Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals.

Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigated. In this study we inferred the patterns and neural signatures of self-induced emotions from electroencephalogram (EEG) signals.

A novel P300 BCI speller based on the Triple RSVP paradigm.

A brain-computer interface (BCI) is an advanced human-machine interaction technology. The BCI speller is a typical application that detects the stimulated source-induced EEG signal to identify the expected characters of the subjects. The current mainstream matrix-based BCI speller involves two problems that remain unsolved, namely, gaze-dependent and space-dependent problems.

An EEG-Based Person Authentication System with Open-Set Capability Combining Eye Blinking Signals.

The electroencephalogram (EEG) signal represents a subject's specific brain activity patterns and is considered as an ideal biometric given its superior forgery prevention. However, the accuracy and stability of the current EEG-based person authentication systems are still unsatisfactory in practical application. In this paper, a multi-task EEG-based person authentication system combining eye blinking is proposed, which can achieve high precision and robustness.

An Efficient Direct Position Determination Method for Multiple Strictly Noncircular Sources.

This paper focuses on the localization methods for multiple sources received by widely separated arrays. The conventional two-step methods extract measurement parameters and then estimate the positions from them. In the contrast to the conventional two-step methods, direct position determination (DPD) localizes transmitters directly from original sensor outputs without estimating intermediate parameters, resulting in higher location accuracy and avoiding the data association.

Method for enhancing single-trial P300 detection by introducing the complexity degree of image information in rapid serial visual presentation tasks.

The application of electroencephalogram (EEG) generated by human viewing images is a new thrust in image retrieval technology. A P300 component in the EEG is induced when the subjects see their point of interest in a target image under the rapid serial visual presentation (RSVP) experimental paradigm. We detected the single-trial P300 component to determine whether a subject was interested in an image.

SCM: A method to improve network service layout efficiency with network evolution.

Network services are an important component of the Internet, which are used to expand network functions for third-party developers. Network function virtualization (NFV) can improve the speed and flexibility of network service deployment. However, with the evolution of the network, network service layout may become inefficient.

Optical sparse aperture imaging with faint objects using improved spatial modulation diversity.

The next generation of optical sparse aperture systems will provide high angular resolution for astronomical observations. Spatial modulation diversity (SMD) is a newly developed post-processing technique for such telescopes, faced with challenges of imaging faint objects, which are very attractive for astronomers but always make raw diversity images suffer serious photon noise. In this paper, we propose an improved SMD with denoising reprocessing embedded to address the problem.

A decoy chain deployment method based on SDN and NFV against penetration attack.

Penetration attacks are one of the most serious network security threats. However, existing network defense technologies do not have the ability to entirely block the penetration behavior of intruders. Therefore, the network needs additional defenses.

Block matching sparsity regularization-based image reconstruction for incomplete projection data in computed tomography.

In medical imaging many conventional regularization methods, such as total variation or total generalized variation, impose strong prior assumptions which can only account for very limited classes of images. A more reasonable sparse representation frame for images is still badly needed. Visually understandable images contain meaningful patterns, and combinations or collections of these patterns can be utilized to form some sparse and redundant representations which promise to facilitate image reconstructions.

Temporary formation of highly conducting domain walls for non-destructive read-out of ferroelectric domain-wall resistance switching memories.

Erasable conductive domain walls in insulating ferroelectric thin films can be used for non-destructive electrical read-out of the polarization states in ferroelectric memories. Still, the domain-wall currents extracted by these devices have not yet reached the intensity and stability required to drive read-out circuits operating at high speeds. This study demonstrated non-destructive read-out of digital data stored using specific domain-wall configurations in epitaxial BiFeO thin films formed in mesa-geometry structures.

Emotion Recognition from EEG Signals Using Multidimensional Information in EMD Domain.

This paper introduces a method for feature extraction and emotion recognition based on empirical mode decomposition (EMD). By using EMD, EEG signals are decomposed into Intrinsic Mode Functions (IMFs) automatically. Multidimensional information of IMF is utilized as features, the first difference of time series, the first difference of phase, and the normalized energy.

Multirapid Serial Visual Presentation Framework for EEG-Based Target Detection.

Target image detection based on a rapid serial visual presentation (RSVP) paradigm is a typical brain-computer interface system with various applications, such as image retrieval. In an RSVP paradigm, a P300 component is detected to determine target images. This strategy requires high-precision single-trial P300 detection methods.

Percolation-theoretic bounds on the cache size of nodes in mobile opportunistic networks.

The node buffer size has a large influence on the performance of Mobile Opportunistic Networks (MONs). This is mainly because each node should temporarily cache packets to deal with the intermittently connected links. In this paper, we study fundamental bounds on node buffer size below which the network system can not achieve the expected performance such as the transmission delay and packet delivery ratio.

Sparse-view image reconstruction via total absolute curvature combining total variation for X-ray computed tomography.

Sparse-view imaging is a promising scanning approach which has fast scanning rate and low-radiation dose in X-ray computed tomography (CT). Conventional L1-norm based total variation (TV) has been widely used in image reconstruction since the advent of compressive sensing theory. However, with only the first order information of the image used, the TV often generates dissatisfactory image for some applications.

Performance Analysis of the Direct Position Determination Method in the Presence of Array Model Errors.

