A note on octonionic support vector regression

This note presents an analysis of the octonionic form of the division algebraic support vector regressor (SVR) first introduced by Shilton A detailed derivation of the dual form is given, and three conditions under which it is analogous to the quaternionic case are exhibited. It is shown that, in the general case of an octonionic-valued feature map, the usual "kernel trick" breaks down. The cause of this (and its interpretation) is discussed in some detail, along with potential ways of extending kernel methods to take advantage of the distinct features present in the general case. Finally, the octonionic SVR is applied to an example gait analysis problem, and its performance is compared to that of the least squares SVR, the Clifford SVR, and the multidimensional SVR.     

Ellipsoidal neighbourhood outlier factor for distributed anomaly detection in resource constrained networks

Anomaly detection in resource constrained wireless networks is an important challenge for tasks such as intrusion detection, quality assurance and event monitoring applications. The challenge is to detect these interesting events or anomalies in a timely manner, while minimising energy consumption in the network. We propose a distributed anomaly detection architecture, which uses multiple hyperellipsoidal clusters to model the data at each sensor node, and identify global and local anomalies in the network. In particular, a novel anomaly scoring method is proposed to provide a score for each hyperellipsoidal model, based on how remote the ellipsoid is relative to their neighbours. We demonstrate using several synthetic and real datasets that our proposed scheme achieves a higher detection performance with a significant reduction in communication overhead in the network compared to centralised and existing schemes.     

Synthesis and characterization of liquid crystalline polymers containing aromatic ester mesogen and a nonmesogenic ferrocene unit in the spacer

A new series of liquid crystalline polymers containing aromatic triad ester mesogen and 1,1′‐disubstituted ferrocene as a nonmesogenic unit along with polymethylene spacer was synthesized. The polymer was synthesized by a room temperature polycondensation reaction between bis(4‐chloroformyl phenyloxy alkyl ferrocene dicarboxylate) and quinol. The alkyl groups have been varied by an even number of methylene groups with a range from two to ten groups. All the polymers were found to possess liquid crystalline properties. The identification of the mesophase is more transparent with an increase in the spacer. The thermal characteristics were studied using thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). The results reveal that the thermal stability of the polymers was decreased with increasing spacer length. The T_g, T_m, and T_i of the polymers decreased with increasing methylene groups. The incorporation of the ferrocene moiety also has a considerable effect on the glass transition temperature. The char yield of the polymer decreases with an increasing methylene chain length. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 86: 3494–3501, 2002     

Implementation of VLSI on Signal Processing-Based Digital Architecture Using AES Algorithm

Continuous improvements in very-large-scale integration (VLSI) technology and design software have significantly broadened the scope of digital signal processing (DSP) applications. The use of application-specific integrated circuits (ASICs) and programmable digital signal processors for many DSP applications have changed, even though new system implementations based on reconfigurable computing are becoming more complex. Adaptable platforms that combine hardware and software programmability efficiency are rapidly maturing with discrete wavelet transformation (DWT) and sophisticated computerized design techniques, which are much needed in today’s modern world. New research and commercial efforts to sustain power optimization, cost savings, and improved runtime effectiveness have been initiated as initial reconfigurable technologies have emerged. Hence, in this paper, it is proposed that the DWT method can be implemented on a field-programmable gate array in a digital architecture (FPGA-DA). We examined the effects of quantization on DWT performance in classification problems to demonstrate its reliability concerning fixed-point math implementations. The Advanced Encryption Standard (AES) algorithm for DWT learning used in this architecture is less responsive to resampling errors than the previously proposed solution in the literature using the artificial neural networks (ANN) method. By reducing hardware area by 57%, the proposed system has a higher throughput rate of 88.72%, reliability analysis of 95.5% compared to the other standard methods.     

Application-Oriented Flow Control: Fundamentals, Algorithms and Fairness

This paper is concerned with flow control and resource allocation problems in computer networks in which real-time applications may have hard quality of service (QoS) requirements. Recent optimal flow control approaches are unable to deal with these problems since QoS utility functions generally do not satisfy the strict concavity condition in real-time applications. For elastic traffic, we show that bandwidth allocations using the existing optimal flow control strategy can be quite unfair. If we consider different QoS requirements among network users, it may be undesirable to allocate bandwidth simply according to the traditional max-min fairness or proportional fairness. Instead, a network should have the ability to allocate bandwidth resources to various users, addressing their real utility requirements. For these reasons, this paper proposes a new distributed flow control algorithm for multiservice networks, where the application's utility is only assumed to be continuously increasing over the available bandwidth. In this, we show that the algorithm converges, and that at convergence, the utility achieved by each application is well balanced in a proportionally (or max-min) fair manner     

An adaptive tracking controller using neural networks for a classof nonlinear systems

A neural-network-based adaptive tracking control scheme is proposed for a class of nonlinear systems in this paper. It is shown that RBF neural networks are used to adaptively learn system uncertainty bounds in the Lyapunov sense, and the outputs of the neural networks are then used as the parameters of the controller to compensate for the effects of system uncertainties. Using this scheme, not only strong robustness with respect to uncertain dynamics and nonlinearities can be obtained, but also the output tracking error between the plant output and the desired reference output can asymptotically converge to zero. A simulation example is performed in support of the proposed neural control scheme.     

WebWork: METEOR2's Web-Based Workflow Management System

METEOR₂ workflow management systems consist of both (1) design/build-time and (2) run-time/enactment components for implementing workflow applications. An enactment system provides the command, communication and control for the individual tasks in the workflow. Tasks are the run-time instances of intra- or inter-enterprise applications. We are developing three implementations of the METEOR₂ model: WebWork, OrbWork and NeoWork. This paper discusses WebWork, an implementation relying solely on Web technology as the infrastructure for the enactment system. WebWork supports a distributed implementation with participation of multiple Web servers. It also supports automatic code generation of workflow applications from design specifications produced by a comprehensive graphical designer. WebWork has been developed as a complement of its more heavyweight counterparts (OrbWork and NeoWork), with the goal of providing ease of workflow application development, installation, use and maintenance. At the time of this writing, WebWork has been installed by several of the LSDIS Lab's industrial partners for testing, evaluation and building workflow applications.     

Microstructure and mechanical properties of a laser welded low carbon–stainless steel joint

This article reports on an experimental investigation to understand and subsequently control the alloying composition in laser welding of austenitic stainless steel and low carbon steel. An energy dispersive spectrometer was used to analyse the alloying composition, while microscopy and tensile tests were used to study the microstructure, and mechanical performance of the welded joint respectively. The investigation revealed that above a certain specific point energy the material within the melt pool is well mixed and the laser beam position can be used to control the mechanical properties of the joint. This finding was confirmed using a numerical model based on computational fluid dynamics (CFD) of melt pool dynamics. Strategies to control the composition leading to dramatic changes in hardness, microstructure and mechanical properties of the dissimilar laser welded joint are discussed.