to reblogs, making it more similar to sites like Twitter and Bluesky. The change was rolled back the next day after heavy user backlash. == Features == === Blog management === Dashboard: The dashboard is the primary tool for the typical Tumblr user. It is a live feed of recent posts from blogs that they follow. Through the dashboard, users are able to comment, reblog, and like posts from other blogs that appear on their dashboard. The dashboard allows the user to upload text posts, images, videos, quotes, or links to their blog with a click of a button displayed at the top of the dashboard. Users are also able to connect their blogs to their Twitter and Facebook accounts, so that whenever they make a post, it will also be sent as a tweet and a status update. As of June 2022, users can also turn off reblogs on specific posts through the dashboard. Queue: Users are able to set up a schedule to delay posts that they make. They can spread their posts over several hours or even days. Tags: Users can help their audience find posts about certain topics by adding tags. If someone were to upload a picture to their blog and wanted their viewers to find pictures, they would add the tag #picture, and their viewers could use that word to search for posts with the tag #picture. HTML editing: Tumblr allows users to edit their blog's theme using HTML to control the appearance of their blog. Custom themes are able to be shared and used by other users, or sold. Custom domains: Tumblr allows users to use custom domains for their blogs. Users must purchase a domain from Tumblr Domains, an in-house registrar that provides domains that can only be used with Tumblr unless removed from the user's blog and transferred to another registrar. Blogs previously were able to be linked with any domain/subdomain from any registrar, however following the introduction of the Tumblr Domains service, now requires you to purchase a domain directly from Tumblr to be used with a blog. Users who kept their blogs connected to a domain after the introduction got to keep their custom domain, as long as they do not disconnect it from Tumblr or let the domain expire. === Tags === The tagging system on the website operates on a hybrid tagging system, involving both self-tagging (user write their own tags on their posts) and an auto-manual function (the website will recommend popular tags and ones that the user has used before.) Only the first 20 tags added to any post will be indexed by the site. The tags are prefaced by a hashtag and separated by commas, and spaces and special characters are allowed, but only up to 140 characters total per tag. There are two main types used by Tumblr users: descriptive tagging, and opinion or commentary tagging. Descriptive tags are usually introduced by the original poster, and describe what is in the post (e.g. #art, #sky). These are important for the original poster to use, so their post will be indexed and searchable by others wishing to view that subject of content. Tags used as a form of communication are unique to Tumblr, and are typically more personal, expressing opinions, reactions, meta-commentary, background information, and more. Instead of adding onto the reblogged post (with their comments becoming an addition to each subsequent reblog from them) a user may add their comments in the tags, not changing the content or appearance of the original post in any way. Not all users choose to use tags this way, but those who do use tags for commentary may prefer it over adding a comment on the actual post. === Mobile === With Tumblr's 2009 acquisition of Tumblerette, an iOS application created by Jeff Rock and Garrett Ross, the service launched its official iPhone app. The site became available to BlackBerry smartphones on April 17, 2010, via a Mobelux application in BlackBerry World. In June 2012, Tumblr released a new version of its iOS app, Tumblr 3.0, allowing support for Spotify integration, hi-res images and offline access. An app for Android is also available. A Windows Phone app was released on April 23, 2013. An app for Google Glass was released on May 16, 2013. === Inbox and messaging === Tumblr blogs have the option to allow users to submit questions, either as themselves or anonymously, to the blog for a response. Tumblr
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Language Computer Corporation

Language Computer Corporation (LCC) is a natural language processing research company based in Richardson, Texas. The company develops a variety of natural language processing products, including software for question answering, information extraction, and automatic summarization. Since its founding in 1995, the low-profile company has landed significant United States Government contracts, with $8,353,476 in contracts in 2006-2008. While the company has focused primarily on the government software market, LCC has also used its technology to spin off three start-up companies. The first spin-off, known as Lymba Corporation, markets the PowerAnswer question answering product originally developed at LCC. In 2010, LCC's CEO, Andrew Hickl, co-founded two start-ups which made use of the company's technology. These included Swingly, an automatic question answering start-up, and Extractiv, an information extraction service that was founded in partnership with Houston, Texas-based 80legs.
