Audience Analytics for News
Imaging Muscle Fibres
The SIXTH sense enabling you access to the hidden sensors all around you.
SIXTH™ is a Java-based middleware for the mobile Sensor Web. The Internet of Things (IoT) offers the potential of a world in which computing has become ubiquitous. A world in which a plethora of devices at any given time can be providing sensor data to allow for the creation of rich sensor-driven mobile applications. The breadth and depth of new applications possibilities, commercial opportunities and innovations within this rapidly emerging and increasingly mobile-centric landscape is truly awesome but to access all the sensors around a given user at any point in time is still a non-trivial task. SIXTH™ creates a producer/consumer sensor web around a user where the user has apps to find and locate sensors automatically around them to create more feature rich app experiences. Developers are provided with an abstraction layer that gives the ability to access these sensors without the technical hurdles associated with previous solutions.
Dome is a Python-based suite for power system modelling and simulation
Dome is a C and Python-based set of libraries for electric power system analysis and control. It is a Unix command line application that links to the state-of-the-art of mathematical libraries for sparse matrix factorization, synthesis of controllers and stability analysis. Parallel computing and GPU support is also provided. A reduced version of the tool can also run on Windows.
PSAT is a Matlab-based suite for power system steady-state and dynamic analysis
PSAT is a Matlab toolbox for electric power system analysis and control. The command line version of PSAT is also GNU Octave compatible. PSAT includes power flow, continuation power flow, optimal power flow, small signal stability analysis and time domain simulation. All operations can be assessed by means of graphical user interfaces (GUIs) and a Simulink-based library provides an user-friendly tool for network design. ** This purchase price is for the electronic documentation, software is provided as open source **
Advanced monitoring of entities in online media sources
In the always-connected 24-7 online marketplace, up-to-date knowledge is paramount for good decision making. In many timezones across the globe, content is emerging which can have an effect on the reputation of a firm, individual or product. Depending on the trust level attached to the source, a single social media message can cause significant damage to the reputation of an entity, as experienced when a tweet was sent from the hacked Associated Press Twitter account, causing the Dow Jones Index on Wall Street to drop sharply. Online monitoring of the reputation of an entity enables quick reactions to current events or market fluctuations.
High-throughput, Scalable Data Stream Clustering
High volume, high throughput data streams are common in many industries including financial services (transaction streams), communications (instant messaging, SMS, micro-blogging), web and gaming (action and event streams) and production line environments (machine generated data). The ability to analyse and gain insights from this type of data as events happen, in real-time, can be hugely beneficial. Traditionally data analytics is performed as an off-line, batch process where the results are available hours or even days after the data was produced. This means that any actions taken based on these insights will be at a considerable time interval after the original events occurred, and in many scenarios being able to analyse the live data stream and hence reduce this response delay is of critical importance. Clustering is a core data analytics technique whereby similar entities are automatically identified and grouped together. This drives many common applications of data analytics such as detecting anomalous or fraudulent activity, identifyting market segments and user behaviours, reporting spam and emerging topics and patterns. CeADAR has developed a high-throughput, scalable clustering solution for data streams that brings real-time, ‘live data’ capabilities to these advanced data analytics tasks.
Personalised forecasting model for more accurate predictions
Different areas of the economy require precise forecasts of future events based on knowledge collected from the past.Technology platforms which are accurate and effective in acquiring and processing such information are of great value to the market because they assist in planning and helping to prepare for what is coming. Unlike typical forecasting which can be based on informal methods, optimal forecasting methodology can be conducted based on scientific tools to assure proper outputs. As a result, appropriate feedback on a given problem can successfully warn if the process under investigation is heading towards an undesired direction. This result, in turn, can support managers with meaningful information that allows them to improve planning. An automated forecasting platform can result in trustworthy analytics which leads to better decision making processes. Successful forecasting can minimize risk which is associated with the unknown future, and if properly utilized may give rise to the profits which would not be achievable otherwise.
Identifying Persons of Interest in Social Networks
Finding persons of interest on social media is not a trivial task and current approaches to people search are simplistic and based on collaborative filtering. There is a growing need to find people in a particular area of expertise, location, and/or business. IdentityMatch provides near real time retrieval of results from user searches.
Continuous Data Stream Analytics for Image Data
Object detection, especially small object detection, is a well known and studied image analysis challenge. An example of which is finding predefined brands/logos in a large number of images or a continuous image stream in real-time. Current logo detection applications are generally run in an offline way, and cannot handle a large, continuous image data stream and process it in real time or near real-time.
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