Solid-state electronics are semiconductor electronics: electronic equipment that use semiconductor devices such as transistors, diodes and integrated circuits (ICs). The term is also used as an adjective for devices in which semiconductor electronics that have no moving parts replace devices with moving parts, such as the solid-state relay, in which transistor switches are used in place of a moving-arm electromechanical relay, or the solid-state drive (SSD), a type of semiconductor memory used in computers to replace hard disk drives, which store data on rotating disks. == History == The term solid-state became popular at the beginning of the semiconductor era in the 1960s to distinguish this new technology. A semiconductor device works by controlling an electric current consisting of electrons or holes moving within a solid crystalline piece of semiconducting material such as silicon, while the thermionic vacuum tubes it replaced worked by controlling a current of electrons or ions in a vacuum within a sealed tube. Although the first solid-state electronic device was the cat's whisker detector, a crude semiconductor diode invented around 1904, solid-state electronics started with the invention of the transistor in 1947. Before that, all electronic equipment used vacuum tubes, because vacuum tubes were the only electronic components that could amplify—an essential capability in all electronics. The transistor, which was invented by John Bardeen and Walter Houser Brattain while working under William Shockley at Bell Laboratories in 1947, could also amplify, and replaced vacuum tubes. The first transistor hi-fi system was developed by engineers at GE and demonstrated at the University of Philadelphia in 1955. In terms of commercial production, The Fisher TR-1 was the first "all transistor" preamplifier, which became available mid-1956. In 1961, a company named Transis-tronics released a solid-state amplifier, the TEC S-15. The replacement of bulky, fragile, energy-hungry vacuum tubes by transistors in the 1960s and 1970s created a revolution not just in technology but in people's habits, making possible the first truly portable consumer electronics such as the transistor radio, cassette tape player, walkie-talkie and quartz watch, as well as the first practical computers and mobile phones. Other examples of solid state electronic devices are the microprocessor chip, LED lamp, solar cell, charge coupled device (CCD) image sensor used in cameras, and semiconductor laser. Also during the 1960s and 1970s, television set manufacturers switched from vacuum tubes to semiconductors, and advertised sets as "100% solid state" even though the cathode-ray tube (CRT) was still a vacuum tube. It meant only the chassis was 100% solid-state, not including the CRT. Early advertisements spelled out this distinction, but later advertisements assumed the audience had already been educated about it and shortened it to just "100% solid state". LED displays can be said to be truly 100% solid-state.
Google Clips
Google Clips is a discontinued miniature clip-on camera device developed by Google. == History == It was announced on October 4, 2017 and went on sale on January 27, 2018. Google Clips automatically captured video clips (without audio) at moments its machine learning algorithms determined to be interesting or relevant. An indicator flashed when the camera was looking for scenes to capture. Google Clips' artificial intelligence (AI) could learn the faces of people to take photographs with certain people, and could automatically set lighting and framing. It had 16 GB of storage built-in storage and could record clips for up to 3 hours. This camera was originally priced at US$249 in the United States. It was withdrawn from sale on October 15, 2019, but supported until the end of December 2021. == Reception == The Independent wrote that Google Clips is "an impressive little device, but one that also has the potential to feel very creepy." According to The Verge's generally negative review, "it didn't capture anything special" over two weeks of testing.
