An efficient online secondary path estimation for feedback active noise control systems

In practical cases for active noise control (ANC), the secondary path has usually a time varying behavior. For these cases, an online secondary path modeling method that uses a white noise as a training signal is required to ensure convergence of the system. The modeling accuracy and the convergence rate are increased when a white noise with a larger variance is used. However, the larger variance increases the residual noise, which decreases performance of the system and additionally causes instability problem to feedback structures. A sudden change in the secondary path leads to divergence of the online secondary path modeling filter. To overcome these problems, this paper proposes a new approach for online secondary path modeling in feedback ANC systems. The proposed algorithm uses the advantages of white noise with larger variance to model the secondary path, but the injection is stopped at the optimum point to increase performance of the algorithm and to prevent the instability effect of the white noise. In this approach, instead of continuous injection of the white noise, a sudden change in secondary path during the operation makes the algorithm to reactivate injection of the white noise to correct the secondary path estimation. In addition, the proposed method models the secondary path without the need of using off-line estimation of the secondary path. Considering the above features increases the convergence rate and modeling accuracy, which results in a high system performance. Computer simulation results shown in this paper indicate effectiveness of the proposed method.

The digital transformation of 21st century news journalism

This paper deals with the transformations that have occurred in news journalism worldwide in the early 21st century. I argue that they havebeen the most significant changes to the profession for 100 years, and the challenges facing the news media industry in responding to them are substantial, as are those facing journalism education. This argument is developed in relation to the crisis of the newspaper business model, and why social media, blogging and citizen journalism have not filled the gap left by the withdrawal of resources from traditional journalism. It also draws upon Wikileaks as a case study in debates about computational and data-driven journalism, and whether large-scale "leaks" of electronic documents may be the future of investigative journalism.

Sensitivity of peak positions to flight-path parameters in a deep-inelastic scattering neutron TOF spectrometer

The effects of small changes in flight-path parameters (primary and secondary flight paths, detector angles), and of displacement of the sample along the beam axis away from its ideal position, are examined for an inelastic time-of-flight (TOF) neutron spectrometer, emphasising the deep-inelastic regime. The aim was to develop a rational basis for deciding what measured shifts in the positions of spectral peaks could be regarded as reliable in the light of the uncertainties in the calibrated flight-path parameters. Uncertainty in the length of the primary or secondary flight path has the least effect on the positions of the peaks of H, D and He, which are dominated by the accuracy of the calibration of the detector angles. This aspect of the calibration of a TOF spectrometer therefore demands close attention to achieve reliable outcomes where the position of the peaks is of significant scientific interest and is discussed in detail. The corresponding sensitivities of the position of peak of the Compton profile, J(y), to flight-path parameters and sample position are also examined, focusing on the comparability across experiments of results for H, D and He. We show that positioning the sample to within a few mm of the ideal position is required to ensure good comparability between experiments if data from detectors at high forward angles are to be reliably interpreted.

Empirical approach to understand the knowledge management process

Knowledge Management (KM) is a process that focuses on knowledge-related activities to facilitate knowledge creation, capture, transformation and use, with the ultimate aim of leveraging organisations’ intellectual capital to achieve organisational objectives. The KM process receives input from its context (e.g. internal business environment), and produces output (i.e. knowledge). It is argued that the validity of such knowledge should be justified by business performance. The study, this paper reports on, provides enhanced empirical understanding of such an input-process-output relationship through investigating the interactions among different KM activities in the context of how construction organisations in Hong Kong manage knowledge. To this end, a theoretical framework along with a number of hypotheses are proposed and empirically tested through correlation, regression and path analyses. A questionnaire survey was administered to a sample of construction contractors operating in Hong Kong to facilitate testing the proposed relationships. More than 140 respondents from 99 organisations responded to the survey. The study findings demonstrate that both organisational and technical environments have the potential to predict the intensity of KM activities. Furthermore, different categories of KM activities interact with each other, and collectively they could be used to predict business performance.

Coordination of UAV path planning in 2D and 3D in environmental/bio sensing applications

This paper is concerned with the optimal path planning and initialization interval of one or two UAVs in presence of a constant wind. The method compares previous literature results on synchronization of UAVs along convex curves, path planning and sampling in 2D and extends it to 3D. This method can be applied to observe gas/particle emissions inside a control volume during sampling loops. The flight pattern is composed of two phases: a start-up interval and a sampling interval which is represented by a semi-circular path. The methods were tested in four complex model test cases in 2D and 3D as well as one simulated real world scenario in 2D and one in 3D.

