A transformation model for heterogeneous servers

One of the characteristics of the current Web services is that many clients request the same or similar service from a group of replicated servers, e.g. music or movie downloading in peer-to-peer networks. Most of the time, servers are heterogeneous ones in terms of service rate. Much of research has been done in the homogeneous environment. However, there is has been little done on the heterogeneous scenario. It is important and urgent that we have models for heterogeneous server groups for the current Internet applications design and analysis. In this paper, we deploy an approximation method to transform heterogeneous systems into a group of homogeneous system. As a result, the previous results of homogeneous studies can be applied in heterogeneous cases. In order to test the approximation ratio of the proposed model to real applications, we conducted simulations to obtain the degree of similarity. We use two common strategies: random selection algorithm and Firs-Come-First-Serve (FCFS) algorithm to test the approximation ratio of the proposed model. The simulations indicate that the approximation model works well.

Robust filtering for uncertain discrete-time systems with uncertain noise covariance and uncertain observations

The use of Kalman filtering is very common in state estimation problems. The problem with Kalman filters is that they require full prior knowledge about the system modeling. It is also assumed that all the observations are fully received. In real applications, the previous assumptions are not true all the time. It is hard to obtain the exact system model and the observations may be lost due to communication problems. In this paper, we consider the design of a robust Kalman filter for systems subject to uncertainties in the state and white noise covariances. The systems under consideration suffer from random interruptions in the measurements process. An upper bound for the estimation error covariance is proposed. The proposed upper bound is further minimized by selection of optimal filter parameters. Simulation example shows the effectiveness of the proposed filter.

Facial image analysis

This thesis focuses on novel technologies for facial image analysis, which involves three topics: face recognition under uncontrolled conditions, automatic facial age estimation, and context-aware fusion of face and gait. They are either key issues bridging laboratorial research and real applications, or innovative problems that have barely been studied before.

Missing value estimation for mixed-attribute data sets

Missing data imputation is a key issue in learning from incomplete data. Various techniques have been developed with great successes on dealing with missing values in data sets with homogeneous attributes (their independent attributes are all either continuous or discrete). This paper studies a new setting of missing data imputation, i.e., imputing missing data in data sets with heterogeneous attributes (their independent attributes are of different types), referred to as imputing mixed-attribute data sets. Although many real applications are in this setting, there is no estimator designed for imputing mixed-attribute data sets. This paper first proposes two consistent estimators for discrete and continuous missing target values, respectively. And then, a mixture-kernel-based iterative estimator is advocated to impute mixed-attribute data sets. The proposed method is evaluated with extensive experiments compared with some typical algorithms, and the result demonstrates that the proposed approach is better than these existing imputation methods in terms of classification accuracy and root mean square error (RMSE) at different missing ratios.

Human action recognition based on 3D SIFT and LDA model

How to recognize human action from videos captured by modern cameras efficiently and effectively is a challenge in real applications. Traditional methods which need professional analysts are facing a bottleneck because of their shortcomings. To cope with the disadvantage, methods based on computer vision techniques, without or with only a few human interventions, have been proposed to analyse human actions in videos automatically. This paper provides a method combining the three dimensional Scale Invariant Feature Transform (SIFT) detector and the Latent Dirichlet Allocation (LDA) model for human motion analysis. To represent videos effectively and robustly, we extract the 3D SIFT descriptor around each interest point, which is sampled densely from 3D Space-time video volumes. After obtaining the representation of each video frame, the LDA model is adopted to discover the underlying structure-the categorization of human actions in the collection of videos. Public available standard datasets are used to test our method. The concluding part discusses the research challenges and future directions.

Fast mining of non-derivable episode rules in complex sequences

Researchers have been endeavoring to discover concise sets of episode rules instead of complete sets in sequences. Existing approaches, however, are not able to process complex sequences and can not guarantee the accuracy of resulting sets due to the violation of anti-monotonicity of the frequency metric. In some real applications, episode rules need to be extracted from complex sequences in which multiple items may appear in a time slot. This paper investigates the discovery of concise episode rules in complex sequences. We define a concise representation called non-derivable episode rules and formularize the mining problem. Adopting a novel anti-monotonic frequency metric, we then develop a fast approach to discover non-derivable episode rules in complex sequences. Experimental results demonstrate that the utility of the proposed approach substantially reduces the number of rules and achieves fast processing.

