This thesis describes research undertaken as an initial step in the development of a nationwide land mobile coverage area prediction model for New Zealand. The most suitable approach is shown to be a computer-based method which includes corrections for both environmental and terrain features. Extensive field trials performed in Auckland at 76 and 465MHz to develop a propagation data base are described. Measurements have been made in a variety of environments over unobstructed, obstructed and mixed land-sea paths. An analysis of these measurements indicates that, subject to the availability of suitable topographic and environmental data bases and with the exception of two special cases, a plane earth based prediction method is suitable for incorporation in a nationwide land mobile coverage area prediction model for New Zealand. Recommendations are made for the implementation and further development of this model.

The deployment of third generation (3G) cellular systems is resulting in a transition from cellular systems that predominantly carry constant-bit-rate (CBR) voice traffic to multi-service packet based systems that predominantly carry variable-bit-rate (VBR) traffic. With 3G DS-CDMA cellular systems there is a direct relationship between user traffic and propagation dependent performance as additional traffic causes increased system interference. This thesis investigates the impact of VBR traffic on the propagation dependent performance of DS-CDMA cellular systems that utilise frame-by-frame dynamic resource allocation on the radio channel. A DS-CDMA cellular system model is developed and the downlink performance of both outdoor macro-cellular and indoor pico-cellular systems is evaluated with a variety of traffic types. Both traffic scheduling performance and propagation dependent performance are evaluated as the two are inter-linked. Scenarios are identified where propagation dependent performance is sensitive to the statistical properties of the user traffic streams and it is shown that a significant performance difference potentially exists between different traffic types when the number of users per cell is low. When a significant performance difference does exist, burstier more variable traffic generally results in superior propagation dependent performance. The base transceiver station (BTS) transmitter power mean and variance provides a good indication of the level of propagation dependent performance regardless of the specific traffic type. Traffic scheduling policies that deliberately reduce the variability of user traffic streams are considered and in terms of propagation dependent performance these are shown to have a minimal impact on the performance difference between different traffic types. The implications of VBR traffic on DS-CDMA cellular system design are outlined and it is shown that VBR traffic can be approximated as CBR traffic in many scenarios and this is a convenient approximation as it simplifies system design and detailed traffic models do not need to be developed.

The proliferation of robots in industry and every day human life is gaining momentum. After the initial few decades of employment of robots in the industry, especially the automotive assembly plants, robots are now entering the home and offices. From being pick-and-place manipulators, robots are slowly being transformed in shape and form to be more anthropomorphic. The wheeled robots are however here to stay for the foreseeable future until such time as artificial muscles, and efficient means to control them, are well developed. The next phase of development of robots will be for the service industry. Robots will cooperate with each other to accomplish collaborative tasks to aid human life. They will also collaborate with human beings to assist them in doing tasks such as lifting loads and moving objects. At the same time, with the advancement of hardware, robots are becoming very fast and are capable of being programmed with more intelligence. Coupled with this is the availability of sophisticated sensors with which the robots can perceive the real world around them. Combinations of these factors have created many challenging areas of research. Several factors affect the performance of robots in a dynamic collaborative environment. The research presented in this thesis has identified the major contributing factors, namely fast vision processing, behaviour programming, predictive movement and interception control, and precise motion control, that collectively have influence on the performance of robots which are engaged in a collaborative effort to accomplish a task. Several novel techniques have been proposed in this thesis to enhance the collective performance of collaborating robots. In many systems, vision is used as one of the sensory inputs for the robot’s perception of the environment. This thesis describes a new colour space and the use of discrete look-up-tables (LUT) for very fast and robust colour segmentation and real-time identification of objects in the robot’s work space. A distributed camera system and a stereo vision using a single camera are reported. Advanced filtering has been applied to the vision data for predictive identification of the position and orientation of moving robots and targets, and for anticipatory interception control. Collaborative tasks are generally complex and robots need to be capable of exhibiting sophisticated behaviours. This thesis has detailed the use of State Transition Based Control (STBC) methodology to build a hierarchy of complex behaviour. Behaviour of robots in a robot soccer game and features such as role selection and obstacle avoidance have been built using STBC. A novel methodology for advanced control of fast robots is detailed. The algorithm uses a combination of Triangular Targeting Algorithm (TTA) and Proximity Positioning Algorithm (PPA) to position a robot behind an object aligned with a target. Various forms of velocity profiling have been proposed and validated with substantial test results. The thesis ends by looking at future scenarios where robots and human beings will coexist and work together to do many collaborative tasks. Anthropomorphic robots will be more prevalent in future and teleoperation will gain momentum. Throughout the thesis, the engineering applicability of proposed algorithms and architectures have been emphasised by testing on real robots.