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Frame (networking)

A frame is a digital data transmission unit in computer networking and telecommunications. In packet switched systems, a frame is a simple container for a single network packet. In other telecommunications systems, a frame is a repeating structure supporting time-division multiplexing. A frame typically includes frame synchronization features consisting of a sequence of bits or symbols that indicate to the receiver the beginning and end of the payload data within the stream of symbols or bits it receives. If a receiver is connected to the system during frame transmission, it ignores the data until it detects a new frame synchronization sequence. == Packet switching == In the OSI model of computer networking, a frame is the protocol data unit at the data link layer. Frames are the result of the final layer of encapsulation before the data is transmitted over the physical layer. A frame is "the unit of transmission in a link layer protocol, and consists of a link layer header followed by a packet." Each frame is separated from the next by an interframe gap. A frame is a series of bits generally composed of frame synchronization bits, the packet payload, and a frame check sequence. Examples are Ethernet frames, Wi-Fi frames, 4G frames, Point-to-Point Protocol (PPP) frames, Fibre Channel frames, and V.42 modem frames. Often, frames of several different sizes are nested inside each other. For example, when using Point-to-Point Protocol (PPP) over asynchronous serial communication, the eight bits of each individual byte are framed by start and stop bits, the payload data bytes in a network packet are framed by the header and footer, and several packets can be framed with frame boundary octets. == Time-division multiplex == In telecommunications, specifically in time-division multiplex (TDM) and time-division multiple access (TDMA) variants, a frame is a cyclically repeated data block that consists of a fixed number of time slots, one for each logical TDM channel or TDMA transmitter. In this context, a frame is typically an entity at the physical layer. TDM application examples are SONET/SDH and the ISDN circuit-switched B-channel, while TDMA examples are Circuit Switched Data used in early cellular voice services. The frame is also an entity for time-division duplex, where the mobile terminal may transmit during some time slots and receive during others.
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European Grid Infrastructure

EGI (originally an initialism for European Grid Infrastructure) is a federation of computing and storage resource providers that deliver advanced computing and data analytics services for research and innovation. The Federation is governed by its participants represented in the EGI Council and coordinated by the EGI Foundation. As of 2024, the EGI Federation supports 160 scientific communities worldwide and over 95,000 users in their intensive data analysis. The most significant scientific communities supported by EGI in 2022 were Medical and Health Sciences, High Energy Physics, and Engineering and Technology. The EGI Federation provideds services through over 150 data centres, of which 25 are cloud sites, in 43 countries and 64 Research Infrastructures (4 of which are members of the Federation). == Name == Originally, EGI stood for European Grid Infrastructure. This reflected its focus on providing access to high-throughput computing resources across Europe using Grid computing techniques. However, as EGI's service offerings expanded beyond traditional grid computing, particularly with the incorporation of federated cloud services, the original meaning of the acronym became less accurate. To emphasise the broader scope of EGI's services and avoid any confusion associated with the outdated term "grid," it is recommended to refer to EGI simply as EGI. == Structure == === EGI Federation === The EGI Federation delivers a scalable digital research infrastructure (e-infrastructure), empowering tens of thousands of researchers across diverse scientific disciplines. Through the EGI Federation, researchers gain access to advanced computing and data analytics capabilities, including large-scale data analysis, while benefiting from the collaborative efforts of hundreds of service providers from both public and private sectors, consolidating resources from Europe and beyond. Overall, the EGI Federation offers a range of services, encompassing distributed high-throughput computing and cloud computing, storage and data management capabilities, co-development of new solutions, expert support, and comprehensive training opportunities. This ecosystem propels collaboration, scientific progress and innovation. === EGI Foundation === The EGI Foundation is the coordinating body of the EGI Federation. It was established in 2010 with headquarters in Amsterdam, Netherlands. The Foundation coordinates the research and innovation efforts of its members, spanning technical areas critical to data-intensive science, including large-scale data processing and analysis, distributed Artificial Intelligence/Machine Learning, federated Identity and access management and the application of digital twins for research. The day-to-day running of the EGI Foundation is supervised by the Executive Board. The board’s members work closely with the EGI Director