Blind source separation by fully nonnegative constrained iterative volume maximization

Blind source separation (BSS) has been widely discussed in many real applications. Recently, under the assumption that both of the sources and the mixing matrix are nonnegative, Wang develop an amazing BSS method by using volume maximization. However, the algorithm that they have proposed can guarantee the nonnegativities of the sources only, but cannot obtain a nonnegative mixing matrix necessarily. In this letter, by introducing additional constraints, a method for fully nonnegative constrained iterative volume maximization (FNCIVM) is proposed. The result is with more interpretation, while the algorithm is based on solving a single linear programming problem. Numerical experiments with synthetic signals and real-world images are performed, which show the effectiveness of the proposed method.

Reduced order functional observers with application to partial state estimation of linear systems with input delay

Reduced order multi-functional observer design for multi-input multi-utput (MIMO) linear time-invariant (LTI) systems with constant delayed inputs is studied. This research is useful in the input estimation of LTI systems with actuator delay, as well as system monitoring and fault detection of these systems. Two approaches for designing an asymptotically stable functional observer for the system are proposed: delay-dependent and delay-free. The delay-dependent observer is infinite-dimensional, while the delay-free structure is finite-dimensional. Moreover, since the delay-free observer does not require any information on the time delay, it is more practical in real applications. However, the delay-dependent observer contains less restrictive assumptions and covers more variety of systems. The proposed observer design schemes are novel, simple to implement, and have improved numerical features compared to some of the other available approaches to design (unknown-input) functional observers. In addition, the proposed observers usually possess lower order than ordinary Luenberger observers, and the design schemes do not need the observability or detectability requirements of the system. The necessary and sufficient conditions of the existence of an asymptoticobserver in each scenario are explored. The extensions of the proposed observers to systems with multiple delayed-inputs are also discussed. Several numerical examples and simulation results are employed to support our theories.

A video-based real-time vehicle detection method by classified background learning

A new two-level real-time vehicle detection method is proposed in order to meet the robustness and efficiency requirements of real world applications. At the high level, pixels of the background image are classified into three categories according to the characteristics of Red, Green, Blue (RGB) curves. The robustness of the classification is further enhanced by using
line detection and pattern connectivity. At the lower level, an exponential forgetting algorithm with adaptive parameters for different categories is utilised to calculate the background and reduce the distortion by the small motion of video cameras. Scene tests show that the proposed method is more robust and faster than previous methods, which is very suitable for real-time vehicle detection in outdoor environments, especially concerning locations where the level of illumination changes frequently and speed detection is important.

Conducting polymer coated fabrics for potential applications in microwave frequencies : a study of electromagnetic properties

The microwave reflection, transmission and complex permittivity of paratoluene-2-sulfonic acid doped conducting polypyrrole (PPy/pTSA) coated Nylon-Lycra textiles in the 1-18 GHz frequency were investigated. The real part of permittivity increased with polymerization time and dopant concentration, reaching a plateau at certain dopant concentration and polymerization time. The imaginary part of permittivity showed a frequency dependent change throughout the tested range. All the samples had higher values of absorption than reflection. The total electromagnetic shielding effectiveness exceeded 80% for the highly pTSA doped samples coated for 3 hours.

On the communal analysis suspicion scoring for identity crime in streaming credit applications

This paper describes a rapid technique: communal analysis suspicion scoring (CASS), for generating numeric suspicion scores on streaming credit applications based on implicit links to each other, over both time and space. CASS includes pair-wise communal scoring of identifier attributes for applications, definition of categories of suspiciousness for application-pairs, the incorporation of temporal and spatial weights, and smoothed k-wise scoring of multiple linked application-pairs. Results on mining several hundred thousand real credit applications demonstrate that CASS reduces false alarm rates while maintaining reasonable hit rates. CASS is scalable for this large data sample, and can rapidly detect early symptoms of identity crime. In addition, new insights have been observed from the relationships between applications.

