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Representation collapse

Representation collapse is a phenomenon in machine learning and representation learning where a model maps different inputs to the same or very similar embeddings, which means it loses important information about how the data is spread out. It is frequently encountered in self-supervised learning, especially within contrastive and non-contrastive frameworks, when training objectives or model architectures do not maintain variance across representations. Collapse results in degenerate solutions characterized by uninformative learned features, significantly impairing downstream task performance. Various techniques have been proposed to mitigate representation collapse, including the use of negative samples, architectural asymmetry, stop-gradient operations, variance regularization, and redundancy reduction objectives, as seen in methods such as SimCLR, BYOL, and VICReg. Comprehending and averting representation collapse is regarded as a fundamental challenge in the advancement of stable and efficient self-supervised learning systems.
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Logistics automation

Logistics automation is the application of computer software or automated machinery to logistics operations in order to improve its efficiency. Typically this refers to operations within a warehouse or distribution center, with broader tasks undertaken by supply chain engineering systems and enterprise resource planning systems. Logistics automation systems can powerfully complement the facilities provided by these higher level computer systems. The focus on an individual node within a wider logistics network allows systems to be highly tailored to the requirements of that node. == Components == Logistics automation systems comprise a variety of hardware and software components: Fixed machinery Automated storage and retrieval systems, including: Cranes serve a rack of locations, allowing many levels of stock to be stacked vertically, and allowing for higher storage densities and better space utilization than alternatives. In systems produced by Amazon Robotics, automated guided vehicles move items to a human picker. Conveyors: Containers can enter automated conveyors in one area of the warehouse and, either through hard-coded rules or data input, be moved to a selected destination. Vertical carousels based on the paternoster lift system or using space optimization, similar to vending machines, but on a larger scale. Sortation systems: similar to conveyors but typically with higher capacity and able to divert containers more quickly. Typically used to distribute high volumes of small cartons to a large set of locations. Industrial robots: four- to six-axis industrial robots, e.g. palletizing robots, are used for palletizing, depalletizing, packaging, commissioning and order picking. Typically all of these will automatically identify and track containers using barcodes or, increasingly, RFID tags. Motion check weighers may be used to reject cases or individual products that are under or over their specified weight. They are often used in kitting conveyor lines to ensure all pieces belonging in the kit are present. Mobile technology Radio data terminals: these are handheld or truck-mounted terminals which connect by radio to logistics automation software and provide instructions to operators moving throughout the warehouse. Many also have barcode scanners to allow identification of containers more quickly and accurately than manual keyboard entry. Software Integration software: this provides overall control of the automation machinery and allows cranes to be connected to conveyors for seamless stock movements. Operational control software: provides low-level decision-making, such as where to store incoming containers, and where to retrieve them when requested. Business control software: provides higher-level functionality, such as identification of incoming deliveries/stock, scheduling order fulfillment, and assignment of stock to outgoing trailers. == Benefits == A typical warehouse or distribution center will receive stock of a variety of products from suppliers and store these until the receipt of orders from customers, whether individual buyers (e.g. mail order), retail branches (e.g. chain stores), or other companies (e.g. wholesalers). A logistics automation system may provide the following: Automated goods in processes: Incoming goods can be marked with barcodes and the automation system notified of the expected stock. On arrival, the goods can be scanned and thereby identified, and taken via conveyors, sortation systems, and automated cranes into an automatically assigned storage location. Automated goods retrieval for orders: On receipt of orders, the automation system is able to immediately locate goods and retrieve them to a pick-face location. Automated dispatch processing: Combining knowledge of all orders placed at the warehouse the automation system can assign picked goods into dispatch units and then into outbound loads. Sortation systems and conveyors can then move these onto the outgoing trailers. If needed, repackaging to ensure proper protection for further distribution or to change the package format for specific retailers/customers. A complete warehouse automation system can drastically reduce the workforce required to run a facility, with human input required only for a few tasks, such as picking units of product from a bulk packed case. Even here, assistance can be provided with equipment such as pick-to-light units. Smaller systems may only be required to handle part of the process. Examples include automated storage and retrieval systems, which simply use cranes to store and retrieve identified cases or pallets, typically into a high-bay storage system which would be unfeasible to access using fork-lift trucks or any other means. The use of Automatic Guided Vehicles maximizes the output compared to humans since they can do repetitive tasks for long hours and with least to no supervision. An AGV is built and programmed for precision and accuracy thereby reducing the chances of errors in a warehouse, especially when dealing with fragile goods. == Automation software == Software or cloud-based SaaS solutions are used for logistics automation which helps the supply chain industry in automating the workflow as well as management of the system. Knowledge @ Wharton staff writers noted in 2011 that some manufacturers and retailers were weathering the Great Recession "by signing up for pay-as-you-go logistics services available through the Internet 'cloud'". They identified the benefits and reduced costs which came from sharing information about shipments with suppliers, hauliers and end users. There is little generalized software available in this market. This is because there is no rule to generalize the system as well as work flow even though the practice is more or less the same. Most of the commercial companies do use one or the other of the custom solutions. But there are various software solutions that are being used within the departments of logistics. There are a few departments in Logistics, namely: Conventional Department, Container Department, Warehouse, Marine Engineering, Heavy Haulage, etc. Software used in these departments Conventional department : CVT software / CTMS software. Container Trucking: CTMS software Warehouse : WMS/WCS Improving Effectiveness of Logistics Management Logistical Network Information Transportation Sound Inventory Management Warehousing, Materials Handling & Packaging
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AVS Video Editor