on operational, technical and financial issues. The Executive Board’s members are appointed by the EGI Council for a two-year term. === EGI Council === The EGI Council is responsible for defining the strategic direction of the EGI Federation. The Council acts as the senior decision-making and supervisory authority of the EGI Foundation, with a mandate to define the strategic direction of the entire EGI ecosystem. === EGI Services === EGI offers a suite of services to support data-intensive research. These services include compute resources, orchestration tools, storage and data management solutions, training programmes, security and identity services, and applications. Compute resources encompass cloud compute, cloud container compute, high-throughput compute, and software distribution. Orchestration tools include the Workload Manager and infrastructure manager. Storage and data management solutions include online storage, data transfer, and DataHub. Training programmes cover FitSM, ISO 27001, and general training infrastructure. EGI Check-in and Secrets Store are key security and identity services, while applications such as Notebooks and Replay enhance research productivity. In addition to services for Research, EGI also provides services for Federation and Business. Services for Federation are designed to help resource providers and user communities collaborate and share resources. EGI also offers a range of services to support businesses in their digital transformation. Through the EGI Digital Innovation Hub (EGI DIH), companies can access advanced computing resources, networking, funding and training opportunities, collaborate with research institutions, and test solutions before investing. == History == In 2002, the first large-scale experimental facility was successfully demonstrated by the DataGrid project under the lead of CERN with tens of technical architects from the major High Energy Physics institutes in the world. For the first time, distributed computing was applied to data-intensive processing. It aimed at developing a large-scale computational grid to facilitate distributed data-intensive scientific computing across High Energy Physics, Earth Observation, and Biology science applications. On 28 February 2003, the first software release of LCG-MW was published. gLite, the Lightweight Middleware for Grid Computing and LCG, Large Hadron Collider Computing Grid, are the cornerstone of the Worldwide LHC Computing Grid, which expanded over time towards the EGI Federation. 2004 marks the year of the first pilot infrastructure, seeing the participation of CERN and data centres in the United Kingdom, Spain, Germany, the Netherlands, France, Canada, Russia, Bulgaria, the Asia-Pacific region and Switzerland. Over the years, the infrastructure has grown into a federation of 128 data centres and 25 cloud providers serving more than 95,000 users worldwide. In 2004, the first data processing tasks started being formally recorded in a central accounting system. The EGI Accounting Portal provides the accounting data for Compute, Storage and Data services gathered from the data centres of the EGI Federation. A few years later, in 2010, EGI was established as the coordinating body of the EGI Federation to build an integrated pan-European infrastructure to support European research communities primarily. In the same year, EGI launched the flagship project EGI Inspire. That project brought together European organisations to establish a sustainable European Grid Infrastructure for large-scale data analysis. The success of the project was due to the adoption of a distributed computing model to solve big data problems. Moreover, EGI-Inspire harmonised operational policies across its federation of affiliated data centres and cloud service providers worldwide, integrating e-infrastructures from 57 countries. The EGI Federation was the first to apply federation to cloud provisioning, opening a new avenue in large-scale interactive data analysis. In 2015, within EGI Engage, opening a new avenue in large-scale interactive data analysis. The EGI Federated Cloud is an IaaS-type cloud, incorporating academic and private clouds and virtualised resources built using open standards. Its development is driven by the needs of the scientific community, resulting in a novel research e-infrastructure that relies on well-established federated operational services, making EGI a dependable resource for scientific endeavours. In 2015, EGI, EUDAT, GÉANT, LIBER and OpenAIRE published a position paper on a 'European Open Science Cloud for Research'. With the EOSC-hub project in 2016, EGI started contributing in practice to shaping the services for the EOSC. The work continued with a series of projects, like EOSC Enhance, EOSC Life and EOSC Synergy. With EGI-ACE and its contribution to EOSC Future, EGI has continued developing the EOSC Core. In early 2024, EGI started providing services to the EOSC EU Node, and with EOSC Beyond it will provide new EOSC Core capabilities and pilot additional national and thematic nodes. In October 2024, EUDAT, GÉANT, OpenAIRE, PRACE and EGI signed a Memorandum of Understanding establishing the European e-Infrastructures Assembly. This collaboration will bolster the position and promote the services of e-Infrastructures, empowering researchers across Europe to drive innovation and advance scientific discovery.
