Comparison of advanced waste heat recovery systems with a novel oil heating system

Latest trends in waste heat recovery include systems like Thermo Electric Generation (TEG), Rankine cycle, and active warm up systems. The advantages and disadvantages of different approaches are critically discussed and compared with a novel and effective oil heating system that can deliver between 7% and 12% reductions of CO2 emissions and fuel consumption. The comparison includes the expected CO2 and fuel saving potential related to the legal drive cycle as well as real world driving, effects on regulated exhaust emissions, utilisation of resources, maintenance and service, vehicle performance, comfort, noise, and durability.

Learning sparse latent representation and distance metric for image retrieval

The performance of image retrieval depends critically on the semantic representation and the distance function used to estimate the similarity of two images. A good representation should integrate multiple visual and textual (e.g., tag) features and offer a step closer to the true semantics of interest (e.g., concepts). As the distance function operates on the representation, they are interdependent, and thus should be addressed at the same time. We propose a probabilistic solution to learn both the representation from multiple feature types and modalities and the distance metric from data. The learning is regularised so that the learned representation and information-theoretic metric will (i) preserve the regularities of the visual/textual spaces, (ii) enhance structured sparsity, (iii) encourage small intra-concept distances, and (iv) keep inter-concept images separated. We demonstrate the capacity of our method on the NUS-WIDE data. For the well-studied 13 animal subset, our method outperforms state-of-the-art rivals. On the subset of single-concept images, we gain 79:5% improvement over the standard nearest neighbours approach on the MAP score, and 45.7% on the NDCG.

On film character retrieval in feature-length films

The problem of automatic face recognition (AFR) concerns matching a detected (roughly localized) face against a database of known faces with associated identities. This task, although very intuitive to humans and despite the vast amounts of research behind it, still poses a significant challenge to computer-based methods. For reviews of the literature and commercial state-of-the-art see [21, 372] and [252, 253]. Much AFR research has concentrated on the user authentication paradigm (e.g. [10, 30, 183]). In contrast, we consider the content-based multimedia retrieval setup: our aim is to retrieve, and rank by confidence, film shots based on the presence of specific actors. A query to the system consists of the user choosing the person of interest in one or more keyframes.

Automatic face recognition for film character retrieval in feature-length films

The objective of this work is to recognize all the frontal faces of a character in the closed world of a movie or situation comedy, given a small number of query faces. This is challenging because faces in a feature-length film are relatively uncontrolled with a wide variability of scale, pose, illumination, and expressions, and also may be partially occluded. We develop a recognition method based on a cascade of processing steps that normalize for the effects of the changing imaging environment. In particular there are three areas of novelty: (i) we suppress the background surrounding the face, enabling the maximum area of the face to be retained for recognition rather than a subset; (ii) we include a pose refinement step to optimize the registration between the test image and face exemplar; and (iii) we use robust distance to a sub-space to allow for partial occlusion and expression change. The method is applied and evaluated on several feature length films. It is demonstrated that high recall rates (over 92%) can be achieved whilst maintaining good precision (over 93%).

Reading ancient coins: automatically identifying denarii using obverse legend seeded retrieval

The aim of this paper is to automatically identify a Roman Imperial denarius from a single query photograph of its obverse and reverse. Such functionality has the potential to contribute greatly to various national schemes which encourage laymen to report their finds to local museums. Our work introduces a series of novelties: (i) this is the first paper which describes a method for extracting the legend of an ancient coin from a photograph; (ii) we are also the first to suggest the idea and propose a method for identifying a coin using a series of carefully engineered retrievals, each harnessed for further information using visual or meta-data processing; (iii) we show how in addition to a unique standard reference number for a query coin, the proposed system can be used to extract salient coin information (issuing authority, obverse and reverse descriptions, mint date) and retrieve images of other coins of the same type.

