California performance tour

Collaborative group improvisations: Betalevel, Los Angeles; KZSU Day of Noise, Stanford; Thingamajigs performance at the Museum of California, Oakland; CCRMA Concert, Stanford; EarMeal LA Art Stream, Los Angeles; Outsound at the Luggage Store Gallery, San Francisco (with Matt Ingalls, John Ingle, Ted Byrnes, Edward Schocker & others).

Principles of the performance-based approach for concrete durability

In general, design approaches for durability can be divided into prescriptive design concepts and performance-based design concepts.

Application examples of performance-based specification and quality control

This chapter presents an overview on several performance-based approaches for concrete durability specification and conformity assessment of the as-built structure.

Invited Interview on Network Performance in the "Transient Gestures" issue

An issue on Networked Performance produceed by Neural for which I was invited to deliver an interview on network performance. Neural is a printed magazine established in 1993 dealing with new media art, electronic music and hacktivism. It was founded by Alessandro Ludovico and Minus Habens Records label owner Ivan Iusco in Bari (Italy).

Performance and Fault Tolerance of Preconditioned Iterative Solvers on Low-Power ARM Architectures

As the complexity of computing systems grows, reliability and energy are two crucial challenges asking for holistic solutions. In this paper, we investigate the interplay among concurrency, power dissipation, energy consumption and voltage-frequency scaling for a key numerical kernel for the solution of sparse linear systems. Concretely, we leverage a task-parallel implementation of the Conjugate Gradient method, equipped with an state-of-the-art pre-conditioner embedded in the ILUPACK software, and target a low-power multi core processor from ARM.In addition, we perform a theoretical analysis on the impact of a technique like Near Threshold Voltage Computing (NTVC) from the points of view of increased hardware concurrency and error rate.

Design, analysis and performance of a polymer-carbon nanotubes based economic solar collector

A low cost flat plate solar collector was developed by using polymeric components as opposed to metal and glass components of traditional flat plate solar collectors. In order to improve the thermal and optical properties of the polymer absorber of the solar collector, Carbon Nanotubes (CNT) were added as a filler. The solar collector was designed as a multi-layer construction with an emphasis on low manufacturing costs. Through the mathematical heat transfer analysis, the thermal performance of the collector and the characteristics of the design parameters were analyzed. Furthermore, the prototypes of the proposed collector were built and tested at a state-of-the-art solar simulator facility to evaluate its actual performance. The inclusion of CNT improved significantly the properties of the polymer absorber. The key design parameters and their effects on the thermal performance were identified via the heat transfer analysis. Based on the experimental and analytical results, the cost-effective polymer-CNT solar collector, which achieved a high thermal efficiency similar to that of a conventional glazed flat plate solar panel, was successfully developed.

High Performance Real-time Pre-Processing for Fixed-Complexity Sphere Decoder

Pre-processing (PP) of received symbol vector and channel matrices is an essential pre-requisite operation for Sphere Decoder (SD)-based detection of Multiple-Input Multiple-Output (MIMO) wireless systems. PP is a highly complex operation, but relative to the total SD workload it represents a relatively small fraction of the overall computational cost of detecting an OFDM MIMO frame in standards such as 802.11n. Despite this, real-time PP architectures are highly inefficient, dominating the resource cost of real-time SD architectures. This paper resolves this issue. By reorganising the ordering and QR decomposition sub operations of PP, we describe a Field Programmable Gate Array (FPGA)-based PP architecture for the Fixed Complexity Sphere Decoder (FSD) applied to 4 × 4 802.11n MIMO which reduces resource cost by 50% as compared to state-of-the-art solutions whilst maintaining real-time performance.

Sound Art and Spatial Practices: Situating Sound Installation Art Since 1958

This dissertation examines the emergence and development of sound installation art, an under-recognized tradition that has developed between music, architecture, and media art practices since the late 1950s. Unlike many musical works, which are concerned with organizing sounds in time, sound installations organize sounds in space; they thus necessitate new theoretical and analytical models that take into consideration the spatial situated-ness of sound. Existing discourses on “spatial sound” privilege technical descriptions of sound localization. By contrast, this dissertation examines the ways in which concepts of space are socially, culturally, and politically construed, and how spatially-organized sound works reflect and resist these different constructions. Using an interdisciplinary methodology of critical spatial analysis and critical studies in music, this dissertation explores such topics as: conceptions of acoustic space in postwar Western art music, architecture, and media theory; the development of sound installation art in relation to philosophies of everyday life and social space; the historical links between musical performance, conceptual art, and sound sculpture; the body as a site for sound installations; and sonicspatial strategies that confront politics of race and gender. Through these different investigations, this dissertation proposes an “ontopological” model for considering sound: a critical model of analysis and reception that privileges an understanding of sound in relation to ontologies of space and place.