AVS Video Editor is a video editing software published by Online Media Technologies Ltd. It is a part of AVS4YOU software suite which includes video, audio, image editing and conversion, disc editing and burning, document conversion and registry cleaner programs. It offers the opportunity to create and edit videos with a vast variety of video and audio effects, text and transitions; capture video from screen, web or DV cameras and VHS tape; record voice; create menus for discs, as well as to save them to plenty of video file formats, burn to discs or publish on Facebook, YouTube, Flickr, etc. == Description == === Interface === The layout consists of the timeline or storyboard view, preview pane and media library (transitions, video effects, text or disc menus) collections. The storyboard view shows the sequence of video clips with the transitions between them and used to change the order of clips or add transitions. Timeline view consists of main video, audio, effects, video overlay and text lines for editing. Once on the timeline video can be duplicated, split, muted, frozen, cropped, stabilized, its speed can be slowed down or increased, audio and color corrected. === Importing footage === Video, audio and image files necessary for video project can be imported into the program from computer hard disk drive. User can also capture video from computer screen, web or mini DV camera, as well as from VHS tape, record voice. === Output (web, device, disc, format) === AVS Video Editor gives the opportunity to save video to a computer hard drive to one of the video formats: AVI, DVD, Blu-ray, MOV, MP4, M4V, MPEG, WMV, MKV, WebM, M2TS, TS, FLV, SWF, RM, 3GP, GIF, DPG, AMV, MTV; burn to DVD or Blu-ray disc with menus; create a video for mobile players, mobile phones or gaming consoles and upload it right to the device. The most popular devices such as Apple iPod, Apple iPhone, Apple iPad, Sony PSP, Samsung Galaxy, Android and BlackBerry smartphones and tablets are supported. There is also an option to create a video that can be streamed via web and save it into Flash or WebM format or for the popular web services: YouTube, Facebook, Telly (Twitvid), Dailymotion, Flickr and Dropbox. === Features === Single and multithread modes: if a computer supports multi-threading, video creation process is performed faster in multithread mode, especially on a multi-core system. Customization of the output file settings, such as bitrate, frame rate, frame size, video and audio codecs, etc. Transitions - help video clips smoothly go into one another, dissolve or overlap two video or image files. Fade in and fade out video and audio files - dissolve a video to and from a blank image, reduce the audio volume at the end of the video and increase at the beginning. Slideshow creation - create a presentation of a series of still images. Voice recording Projects - once a project is created and saved, the next time saving video to some other format will be fast, projects are also used if a user do not have a possibility to create, edit and save video all at once. Video overlay option - superpose video image over the video clip that is being edited. Disk menu and chapters creation - an option for DVD and Blu-ray video. Freeze frame - make a still shot from a video clip. Stabilization feature - reduce jittering or blurring caused by shaky motions of a camera. Enhanced deinterlacing method - increase video quality for interlaced input file - spots and blurred areas are compensated. Scene detection - search and separate one scene of the video from the other. Loop DVD and SWF - output SWF and DVD video are played back continuously. Caching for processing high definition files - create a duplicate video file smaller in size to use it on the preview window and accelerate processing of HD files. Chroma key option - add video overlay half transparent so that only part of it is visible and all the rest disappears to reveal the video underneath. Capture video material from DV tapes, VHS tapes, web cameras, etc. Movie closing credits - add information on movie editing, e.g. crew, cast, data, etc. Creeping line, subtitles, text - add different captions (static and animated), shapes and images to video. Speech balloons and other graphic objects - geometrical shapes to highlight an object in the video. Zoom effect - magnify or reduce the view of the image. Rotate effect - rotate video image at different degrees, e.g. 90, 180, etc. Grayscale and old movie effects - create a black and white video image. Old movie adds also scratches, noise, shake and dust to video, as if it's being played on an old projector. Blur and sharpen effects - visually smooth and soften an image, or make video image better focused. Snow and particles effects - adds snow or various objects (bubbles, flowers, leaves, butterflies etc.) that are moving, flying or falling on the video. Pan and zoom Timer, countdown effects - add a timepiece that measures or counts down a time interval to the video being edited. Snapshots - capture a particular moment of a video clip. Sound track replacement - mute audio track from video and add another one. Audio amplify, noise removal, equalizer, etc. - make video sound louder, attenuate the noise, change frequency pattern of the audio, make some other audio adjustments. Trim and multi-trim options - change video clip duration cutting out unnecessary parts or detect scenes and cut out parts in any place of the video clip. Color correction (brightness, temperature, contrast, saturation, gamma, etc.) effects - allow adjustment of tonal range, color, and sharpness of video files. Crop scale effect - get rid of mattes that appear after changing aspect ratio of a video file. Adjusting the Playback Speed Volume and balance - change sound volume in the output video. Change volume value proportion for main video and added soundtrack, completely mute main video audio and leave added soundtrack only, etc. === Utilities embedded into AVS Video Editor === AVS Mobile Uploader is used to transfer edited and converted media files to portable devices via Bluetooth, Infrared or USB connection. AVS Video Burner is used to burn converted video files to different disc types: CD, DVD, Blu-ray. AVS Video Recorder is used to capture video from analog video sources and supports different types of devices: capture card, web camera (webcam), DV camera, HDV camera. AVS Video Uploader is used to transfer video files to popular video-sharing websites, like Facebook, Dailymotion, YouTube, Photobucket, TwitVid, MySpace, Flickr. AVS Screen Capture is used to capture any actions on the desktop to make presentations or video tutorials more vivid and easily comprehensible. == Important upgrades == The initial release of AVS Video Editor was in 2003 when the program was offered inside AVS software bundles together with AVS Video Tools, AVS Audio Tools and DVD Copy software. In 2005 the program is offered as a part of multifunctional AVS4YOU software suite. AVS Video Editor is frequently updated. The main updates include adding several important features for video editing
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WinFIG

WinFIG is a proprietary shareware vector graphics editor application. The file format and rendering are as close to Xfig as possible, but the program takes advantage of Windows features like clipboard, printer preview, multiple documents etc. As of 2011, WinFIG is under active development, with new features being added regularly. == History == The first release was in March 2003 and based on the Amiga program AmiFIG by the same author, which is also an Xfig compatible vector drawing application. WinFIG was not created by porting the Xfig source code to Windows. It is an independent implementation. Starting with release 4.0 WinFIG was ported from MFC to the Qt toolkit as the application framework and thereby enabling the first release of a Linux version. After Version 7.8 the Version scheme changes to years with version 2021.1. == Interface and usability == WinFIG is designed to provide a clear, efficient and convenient graphical user interface. It allows working on multiple documents using an MDI user interface and provides unlimited undo and redo of actions. == Features == === Object creation === The basic types of objects in WinFIG are: Open and closed Splines Ellipses Polylines and Polygons Texts LaTeX formatted texts Arcs Images: PNG, GIF, JPEG, EPS and more Compound objects, which are hierarchical compositions of objects Objects can have several attributes, which depend on the object type: Line width Line style Line cap style Line join style Arrows Outline color, fill color and fill pattern === Object manipulation === move copy scale rotate align add/delete points from lines or splines copy object attributes Numerical input of point coordinates === Exports === WinFIG can export into various formats: Raster formats: GIF, JPEG, PNG, PPM, XBM, XPM, PCX, TIFF, SLD Formats for printed documents: PostScript, PDF, LaTeX, HP-GL (printer control language used by Hewlett-Packard plotters), Vector graphics formats: EPS, SVG, PSTricks, TPIC, PIC, CGM, Metafont, MetaPost, EMF, Tk. === Miscellaneous === Winfig can handle smart links. A smart link is a moving connection from a source to a target object. It is established by connecting the end point of a line or spline to another object. The connecting line or spline segment follows the movements of the target object. Smart links are useful for diagrams, graphs etc. WinFIG can show a grid and provides several magnet modes for constraining editing operations to discrete coordinates. Objects can be organized in layers to control their Z-order. This is important to control overlapping of filled shapes. Object library: drawings can be stored in a special sub-folder in the program installation directory, which makes them available in the library dialog for easy reuse.
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Vulnerability assessment (computing)

