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Emotions are important factors in decision-making. With the advent of brain-computer interface (BCI) techniques, researchers developed a strong interest in predicting decisions based on emotions, which is a challenging task. To predict decision-making performance using emotion, we have proposed the Maximizing Mutual Information between Emotion and Decision relevant features (MMI-ED) method, with three modules: (1) Temporal-spatial encoding module captures spatial correlation and temporal dependence from electroencephalogram (EEG) signals; (2) Relevant feature decomposition module extracts emotion-relevant features and decision-relevant features; (3) Relevant feature fusion module maximizes the mutual information to incorporate useful emotion-related feature information during the decision-making prediction process. To construct a dataset that uses emotions to predict decision-making performance, we designed an experiment involving emotion elicitation and decision-making tasks and collected EEG, behavioral, and subjective data. We performed a comparison of our model with several emotion recognition and motion imagery models using our dataset. The results demonstrate that our model achieved state-of-the-art performance, achieving a classification accuracy of 92.96 $\%$ % . This accuracy is 6.83 $\%$ % higher than the best-performing model. Furthermore, we conducted an ablation study to demonstrate the validity of each module and provided explanations for the brain regions associated with the relevant features. [ABSTRACT FROM AUTHOR]

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 dierent 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 dierent 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 eective 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. [ABSTRACT FROM AUTHOR]

Flexible and stretchable circuit is the key element for the upcoming flexible hybrid electronics in addition to high performance silicon based active devices and passive components. The circuitry and structure design of the circuit carrier have to be able to withstand the stress and tolerate biometric signal variation induced by external force. These circuit carriers can be fabricated by use of the flexible display technologies and facilities. In this study, we integrated ICs, passive components and sensors to form a system upon flexible/plastic circuit carrier. Bendable and/or stretchable smart textiles and patches were successful demonstrated with the functionality of electromyography (EMG) and electrocardiography (ECG). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

3D display has become increasingly popular in many fields. It can provide incredible immersion compared with 2D display. There are many researches referring to the change of user about no matter subjective experience or objective physiology signal while watching 3D content. Similarly, in this paper, we study the variation of user's EEG when the user watched 3D content. However, we only focus on parallax cue and avoid other cues of 3D content by using Random Dot Stereogram(RDS) as visual stimulus. We designed an experiment which asked the subject to watch RDS with nine kinds of parallaxes(0,±0.5,±1.1,±1.3,±1.7). The users watched one RDS for 1 minute and then closed eyes and rest for 1 minute every time. During watching, EEG signal with 30‐channel was recorded. The result suggests that significant variation of the user's EEG data occurred in the Alpha (8–13Hz) and Beta(14–30Hz) frequency band by different parallax's comparison. And we found not only parallax but also electrode position can impact the user's EEG data significantly. Accurately, the EEG data has a significant change in six electrodes position in both frequency bands including CZ, PZ, OZ, O2 in international 10–20 system. It means those electrode positions we detect can be regarded as typical positions where we record EEG signal. It will simplify the process of EEG signal acquisition when we need to study the relationship between parallax cue of 3D display and the user further. [ABSTRACT FROM AUTHOR]

In this paper, we designed an experiment to observe changes for different fatigue level in human brain when watching random dot stereogram (RDS) using the method of EEG. The subjects were asked to watch 3D content for 30 minutes and the disparity of RDS changed every 10 seconds. During this watching task, subjects should rate their extent of fatigue during the experiment on the scale of one to five only when they felt worse or better. 30 channels electroencephalogram (EEG) signals were recorded through the experiment. Finally, the relationship between various fatigue levels and disparities which revealed by EEG signals were investigated. Our results show that the power of waveband, namely alpha increased with the fatigue level increasing from one to four during watching RDS, but decreased in level five. We have employed logistic regression to model fatigue level and achieved classification accuracy of 94.18%. Our research has important implications for the continuous monitoring of user's fatigue in the future. [ABSTRACT FROM AUTHOR]