Performance of polarisation functionals for linear and nonlinear optical properties of bulk zinc chalcogenides ZnX (X=S,Se,Te).

We calculated the frequency dependent macroscopic dielectric function and second-harmonic generation of cubic ZnS, ZnSe and ZnTe within time-dependent density-polarisation functional theory. The macroscopic dielectric function is calculated in a linear response framework, and second-harmonic generation in a real-time framework. The macroscopic exchange–correlation electric field that enters the time-dependent Kohn–Sham equations and accounts for long range correlation is approximated as a simple polarisation functional αP, where P is the macroscopic polarisation. Expressions for α are taken from the recent literature. The performance of the resulting approximations for the exchange–correlation electric field is analysed by comparing the theoretical spectra with experimental results and results obtained at the levels of the independent particle approximation and the random-phase approximation. For the dielectric function we also compare with state-of-the art calculations at the level of the Bethe–Salpeter equation.

Statistical Tests for Joint Analysis of Performance Measures

Recently there has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and architectural complexity). Once one has learned a model based on their devised method, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Unfortunately, the standard tests used for this purpose are not able to jointly consider performance measures. The aim of this paper is to resolve this issue by developing statistical procedures that are able to account for multiple competing measures at the same time. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameter of such models, as usually the number of studied cases is very reduced in such comparisons. Real data from a comparison among general purpose classifiers is used to show a practical application of our tests.

Joint Analysis of Multiple Algorithms and Performance Measures

There has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and time complexity). Once one has developed an approach to a problem of interest, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Standard tests used for this purpose are able to consider jointly neither performance measures nor multiple competitors at once. The aim of this paper is to resolve these issues by developing statistical procedures that are able to account for multiple competing measures at the same time and to compare multiple algorithms altogether. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameters of such models, as usually the number of studied cases is very reduced in such comparisons. Data from a comparison among general purpose classifiers is used to show a practical application of our tests.

Investigation of the effects of flow conditions at rotor inlet on mixed flow turbine performance for automotive applications

Current trends in the automotive industry have placed increased importance on engine downsizing for passenger vehicles. Engine downsizing often results in reduced power output and turbochargers have been relied upon to restore the power output and maintain drivability. As improved power output is required across a wide range of engine operating conditions, it is necessary for the turbocharger to operate effectively at both design and off-design conditions. One off-design condition of considerable importance for turbocharger turbines is low velocity ratio operation, which refers to the combination of high exhaust gas velocity and low turbine rotational speed. Conventional radial flow turbines are constrained to achieve peak efficiency at the relatively high velocity ratio of 0.7, due the requirement to maintain a zero inlet blade angle for structural reasons. Several methods exist to potentially shift turbine peak efficiency to lower velocity ratios. One method is to utilize a mixed flow turbine as an alternative to a radial flow turbine. In addition to radial and circumferential components, the flow entering a mixed flow turbine also has an axial component. This allows the flow to experience a non-zero inlet blade angle, potentially shifting peak efficiency to a lower velocity ratio when compared to an equivalent radial flow turbine.
This study examined the effects of varying the flow conditions at the inlet to a mixed flow turbine and evaluated the subsequent impact on performance. The primary parameters examined were average inlet flow angle, the spanwise distribution of flow angle across the inlet and inlet flow cone angle. The results have indicated that the inlet flow angle significantly influenced the degree of reaction across the rotor and the turbine efficiency. The rotor studied was a custom in-house design based on a state-of-the-art radial flow turbine design. A numerical approach was used as the basis for this investigation and the numerical model has been validated against experimental data obtained from the cold flow turbine test rig at Queen’s University Belfast. The results of the study have provided a useful insight into how the flow conditions at rotor inlet influence the performance of a mixed flow turbine.