The direct position determination approach was recently presented as a promising technique for the localization of a transmitting source with accuracy higher than that of the conventional two-step localization method. In this paper, the theoretical performance of a direct position determination estimator proposed by Weiss is examined for situations in which the array model errors are present. Our study starts from a matrix eigen-perturbation result, which expresses the perturbation of eigenvalues as a function of the disturbance added to the Hermitian matrix.

A Synthetic Recombinase-Based Feedback Loop Results in Robust Expression.

Accurate control of a biological process is essential for many critical functions in biology, from the cell cycle to proteome regulation. To achieve this, negative feedback is frequently employed to provide a highly robust and reliable output. Feedback is found throughout biology and technology, but due to challenges posed by its implementation, it is yet to be widely adopted in synthetic biology.

A weighted difference of L1 and L2 on the gradient minimization based on alternating direction method for circular computed tomography.

Iterative reconstruction algorithms for computed tomography (CT) through total variation (TV) regularization can provide accurate and stable reconstruction results. TV minimization is the L1-norm of gradient-magnitude images and can be regarded as a convex relaxation method to replace the L0 norm. In this study, a fast and efficient algorithm, which is named a weighted difference of L1 and L2 (L1 - αL2) on the gradient minimization, was proposed and investigated.

A top-down manner-based DCNN architecture for semantic image segmentation.

Given their powerful feature representation for recognition, deep convolutional neural networks (DCNNs) have been driving rapid advances in high-level computer vision tasks. However, their performance in semantic image segmentation is still not satisfactory. Based on the analysis of visual mechanism, we conclude that DCNNs in a bottom-up manner are not enough, because semantic image segmentation task requires not only recognition but also visual attention capability.

Innovation & evaluation of tangible direct manipulation digital drawing pens for children.

Focusing on the theme of direct manipulation, in this study, we proposed a new and innovative tangible user interface (TUI) design concept for a manipulative digital drawing pen. Based on interviews with focus groups brainstorming and experts and the results of a field survey, we selected the most suitable tangible user interface for children between 4 and 7 years of age. Using the new tangible user interface, children could choose between the brush tools after touching and feeling the various patterns.

Modularized compact positron emission tomography detector for rapid system development.

We report the development of a modularized compact positron emission tomography (PET) detector that outputs serial streams of digital samples of PET event pulses via an Ethernet interface using the UDP/IP protocol to enable rapid configuration of a PET system by connecting multiple such detectors via a network switch to a computer. Presently, the detector is [Formula: see text] in extent (excluding I/O connectors) and contains an [Formula: see text] array of [Formula: see text] one-to-one coupled lutetium-yttrium oxyorthosilicate/silicon photomultiplier pixels. It employs cross-wire and stripline readouts to merge the outputs of the 216 detector pixels to 24 channels.

Artificial Neuron Based on Integrated Semiconductor Quantum Dot Mode-Locked Lasers.

Neuro-inspired implementations have attracted strong interest as a power efficient and robust alternative to the digital model of computation with a broad range of applications. Especially, neuro-mimetic systems able to produce and process spike-encoding schemes can offer merits like high noise-resiliency and increased computational efficiency. Towards this direction, integrated photonics can be an auspicious platform due to its multi-GHz bandwidth, its high wall-plug efficiency and the strong similarity of its dynamics under excitation with biological spiking neurons.

Design, Modeling, and Fabrication of Chemical Vapor Deposition Grown MoS Circuits with E-Mode FETs for Large-Area Electronics.

Two-dimensional electronics based on single-layer (SL) MoS offers significant advantages for realizing large-scale flexible systems owing to its ultrathin nature, good transport properties, and stable crystalline structure. In this work, we utilize a gate first process technology for the fabrication of highly uniform enhancement mode FETs with large mobility and excellent subthreshold swing. To enable large-scale MoS circuit, we also develop Verilog-A compact models that accurately predict the performance of the fabricated MoS FETs as well as a parametrized layout cell for the FET to facilitate the design and layout process using computer-aided design (CAD) tools.

A promising limited angular computed tomography reconstruction via segmentation based regional enhancement and total variation minimization.

X-ray computed tomography (CT) is a powerful and common inspection technique used for the industrial non-destructive testing. However, large-sized and heavily absorbing objects cause the formation of artifacts because of either the lack of specimen penetration in specific directions or the acquisition of data from only a limited angular range of views. Although the sparse optimization-based methods, such as the total variation (TV) minimization method, can suppress artifacts to some extent, reconstructing the images such that they converge to accurate values remains difficult because of the deficiency in continuous angular data and inconsistency in the projections.

Self-regulating positive emotion networks by feedback of multiple emotional brain states using real-time fMRI.

Disordered emotion regulation may affect work efficiency, induce social disharmony, and even cause psychiatric diseases. Despite recent neurocomputing advances, whether positive and negative emotion networks can be voluntarily modulated is still unknown. In the present study, we addressed this question through multivariate voxel pattern analysis and real-time functional MRI neurofeedback (rtfMRI-nf).

Quantitative study on exact reconstruction sampling condition by verifying solution uniqueness in limited-view CT.

In limited-view computed tomography reconstruction, iterative image reconstruction with sparsity-exploiting methods, such as total variation (TV) minimization, inspired by compressive sensing, potentially claims large reductions in sampling requirements. However, a quantitative notion of this claim is non-trivial because of the ill-defined reduction in sampling achieved by the sparsity-exploiting method. In this paper, exact reconstruction sampling condition for limited-view problem is studied by verifying the uniqueness of solution in TV minimization model.