What Are You Looking at?

In this paper we present a novel mechanism to obtain enhanced gaze estimation for subjects looking at a scene or an image. The system makes use of prior knowledge about the scene (e.g. an image on a computer screen), to define a probability map of the scene the subject is gazing at, in order to find the most probable location. The proposed system helps in correcting the fixations which are erroneously estimated by the gaze estimation device by employing a saliency framework to adjust the resulting gaze point vector. The system is tested on three scenarios: using eye tracking data, enhancing a low accuracy webcam based eye tracker, and using a head pose tracker. The correlation between the subjects in the commercial eye tracking data is improved by an average of 13.91%. The correlation on the low accuracy eye gaze tracker is improved by 59.85%, and for the head pose tracker we obtain an improvement of 10.23%. These results show the potential of the system as a way to enhance and self-calibrate different visual gaze estimation systems.     

A Decentralized Grid Market Infrastructure for Service Oriented Grids

Service Oriented Computing has a deep impact on how IT infrastructures are conceived both in academia (e-science) and in industry (Service Oriented Architecture and commercial Web Services). Increasingly, economic models are being considered as suitable coordination mechanism for the management of service allocations to clients. However, few complete infrastructures have demonstrated the enabling of economics-based Service Oriented Grids (SOGs). The authors propose a complete infrastructure for economics-based SOGs and demonstrate its application in a prototype. They conduct experiments showing that practical agent-based automatic and fair trading of services at stable prices can be achieved using the proposed infrastructure.     

Digital self-calibration method for MEMS sensors

This paper presents a digital self-calibration method for microelectromechanical systems (MEMS) using a relay-feedback loop and a set of discrete-time filters. This method is based on the measurement of limit-cycles at the comparator's output, which result from a synchronization phenomenon between the natural frequency of the MEMS and the clock frequency of the discrete-time components. We show how quantized information concerning the MEMS parameters may be extracted from the shape of the limit-cycle, which depends on the characteristics of the MEMS (pulsation, damping, etc.). This digital technique is amplitude-independent, relatively insensitive to noise, and not costly to implement. Details concerning its implementation are discussed, and some simulation and experimental results are given.     

The MFS for numerical boundary identification in two-dimensional harmonic problems

In this study, we briefly review the applications of the method of fundamental solutions to inverse problems over the last decade. Subsequently, we consider the inverse geometric problem of identifying an unknown part of the boundary of a domain in which the Laplace equation is satisfied. Additional Cauchy data are provided on the known part of the boundary. The method of fundamental solutions is employed in conjunction with regularization in order to obtain a stable solution. Numerical results are presented and discussed.     

A New Fuzzy Adaptive Algorithm to Classify Imbalanced Data

Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other classes. The Imbalanced distribution of data is a natural occurrence in real world datasets, so needed to be dealt with carefully to get important insights. In case of imbalance in data sets, traditional classifiers have to sacrifice their performances, therefore lead to misclassifications. This paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this issue. We have adapted the ‘existing algorithm modification solution’ to learn from imbalanced datasets that classify data without manipulating the natural distribution of data unlike the other popular data balancing methods. The K nearest neighbor is a non-parametric classification method that is mostly used in machine learning problems. Fuzzy classification with the nearest neighbor clears the belonging of an instance to classes and optimal weights with improved nearest neighbor concept helping to correctly classify imbalanced data. The proposed hybrid approach takes care of imbalance nature of data and reduces the inaccuracies appear in applications of original and traditional classifiers. Results show that it performs well over the existing fuzzy nearest neighbor and weighted neighbor strategies for imbalanced learning.     

Fourier transform infrared-attenuated total reflection nitrate determination of soil pastes using principal component regression, partial least squares, and cross-correlation

This paper investigates the use of Fourier transform infrared (FTIR) attenuated total reflectance (ATR) spectroscopy as a fast and simple way for direct determination of nitrate concentration in soil pastes, which would assist precision fertilizer placement and reduce nitrate pollution. Eight types of soils are investigated, with nitrate concentrations ranging from 0 to 1000 ppm-N. The spectral region around the nitrate band (1300-1550 cm(-1)) is analyzed by (1) principal component regression (PCR), (2) partial least squares (PLS), and (3) cross-correlation with reference libraries that include spectra of pure ions and/or soils. The main obstacle to accurate nitrate measurement appears to be an interfering band present in calcareous soils. This band, which may be due to carbonate, is located around 1450 cm(-1) and overlaps with the nitrate band centered around 1370 cm(-1). For non-calcareous soils, and in particular for light sandy agricultural soils, PLS and cross-correlation with a reference library containing only spectra of ions in water give similar results (about 8 ppm-N on dry soil basis), while PCR leads to slightly poorer results. When calcareous soils are included in the analysis, the prediction errors are about twice as large. In this case, the best results are obtained using PLS, followed by PCR, while cross-correlation with reference libraries leads to poorer results.     

Investigation of Mixed Mode I/II Brittle Fracture Using ASCB Specimen

Using the asymmetric semi-circular bend specimen (ASCB) with vertical crack, a series of mixed mode I/II fracture tests were performed on two different rigid polyurethanes materials. The experimental results obtained for brittle fracture are compared with the theoretical predictions based on MTS and SED criteria. The results demonstrate that the SED criterion gives better results for mode II loading conditions, while for mode I and predominantly mode I both criteria are accurate.     

Wafer scale interdigitated nanoelectrode devices functionalized using a MEMS-based deposition system

This paper reports on a methodology to elaborate interdigitated nanoelectrode devices (INDs) at the wafer scale, relying on a mix-and-match process which combines proximity optical lithography and electron beam lithography. An optimum exposure dose allowed fabricating nanodevices, at the wafer level, with a successful yield of 97%. The final devices are bonded onto conventional TO-8 packages. Electrical characterization in a short-circuited nanoelectrode is performed, revealing a 230?μΩ?cm resistivity value at 23?°C. A MEMS-based spotter made of cantilevers (called Bioplume) has been used to obtain precise functionalization of the INDs with sub-picoliter volume solutions. These INDs are the basis of multiple tunnel junction nanodevices, intended to serve as novel highly sensitive nanobiosensors.