ACT! (previously known as Activity Control Technology, Automated Contact Tracking, ACT! by Sage, and Sage ACT!) is a customer relationship management and marketing automation software platform designed for small and medium-sized businesses. It has over 2.8 million registered users as of December 2014. == History == The company Conductor Software was founded in 1986, in Dallas, Texas, by Pat Sullivan and Mike Muhney. The original name for the software was Activity Control Technology; it was renamed to Automated Contact Tracking, later abbreviated to ACT. The name of the company was subsequently changed to Contact Software International and it was sold in 1993 to Symantec Corporation, who in 1999 then sold it to SalesLogix. The Sage Group purchased Interact Commerce (formerly SalesLogix) in 2001 through Best Software, then its North American software division. Swiftpage acquired it in 2013. Beginning with the 2006 version, the name was styled ACT! by Sage, and in 2010 revised to Sage ACT!. Following its 2013 acquisition by Swiftpage, it was renamed to ACT! Swiftpage. In May 2018, ACT! was sold to SFW Advisors. In December 2018, Kuvana, a marketing automation software solution, was acquired by SFW and merged with ACT! This add-on is now a complementary service to the core CRM solution. In December 2019, ACT! hired Steve Oriola as chairman and CEO. In 2020, Swiftpage changed its company name to ACT!. In March 2023, ACT! hired Bruce Reading as President and CEO. == Software == ACT! features include contact, company and opportunity management, a calendar, marketing automation and e-marketing tools, reports, interactive dashboards with graphical visualizations, and the ability to track prospective customers. ACT! integrates with Microsoft Word, Excel, Outlook, Google Contacts, Gmail, and other applications via Zapier. For custom integrations, ACT! has an in-built API. ACT! can be accessed from Windows desktops (Win7 and later) with local or network shared database; synchronized to laptops or remote officers; Citrix or Remote Desktop; Web browsers (Premium only) with self or SaaS hosting; smartphones and tablets via HTML5 Web (Premium only); smartphones and tablets via sync with Handheld Contact.
ImageNet
The ImageNet project is a large visual database designed for use in visual object recognition software research. More than 14 million images have been hand-annotated by the project to indicate what objects are pictured and in at least one million of the images, bounding boxes are also provided. ImageNet contains more than 20,000 categories, with a typical category, such as "balloon" or "strawberry", consisting of several hundred images. The database of annotations of third-party image URLs is freely available directly from ImageNet, though the actual images are not owned by ImageNet. Since 2010, the ImageNet project runs an annual software contest, the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), where software programs compete to correctly classify and detect objects and scenes. The challenge uses a "trimmed" list of one thousand non-overlapping classes. == History == AI researcher Fei-Fei Li began working on the idea for ImageNet in 2006. At a time when most AI research focused on models and algorithms, Li wanted to expand and improve the data available to train AI algorithms. In 2007, Li met with Princeton professor Christiane Fellbaum, one of the creators of WordNet, to discuss the project. As a result of this meeting, Li went on to build ImageNet starting from the roughly 22,000 nouns of WordNet and using many of its features. She was also inspired by a 1987 estimate that the average person recognizes roughly 30,000 different kinds of objects. As an assistant professor at Princeton, Li assembled a team of researchers to work on the ImageNet project. They used Amazon Mechanical Turk to help with the classification of images. Labeling started in July 2008 and ended in April 2010. It took 49K workers from 167 countries filtering and labeling over 160M candidate images. They had enough budget to have each of the 14 million images labelled three times. The original plan called for 10,000 images per category, for 40,000 categories at 400 million images, each verified 3 times. They found that humans can classify at most 2 images/sec. At this rate, it was estimated to take 19 human-years of labor (without rest). They presented their database for the first time as a poster at the 2009 Conference on Computer Vision and Pattern Recognition (CVPR) in Florida, titled "ImageNet: A Preview of a Large-scale Hierarchical Dataset". The poster was reused at Vision Sciences Society 2009. In 2009, Alex Berg suggested adding object localization as a task. Li approached PASCAL Visual Object Classes contest in 2009 for a collaboration. It resulted in the subsequent ImageNet Large Scale Visual Recognition Challenge starting in 2010, which has 1000 classes and object localization, as compared to PASCAL VOC which had just 20 classes and 19,737 images (in 2010). === Significance for deep learning === On 30 September 2012, a convolutional neural network (CNN) called AlexNet achieved a top-5 error of 15.3% in the ImageNet 2012 Challenge, more than 10.8 percentage points lower than that of the runner-up. Using convolutional neural networks was feasible due to the use of graphics processing units (GPUs) during