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Computer network engineering

Computer network engineering is a technology discipline within engineering that deals with the design, implementation, and management of computer networks. These systems contain both physical components, such as routers, switches, cables, and some logical elements, such as protocols and network services. Computer network engineers attempt to ensure that the data is transmitted efficiently, securely, and reliably over both local area networks (LANs) and wide area networks (WANs), as well as across the Internet. Computer networks often play a large role in modern industries ranging from telecommunications to cloud computing, enabling processes such as email and file sharing, as well as complex real-time services like video conferencing and online gaming. == Background == The evolution of network engineering is marked by significant milestones that have greatly impacted communication methods. These milestones particularly highlight the progress made in developing communication protocols that are vital to contemporary networking. This discipline originated in the 1960s with projects like ARPANET, which initiated important advancements in reliable data transmission. The advent of protocols such as TCP/IP revolutionized networking by enabling interoperability among various systems, which, in turn, fueled the rapid growth of the Internet. Key developments include the standardization of protocols and the shift towards increasingly complex layered architectures. These advancements have profoundly changed the way devices interact across global networks. == Network infrastructure design == The foundation of computer network engineering lies in the design of the network infrastructure. This involves planning both the physical layout of the network and its logical topology to ensure optimal data flow, reliability, and scalability. === Physical infrastructure === The physical infrastructure consists of the hardware used to transmit data, which is represented by the first layer of the OSI model. ==== Cabling ==== Copper cables such as ethernet over twisted pair are commonly used for short-distance connections, especially in local area networks (LANs), while fiber optic cables are favored for long-distance communication due to their high-speed transmission capabilities and lower susceptibility to interference. Fiber optics play a significant role in the backbone of large-scale networks, such as those used in data centers and internet service provider (ISP) infrastructures. ==== Wireless networks ==== In addition to wired connections, wireless networks have become a common component of physical infrastructure. These networks facilitate communication between devices without the need for physical cables, providing flexibility and mobility. Wireless technologies use a range of transmission methods, including radio frequency (RF) waves, infrared signals, and laser-based communication, allowing devices to connect to the network. Wi-Fi based on IEEE 802.11 standards is the most widely used wireless technology in local area networks and relies on RF waves to transmit data between devices and access points. Wireless networks operate across various frequency bands, including 2.4 GHz and 5 GHz, each offering unique ranges and data rates; the 2.4 GHz band provides broader coverage, while the 5 GHz band supports faster data rates with reduced interference, ideal for densely populated environments. Beyond Wi-Fi, other wireless transmission methods, such as infrared and laser-based communication, are used in specific contexts, like short-range, line-of-sight links or secure point-to-point communication. In mobile networks, cellular technologies like 3G, 4G, and 5G enable wide-area wireless connectivity. 3G introduced faster data rates for mobile browsing, while 4G significantly improved speed and capacity, supporting advanced applications like video streaming. The latest evolution, 5G, operates across a range of frequencies, including millimeter-wave bands, and provides high data rates, low latency, and support for more device connectivity, useful for applications like the Internet of Things (IoT) and autonomous systems. Together, these wireless technologies allow networks to meet a variety of connectivity needs across local and wide areas. ==== Network devices ==== Routers and switches help direct data traffic and assist in maintaining network security; network engineers configure these devices to optimize traffic flow and prevent network congestion. In wireless networks, wireless access points (WAP) allow devices to connect to the network. To expand coverage, multiple access points can be placed to create a wireless infrastructure. Beyond Wi-Fi, cellular network components like base stations and repeaters support connectivity in wide-area networks, while network controllers and firewalls manage traffic and enforce security policies. Together, these devices enable a secure, flexible, and scalable network architecture suitable for both local and wide-area coverage. === Logical topology === Beyond the physical infrastructure, a network must be organized logically, which defines how data is routed between devices. Various topologies, such as star, mesh, and hierarchical designs, are employed depending on the network’s requirements. In a star topology, for example, all devices are connected