The kernel common vector method: a novel nonlinear subspace classifier for pattern recognition.

The common vector (CV) method is a linear subspace classifier method which allows one to discriminate between classes of data sets, such as those arising in image and word recognition. This method utilizes subspaces that represent classes during classification. Each subspace is modeled such that common features of all samples in the corresponding class are extracted. To accomplish this goal, the method eliminates features that are in the direction of the eigenvectors corresponding to the nonzero eigenvalues of the covariance matrix of each class. In this paper, we introduce a variation of the CV method, which will be referred to as the modified CV (MCV) method. Then, a novel approach is proposed to apply the MCV method in a nonlinearly mapped higher dimensional feature space. In this approach, all samples are mapped into a higher dimensional feature space using a kernel mapping function, and then, the MCV method is applied in the mapped space. Under certain conditions, each class gives rise to a unique CV, and the method guarantees a 100% recognition rate with respect to the training set data. Moreover, experiments with several test cases also show that the generalization performance of the proposed kernel method is comparable to the generalization performances of other linear subspace classifier methods as well as the kernel-based nonlinear subspace method. While both the MCV method and its kernel counterpart did not outperform the support vector machine (SVM) classifier in most of the reported experiments, the application of our proposed methods is simpler than that of the multiclass SVM classifier. In addition, it is not necessary to adjust any parameters in our approach.     

Intelligent association selection of embedded agents in intelligent inhabited environments

Recent advances in technology and manufacturing have resulted in more powerful and smaller processors to be embedded in the various artefacts within smart environments. Most of these artefacts are network enabled and thanks to pervasive networking such artefacts can communicate and collaborate together to support our daily lives. Furthermore, these artefacts can also be equipped with embedded agents to provide intelligent reasoning, planning and learning capabilities. However, the multitude of interconnected devices and artefacts can result in major network and processing delays as well as creating inherent complexities in programming and configuring smart environments to personalise themselves to suit the individual needs. Hence, a major challenge to the design and use of smart environments involves finding the best set of device associations and interconnections that are most suitable to the environment and user needs. In this paper, we will present a novel intelligent method for reducing the number of associations and interconnections between the various devices and artefacts within smart environments to minimise the network and processing overheads while reducing the cognitive load associated with configuring and programming smart environments.     

Discriminative touch and emotional touch.

Somatic sensation comprises four main modalities, each relaying tactile, thermal, painful, or pruritic (itch) information to the central nervous system. These input channels can be further classified as subserving a sensory function of spatial and temporal localization, discrimination, and provision of essential information for controlling and guiding exploratory tactile behaviours, and an affective function that is widely recognized as providing the afferent neural input driving the subjective experience of pain, but not so widely recognized as also providing the subjective experience of affiliative or emotional somatic pleasure of touch. The discriminative properties of tactile sensation are mediated by a class of fast-conducting myelinated peripheral nerve fibres--A-beta fibres--whereas the rewarding, emotional properties of touch are hypothesized to be mediated by a class of unmyelinated peripheral nerve fibres--CT afferents (C tactile)--that have biophysical, electrophysiological, neurobiological, and anatomical properties that drive the temporally delayed emotional somatic system. CT afferents have not been found in the glabrous skin of the hand in spite of numerous electrophysiological explorations of this area. Hence, it seems reasonable to conclude that they are lacking in the glabrous skin. A full understanding of the behavioural and affective consequences of the differential innervation of CT afferents awaits a fuller understanding of their function. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A Novel Control Unit for a Friction Welding Machine Based on Computer

Typical friction welds are made by holding a non-rotating sample in contact with a rotating sample under constant or gradually increasing pressure until the interface reaches the welding temperature and then stopping pressure rotating to complete the weld. This process requires additional time and is labor intensive. Recently, several control systems have been introduced to the industry for improving the welding process such as conventional automatic control, microcontroller control, and Programmable Logic Controller (PLC). However, these processes have limitations either on materials selection or on visualization. In this study, continuous monitoring, control and recording are implemented. Computer-controlled platform for the simulation and control of mechanisms is based on a Microsoft Visual Basic environment. Welding parameters such as friction time, forge time and breaking time are entered with a keyboard and can be saved and used again. To show the feasibility and versatility of the study, the evaluation is used for sample joining. It has been observed that this system works successfully and gives good performance.     

