Fast adaptive algorithms for abrupt change detection

We propose two fast algorithms for abrupt change detection in streaming data that can operate on arbitrary unknown data distributions before and after the change. The first algorithm, MB-GT\textsf{MB-GT} , computes efficiently the average Euclidean distance between all pairs of data points before and after the hypothesized change. The second algorithm, MB-CUSUM\textsf{MB-CUSUM} , computes the log-likelihood ratio statistic for the data distributions before and after the change, similarly to the classical CUSUM algorithm, but unlike that algorithm, MB-CUSUM\textsf{MB-CUSUM} does not need to know the exact distributions, and uses kernel density estimates instead. Although a straightforward computation of the two change statistics would have computational complexity of O(N ⁴) with respect to the size N of the streaming data buffer, the proposed algorithms are able to use the computational structure of these statistics to achieve a computational complexity of only O(N ²) and memory requirement of O(N). Furthermore, the algorithms perform surprisingly well on dependent observations generated by underlying dynamical systems, unlike traditional change detection algorithms.     

A wideband rectangular and circular ring‐shaped patch antenna with gap coupled meandered parasitic elements for wireless applications

A new design of a gap coupled rectangular patch antenna with meandered parasitic elements and a circular ring at the feed line has been proposed for Bluetooth, WLAN, WiMAX, C, and X band applications. Multibands at 1.26 to 1.36 GHz, 1.83 to 3.81 GHz, 7.6 to 7.85 GHz, 9.6 to 10.74 GHz, and 13.58 to 14.23 GHz with impedance bandwidth of 6.0% and 70.21%, 3.23%, 11.2%, and 4.67%, respectively, are observed. Multiple bandwidth enhancement techniques such as using symmetrical meandered parasitic patches, gap coupling, and defected ground have been employed in one design. Circular ring feed structure and meandered parasitic patches enhance the percentage impedance bandwidth from 12.11% to 70.21%.     

Stability and weighted sensitivity analysis of robust controller for heat exchanger

This study presents a parametric system identification approach to estimate the dynamics of a chemical plant from experimental data and develops a robust PID controller for the plant. Parametric system identification of the heat exchanger system has been carried out using experimental data and prediction error method. The estimated model of the heat exchanger system is a time-delay model and a robust PID controller for the time-delayed model has been designed considering weighted sensitivity criteria. The mathematical background of parametric system identification, stability analysis, and ${{\rm H}_\infty }$ weighted sensitivity analysis have been provided in this paper. A graphical plot has been provided to determine the stability region in the $( {{K_{\rm p}},{K_{\rm i}}} )$, $( {{K_{\rm p}},{K_{\rm d}}} )$ and $( {{K_{\rm i}},{K_{\rm d}}} )$ plane. The stability region is a locus dependent on parameters of the controller and frequency, in the parameter plane.     

Fabrication of comb structure with vertical sidewalls in Si (110) substrate by wet etching in boiling KOH solution

Microsystem Technologies - This paper discusses about the fabrication of comb-structure with vertical sidewall profile by wet chemical etching of Si (110) substrate in boiling KOH solution. Etch...     

Finite element analysis of the temperature profile of rolls

The thermal conditions during hot rolling operations can adversely affect the rolls’ service life and the quality of the rolled product. Analysis of the temperature profile of the rolls is an essential step towards the development of precise cooling systems to minimise their surface deterioration and thermal bending or camber. This paper makes an attempt at developing a finite element model for analysing the steady state temperature profile of rolls of varying dimensions. Rolls of large surface area exhibit greater differences in temperature between the surface and the interior as compared to those having smaller surface area and therefore make greater demands on the cooling system.     

Energy-aware Virtual Machine Migration for Cloud Computing - A Firefly Optimization Approach

Energy efficiency has grown into a latest exploration area of virtualized cloud computing paradigm. The increase in the number and the size of the cloud data centers has propagated the need for energy efficiency. An extensively practiced technology in cloud computing is live virtual machine migration and is thus focused in this work to save energy. This paper proposes an energy-aware virtual machine migration technique for cloud computing, which is based on the Firefly algorithm. The proposed technique migrates the maximally loaded virtual machine to the least loaded active node while maintaining the performance and energy efficiency of the data centers. The efficacy of the proposed technique is exhibited by comparing it with other techniques using the CloudSim simulator. An enhancement in the average energy consumption of about 44.39 % has been attained by reducing an average of 72.34 % of migrations and saving 34.36 % of hosts, thereby, making the data center more energy-aware.     

