A method for generating non-Gaussian noise series with specified probability distribution and power spectrum

It is necessary to generate a non-Gaussian noise sequence in the simulation of the communication system and signal processing. In the design of some practical system such as radar system, we must generate a stationary noise sequence or clutter series with a specified non-Gaussian probability density function and a desired power spectrum for the purpose of testing the performance of the system. This paper presents a novel method by which such a non-Gaussian noise sequence can be generated. The simulation results are also demonstrated to show the effectiveness of the proposed method.published_or_final_versio

Novel Approach for Time-Varying Bispectral Analysis of Non-Stationary EEG Signals

novel parametric method, based on the non-Gaussian AR model, is proposed for the partition of on-stationary EEG data into a finite set of third-order stationary segments. With the assumption of piecewise third-order stationarity of the signal, a series of parametric bispectral estimations of the non-stationary EEG data can be performed so as to describe the time-varying non-Gaussian nonlinear characteristics of the observed EEG signals. A practical method based on the fitness of third-order statistics of the signal by using the non-Gaussian AR model, together with an algorithm with CMI is presented. The experimental results with several simulations and clinical EEG signals have also been investigated and discussed. The results show successful performance of the proposed method in estimating the time-varying bispectral structures of the EEG signals.published_or_final_versio

Analysis of time-varying synchronization of EEG during sentences identification

The study of the synchronization of EEG signals can help us to understand the underlying cognitive processes and detect the learning deficiencies since the oscillatory states in the EEG reveal the rhythmic synchronous activity in large networks of neurons. As the changes of the physiological states and the relative environment exist when cognitive and information processing take place in different brain regions at different times, the practical EEGs therefore turn out to be extremely non-stationary processes. To investigate how these distributed brain regions are linked together and the information is exchanged with time, this paper proposes a modern time-frequency coherent analysis method that employs an alternative way for quantifying synchronization with both temporal and spatial resolution. Wavelet coherent spectrum is defined such that the degree of synchronization and information flow between different brain regions can be described. Several real EEG data are analysed under the cognitive tasks of sentences identification in both English and Chinese. The time-varying synchronization between the brain regions involved in the processing of sentences exhibited that a common neural network is activated by both English and Chinese sentences. The results of the presented method are helpful for studying English and Chinese learning for Chinese students.published_or_final_versio

Learning and Matching Multi-View Descriptors for Registration of Point Clouds

Critical to the registration of point clouds is the establishment of a set of accurate correspondences between points in 3D space. The correspondence problem is generally addressed by the design of discriminative 3D local descriptors on the one hand, and the development of robust matching strategies on the other hand. In this work, we first propose a multi-view local descriptor, which is learned from the images of multiple views, for the description of 3D keypoints. Then, we develop a robust matching approach, aiming at rejecting outlier matches based on the efficient inference via belief propagation on the defined graphical model. We have demonstrated the boost of our approaches to registration on the public scanning and multi-view stereo datasets. The superior performance has been verified by the intensive comparisons against a variety of descriptors and matching methods

Quantifying Robotic Swarm Coverage

In the field of swarm robotics, the design and implementation of spatial density control laws has received much attention, with less emphasis being placed on performance evaluation. This work fills that gap by introducing an error metric that provides a quantitative measure of coverage for use with any control scheme. The proposed error metric is continuously sensitive to changes in the swarm distribution, unlike commonly used discretization methods. We analyze the theoretical and computational properties of the error metric and propose two benchmarks to which error metric values can be compared. The first uses the realizable extrema of the error metric to compute the relative error of an observed swarm distribution. We also show that the error metric extrema can be used to help choose the swarm size and effective radius of each robot required to achieve a desired level of coverage. The second benchmark compares the observed distribution of error metric values to the probability density function of the error metric when robot positions are randomly sampled from the target distribution. We demonstrate the utility of this benchmark in assessing the performance of stochastic control algorithms. We prove that the error metric obeys a central limit theorem, develop a streamlined method for performing computations, and place the standard statistical tests used here on a firm theoretical footing. We provide rigorous theoretical development, computational methodologies, numerical examples, and MATLAB code for both benchmarks.Comment: To appear in Springer series Lecture Notes in Electrical Engineering (LNEE). This book contribution is an extension of our ICINCO 2018 conference paper arXiv:1806.02488. 27 pages, 8 figures, 2 table

Corticosterone Potentiation of Cocaine-Induced Reinstatement of Conditioned Place Preference in Mice is Mediated by Blockade of the Organic Cation Transporter 3