Adaptive dynamic path re-planning RRT algorithms with game theory for UAVs

The main aim of this paper is to describe an adaptive re-planning algorithm based on a RRT and Game Theory to produce an efficient collision free obstacle adaptive Mission Path Planner for Search and Rescue (SAR) missions. This will provide UAV autopilots and flight computers with the capability to autonomously avoid static obstacles and No Fly Zones (NFZs) through dynamic adaptive path replanning. The methods and algorithms produce optimal collision free paths and can be integrated on a decision aid tool and UAV autopilots.

A variable step-size FxLMS algorithm for feedforward active noise control systems based on a new online secondary path modelling technique

Several approaches have been introduced in literature for active noise control (ANC) systems. Since FxLMS algorithm appears to be the best choice as a controller filter, researchers tend to improve performance of ANC systems by enhancing and modifying this algorithm. This paper proposes a new version of FxLMS algorithm. In many ANC applications an online secondary path modelling method using a white noise as a training signal is required to ensure convergence of the system. This paper also proposes a new approach for online secondary path modelling in feedfoward ANC systems. The proposed algorithm stops injection of the white noise at the optimum point and reactivate the injection during the operation, if needed, to maintain performance of the system. Benefiting new version of FxLMS algorithm and not continually injection of white noise makes the system more desirable and improves the noise attenuation performance. Comparative simulation results indicate effectiveness of the proposed approach.

A new online secondary path modelling method for feedforward active noise control systems

An online secondary path modelling method using a white noise as a training signal is required in many applications of active noise control (ANC) to ensure convergence of the system. Not continually injection of white noise during system operation makes the system more desirable. The purposes of the proposed method are two folds: controlling white noise by preventing continually injection, and benefiting white noise with a larger variance. The modelling accuracy and the convergence rate increase when a white noise with larger variance is used, however larger the variance increases the residual noise, which decreases performance of the system. This paper proposes a new approach for online secondary path modelling in feedfoward ANC systems. The proposed algorithm uses the advantages of the white noise with larger variance to model the secondary path, but the injection is stopped at the optimum point to increase performance of the system. Comparative simulation results shown in this paper indicate effectiveness of the proposed approach in controlling active noise.

One-stop government portals : transformation or navigation?

E-government is seen as a promising approach for governments to improve their service towards citizens and become more cost-efficient in service delivery. This is often combined with one-stop government, which is a citizen-oriented approach stressing integrated provision of services from multiple departments via a single access point, the one-stop government portal. While the portal concept is gaining prominence in practice, there is little know about its status in academic literature. This hinders academics in building an accumulated body of knowledge around the concept and makes it hard for practitioners to access relevant academic insights on the topic. The objective of this study is to identify and understand the key themes of the one-stop government portal concept in academic, e-government research. A holistic analysis is provided by addressing different viewpoints: social-political, legal, organizational, user, security, service, data & information, and technical. As overall finding we conclude that there are two different approaches: a more pragmatic approach focuses on quick wins in particular related to usability and navigation and a more ambitious, transformational approach having far reaching social-political, legal, organizational implications.

An optimized online secondary path modeling method for single-channel feedback ANC systems

This paper proposes a new method for online secondary path modeling in feedback active noise control (ANC) systems. In practical cases, the secondary path is usually time varying. For these cases, online modeling of secondary path is required to ensure convergence of the system. In literature the secondary path estimation is usually performed offline, prior to online modeling, where in the proposed system there is no need for using offline estimation. The proposed method consists of two steps: a noise controller which is based on an FxLMS algorithm, and a variable step size (VSS) LMS algorithm which is used to adapt the modeling filter with the secondary path. In order to increase performance of the algorithm in a faster convergence and accurate performance, we stop the VSS-LMS algorithm at the optimum point. The results of computer simulation shown in this paper indicate effectiveness of the proposed method.

