Screenless video is any system for transmitting visual information from a video source without the use of a screen. Screenless computing systems can be divided into three groups: Visual Image, Retinal Direct, and Synaptic Interface. == Visual image == Visual Image screenless display includes any image that the eye can perceive. The most common example of Visual Image screenless display is a hologram. In these cases, light is reflected off some intermediate object (hologram, LCD panel, or cockpit window) before it reaches the retina. In the case of LCD panels the light is refracted from the back of the panel, but is nonetheless a reflected source. Google has proposed a similar system to replace the screens of tablet computers and smartphones. == Retinal display == Virtual retinal display systems are a class of screenless displays in which images are projected directly onto the retina. They are distinguished from visual image systems because light is not reflected from some intermediate object onto the retina, it is instead projected directly onto the retina. Retinal Direct systems, once marketed, hold out the promise of extreme privacy when computing work is done in public places because most snooping relies on viewing the same light as the person who is legitimately viewing the screen, and retinal direct systems send light only into the pupils of their intended viewer. == Synaptic interface == Synaptic Interface screenless video does not use light at all. Visual information completely bypasses the eye and is transmitted directly to the brain. While such systems have only been implemented in humans in rudimentary form - for example, displaying single Braille characters to blind people – success has been achieved in sampling usable video signals from the biological eyes of a living horseshoe crab through their optic nerves, and in sending video signals from electronic cameras into the creatures' brains using the same method.
Stride (software)
Stride was a cloud-based team business communication and collaboration tool, launched by Atlassian on 7 September 2017 to replace the cloud-based version of HipChat. Stride software was available to download onto computers running Windows, Mac or Linux, as well as Android, iOS smartphones, and tablets. Stride was bought by Atlassian's competitor Slack Technologies and was discontinued on February 15, 2019. The features of Stride include chat rooms, one-on-one messaging, file sharing, 5 GB of file storage, group voice and video calling, built-in collaboration tools, and up to 25,000 of searchable message history. Premium features include unlimited file storage, users, group chat rooms, file sharing and storage, apps, and history retention. The premium version, priced at $3/user/month, also includes advanced meeting functionality like group screen sharing, remote desktop control, and dial-in/dial-out capabilities. Stride offered integrations with Atlassian's other products as well as other third-party applications listed in the Atlassian Marketplace, such as GitHub, Giphy, Stand-Bot and Google Calendar. Stride offered additional features beyond messaging to improve efficiency and productivity. It aimed to reduce collaboration noise by introducing a "focus" mode, and eliminates the divisions between text chat, voice meetings, and videoconferencing, by simplifying transitioning between these modes in the same channel. On July 26, 2018, Atlassian announced that HipChat and Stride would be discontinued February 15, 2019, and that it had reached a deal to sell their intellectual property to Slack. Slack paid an undisclosed amount over three years to assume the user bases of the services, while Atlassian took a minority investment in Slack. The companies also announced a commitment to work on integration of Slack with Atlassian services.
Apache ORC
Apache ORC (Optimized Row Columnar) is a free and open-source column-oriented data storage format. It is similar to the other columnar-storage file formats available in the Hadoop ecosystem such as RCFile and Parquet. It is used by most of the data processing frameworks Apache Spark, Apache Hive, Apache Flink, and Apache Hadoop. In February 2013, the Optimized Row Columnar (ORC) file format was announced by Hortonworks in collaboration with Facebook. A calendar month later, the Apache Parquet format was announced, developed by Cloudera and Twitter. Apache ORC format is widely supported including Amazon Web Services' Glue,Google Cloud Platform's BigQuery, and Pandas (software). == History ==
WorkingPoint