The RF high power amplifier is a key module in the NMR spectrometer. Robustness, lower power consumption, and small size are requirements. In this thesis, devices are studied and different design approaches are considered. New ideas are introduced, and simulations are used to show if it these work. A real prototype is developed. Results from the prototype are satisfactory and in good agreement with the simulation results. This allows for the possibility of a real portable NMR spectrometer 'Lapspec'. Points of note: • Feedback to stabilize amplifier, • Hard bias to improve rise time of pulse, • A rugged device is chosen, • Power limiter technology is used to avoid overdrive amplifier, • Lower value attenuator at output of final stage to reduce load VSWR, • Reason of spike is studied, the solution to reduce spike is given, • The reason of instability of amplifier with NMR load is analyzed, • A method is introduced to ensure there is no oscillation while the High Power Amplifier (HPA) is connected with the NMR probe.

The vision of Ambient Intelligence (AmI) can be realised through the integration of embedded technologies, distributed systems, middleware and human machine interfaces and many research efforts have been made to advance these technologies. However, the exclusiveness of these ambient intelligence technologies has reduced their practical values. In this thesis, a novel AmI platform is proposed to facilitate the integration and interoperability of various technologies in the process of developing AmI applications. The platform defines the overall software/hardware architecture and communication interfaces and provides a common base for development, operation and future adaptation of AmI applications. The proposed platform consists of four layers, the physical ubiquitous environment, middleware, multi-agent system (MAS) and application layer. The ubiquitous environment layer accommodates any type of embedded device network for interconnecting different sensors, actuators and computing devices. The middleware layer is built using an IP-based service discovery protocol, Universal Plug and Play (UPnP), which provides a unique communication interface for controlling and monitoring embedded devices. The MAS handles the core distributed and adaptive control functionality and communication with user interfaces. The application layer contains any type of user interface for different AmI applications. An XML-based content language is designed with an XML schema and seven XML messages. The content language standardises the way of interpreting contents of communication between different user interfaces and the MAS. Based on the proposed platform, a complete AmI application prototype called Distributed Embedded Intelligence Room (DEIR) has been implemented. Four different device networks, the SmartHouse network, IP network, Bluetooth and Zigbee network, have been integrated in DEIR to interconnect various embedded sensors and devices. The MAS is implemented using Java Agent DEvelopment framework (JADE). Four application specific agents, known as the UPnP control point agent, IP interface agent, fuzzy inference agent and decision tree agent, are designed and implemented. The UPnP control point agent provides MAS the ability to monitor and to control the underlying hardware devices through the UPnP middleware layer. The IP interface agent handles communication with user interfaces over socket connections. Fuzzy inference and decision tree agents are implemented to provide personalised learning and automated control capabilities. Three user interfaces, including a remote graphical user interface, a mobile PDA interface and a 3D virtual reality interface are implemented. Contents of communication between these user interfaces and the MAS are encoded using the proposed XML content language and transmitted over socket connections. The AmI application prototype, DEIR, has demonstrated the ability of integrating multiple device networks and multiple user interfaces, which is a vital feature for most AmI applications. Two case studies have been carried out to incorporate two adaptive learning and controlling algorithms, known as the adaptive online fuzzy inference system (AOFIS) and ID3 decision tree algorithm, in the MAS of DEIR. The results of case studies show that DEIR has the ability of incorporating multiple adaptive control algorithms as multiple agents. In addition, comparable or better offline learning accuracy and learning speed have been achieved by DEIR compared with other advanced adaptive control algorithms. / Whole document restricted, but available by request, use the feedback form to request access.

