We consider the problem of estimating the latent state of a spatiotemporally evolving continuous function using very few sensor measurements. We show that a dynamical systems layer over temporal evolution of the weights of a kernel model is a valid approach to spatiotemporal modeling that does not necessarily require the design of complex nonstationary kernels. Furthermore, we show that such a ...

Iran has suitable conditions for cultivating high-quality varieties of hazelnuts (Corylus avellana L.). Most of hazelnut orchards in Iran have been established by planting native genotypes. Aspergillus flavus Link. (AF) is a filamentous fungus affecting hazelnut kernels in orchards and during storage conditions. The most widely explored strategy for reducing aflatoxin contamin...

Nonlinear adaptive filtering techniques, based on the Volterra model, are widely used for the nonlinearities identification in many applications. This paper proposes a new implementation of the third order LMS Volterra filter. A third order nonlinear system with memory is identified using the new LMS algorithm implementation for the Volterra kernels estimation. The accuracy of the proposed algo...

On the one hand, Support Vector Machines have met with significant success in solving difficult pattern recognition problems with global features representation. On the other hand, local features in images have shown to be suitable representations for efficient object recognition. Therefore, it is natural to try to combine SVM approach with local features representation to gain advantages on bo...

Feature selection and weighting has been an active research area in the last few decades finding success in many different applications. With the advent of Big Data, the adequate identification of the relevant features has converted feature selection in an even more indispensable step. On the other side, in kernel methods features are implicitly represented by means of feature mappings and kern...

Nowadays high performance computing devices are more common than ever before. The capacity of main memories becomes very huge; CPUs get more cores and computing units that have greater performance. There are more and more machines get accelerators such as GPUs, too. Take full advantages of modern machines that use heterogeneous architectures to get higher performance solutions is a real challen...

This work describes an auto-calibrated method for parallel imaging with spiral trajectory. The method is a k-space approach where an interpolation kernel, accounting for coil sensitivity factors, is derived from experimental data and used to interpolate the reduced data set in parallel imaging to estimate the missing k-space data. For the case of spiral imaging, this interpolation kernel is def...

Digital signal processing (DSP) industry has been growing rapidly over the past few years; it remains the technology driver for the recovering semiconductor industry. Performance evaluation is essential for the users and manufacturers of DSP processors. Since DSP application programs become larger and more complicated, people need new benchmarks for performance evaluation of different DSP proce...

These kernels combine the benefits of two other important classes of kernels: the homogeneous additive kernels (e.g. the χ2 kernel) and the RBF kernels (e.g. the exponential kernel). However, large scale problems require machine learning techniques of at most linear complexity and these are usually limited to linear kernels. Recently, Maji and Berg [2] and Vedaldi and Zisserman [4] proposed exp...

Extensive research works demonstrate that the attention mechanism in convolutional neural networks (CNNs) effectively improves accuracy. Nevertheless, few design mechanisms using large receptive fields. In this work, we propose a novel method named Rega-Net to increase CNN accuracy by enlarging field. To best of our knowledge, increasing field network requires size convolution kernel, which als...