Nonuniform Sampling for Detection of Abrupt Changes

In this work, detection of abrupt changes in continuous-time linear stochastic systems and selection of the sampling interval to improve the detection performance are considered. Cost functions are proposed to optimize both uniform and nonuniform sampling intervals for the well-known cumulative sum algorithm. Some iterative techniques are presented to make online optimization computationally feasible. It is shown that considerable improvement in the detection performance can be obtained by using nonuniform sampling intervals.     

Poly(3-hydroxybutyric acid-co-3-hydroxyvaleric acid) based tissue engineering matrices

In this study, the aim was to produce tissue-engineered bone using osteoblasts and a novel matrix material, poly(3-hydroxybutyric acid-co-3-hydroxyvaleric acid) (PHBV). In order to prepare a porous PHBV matrix with uniform pore size, sucrose crystals were loaded in the foam and then leached leaving pores behind. The surface of the PHBV matrix was treated with rf-oxygen plasma to increase the surface hydrophilicity. SEM examination of the PHBV matrices was carried out. Stability of PHBV foams in aqueous media was studied. The pH decrease is an indication of the degradation extent. The weight and density were unchanged for a period of 120 days but then a significant decrease was observed for the rest of the study. Osteoblast cells were then isolated from rat bone marrow and seeded onto PHBV matrices. The metabolization and proliferation on the foams was determined with MTS assay which showed that osteoblasts proliferated on PHBV. It was also found that cells proliferated better on large pore size foams (300–500 m) than on the small pore size foams (75–300 m). Production of ALP was measured spectrophotometrically. The present study demonstrated that PHBV matrices are suitable substrates for osteoblast proliferation and differentiation.     

Model-based cache-aware dispatching of object-oriented software for multicore systems

In recent years, processor technology has evolved towards multicore processors, which include multiple processing units (cores) in a single package. Those cores, having their own private caches, often share a higher level cache memory dedicated to each processor die. This multi-level cache hierarchy in multicore processors raises the importance of cache utilization problem. Assigning parallel-running software components with common data to processor cores that do not share a common cache increases the number of cache misses. In this paper we present a novel approach that uses model-based information to guide the OS scheduler in assigning appropriate core affinities to software objects at run-time. We build graph models of software and cache hierarchies of processors and devise a graph matcher algorithm that provides mapping between these two graphs. Using this mapping we obtain candidate core sets that each software object can be affiliated with at run-time. These affiliations are determined based on the idea that software components that have the potential to share common data at run-time should run on cores that share a common cache. We also develop an object dispatcher algorithm that keeps track of object affiliations at run-time and dispatches objects by using the information from the compile-time graph matcher. We apply our approach on design pattern implementations and two different application program running on servers using CFS scheduling. Our results show that cache-aware dispatching based on information obtained from software model, decreases number of cache misses significantly and improves CFS’ scheduling performance.     

Gain adaptation in sliding mode control of robotic manipulators

In this paper, a novel scheme is proposed to adapt the gains of a sliding mode controller (SMC) so that the problems faced in its practical implementations as a motion controller are overcome. A Lyapunov function is selected for the design of the SMC and an MIT rule is used for gain adaptation. The criterion that is minimized for gain adaptation is selected as the sum of the squares of the control signal and the sliding surface function. This novel approach is tested on a scara-type robot manipulator. The experimental results presented prove its efficacy.     

Optimal diversity combining under correlated noise in mobile radio

In this paper the performance of predetection maximal ratio and equal gain combiners are investigated under conditions of correlated branch noise. A statistical model is devised to determine the spatial noise correlation coefficients at metropolitan‐area base stations, and the cases where significant correlation is likely are clarified. Optimal weighting coefficients for a maximal ratio combiner with two‐branch space diversity are derived under correlated noise. Based on this result it is shown that correlation in branch noise can be used to improve the combiner performance by dynamically adjusting the weightings so as to partially cancel the noise. Performance of equal gain combiners is also shortly discussed. This revised version was published online in June 2006 with corrections to the Cover Date.     

A neural network-based approach for calculating dissolved oxygen profiles in reservoirs

A Neural Network (NN) modelling approach has been shown to be successful in calculating pseudo steady state time and space dependent Dissolved Oxygen (DO) concentrations in three separate reservoirs with different characteristics using limited number of input variables. The Levenberg–Marquardt algorithm was adopted during training. Pre-processing before training and post processing after simulation steps were the treatments applied to raw data and predictions respectively. Generalisation was improved and over-fitting problems were eliminated: Early stopping method was applied for improving generalisation. The correlation coefficients between neural network estimates and field measurements were as high as 0.98 for two of the reservoirs with experiments that involve double layer neural network structure with 30 neurons within each hidden layer. A simple one layer neural network structure with 11 neurons has yielded comparable and satisfactorily high correlation coefficients for complete data set, and training, validation and test sets of the third reservoir.     

An efficient and adaptive channel handover procedure for cognitive radio networks

In designing cognitive radio systems, one of the most critical issues is handling the channel handover process (CHP). The CHP consists of spectrum sensing, spectrum decision, negotiation on the common control channel, and adjustment of frequency and modulation settings, and such, it can be a time‐consuming process. Consequently, initiating the CHP after each detected user activity (UA) can decrease the aggregate spectrum utilization. To alleviate this problem, we introduce a novel handover strategy to find the optimal trade‐off between the durations of the CHP and UAs. With the use this model, secondary users (SUs) track only local information on their current data channel to make the decision to initiate the CHP or to wait for the termination of the ongoing UA. The system adapts to the dynamic conditions of the data channels and reduces the frequency of handovers to increase throughput and decrease access delay. We give analytical utilization bounds for SUs and also compare the performance of our model to those of other channel handover strategies by using extensive simulations. Our results for channels with heterogeneous loads and dynamic environments show that this model can clearly decrease the frequency of handover and consequently increase the aggregate SU utilization. Copyright © 2013 John Wiley & Sons, Ltd.     

Virtual topology reconfiguration on optical WDM networks considering traffic grooming

This paper investigates the virtual topology reconfiguration (VTR) problem of optical WDM networks by taking the traffic grooming factor into consideration. Firstly, by applying a common “divide and conquer” approach, the problem is categorized and handled as two independent sub-problems, triggering policy and the proper algorithm. Secondly, the VTR problem considering traffic grooming is formulated with new variables and constraints by a mixed-integer linear program (MILP). In order to handle the tradeoff between the advantages and disadvantages of VTR, both network resource utilization and network disruption are examined and quantified in terms of measurable parameters. A new multi-objective VTR algorithm called integrated reconfiguration (IR) algorithm is proposed to provide better overall VTR performance. Different from previous studies this newly proposed VTR algorithm combines three main factors (traffic load, traffic grooming ratio and route length of lightpaths) into one single objective and considers them all when reconfiguring. The results of simulations indicate that proposed VTR policy, periodic VTR triggering policy with IR algorithm, achieves performance improvements for overall VTR performance.