training, an essential ingredient of the deep learning revolution. According to The Economist, "Suddenly people started to pay attention, not just within the AI community but across the technology industry as a whole." In 2015, AlexNet was outperformed by Microsoft's very deep CNN with over 100 layers, which won the ImageNet 2015 contest, having 3.57% error on the test set. Andrej Karpathy estimated in 2014 that with concentrated effort, he could reach 5.1% error rate, and ~10 people from his lab reached ~12-13% with less effort. It was estimated that with maximal effort, a human could reach 2.4%. == Dataset == ImageNet crowdsources its annotation process. Image-level annotations indicate the presence or absence of an object class in an image, such as "there are tigers in this image" or "there are no tigers in this image". Object-level annotations provide a bounding box around the (visible part of the) indicated object. ImageNet uses a variant of the broad WordNet schema to categorize objects, augmented with 120 categories of dog breeds to showcase fine-grained classification. In 2012, ImageNet was the world's largest academic user of Mechanical Turk. The average worker identified 50 images per minute. The original plan of the full ImageNet would have roughly 50M clean, diverse and full resolution images spread over approximately 50K synsets. This was not achieved. The summary statistics given on April 30, 2010: Total number of non-empty synsets: 21841 Total number of images: 14,197,122 Number of images with bounding box annotations: 1,034,908 Number of synsets with SIFT features: 1000 Number of images with SIFT features: 1.2 million === Categories === The categories of ImageNet were filtered from the WordNet concepts. Each concept, since it can contain multiple synonyms (for example, "kitty" and "young cat"), so each concept is called a "synonym set" or "synset". There were more than 100,000 synsets in WordNet 3.0, majority of them are nouns (80,000+). The ImageNet dataset filtered these to 21,841 synsets that are countable nouns that can be visually illustrated. Each synset in WordNet 3.0 has a "WordNet ID" (wnid), which is a concatenation of part of speech and an "offset" (a unique identifying number). Every wnid starts with "n" because ImageNet only includes nouns. For example, the wnid of synset "dog, domestic dog, Canis familiaris" is "n02084071". The categories in ImageNet fall into 9 levels, from level 1 (such as "mammal") to level 9 (such as "German shepherd"). === Image format === The images were scraped from online image search (Google, Picsearch, MSN, Yahoo, Flickr, etc) using synonyms in multiple languages. For example: German shepherd, German police dog, German shepherd dog, Alsatian, ovejero alemán, pastore tedesco, 德国牧羊犬. ImageNet consists of images in RGB format with varying resolutions. For example, in ImageNet 2012, "fish" category, the resolution ranges from 4288 x 2848 to 75 x 56. In machine learning, these are typically preprocessed into a standard constant resolution, and whitened, before further processing by neural networks. For example, in PyTorch, ImageNet images are by default normalized by dividing the pixel values so that they fall between 0 and 1, then subtracting by [0.485, 0.456, 0.406], then dividing by [0.229, 0.224, 0.225]. These are the mean and standard deviations for ImageNet, so this whitens the input data. === Labels and annotations === Each image is labelled with exactly one wnid. Dense SIFT features (raw SIFT descriptors, quantized codewords, and coordinates of each descriptor/codeword) for ImageNet-1K were available for download, designed for bag of visual words. The bounding boxes of objects were available for about 3000 popular synsets with on average 150 images in each synset. Furthermore, some images have attributes. They released 25 attributes for ~400 popular synsets: Color: black, blue, brown, gray, green, orange, pink, red, violet, white, yellow Pattern: spotted, striped Shape: long, round, rectangular, square Texture: furry, smooth, rough, shiny, metallic, vegetation, wooden, wet === ImageNet-21K === The full original dataset is referred to as ImageNet-21K. ImageNet-21k contains 14,197,122 images divided into 21,841 classes. Some papers round this up and name it ImageNet-22k. The full ImageNet-21k was released in Fall of 2011, as fall11_whole.tar. There is no official train-validation-test split for ImageNet-21k. Some classes contain only 1-10 samples, while others contain thousands. === ImageNet-1K === There are various subsets of the ImageNet dataset used in various context, sometimes referred to as "versions". One of the most highly used subsets of ImageNet is the "ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012–2017 image classification and localization dataset". This is also referred to in the research literature as ImageNet-1K or ILSVRC2017, reflecting the original ILSVRC challenge that involved 1,000 classes. ImageNet-1K contains 1,281,167 training images, 50,000 validation images and 100,000 test images. Each category in ImageNet-1K is a leaf category, meaning that there are no child nodes below it, unlike ImageNet-21K. For example, in ImageNet-21K, there are some images categorized as simply "mammal", whereas in ImageNet-1K, there are only images categorized as things like "German shepherd", since there are no child-words below "German shepherd". === Later developments === In the WordNet they built ImageNet on, there were 2832 synsets in the "person" subtree. During 2018--2020 period, they removed the download of the ImageNet-21k as they went through extensive filtering in these person synsets. Out of these 2832 synsets, 1593 were deemed "potentially offensive". Out of the remaining 1239, 1081 were deemed not really "visual". The result was that only 158 syn