The mechanisms by which stressful life events increase the risk of relapse in recovering cocaine addicts are not well understood. We previously reported that stress, via elevated corticosterone, potentiates cocaine-primed reinstatement of cocaine seeking following self-administration in rats and that this potentiation appears to involve corticosterone-induced blockade of dopamine clearance via the organic cation transporter 3 (OCT3). In the present study, we use a conditioned place preference/reinstatement paradigm in mice to directly test the hypothesis that corticosterone potentiates cocaine-primed reinstatement by blockade of OCT3. Consistent with our findings following self-administration in rats, pretreatment of male C57/BL6 mice with corticosterone (using a dose that reproduced stress-level plasma concentrations) potentiated cocaine-primed reinstatement of extinguished cocaine-induced conditioned place preference. Corticosterone failed to re-establish extinguished preference alone but produced a leftward shift in the dose–response curve for cocaine-primed reinstatement. A similar potentiating effect was observed upon pretreatment of mice with the non-glucocorticoid OCT3 blocker, normetanephrine. To determine the role of OCT3 blockade in these effects, we examined the abilities of corticosterone and normetanephrine to potentiate cocaine-primed reinstatement in OCT3-deficient and wild-type mice. Conditioned place preference, extinction and reinstatement of extinguished preference in response to low-dose cocaine administration did not differ between genotypes. However, corticosterone and normetanephrine failed to potentiate cocaine-primed reinstatement in OCT3-deficient mice. Together, these data provide the first direct evidence that the interaction of corticosterone with OCT3 mediates corticosterone effects on drug-seeking behavior and establish OCT3 function as an important determinant of susceptibility to cocaine use

Vectorial Control of Magnetization by Light

Coherent light-matter interactions have recently extended their applications to the ultrafast control of magnetization in solids. An important but unrealized technique is the manipulation of magnetization vector motion to make it follow an arbitrarily designed multi-dimensional trajectory. Furthermore, for its realization, the phase and amplitude of degenerate modes need to be steered independently. A promising method is to employ Raman-type nonlinear optical processes induced by femtosecond laser pulses, where magnetic oscillations are induced impulsively with a controlled initial phase and an azimuthal angle that follows well defined selection rules determined by the materials' symmetries. Here, we emphasize the fact that temporal variation of the polarization angle of the laser pulses enables us to distinguish between the two degenerate modes. A full manipulation of two-dimensional magnetic oscillations is demonstrated in antiferromagnetic NiO by employing a pair of polarization-twisted optical pulses. These results have lead to a new concept of vectorial control of magnetization by light

Biomolecular condensates undergo a generic shear-mediated liquid-to-solid transition.

Membrane-less organelles resulting from liquid-liquid phase separation of biopolymers into intracellular condensates control essential biological functions, including messenger RNA processing, cell signalling and embryogenesis1-4. It has recently been discovered that several such protein condensates can undergo a further irreversible phase transition, forming solid nanoscale aggregates associated with neurodegenerative disease5-7. While the irreversible gelation of protein condensates is generally related to malfunction and disease, one case where the liquid-to-solid transition of protein condensates is functional, however, is that of silk spinning8,9. The formation of silk fibrils is largely driven by shear, yet it is not known what factors control the pathological gelation of functional condensates. Here we demonstrate that four proteins and one peptide system, with no function associated with fibre formation, have a strong propensity to undergo a liquid-to-solid transition when exposed to even low levels of mechanical shear once present in their liquid-liquid phase separated form. Using microfluidics to control the application of shear, we generated fibres from single-protein condensates and characterized their structural and material properties as a function of shear stress. Our results reveal generic backbone-backbone hydrogen bonding constraints as a determining factor in governing this transition. These observations suggest that shear can play an important role in the irreversible liquid-to-solid transition of protein condensates, shed light on the role of physical factors in driving this transition in protein aggregation-related diseases and open a new route towards artificial shear responsive biomaterials

Structural analysis and corrosion studies on an ISO 5832-9 biomedical alloy with TiO2 sol–gel layers

The aim of this study was to demonstrate the relationship between the structural and corrosion properties of an ISO 5832-9 biomedical alloy modified with titanium dioxide (TiO2) layers. These layers were obtained via the sol–gel method by acid-catalyzed hydrolysis of titanium isopropoxide in isopropanol solution. To obtain TiO2 layers with different structural properties, the coated samples were annealed at temperatures of 200, 300, 400, 450, 500, 600 and 800 C for 2 h. For all the prepared samples, accelerated corrosion measurements were performed in Tyrode’s physiological solution using electrochemical methods. The most important corrosion parameters were determined: corrosion potential, polarization resistance, corrosion rate, breakdown and repassivation potentials. Corrosion damage was analyzed using scanning electron microscopy. Structural analysis was carried out for selected TiO2 coatings annealed at 200, 400, 600 and 800 C. In addition, the morphology, chemical composition, crystallinity, thickness and density of the deposited TiO2 layers were determined using suitable electron and X-ray measurement methods. It was shown that the structure and character of interactions between substrate and deposited TiO2 layers depended on annealing temperature. All the obtained TiO2 coatings exhibit anticorrosion properties, but these properties are related to the crystalline structure and character of substrate–layer interaction. From the point of view of corrosion, the best TiO2 sol–gel coatings for stainless steel intended for biomedical applications seem to be those obtained at 400 C.This study was supported by Grant No. N N507 501339 of the National Science Centre. The authors wish to express their thanks to J. Borowski (MEDGAL, Poland) for the Rex 734 alloy