基于单试验脑电图的n-back任务中的脑力负荷分类

近年来,脑力负荷估计已经经历了广泛的研究,因为监测认知负荷的能力能够防止认知超负荷并且改善工作场所安全。脑电图（EEG）信号已经被发现是一种客观和非侵入性的脑力负荷的测量方式。然而,作为实时脑力负荷监测和脑机接口研究的重要一步,基于单试验EEG数据的认知负荷的评估一直是一个重大的挑战。最近,许多高级的特征提取方法和机器学习算法已经被采用于基于EEG的脑力负荷评估中。在本研究中,使用在具有2个难度水平的n back任务的执行期间记录的EEG数据进行了单试验脑力负荷分类,测试了3种类型的特征提取的有效性（谱功率、离散小波变换和公共空间滤波）,并评估了4种分类算法的性能（支持向量机、K 近邻、随机森林和梯度推进分类器）。研究结果表明,公共空间滤波是性能最好的基于单试验的脑力负荷分类的特征提取方法,而且最佳性能可以通过将来自谱功率或离散小波变换的特征与来自公共空间滤波的特征相结合,并采用随机森林分类器来实现。这项研究可能对基于单试验脑电图数据的脑力负荷评估中的特征提取方法以及机器学习算法的选择提供一些有用的指导。     

Correction to: Automatic stress analysis from facial videos based on deep facial action units recognition

Pattern Analysis and Applications - A correction to this paper has been published: 10.1007/s10044-021-01012-9     

Development of a decision support tool for the assessment of biofuels

Alternative fuels for the transport sector are gaining growing attention as a means against fossil fuel dependence and towards greener forms of energy. At the same time, however, they are surrounded with doubts concerning sustainability of their production. This work presents the basic framework for a decision support tool to evaluate biofuel production pathways, with the purpose of providing the decision maker with a structured methodology that will lead him to the final decision. The tool integrates the most important aspects along the entire value chain (i.e. from biomass production to biofuel end-use), namely the technical, economic, environmental and social aspect. The tool consists of a computational part, which can be combined with the personal preferences of the user. The analysis provides a score for the respective pathway that can be used to rank different options and select among them the optimal solution. The functionality of the tool has been tested for the case of biodiesel from rapeseed in Germany.     

A study of in situ plated tin-film electrodes for the determination of trace metals by means of square-wave anodic stripping voltammetry

This work is a study of tin-film electrodes (SnFEs) for the determination of trace metals by means of square-wave anodic stripping voltammetry (SWASV). SnFEs represent a new promising type of environment-friendly electrodes but their analytical performance has not been explored in detail so far. Initially, the operational parameters for the simultaneous determination of Cd(II) and Zn(II) on in situ plated SnFEs on different substrates were thoroughly investigated. Using the selected conditions, at a preconcentration time of 120 s the limits of detection were 0.7 μg l⁻¹ for Cd and 0.9 μg l⁻¹ for Zn and the percent relative standard deviations were 4.2% for Zn(II) and 3.6% for Cd(II) at the 20 μg l⁻¹ level (n = 8). Subsequently, a study was conducted to investigate the utility of SnFEs as sensors for the determination of other metals, including those with oxidation potentials close to, or more positive than, the oxidation potential of Sn (Tl(I), Pb(II), Cu(II)). Finally, the SnFEs were successfully applied for the determination of Pb(II) and Zn(II) in tap water.     

Spatiotemporal analysis of human activities for biometric authentication

This paper presents a novel framework for unobtrusive biometric authentication based on the spatiotemporal analysis of human activities. Initially, the subject’s actions that are recorded by a stereoscopic camera, are detected utilizing motion history images. Then, two novel unobtrusive biometric traits are proposed, namely the static anthropometric profile that accurately encodes the inter-subject variability with respect to human body dimensions, while the activity related trait that is based on dynamic motion trajectories encodes the behavioral inter-subject variability for performing a specific action. Subsequently, score level fusion is performed via support vector machines. Finally, an ergonomics-based quality indicator is introduced for the evaluation of the authentication potential for a specific trial. Experimental validation on data from two different datasets, illustrates the significant biometric authentication potential of the proposed framework in realistic scenarios, whereby the user is unobtrusively observed, while the use of the static anthropometric profile is seen to significantly improve performance with respect to state-of-the-art approaches.     