Seventh-order elliptic video filter with 0.1 dB pass band ripple employing CMOS CDTAs

In this paper, a CMOS realization of the current differencing transconductance amplifier (CDTA) is given, which is a newly reported active building block for current-mode signal processing. Current differencing stage of the CDTA element is realized using a differential current-controlled current source and in the output stage, floating current sources are used to convert intermediate voltage of z terminal to output currents. Due to the compactness of the circuit, it is capable of high-frequency operation and suitable for video signal-processing applications. The CDTA element is used in a current-mode anti-aliasing video filter, which is designed using operational simulation of a seventh-order passive elliptic filter, so the resulting active filter has the low sensitivity feature of its passive counterpart. The filter has 0.1 dB maximum pass band ripple as imposed by ITU video anti-aliasing filter standard. SPICE-simulation results of both the CDTA element and the seventh-order elliptic filter are given. Simulation results are found in close agreement with theoretical results.     

Using multiplicative neuron model to establish fuzzy logic relationships

Determination of fuzzy logic relationships between observations is quite effective on the forecasting performance of fuzzy time series approaches. In various studies available in the literature, it has been seen that utilizing artificial neural networks for establishing fuzzy relations increase the forecasting accuracy. In this study, a novel high order fuzzy time series forecasting approach in which multiplicative neuron model is used to define fuzzy relations is proposed in order to reach high forecasting level. Also, particle swarm optimization method is utilized to train multiplicative neuron model. In order to show forecasting performance of the proposed method, it is applied to a well-known data Taiwan future exchange and the results produced by the proposed approach is compared to those obtained from other fuzzy time series forecasting models. As a result of the implementation, it is observed that the proposed approach gives the best forecasts for Taiwan future exchange time series.     

Tensile Bond Strength of a Highly Cross-Linked Denture Tooth to the Compression-Molded and Injection-Molded Denture Base Polymers

This study compared tensile bond strengths between conventional compression-molded heat (HC)-, auto (AP)-, and microwave-polymerized (MC) poly(methyl methacrylate)-based denture resins and a relatively new injection-molded, microwave-polymerized polyurethane based resin (MI) bonded to a highly cross-linked denture tooth. In the first part of the experiments, denture teeth were used as received. In the second part, they were treated with dichloromethane to see its effect on bonding of conventional denture bases (HCS and APS). Bond strength was tested in tension according to ADA specification No.15. The results showed that the HC group failed cohesively because of higher interface bonding (49.95 MPa) compared with those of the others (AP: 25.41 MPa; MC: 22.06 MPa; MI: 20.02 MPa). The application of dichloromethane improved bond strengths of HCS and APS groups (60.61 and 32.03 MPa, respectively). It was suggested that dichloromethane could be applied on the denture teeth ridge lap area prior to denture base processing to enhance adhesion between the tooth/resin.     

An experimental study on the bond strength between reinforcement bars and concrete as a function of concrete cover,strength and corrosion level

The effect of corrosion on the bond strength between reinforcement bars and concrete was studied in a series of experiments. An accelerated corrosion method was used to corrode the reinforcement bars embedded in concrete specimens. Pullout tests were performed to develop an empirical model for the ultimate bond strength by evaluating bond strengths in two different concrete mixes, three concrete cover depths and different mass losses of reinforcement bars after corrosion. Bond-slip relationships for the different corrosion levels were compared. It was found that the relationship between bond strength and concrete strength in uncorroded specimens differed from that of corroded specimens set in high-strength concrete because of brittleness in the corroded specimens, which caused a sudden loss of bond strength. The results revealed that specimens with higher concrete strength levels and corroded reinforcements showed a higher percentage of bond strength degradation due to concrete cracking during the pullout tests.     

The impact of a realistic packet traffic model on the performance of surveillance wireless sensor networks

It is quite common to see that classical periodic or Poisson packet traffic models are used for evaluating the performance of wireless sensor networks (WSNs). However, these models may not be appropriate for modeling the data traffic resulting from a particular application. Furthermore, they may be overestimating the performance of a WSN. In this paper, we show the significance of using a realistic and application-specific packet traffic model by comparing the performance of a well-known WSN protocol under the Surveillance WSN packet traffic model (SPTM), as well as under periodic and binomial traffic models. A packet traffic framework specific to surveillance applications is proposed which is then used for deriving SPTM analytically. In order to be adaptable and flexible, SPTM incorporates a probabilistic and parametric sensor detection model. Simulation results show that to employ an application-specific packet traffic model has significant impact on the performance evaluation of the WSN and ordinary traffic models may overestimate the capacity of the WSN.