Vulnerability assessment is a process of defining, identifying and classifying the security holes in information technology systems. An attacker can exploit a vulnerability to violate the security of a system. Some known vulnerabilities are Authentication Vulnerability, Authorization Vulnerability and Input Validation Vulnerability. == Purpose == Before deploying a system, it first must go through from a series of vulnerability assessments that will ensure that the build system is secure from all the known security risks. When a new vulnerability is discovered, the system administrator can again perform an assessment, discover which modules are vulnerable, and start the patch process. After the fixes are in place, another assessment can be run to verify that the vulnerabilities were actually resolved. This cycle of assess, patch, and re-assess has become the standard method for many organizations to manage their security issues. The primary purpose of the assessment is to find the vulnerabilities in the system, but the assessment report conveys to stakeholders that the system is secured from these vulnerabilities. If an intruder gained access to a network consisting of vulnerable Web servers, it is safe to assume that he gained access to those systems as well. Because of assessment report, the security administrator will be able to determine how intrusion occurred, identify compromised assets and take appropriate security measures to prevent critical damage to the system. == Assessment types == Depending on the system a vulnerability assessment can have many types and level. === Host assessment === A host assessment looks for system-level vulnerabilities such as insecure file permissions, application level bugs, backdoor and Trojan horse installations. It requires specialized tools for the operating system and software packages being used, in addition to administrative access to each system that should be tested. Host assessment is often very costly in term of time, and thus is only used in the assessment of critical systems. Tools like COPS and Tiger are popular in host assessment. === Network assessment === In a network assessment one assess the network for known vulnerabilities. It locates all systems on a network, determines what network services are in use, and then analyzes those services for potential vulnerabilities. This process does not require any configuration changes on the systems being assessed. Unlike host assessment, network assessment requires little computational cost and effort. == Vulnerability assessment vs penetration testing == Vulnerability assessment and penetration testing are two different testing methods. They are differentiated on the basis of certain specific parameters. == Regulatory requirements == Vulnerability assessments are mandated or strongly recommended by several regulatory frameworks. In the United States healthcare sector, the Health Insurance Portability and Accountability Act (HIPAA) Security Rule requires covered entities to conduct periodic evaluations of their security posture, and a December 2024 Notice of Proposed Rulemaking would explicitly require vulnerability scanning at least every six months for systems containing electronic protected health information. The Payment Card Industry Data Security Standard (PCI DSS) requires quarterly vulnerability scans for organizations that process credit card transactions, and the NIST Cybersecurity Framework includes vulnerability assessment as a core component of its Identify function.
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Microsoft Fresh Paint

Fresh Paint is a painting app developed by Microsoft and released on May 25, 2012. == History == Fresh Paint originated from a Microsoft Research project known as Project Gustav, an endeavor to reproduce the behavior of physical oil paint on a digital medium. To push the boundaries of simulating oil on a digital medium, the research team created a physics model that precisely replicated on a screen what would happen in the real world if you combined oil, a surface and a tool such as a paint brush. Two publications, Detail-Preserving Paint Modeling for 3D Brushes and Simple Data-Driven Modeling of Brushes, were released as a result of the team’s findings. After a variety of internal testing Project, Gustav was codenamed Digital Art. Partnering with The Museum of Modern Art, Digital Art was tested for a year by 60,000 people. With feedback culled from MoMA, developers expanded the existing physics model, experimenting with how real oil paint blended and reacted to the texture of a canvas. After final adjustments were made, Digital Art was rebranded as Fresh Paint. It was released to the public on 25 May 2012.
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Clue (mobile app)

Clue is a menstrual health app developed by the Berlin-based technology company BioWink GmbH. The app has over 15 million users from 180 countries. The startup has raised over $17 million from backers that include Union Square Ventures and Mosaic Ventures. == History == Clue was co-founded by Ida Tin, Hans Raffauf, Mike LaVigne and Moritz von Buttlar in 2012. BioWink GmbH launched the app in 2013. Ida Tin's stated goal was to take female reproductive health “out of taboo land” and to start “a reproductive health revolution.” Tin previously led motorbike tours around the world and wrote a book about her experience. By July 2017, the Clue app had more than 8 million active users on both Android and iOS. Users were representative of more than 180 countries. In 2015, BioWink GmbH closed a $7 million Series A funding round led by Union Square Ventures and Mosaic Ventures, bringing the company's total funding to $10 million. The company was listed as one of Europe's Hottest Startups in 2015 by Wired UK, with Clue being named one of the best apps in 2015 by both Apple and Google. In March 2018, the company launched an editorial site to serve as a resource for accessible and scientific menstrual health information. == Mobile app == The Clue mobile application calculates and predicts a user's period, fertile window, and premenstrual syndrome. It also informs users the most or least likely time for becoming pregnant and allows them to track more than 30 health categories, including sex, sleep, pain, exercise, hair, skin, digestion, emotions and energy. The app can also explain how pill dosages impact fertility and includes an alarm system to allow for reminders for taking pills. In 2015, the company closed a Series A funding round and announced plans to use the proceeds to expand features of the mobile app and hire more staff. Clue also partnered with universities such as Stanford University, Columbia University, University of Washington, and University of Oxford to advance female health research. Clue integrated with Apple Inc.'s HealthKit for iOS 9 in September 2015, allowing data such as body temperature, cervical mucus quality, menstruation, ovulation test results, sexual activity, and spotting directly to the app. In 2016, Clue was available in 15 languages on both iOS and Android. That same year, Clue introduced a cycle-sharing feature and in 2017 a pill-tracking option. In February 2018, Clue made its app available on the Fitbit Ionic smartwatch. In 2026, Clue partnered with UK-based digital healthcare platform Evaro, an NHS-licensed provider, to offer embedded prescription services within the app.
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Optical granulometry

Optical granulometry is the process of measuring the different grain sizes in a granular material, based on a photograph. Technology has been created to analyze a photograph and create statistics based on what the picture portrays. This information is vital in maintaining machinery in various trades worldwide. Mining companies can use optical granulometry to analyze inactive or moving rock to quantify the size of these fragments. Forestry companies can zero in on wood chip sizes without stopping the production process, and minimize sizing errors. With more photoanalysis technologies being produced, mining companies have shown an increased interest in these types of systems because of their ability to maintain efficiency throughout the mining process. Companies are saving millions of dollars annually because of this new technology, and are cutting back on maintenance costs on equipment. In order for optical granulometry to be completely successful, an accurate photo must be taken – under sufficient lighting, and using proper technology – to obtain quantified results. If these requirements are met, an image analysis system can be implemented. == The process == Software uses four basic steps in determining the average size of material: See the Wikipedia article on Photoanalysis to see how mining, forestry and agricultural companies are using this technology to improve quality control techniques. == Smartphone-based, segmentation-free estimation of grain size distribution == Recently, a methodology has emerged by which soil grain size distribution can be inferred from optical images acquired with commodity smartphones by training convolutional neural networks to predict parameters of the distribution curve directly from the image, without explicit image segmentation . In this approach, a standardized image of a soil surface is captured under controlled conditions, preprocessed to reduce device-specific variability, and passed to a regression model that outputs the parameters of a cumulative distribution function e.g., a two-parameter Weibull curve. The resulting distribution can be used to derive geotechnical descriptors and class boundaries.
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Metadatabase

Metadatabase is a database model for (1) metadata management, (2) global query of independent databases, and (3) distributed data processing. The word metadatabase is an addition to the dictionary. Originally, metadata was only a common term referring simply to "data about data", such as tags, keywords, and markup headers. However, in this technology, the concept of metadata is extended to also include such data and knowledge representation as information models (e.g., relations, entities-relationships, and objects), application logic (e.g., production rules), and analytic models (e.g., simulation, optimization, and mathematical algorithms). In the case of analytic models, it is also referred to as a Modelbase. These classes of metadata are integrated with some modeling ontology to give rise to a stable set of meta-relations (tables of metadata). Individual models are interpreted as metadata and entered into these tables. As such, models are inserted, retrieved, updated, and deleted in the same manner as ordinary data do in an ordinary (relational) database. Users will also formulate global queries and requests for processing of local databases through the Metadatabase, using the globally integrated metadata. The Metadatabase structure can be implemented in any open technology for relational databases. == Significance == The Metadatabase technology is developed at Rensselaer Polytechnic Institute at Troy, New York, by a group of faculty and students (see the references at the end of the article), starting in late 1980s. Its main contribution includes the extension of the concept of metadata and metadata management, and the original approach of designing a database for metadata applications. These conceptual results continue to motivate new research and new applications. At the level of particular design, its openness and scalability is tied to that of the particular ontology proposed: It requires reverse-representation of the application models in order to save them into the meta-relations. In theory, the ontology is neutral, and it has been proven in some industrial applications. However, it needs more development to establish it for the field as an open technology. The requirement of reverse-representation is common to any global information integration technology. A way to facilitate it in the Metadatabase approach is to distribute a core portion of it at each local site, to allow for peer-to-peer translation on the fly.
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Afghan Girls Robotics Team