Amazon Kinesis
Amazon Kinesis is a family of services provided by Amazon Web Services (AWS) for processing and analyzing real-time streaming data at a large scale. Launched in November 2013, it offers developers the ability to build applications that can consume and process data from multiple sources simultaneously. Kinesis supports multiple use cases, including real-time analytics, log and event data collection, and real-time processing of data generated by IoT devices. == History == Amazon Kinesis was launched by Amazon Web Services (AWS) in November 2013 as a managed service for processing and analyzing real-time streaming data at a large scale. The service was introduced to address the growing need for businesses to process and analyze data as it was generated, rather than in batches, allowing for real-time insights and decision-making. Since its launch, the Amazon Kinesis family of services has expanded to include four main components: Kinesis Data Streams, Kinesis Data Firehose, Kinesis Data Analytics, and Kinesis Video Streams. Each of these components serves a specific purpose in the processing and analysis of real-time streaming data. In August 2015, AWS announced the availability of Kinesis Data Firehose, a fully managed service for delivering real-time streaming data to destinations such as Amazon S3, Amazon Redshift, and Amazon Elasticsearch. A year later in August 2016, AWS launched Kinesis Data Analytics, enabling customers to analyze streaming data in real time using standard SQL queries. AWS introduced Kinesis Video Streams, a fully managed service for securely capturing, processing, and storing video streams for analytics and machine learning applications, was introduced by AWS in November 2017. == Components == Amazon Kinesis is composed of four main services: Kinesis Data Streams, Kinesis Data Firehose, Kinesis Data Analytics, and Kinesis Video Streams. === Kinesis Data Streams === Kinesis Data Streams is a scalable and durable real-time data streaming service that captures and processes gigabytes of data per second from multiple sources. It enables the storage and processing of data in real time, making it useful for applications that require immediate insights, such as monitoring and alerting. === Kinesis Data Firehose === Kinesis Data Firehose is a fully managed service for delivering real-time streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon Elasticsearch, and AWS-partner data stores. With Data Firehose, users can configure and scale data delivery without manual intervention. === Kinesis Data Analytics === Kinesis Data Analytics enables the analysis of streaming data in real time using standard SQL or Apache Flink. === Kinesis Video Streams === Kinesis Video Streams is a fully managed service for securely capturing, processing, and storing video streams for analytics and machine learning. It supports multiple video codecs and streaming protocols, making it suitable for various use cases, such as security and surveillance, video-enabled IoT devices, and live event broadcasting. == Integration == Amazon Kinesis can be easily integrated with other AWS services, such as AWS Lambda, Amazon S3, Amazon Redshift, and Amazon OpenSearch. This integration enables developers to build end-to-end streaming data processing applications, taking advantage of the extensive AWS ecosystem. == Use cases == Some common use cases for Amazon Kinesis include: Real-time analytics: Analyzing streaming data in real time to provide immediate insights and make data-driven decisions. Log and event data collection: Collecting, processing, and analyzing log and event data generated by applications, infrastructure, and devices. IoT data processing: Processing and analyzing large volumes of data generated by IoT devices in real time. Machine learning: Ingesting and processing video streams for machine learning applications, such as object recognition, facial recognition, and sentiment analysis. == Pricing == Amazon Kinesis follows a pay-as-you-go pricing model, with costs depending on the chosen service, data volume, and processing power required. AWS provides a free tier for Kinesis Data Streams and Kinesis Data Firehose, allowing users to get started with the services at no cost.
Cloud robotics
Cloud robotics is a field of robotics that attempts to invoke cloud technologies such as cloud computing, cloud storage, and other Internet technologies centered on the benefits of converged infrastructure and shared services for robotics. When connected to the cloud, robots can benefit from the powerful computation, storage, and communication resources of a modern data center in the cloud, which can process and share information from various robots or agents (other machines, smart objects, humans, etc.). Humans can also delegate tasks to robots remotely through networks. Cloud computing technologies enable robot systems to be gain capability whilst reducing costs through cloud technologies. Thus, it is possible to build lightweight, low-cost, smarter robots with an intelligent "brain" in the cloud. The "brain" consists of data center, knowledge base, task planners, deep learning, information processing, environment models, communication support, etc. == Components == A cloud for robots potentially has at least six significant components: Building a "cloud brain" for robots, the main object of cloud robotics; Offering a global library of images, maps, and object data, often with geometry and mechanical properties, expert system, knowledge base (i.e. semantic web, data centres); Massively-parallel computation on demand for sample-based statistical modelling and motion planning, task planning, multi-robot collaboration, scheduling and coordination of system; Robot sharing of outcomes, trajectories, and dynamic control policies and robot learning support; Human sharing of open-source code, data, and designs for programming, experimentation, and hardware construction; On-demand human guidance and assistance for evaluation, learning, and error recovery; Augmented human–robot interaction through various ways (semantics knowledge base, Apple SIRI like service, etc.). == Applications == Autonomous mobile robots Google's self-driving cars are cloud robots. The cars use the network to access Google's enormous database of maps and satellite and environment model (like Streetview) and combines it with streaming data from GPS, cameras, and 3D sensors to monitor its own position within centimetres, and with past and current traffic patterns to avoid collisions. Each car can learn something about environments, roads, or driving, or conditions, and it sends the information to the Google cloud, where it can be used to improve the performance of other cars. Cloud medical robots a medical cloud (also called a healthcare cluster) consists of various services such as a disease archive, electronic medical records, a patient health management system, practice services, analytics services, clinic solutions, expert systems, etc. A robot can connect to the cloud to provide clinical service to patients, as well as deliver assistance to doctors (e.g. a co-surgery robot). Moreover, it also provides a collaboration service by sharing information between doctors and care givers about clinical treatment. Assistive robots A domestic robot can be employed for healthcare and life monitoring for elderly people. The system collects the health status of users and exchange information with cloud expert system or doctors to facilitate elderly peoples life, especially for those with chronic diseases. For example, the robots are able to provide support to prevent the elderly from falling down, emergency healthy support such as heart disease, blooding disease. Care givers of elderly people can also get notification when in emergency from the robot through network. Industrial robots As highlighted by the German government's Industry 4.0 Plan, "Industry is on the threshold of the fourth industrial revolution. Driven by the Internet, the real and virtual worlds are growing closer and closer together to form the Internet of Things. Industrial production of the future will be characterised by the strong individualisation of products under the conditions of highly flexible (large series) production, the extensive integration of customers and business partners in business and value-added processes, and the linking of production and high-quality services leading to so-called hybrid products." In manufacturing, such cloud based robot systems could learn to handle tasks such as threading wires or cables, or aligning gaskets from a professional knowledge base. A group of robots can share information for some collaborative tasks. Even more, a consumer is able to place customised product orders to manufacturing robots directly with online ordering systems. Another potential paradigm is shopping-delivery robot systems. Once an order is placed, a warehouse robot dispatches the item to an autonomous car or autonomous drone to deliver it to its recipient. == Research == RoboEarth was funded by the European Union's Seventh Framework Programme for research, technological development projects, specifically to explore the field of cloud robotics. The goal of RoboEarth is to allow robotic systems to benefit from the experience of other robots, paving the way for rapid advances in machine cognition and behaviour, and ultimately, for more subtle and sophisticated human-machine interaction. RoboEarth offers a Cloud Robotics infrastructure. RoboEarth's World-Wide-Web style database stores knowledge generated by humans – and robots – in a machine-readable format. Data stored in the RoboEarth knowledge base include software components, maps for navigation (e.g., object locations, world models), task knowledge (e.g., action recipes, manipulation strategies), and object recognition models (e.g., images, object models). The RoboEarth Cloud Engine includes support for mobile robots, autonomous vehicles, and drones, which require much computation for navigation. Rapyuta is an open source cloud robotics framework based on RoboEarth Engine developed by the robotics researcher at ETHZ. Within the framework, each robot connected to Rapyuta can have a secured computing environment (rectangular boxes) giving them the ability to move their heavy computation into the cloud. In addition, the computing environments are tightly interconnected with each other and have a high bandwidth connection to the RoboEarth knowledge repository. FogROS2 is an open-source extension to the Robot Operating System 2 (ROS 2) developed by researchers at UC