Digital data
Digital data or digital information, in information theory and information systems, is data or information represented as a string of discrete symbols, each of which can take on one of only a finite number of values from some alphabet, such as letters or digits. An example is a text document, which consists of a string of alphanumeric characters. The most common form of digital data in modern information systems is binary data, which is represented by a string of binary digits (bits) each of which can have one of two values, either 0 or 1. Digital data can be contrasted with analog data, which is represented by a value from a continuous range of real numbers. Analog data is transmitted by an analog signal, which not only takes on continuous values but can vary continuously with time, a continuous real-valued function of time. An example is the air pressure variation in a sound wave. Data requires interpretation to become information. In modern (post-1960) computer systems, all data is digital. The word digital comes from the same source as the words digit and digitus (the Latin word for finger), as fingers are often used for counting. Mathematician George Stibitz of Bell Telephone Laboratories used the word digital in reference to the fast electric pulses emitted by a device designed to aim and fire anti-aircraft guns in 1942. The term is most commonly used in computing and electronics, especially where real-world information is converted to binary numeric form as in digital audio and digital photography. == Symbol to digital conversion == Since symbols (for example, alphanumeric characters) are not continuous, representing symbols digitally is rather simpler than conversion of continuous or analog information to digital. Instead of sampling and quantization as in analog-to-digital conversion, such techniques as polling and encoding are used. A symbol input device usually consists of a group of switches that are polled at regular intervals to see which switches are switched. Data will be lost if, within a single polling interval, two switches are pressed, or a switch is pressed, released, and pressed again. This polling can be done by a specialized processor in the device to prevent burdening the main CPU. When a new symbol has been entered, the device typically sends an interrupt, in a specialized format, so that the CPU can read it. For devices with only a few switches (such as the buttons on a joystick), the status of each can be encoded as bits (usually 0 for released and 1 for pressed) in a single word. This is useful when combinations of key presses are meaningful, and is sometimes used for passing the status of modifier keys on a keyboard (such as shift and control). But it does not scale to support more keys than the number of bits in a single byte or word. Devices with many switches (such as a computer keyboard) usually arrange these switches in a scan matrix, with the individual switches on the intersections of x and y lines. When a switch is pressed, it connects the corresponding x and y lines together. Polling (often called scanning in this case) is done by activating each x line in sequence and detecting which y lines then have a signal, thus which keys are pressed. When the keyboard processor detects that a key has changed state, it sends a signal to the CPU indicating the scan code of the key and its new state. The symbol is then encoded or converted into a number based on the status of modifier keys and the desired character encoding. A custom encoding can be used for a specific application with no loss of data. However, using a standard encoding such as ASCII is problematic if a symbol such as 'ß' needs to be converted but is not in the standard. It is estimated that in the year 1986, less than 1% of the world's technological capacity to store information was digital and in 2007 it was already 94%. The year 2002 is assumed to be the year when humankind was able to store more information in digital than in analog format (the "beginning of the digital age"). == States == Digital data come in these three states: data at rest, data in transit, and data in use. The confidentiality, integrity, and availability have to be managed during the entire lifecycle from 'birth' to the destruction of the data. === Data at rest === Data at rest in information technology means data that is housed physically on computer data storage in any digital form (e.g. cloud storage, file hosting services, databases, data warehouses, spreadsheets, archives, tapes, off-site or cloud backups, mobile devices etc.). Data at rest includes both structured and unstructured data. This type of data is subject to threats from hackers and other malicious threats to gain access to the data digitally or physical theft of the data storage media. To prevent this data from being accessed, modified or stolen, organizations will often employ security