WorkingPoint is a web-based application that provides a suite of small business management tools. It is designed to serve as a single point of access for various business operations, featuring a user-friendly interface. WorkingPoint's functionalities include double-entry bookkeeping, contact management, inventory management, invoicing, and bill and expense management. == Company == WorkingPoint, formerly Netbooks Inc, is a privately held corporation based in San Francisco, CA. The company is backed by CMEA Capital, also based in San Francisco. WorkingPoint has about ten employees and is led by CEO Tate Holt and Chairman Tom Proulx. Proulx is a co-founder of Intuit and an original author of that company’s Quicken personal finance software. The company was founded in 2007 under its original name Netbooks by co-creator Ridgely Evers. Evers set out to design a product that was more user-friendly than Intuit’s Quickbooks, which he also co-created. In mid-2009 the company officially rebranded itself and its flagship product “WorkingPoint”. The purpose of the re-branding was to disassociate the company from the product category of small laptops also known as netbooks. == Social Media Presence == WorkingPoint maintains a daily blog geared toward small business owners and managers. Each week the blog is updated with 3 WorkingPoint product feature or “how-to” posts, 2 subscriber company profiles, and 2 small business coaching posts. The company also maintains a Twitter page and a Facebook page. == Product Description (Free Version) == WorkingPoint allows businesses to invoice up to five customers (repeatedly) and provides account access for up to two individual users free of charge. Online Invoicing WorkingPoint allows users to create customized quotes and invoices online. The invoices can be used to bill customers via email or hardcopy post. WorkingPoint compiles the info from these invoices so users can track customer payments, inventory costs, shipping charges, accounts receivable and sales taxes. Users can also manage customer overpayments, provide customer loyalty discounts, and view a customer invoice history. Bill & Expense Management Users can track their bills and expenses by entering info into the WorkingPoint interface. WorkingPoint compiles this info so users can track categorized expenses, accounts paid, accounts payable, and vendor purchase history. The interface also allows users to add to their inventory while entering billing info. Double-Entry Bookeeping WorkingPoint automatically records entries under the double-entry bookkeeping system (also known as debits and credits) when the user completes invoicing and expense forms. Users can view transactions in general ledger format and perform closing entries if necessary. This functionality is designed for users who do not have an accounting background. Business Contact Management WorkingPoint provides an interface for users to manage their customer and vendor contact info. The software automatically tracks the user’s relationship with contacts, so users can track a contact’s sales and purchase history. Contacts can be imported and exported via numerous email clients including Microsoft Outlook, Yahoo! Mail, Google Gmail, and Mac Address Book. Inventory Management The software automatically adjusts inventory quantities after every purchase and sale. Users can track their current inventory quantity, average cost of inventory on-hand, cost of goods sold (COGS) and top-selling products. Users can also make manual adjustments to inventory when necessary. Financial Reporting Users can view a balance sheet, income statement, or cash flow statement pertaining to their business. The software automatically manages accruals to produce the balance sheet and income statement. Users can choose a data range from which to draw any of these reports. Financial reports can be converted to pdf format or exported (with formulas intact) to OpenOffice or Microsoft Excel. Cash Management WorkingPoint enables users to monitor cash balances on their bank accounts. The software automatically tracks cash inflows and outflows when users manage their accounts payable and accounts receivable. Business Dashboard The Business Dashboard visually and graphically displays key real-time business data. Users can customize the Dashboard to display data of their choosing. Online Company Profile Users can create an online company profile in order to have a presence on the Internet and as a basis for participation in WorkingPoint’s small business community features. Public profiles are featured in the WorkingPoint Company Directory and can be viewed externally using the URL format: https://businessname.workingpoint.com. == Product Description (Premium Version) == The premium version of WorkingPoint costs $10 per month. It includes all of the functionalities of the free version, allowing unlimited invoicing and account access. It also offers the following functions: 1099 Tax Reporting, invoice payment collection via PayPal, Email Marketing via VerticalResponse, and the Premium Reports & Accounting Package. 1099 Tax Reporting Users can identify qualifying companies and individuals for IRS Form 1099 or IRS Form 1096 reporting. WorkingPoint automatically tracks payments made to these companies and individuals. Users can then generate 1099 reports for distribution. Premium Reports & Accounting Package This includes: a Daily Operating Report providing users with sales and cash flow information, customizable accounts categorization, and cash flow statements using the indirect method of reporting. Invoice Payment Collection via PayPal Users can collect payment on their invoices via PayPal. Email Marketing via VerticalResponse The WorkingPoint premium package includes 500 email credits with the email marketing firm VerticalResponse.