Medical image retrieval based on unclean image bags

Traditional content-based image retrieval (CBIR) scheme with assumption of independent individual images in large-scale collections suffers from poor retrieval performance. In medical applications, images usually exist in the form of image bags and each bag includes multiple relevant images of the same perceptual meaning. In this paper, based on these natural image bags, we explore a new scheme to improve the performance of medical image retrieval. It is feasible and efficient to search the bag-based medical image collection by providing a query bag. However, there is a critical problem of noisy images which may present in image bags and severely affect the retrieval performance. A new three-stage solution is proposed to perform the retrieval and handle the noisy images. In stage 1, in order to alleviate the influence of noisy images, we associate each image in the image bags with a relevance degree. In stage 2, a novel similarity aggregation method is proposed to incorporate image relevance and feature importance into the similarity computation process. In stage 3, we obtain the final image relevance in an adaptive way which can consider both image bag similarity and individual image similarity. The experiments demonstrate that the proposed approach can improve the image retrieval performance significantly.

Developing Abu Dhabi's solid waste sustainability index (ADSWMI)

Fast urbanization and population and economic growth led to increased solid waste generation in Abu Dhabi in the last decades. Abu Dhabi generates 5.8 kg of municipal waste per day per person. This is well above the world average of 1.2 kg per day per person. Treatment and destination of the municipal solid waste is also problematic. Only 3.5% of the total municipal solid waste generation is recycled, and the remaining waste is disposed in landfills which are technically not adequate. In this context, sustainability indicators can play an important role in supporting decision makers in planning and managing the solid waste system. In this study, the waste management system in Abu Dhabi Emirate was analyzed through the implementation of a set of proposed sustainability indicators. The DSR Driving force-State-Response approach was used as the methodology to develop a framework for the context of Abu Dhabi. Twenty indicators, based on literature review and benchmarking, were divided into five categories: quantity & composition, environmental controls & resource management, construction & demolition waste, financial sustainability, and governances & policies. These indictors can be a baseline to assist decision makers to develop an integrated waste management system able to meet the high international standards and target in the field.

Australian SMEs waste to landfill

Landfill waste has a negative impact on the environment and small and medium sized enterprises (SMEs) are believed to be significant contributors. There is little government or scholarly research, however, quantifying the collective volume of waste SMEs send to landfill. Where studies do exist they measure total volumes (landfill and recycling combined) and/or do not distinguish between specific waste streams (e.g. wood) and subcategories (e.g. dust). This paper contributes to knowledge by giving insight into the collective volume of waste of 404 SMEs, reconceptualising SME waste into subcategories and by measuring landfill volumes. It presents findings from these 404 Australian SMEs which found that, in descending order, cardboard, paper, plastic wrap, wood dust and particleboard were the subcategories these SMEs sent to landfill in the greatest volumes. It also argues that this reconceptualisation, and associated data collection protocols, have the potential to enable scholars and policy makers to determine the waste subcategories to which SMEs contribute most, formulate targeted interventions and research or evaluate environmental outcomes. © 2014 © 2014 Environment Institute of Australia and New Zealand Inc.

Evaluating non-deterministic retrieval systems

The use of sampling, randomized algorithms, or training based on the unpredictable inputs of users in Information Retrieval often leads to non-deterministic outputs. Evaluating the effectiveness of systems incorporating these methods can be challenging since each run may produce different effectiveness scores. Current IR evaluation techniques do not address this problem. Using the context of distributed information retrieval as a case study for our investigation, we propose a solution based on multivariate linear modeling. We show that the approach provides a consistent and reliable method to compare the effectiveness of non-deterministic IR algorithms, and explain how statistics can safely be used to show that two IR algorithms have equivalent effectiveness. Copyright 2014 ACM.

A noisy-smoothing relevance feedback method for content-based medical image retrieval

In this paper, we address a new problem of noisy images which present in the procedure of relevance feedback for medical image retrieval. We concentrate on the noisy images, caused by the users mislabeling some irrelevant images as relevant ones, and a noisy-smoothing relevance feedback (NS-RF) method is proposed. In NS-RF, a two-step strategy is proposed to handle the noisy images. In step 1, a noisy elimination algorithm is adopted to identify and eliminate the noisy images. In step 2, to further alleviate the influence of noisy images, a fuzzy membership function is employed to estimate the relevance probabilities of retained relevant images. After noisy handling, the fuzzy support vector machine, which can take into account different relevant images with different relevance probabilities, is adopted to re-rank the images. The experimental results on the IRMA medical image collection demonstrate that the proposed method can deal with the noisy images effectively.