Microearthquakes, seismicity and tectonics of the North-Central Persian plateau

Imperial Users onl

Waste avoidance and reuse strategies for residential buildings in Australia

Introduction The Beyond Waste Fund is an initiative managed by Sustainability Victoria to help businesses avoid waste sent to landfill. The fund supports innovations that focus on waste avoidance, reduction and reuse, leading to improved resource management, and better environmental outcomes. As part of the Beyond Waste Fund, a partnership between Burbank Australia, the Housing Industry Association (HIA), and the Centre for Design at RMIT University was established to conduct a research study aiming at reducing the waste generated in the construction phase of building by the volume residential building sector in Australia. The major steps in this project were to: 1.    Establish a waste audit methodology 2.    Undertake and assess an initial waste audit on a typical volume-built house 3.    Develop waste avoidance strategies 4.    Assess the efficacy of waste avoidance strategies by undertaking a final waste audit on a typical volume build-house, which utilises the identified strategies. This report presents the final outcomes of this study, outlining the goal and scope of the study, the waste audit methodology and initial audit results, waste avoidance strategies, final audit results and an assessment on the efficacy and implementation of waste avoidance strategies

MRSL: AUTONOMOUS NEURAL NETWORK-BASED SELF-STABILIZING SYSTEM

Stabilizing and localizing the positioning systems autonomously in the areas without GPS accessibility is a difficult task. In this thesis we describe a methodology called Most Reliable Straight Line (MRSL) for stabilizing and positioning camera-based objects in 3-D space. The camera-captured images are used to identify easy-to-track points “interesting points� and track them on two consecutive images. The distance between each of interesting points on the two consecutive images are compared and one with the maximum length is assigned to MRSL, which is used to indicate the deviation from the original position. To correct this our trained algorithm is deployed to reduce the deviation by issuing relevant commands, this action is repeated until MRSL converges to zero. To test the accuracy and robustness, the algorithm was deployed to control positioning of a Quadcopter. It was demonstrated that the Quadcopter (a) was highly robust to any external forces, (b) can fly even if the Quadcopter experiences loss of engine, (c) can fly smoothly and positions itself on a desired location

Light Field compression and manipulation via residual convolutional neural network

Light field (LF) imaging has gained significant attention due to its recent success in microscopy, 3-dimensional (3D) displaying and rendering, augmented and virtual reality usage. Postprocessing of LF enables us to extract more information from a scene compared to traditional cameras. However, the use of LF is still a research novelty because of the current limitations in capturing high-resolution LF in all of its four dimensions. While researchers are actively improving methods of capturing high-resolution LF\u27s, using simulation, it is possible to explore a high-quality captured LF\u27s properties. The immediate concerns following the LF capture are its storage and processing time. A rich LF occupies a large chunk of memory ---order of multiple gigabytes per LF---. Also, most feature extraction techniques associated with LF postprocessing involve multi-dimensional integration that requires access to the whole LF and is usually time-consuming. Recent advancements in computer processing units made it possible to simulate realistic images using physical-based rendering software. In this work, at first, a transformation function is proposed for building a camera array (CA) to capture the same portion of LF from a scene that a standard plenoptic camera (SPC) can acquire. Using this transformation, LF simulation with similar properties as a plenoptic camera will become trivial in any rendering software. Artificial intelligence (AI) and machine learning (ML) algorithms ---when deployed on the new generation of GPUs--- are faster than ever. It is possible to generate and train large networks with millions of trainable parameters to learn very complex features. Here, residual convolutional neural network (RCNN) structures are employed to build complex networks for compression and feature extraction from an LF. By combining state-of-the-art image compression and RCNN, I have created a compression pipeline. The proposed pipeline\u27s bit per pixel (bpp) ratio is 0.0047 on average. I show that with a 1% compression time cost and 18x speedup for decompression, our methods reconstructed LFs have better structural similarity index metric (SSIM) and comparable peak signal-to-noise ratio (PSNR) compared to the state-of-the-art video compression techniques used to compress LFs. In the end, using RCNN, I created a network called RefNet, for extracting a group of 16 refocused images from a raw LF. The training parameters of the 16 LFs are set to (\alpha=0.125, 0.250, 0.375, ..., 2.0) for training. I show that RefNet is 134x faster than the state-of-the-art refocusing technique. The RefNet is also superior in color prediction compared to the state-of-the-art ---Fourier slice and shift-and-sum--- methods

Identifying the barriers in the development of building information modeling in construction engineering and management of educational systems

After the introduction of Building Information Modeling to construction industry in 1987, today industry are being faced with a demand for tools and a trained professionals capable of implementing it. Recently, the new idea of having a comprehensive 3D intelligent model with the ability of being extended to a 4D, 5D, 6D and 7D model has caught a lot of attention and forced the construction companies to move toward adopting the new knowledge and implementing it in their projects. However, there are deficiencies associated with the integration of this new technology which is basically due to the lack of well-trained individuals in the field. These deficiencies are basically relates to the scarcity of construction engineering programs within the universities with a dedicated course in Building Information Modeling. Although the utilization of BIM has been recently developed in some universities curriculums, this adoption has been overlooked in roughly all Malaysians’ institutions curriculums. As a result, this research is aimed to identify obstacles regarding the use of BIM in educational system. The scope of this research is limited to academicians including both lecturers and students of the faculties of civil and Build Environment at University Technology of Malaysia. The results have indicated that, although, majority of students are aware about advantages of BIM technology at construction stages, they are not capable to use related software. By doing further research some barriers identified and recommendations stated. From this research that is undertaken, it is practicable to conclude that the usage of building information modeling in academic systems is effective in increasing students’ ability in working fields in future. Besides, with eliminating the identified barriers, the utilization of BIM in academic fields would be accelerated