protection measures such as password protection, data encryption, or a combination of both. The security options used for this type of data are broadly referred to as data-at-rest protection (DARP). Definitions include: "...all data in computer storage while excluding data that is traversing a network or temporarily residing in computer memory to be read or updated." "...all data in storage but excludes any data that frequently traverses the network or that which resides in temporary memory. Data at rest includes but is not limited to archived data, data which is not accessed or changed frequently, files stored on hard drives, USB thumb drives, files stored on backup tape and disks, and also files stored off-site or on a storage area network (SAN)." While it is generally accepted that archive data (i.e. which never changes), regardless of its storage medium, is data at rest and active data subject to constant or frequent change is data in use. “Inactive data” could be taken to mean data which may change, but infrequently. The imprecise nature of terms such as “constant” and “frequent” means that some stored data cannot be comprehensively defined as either data at rest or in use. These definitions could be taken to assume that Data at Rest is a superset of data in use; however, data in use, subject to frequent change, has distinct processing requirements from data at rest, whether completely static or subject to occasional change. ==== Security ==== Because of its nature data at rest is of increasing concern to businesses, government agencies and other institutions. Mobile devices are often subject to specific security protocols to protect data at rest from unauthorized access when lost or stolen and there is an increasing recognition that database management systems and file servers should also be considered as at risk; the longer data is left unused in storage, the more likely it might be retrieved by unauthorized individuals outside the network. Data encryption, which prevents data visibility in the event of its unauthorized access or theft, is commonly used to protect data in motion and increasingly promoted for protecting data at rest. The encryption of data at rest should only include strong encryption methods such as AES or RSA. Encrypted data should remain encrypted when access controls such as usernames and password fail. Increasing encryption on multiple levels is recommended. Cryptography can be implemented on the database housing the data and on the physical storage where the databases are stored. Data encryption keys should be updated on a regular basis. Encryption keys should be stored separately from the data. Encryption also enables crypto-shredding at the end of the data or hardware lifecycle. Periodic auditing of sensitive data should be part of policy and should occur on scheduled occurrences. Finally, only store the minimum possible amount of sensitive data. Tokenization is a non-mathematical approach to protecting data at rest that replaces sensitive data with non-sensitive substitutes, referred to as tokens, which have no extrinsic or exploitable meaning or value. This process does not alter the type or length of data, which means it can be processed by legacy systems such as databases that may be sensitive to data length and type. Tokens require significantly less computational resources to process and less storage space in databases than traditionally encrypted data. This is achieved by keeping specific data fully or partially visible for processing and analytics while sensitive information is kept hidden. Lower processing and storage requirements makes tokenization an ideal method of securing data at rest in systems that manage large volumes of data. A further method of preventing unwanted access to data at rest is the use of data federation especially when data is distributed globally (e.g. in off-shore archives). An example of this would be a European organisation which stores its archived data off-site in the US. Under the terms of the USA PATRIOT Act the American authorities can demand
Temporal resolution
Temporal resolution (TR) refers to the discrete resolution of a measurement with respect to time. It is defined as the amount of time needed to revisit and acquire data for the same location. When applied to remote sensing, this amount of time is influenced by the sensor platform's orbital characteristics and the features of the sensor itself. The temporal resolution is low when the revisiting delay is high and vice versa. Temporal resolution is typically expressed in days. == Physics == Often there is a trade-off between the temporal resolution of a measurement and its spatial resolution, due to Heisenberg's uncertainty principle. In some contexts, such as particle physics, this trade-off can be attributed to the finite speed of light and the fact that it takes a certain period of time for the photons carrying information to reach the observer. In this time, the system might have undergone changes itself. Thus, the longer the light has to travel, the lower the temporal resolution. == Technology == === Computing === In another context, there is often a tradeoff between temporal resolution and computer storage. A transducer may be able to record data every millisecond, but available storage may not allow this, and in the case of 4D PET imaging the resolution