The transfer of market power in electric generation from utilities to end-users spurred by the diffusion of distributed energy resources necessitates a new system of settlement in the electricity business that can better manage generation assets at the grid-edge. A new concept in facilitating distributed generation is peer-to-peer energy trading, where households exchange excess power with neighbors at a price they set themselves. However, little is known about the effects of peer-to-peer energy trading on the sociotechnical dynamics of electric power systems. Further, given the novelty of the concept, there are knowledge gaps regarding the impact of alternative electricity market structures and individual decision strategies on neighborhood exchanges and market outcomes. This study develops an empirical agent-based modeling (ABM) framework to simulate peer-to-peer electricity trades in a decentralized residential energy market. The framework is applied for a case study in Perth, Western Australia, where a blockchain-enabled energy trading platform was trialed among 18 households, which acted as prosumers or consumers. The ABM is applied for a set of alternative electricity market structures. Results assess the impact of solar generation forecasting approaches, battery energy storage, and ratio of prosumers to consumers on the dynamics of peer-to-peer energy trading systems. Designing an efficient, equitable, and sustainable future energy system hinges on the recognition of trade-offs on and across, social, technological, economic, and environmental levels. Results demonstrate that the ABM can be applied to manage emerging uncertainties by facilitating the testing and development of management strategies.

Discretionary foods dominate food advertising. On average, discretionary food advertising was higher during PVTs for children and during the summer school holidays (January).

Within the expanding paradigm of medical imaging and wireless communications there is increasing demand for transmitting diagnostic medical imagery over error-prone wireless communication channels such as those encountered in cellular phone technology. Medical images must be compressed with minimal file size to minimize transmission time and robustly coded to withstand these wireless environments. It has been reinforced through extensive research that the most crucial regions of medical images must not be degraded and compressed by a lossless or near lossless algorithm. This type of area is called the Region of Interest (ROI). Conversely, the Region of Backgrounds (ROB) may be compressed with some loss of information to achieve a higher compression level. This type of hybrid coding scheme is most useful for wireless communication where the 'bit-budget' is devoted to the ROI. This paper also develops a way for this system to operate externally to the Joint Picture Experts Group (JPEG) still image compression standard without the use of hybrid coding. A multiple watermarking technique is developed to verify the integrity of the ROI after transmission and in the situation where there may be incidental degradation that is hard to perceive or unexpected levels of compression that may degrade ROI content beyond an acceptable level. The most useful contribution in this work is assurance of ROI image content integrity after image files are subject to incidental degradation in these environments. This is made possible with extraction of DCT signature coefficients from the ROI and embedding multiply in the ROB. Strong focus is placed on the robustness to JPEG compression and the mobile channel as well as minimizing the image file size while maintaining its integrity with the use of semi-fragile, robust watermarking.

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In this paper we reverse engineer the Sony IMX219PQ image sensor, otherwise known as the Raspberry Pi Camera v2.0. We provide a visual reference for pixel non-uniformity by analysing variations in transistor length, microlens optic system and in the photodiode. We use these measurements to demonstrate irregularities at the microscopic level and link this to the signal variation measured as pixel non-uniformity used for unique identification of discrete image sensors.