In modern point to point microwave radio systems used to backhaul cellular voice and data traffic, quadrature amplitude modulation (QAM) is the norm. These systems require a highly linear power amplifier which is expensive and has relatively low power efficiency. Recently, continuous phase modulation (CPM) has been deployed in this market. The CPM transmitted waveform has a constant envelope and so a non-linear RF power amplifier can be used. This significantly reduces cost and improves power efficiency. Two important disadvantages of CPM are receiver complexity and inferior spectral efficiency compared to QAM. This thesis demonstrates a 50% spectral efficiency improvement over an existing CPM configuration without loss of detection efficiency. This is achieved by moving to coherent demodulation and extending the duration of the CPM phase pulse to 3 symbol periods. This new CPM configuration of h=1/4, M=4, L=3, is evaluated against ETSI requirements for a 28 MHz channel carrying 24 E1 circuits. Simulation of the receiver floating point model demonstrates all requirements are met. The detection efficiency requirement is exceeded by 4.7 dB. Carrier recovery, phase and timing synchronisation are assumed to be ideal. The 50% increased symbol rate, coherent reception and a longer smoother phase pulse, conspire to increase receiver complexity substantially. The Viterbi algorithm is used to perform maximum-likelihood detection resulting in a 128 state trellis. This application has a stringent cost requirement that limits the implementation target to a Field Programmable Gate Array (FPGA) costing less than US$30. To demonstrate this demanding cost target is met, the two most computationally expensive receiver functions, the branch metric unit and path metric processing unit, are implemented in VHDL and targeted to a Xilinx Spartan 3A-DSP 1800 FPGA. The implementation uses 67% of the available logic resources, thus meeting the cost requirement. The branch metric unit is implemented using a distributed arithmetic technique that performs the equivalent of 27.6 giga-multiplies/s, consuming only 23% of the available FPGA logic cells. This is very efficient compared to a conventional approach using all the FPGA’s embedded multipliers which combined can only achieve 21 giga-multiplies/s. The Viterbi path metric processing unit is implemented using a more conventional state-parallel architecture. To reduce state metric routing complexity, states are grouped into radix-4 units comprising dual add-compare-select (ACS) units. By utilising a spare cycle in the deep ACS pipeline, each ACS unit processes two output state metrics, thus halving the number of ACS units required. This implementation uses 44% of the available FPGA resources and meets timing at 204.5 MHz, exceeding the throughput requirement of 54 Mbit/s.

The vision of Ambient Intelligence (AmI) can be realised through the integration of embedded technologies, distributed systems, middleware and human machine interfaces and many research efforts have been made to advance these technologies. However, the exclusiveness of these ambient intelligence technologies has reduced their practical values. In this thesis, a novel AmI platform is proposed to facilitate the integration and interoperability of various technologies in the process of developing AmI applications. The platform defines the overall software/hardware architecture and communication interfaces and provides a common base for development, operation and future adaptation of AmI applications. The proposed platform consists of four layers, the physical ubiquitous environment, middleware, multi-agent system (MAS) and application layer. The ubiquitous environment layer accommodates any type of embedded device network for interconnecting different sensors, actuators and computing devices. The middleware layer is built using an IP-based service discovery protocol, Universal Plug and Play (UPnP), which provides a unique communication interface for controlling and monitoring embedded devices. The MAS handles the core distributed and adaptive control functionality and communication with user interfaces. The application layer contains any type of user interface for different AmI applications. An XML-based content language is designed with an XML schema and seven XML messages. The content language standardises the way of interpreting contents of communication between different user interfaces and the MAS. Based on the proposed platform, a complete AmI application prototype called Distributed Embedded Intelligence Room (DEIR) has been implemented. Four different device networks, the SmartHouse network, IP network, Bluetooth and Zigbee network, have been integrated in DEIR to interconnect various embedded sensors and devices. The MAS is implemented using Java Agent DEvelopment framework (JADE). Four application specific agents, known as the UPnP control point agent, IP interface agent, fuzzy inference agent and decision tree agent, are designed and implemented. The UPnP control point agent provides MAS the ability to monitor and to control the underlying hardware devices through the UPnP middleware layer. The IP interface agent handles communication with user interfaces over socket connections. Fuzzy inference and decision tree agents are implemented to provide personalised learning and automated control capabilities. Three user interfaces, including a remote graphical user interface, a mobile PDA interface and a 3D virtual reality interface are implemented. Contents of communication between these user interfaces and the MAS are encoded using the proposed XML content language and transmitted over socket connections. The AmI application prototype, DEIR, has demonstrated the ability of integrating multiple device networks and multiple user interfaces, which is a vital feature for most AmI applications. Two case studies have been carried out to incorporate two adaptive learning and controlling algorithms, known as the adaptive online fuzzy inference system (AOFIS) and ID3 decision tree algorithm, in the MAS of DEIR. The results of case studies show that DEIR has the ability of incorporating multiple adaptive control algorithms as multiple agents. In addition, comparable or better offline learning accuracy and learning speed have been achieved by DEIR compared with other advanced adaptive control algorithms. / Whole document restricted, but available by request, use the feedback form to request access.