may be limited to several minutes. === Electronic displays === In some applications, temporal resolution may instead be equated to the sampling period, or its inverse, the refresh rate, or update frequency in Hertz, of a TV, for example. The temporal resolution is distinct from temporal uncertainty. This would be analogous to conflating image resolution with optical resolution. One is discrete, the other, continuous. The temporal resolution is a resolution somewhat the 'time' dual to the 'space' resolution of an image. In a similar way, the sample rate is equivalent to the pixel pitch on a display screen, whereas the optical resolution of a display screen is equivalent to temporal uncertainty. Note that both this form of image space and time resolutions are orthogonal to measurement resolution, even though space and time are also orthogonal to each other. Both an image or an oscilloscope capture can have a signal-to-noise ratio, since both also have measurement resolution. === Oscilloscopy === An oscilloscope is the temporal equivalent of a microscope, and it is limited by temporal uncertainty the same way a microscope is limited by optical resolution. A digital sampling oscilloscope has also a limitation analogous to image resolution, which is the sample rate. A non-digital non-sampling oscilloscope is still limited by temporal uncertainty. The temporal uncertainty can be related to the maximum frequency of continuous signal the oscilloscope could respond to, called the bandwidth and given in Hertz. But for oscilloscopes, this figure is not the temporal resolution. To reduce confusion, oscilloscope manufacturers use 'Sa/s' instead of 'Hz' to specify the temporal resolution. Two cases for oscilloscopes exist: either the probe settling time is much shorter than the real time sampling rate, or it is much larger. The case where the settling time is the same as the sampling time is usually undesirable in an oscilloscope. It is more typical to prefer a larger ratio either way, or if not, to be somewhat longer than two sample periods. In the case where it is much longer, the most typical case, it dominates the temporal resolution. The shape of the response during the settling time also has as strong effect on the temporal resolution. For this reason probe leads usually offer an arrangement to 'compensate' the leads to alter the trade off between minimal settling time, and minimal overshoot. If it is much shorter, the oscilloscope may be prone to aliasing from radio frequency interference, but this can be removed by repeatedly sampling a repetitive signal and averaging the results together. If the relationship between the 'trigger' time and the sample clock can be controlled with greater accuracy than the sampling time, then it is possible to make a measurement of a repetitive waveform with much higher temporal resolution than the sample period by upsampling each record before averaging. In this case the temporal uncertainty may be limited by clock jitter.
SPACEMAP
SPACEMAP (Korean: 스페이스맵) is a South Korean satellite orbit optimization and satellite communications company headquartered in Seoul, South Korea. The company was founded in 2021 by CEO, Douglas Deok-Soo Kim, as an offshoot of Hanyang University. It was funded by the Leader Research grant from the National Research Foundation of Korea with the goal of capitalizing on the growing space industry. == History == Kim initially began research into Voronoi diagrams at the University of Michigan. He met with Dr. Misoon Ma, former director of the Asia Division of the U.S. Air Force Office of Scientific Research (AFOSR) and was recruited to work with the U.S. Air force, using Voronoi diagrams for a satellite collision prevention program. After his work with the U.S. Air Force, Kim founded SPACEMAP Inc in September 2021. In 2023, the company was selected by Korea's Tech Incubator Program for Startups (TIPS) to be funded up to 17 billion KRW (approx. US$13 million) in 3 years. == Technology == The services provided by SPACEMAP are based on using dynamic Voronoi diagrams to predict satellite orbits with the aim of enhancing space mission safety and efficiency. For complex problems involving many moving points, Voronoi diagrams maintain a near-constant computation time regardless of the number of points involved. By utilizing Voronoi diagrams and artificial intelligence, the software can easily determine the number of neighboring satellites surrounding a specific satellite and calculate the distances between them, thereby predicting the probability of a collision. SPACEMAP claims their method to be superior in computational time and memory efficiency, compared to the previously established three-filter method. == Products == SPACEMAP offers satellite products and services including the following: AstroOne, a conjunction assessment, and optimal collision avoidance service for all space vehicles in both orbital and non-orbital motions. AstroOrca, providing data transmission for satellites in multiple orbits, launch optimization, shuttle logistics for space gas stations, and Active Debris Removal (ADR) itinerary. AstroLibrary, a library of RESTful APIs to access the C++ implementation of SPACEMAP's Voronoi diagram algorithms wrapped in a Python interface. It also provides real-time tracking of the North Korean reconnaissance satellite, Malligyong-1.