The Afghan Girls Robotics Team, also known as the Afghan Dreamers, is an all-girl robotics team from Herat, Afghanistan, founded through the Digital Citizen Fund (DCF) in 2017 by Roya Mahboob and Alireza Mehraban. It is made up of girls between ages 12 and 18 and their mentors. Several members of the team were relocated to Qatar and Mexico by humanitarian and tech entrepreneur Sarah Porter following the fall of Kabul in August 2021. A documentary film featuring members of the team, titled Afghan Dreamers, was released by MTV Documentary Films in 2023. == Origins == The Afghan Girls Robotics Team was co-founded in 2017 by Roya Mahboob, who is their coach, mentor and sponsor, and founder of the Digital Citizen Fund (DCF), which is the parent organization for the team. Dean Kamen was planning a 2017 competition in the United States and had recruited Mahboob to form a team from Afghanistan. Out of 150 girls, 12 were selected for the first team. Before parts were sent by Kamen, they trained in the basement of the home of Mahboob's parents, with scrap metal and without safety equipment under the guidance of their coach, Mahboob's brother Alireza Mehraban, who is also a co-founder of the team. == 2017 and 2018 == In 2017, six members of the Afghan Girls Robotics Team traveled to the United States to participate in the international FIRST Global Challenge robotics competition. Their visas were rejected twice after they made two journeys from Herat to Kabul through Taliban-controlled areas, before officials in the United States government intervened to allow them to enter the United States. Customs officials also detained their robotics kits, which left them two weeks to construct their robot, unlike some teams that had more time. They were awarded a Silver medal for Courageous Achievement. One week after they returned home from the competition, the father of team captain Fatemah Qaderyan, Mohammad Asif Qaderyan, was killed in a suicide bombing. After their United States visas expired, the team participated in competitions in Estonia and Istanbul. Three of the 12 members participated in the 2017 Entrepreneurial Challenge at the Robotex festival in Estonia, and won the competition for their solar-powered robot designed to assist farmers. In 2018, the team trained in Canada, continued to travel in the United States for months and participate in competitions. == 2019 == The Afghan Girls Robotics team had aspirations to develop a science and technology school for girls in Afghanistan. Roya Mahboob interfaced with the School of Engineering and Applied Sciences (SEAS), the School of Architecture, and the Whitney and Betty MacMillan Center for International and Area Studies Yale University to design the infrastructure for what they named The Dreamer Institute. == 2020 == In March 2020, the governor of Herat at the time, in response to the COVID-19 pandemic in Afghanistan and a scarcity of ventilators, sought help with the design of low-cost ventilators, and the Afghan Girls Robotics Team was one of six teams contacted by the government. Using a design from Massachusetts Institute of Technology and with guidance from MIT engineers and Douglas Chin, a surgeon in California, the team developed a prototype with Toyota Corolla parts and a chain drive from a Honda motorcycle. UNICEF also supported the team with the acquisition of necessary parts during the three months they spent building the prototype that was completed in July 2020. Their design costs around $500 compared to $50,000 for a ventilator. In December 2020, Minister of Industry and Commerce Nizar Ahmad Ghoryani donated funding and obtained land for a factory to produce the ventilators. Under the direction of their mentor Roya Mahboob, the Afghan Dreamers also designed a UVC Robot for sanitization, and a Spray Robot for disinfection, both of which were approved by the Ministry of Health for production. == 2021 == In early August 2021, Somaya Faruqi, former captain of the team, was quoted by Public Radio International about the future of Afghanistan, stating, "We don’t support any group over another but for us what’s important is that we be able to continue our work. Women in Afghanistan have made a lot of progress over the past two decades and this progress must be respected." On August 17, 2021, the Afghan Girls Robotics Team and their coaches were reported to be attempting to evacuate, but unable to obtain a flight out of Afghanistan, and a lawyer appealed to Canada for assistance regarding the evacuation of the team members. As of August 19, 2021, nine members of the team and their coaches had evacuated to Qatar. The founder of the team, Roya Mahboob, and DCF board member, Elizabeth Schaeffer Brown, were previously in contact with the Qatari government to assist the team members in their evacuation from Afghanistan. By August 25, 2021, some members arrived in Mexico. Saghar, a team member who evacuated to Mexico, said, "We wanted to continue the path that we started to continue to go for our achievements and to go for having our dreams through reality. So that's why we decided to leave Afghanistan and go for somewhere safe" in an interview with The Associated Press. The members who have left Afghanistan participated in an online robotics competition in September and plan to continue their education. A documentary film titled Afghan Dreamers, produced by Beth Murphy and directed by David Greenwald, was in post-production when the team began to evacuate. == 2022 == The Afghan Dreamers were involved in a training program at the Texas A&M University at Qatar’s STEM Hub. == 2023 == The Afghan Girls Robotics Team had a booth at the 5th UN Conference on the Least Developed Countries, where they displayed some of the robots the team had constructed. == Afghan Dreamers documentary == The Afghan Dreamers documentary from MTV Documentary Films premiered in May 2023 on Paramount+. The film was directed by David Greenwald and produced by David Cowan and Beth Murphy. In a review for Screen Daily, Wendy Ide wrote, "This film, with its likeable cast of girl nerds and positive message, should enjoy a warm reception on the festival circuit, and will be of particular interest to events seeking to showcase women's stories from around the world. It also serves as a timely cautionary tale – a case study on just how quickly the rights and the opportunities of women can be curtailed, at the behest of the men in power." == Honors and awards == 2017 Silver medal for Courageous Achievement at the FIRST Global Challenge, science and technology 2017 Benefiting Humanity in AI Award at World Summit AI 2017 Winner, Entrepreneurship Challenge at Robotex in Estonia 2018 Permission to Dream Award, Raw Film Festival 2018 Conrad Innovation Challenge, Raw Film Festival 2018 Rookie All Star – District Championship, Canada 2018 Asia Game Changer Award Honoree 2019 Inspiring in Engineering Award – FIRST Detroit World Championship 2019 Asia Game Changer Award of California 2019 Safety Award – FIRST Global, Dubai 2021 Forbes 30 Under 30 Asia 2022 World Championships, Genoa, Switzerland
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Photoanalysis

