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In recent decades, a lot of achievements have been obtained in imaging and cognitive neuroscience of human brain. Brain's activities can be shown by a number of different kinds of non-invasive technologies, such as: Near-Infrared Spectroscopy (NIRS), Magnetic Resonance Imaging (MRI), and ElectroEncephaloGraphy (EEG; Wolpaw et al., 2002; Weiskopf et al., 2004; Blankertz et al., 2006). NIRS has become the convenient technology for experimental brain purposes. The change of oxygenation changes (oxy-Hb) along task period depending on location of channel on the cortex has been studied: sustained activation in the motor cortex, transient activation during the initial segments in the somatosensory cortex, and accumulating activation in the frontal lobe (Gentili et al., 2010). Oxy-Hb concentration at the aforementioned sites in the brain can also be used as a predictive factor allows prediction of subject's investigation behavior with a considerable degree of precision (Shimokawa et al., 2009). In this paper, a study of recognition algorithm will be described for recognition whether one taps the left hand (LH) or the right hand (RH). Data with noises and artifacts collected from a multi-channel system will be pre-processed using a Savitzky–Golay filter for getting more smoothly data. Characteristics of the filtered signals during LH and RH tapping process will be extracted using a polynomial regression (PR) algorithm. Coefficients of the polynomial, which correspond to Oxygen-Hemoglobin (Oxy-Hb) concentration, will be applied for the recognition models of hand tapping. Support Vector Machines (SVM) will be applied to validate the obtained coefficient data for hand tapping recognition. In addition, for the objective of comparison, Artificial Neural Networks (ANNs) was also applied to recognize hand tapping side with the same principle. Experimental results have been done many trials on three subjects to illustrate the effectiveness of the proposed method.

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Tomato leaves can have different diseases which can affect harvest performance. Therefore, accurate classification for the early detection of disease for treatment is very important. This article proposes one classification model, in which 16,010 tomato leaf images obtained from the Plant Village database are segmented before being used to train a deep convolutional neural network (DCNN). This means that this classification model will reduce training time compared with that of the model without segmenting the images. In particular, we applied a VGG-19 model with transfer learning for re-training in later layers. In addition, the parameters such as epoch and learning rate were chosen to be suitable for increasing classification performance. One highlight point is that the leaf images were segmented for extracting the original regions and removing the backgrounds to be black using a hue, saturation, and value (HSV) color space. The segmentation of the leaf images is to synchronize the black background of all leaf images. It is obvious that this segmentation saves time for training the DCNN and also increases the classification performance. This approach improves the model accuracy to 99.72% and decreases the training time of the 16,010 tomato leaf images. The results illustrate that the model is effective and can be developed for more complex image datasets.

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Charged organic adsorbates play an important role in a number of electrochemical reactions, e.g. as additives for metal plating relevant for device fabrication in the semiconductor industry. Fundamental investigations are mandatory in order to acquire profound knowledge of the structural and electronic properties of these layers parallel and perpendicular to the surface, and to finally achieve a deeper mechanistic understanding of the kinetics of involved charge transfer reactions taking place at these complex metal/organic/electrolyte interfaces. A key structural motif of these interfaces consists in 'paired' (inorganic)anion/(organic)cation layers that can have an enormous stability even during an ongoing charge transfer reaction. In this contribution we present and discuss a selected case study on the co-adsorption of halide anions and cationic organic molecules that exhibit a pronounced redox activity. It will be demonstrated that their phase behavior at the interface crucially depends on both their particular redox-state and the surface concentration of the halide counter ions. The subtle balance between adsorbate–adsorbate and adsorbate–substrate interaction of the poly-cationic organic layer can be carefully controlled by potential dependent anion adsorption and desorption processes through the organic layer. This process can be followed by in situ high-resolution scanning tunnelling microscopy, while additional information about the structural and chemical state of the respective phase is obtained from in situ X-ray diffraction and ex situ photoelectron spectroscopy.

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