Automated computational analysis of nanoparticles is the key approach urgently required to achieve further progress in catalysis, the development of new nanoscale materials, and applications. Analysis of nanoscale objects on the surface relies heavily on scanning electron microscopy (SEM) as the experimental analytic method, allowing direct observation of nanoscale structures and morphology. One of the important examples of such objects is palladium on carbon catalysts, allowing access to various chemical reactions in laboratories and industry. SEM images of Pd/C catalysts show a large number of nanoparticles that are usually analyzed manually. Manual analysis of a statistically significant number of nanoparticles is a tedious and highly time-consuming task that is impossible to perform in a reasonable amount of time for practically needed large amounts of samples. This work provides a comprehensive comparison of various computer vision methods for the detection of metal nanoparticles. In addition, multiple new types of data representations were developed, and their applicability in practice was assessed.

This paper addresses the actual problem of multiclass classification in large training sets. Classical Support Vector Machines (SVM) is a popular, convenient and well-interpreted classification method, but it has a high computational complexity of a training stage in a nonlinear case and a low data parallelism. The aim of this paper is to improve the scalability of nonlinear multiclass SVM.In the basis of this paper is Kernel-based Mean Decision Rule method with smart sampling (SS-KMDR) we previously proposed for fast solving large-scale binary SVM problems. In this paper we, at first, extend SS-KMDR for the multiclass classification problem. At second, we propose the modified algorithm of smart sample construction that allows to improve its characteristics and also extend it to possess the possibility to solve large-scale multiclass SVM problems. Experimental investigation of proposed methods was made on three large handwritten digit images data sets of different size and one large intrusion detection data set. Experiments show that both proposed multiclass methods allow to reach the quality near state-of-the-art SVC quality, but they essentially outperform it in the training time. The proposed Dual-Layer Smart Sampling SVM (DLSS-SVM method) allows additionally reduce training and test times in contrast to the basic smart sampling technique.

Electroencephalography is a method of testing the electrical activity of the brain by jointly processing electrical signals registered at several points on the surface of the skull. It was originally invented to study mechanisms by which human behavior is generated, in particular, for brain diseases diagnosis. However, in the past decades, electroencephalography has become the basis of many brain-computer interfaces, which decode neural response to different stimuli into commands that, for instance, operate external devices. Our research is concerned with another purpose of analyzing responses of a multi-channel electroencephalogram (EEG) to outward stimuli. It is assumed that the person whose EEG is processed is an experienced mammologist able to reliably distinguish between X-ray mammograms of women with breast cancer and those of healthy women. These studies pursue the aim to essentially improve productivity of rare pronounced experts by way of, first, accelerating the screening of mammographic images up to ten pictures per second, and, second, immediately detecting the eventual potentials evoked in the expert's EEG by a target (cancer) image among a crowd of nontarget ones before the expert becomes aware of this fact.

In our previous work, an exponential approximation (EA) method was proposed for the detection of nanoparticles in scanning electron microscope (SEM) images. It shows the best quality of nanoparticle detection compared to other methods. But its main drawback is that it takes a lot of time. In this paper, we propose a two-level parallel computing scheme and a corresponding high-performance python+MPI implementation of the EA method. Experiments have shown that the developed parallel implementation can significantly speed up the computational process.

A typical characteristic of modern applied multiclass classification problems is the large scale. It significantly complicates or even makes it impossible an application of such popular, convenient and well-interpreted method as Support Vector Machines (SVM), which is well-proven for small-size classification problems. In this connection the actual problem is to increase SVM’s computational performance. The Double-Layer Smart Sampling SVM (DLSS-SVM) method allows to reduce the training time of multiclass SVM via double using the smart sampling technique. This paper proposes the high-performance version of DLSS-SVM (HP-DLSS-SVM). It is based on two-level parallel computing scheme, which exploits useful DLSS-SVM properties and computing system capabilities more fully. Experimental investigation of the proposed HPDLSS-SVM method was made on three large handwritten digit images data sets of different size. Experiments show that the proposed approach allows to essentially decrease training and testing times and at that to maintain the obtained recognition accuracy close to the best.