A 16-Mbit/s adapter chip for the IBM token-ring local area network

The authors describe a 9.02×9.02-mm chip built in 1-μm CMOS with two levels of metal and an additional mask level for fabricating capacitors. It contains both analog and digital circuits and has provisions for self-test. The function includes the transmitter, receiver, protocol handler, an microprocessor, as well as interfaces for RAM/ROM storage, IBM PC bus, IBM PS/2 bus, IBM 3174 bus, and Motorola 68000 bus. The physical design terrains are formed by 24K circuits of standard cell gates, a 10K-circuit equivalent hand-honed custom microprocessor, and an analog macro. The chip operates from a single 5-V supply, and the power consumption is 0.8 W nominal at 16 Mb/s. The chip can also be operated at 4 Mb/s     

DYRE: a DYnamic REconfigurable solution to increase GPGPU’s reliability

The Journal of Supercomputing - General-purpose graphics processing units (GPGPUs) are extensively used in high-performance computing. However, it is well known that these devices’...     

Autoregressive model-based fuzzy clustering and its application for detecting information redundancy in air pollution monitoring networks

Fuzzy clustering enables the simultaneous membership of objects in two or more clusters. This is particularly pertinent where time series are concerned, because very often patterns of time series change over time. Thus, a time series might belong to different clusters over different periods of time, in which case, crisp clustering is unable to capture this multi-cluster membership. In this paper, we adopt a Fuzzy C-Medoids approach to clustering time series based on autoregressive estimates of models fitted to the time series. We illustrate very good performance of this approach in a range of simulation studies. By means of two applications, we also show the usefulness of this clustering approach in the air pollution monitoring, by considering air pollution time series, i.e., CO time series, CO₂ time series and NO time series monitored on world and urban scales. In particular, we show that, by considering in the clustering process, the autoregressive representation of these air pollution time series, we are able to detect possible information redundancy in the monitoring networks and then, decreasing the number of monitoring stations, to reduce the monitoring costs and then to increase the monitoring efficiency of the networks.     

Fuzzy clustering of time series in the frequency domain

Traditional and fuzzy cluster analyses are applicable to variables whose values are uncorrelated. Hence, in order to cluster time series data which are usually serially correlated, one needs to extract features from the time series, the values of which are uncorrelated. The periodogram which is an estimator of the spectral density function of a time series is a feature that can be used in the cluster analysis of time series because its ordinates are uncorrelated. Additionally, the normalized periodogram and the logarithm of the normalized periodogram are also features that can be used. In this paper, we consider a fuzzy clustering approach for time series based on the estimated cepstrum. The cepstrum is the spectrum of the logarithm of the spectral density function. We show in our simulation studies for the typical generating processes that have been considered, fuzzy clustering based on the cepstral coefficients performs very well compared to when it is based on other features.     

A least squares approach to principal component analysis for interval valued data

Principal Component Analysis (PCA) is a well-known technique, the aim of which is to synthesize huge amounts of numerical data by means of a low number of unobserved variables, called components. In this paper, an extension of PCA to deal with interval valued data is proposed. The method, called Midpoint Radius Principal Component Analysis (MR-PCA), recovers the underlying structure of interval valued data by using both the midpoints (or centers) and the radii (a measure of the interval width) information. In order to analyze how MR-PCA works, the results of a simulation study and two applications on chemical data are proposed.     

A formal framework for secure and complying services

Internet is offering a variety of services that are assembled to accomplish requests made by clients. While serving a request, security of the communications and of the data exchanged among services is crucial. Since communications occur along specific channels, it is equally important to guarantee that the interactions between a client and a server never get blocked because either cannot access a selected channel. We address here both these problems, from a formal point of view. A static analysis is presented, guaranteeing that a composition of a client and of possibly nested services respects both security policies for access control, and compliance between clients and servers.     