Development of DAL and DAPL languages for building distributed applications

A common characteristic among parallel/distributed programming languages is that the one language is used to specify not only the overall organisation of the distributed application, but also the functionality of the application. That is, the connectivity and functionality of processes are specified within a single program. Connectivity and functionality are independent aspects of a distributed application. This thesis shows that these two aspects can be specified separately, therefore allowing application designers to freely concentrate on either aspect in a modular fashion. Two new programming languages have been developed for specifying each aspect. These languages are for loosely coupled distributed applications based on message passing, and have been designed to simplify distributed programming by completely removing all low level interprocess communication. A suite of languages and tools has been designed and developed. It includes the two new languages, parsers, a compilation system to generate intermediate C code that is compiled to binary object modules, a run-time system to create, manage and terminate several distributed applications, and a shell to communicate with the run-tune system. DAL (Distributed Application Language) and DAPL (Distributed Application Process Language) are the new programming languages for the specification and development of process oriented, asynchronous message passing, distributed applications. These two languages have been designed and developed as part of this doctorate in order to specify such distributed applications that execute on a cluster of computers. Both languages are used to specify orthogonal components of an application, on the one hand the organisation of processes that constitute an application, and on the other the interface and functionality of each process. Consequently, these components can be created in a modular fashion, individually and concurrently. The DAL language is used to specify not only the connectivity of all processes within an application, but also a cluster of computers for which the application executes. Furthermore, sub-clusters can be specified for individual processes of an application to constrain a process to a particular group of computers. The second language, DAPL, is used to specify the interface, functionality and data structures of application processes. In addition to these languages, a DAL parser, a DAPL parser, and a compilation system have been designed and developed (in this project). This compilation system takes DAL and DAPL programs to generate object modules based on machine code, one module for each application process. These object modules are used by the Distributed Application System (DAS) to instantiate and manage distributed applications. The DAS system is another new component of this project. The purpose of the DAS system is to create, manage, and terminate many distributed applications of similar and different configurations. The creation procedure incorporates the automatic allocation of processes to remote machines. Application management includes several operations such as deletion, addition, replacement, and movement of processes, and also detection and reaction to faults such as a processor crash. A DAS operator communicates with the DAS system via a textual shell called DASH (Distributed Application SHell). This suite of languages and tools allowed distributed applications of varying connectivity and functionality to be specified quickly and simply at a high level of abstraction. DAL and DAPL programs of several processes may require a few dozen lines to specify as compared to several hundred lines of equivalent C code that is generated by the compilation system. Furthermore, the DAL and DAPL compilation system is successful at generating binary object modules, and the DAS system succeeds in instantiating and managing several distributed applications on a cluster.

Key success factors in real-time industrial system assurance

The thesis analyses the issues of implementing real-time software systems in industrial applications. The benefit is the development of the Integrated Mega Project Development Model, to improve the effectiveness of planning and timely delivery of software, the quality of the delivered software and reducing problems associated with integrating software systems.

Kernel performance in meeting hard real-time system characteristics (the ARTOS real-time kernel)

Describes the design and implementation of an operating system kernel specifically designed to support real-time applications. It emphasises portability and aims to support state-of-the-art concepts in real-time programming. Discusses architectural aspects of the ARTOS kernel, and introduces new concepts on the areas of interrupt processing, scheduling, mutual exclusion and inter-task communication. Also explains the programming environment of ARTOS kernal and its task model, defines the real-time task states and system data structures and discusses exception handling mechanisms which are used to detect missed deadlines and take corrective action.

Flexible deterministic packet marking: an IP traceback system to find the real source of attacks

Internet Protocol (IP) traceback is the enabling technology to control Internet crime. In this paper, we present a novel and practical IP traceback system called Flexible Deterministic Packet Marking (FDPM) which provides a defense system with the ability to find out the real sources of attacking packets that traverse through the network. While a number of other traceback schemes exist, FDPM provides innovative features to trace the source of IP packets and can obtain better tracing capability than others. In particular, FDPM adopts a flexible mark length strategy to make it compatible to different network environments; it also adaptively changes its marking rate according to the load of the participating router by a flexible flow-based marking scheme. Evaluations on both simulation and real system implementation demonstrate that FDPM requires a moderately small number of packets to complete the traceback process; add little additional load to routers and can trace a large number of sources in one traceback process with low false positive rates. The built-in overload prevention mechanism makes this system capable of achieving a satisfactory traceback result even when the router is heavily loaded. The motivation of this traceback system is from DDoS defense. It has been used to not only trace DDoS attacking packets but also enhance filtering attacking traffic. It has a wide array of applications for other security systems.