Accelerating the Industrial Internet of Things
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Number of Case Studies51
Oil Well Pump Control System
Oil Well Pump Control System
Naftamatika was looking to develop an oil well pump control system which could optimize oil well operation using only sensors installed on surface equipment.
Artificial Intelligence and the implications on Medical Imaging
Artificial Intelligence and the implications on Medical Imaging
There are several factors simultaneously driving integration of AI in radiology. Firstly, in many countries around the world there is a discrepancy between the number of doctors trained in radiology and the rising demand for diagnostic imaging. This leads to greater demands for work efficiency and productivity. For example, the number of radiology specialists (consultant work- force) in England went up 5% between 2012 and 2015, while in the same period the number of CT and MR scans increased by 29 and 26 percentage points respectively. In Scotland, the gap widened even further (The Royal College of Radiologists 2016). Today, the average radiologist is interpreting an image every three to four seconds, eight hours a day (Choi et al. 2016).Secondly, the image resolution of today’s scanners is continuously improving – resulting in an ever greater volume of data. Indeed, the estimated overall medical data volume doubles every three years, making it harder and harder for radiologists to make good use of the available information without extra help from computerized digital processing. It is desirable, both in radiological research and in clinical diagnostics, to be able to quantitatively analyze this largely unexploited wealth of data and, for example, utilize new measurable imaging biomarkers to assess disease progression and prognosis (O’Connor et al. 2017). Experts see considerable future potential in the transformation of radiology from a discipline of qualitative interpretation to one of quantita- tive analysis, which derives clinically relevant information from extensive data sets (“radiomics”). “Images are more than pictures, they are data,” American radiologist Robert Gillies and his colleagues write (Gillies et al. 2016). Of course, this direction for radiology will require powerful, automated procedures, some of which at least will come under the field of artificial intelligence.
Controlling and Monitoring Microgrids
Controlling and Monitoring Microgrids
Microgrid topologies offer several advantages over large traditional grids including increased resiliency and easier integration of distributed renewables. However, some challenges such as maintaining scalability and interoperability between diffferent vendors and connectivity protocol standards are challenges that have to be overcome in order to reap the benefits of such a system.
Number of Suppliers55
Eigen Innovations
Eigen Innovations
Eigen Innovations provides solutions that combine the data being produced by traditional Operational Technology running within industrial environments with Information Technology that uses cloud-based computing and advanced data science. This convergence creates unique opportunities for significant process efficiency gains within industrial manufacturing.Year founded: 2012
DorsaVi
DorsaVi
DorsaVi is a biotechnology company developing innovative motion analysis device technologies. DorsaVi makes wearable sensors, software and sophisticated algorithms that objectively measure movement and muscle activation.
Futuretext
Futuretext
futuretext applies machine learning techniques to complex problems in the IoT (Internet of Things) and Telecoms domains. The company works on complex projects which use Machine learning algorithms to provide a distinct competitive advantage to our clients. Their areas of focus include Sensor fusion techniques, Real Time algorithms (ex Apache Spark) and Deep learning. We apply machine learning techniques to the IoT and Telecoms datasets.
Number of Organizations2
Zetta Venture Partners
Zetta Venture Partners
Zetta Venture Partners is the first focused fund committed to delivering exceptional returns from the high-growth analytics market. The firm, founded by industry veteran Mark Gorenberg, launched in 2013. With offices in San Francisco and Utah, and a worldwide network of partners, entrepreneurs and investors, Zetta Venture Partners is at the forefront of the next huge and long-lasting investment opportunity. Focused funds deliver exceptional returns. In fact, over the past 10 years, focused funds have outperformed all funds by 72 basis points, according to the National Venture Capital Association. As an analytics-only fund, Zetta delivers the expertise, network and portfolio to create ongoing, exceptional deal flow.