Stixel
In computer vision, a stixel (portmanteau of "stick" and "pixel") is a superpixel representation of depth information in an image, in the form of a vertical stick that approximates the closest obstacles within a certain vertical slice of the scene. Introduced in 2009, stixels have applications in robotic navigation and advanced driver-assistance systems, where they can be used to define a representation of robotic environments and traffic scenes with a medium level of abstraction. == Definition == One of the problems of scene understanding in computer vision is to determine horizontal freespace around the camera, where the agent can move, and the vertical obstacles delimiting it. An image can be paired with depth information (produced e.g. from stereo disparity, lidar, or monocular depth estimation), allowing a dense tridimensional reconstruction of the observed scene. One drawback of dense reconstruction is the large amount of data involved, since each pixel in the image is mapped to an element of a point cloud. Vision problems characterised by planar freespace delimited by mostly vertical obstacles, such as traffic scenes or robotic navigation, can benefit from a condensed representation that allows to save memory and processing time. Stixels are thin vertical rectangles representing a slice of a vertical surface belonging to the closest obstacle in the observed scene. They allow to dramatically reduce the amount of information needed to represent a scene in such problems. A stixel is characterised by three parameters: vertical coordinate of the bottom, height of the stick, and depth. Stixels have fixed width, with each stixel spanning over a certain number of image columns, allowing downsampling of the horizontal image resolution. In the original formulation, each column of the image would contain at most one stixel, and later extensions were developed to allow multiple stixels on each column, allowing to represent multiple objects at different distances. == Stixel estimation == The input to stixel estimation is a dense depth map, that can be computed from stereo disparity or other means. The original approach computes an occupancy grid that can be segmented to estimate the freespace, with dynamic programming providing an efficient method to find an optimal segmentation. Alternative approaches can be used instead of occupancy grid mapping, such as manifold-based methods. The freespace boundary provides the base points of the obstacles at closest longitudinal distance, however multiple objects at different distances might appear in each column of the image. To fully define the obstacles, their height should be estimated, and this is accomplished by segmenting the depth of the object from the depth of the background. A membership function over the pixels can be defined based on the depth value, where the membership represents the confidence of a pixel belonging to the closest vertical obstacle or to the background, and a cut separating the obstacles from the background can again be computed effectively with dynamic programming. Once both the freespace and the obstacle height are known, the stixels can be estimated by fusing the information over the columns spanned by each stixel, and finally a refined depth of the stixel can be estimated via model fitting over the depth of the pixels covered by the stixel, possibly paired with confidence information (e.g. disparity confidence produced by methods such as semi-global matching).