Berkeley. It enables robots to offload computationally intensive tasks—such as SLAM, grasp planning, and motion planning—to cloud resources, thereby enhancing performance and reducing onboard computational requirements. FogROS2 automates the provisioning of cloud instances, deployment of ROS 2 nodes, and secure communication between robots and cloud services. The platform is designed to be compatible with existing ROS 2 applications without requiring code modifications. Further advancements include FogROS2-SGC, which facilitates secure global connectivity across different networks and locations, and FogROS2-FT, which introduces fault tolerance by replicating services across multiple cloud providers to ensure robustness against failures. KnowRob is an extensional project of RoboEarth. It is a knowledge processing system that combines knowledge representation and reasoning methods with techniques for acquiring knowledge and for grounding the knowledge in a physical system and can serve as a common semantic framework for integrating information from different sources. RoboBrain is a large-scale computational system that learns from publicly available Internet resources, computer simulations, and real-life robot trials. It accumulates everything robotics into a comprehensive and interconnected knowledge base. Applications include prototyping for robotics research, household robots, and self-driving cars. The goal is as direct as the project's name—to create a centralised, always-online brain for robots to tap into. The project is dominated by Stanford University and Cornell University. And the project is supported by the National Science Foundation, the Office of Naval Research, the Army Research Office, Google, Microsoft, Qualcomm, the Alfred P. Sloan Foundation and the National Robotics Initiative, whose goal is to advance robotics to help make the United States more competitive in the world economy. MyRobots is a service for connecting robots and intelligent devices to the Internet. It can be regarded as a social network for robots and smart objects (i.e. Facebook for robots). With socialising, collaborating and sharing, robots can benefit from those interactions too by sharing their sensor information giving insight on their perspective of their current state. COALAS is funded by the INTERREG IVA France (Channel) – England European cross-border co-operation programme. The project aims to develop new technologies for disabled people through social and technological innovation and through the users' social and psychological integrity. The objective is to produce a cognitive ambient
Telligent Community
Telligent Community is a community and collaboration software platform developed by Telligent Systems and was first released in 2004. Telligent Community is built on the Telligent Evolution platform, with a variety of core applications running on top of it such as blogs, forums, media galleries, and wikis. Additional applications from third parties using the API's and REST stack can be installed or integrated with the platform. Telligent Community is built with ASP.NET, C#, and Microsoft SQL Server. It is available as downloadable software that can be installed on a web server or via hosting providers. The current version is Verint Community 12.0 which was released February 2012. The product used to be named Community Server before being rebranded as part of the 5.0 release. == History == Telligent Systems was founded by Rob Howard in 2004, who was previously part of Microsoft's ASP.NET team. Telligent introduced its first product, Community Server, in the fall of 2004. Community Server was one of the first integrated community platforms that brought together blogs, photo galleries, wikis, forums, user profiles and more. Community Server was based on the merger of three then-widely used open source ASP.NET projects: the ASP.NET Forums, nGallery photo gallery, and .Text blog engine. The people behind those projects (Scott Watermasysk, Jason Alexander, and Rob Howard) joined together as Telligent Systems and along with several other software developers created Community Server 1.0. Between 2004 and 2009 Community Server steadily grew in scope, features, and capabilities. In 2008 Telligent Systems released a second version of Community Server that targeted as an Enterprise Social Software platform used to create and manage internal employee communities and intranets. Originally branded as Community Server Evolution this was later renamed Telligent Enterprise. Telligent also announced a new Enterprise Reporting platform at its first Community Server Developers Conference in 2008, which was later renamed Harvest. It was one of the first analytics suites for enterprise collaboration software, and provides social analytics including sentiment analysis, social fingerprints, and buzz analysis on social networking sites such as Twitter. Telligent rebranded all of its products on June 23, 2009 at the Enterprise 2.0 conference when it launched its new Evolution platform product suite. Community Server became known as Telligent Community, Community Server Evolution became known as Telligent Enterprise and the underlying platform that both run on is now referred to as Telligent Evolution. The Social Analytics suite was renamed Telligent Analytics.
