A multimodal and adaptive real-time human -computer interface for peoples with severe motor disability

This dissertation introduces the design of a multimodal, adaptive real-time assistive system as an alternate human computer interface that can be used by individuals with severe motor disabilities. The proposed design is based on the integration of a remote eye-gaze tracking system, voice recognition software, and a virtual keyboard. The methodology relies on a user profile that customizes eye gaze tracking using neural networks. The user profiling feature facilitates the notion of universal access to computing resources for a wide range of applications such as web browsing, email, word processing and editing. ^ The study is significant in terms of the integration of key algorithms to yield an adaptable and multimodal interface. The contributions of this dissertation stem from the following accomplishments: (a) establishment of the data transport mechanism between the eye-gaze system and the host computer yielding to a significantly low failure rate of 0.9%; (b) accurate translation of eye data into cursor movement through congregate steps which conclude with calibrated cursor coordinates using an improved conversion function; resulting in an average reduction of 70% of the disparity between the point of gaze and the actual position of the mouse cursor, compared with initial findings; (c) use of both a moving average and a trained neural network in order to minimize the jitter of the mouse cursor, which yield an average jittering reduction of 35%; (d) introduction of a new mathematical methodology to measure the degree of jittering of the mouse trajectory; (e) embedding an onscreen keyboard to facilitate text entry, and a graphical interface that is used to generate user profiles for system adaptability. ^ The adaptability nature of the interface is achieved through the establishment of user profiles, which may contain the jittering and voice characteristics of a particular user as well as a customized list of the most commonly used words ordered according to the user's preferences: in alphabetical or statistical order. This allows the system to successfully provide the capability of interacting with a computer. Every time any of the sub-system is retrained, the accuracy of the interface response improves even more. ^

Deep Corals, Deep Learning: Moving the Deep Net Towards Real-Time Image Annotation

The mismatch between human capacity and the acquisition of Big Data such as Earth imagery undermines commitments to Convention on Biological Diversity (CBD) and Aichi targets. Artificial intelligence (AI) solutions to Big Data issues are urgently needed as these could prove to be faster, more accurate, and cheaper. Reducing costs of managing protected areas in remote deep waters and in the High Seas is of great importance, and this is a realm where autonomous technology will be transformative.

A Valuation Analysis of the Physical Oceanographic Real Time System (PORTS)

This analysis estimates several economic benefits derived from national implementation of the National Oceanic and Atmospheric Administration’s Physical Oceanographic Real-Time System (PORTS®) at the 175 largest ports in the United States. Significant benefits were observed owing to: (1) lower commercial marine accident rates and resultant reductions in morbidity, mortality and property damage; (2) reduced pollution remediation costs; and, (3) increased productivity associated with operation of more fully loaded commercial vessels. Evidence also suggested additional benefits from heightened commercial and recreational fish catch and diminished recreational boating accidents. Annual gross benefits from 58 current PORTS® locations exceeded $217 million with an addition $83 million possible if installed at the largest remaining 117 ports in the United States. Over the ten-year economic life of PORTS® instruments, the present value for installation at all 175 ports could approach $2.5 billion.

Supporting business intelligence usage: an integrated framework with automatic weighting

This paper presents an integrated framework that comprises an automatic weighting method for assessing data quality (DQ) of the framework so as to better support the business intelligence (BI) usage. Specifically, we utilize business process modeling (BPM) notation and information product map and frame them into a hierarchical mapping structure. Furthermore, we develop and demonstrate an automatic weight-assignment method for evaluating critical dimensions (i.e., completeness and accuracy) of DQ of the integrated framework. Through a design science paradigm, the effectiveness of the framework and the associated DQ weighting method has been rigorously validated by faculty management users of a university. The framework together with the DQ weighting method builds user confidence by enhancing the traceability of a BI product. The automatic DQ weight assignment also provides better time efficiency because the weight of each data attribute is determined automatically based on its usage on the BI dashboard.

An investigation of two real time machine learning techniques that could enhance the adaptability of game AI agents

Developers strive to create innovative Artificial Intelligence (AI) behaviour in their games as a key selling point. Machine Learning is an area of AI that looks at how applications and agents can be programmed to learn their own behaviour without the need to manually design and implement each aspect of it. Machine learning methods have been utilised infrequently within games and are usually trained to learn offline before the game is released to the players. In order to investigate new ways AI could be applied innovatively to games it is wise to explore how machine learning methods could be utilised in real-time as the game is played, so as to allow AI agents to learn directly from the player or their environment. Two machine learning methods were implemented into a simple 2D Fighter test game to allow the agents to fully showcase their learned behaviour as the game is played. The methods chosen were: Q-Learning and an NGram based system. It was found that N-Grams and QLearning could significantly benefit game developers as they facilitate fast, realistic learning at run-time.

Communication Architecture Design for Real-Time Networked Control Systems

Networked control over data networks has received increasing attention in recent years. Among many problems in networked control systems (NCSs) is the need to reduce control latency and jitter and to deal with packet dropouts. This paper introduces our recent progress on a queuing communication architecture for real-time NCS applications, and simple strategies for dealing with packet dropouts. Case studies for a middle-scale process or multiple small-scale processes are presented for TCP/IP based real-time NCSs. Variations of network architecture design are modelled, simulated, and analysed for evaluation of control latency and jitter performance. It is shown that a simple bandwidth upgrade or adding hierarchy does not necessarily bring benefits for performance improvement of control latency and jitter. A co-design of network and control is necessary to maximise the real-time control performance of NCSs