Photoanalysis (or photo analysis) refers to the study of pictures to compile various types of data, for example, to measure the size distribution of virtually anything that can be captured by photo. Photoanalysis technology has changed the way mines and mills quantify fragmented material. Images are an effective way to document conditions before, after, and even during blasting activities. The technology is advancing at a high rate, and lenses, storage media memory, light sensitivity and resolution have been improving steadily. Today's digital cameras and camcorders include high-resolution optics, compact size, automatic time and date stamps, good battery life, shutters to freeze motion, and computers to autofocus and eliminate jitter using image stabilization. == Mining == Photoanalysis in mining operations can provide an automated system that forewarns a company of potential problems with materials, leading to economies and reduced damage caused from over-sized materials. It can also help determine the effectiveness of blasts. A company can use this technology to monitor materials moving on a conveyor belt in an underground environment, to measure piles left over from a blast, and even measure the amount of material being carried by dump trucks or vessels to a destination. Photoanalysis is being used on SAG mills worldwide to control the size of rock being crushed. Companies are using this technology to determine the size of particles being processed in the SAG Mill.[1] Archived 2009-05-23 at the Wayback Machine Having oversize material entering the SAG mill makes an operation less efficient, costing companies money in electrical and maintenance costs. Photoanalysis technology can eliminate unwanted material before it enters the mill, keeping rock crushing costs low. == Forestry == Wood chip size can affect the overall quality of a product. With automated photoanalysis systems, companies can remove any unwanted wrong-size particles without stopping their mill process. Photoanalysis can affect how efficiently forestry companies operate. In mills worldwide, photoanalysis technology is improving the use of lumber products, cutting back on the amount of trees being used to operate, and saving companies money through quality control optimization.[2] With the current downturn in the North American forestry industry, operators are looking at making their mills more efficient and effective when processing materials. Photoanalysis technology helps identify any weaknesses in the process by continuously monitoring different sections of an operation. == Agriculture == Agricultural companies can, using photoanalysis, monitor conveyor belts of food without contaminating the product by touching it. Other benefits of photoanalysis systems include: Automated removal of any unwanted material on food conveyor Improved quality control for the most important parts of the agricultural process Pinpoint accuracy that helps the efficiency and effectiveness of product handling techniques The importance of photoanalysis technology is being noticed by the agricultural industry as it identifies any unwanted materials going through the process. In an example, if a mouse is on a conveyor of corn, photoanalysis technology would be able to identify the unwanted object and remove it before it contaminates the whole process. == Origins of photoanalysis technology == Photoanalysis technology was created by using the Waterloo Image Enhancement Process in the 1980s. After further development of the imaging process with explosives producer DuPont, engineers Tom Palangio and Takis Katsabanis began selling photoanalysis software commercially. They later renamed the process WipFrag, standing for Waterloo Image Process Fragmentation Today, photoanalysis technology has evolved into stabilized and portable systems that can automatically capture and analyze results instantly. Thousands of these products are currently being used around the world to measure fragmented material. == Photoanalysis equipment photos == == Fragmentation analysis == Fragmentation analysis is becoming a popular term in mining, agricultural and forestry industries. With the majority of money in these industries directed towards the proper sizing of materials, companies are using fragmentation analysis to determine various factors within an operation.[3] The two main ways a company keeps track of fragmented material are through manual and automated sieving procedures. Manual sieving involves extracting a sample of material to analyze the size distribution. The results can be tabulated within two days. Automated sieving is an advanced way of sieving materials running through a process. Without having to extract the material, photoanalysis can take place, allowing for immediate results with pinpoint accuracy. == Blast Fragmentation Software == Operators are using fragmentation analysis to determine the effectiveness of various blasts. With automated sieving technology, workers can track the success of these blasts and receive instant results. Companies are using these results to determine what blasting method yielded the best results for their specific operation. The common variables associated with blast optimization are the provided Particle Size Distribution (PSD) from a shovel fragmentation system, geology including rock type and fracturing, and energy factor. By using photoanalysis the fragmented materials can be monitored, offering pinpoint accuracy and allowing mine operators to make adjustments to future blasting procedures. See Optical Granulometry to view the automated sieving process. == Pre-crushing analysis == Maintenance costs can be significantly reduced if an operation focuses on the fragmentation of the particles passing through their process. Automated sieving systems can detect and help remove any oversize material before it enters the crusher and causes maintenance problems. It also helps determine the effectiveness of the mining process prior to crushing; the sizing of material is always a critical part of operations in the mining, forestry and agricultural industries. Having an analysis taking place at every major point in an operation allows for the proper tracking of material being processed. Engineers can then determine what part of the process needs improving based solely on the size of material. == Post-crushing analysis == Measuring how effective industrial crushers are, can help save a company millions of dollars in energy costs on an annual basis. There are two components that affect a typical crusher: the size of the material inputted, and the speed at which the crusher is moving. If the user can find a perfect balance between these two components, the materials will be crushed to the right size in the shortest time possible. Meeting the material standards set by governments and large companies can be hard. Having a post-crushing analysis taking place ensures that no oversize material gets shipped; eliminating the chance of getting fined for not meeting industry specifications.
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Phase stretch transform