Wumpus world
Wumpus world is a simple world use in artificial intelligence for which to represent knowledge and to reason. Wumpus world was introduced by Michael Genesereth, and is discussed in the Russell-Norvig Artificial Intelligence book Artificial Intelligence: A Modern Approach. Wumpus World is loosely inspired by the 1972 video game Hunt the Wumpus. == Problem description == In Artificial Intelligence: A Modern Approach, the wumpus world features a 4x4 grid, containing a monster called a wumpus, multiple bottomless pits and hidden gold. The agent starts at (1,1) and has to find the gold and return to the starting position. The agent loses 1 point for every move and gains 1000 points for bringing the gold to the starting position. The agent can sense pits by a breeze, stench indicates a wumpus, and sparkle indicates gold. The wumpus can be killed by an arrow but costs 10 points.
Elastic cloud storage
An elastic cloud is a cloud computing offering that provides variable service levels based on changing needs. Elasticity is an attribute that can be applied to most cloud services. It states that the capacity and performance of any given cloud service can expand or contract according to a customer's requirements and that this can potentially be changed automatically as a consequence of some software-driven event or, at worst, can be reconfigured quickly by the customer's infrastructure management team. Elasticity has been described as one of the five main principles of cloud computing by Rosenburg and Mateos in The Cloud at Your Service - Manning 2011. == History == Cloud computing was first described by Gillet and Kapor in 1996; however, the first practical implementation was a consequence of a strategy to leverage Amazon's excess data center capacity. Amazon and other pioneers of the commercial use of this technology were primarily interested in providing a “public” cloud service, whereby they could offer customers the benefits of using the cloud, particularly the utility-based pricing model benefit. Other suppliers followed suit with a range of cloud-based models all offering elasticity as a core component, but these suppliers were only offering this service as an element of their public cloud service. Due to perceived weaknesses in security, or at least a lack of proven compliance, many organizations, particularly in the financial and public sectors, have been slow adopters of cloud technologies. These wary organizations can achieve some of the benefits of cloud computing by adopting private cloud technologies. An alternative form of the elastic cloud has been offered by vendors such as EMC and IBM, whereby the service is based around an enterprise's own infrastructure but still retains elements of elasticity and the potential to bill by consumption. == Description == Elasticity in cloud computing is the ability for the organization to adjust its storage requirements in terms of capacity and processing with respect to operational requirements. This has the following benefits: Operational Benefits - Services can be acquired quickly, meaning that the evolving requirements of the business can be addressed almost immediately, giving an organization a potential agility advantage. A properly implemented elastic system will provision/de-provision according to application demands, so if a particular business has activity spikes then the provision can be enabled to match the demand and the capacity can be re-allocated. Research and Development (R&D) Projects - R&D activities are no longer hindered by a requirement to secure a capex budget prior to a project starting. Capability can simply be provisioned from the cloud and released at the end of the exercise. Testing and Deployment - With most large-scale projects a size test needs to be performed prior to final rollout. By taking advantage of the elasticity of the cloud and creating a full-scale avatar of the proposed production system, realistic data and traffic volumes can be provisioned and released as needed. Expensive Resources Allocated - This will normally apply only in the context where a customer is applying at least some of their own servers as part of a cloud infrastructure, specifically where a business (for performance reasons) has decided to invest in solid-state storage as opposed to spinning platters. There are instances when, due to activity spikes, a less critical process may need to be moved from the high-performance resources to more traditional storage. Server Specification - When a customer has elected to own/lease hardware, they can select and specify servers that are specifically tuned to meet the likely needs of their operation (i.e., directly controlling the cost/benefit equation). Utility Based Payments - There is, of course, a key cost driver in this process, and the notion that you should pay for what you consume is acceptable for many organizations. When hardware capacity is sourced internally, organizations need to over-provision. This applies just as much to traditional outsourcing as it does to capex-related expenditure on in-house servers. Cloud Platform – At the heart of any cloud storage system is the ability to manage hyperscale object storage and a Hadoop Distributed Files System (HDFS). Elastic storage capability is particularly well suited to hyperscale and Hadoop environments, where its capability to rapidly respond to changing circumstances and priorities is essential