This article introduces the first comprehensive demonstration of new innovative technology comprising multiple key technologies for highly cost-effective and high-performance Xilinx field programmable gate array (FPGA), which is so-called stack silicon-less interconnect technology (SLIT) that provides the equivalent high-bandwidth connectivity and routing design-rule as stack silicon interconnect (SSI) technology at a cost-effective manner. We have successfully demonstrated the overall process integration and functions of our new SLIT-employed package using Virtex®-7 2000T FPGA product with chip-to-wafer stacking, wafer-level flux cleaning, microbump underfilling, mold encapsulation, and backside silicon removal. Of all technology elements, both full silicon removal process with faster etching and no dielectric layer damage and wafer warpage management after full silicon etching are most crucial elements to realize the SLIT technology. To manage the wafer warpage after full Si removal, a couple of knobs are identified and used such as top reinforcement layer, microbump underfill properties tuning, die thickness, die-to-die space, and total thickness adjustments. It is also discussed in the article how the wafer warpage behaves and how the wafer warpage is managed. New SLIT module shows excellent warpage characteristics of only -30 mm ~ -40 mm at room temperature (25°C) for 25 mm x 31 mm in size and +20 mm ~ +25 mm at reflow temperature (250°C). Thermal simulation results shows that thermal resistance of new SLIT package is almost comparable to that of standard 2000T flip-chip ball grid array (FC-BGA) package using through silicon via interposer with standard heat sink configuration and air wind condition. The reliability assessment is now under the study. [ABSTRACT FROM AUTHOR]

In computer vision and pattern recognition applications, there are usually a vast number of unlabelled data whereas the labelled data are very limited. Active learning is a kind of method that selects the most representative or informative examples for labelling and training; thus, the best prediction accuracy can be achieved. A novel active learning algorithm is proposed here based on one-versus-one strategy support vector machine (SVM) to solve multi-class image classification. A new uncertainty measure is proposed based on some binary SVM classifiers and some of the most uncertain examples are selected from SVM output. To ensure that the selected examples are diverse from each other, Gaussian kernel is adopted to measure the similarity between any two examples. From the previous selected examples, a batch of diverse and uncertain examples are selected by the dynamic programming method for labelling. The experimental results on two datasets demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Processor-embedded disks, or smart disks, with their network interface controller, can in effect be viewed as processing elements with on-disk memory and secondary storage. The data sizes and access patterns of today''s large I/O-intensive workloads require architectures whose processing power scales with increased storage capacity. To address this concern, we propose and evaluate disk-based distributed smart storage architectures. Based on analytically derived performance models, our evaluation with representative workloads show that offloading processing and performing point-to-point data communication improve performance over centralized architectures. Our results also demonstrate that distributed smart disk systems exhibit desirable scalability and can efficiently handle I/O-intensive workloads, such as commercial decision support database (TPC-H) queries, association rules mining, data clustering, and two-dimensional fast Fourier transform, among others. [Copyright &y& Elsevier]

Processor-embedded disks, or smart disks, with their network interface controller, can in effect be viewed as processing elements with on-disk memory and secondary storage. The data sizes and access patterns of today''s large I/O-intensive workloads require architectures whose processing power scales with increased storage capacity. To address this concern, we propose and evaluate disk-based distributed smart storage architectures. Based on analytically derived performance models, our evaluation with representative workloads show that offloading processing and performing point-to-point data communication improve performance over centralized architectures. Our results also demonstrate that distributed smart disk systems exhibit desirable scalability and can efficiently handle I/O-intensive workloads, such as commercial decision support database (TPC-H) queries, association rules mining, data clustering, and two-dimensional fast Fourier transform, among others. [Copyright &y& Elsevier]

In this study, three single-chamber microbial fuel cells (MFCs), each having Pt-coated carbon cloth as a cathode and four bamboo charcoal (BC) plates as an anode, were run in a fed-batch mode, individually and in series. Simulated potato-processing wastewater was used as a substrate for supporting the growth of a mixed bacterial culture. The maximum power output increased from 0.386 mW with one MFC to 1.047 mW with three MFCs connected in series. The maximum power density, however, decreased from 576 mW/m2 (normalized to the cathode area) with one MFC to 520 mW/m2 with three MFCs in series. The experimental results showed that power can be increased by connecting the MFCs in series; however, choosing low resistance BC is crucial for increasing power density. [ABSTRACT FROM AUTHOR]