a b s t r a c tOver half of the world's population will live in urban areas in the next decade, which will impose significant pressure on water security. The advanced management of water resources and their consumption is pivotal to maintaining a sustainable water future. To contribute to this goal, the aim of this study was to develop an autonomous and intelligent system for residential water end-use classification that could interface with customers and water business managers via a user-friendly web-based application. Water flow data collected directly from smart water metres connected to dwellings includes both single (e.g., a shower event occurring alone) and combined (i.e., an event that comprises several overlapping single events) water end use events. The authors recently developed an intelligent application called Autoflow which served as a prototype tool to solve the complex problem of autonomously categorising residential water consumption data into a registry of single and combined events. However, this first prototype application achieved overall recognition accuracy of 85%, which is not sufficient for a commercial application. To improve this accuracy level, a larger dataset consisting of over 82,000 events from over 500 homes in Melbourne and South-east Queensland, Australia, were employed to derive a new single event recognition method employing a hybrid combination of Hidden Markov Model (HMM), Artificial Neural Networks (ANN) and the Dynamic Time Warping (DTW) algorithm. The classified single event registry was then used as the foundations of a sophisticated hybrid ANN-HMM combined event disaggregation module, which was able to strip apart concurrently occurring end use events. The new hybrid model's recognition accuracy ranged from 85.9% to 96.1% for single events and 81.8-91.5% for combined event disaggregation, which was a 4.9% and 8.0% improvement, respectively, when compared to the first prototype model. The developed Autoflow tool has far-reaching implications for enhanced urban water demand planning and management, sustained customer behaviour change through more granular water conservation awareness, and better customer satisfaction with water utility providers.

Standard Form 298 (Rev. 8-98)Prescribed by ANSI Std. Z39.18Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. This report describes the data collected during one of a series of Naval Research Laboratory (NRL) remote sensing and calibration and validation (Cal/Val) campaigns, providing data and information for the development of models of coast types and their associated environmental factors. Models allow rapid processing of hyperspectral imagery (HSI), generating shallow water bathymetric charts and trafficability maps. Cal/Val data collected during the Mariana Islands Hyperspectral Airborne Remote Environmental Sensing 2010 (MIHARES 2010) campaign focused on spectral and geotechnical library development, bathymetry, and location of WWII remnant hazards on Pagan, Tinian, and Guam. Ground control data collected during the remote sensing experiment will be useful in building digital elevation models and maps for remote areas such as Pagan, a volcanic island in the Commonwealth of the Northern Mariana Islands (CNMI). Surveyed calibration panels, WWII relics, and underwater panels are all useful in developing anomaly detection algorithms. The primary purpose of this memorandum report is to summarize imagery collections and all Cal/Val data and the project geodatabase, with products described in future publications. REPORT TYPE 1. REPORT DATE (DD-MM-YYYY) TITLE AND SUBTITLE AUTHOR(S) PERFORMING ORGANIZATION REPORT NUMBER PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) SPONSOR / MONITOR'S ACRONYM(S) 9. SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) SPONSOR / MONITOR'S REPORT NUMBER(S)

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High Brightness Light Emitting Diode's (HB-LED's) have received considerable attention during the last few years due to their utilization in numerous consumer products (automotive, displays, etc.). Recently, one of the largest emerging markets for HB-LED's is the lighting industry because of its lower power requirements and longer lifetime. One of the key limitations for its universal consumer adoption is its higher cost. If the cost for production of an HB-LED is broken up into materials and process steps the price of the sapphire substrate is noticed to be significantly higher than all the individual process and material steps. In such a circumstance the key to making HB-LED's cheaper is by substrate engineering. Another aspect of the cost is the fact that the traditional sapphire substrates are usually 2 or 4 inches. Therefore, a logical step forward is to move to bigger substrates where yield can be higher. To make this a reality different groups have been working on alternative cheaper and larger substrates (Si/Glass). However, before any technology becomes mature numerous reliability and yield issues need to be fixed. As part of process optimization identifying killer defects is critical. In order to do so we use the Candela platform from KLA Tencor to monitor our epitaxial process. Since, silicon wafers are one of the most common substrates available it obviously emerged as a first choice. We at IMEC have developed a GaN on Si process for making HB-LED's on 200mm Si (111) substrates. The control of the first epitaxial layers on Si is the key to a successful HB-LED fabrication. Lattice mismatch and thermal coefficient mismatch often lead to wafer bow and defect propagation to the p-GaN surface which can be detrimental to the IQE (Internal Quantum Efficiency). The goal of this work is to understand the different types of defect and the nature of their origin on a typical HB LED stack as well as the detection capability of the tool. Typical defects detected are the cracks/hexagonal defects/pits and particles. Defect data will be analyzed in terms of compressive or tensile stress in the film. This paper focuses on un-optimized EPI wafers in terms of stress/defectivity and crystalline quality to help define the correct inspection thresholds.