Model-based approaches to handling additive noise in reverberant environments

Model-based approaches to handle additive and convolutional noise have been extensively investigated and used. However, the application of these schemes to handling reverberant noise has received less attention. This paper examines the extension of two standard additive/convolutional noise approaches to handling reverberant noise. The first is an extension of vector Taylor series (VTS) compensation, reverberant VTS, where a mismatch function including reverberant noise is used. The second scheme modifies constrained MLLR to allow a wide-span of frames to be taken into account and projected into the required dimensionality. To allow additive noise to be handled, both these schemes are combined with standard VTS. The approaches are evaluated and compared on two tasks, MC-WSJ-AV, and a reverberant simulated version of AURORA-4. © 2011 IEEE.

New approaches to materials education for students of engineering

A novel approach to the teaching of materials to engineering students is outlined. It starts from the overview of the "world" of materials made possible by material property charts, and develops both an understanding of material properties and skills in selecting materials and processes to meet design specifications. It is supported by extensive computer-based methods and tools, and is well adapted both for elementary and for advanced courses.

Data fusion approaches and applications for construction engineering

Data fusion can be defined as the process of combining data or information for estimating the state of an entity. Data fusion is a multidisciplinary field that has several benefits, such as enhancing the confidence, improving reliability, and reducing ambiguity of measurements for estimating the state of entities in engineering systems. It can also enhance completeness of fused data that may be required for estimating the state of engineering systems. Data fusion has been applied to different fields, such as robotics, automation, and intelligent systems. This paper reviews some examples of recent applications of data fusion in civil engineering and presents some of the potential benefits of using data fusion in civil engineering.

Alternative approaches to slum upgrading in Kibera, Nairobi

This paper discusses the sustainability of two different approaches to upgrade water and sanitation infrastructure in Kenya’s largest informal settlement, Kibera. A background to the urbanization of poverty is outlined along with approaches to urban slums. Two case-studies of completed interventions of infrastructure upgrading have been investigated. In one case-study, the upgrading method driven by an NGO uses an integrated livelihoods and partnership technique at community level to create an individual project. in the other case-study, the method is a collaboration between the government and a multi-lateral agency to deliver upgraded services as a part of a country-wide programme. The ‘bottom-up’ (project) and ‘top-down’ (programme) approaches each seek sustainability and aim to achieve this in the same context using different techniques. This paper investigates the sustainability of each approach. The merits and challenges of the approaches are discussed with the projected future of Kibera. The paper highlights the valuable opportunity for the role of appropriate engineering infrastructure for sustainable urban development, as well as the alleviation of poverty in a developing context.

Convex approaches to model wavelet sparsity patterns

Statistical dependencies among wavelet coefficients are commonly represented by graphical models such as hidden Markov trees (HMTs). However, in linear inverse problems such as deconvolution, tomography, and compressed sensing, the presence of a sensing or observation matrix produces a linear mixing of the simple Markovian dependency structure. This leads to reconstruction problems that are non-convex optimizations. Past work has dealt with this issue by resorting to greedy or suboptimal iterative reconstruction methods. In this paper, we propose new modeling approaches based on group-sparsity penalties that leads to convex optimizations that can be solved exactly and efficiently. We show that the methods we develop perform significantly better in de-convolution and compressed sensing applications, while being as computationally efficient as standard coefficient-wise approaches such as lasso. © 2011 IEEE.

System-of-system Approaches and Challenges for Multi-Site Manufacturing

In the multi-site manufacturing domain, systems-of-systems (SoS) are rarely called so. However, there exist a number of collaborative manufacturing paradigms which closely relate to system-of-system principles. These include distributed manufacturing, dispersed network manufacturing, virtual enterprises and cloud manufacturing/manufacturing-as-a-service. This paper provides an overview of these terms and paradigms, exploring their characteristics, overlaps and differences. These manufacturing paradigms are then considered in relation to five key system-of-systems characteristics: autonomy, belonging, connectivity, diversity and emergence. Data collected from two surveys of academic and industry experts is presented and discussed, with key challenges and barriers to multi-site manufacturing SoS identified.