to a central hub that directs traffic. This configuration is relatively easy to manage and troubleshoot but can create a single point of failure. In contrast, a mesh topology, where each device is interconnected with several others, offers high redundancy and reliability but requires a more complex design and larger hardware investment. Large networks, especially those in enterprises, often employ a hierarchical model, dividing the network into core, distribution, and access layers to enhance scalability and performance. == Network protocols and communication standards == Communication protocols dictate how data in a network is transmitted, routed, and delivered. Depending on the goals of the specific network, protocols are selected to ensure that the network functions efficiently and securely. The Transmission Control Protocol/Internet Protocol (TCP/IP) suite is fundamental to modern computer networks, including the Internet. It defines how data is divided into packets, addressed, routed, and reassembled. The Internet Protocol (IP) is critical for routing packets between different networks. In addition to traditional protocols, advanced protocols such as Multiprotocol Label Switching (MPLS) and Segment Routing (SR) enhance traffic management and routing efficiency. For intra-domain routing, protocols like Open Shortest Path First (OSPF) and Enhanced Interior Gateway Routing Protocol (EIGRP) provide dynamic routing capabilities. On the local area network (LAN) level, protocols like Virtual Extensible LAN (VXLAN) and Network Virtualization using Generic Routing Encapsulation (NVGRE) facilitate the creation of virtual networks. Furthermore, Internet Protocol Security (IPsec) and Transport Layer Security (TLS) secure communication channels, ensuring data integrity and confidentiality. For real-time applications, protocols such as Real-time Transport Protocol (RTP) and WebRTC provide low-latency communication, making them suitable for video conferencing and streaming services. Additionally, protocols like QUIC enhance web performance and security by establishing secure connections with reduced latency. == Network security == As networks have become essential for business operations and personal communication, the demand for robust security measures has increased. Network security is a critical component of computer network engineering, concentrating on the protection of networks against unauthorized access, data breaches, and various cyber threats. Engineers are responsible for designing and implementing security measures that ensure the integrity and confidentiality of data transmitted across networks. Firewalls serve as barriers between trusted internal networks and external environments, such as the Internet. Network engineers configure firewalls, including next-generation firewalls (NGFW), which incorporate advanced features such as deep packet inspection and application awareness, thereby enabling more refined control over network traffic and protection against sophisticated attacks. In addition to firewalls, engineers use encryption protocols, including Internet Protocol Security (IPsec) and Transport Layer Security (TLS), to secure data in transit. These protocols provide a means of safeguarding sensitive information from interception and tampering. For secure remote access, Virtual Private Networks (VPNs) are deployed, using technologies to create encrypted tunnels for data transmission over public networks. These VPNs are often used for maintaining security when remote users access corporate networks but are also used ion other settings. To enhance threat detection and r
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Aphelion (software)

The Aphelion Imaging Software Suite is a software suite that includes three base products - Aphelion Lab, Aphelion Dev, and Aphelion SDK for addressing image processing and image analysis applications. The suite also includes a set of extension programs to implement specific vertical applications that benefit from imaging techniques. The Aphelion software products can be used to prototype and deploy applications, or can be integrated, in whole or in part, into a user's system as processing and visualization libraries whose components are available as both DLLs or .Net components. == History and evolution == The development of Aphelion started in 1995 as a joint project of a French company, ADCIS S.A., and an American company, Amerinex Applied Imaging, Inc. (AAI) Aphelion's image processing and analysis functions were made from operators available from the KBVision software developed and sold by Amerinex's predecessor, Amerinex Artificial Intelligence Inc. In the 1990s, the XLim software library was developed at the Center of Mathematical Morphology of Mines ParisTech, and both companies carried out its development tasks. The first version of Aphelion was completed and released in April 1996. Successive versions were released before the first official stable release in December 1996 at the Photonics East conference in Boston and the Solutions Vision show in Paris in January 1997, where at the latter it competed with Stemmer Imaging's CVB imaging toolbox. In 1998, version 2.3 of Aphelion for Windows 98 was released, and its user base was growing in both France and the United States. Version 3.0, totally rewritten to take advantage of Microsoft's then-recent ActiveX technology, was officially