SmartSantander
SmartSantander
SmartSantander is a EU ICT FP7 research project for future Internet research and experimentation.This unique experimental facility will be sufficiently large, open and flexible to enable horizontal and vertical federation with other experimental facilities and stimulates development of new applications by users of various types including experimental advanced research on IoT technologies and realistic assessment of users’ acceptability tests. The project envisions the deployment of 20,000 sensors in Belgrade, Guildford, Lübeck and Santander (12,000), exploiting a large variety of technologies.A scalable, heterogeneous and trustable large-scale real-world experimental facility will be deployed. During the RWI sessions of the Future Internet Assembly in Prague in early 2009, the main requirements for a real-world IoT experimental platform were identified. SmartSantander will address all these requirements by specifying, designing, and implementing the necessary building blocks. An initial high-level architecture for the resulting new experimental facility has already been worked out, and is shown in Figure below. This architecture heavily relies on existing components which will be supplemented by the so far missing building blocks.One of the main objectives of the project is to fuel the use of the Experimentation Facility among the scientific community, end users and service providers in order to reduce the technical and societal barriers that prevent the IoT concept to become an everyday reality. To attract the widest interest and demonstrate the usefulness of the SmartSantander platform, a key aspect that will be addressed is the inclusion of a wide set of applications. Application areas will be selected based on their high potential impact on the citizens as well as to exhibit the diversity, dynamics and scale that are essential in advanced protocol solutions, and will be able to be evaluated through the platform. The platform will be attractive for all involved stakeholders: Industries, communities of users, other entities that are willing to use the experimental facility for deploying, and assessing new services and applications, and Internet researchers to validate their cutting-edge technologies (protocols, algorithms, radio interfaces, etc.).
Number of Use Cases7
Fog Computing
Fog Computing
Fog computing refers to a decentralized computing structure, where resources, including the data and applications, get placed in logical locations between the data source and the cloud; it also is known by the terms ‘fogging’ and ‘fog networking.’The goal of this is to bring basic analytic services to the network edge, improving performance by positioning computing resources closer to where they are needed, thereby reducing the distance that data needs to be transported on the network, improving overall network efficiency and performance. Fog computing can also be deployed for security reasons, as it has the ability to segment bandwidth traffic and introduce additional firewalls to a network for higher security. 
Water Quality and Leakage Monitoring
Water Quality and Leakage Monitoring
Smart Water Monitoring Platforms are ultra-low-power sensor nodes designed for use in rugged environments and deployment in hard-to-access locations. They detect damages in the water supply infrastructure and potential risks to public health or environmental damage in real-time.Water leaks typically go undetected or are responded to only after the event. Therefore, a significant amount of water is lost due to excessive irrigation (only 70% of the water supplied is consumed by agriculture).The demand for innovative solutions to enable more efficient use of available water resources, to improve drinking water quality, and improve water resource planning is growing. Due to this, some analysts estimate the global water sector to be worth 1 trillion USD per year by 2025.IoT enables precise control over water resources data, thus allowing an efficient and optimized management of water companies. Smart water management systems can make a fast and significant improvement to the cost and reliability of water supplies, especially in urban areas and in agriculture.There are various applications for smart water management such as water leakage detection, watering management through sensors, drinking water quality monitoring, quality control of pools and water reserves, etc. At present, water companies have numerous sensors and devices which are able to provide input for detailed reports about relevant business critical factors – including water temperature, water quality/composition, water pressure, water flow, etc. However, most water companies still lack advanced real-time reporting and prediction capabilities to monitor “changing” factors. As a consequence, there is a focus on real-time data extraction, reporting, visualization. 
Asset Health Management (AHM)
Asset Health Management (AHM)
Asset Health Management refers to the process of analyzing the health of an asset as determined by operational requirements. The health of an asset in itself relates to the asset's utility, its need to be replaced, and its need for maintenance. It can be broken down into three key components:Monitoring: Tracking the current operating status of the asset.Diagnostic Analysis: Comparing real-time data to historical data in order to detect anomalies.Prognostic Analysis: Identifying and prioritizing specific actions to maximize the remaining useful life of the asset based on analysis of real-time and historical data.
Number of Terms14
Smart Cards
Smart Cards
A smart card is a device that includes an embedded integrated circuit chip (ICC) that can be either a secure microcontroller or equivalent intelligence with internal memory or a memory chip alone.
Proof of Stake (POS)
Proof of Stake (POS)
A consensus distribution algorithm that rewards earnings based on the number of coins you hold.
Machine Learning
Machine Learning
A subfield of computer science that evolved from the study of pattern recognition in artificial intelligence.
Number of Guides1
Can an algorithm select the perfect team?
Can an algorithm select the perfect team?
Adi Gaskell
51 Case Studies
55 Suppliers
2 Organizations
7 Use Cases
14 Terms
1 Guide
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