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It is common for people to make bad decisions because of their emotions in life. When these decisions are important, such as aeronautical decisions and driving decisions, the mistakes of decisions can cause irreversible damage. Therefore, it is important to explore how emotions influence decision-making, so as to avoid the negative influence of emotions on decision-making as much as possible. Although existing researchers have found some mechanisms of emotion's influence on decision-making, only a few studies focused on the influence of emotions on decision-making based on electroencephalography (EEG). In addition, most of them were focused on risky and uncertain decision-making. We designed a novel experimental task to explore the influence of emotion on spatial decision-making and recorded subjective data, decision-making behavioral data, and EEG data. By analyzing these data, we came to three conclusions. Firstly, we observed three similar event-related potentials (ERP) microstates in the decision-making process under different emotions by microstate analysis. Additionally, the prefrontal, parietal and occipital lobes played key roles in decision-making. Secondly, we found that the P2 component of the prefrontal lobe presented the influence of different emotions on decision-making by ERP analysis. Among them, positive emotion evoked the largest P2 amplitude compared to negative emotions and no stimuli. Thirdly, we found some graph metrics that were significantly associated with decision accuracy by effective connectivity analysis combined with graph theoretic analysis. In consequence, the finding of our study may shed more light on the brain mechanisms underlying the influence of emotions on spatial decision-making, thereby providing a basis for avoiding decision-making accidents caused by emotions and realizing better decision-making.

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Prolonged viewing of 3D content may result in severe fatigue symptoms, giving negative user experience thus hindering the development of 3D industry. For 3D visual fatigue evaluation, previous studies focused on exploring the changes of frequency-domain features in EEG for various fatigue degrees. However, their time-domain features were scarcely investigated. In this study, a modified paradigm with a random disparities order is adopted to evoke the depth-related visual evoked potentials (DVEPs). Then the characteristics of the DVEPs components for various fatigue degrees are compared using one-way repeated-measurement ANOVA. Point-by-point permutation statistics revealed sample points from 100ms to 170ms - including P1 and N1 - in sensors Pz and P4 changed significantly with visual fatigue. More specifically, we find that the amplitudes of P1 and N1 change significantly when visual fatigue increases. Additionally, independent component analysis identify P1 and N1 which originate from posterior cingulate cortex are associated statistically with 3D visual fatigue. Our results indicate there is a significant correlation between 3D visual fatigue and P1 amplitude, as well as N1, of DVEPs on right parietal areas. We believe the characteristics (e.g., amplitude and latency) of identified components may be the indicators of 3D visual fatigue evaluation. Furthermore, we argue that 3D visual fatigue may be associated with the activities decrease of the attention and the processing capacity of disparity.

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Compared to Beowulf clusters and shared-memory machines, GPU and FPGA are emerging alternative architectures that provide massive parallelism and great computational capabilities. These architectures can be utilized to run compute-intensive algorithms to analyze ever-enlarging datasets and provide scalability. In this paper, we present four implementations of K-means data clustering algorithm for different high performance computing platforms. These four implementations include a CUDA implementation for GPUs, a Mitrion C implementation for FPGAs, an MPI implementation for Beowulf compute clusters, and an OpenMP implementation for shared-memory machines. The comparative analyses of the cost of each platform, difficulty level of programming for each platform, and the performance of each implementation are presented.

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Estimating skeletal muscle (finger) forces using surface Electromyography (sEMG) signals poses many challenges. In general, the sEMG measurements are based on single sensor data. In this paper, two novel hybrid fusion techniques for estimating the skeletal muscle force from the sEMG array sensors are proposed. The sEMG signals are pre-processed using five different filters: Butterworth, Chebychev Type II, Exponential, Half-Gaussian and Wavelet transforms. Dynamic models are extracted from the acquired data using Nonlinear Wiener Hammerstein (NLWH) models and Spectral Analysis Frequency Dependent Resolution (SPAFDR) models based system identification techniques. A detailed comparison is provided for the proposed filters and models using 18 healthy subjects. Wavelet transforms give higher mean correlation of 72.6 ± 1.7 (mean ± SD) and 70.4 ± 1.5 (mean ± SD) for NLWH and SPAFDR models, respectively, when compared to the other filters used in this work. Experimental verification of the fusion based hybrid models with wavelet transform shows a 96% mean correlation and 3.9% mean relative error with a standard deviation of ± 1.3 and ± 0.9 respectively between the overall hybrid fusion algorithm estimated and the actual force for 18 test subjects' k-fold cross validation data.

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In this paper, we present a method of combining spectral models using a Kullback Information Criterion (KIC) data fusion algorithm. Surface Electromyographic (sEMG) signals and their corresponding skeletal muscle force signals are acquired from three sensors and pre-processed using a Half-Gaussian filter and a Chebyshev Type- II filter, respectively. Spectral models - Spectral Analysis (SPA), Empirical Transfer Function Estimate (ETFE), Spectral Analysis with Frequency Dependent Resolution (SPFRD) - are extracted from sEMG signals as input and skeletal muscle force as output signal. These signals are then employed in a System Identification (SI) routine to establish the dynamic models relating the input and output. After the individual models are extracted, the models are fused by a probability based KIC fusion algorithm. The results show that the SPFRD spectral models perform better than SPA and ETFE models in modeling the frequency content of the sEMG/skeletal muscle force data.

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This paper presents a surface electromyographic (sEMG)-based, optimal control strategy for a prosthetic hand. System Identification (SI) is used to obtain the dynamic relation between the sEMG and the corresponding skeletal muscle force. The input sEMG signal is preprocessed using a Half-Gaussian filter and fed to a fusion-based Multiple Input Single Output (MISO) skeletal muscle force model. This MISO system model provides the estimated finger forces to be produced as input to the prosthetic hand. Optimal tracking method has been applied to track the estimated force profile of the Fusion based sEMG-force model. The simulation results show good agreement between reference force profile and the actual force.

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Extracting or estimating skeletal hand/finger forces using surface electro myographic (sEMG) signals poses many challenges due to cross-talk, noise, and a temporal and spatially modulated signal characteristics. Normal sEMG measurements are based on single sensor data. In this paper, array sensors are used along with a proposed sensor fusion scheme that result in a simple Multi-Input-Single-Output (MISO) transfer function. Experimental data is used along with system identification to find this MISO system. A Genetic Algorithm (GA) approach is employed to optimize the characteristics of the MISO system. The proposed fusion-based approach is tested experimentally and indicates improvement in finger/hand force estimation.

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We report an approach for determining the structure of macromolecular assemblies by the combined application of cryo-electron microscopy (cryo-EM) and site-directed spin labeling electron paramagnetic resonance spectroscopy (EPR). This approach is illustrated for Hsp16.5, a small heat shock protein that prevents the aggregation of nonnative proteins. The structure of Hsp16.5 has been previously studied by both cryo-EM and X-ray crystallography. The crystal structure revealed a roughly spherical protein shell with dodecameric symmetry; however, residues 1-32 were found to be disordered. The cryo-EM reconstruction at 13 A resolution appeared similar to the crystal structure but with additional internal density corresponding to the N-terminal regions of the 24 subunits. In this study, a systematic application of site-directed spin labeling and EPR spectroscopy was carried out. By combining the EPR constraints from spin label accessibilities and proximities with the cryo-EM density, we obtained an atomic model for a portion of the Hsp16.5 N-terminal region in the context of the oligomeric complex.

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