Self-adaptive clock synchronisation based on clock precision difference

This paper presents an innovative strategy to synchronize all virtual clocks in asynchronous Internet environments. Our model is based on the architecture of one reference clock and many slave clocks communicating with each other over the Internet. The paper makes three major contributions to this research area. Firstly, one-way information transmission is applied to reduce traffic overhead on the Internet for the purpose of clock synchronization. Secondly, the slave nodes use local virtual time and the arrival timestamp, from the reference node, to create linear mathematical trend models and to retrieve the clock precision differences between reference clock and slave clocks. Finally, a fault-tolerant and self-adaptive model executed by each slave node based on the above linear trend model is created in order to ensure that the virtual clock is running normally, even when the link between the reference node and this slave node has crashed. We also present detailed simulations of this strategy and mathematical analysis on real Internet environments.

Performance optimization for energy-aware adaptive checkpointing in embedded real-time systems

Using additional store-checkpoinsts (SCPs) and compare-checkpoints (CCPs), we present an adaptive checkpointing for double modular redundancy (DMR) in this paper. The proposed approach can dynamically adjust the checkpoint intervals. We also design methods to calculate the optimal numbers of checkpoints, which can minimize the average execution time of tasks. Further, the adaptive checkpointing is combined with the DVS (dynamic voltage scaling) scheme to achieve energy reduction. Simulation results show that, compared with the previous methods, the proposed approach significantly increases the likelihood of timely task completion and reduces energy consumption in the presence of faults.

Self-adaptive clock synchronization for computational grid

Abstract This paper presents an innovative method to synchronize physical clocks for a computational grid, in particular for a computational grid linked through the asynchronous Intranet or Internet environments. The method discussed is an asynchronous self-adaptive clock synchronization mechanism. Two strategies for clock synchronization are introduced. (1), Use continuous time intervals to calculate the precision of clocks, which can reduce the effect of network delay efficiently. (2) Every node synchronizes its clock, with its leader actively. In addition, a node self-adaptive model is presented, and the relationship between the clock precision and synchronization time is induced, hence a node can predict when it should begin the synchronization process. Detailed simulation and extension of this issue are provided at the end of the paper. The presented model is both practical and feasible

Culturally adaptive learning objects : challenges for educators and developers

In this paper the authors argue that the major problem facing local
courseware developers is that of representing cultural artefacts in their
courseware. They also discuss an emerging learning-object model that can be
adopted to address this problem by providing a systematic way to approach
implementing and representing cultural artefacts in courseware.

Supporting adaptive learning in hypertext environments : a high level timed Petri net-based approach

A problem for hypertext-based learning application is to control learning paths for different learning activities. This paper first introduces related concepts of hypertext learning state space and high level Petri Nets (PNs), then proposes a high level timed PN based approach used to providing kinds of adaptation for learning activities by adjusting time attributes of targeted learning state space. Examples are given while explaining ways to realising adaptive instructions. Possible future directions are also discussed at the end of this paper.

Adaptive fusion of gait and face for human identification in video

Most work on multi-biometric fusion is based on static fusion rules which cannot respond to the changes of the environment and the individual users. This paper proposes adaptive multi-biometric fusion, which dynamically adjusts the fusion rules to suit the real-time external conditions. As a typical example, the adaptive fusion of gait and face in video is studied. Two factors that may affect the relationship between gait and face in the fusion are considered, i.e., the view angle and the subject-to-camera distance. Together they determine the way gait and face are fused at an arbitrary time. Experimental results show that the adaptive fusion performs significantly better than not only single biometric traits, but also those widely adopted static fusion rules including SUM, PRODUCT, MIN, and MAX.

An adaptive policy-based vertical handoff algorithm for heterogeneous wireless networks

The next generation of wireless networks is envisioned as convergence of heterogeneous radio access networks. Since technologies are becoming more collaborative, a possible integration between IEEE 802.16 based network and previous generation of telecommunication systems (2G, ..., 3G) must be considered. A novel quality function based vertical handoff (VHO) algorithm, based on proposed velocity and average receive power estimation algorithms is discussed in this paper. The short-time Fourier analysis of received signal strength (RSS) is employed to obtain mobile speed and average received power estimates. Performance of quality function based VHO algorithm is evaluated by means of measure of quality of service (QoS). Simulation results show that proposed quality function, brings significant gains in QoS and more efficient use of resources can be achieved.