Phase stretch transform (PST) is a computational approach to signal and image processing. One of its utilities is for feature detection and classification. PST is related to time stretch dispersive Fourier transform. It transforms the image by emulating propagation through a diffractive medium with engineered 3D dispersive property (refractive index). The operation relies on symmetry of the dispersion profile and can be understood in terms of dispersive eigenfunctions or stretch modes. PST performs similar functionality as phase-contrast microscopy, but on digital images. PST can be applied to digital images and temporal (time series) data. It is a physics-based feature engineering algorithm. == Operation principle == Here the principle is described in the context of feature enhancement in digital images. The image is first filtered with a spatial kernel followed by application of a nonlinear frequency-dependent phase. The output of the transform is the phase in the spatial domain. The main step is the 2-D phase function which is typically applied in the frequency domain. The amount of phase applied to the image is frequency dependent, with higher amount of phase applied to higher frequency features of the image. Since sharp transitions, such as edges and corners, contain higher frequencies, PST emphasizes the edge information. Features can be further enhanced by applying thresholding and morphological operations. PST is a pure phase operation whereas conventional edge detection algorithms operate on amplitude. == Physical and mathematical foundations of phase stretch transform == Photonic time stretch technique can be understood by considering the propagation of an optical pulse through a dispersive fiber. By disregarding the loss and non-linearity in fiber, the non-linear Schrödinger equation governing the optical pulse propagation in fiber upon integration reduces to: E o ( z , t ) = 1 2 π ∫ − ∞ ∞ E ~ i ( 0 , ω ) ⋅ e − i β 2 z ω 2 2 ⋅ e i ω t d ω {\displaystyle E_{o}(z,t)={\frac {1}{2\pi }}\int _{-\infty }^{\infty }{\tilde {E}}_{i}(0,\omega )\cdot e^{\frac {-i\beta _{2}z\omega ^{2}}{2}}\cdot e^{i\omega {t}}\,d\omega } (1) where β 2 {\displaystyle \beta _{2}} = GVD parameter, z is propagation distance, E o ( z , t ) {\displaystyle E_{o}(z,t)} is the reshaped output pulse at distance z and time t. The response of this dispersive element in the time-stretch system can be approximated as a phase propagator as presented in H ( ω ) = e i φ ( ω ) = e i ∑ m = 0 ∞ φ m ( ω ) = ∏ m = 0 ∞ H m ( ω ) {\displaystyle H(\omega )=e^{i\varphi (\omega )}=e^{i\sum _{m=0}^{\infty }\varphi _{m}(\omega )}=\prod _{m=0}^{\infty }H_{m}(\omega )} (2) Therefore, Eq. 1 can be written as following for a pulse that propagates through the time-stretch system and is reshaped into a temporal signal with a complex envelope given by E o ( t ) = 1 2 π ∫ − ∞ ∞ E ~ i ( ω ) ⋅ H ( ω ) ⋅ e i ω t d ω {\displaystyle E_{o}(t)={\frac {1}{2\pi }}\int _{-\infty }^{\infty }{\tilde {E}}_{i}(\omega )\cdot H(\omega )\cdot e^{i\omega t}\,d\omega } (3) The time stretch operation is formulated as generalized phase and amplitude operations, S { E i ( t ) } = ∫ − ∞ + ∞ F { E i ( t ) } ⋅ e i φ ( ω ) ⋅ L ~ ( ω ) ⋅ e i ω t d ω {\displaystyle \mathbb {S} \{E_{i}(t)\}=\int _{-\infty }^{+\infty }{\mathcal {F}}\{E_{i}(t)\}\cdot e^{i\varphi (\omega )}\cdot {\tilde {L}}(\omega )\cdot e^{i\omega {t}}d\omega } (4) where e i φ ( ω ) {\displaystyle e^{i\varphi (\omega )}} is the phase filter and L ~ ( ω ) {\displaystyle {\tilde {L}}(\omega )} is the amplitude filter. Next the operator is converted to discrete domain, S { E i [ n ] } = 1 N ∑ u = 0 N − 1 F F T { E i ( n ) } ⋅ K ~ ( u ) ⋅ L ~ ( u ) ⋅ e i 2 π N u n {\displaystyle \mathbb {S} \{E_{i}[n]\}={\frac {1}{N}}\sum _{u=0}^{N-1}FFT\{E_{i}(n)\}\cdot {\tilde {K}}(u)\cdot {\tilde {L}}(u)\cdot e^{i{\frac {2\pi }{N}}un}} (5) where u {\displaystyle u} is the discrete frequency, K ~ ( u ) {\displaystyle {\tilde {K}}(u)} is the phase filter, L ~ ( u ) {\displaystyle {\tilde {L}}(u)} is the amplitude filter and FFT is fast Fourier transform. The stretch operator S { } {\displaystyle \mathbb {S} \{\}} for a digital image is then S { E i [ n , m ] } = 1 M N ∑ v = 0 N − 1 ∑ u = 0 M − 1 F F T 2 { E i ( n , m ) } ⋅ K ~ ( u , v ) ⋅ L ~ ( u , v ) ⋅ e i 2 π M u m ⋅ e i 2 π N v n {\displaystyle \mathbb {S} \{E_{i}[n,m]\}={\frac {1}{MN}}\sum _{v=0}^{N-1}\sum _{u=0}^{M-1}FFT^{2}\{E_{i}(n,m)\}\cdot {\tilde {K}}(u,v)\cdot {\tilde {L}}(u,v)\cdot e^{i{\frac {2\pi }{M}}um}\cdot e^{i{\frac {2\pi }{N}}vn}} (6) In the above equations, E i [ n , m ] {\displaystyle E_{i}[n,m]} is the input image, n {\displaystyle n} and m {\displaystyle m} are the spatial variables, F F T 2 {\displaystyle FFT^{2}} is the two-dimensional fast Fourier transform, and u {\displaystyle u} and v {\displaystyle v} are spatial frequency variables. The function K ~ ( u , v ) {\displaystyle {\tilde {K}}(u,v)} is the warped phase kernel and the function L ~ ( u , v ) {\displaystyle {\tilde {L}}(u,v)} is a localization kernel implemented in frequency domain. PST operator is defined as the phase of the Warped Stretch Transform output as follows P S T { E i [ n , m ] } ≜ ∡ { S { E i [ x , y ] } } {\displaystyle PST\{E_{i}[n,m]\}\triangleq \measuredangle \{\mathbb {S} \{E_{i}[x,y]\}\}} (7) where ∡ { } {\displaystyle \measuredangle \{\}} is the angle operator. == PST kernel implementation == The warped phase kernel K ~ ( u , v ) {\displaystyle {\tilde {K}}(u,v)} can be described by a nonlinear frequency dependent phase K ~ ( u , v ) = e i φ ( u , v ) {\displaystyle {\tilde {K}}(u,v)=e^{i\varphi (u,v)}} While arbitrary phase kernels can be considered for PST operation, here we study the phase kernels for which the kernel phase derivative is a linear or sublinear function with respect to frequency variables. A simple example for such phase derivative profiles is the inverse tangent function. Consider the phase profile in the polar coordinate system φ ( u , v ) = φ polar ( r , θ ) = φ polar ( r ) {\displaystyle \varphi (u,v)=\varphi _{\text{polar}}(r,\theta )=\varphi _{\text{polar}}(r)} From d φ ( r ) d r = tan − 1 ⁡ ( r ) {\displaystyle {\frac {d\varphi (r)}{dr}}=\tan ^{-1}(r)} we have φ ( r ) = r tan − 1 ⁡ ( r ) − 1 2 log ⁡ ( r 2 + 1 ) {\displaystyle \varphi (r)=r\tan ^{-1}(r)-{\frac {1}{2}}\log(r^{2}+1)} Therefore, the PST kernel is implemented as φ ( r ) = S ⋅ ( W r ) ⋅ tan − 1 ⁡ ( W r ) − 1 2 log ⁡ ( 1 + ( W r ) 2 ) ( W r max ) ⋅ tan − 1 ⁡ ( W r max ) − 1 2 log ⁡ ( 1 + ( W r max ) 2 ) {\displaystyle \varphi (r)=S\cdot {\frac {(Wr)\cdot \tan ^{-1}(Wr)-{\frac {1}{2}}\log(1+(Wr)^{2})}{(Wr_{\max })\cdot \tan ^{-1}(Wr_{\max })-{\frac {1}{2}}\log(1+(Wr_{\max })^{2})}}} where S {\displaystyle S} and W {\displaystyle W} are real-valued numbers related to the strength and warp of the phase profile == Applications == PST has been used for edge detection in biological and biomedical images as well as synthetic-aperture radar (SAR) image processing, as well as detail and feature enhancement for digital images. PST has also been applied to improve the point spread function for single molecule imaging in order to achieve super-resolution. The transform exhibits intrinsic superior properties compared to conventional edge detectors for feature detection in low contrast visually impaired images. The PST function can also be performed on 1-D temporal waveforms in the analog domain to reveal transitions and anomalies in real time. == Open source code release == On February 9, 2016, a UCLA Engineering research group has made public the computer code for PST algorithm that helps computers process images at high speeds and "see" them in ways that human eyes cannot. The researchers say the code could eventually be used in face, fingerprint, and iris recognition systems for high-tech security, as well as in self-driving cars' navigation systems or for inspecting industrial products. The Matlab implementation for PST can also be downloaded from Matlab Files Exchange. However, it is provided for research purposes only, and a license must be obtained for any commercial applications. The software is protected under a US patent. The code was then significantly refactored and improved to support GPU acceleration. In May 2022, it became one algorithm in PhyCV: the first physics-inspired computer vision library.
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Nanosemantics

