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Opportunistic communication techniques have shown to provide significant performance improvements in centralised random access wireless networks. The key mechanism of opportunistic communication is to send back-to-back data packets whenever the channel quality is deemed "good". Recently there have been attempts to introduce opportunistic communication techniques in distributed wireless networks such as wireless ad hoc networks. In line of this research, we propose a new paradigm of medium access control, called Channel MAC based on the channel randomness and opportunistic communication principles. Scheduling in Channel MAC depends on the instance at which the channel quality improves beyond a threshold, while neighbouring nodes are deemed to be silent. Once a node starts transmitting, it will keep transmitting until the channel becomes "bad". We derive an analytical throughput equation of the proposed MAC in a multiple access environment and validate it by simulations. It is observed that Channel MAC outperforms IEEE 802.11 for all probabilities of good channel condition and all numbers of nodes. For higher number of nodes, Channel MAC achieves higher throughput at lower probabilities of good channel condition increasing the operating range. Furthermore, the total throughput of the network grows with increasing number of nodes considering negligible propagation delay in the network. A scalable channel prediction scheme is required to implement the practical Channel MAC protocol in practice. We propose a mean-value based channel prediction scheme, which provides prediction with enough accuracy to be used in the Channel MAC protocol. NS2 simulation result shows that the Channel MAC protocol outperforms the IEEE 802.11 in throughput due to its channel diversity mechanism in spite of the prediction errors and packet collisions. Next, we extend the Channel MAC protocol to support multi-rate communications. At present, two prominent multi-rate mechanisms, Opportunistic Auto Rate (OAR) and Receiver Based Auto Rate (RBAR) are unable to adapt to short term changes in channel conditions during transmission as well as to use optimum power and throughput during packet transmissions. On the other hand, using channel predictions, each source-destinations pair in Channel MAC can fully utilise the non-fade durations. We combine the scheduling of Channel MAC and the rate adaptive transmission based on the channel state information to design the 'Rate Adaptive Channel MAC' protocol. However, to implement the Rate adaptive Channel MAC, we need to use a channel prediction scheme to identify transmission opportunities as well as auto rate adaptation mechanism to select rates and number of packets to transmit during those times. For channel prediction, we apply the scheme proposed for the practical implementation of Channel MAC. We propose a "safety margin" based technique to provide auto rate adaptation. Simulation results show that a significant performance improvement can be achieved by Rate adaptive Channel MAC as compared to existing rate adaptive protocols such as OAR.

Opportunistic communication techniques have shown to provide significant performance improvements in centralised random access wireless networks. The key mechanism of opportunistic communication is to send back-to-back data packets whenever the channel quality is deemed "good". Recently there have been attempts to introduce opportunistic communication techniques in distributed wireless networks such as wireless ad hoc networks. In line of this research, we propose a new paradigm of medium access control, called Channel MAC based on the channel randomness and opportunistic communication principles. Scheduling in Channel MAC depends on the instance at which the channel quality improves beyond a threshold, while neighbouring nodes are deemed to be silent. Once a node starts transmitting, it will keep transmitting until the channel becomes "bad". We derive an analytical throughput equation of the proposed MAC in a multiple access environment and validate it by simulations. It is observed that Channel MAC outperforms IEEE 802.11 for all probabilities of good channel condition and all numbers of nodes. For higher number of nodes, Channel MAC achieves higher throughput at lower probabilities of good channel condition increasing the operating range. Furthermore, the total throughput of the network grows with increasing number of nodes considering negligible propagation delay in the network. A scalable channel prediction scheme is required to implement the practical Channel MAC protocol in practice. We propose a mean-value based channel prediction scheme, which provides prediction with enough accuracy to be used in the Channel MAC protocol. NS2 simulation result shows that the Channel MAC protocol outperforms the IEEE 802.11 in throughput due to its channel diversity mechanism in spite of the prediction errors and packet collisions. Next, we extend the Channel MAC protocol to support multi-rate communications. At present, two prominent multi-rate mechanisms, Opportunistic Auto Rate (OAR) and Receiver Based Auto Rate (RBAR) are unable to adapt to short term changes in channel conditions during transmission as well as to use optimum power and throughput during packet transmissions. On the other hand, using channel predictions, each source-destinations pair in Channel MAC can fully utilise the non-fade durations. We combine the scheduling of Channel MAC and the rate adaptive transmission based on the channel state information to design the 'Rate Adaptive Channel MAC' protocol. However, to implement the Rate adaptive Channel MAC, we need to use a channel prediction scheme to identify transmission opportunities as well as auto rate adaptation mechanism to select rates and number of packets to transmit during those times. For channel prediction, we apply the scheme proposed for the practical implementation of Channel MAC. We propose a "safety margin" based technique to provide auto rate adaptation. Simulation results show that a significant performance improvement can be achieved by Rate adaptive Channel MAC as compared to existing rate adaptive protocols such as OAR.

This thesis describes the theoretical, experimental, and practical aspects of a noise-limited method for scene-change detection in images. The research is divided into three sections: noise analysis and modelling, dual illumination scene-change modelling, and integration of noise into the scene-change model. The sources of noise within commercially available digital cameras are described, with a new model for image noise derived for charge-coupled device (CCD) cameras. The model is validated experimentally through the development of techniques that allow the individual noise components to be measured from the analysis of output images alone. A generic model for complementary metal-oxide-semiconductor (CMOS) cameras is also derived. Methods for the analysis of spatial (inter-pixel) and temporal (intra-pixel) noise are developed. These are used subsequently to investigate the effects of environmental temperature on camera noise. Based on the cameras tested, the results show that the CCD camera noise response to variation in environmental temperature is complex whereas the CMOS camera response simply increases monotonically. A new concept for scene-change detection is proposed based upon a dual illumination concept where both direct and ambient illumination sources are present in an environment, such as that which occurs in natural outdoor scenes with direct sunlight and ambient skylight. The transition of pixel colour from the combined direct and ambient illuminants to the ambient illuminant only is modelled. A method for shadow-free scene-change is then developed that predicts a pixel's colour when the area in the scene is subjected to ambient illumination only, allowing pixel change to be distinguished as either being due to a cast shadow or due to a genuine change in the scene. Experiments on images captured in controlled lighting demonstrate 91% of scene-change and 83% of cast shadows are correctly determined from analysis of pixel colour change alone. A statistical method for detecting shadow-free scene-change is developed. This is achieved by bounding the dual illumination model by the confidence interval associated with the pixel's noise. Three benefits arise from the integration of noise into the scene-change detection method: - The necessity for pre-filtering images for noise is removed; - All empirical thresholds are removed; and - Performance is improved. The noise-limited scene-change detection algorithm correctly classifies 93% of scene-change and 87% of cast shadows from pixel colour change alone. When simple post-analysis size-filtering is applied both these figures increase to 95%.