The business case for incorporating sustainability in office buildings : the adaptive reuse of existing buildings

The effect of climate change and global warming has received increased attention in society with constant reminders about the importance of energy efficiency and sustainability in buildings. Whilst the focus is often placed on smaller items such as plastic bags, air conditioners and motor vehicles, the emphasis has recently shifted to structures in the built environment. Office buildings have been identified as contributing significantly to global warming during their building lifecycle with a substantial contribution to CO2 omissions. In response, building designs and construction techniques have evolved over time to improve energy efficiency and reduce CO2 omissions. Whilst property valuers, managers and analysts must remain up-to-date regarding changes, relatively little research has been conducted about whether and how increased sustainability, such as signified in a Green Star rating affects a property’s highest and best value and long-term lifecycle. This study investigates the degree to which sustainability is understood in the property marketplace, especially in relation to property values with the emphasis placed on a cost- benefit analysis from both an owner’s and tenant’s perspective. Whilst it may be argued that incorporating sustainability into a new office building is cost prohibitive on a financial cost-benefit analysis, often various minor steps can be taken to upgrade the sustainability of an existing building. This project examines recent trends in capital expenditure to increase the sustainability of offices and where Green Star ratings have been applied to establish whether any corresponding increase in value is evident. In order to develop a researchframework, a thorough literature review will be conducted of recent Australian and overseas studies. This will enable links between sustainability and office buildings to be highlighted, and vitally how they affect a property’s value in both the short and long-term.

Comments on "Adaptive multiple-surface sliding control for non-autonomous systems with mismatched uncertainties"

This note points out that the time complexity of the main multiple-surface sliding control (MSSC) algorithm in Huang and Chen [Huang, A. C. & Chen, Y. C. (2004). Adaptive multiple-surface sliding control for non-autonomous systems with mismatched uncertainties. Automatica, 40(11), 1939-1945] is O(2^n). Here, we propose a simplified recursive design MSSC algorithm with time complexity O(n), and, using mathematical induction, we show that this algorithm agrees with this MSSC law.

A generalized data windowing scheme for adaptive conjugate gradient algorithms

The performance of the modified adaptive conjugate gradient (CG) algorithms based on the iterative CG method for adaptive filtering is highly related to the ways of estimating the correlation matrix and the cross-correlation vector. The existing approaches of implementing the CG algorithms using the data windows of exponential form or sliding form result in either loss of convergence or increase in misadjustment. This paper presents and analyzes a new approach to the implementation of the CG algorithms for adaptive filtering by using a generalized data windowing scheme. For the new modified CG algorithms, we show that the convergence speed is accelerated, the misadjustment and tracking capability comparable to those of the recursive least squares (RLS) algorithm are achieved. Computer simulations demonstrated in the framework of linear system modeling problem show the improvements of the new modifications.

Stability and convergence analysis of transform-domain LMS adaptive filters with second-order autoregressive process

In this paper, the stability and convergence properties of the class of transform-domain least mean square (LMS) adaptive filters with second-order autoregressive (AR) process are investigated. It is well known that this class of adaptive filters improve convergence property of the standard LMS adaptive filters by applying the fixed data-independent orthogonal transforms and power normalization. However, the convergence performance of this class of adaptive filters can be quite different for various input processes, and it has not been fully explored. In this paper, we first discuss the mean-square stability and steady-state performance of this class of adaptive filters. We then analyze the effects of the transforms and power normalization performed in the various adaptive filters for both first-order and second-order AR processes. We derive the input asymptotic eigenvalue distributions and make comparisons on their convergence performance. Finally, computer simulations on AR process as well as moving-average (MA) process and autoregressive-moving-average (ARMA) process are demonstrated for the support of the analytical results.

Adaptive fast finite time control of a class of uncertain nonlinear systems

This paper concerns the adaptive fast finite time control of a class of nonlinear uncertain systems of which the upper bounds of the system uncertainties are unknown. By using the fast non-smooth control Lyapunov function and the method of so-called adding a power integrator merging with adaptive technique, a recursive design procedure is provided, which guarantees the fast finite time stability of the closed-loop system. It is proved that the control input is bounded, and a simulation example is given to illustrate the effectiveness of the theoretical results.

On performance of transform domain adaptive filters with Markov-2 inputs

In this paper, the analysis for the performance of the discrete Fourier transform LMS adaptive filter (DFT-LMS) and the discrete cosine transform LMS adaptive filter (DCT-LMS) for the Markov-2 inputs is presented. To improve the convergence property of the least mean squares (LMS) adaptive filter, the DFT-LMS and DCT-LMS preprocess the inputs with the fixed orthogonal transforms and power normalization. We derive the asymptotic results for the eigenvalues and eigenvalue distributions of the preprocessed input autocorrelation matrices with DFT-LMS and DCT-LMS for Markov-2 inputs. These results explicitly show the superior decorrelation property of DCT-LMS over that of DFT-LMS, and also provide the upper bounds for the eigenvalue spreads of the finite-length DFT-LMS and DCT-LMS adaptive filters. Simulation results are demonstrated to support the analytic results.

The adaptive significance of ontogenetic colour change in a tropical python

Ontogenetic colour change is typically associated with changes in size, vulnerability or habitat, but assessment of its functional significance requires quantification of the colour signals from the receivers' perspective. The tropical python, Morelia viridis, is an ideal species to establish the functional significance of ontogenetic colour change. Neonates hatch either yellow or red and both the morphs change to green with age. Here, we show that colour change from red or yellow to green provides camouflage from visually oriented avian predators in the different habitats used by juveniles and adults. This reflects changes in foraging behaviour and vulnerability as individuals mature and provides a rare demonstration of the adaptive value of ontogenetic colour change.