Identifying the triggers for management services in business transformation management

Business transformations are complex organisational change endeavours that result in a business performing current work differently, or performing different work. Information Technology (IT) is a key enabler of such initiatives, but comes with its challenges, as revamping the IT infrastructure in large-scale organisations implies high complexity, high risk, and often high failure rates. We view business transformations as a collection of management services that are demanded and enacted at a program level, defined as abstract resources that provide the managerial capabilities necessary for business transformations. In this research-in-progress, we explore what triggers the need for management services in response to the challenges in business transformation management. We analyse data from two exploratory case studies using the critical incident technique as our qualitative analysis method. Early findings indicate that management service triggers reside on either the strategic level, which may be internally or externally driven, or at the program management level, which may be situational, influential or reactional. We detail implications for our on-going research.

Career-path analysis using optimal matching and self-organizing maps

This paper is devoted to the analysis of career paths and employability. The state-of-the-art on this topic is rather poor in methodologies. Some authors propose distances well adapted to the data, but are limiting their analysis to hierarchical clustering. Other authors apply sophisticated methods, but only after paying the price of transforming the categorical data into continuous, via a factorial analysis. The latter approach has an important drawback since it makes a linear assumption on the data. We propose a new methodology, inspired from biology and adapted to career paths, combining optimal matching and self-organizing maps. A complete study on real-life data will illustrate our proposal.

Thermal treatment of natural goethite : thermal transformation and physical properties

XRD (X-ray diffraction), XRF (X-ray fluorescence), TG (thermogravimetry), FT-IES (Fourier transform infrared emission spectroscopy), FESEM (field emission scanning electron microscope), TEM (transmission electron microscope) and nitrogen–adsorption–desorption analysis were used to characterize the composition and thermal evolution of the structure of natural goethite. The in situ FT-IES demonstrated the start temperature (250 °C) of the transformation of natural goethite to hematite and the thermodynamic stability of protohematite between 250 and 600 °C. The heated products showed a topotactic relationship to the original mineral based on SEM analysis. Finally, the nitrogen–adsorption–desorption isotherm provided the variation of surface area and pore size distribution as a function of temperature. The surface area displayed a remarkable increase up to 350 °C, and then decreased above this temperature. The significant increase in surface area was attributed to the formation of regularly arranged slit-shaped micropores running parallel to elongated direction of hematite microcrystal. The main pore size varied from 0.99 nm to 3.5 nm when heating temperature increases from 300 to 400 °C. The hematite derived from heating goethite possesses high surface area and favors the possible application of hematite as an adsorbent as well as catalyst carrier.

An investigation into painting's transformation and regeneration through post-photographic intervention in the digital archive

This PhD practice-led research inquiry sets out to examine and describe how the fluid interactions between memory and time can be rendered via the remediation of my painting and the construction of a digital image archive. My abstract digital art and handcrafted practice is informed by Deleuze and Guattari’s rhizomics of becoming. I aim to show that the technological mobility of my creative strategies produce new conditions of artistic possibility through the mobile principles of rhizomic interconnection, multiplicity and diversity. Subsequently through the ongoing modification of past painting I map how emergent forms and ideas open up new and incisive engagements with the experience of a ‘continual present’. The deployment of new media and cross media processes in my art also deterritorialises the modernist notion of painting as a static and two dimensional spatial object. Instead, it shows painting in a postmodern field of dynamic and transformative intermediality through digital formats of still and moving images that re-imagines the relationship between memory, time and creative practice.

Dynamic robot path planning using an enhanced simulated annealing approach

Evolutionary computation is an effective tool for solving optimization problems. However, its significant computational demand has limited its real-time and on-line applications, especially in embedded systems with limited computing resources, e.g., mobile robots. Heuristic methods such as the genetic algorithm (GA) based approaches have been investigated for robot path planning in dynamic environments. However, research on the simulated annealing (SA) algorithm, another popular evolutionary computation algorithm, for dynamic path planning is still limited mainly due to its high computational demand. An enhanced SA approach, which integrates two additional mathematical operators and initial path selection heuristics into the standard SA, is developed in this work for robot path planning in dynamic environments with both static and dynamic obstacles. It improves the computing performance of the standard SA significantly while giving an optimal or near-optimal robot path solution, making its real-time and on-line applications possible. Using the classic and deterministic Dijkstra algorithm as a benchmark, comprehensive case studies are carried out to demonstrate the performance of the enhanced SA and other SA algorithms in various dynamic path planning scenarios.