A hand gesture recognition technique for human–computer interaction

We propose an approach to recognize trajectory-based dynamic hand gestures in real time for human–computer interaction (HCI). We also introduce a fast learning mechanism that does not require extensive training data to teach gestures to the system. We use a six-degrees-of-freedom position tracker to collect trajectory data and represent gestures as an ordered sequence of directional movements in 2D. In the learning phase, sample gesture data is filtered and processed to create gesture recognizers, which are basically finite-state machine sequence recognizers. We achieve online gesture recognition by these recognizers without needing to specify gesture start and end positions. The results of the conducted user study show that the proposed method is very promising in terms of gesture detection and recognition performance (73% accuracy) in a stream of motion. Additionally, the assessment of the user attitude survey denotes that the gestural interface is very useful and satisfactory. One of the novel parts of the proposed approach is that it gives users the freedom to create gesture commands according to their preferences for selected tasks. Thus, the presented gesture recognition approach makes the HCI process more intuitive and user specific.     

Frame detection using gradients fuzzy logic and morphological processing for distant color eye images in an intelligent iris recognition system

The capture of an eye image with the occlusion of spectacles in a non-cooperative environment compromises the accuracy in identifying a person in an iris recognition system. This is due to the obstruction of the iris by the frame which tends to produce an incorrect estimation of the initial center of the iris and the pupil during the iris segmentation process. In addition, it also causes incorrect localization of the upper eyelid during the process of iris segmentation and sometimes, the edges of the frame are wrongly identified as the edges of the upper eyelid. A frame detection method which involves the combination of two gradients, namely the Sobel operator and high pass filter, followed by fuzzy logic and the dilation operation of morphological processing is proposed to identify the frame on the basis of different frame factors in the capture of a distant eye image. In addition, a different color space is applied and only a single channel is used for the process of frame detection. The proposed frame detection method provides the highest frame detection rate compared to the other methods, with a detection rate of more than 80.0%. For the accuracy of the iris localization, upper eyelid localization and iris recognition system, the proposed method gives more than 96.5% accuracy compared to the other methods. The index of decidability showed that the proposed method gives more than 2.35 index compared to the existing methods.     

Younger and older users׳ recognition of virtual agent facial expressions

As technology advances, robots and virtual agents will be introduced into the home and healthcare settings to assist individuals, both young and old, with everyday living tasks. Understanding how users recognize an agent׳s social cues is therefore imperative, especially in social interactions. Facial expression, in particular, is one of the most common non-verbal cues used to display and communicate emotion in on-screen agents (Cassell et al., 2000). Age is important to consider because age-related differences in emotion recognition of human facial expression have been supported (Ruffman et al., 2008), with older adults showing a deficit for recognition of negative facial expressions. Previous work has shown that younger adults can effectively recognize facial emotions displayed by agents (Bartneck and Reichenbach, 2005, Courgeon et al., 2009, Courgeon et al., 2011, Breazeal, 2003); however, little research has compared in-depth younger and older adults’ ability to label a virtual agent׳s facial emotions, an import consideration because social agents will be required to interact with users of varying ages. If such age-related differences exist for recognition of virtual agent facial expressions, we aim to understand if those age-related differences are influenced by the intensity of the emotion, dynamic formation of emotion (i.e., a neutral expression developing into an expression of emotion through motion), or the type of virtual character differing by human-likeness. Study 1 investigated the relationship between age-related differences, the implication of dynamic formation of emotion, and the role of emotion intensity in emotion recognition of the facial expressions of a virtual agent (iCat). Study 2 examined age-related differences in recognition expressed by three types of virtual characters differing by human-likeness (non-humanoid iCat, synthetic human, and human). Study 2 also investigated the role of configural and featural processing as a possible explanation for age-related differences in emotion recognition. First, our findings show age-related differences in the recognition of emotions expressed by a virtual agent, with older adults showing lower recognition for the emotions of anger, disgust, fear, happiness, sadness, and neutral. These age-related difference might be explained by older adults having difficulty discriminating similarity in configural arrangement of facial features for certain emotions; for example, older adults often mislabeled the similar emotions of fear as surprise. Second, our results did not provide evidence for the dynamic formation improving emotion recognition; but, in general, the intensity of the emotion improved recognition. Lastly, we learned that emotion recognition, for older and younger adults, differed by character type, from best to worst: human, synthetic human, and then iCat. Our findings provide guidance for design, as well as the development of a framework of age-related differences in emotion recognition.     