Electrified fencing is commonly used throughout the world to control animals with smaller and cheaper fence constructions than would otherwise be necessary with non-electrified wires. Typical installations have a long wire or wires starting from an electric fence energiser and then surrounding fields in various complex configurations. Faults on electric fences can be difficult to locate, with the average fence using tens of kilometres of wire. Basic fault-finding tools allow an operator to read the peak fence voltage, requiring the user to decide whether a fault is present and to randomly search for the source of the problem. The focus of this thesis is to develop a device that reduces the time to locate faults on a fence by providing more information about the location and nature of a fault, and will point in the direction of the fault.

Apart from recognition accuracy, decoding speed and vocabulary size, another point of consideration when developing a practical ASR application is the adaptability of the system. An ASR system is more useful if it can cope with changes that are introduced by users, for example, new words and new grammar rules. In addition, the system can also automatically update the underlying knowledge sources, such as language model probabilities, for better recognition accuracy. Since the knowledge sources need to be adaptable, it is in°exible to statically combine them. It is because on-line modi¯cation becomes di±cult once all the knowledge sources have been combined into one static search space. The second objective of the thesis is to develop an algorithm which allows dynamic integration of knowledge sources during decoding. In this approach, each knowledge source is represented by a weighted ¯nite state transducer (WFST). The knowledge source that is subject to adaptation is factorized from the entire search space. The adapted knowledge source is then combined with the others during decoding. In this thesis, we propose a generalized dynamic WFST composition algorithm, which avoids the creation of non- coaccessible paths, performs weight look-ahead and does not impose any constraints to the topology of the WFSTs. Experimental results on Wall Street Journal (WSJ1) 20k- word trigram task show that our proposed approach has a better word accuracy versus real-time factor characteristics than other dynamic composition approaches.

The vision of Ambient Intelligence (AmI) can be realised through the integration of embedded technologies, distributed systems, middleware and human machine interfaces and many research efforts have been made to advance these technologies. However, the exclusiveness of these ambient intelligence technologies has reduced their practical values. In this thesis, a novel AmI platform is proposed to facilitate the integration and interoperability of various technologies in the process of developing AmI applications. The platform defines the overall software/hardware architecture and communication interfaces and provides a common base for development, operation and future adaptation of AmI applications. The proposed platform consists of four layers, the physical ubiquitous environment, middleware, multi-agent system (MAS) and application layer. The ubiquitous environment layer accommodates any type of embedded device network for interconnecting different sensors, actuators and computing devices. The middleware layer is built using an IP-based service discovery protocol, Universal Plug and Play (UPnP), which provides a unique communication interface for controlling and monitoring embedded devices. The MAS handles the core distributed and adaptive control functionality and communication with user interfaces. The application layer contains any type of user interface for different AmI applications. An XML-based content language is designed with an XML schema and seven XML messages. The content language standardises the way of interpreting contents of communication between different user interfaces and the MAS. Based on the proposed platform, a complete AmI application prototype called Distributed Embedded Intelligence Room (DEIR) has been implemented. Four different device networks, the SmartHouse network, IP network, Bluetooth and Zigbee network, have been integrated in DEIR to interconnect various embedded sensors and devices. The MAS is implemented using Java Agent DEvelopment framework (JADE). Four application specific agents, known as the UPnP control point agent, IP interface agent, fuzzy inference agent and decision tree agent, are designed and implemented. The UPnP control point agent provides MAS the ability to monitor and to control the underlying hardware devices through the UPnP middleware layer. The IP interface agent handles communication with user interfaces over socket connections. Fuzzy inference and decision tree agents are implemented to provide personalised learning and automated control capabilities. Three user interfaces, including a remote graphical user interface, a mobile PDA interface and a 3D virtual reality interface are implemented. Contents of communication between these user interfaces and the MAS are encoded using the proposed XML content language and transmitted over socket connections. The AmI application prototype, DEIR, has demonstrated the ability of integrating multiple device networks and multiple user interfaces, which is a vital feature for most AmI applications. Two case studies have been carried out to incorporate two adaptive learning and controlling algorithms, known as the adaptive online fuzzy inference system (AOFIS) and ID3 decision tree algorithm, in the MAS of DEIR. The results of case studies show that DEIR has the ability of incorporating multiple adaptive control algorithms as multiple agents. In addition, comparable or better offline learning accuracy and learning speed have been achieved by DEIR compared with other advanced adaptive control algorithms. / Whole document restricted, but available by request, use the feedback form to request access.