Mortimer Rogoff
Mortimer Alan Rogoff (May 2, 1921 – August 1, 2008) was an American inventor, businessman, and author as well as an amateur photographer and radio operator. He is recognized for his work in spread spectrum technology which is the technology that modern cell phones and GPS systems are based on. He is also considered the grandfather of the electronic navigation chart. == Early life == Rogoff was born in Brooklyn, New York. He earned his B.S.E.E. from Rensselaer Polytechnic Institute in 1943 and his M.S.E.E. from Columbia University in 1948. While at Rensselaer he was a member of Kappa Nu fraternity and the Features Editor for the student newspaper. During World War II, he enlisted in the United States Navy and worked on developing radio communication and aerial navigation systems. One of the techniques he developed was undetectable by Axis forces because its power was below that of the background noise and its frequency varied in random ways. This secure transmission was the beginning of spread spectrum technology which would become the basis for GPS and CDMA cellular telephone systems. Although he was never able to patent the technology because it was a military secret he did get some recognition for it almost forty years later when he received the Institute of Electrical and Electronics Engineers’ Pioneer Award in 1981. == Career == Rogoff worked for twenty-two years (1946 to 1968) for ITT Laboratories in New Jersey. In 1958, he became their deputy director of Engineering. He was Vice President of ITT Laboratories from 1962 to 1963. From 1963 to 1968, he was promoted to the corporate staff where he became head of European operations. In 1968 he left ITT to work for the Diebold Group where he became an Executive Vice President. After leaving the Diebold Group he founded several technology and automation businesses, including his own consulting firm, and Teletext Communications Corporation. Later in the 1970s, he was a Principal with Booz Allen Hamilton. In 1979, his book ‘’Calculator Navigation’’ was published. This book demonstrated practical methods for calculating precise ship locations using radio navigation with a consumer calculator. In 1981, he founded a new company, Navigation Sciences Inc., in Bethesda, Maryland. With this company he patented a method for marine navigation that combined radar maps with electronic charts in 1986. This was a major advancement in field. Today, this system is known as the Electronic Chart Display and Information System (ECDIS). Rogoff had seen the need for a new charting system in 1968 from his apartment at 180 East End Avenue in New York City. From there, he saw a boating accident where a life was lost and decided there had to be a way to automate navigation. Rogoff then became of member of the International Maritime Organization’s (IMO) sub-committee on Safety of Navigation, a representative to the International Electrotechnical Commission, and became the chairman of the Radio Technical Commission for Maritime Services Special Committee 109 on Electronic Charts. He was able to use his influence on these boards to push through a proposal of ECDIS standards in 1989 where none has been before. As his friend Giuseppe Carnevali said, “Although nobody could argue against the need for a standard, no one was ready to endorse one; however, nobody was brave enough to oppose it.” A Test Bed project on these proposals was conducted by the United States Coast Guard. The amended standards were accepted by the IMO in November, 1995. In 2000, he was named as a Fellow of the Institute of Navigation. He was also a Fellow of the Institute of Electrical and Electronics Engineers. During this time, he was also president of the Navigational Electronic Charts System Association. == Personal == In 1979, he moved to Washington, D.C. and bought a home in Nantucket, Massachusetts. He married Sheila Zunser in 1943 and they were together for sixty-five years. They had three daughters: Louisa Thompson, Alice Rogoff, and Julia Peach. His sister was sociologist Natalie Rogoff Ramsøy of the University of Oslo. He was a member of the Cosmos Club and President of The Navigational Electronic Chart System Association (NECSA). He was a very good amateur photographer and liked amateur radio (call sign W2EE). He died in Nantucket from bladder cancer. == Patents == Patent number: 4176316 – Secure Communication System – November 27, 1979 With Louis A. DeRosa Patent number: 4590569 – Electronic Navigation System – May 20, 1986 With Peter M. Winkler and John N. Ackley Patent number: RE34004 – Secure Communication System – July 21, 1992 With Louis A. DeRosa == Publications == Rogoff, Mortimer September 1957. Automatic Analysis of Infrared Spectra. Annals of the New York Academy of Sciences; vol. 69: no. 1: 27–37. Gen. P.C. Sandretto and Mortimer Rogoff. 1958 “A Novel Concept for Application to the Control of Airways Traffic.” NAVIGATION: Journal of The Institute of Navigation; vol. 6: no. 2: 102–107 Rogoff, Mortimer 1979. Calculator Navigation; ISBN 0-393-03192-6. Published by W.W. Norton & Company (New York and London). Rogoff, Mortimer December 1985. Electronic Charting. Yachting; vol. 158: no. 6: 54–57. Rogoff, Mortimer Winter 1990. Electronic Charts in the Nineties. NAVIGATION: Journal of The Institute of Navigation; vol. 37: no. 4: 305–318.