CAMeL-View TestRig
CAMeL-View is a software application, which is used for the model based design of mechatronic systems (multi-body simulation, block diagrams, pneumatic systems, hydraulic systems, general simulation, linear analysis and Hardware-in-the-Loop). CAMeL-View enables object-oriented model creation of mechatronic systems through the use of graphic blocks. The basic elements of multi-body system dynamics, control technology, hydraulics and hardware connectivity support the modeling process. The user’s proprietary C-Code can also be integrated into the models, which allows CAMeL-View TestRig to be implemented in all phases of the model based design process ( modeling, physical testing and prototyping), and lends itself especially well to mechatronic system design. The model’s structure is described and displayed with the help of directional connectors. Physical connections (such as mechanical or hydraulic linkages) as well as input and output connections (signal flow) are also available. The input of equations is done via mathematical expressions, e.g. the input of constitutive differential equations in vector and matrix form. Based on the model’s structure, the descriptive equations are converted into non-linear state space representations and converted into executable C-Code. CAMeL-View supports the simulation process with a configurable “experiment environment” (for simulator and instrumentation components) which allows the user to apply simulation models to supported targets (MPC5200, TriCore, X86, etc.) without the need for additional software tools for Hardware-in-the-Loop applications. In addition, the generation of so-called S-Functions for use in Simulink and the generation of ANSI C-Code for use in stand-alone simulators is also supported. A particularly noteworthy feature in CAMeL-View TestRig is the way in which the descriptive equations for multi-body system models are created. All multi-body simulation formalisms used for code generation create their equations in the form of typical explicit differential equations (ODE). This is especially important in Hardware-in-the-Loop applications where the calculation of simulation results within a specific, defined time frame must be assured. Only then is it possible to implement complex multi-body simulation models for Hardware-in-the-Loop applications under stringent real-time conditions. These constraints cannot be met when using DAE-based methods. Additional Toolboxes are available for linear analysis (Eigenvalues, pole-zero analysis, frequency response, etc.) of VRML-based animation. Development of CAMeL-View began in 1991 in the Paderborn Mechatronic Laboratory of Professor Dr. Ing. J. Lückel. The software was based on predecessors that had been developed there since 1986. The name stands for Computer Aided Mechatronic Laboratory – Virtual Engineering Workbench and describes the basic intent of one of the specific demands placed on development engineers in the computer lab.
Neural processing unit
A neural processing unit (NPU), also known as an AI accelerator or deep learning processor, is a class of specialized hardware accelerator or computer system designed to accelerate artificial intelligence and machine learning applications, including artificial neural networks and computer vision. == Use == Their purpose is either to efficiently execute already trained AI models (inference) or to train AI models. NPUs can be more efficient in terms of speed or power consumption. NPU applications include algorithms for robotics, Internet of things, and data-intensive or sensor-driven tasks. They are often manycore or spatial designs and focus on low-precision arithmetic, novel dataflow architectures, or in-memory computing capability. As of 2024, a widely used datacenter-grade AI integrated circuit chip, the Nvidia H100 GPU, contains tens of billions of MOSFETs. === Consumer devices === AI accelerators are used in Apple silicon, Qualcomm, Samsung, Huawei, and Google Tensor smartphone processors. Vision processing units are accelerators specialized for machine vision algorithms such as CNN (convolutional neural networks) and SIFT (scale-invariant feature transform). They are used in devices that need to keep track of objects visually such as AR headsets and drones. It is more recently (circa 2017) added to processors from Apple and (circa 2022) to processors from Intel and AMD. All models of Intel Meteor Lake processors have a built-in versatile processor unit (VPU) for accelerating inference for computer vision and deep learning. On consumer devices, the NPU is intended to be small, power-efficient, but reasonably fast when used to run small models. To do this they are designed to support low-bitwidth operations using data types such as INT4, INT8, FP8, and FP16. A common metric is trillions of operations per second (TOPS). Although TOPS does not explicitly specify the kind of operations, it is typically INT8 additions and multiplications. === Datacenters === Accelerators are used in cloud computing servers: e.g., tensor processing units (TPU) for Google Cloud Platform, and Trainium and Inferentia chips for Amazon Web Services. Many vendor-specific terms exist for devices in this category, and it is an emerging technology without a dominant design. Since the late 2010s, graphics processing units designed by companies such as Nvidia and AMD often include AI-specific hardware in the form of dedicated functional units for low-precision matrix-multiplication operations. These GPUs are commonly used as AI accelerators, both for training and inference. === Scientific computation === Although NPUs are tailored for low-precision (e.g., FP16, INT8) matrix multiplication operations, they can be used to emulate higher-precision matrix multiplications in scientific computing. As modern GPUs place much focus on making the NPU part fast, using emulated FP64 (Ozaki scheme) on NPUs can potentially outperform native FP64. This has been demonstrated using FP16-emulated FP64 on NVIDIA TITAN RTX and using INT8-emulated FP64 on NVIDIA consumer GPUs and the A100 GPU. Consumer GPUs especially benefited as they have limited FP64 hardware capacity, showing a 6× speedup. Since CUDA Toolkit 13.0 Update 2, cuBLAS automatically uses INT8-emulated FP64 matrix multiplication of the equivalent precision if it is faster than native. This is in addition to the FP16-emulated FP32 feature introduced in version 12.9. == Programming == An operating system or a higher-level library may provide application programming interfaces such as TensorFlow with LiteRT Next (Android), CoreML (iOS, macOS) or DirectML (Windows). Formats such as ONNX are used to represent trained neural networks. Consumer CPU-integrated NPUs are accessible through vendor-specific APIs. AMD (Ryzen AI), Intel (OpenVINO), Apple silicon (CoreML), and Qualcomm (SNPE) each have their own APIs, which can be built upon by a higher-level library. GPUs generally use existing GPGPU pipelines such as CUDA and OpenCL adapted for lower precisions and specialized matrix-multiplication operations. Vulkan is also being used. Custom-built systems such as the Google TPU use private interfaces. There are a large number of separate underlying acceleration APIs and compilers/runtimes in use in the AI field, causing a great increase in software development effort due to the many combinations involved. As of 2025, the open standard organization Khronos Group is pursuing standardization of AI-related interfaces to reduce the amount of work needed. Khronos is working on three separate fronts: expansion of data types and intrinsic operations in OpenCL and Vulkan, inclusion of compute graphs in SPIR-V, and a NNEF/SkriptND file format for describing a neural network.