released in 2000. It also became available as a « Developer » version, for rapid prototyping of applications using its intuitive GUI and the macro recording capability, and a « Core » version, including the full library as a set of ActiveX components to be used by software developers, integrators and original equipment manufacturers (OEM). As AAI turned its focus to security, in 2001, ADCIS took the lead on developing Aphelion. AAI focused on millimeter wave scanners for concealed weapon detection at airports, and eventually merged with Millimetrics to become Millivision. In 2004, ADCIS specified version 4.0 of Aphelion. The set of image processing/analysis functions was rewritten one more time to be compatible with the .NET technology and the emergence of 64 bit architecture PCs. In addition, the GUI was redesigned to address two usage types: a semi-automatic use where the user is guided through the different steps of functions, and a fully automatic use where the expert user can quickly invoke imaging functions. Its first release was presented at the IPOT exhibition in Birmingham, UK the same year. During the Vision Show in Paris in October 2008, the new Aphelion Lab product was launched for users that are not specialists in image processing. It is easier to use, and only includes fewer image processing functions. It was then included in the Aphelion Image Processing Suite, consisting of Aphelion Dev (replacing Aphelion Developer), Aphelion Lab, Aphelion SDK (replacing Aphelion Core), and a set of extensions. Nowadays, ADCIS is still working on the suite, and updated versions with new extensions and functionalities continually become available from the websites of both companies. In 2015, support was added for very large images and scan microscope images (virtual slides compound into a very large JPEG 2000 image) for high throughput imaging, and new specific extensions were also added. In late 2015, ADCIS announced Aphelion's port for tablets and smartphones, for vertical applications. The name "Aphelion" comes from the astronomical term of the same name, meaning the point on a planet rotating around the Sun where it lies farthest from it, applying the term in a metaphorical sense. Unix was the operating system used on scientific workstations in the 1990s, such as on the workstations manufactured by market leader Sun Microsystems, which Windows suite Aphelion was quite removed from. == Description == Aphelion is a software suite to be used for image processing and image analysis. It supports 2D and 3D, monochrome, color, and multi-band images. It is developed by ADCIS, a French software house located in Saint-Contest, Calvados, Normandy. Aphelion is widely used in the scientific/industry community to solve basic and complex imaging applications. First, the imaging application is quickly developed from the Graphical User Interface, involving a set of functions that can be automatically recorded into a macro command. The macro languages available in Aphelion (i.e. BasicScript, Python, and C#) help to process batch of images, and prompt the user if needed for specific parameters that are applied to the imaging functions. All Aphelion image processing functions are written in C++, and the Aphelion user interface is written in C#. C++ functions can be called from the C# language thanks the use of dedicated wrappers. The main principle of image processing is to automatically process pixels of a digital image, then extract one or more objects of interest (i.e. cells in the field of biology, inclusions in the field of material science) and compute one or more measurements on those objects to quantify the image and generate a verdict (good image, image with defects, cancerous cells). In other words, starting from an image, pixels are processed by a set of successive functions or operators until only measurements are computed and used as the input of a 3rd party system or a classification software that will classify objects of interest that have been extracted during the imaging process. An acquisition system such as a digital camera, a video camera, an optical or electron microscope, a medical scanner, or a smartphone can be used to capture images. The set of values or pixels can be processed as a 1D image (1D signal), a 2D image (array of pixel values corresponding to a monochrome or color image), or a 3D image displayed using volume rendering (array of voxels in the 3D space) or displaying surfaces by using 3D rendering. A 2D color image is made of 3 value pixels (typically Red, Green, and Blue information or another color space), and a 3D image is made of monochrome, color (indexed color are often used), multispectral, or hyperspectral data. When dealing with videos, an additional band is added corresponding to temporal information. The Aphelion Software Suite includes three base products, and a set of optional extensions for specific applications: Aphelion Lab: Entry-level package for non-experts in image processing. It helps to quickly segment an image in a semi-automatic or manual ways, and compute a set of measurements computed on objects of interest that have been extracted during the segmentation process. A set of wizards guides the user from image acquisition to report generation. Aphelion Dev: Full imaging environment including over 450 functions to develop and deploy an application that involves image processing and analysis. It