IEA Mobility Model (MoMo) and its use in the ETP 2008

The IEA published “Energy Technology Perspectives” (ETP) in June 2008. That document reports on IEA scenarios for baseline and low-CO₂ alternative scenarios to 2050, across the energy economy. The study included creating scenarios for transport, using the IEA Mobility Model (MoMo). This paper reports on the transport-related ETP scenarios and describes the model used in the analysis. According to the ETP Baseline scenario, world transport energy use and CO₂ emissions will more than double by 2050. In the most challenging scenario, called “BLUE”, transport emissions are reduced by 70% in 2050 compared to their baseline level in that year (and about 25% below their 2005 levels). There are several versions of the BLUE scenario, but all involve: a 50% or greater improvement in LDV efficiency, 30–50% improvement in efficiency of other modes (e.g. trucks, ships and aircraft), 25% substitution of liquid fossil fuels by biofuels, and considerable penetration of electric and/or fuel-cell vehicles. In the second half of this paper, an overview of the MoMo model is provided. Details on the complete analysis are contained in the ETP 2008 document, available at www.iea.org. Details of the LDV fuel economy analysis are contained in a separate paper in this collection.     

PolyMorph: Increasing the Spelling Efficiency of P300 by Selection Matrix PolyMorphism and Sentence-Based Predictions

One application of the P300 brain electric signal is sentence spelling, which enables subjects who have lost control of their motor pathways to communicate by selecting characters in a matrix containing all alphabet symbols. This technology still suffers from both low communication/high error rates. A P300 speller, named PolyMorph, which jointly introduces the selection matrix polymorphism (reducing the matrix size by removing useless symbols) and sentence-based predictions (which forecast words on the basis of language statistics) is presented. This is accomplished by using a custom dynamic knowledge-base, tailored to the subject lexicon, and updated in real time with the selections of the subject. The effectiveness of the presented speller is measured in vivo and in silico. The results suggest that the use of PolyMorph increases the number of spelt characters per time unit and reduces the error rate.     

Fuzzy clusterwise linear regression analysis with symmetrical fuzzy output variable

The traditional regression analysis is usually applied to homogeneous observations. However, there are several real situations where the observations are not homogeneous. In these cases, by utilizing the traditional regression, we have a loss of performance in fitting terms. Then, for improving the goodness of fit, it is more suitable to apply the so-called clusterwise regression analysis. The aim of clusterwise linear regression analysis is to embed the techniques of clustering into regression analysis. In this way, the clustering methods are utilized for overcoming the heterogeneity problem in regression analysis. Furthermore, by integrating cluster analysis into the regression framework, the regression parameters (regression analysis) and membership degrees (cluster analysis) can be estimated simultaneously by optimizing one single objective function. In this paper the clusterwise linear regression has been analyzed in a fuzzy framework. In particular, a fuzzy clusterwise linear regression model (FCWLR model) with symmetrical fuzzy output and crisp input variables for performing fuzzy cluster analysis within a fuzzy linear regression framework is suggested. For measuring the goodness of fit of the suggested FCWLR model with fuzzy output, a fitting index is proposed. In order to illustrate the usefulness of FCWLR model in practice, several applications to artificial and real datasets are shown.     

Clinical profile of a new monoclonal antibody-based immunoassay for tissue polypeptide antigen

Our preliminary evaluation of a new monoclonal antibody-based assay for tissue polypeptide antigen (TPA) has shown it to be clinically equivalent to the polyclonal antibody-based assay for TPA. The new assay (TPA-M) employs three monoclonal antibodies to epitopes on cytokeratins 8, 18 and 19. This multicenter, multinational study included 266 patients with newly diagnosed carcinomas of the lung, breast, large bowel and urinary bladder. TPA values from the two assays were compared with three other cytokeratin markers (TPS, CYFRA 21-1 and TPACyk) and with the established reference markers for these malignancies (CEA and NSE for lung, CA 15-3 for breast, CEA and CA 19-9 for colorectal tumors). Analysis of receiver operating characteristic (ROC) curves in lung, colorectal and bladder cancer showed similar sensitivities for the two assays, ranging from 50% to 80% with a specificity of 95%. In breast cancer all the markers studied showed poor sensitivity. However, TPA determination by either method could discriminate advanced stage (stages III and IV) from early stage disease (stages 0 to II). TPA showed similar discriminating ability in bladder cancer. On the basis of the results obtained in our patient series, it seems that of the cytokeratin markers studied, TPA and TPA-M are the most sensitive and offer a wide range of clinical applications.