A study of fastener pull-through failure of composite laminates. Part 2: Failure prediction

The experimental study of fastener pull-through failure in composite laminates reported in Part 1 of this paper found pull-through failure to be characterised by substantial internal damage similar to that observed for low-velocity impacted composite panels. Damage is manifested in the form of a conically distributed network of matrix cracking and delaminations extending through-the-thickness from the fastener head outer edge, directed away from the fastener hole. Analysis is conducted in this paper to identify the mechanisms responsible for failure. Finite element analysis indicated high shear stresses at the fastener head outer edge to be responsible for the matrix cracking in this region. Tensile in-plane stresses are the cause of flexural failures found elsewhere in laminates of reduced bending stiffness. Fastener pull-through failure results from the tensile strength of the resin being exceeded. Matrix cracking was found to be the initial mode of failure with cracks aligning themselves perpendicular to the direction of principal stresses. Interply delamination is a secondary mode of failure and represents a propagation of cracking along the path of least resistance. Delaminations are induced due to excessive interlaminar shear and peel strains in the laminate due to through-thickness deformation and matrix cracking respectively. A numerical procedure for the prediction of failure was developed based upon a progressive damage model and a maximum principal strain criterion. Very good correlation between experimental and predicted pull-through failure loads, failure location and failure sequence were achieved. This research constitutes work performed as part of the Cooperative Research Centre for Advanced Composite Structures (CRC-ACS) task on highly loaded joints.     

Artificial neural networks: a tutorial

Artificial neural nets (ANNs) are massively parallel systems with large numbers of interconnected simple processors. The article discusses the motivations behind the development of ANNs and describes the basic biological neuron and the artificial computational model. It outlines network architectures and learning processes, and presents some of the most commonly used ANN models. It concludes with character recognition, a successful ANN application     

Polyhedral Flows in Hybrid Automata

A hybrid automaton is a mathematical model for hybrid systems, which combines, in a single formalism, automaton transitions for capturing discrete updates with differential constraints for capturing continuous flows. Formal verification of hybrid automata relies on symbolic fixpoint computation procedures that manipulate sets of states. These procedures can be implemented using boolean combinations of linear constraints over system variables, equivalently, using polyhedra, for the subclass of linear hybrid automata. In a linear hybrid automaton, the flow at each control mode is given by a rate polytope that constrains the allowed values of the first derivatives. The key property of such a flow is that, given a state-set described by a polyhedron, the set of states that can be reached as time elapses, is also a polyhedron. We call such a flow a polyhedral flow. In this paper, we study if we can generalize the syntax of linear hybrid automata for describing flows without sacrificing the polyhedral property. In particular, we consider flows described by origin-dependent rate polytopes, in which the allowed rates depend, not only on the current control mode, but also on the specific state at which the mode was entered. We identify necessary and sufficient conditions for a class of flows described by origin-dependent rate polytopes to be polyhedral. We also propose and study additional classes of flows: strongly polyhedral flows, in which the set of states that can be reached up to a given time starting from a polyhedron is guaranteed to be a polyhedron, and polyhedrally sliced flows, in which the set of states that can be reached at a given time starting from a polyhedron is guaranteed to be a polyhedron. Finally, we discuss an application of the above classes of flows to approximate exponential behaviours.     

The ROI of software dependability: The iDAVE model

In most organizations, proposed investments in software dependability compete for limited resources with proposed investments in software and system functionality, response time, adaptability, speed of development, ease of use, and other system capabilities. The lack of good return-on-investment models for software dependability makes determining the overall business case for dependability investments difficult. So, with a weak business case, investments in software dependability and the resulting system dependability are frequently inadequate. Dependability models will need to support stakeholders in determining their desired levels for each dependability attribute and estimating the cost, value, and ROI for achieving those. At the University of Southern California, researchers have developed software cost- and quality-estimation models and value-based software engineering processes, methods, and tools. We used these models and the value-based approach to develop an Information Dependability Attribute Value Estimation model (iDAVE) for reasoning about software dependability's ROI.