also includes a set of macro-command languages to automate any application to be invoked from the user interface. It also helps to run the imaging algorithm on more than one image that are stored on disk, available on the network, or captured by an acquisition device. Aphelion libraries for image processing and visualization are provided in Aphelion Dev as DLLs and .Net components. Aphelion SDK: A set of libraries to develop a stand-alone application with a custom interface based on the Aphelion libraries. This software development kit including display, processing and analysis functions that can be used by software developers and OEMs. It is provided as DLLs and .Net components. The stand-alone application is typically developed in C# on one computer, and then deployed on multiple PCs and systems. A set of optional extensions can be added to the « Aphelion Dev » product, depending on the application. An evaluation version of Aphelion can be run on a PC for 30 days. A permanent version of Aphelion is available based on a perpetual license. Upgrades are available through a maintenance agreement based on a yearly fee. Technical support is provided by the engineers who are developing the product. The goal of image processing is usually to extract object(s) of interest in an image, and then to classify them based on some characteristics such as shape, density, position, etc. Using Aphelion, this goal is achieved by performing the following tasks: Load an image from disk or acquire an image using an acquisition device. Enhance the image removing noise or modifying its contrast. Segment the image extracting objects of interest to be measured and analyzed. Typically, for simple applications, a threshold is performed to generate a binary image. Then, morphological operators are applied to clean the image and only keep obj
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G.9972

G.9972 (also known as G.cx) is a Recommendation developed by ITU-T that specifies a coexistence mechanism for networking transceivers capable of operating over electrical power line wiring. It allows G.hn devices to coexist with other devices implementing G.9972 and operating on the same power line wiring. G.9972 received consent during the meeting of ITU-T Study Group 15, on October 9, 2009, and final approval on June 11, 2010. G.9972 specifies two mechanisms for coexistence between G.hn home networks and broadband over power lines (BPL) Internet access networks: Frequency-division multiplexing (FDM), in which the available spectrum is divided into two parts: frequencies below 10 or 14 MHz (specific value can be selected by the access network) are reserved for the access network, while frequencies above them are reserved for the in-home network. Time-division multiplexing (TDM), in which the available channel time is split equally between both networks. 50% of time slots are allocated for the access network, and 50% are allocated to the in-home network.
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ACTS Gigabit Satellite Network

The ACTS Gigabit Satellite Network was a pioneering, high-speed communications satellite network in the years 1993-2004, created as a prototype system to explore high-speed networking of digital endpoints. The system was jointly sponsored by NASA and ARPA, implemented by BBN Technologies and Motorola, and was inducted into the Space Technology Hall of Fame in April 1997. The Advanced Communications Technology Satellite (ACTS) network was designed to provide fiber-compatible SONET service to remote nodes and networks through a wideband satellite system, and provided long-haul, point-to-point and point-to-multipoint full-duplex SONET services, at rates up to 622 Mbit/s, over NASA's Advanced Communication Technology Satellite (ACTS). The Advanced Communications Technology Satellite itself, built and operated by Lockheed Martin, was launched on STS-51 on September 12, 1993, by the Space Shuttle Discovery, and occupied a geostationary orbit at 100° west longitude. It was the first communication satellite to operate in the 20–30 GHz frequency band (Ka band), with 30 GHz uplink and 20 GHz downlink signals. The satellite incorporated advanced on-board switching and multiple dynamically-hopping spot-beam antennas for selected areas of the United States including Hawaii. Up to 3 uplink and 3 downlink antenna beams could be active simultaneously. The ACTS network ground terminals were transportable Gigabit Earth Stations (GES) with fiber-optic SONET interfaces (OC-3 and OC-12), which also supported the Asynchronous Transfer Mode (ATM) protocol suite. The network control and management functions are distributed in the various Gigabit Earth Stations, with the operator's interface being centralized in a Network Management Terminal (NMT), which could be collocated at a GES, or anywhere in the Internet. The system was operational and used for experiments for 127 months, instead of the originally planned 24–48 months. In all, 53 terminals were built and used by more than 100 experimenters to test ACTS abilities. In Nov. 1997 a record data rate of 520 Mbit/s TCP/IP throughput was achieved using ATM between several ground stations via ACTS. On May 31, 2000 the ACTS experiments program officially came to a close, but the system continued to support experiments until it was deactivated on April 28, 2004.
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