System context diagram
A system context diagram in engineering is a diagram that defines the boundary between the system, or part of a system, and its environment, showing the entities that interact with it. This diagram is a high level view of a system. It is similar to a block diagram. == Overview == System context diagrams show a system, as a whole and its inputs and outputs from/to external factors. According to Kossiakoff and Sweet (2011): System Context Diagrams ... represent all external entities that may interact with a system ... Such a diagram pictures the system at the center, with no details of its interior structure, surrounded by all its interacting systems, environments and activities. The objective of the system context diagram is to focus attention on external factors and events that should be considered in developing a complete set of systems requirements and constraints. System context diagrams are used early in a project to get agreement on the scope under investigation. Context diagrams are typically included in a requirements document. These diagrams must be read by all project stakeholders and thus should be written in plain language, so the stakeholders can understand items within the document. == Building blocks == Context diagrams can be developed with the use of two types of building blocks: Entities (Actors): labeled boxes; one in the center representing the system, and around it multiple boxes for each external actor Relationships: labeled lines between the entities and system For example, "customer places order." Context diagrams can also use many different drawing types to represent external entities. They can use ovals, stick figures, pictures, clip art or any other representation to convey meaning. Decision trees and data storage are represented in system flow diagrams. A context diagram can also list the classifications of the external entities as one of a set of simple categories (Examples:), which add clarity to the level of involvement of the entity with regards to the system. These categories include: Active: Dynamic to achieve some goal or purpose (Examples: "Article readers" or "customers"). Passive: Static external entities which infrequently interact with the system (Examples: "Article editors" or "database administrator"). Cooperative: Predictable external entities which are used by the system to bring about some desired outcome (Examples: "Internet service providers" or "shipping companies"). Autonomous (Independent): External entities which are separated from the system, but affect the system indirectly, by means of imposed constraints or similar influences (Examples: "regulatory committees" or "standards groups"). == Alternatives == The best system context diagrams are used to display how a system interoperates at a very high level, or how systems operate and interact logically. The system context diagram is a necessary tool in developing a baseline interaction between systems and actors; actors and a system or systems and systems. Alternatives to the system context diagram are: Architecture Interconnect Diagram: The figure gives an example of an Architecture Interconnect Diagram: A representation of the Albuquerque regional ITS architecture interconnects for the Albuquerque Police Department that was generated using the Turbo Architecture tool is shown in the figure. Each block represents an ITS inventory element, including the name of the stakeholder in the top shaded portion. The interconnect lines between elements are solid or dashed, indicating existing or planned connections. Business Model Canvas, a strategic management template for developing new or documenting existing business models. It is a visual chart with elements describing a firm's value proposition, infrastructure, customers, and finances.[1] It assists firms in aligning their activities by illustrating potential trade-offs. Enterprise data model: this type of data model according to Simsion (2005) can contain up to 50 to 200 entity classes, which results from specific "high level of generalization in data modeling". IDEF0 Top Level Context Diagram: The IDEF0 process starts with the identification of the prime function to be decomposed. This function is identified on a "Top Level Context Diagram" that defines the scope of the particular IDEF0 analysis. Problem Diagrams (Problem Frames): In addition to the kinds of things shown on a context diagram, a problem diagram shows requirements and requirements references. Use case diagram: One of the Unified Modeling Language diagrams. They also represent the scope of the project at a similar level of abstraction. - Use Cases, however, tend to focus more on the goals of 'actors' who interact with the system, and do not specify any solution. Use Case diagrams represent a set of Use Cases, which are textual descriptions of how an actor achieves the goal of a use case. for Example Customer Places Order. ArchiMate: ArchiMate is an open and independent enterprise architecture modeling language to support the description, analysis and visualization of architecture within and across business domains in an unambiguous way. Most of these diagrams work well as long as a limited number of interconnects will be shown. Where twenty or more interconnects must be displayed, the diagrams become quite complex and can be difficult to read.