Nanosemantics Lab is a Russian IT company specializing in natural language processing (NLP), computer vision (CV), speech technologies (ASR/TTS) and creation of interactive dialog interfaces, particularly chatbots and virtual assistants, based on artificial intelligence (AI). The company uses neural network platforms, including its own-made platform PuzzleLib which works on Russian-made microprocessor architecture Elbrus and Russia-based Astra Linux operating system. The company was founded in 2005 by Igor Ashmanov and Natalya Kaspersky. == Profile == The company was one of the first on Russian market to develop dialog interfaces for different branches of businesses, as well as to support community of AI developers. The company's most demanded product, as for beginning of the 2020s, is the automated "online advisers", functioning as chat bots, made for helping customers with usage of commercial products. In 2009 the company released an online service called iii.ru, where visitors were able to create their own AI-based virtual personalities entitles "infs" (for free). A visitor was able to train its own "inf" and let them chat to other "live" visitors as well with other "infs". More than 2.3 million of "infs" were created and trained by visitors over several years. Nanosemantics Lab maintains its own linguistic programming language for AI development called Dialog Language (DL). Popular social networks and instant messaging services may be used as base platforms. Nanosemantics' AI bots support different types of businesses: banks and financial services, telecommunications, retail, travel and automobile industry, home appliances production, etc. Among its solutions, Nanosemantics lists projects for various companies and institutions, among them VTB, Beeline, MTS, Sberbank, Higher School of Economics, Webmoney, Gazpromneft, Rostelecom, Ford Motors, Ministry of Health of the Russian Federation and others. The company uses the term "inf" for naming its numerous types of chat bots. The term was coined by co-founder Igor Ashmanov, head of Ashmanov & Partners. A 2014 scholarly research at Higher School of Economics, called "Basics of Business Informatics", states that such "infs", when used at business, may lower load on employees, collect statistics useful for understanding market demand and also may increase customer loyalty by providing fast and informative answers due to usage of large databases. The same research describes Nanosemantics' project for Russian branch of Ford Motors company, when AI capabilities were used for promoting the car model Ford Kuga. The research pointed out that within 2 months since beginning, the promo-website conducted 47774 talks of visitors with the specialized "inf", which indicated several hundred thousand of questions and the longest chat lasted for 3 hours 10 minutes. One-year promo campaign showed that 28.6% of people who made pre-orders talked to an "inf". In 2016 Nanosemantics launched a SaaS platform aimed at creating customized virtual assistants by users. The company's flagship product is considered to be Dialog Operating System (DialogOS), a professional corporate platform for creating intellectual voice and textual bots. It has its own linguistic programming language for creation of flexible scenarios and ready-studied neural natural language processing modules that are able to understand human interlocutors. In 2021 the company presented technology called NLab Speech ASR which contains a set of neural-networking algorithms for processing audio signals and analysis of texts that were trained and calibrated using speech-based big data marked up manually. The technology allows speed of processing of data up to "6 real-time factor" and precision values in noisy audio data may exceed 82%. In March 2022 the technology was included in Russia's Joint Registry for Russian Programs for Computers and Databases. As well, another technology was included: NLab Speech TTS, which is text-to-speech system that produces synthesized speech from printed text. == Joint projects == Nanosemantics participates in Ashmanov & Partners' projects related to AI. Since 2014, it helps in development of hardware "personal assistant" called Lexy, a solution similar to Amazon Alexa and the analogues. In August 2019 it was announced that Nanosemantics is going to participate in creation of open operating system for creating automated voice assistants. The project was called SOVA (Smart Open Virtual Assistant) and received investment of 300 million roubles (~$4,6 million) from Russian state-maintained National Technological Initiative. The company maintains long-term partnerships with Skolkovo Innovation Center (resident of IT cluster), branch association "Neuronet" and Yandex. Together with USA-based startup Remedy Logic, Nanosemantics has developed a medical diagnostic system for finding, using AI, spinal pathologies in tomography images of human bodies. Among them: central, foraminal and lateral lumbar stenosis, hernias, arthrosis. The system offers options of treatment. Since August 2021 the company is the resident of Technology Valley of Moscow State University. Also in 2021, Nanosemantics became a member of Committee on Artificial Intelligence within the Russian Association of Software Developers "Native Soft". The company states as one of its missions support of initiatives aimed at preservation and development of the Russian language. In May 2021, together with Pushkin Institute, the company created a chat bot called Phil, that explains to Russian people meaning of different Russian neologisms, and offers synonyms for them. Bot's vocabulary contains more than 500 neologisms, as well the bot can give advice on jargonisms and other types of specific words. Also in 2021, Nanosemanics Lab has signed the first-ever Russian "Codex of ethics of artificial intelligence". It establishes guidelines for ethical behavior of businesses that implement AI-based solutions. === IT contests === The company regularly organizes All-Russian Turing Test competitions for IT developers. Some of these events are co-organized with Microsoft. During the competitions, judges randomly choose virtual interlocutor and have a short conversation with them. They have to determine if a human or a machine is talking to them. An interlocutor may be either a bot or its human creator or operator. The results are measured in per cent of judges that were successfully convinced by a machine that it was a human. In 2021 Nanosemantics took part in federal project "Artificial Intelligence" by National Technological Initiative. In December 2021 the company together with state enterprise "Resource Center of Universal Design and Rehabilitation Technologies" (RCUD-RT) held an all-Russian hackathon aimed at development of AI solutions for medicine. During 3 days, participants created several training programs for patients with speech disorders. In April 2022, another hackathon by Nanosemantics was held together with MIREA – Russian Technological University. Students were participating and trying to generate algorithms for voice deepfakes. 17 teams contested in creation of software that generated artificial voice of a certain person. == Recognition == Since its foundation, Nanosemantics Lab has received a number of recognitions and awards. Among them are several professional ROTOR awards for the website iii.ru (created in 2009). The website gives the general public the means to create and train virtual assistants, which can then be used on a website or integrated into social networks. In 2013, a virtual assistant called Dana, created for Beeline Kazakhstan, was awarded with professional prize "Crystal Headset" in nomination "the best applying of technology". In 2015, the RBTH international media service included Nanosemantics in its list of "Top 50 Startups" in Russia. In 2016, the company received Russian state-maintained award called Runet Prize in two nominations: "State and Society" and "Technology and Innovation". In 2021, in Velikiy Novgorod, Nanosemantics team has won a hackathon aimed at finding means of discovering corruption schemes in Russian laws. In February 2022 the company won another contest by National Technological Initiative, called "Prochtenie", aimed at creation of AI systems for checking schoolchildren's school essays. The Nanosemantics team was awarded 20 million rubles for "overcoming technological barrier" in contest dedicated to English language, and 12 million for 1st place in special nomination "Structure" in Russian-language essay contest.
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Sensory, Inc.