同频段混合信号中的无人机信号盲检测识别

无人机信号的探测识别技术是应对无人机黑飞滥用的关键技术之一。在实际信号监测环境中，经常会接收到多个信号的混合信号，它们在时域和频域上混叠且各信号分量调制样式相同。为解决在同频段混合信号中检测识别出无人机信号的问题，提出了一种通过谱特征分析判断无人机信号存在性的方法。分别采用基于二次方谱特征的无人机图传和WiFi混合信号检测识别算法以及基于频谱带宽特征的多无人机混合信号检测识别算法，通过对射频电路采集的信号进行仿真验证，实现了从同频段混合信号中检测识别出无人机信号分量。理论分析和实验测试结果证实了所提检测识别算法的有效性。     

基于目标跟踪的变电站隔离开关状态图像识别方法

为解决变电站中隔离开关状态的自动识别问题，提出了基于目标跟踪的开关分合状态图像识别方法。首先，基于隔离开关开合过程的动态视频，通过 Ｌａｓｓｏ正则化稀疏表示的目标跟踪模型对隔离开关运动轨迹进行识别；其次，利用隔离开关２个闸刀之间的距离自动识别算法获得开关的分合状态。实测结果表明，该方法可快速跟踪开关运行轨迹，实时对变电站中隔离开关状态进行准确识别。     

压缩合成食品对人体运动能力的影响

研究选取12名生理指标相对类似的试验对象,通过采用自身对照和交叉平衡的试验设计方法,针对普通膳食和以90军用干粮为例的压缩合成食品对人体运动能力的影响进行了分析,通过测定试验对象进食2组不同营养组成的食品进行运动或训练后、恢复体内的血糖浓度水平、血乳酸浓度、血乳酸清除率、心率变化、相关激素水平变化以及不同氧化变化情况评价普通膳食和压缩合成食品对人体运动能力的影响。试验结果显示,相比于普通膳食,压缩合成食品在高强度运动以及力竭运动后维持人体运动能力方面以及体能恢复方面具有明显优越性。     

GCAT-U-Net嵌入全局坐标注意力机制的遥感地块分割网络

遥感影像的地块背景特征复杂,当前地块分割方法不能较好地处理模糊的边缘信息,导致分割精度不理想;文章利用注意力机制处理地块特征,提出了一种基于全局坐标注意力机制的遥感地块分割网络:GCAT-U-Net;该方法在U-Net网络基础上嵌入了全局坐标注意力机制,加强了深度神经网络对于遥感影像数据中重要特征的关注度;在公开的GID数据集上的实验结果表明,文章提出的模型将准确率从0.9041提升到了0.9227,比传统U-Net网络提高了2百分点;结合特征自身重要性和特征位置信息的全局坐标注意力机制有助于更精确的目标定位,其输出相较于嵌入单一注意力机制,地块边界更为清晰,提升效果更为显著。     

System for augmented human–robot interaction through mixed reality and robot training by non-experts in customer service environments

Human–robot interaction during general service tasks in home or retail environment has been proven challenging, partly because (1) robots lack high-level context-based cognition and (2) humans cannot intuit the perception state of robots as they can for other humans. To solve these two problems, we present a complete robot system that has been given the highest evaluation score at the Customer Interaction Task of the Future Convenience Store Challenge at the World Robot Summit 2018, which implements several key technologies: (1) a hierarchical spatial concepts formation for general robot task planning and (2) a mixed reality interface to enable users to intuitively visualize the current state of the robot perception and naturally interact with it. The results obtained during the competition indicate that the proposed system allows both non-expert operators and end users to achieve human–robot interactions in customer service environments. Furthermore, we describe a detailed scenario including employee operation and customer interaction which serves as a set of requirements for service robots and a road map for development. The system integration and task scenario described in this paper should be helpful for groups facing customer interaction challenges and looking for a successfully deployed base to build on.     

Ageing workforce management in manufacturing systems: state of the art and future research agenda

The workforce ageing phenomenon is recently affecting most of the Organisation for Economic Co-operation and Development (OECD) member countries, due to a general ageing of their populations and a higher average retirement age of the workforce. In this paper, the topic of ageing workforce management is addressed from a production research standpoint, with the aim of understanding how older workers can be supported and involved in a manufacturing system. First, the current state of the art related to the ageing workforce in production systems is presented. This is structured according to four main topics: (1) analysis and evaluation of ageing workers’ functional capacities, (2) consideration of ageing workers’ capacities in industrial system modelling and management, (3) analysis and exploitation of ageing workers’ expertise, (4) acknowledgement, analysis, design and integration of supporting technologies. Next, the discussion on the impact of the ageing workforce on manufacturing systems’ performances leads to the comparison of some technological advances that are related to the Industry 4.0 paradigms. Finally, a future research agenda on this topic is proposed, based on the same topics classification proposed for the literature analysis. Five different research areas are derived, suggesting future directions for appropriate research concerning the employ of older workers in production environments.     

Chemical-Shift Perturbations Reflect Bile Acid Binding to Norovirus Coat Protein: Recognition Comes in Different Flavors

Bile acids have been reported as important cofactors promoting human and murine norovirus (NoV) infections in cell culture. The underlying mechanisms are not resolved. Through the use of chemical shift perturbation (CSP) NMR experiments, we identified a low-affinity bile acid binding site of a human GII.4 NoV strain. Long-timescale MD simulations reveal the formation of a ligand-accessible binding pocket of flexible shape, allowing the formation of stable viral coat protein–bile acid complexes in agreement with experimental CSP data. CSP NMR experiments also show that this mode of bile acid binding has a minor influence on the binding of histo-blood group antigens and vice versa. STD NMR experiments probing the binding of bile acids to virus-like particles of seven different strains suggest that low-affinity bile acid binding is a common feature of human NoV and should therefore be important for understanding the role of bile acids as cofactors in NoV infection.