Kernel-Based Positioning in Wireless Local Area Networks

The recent proliferation of location-based services (LBSs) has necessitated the development of effective indoor positioning solutions. In such a context, wireless local area network (WLAN) positioning is a particularly viable solution in terms of hardware and installation costs due to the ubiquity of WLAN infrastructures. This paper examines three aspects of the problem of indoor WLAN positioning using received signal strength (RSS). First, we show that, due to the variability of RSS features over space, a spatially localized positioning method leads to improved positioning results. Second, we explore the problem of access point (AP) selection for positioning and demonstrate the need for further research in this area. Third, we present a kernelized distance calculation algorithm for comparing RSS observations to RSS training records. Experimental results indicate that the proposed system leads to a 17 percent (0.56 m) improvement over the widely used K-nearest neighbor and histogram-based methods     

Broadband multimedia on the move with DVB-H

DVB-H is the newly standardized extension to DVB-T, aiming at the provision of IP datacasting (IPDC) services to mobile terminals. This tutorial paper outlines the structure of an interactive DVB-H platform and presents in brief the technical advances of the new specification. It also discusses some interesting scenarios of interactive and non-interactive services which can be directly deployed with the use of the DVB-H technology either as a stand-alone broadcast network or as a complement to existing cellular (2G/3G/WLAN) infrastructures.     

Effect of heat treatment on internal atmosphere and leaf extension of minimally processed leek stalks

Leeks trimmed at 20 cm from their base were immersed in a 55 °C hot water bath. During the heat treatment (HT), the internal gases released from each stalk were collected, and their volume as well as CO₂ and O₂ concentrations were determined. After HT, cooling and storage at 10 °C for 8 days, the weight, maximum leaf extension and internal atmosphere (volume and gas composition) were measured. It was found that HT inhibited leaf extension during storage at 10 °C for 8 days and induced significant modification of the stalk internal atmosphere, both immediately after HT and after storage. Therefore, in heat‐treated leek stalks, the growth zone of the innermost leaves was subjected to an environment of elevated CO₂ concentration, which was accompanied by the inhibition of leaf extension growth.     

Microfabricated Ion-Selective Transistors with Fast and Super-Nernstian Response

Transistor-based ion sensors have evolved significantly, but the best-performing ones rely on a liquid electrolyte as an internal ion reservoir between the ion-selective membrane and the channel. This liquid reservoir makes sensor miniaturization difficult and leads to devices that are bulky and have limited mechanical flexibility, which is holding back the development of high-performance wearable/implantable ion sensors. This work demonstrates microfabricated ion-selective organic electrochemical transistors (OECTs) with a transconductance of 4 mS, in which a thin polyelectrolyte film with mobile sodium ions replaces the liquid reservoir. These devices are capable of selective detection of various ions with a fast response time (≈1 s), a super-Nernstian sensitivity (85 mV dec⁻¹), and a high current sensitivity (224 µA dec⁻¹), comparing favorably to other ion sensors based on traditional and emerging materials. Furthermore, the ion-selective OECTs are stable with highly reproducible sensitivity even after 5 months. These characteristics pave the way for new applications in implantable and wearable electronics.     

Prediction of student’s mood during an online test using formula-based and neural network-based method

Building computerized mechanisms that will accurately, immediately and continually recognize a learner’s affective state and activate an appropriate response based on integrated pedagogical models is becoming one of the main aims of artificial intelligence in education. The goal of this paper is to demonstrate how the various kinds of evidence could be combined so as to optimize inferences about affective states during an online self-assessment test. A formula-based method has been developed for the prediction of students’ mood, and it was tested using data emanated from experiments made with 153 high school students from three different regions of a European country. The same set of data is analyzed developing a neural network method. Furthermore, the formula-based method is used as an input parameter selection module for the neural network method. The results vindicate to a great degree the formula-based method’s assumptions about student’s mood and indicate that neural networks and conventional algorithmic methods should not be in competition but complement each other for the development of affect recognition systems. Moreover, it becomes apparent that neural networks can provide an alternative for and improvements over tutoring systems’ affect recognition methods.