Sensory, Inc. is an American company which develops software AI technologies for speech, sound and vision. It is based in Santa Clara, California. Sensory’s technologies have shipped in over three billion products from hundreds of leading consumer electronics manufacturers including AT&T, Hasbro, Huawei, Google, Amazon, Samsung, LG, Mattel, Motorola, Plantronics, GoPro, Sony, Tencent, Garmin, LG, Microsoft, Lenovo, and more. Sensory has over 60 issued patents covering speech recognition in consumer electronics, biometric authentication, sensor/speech combinations, wake word technology, and more. == History == Sensory, Inc. was founded in 1994, originally as Sensory Circuits, by Forrest Mozer, Mike Mozer and Todd Mozer. The three had also co-founded ESS Technology years earlier. In 1999 Sensory acquired Fluent Speech Technologies, which was formed and started by a group of professors out of the Oregon Graduate Institute (formerly OGI, now OHSU). Fluent Speech Technologies developed high performance embedded speech engines, the technology from this acquisition is now the core technology used throughout Sensory's chip and software line. === Company timeline === 1994 – Founded 1995 – Introduces the RSC 164 - first commercially successful speech recognition IC 1998 – Introduces first speaker verification IC 2000 – Acquires Oregon based Fluent-Speech Technologies 2002 – Acquires Texas Instruments line of speech output ICs (the SC series) 2007 – Introduces first Voice User Interface for Bluetooth silicon (CSR BC-5) - BlueGenie 2008 - Sensory and BlueAnt partner on the V1 - Revolutionary new Bluetooth headset with a voice user interface. First wearable to use a voice user interface for control and best-reviewed speech recognition product in history 2009 – Introduced world's smallest text to speech system (TTS) and Truly HandsfreeTM Triggers/ wake words. 2010 – Introduced the NLP-5x – First Natural Language Voice Processor and TrulyHandsfree wake words in SDKs for Android, iOS, Linux, and Windows. NLP5x used the first generation of TrulyHandsfree wake words with low power and enhanced accuracy. 2011 – Sensory partners with Google and Microsoft to enable TrulyHandsfree as a front end to Goog411 and Bing411 2012 – Partnered with Tensilica to offer ultra-low power TrulyHandsfree wake words; introduced Speaker Verification and Speaker Identification for mobile phones and other consumer electronics. 2012 - TrulyHandsfree released into Samsung's Galaxy S2 for "Hey Galaxy" wake word 2013 – TrulyHandsfree wake words migrated to many new platforms and began shipping as MotoVoice in the Google-owned MotoX. Sensory's TrulyHandsfree in mobile takes off with the Galaxy S3 and S4 and Galaxy Note and is licensed into wearables like Google Glass. 2014 – Announced new initiative in Vision; added LG and Motorola as customers; received the 2014 Global Mobile Award for Best Mobile Technology Breakthrough at the GSMA Mobile World Congress in Barcelona, Spain (judges commented, "A big advance for the wearables market, this offers many benefits for consumers, increasing uptake and usage of many mobile apps, driving revenue for operators and content providers.") 2015-2018 - Licensed Google, Amazon, MSFT, Baidu, Huawei, ZTE, and many others with TrulyHandsfree wake words. Sensory develops first wake words for OK Google, Hey Siri, and Hey Cortana. 2019 - Sensory launched two new solutions: SoundID, sound identification, and TrulyNatural, embedded large vocabulary speech recognition. Sensory also acquired Vocalize.ai, an independent testing lab. 2020 - Sensory introduced VoiceHub, which allows the automated generation of wake words. 2021 - Sensory expands VoiceHub with speech recognition and NLU capabilities. The company initiated a new cloud platform, SensoryCloud.ai. 2022-Sensory rolls out SensoryCloud.ai with speech to text, text to speech, face & voice biometrics 2024- Sensory Automotive & TrulyNatural Speech-to-text On-Device launched == Technology and products == Sensory originally developed both hardware (Integrated Circuit - IC or "chip") and software platforms but migrated to software only around 2005 and added cloud and hybrid computing capabilities in 2021. Sensory's RSC-164 IC (Integrated Circuit or "chip") was used on NASA's Mars Polar Lander in the Mars Microphone on the Lander. Speech Synthesis SC-6x chips – acquired some speech synthesis technology from Texas Instruments. Sensory’s embedded AI solutions include the following: TrulyHandsfree (THF) - wake word detection and phrase spotting. TrulyNatural (TNL) - large vocabulary continuous speech recognition with NLU. TrulySecure (TS) - face and voice biometrics. TrulySecureSpeakerVerification (TSSV) - speaker and sound identification. VoiceHub - Online portal for creating custom wake words and speech recognition models with NLU. Sensory Automotive- Sensory Automotive is a full voice and vision suite of AI technologies that operate efficiently in the car without connecting to a network. The cloud initiative, SensoryCloud.ai, is targeting Speech To Text (STT), Text To Speech (TTS), Wake Word verification, face and voice recognition, and sound identification.
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Multi-focus image fusion

Multi-focus image fusion is a multiple image compression technique using input images with different focus depths to make one output image that preserves all information. == Overview == The main idea of image fusion is gathering important and the essential information from the input images into one single image which ideally has all of the information of the input images. The research history of image fusion spans over 30 years and many scientific papers. Image fusion generally has two aspects: image fusion methods and objective evaluation metrics. In visual sensor networks (VSN), sensors are cameras which record images and video sequences. In many applications of VSN, a camera can't give a perfect illustration including all details of the scene. This is because of the limited depth of focus of the optical lens of cameras. Therefore, just the object located in the focal length of camera is focused and clear, and other parts of the image are blurred. VSN captures images with different depths of focus using several cameras. Due to the large amount of data generated by cameras compared to other sensors such as pressure and temperature sensors and some limitations of bandwidth, energy consumption and processing time, it is essential to process the local input images to decrease the amount of transmitted data. == Multi-Focus image fusion in the spatial domain == Huang and Jing have reviewed and applied several focus measurements in the spatial domain for the multi-focus image fusion process, suitable for real-time applications. They mentioned some focus measurements including variance, energy of image gradient (EOG), Tenenbaum's algorithm (Tenengrad), energy of Laplacian (EOL), sum-modified-Laplacian (SML), and spatial frequency (SF). Their experiments showed that EOL gave better results than other methods like variance and spatial frequency. == Multi-Focus image fusion in multi-scale transform and DCT domain == Image fusion based on the multi-scale transform is the most commonly used and promising technique. Laplacian pyramid transform, gradient pyramid-based transform, morphological pyramid transform and the premier ones, discrete wavelet transform, shift-invariant wavelet transform (SIDWT), and discrete cosine harmonic wavelet transform (DCHWT) are some examples of image fusion methods based on multi-scale transform. These methods are complex and have some limitations e.g. processing time and energy consumption. For example, multi-focus image fusion methods based on DWT require a lot of convolution operations, so they take more time and energy to process. Therefore, most methods in multi-scale transform are not suitable for real-time applications. Moreover, these methods are not very successful along edges, due to the wavelet transform process missing the edges of the image. They create ringing artefacts in the output image and reduce its quality. Due to the aforementioned problems in the multi-scale transform methods, researchers are interested in multi-focus image fusion in the DCT domain. DCT-based methods are more efficient in terms of transmission and archiving images coded in Joint Photographic Experts Group (JPEG) standard to the upper node in the VSN agent. A JPEG system consists of a pair of an encoder and a decoder. In the encoder, images are divided into non-overlapping 8×8 blocks, and the DCT coefficients are calculated for each. Since the quantization of DCT coefficients is a lossy process, many of the small-valued DCT coefficients are quantized to zero, which corresponds to high frequencies. DCT-based image fusion algorithms work better when the multi-focus image fusion methods are applied in the compressed domain. In addition, in the spatial-based methods, the input images must be decoded and then transferred to the spatial domain. After implementation of the image fusion operations, the output fused images must again be encoded. DCT domain-based methods do not require complex and time-consuming consecutive decoding and encoding operations. Therefore, the image fusion methods based on DCT domain operate with much less energy and processing time. Recently, a lot of research has been carried out in the DCT domain. DCT+Variance, DCT+Corr_Eng, DCT+EOL, and DCT+VOL are some prominent examples of DCT based methods.
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