Motion Gesture Delimiters for Smartwatch Interaction

Smartwatches are increasingly popular in our daily lives. Motion gestures are a common way of interacting with smartwatches, e.g., users can make a movement in the air with their arm wearing the watch to trigger a specific command of the smartwatch. Motion gesture interaction can compensate for the small screen size of the smartwatch to some extent and enrich smartwatch-based interactions. An important aspect of motion gesture interaction lies in how to determine the start and end of a motion gesture. This paper is aimed at selecting gestures as suitable delimiters for motion gesture interaction with the smartwatch. We designed six gestures ("shaking wrist left and right,""shaking wrist up and down,""holding fist and opening,""turning wrist clockwise,""turning wrist anticlockwise,"and "shaking wrist up") and conducted two experiments to compare the performance of these six gestures. Firstly, we used dynamic time warping (DTW) and feature extraction with KNN (K-nearest neighbors) to recognize these six gestures. The average recognition rate of the latter algorithm for the six gestures was higher than that of the former. And with the latter algorithm, the recognition rate for the first three of the six gestures was greater than 98%. According to experiment one, gesture 1 (shaking wrist left and right), gesture 2 (shaking wrist up and down), and gesture 3 (holding fist and opening) were selected as the candidate delimiters. In addition, we conducted a questionnaire data analysis and obtained the same conclusion. Then, we conducted the second experiment to investigate the performance of these three candidate gestures in daily scenes to obtain their misoperation rates. The misoperation rates of two candidate gestures ("shaking wrist left and right"and "shaking wrist up and down") were approximately 0, which were significantly lower than that of the third candidate gesture. Based on the above experimental results, gestures "shaking wrist left and right"and "shaking wrist up and down"are suitable as motion gesture delimiters for smartwatch interaction

Experimental and computational investigations on ring-opening polymerization mechanisms of amide-functional benzoxazines

We observed an unusual low polymerization temperature for the ortho-amide benzoxazine in comparison with its para-isomer. Density functional theory (DFT) calculations suggested that the intramolecular hydrogen bond between the oxazine ring and the adjacent amide softens the C–O bond, resulting in a reduced activation energy and thus a low ring-opening polymerization temperature. In addition, the polymerization kinetics of both para- and ortho-amide functional benzoxazines were investigated using the Starink method, which confirmed a relatively lower activation energy for the ortho-amide functional benzoxazine compared with its para-isomer. Our work suggests that softening chemical bonds by intramolecular hydrogen bonding may become a new strategy for the design of high-performance polybenzoxazine thermosets with low processing temperatures. Graphical abstract: [Figure not available: see fulltext.

Experimental and computational investigations on ring-opening polymerization mechanisms of amide-functional benzoxazines

We observed an unusual low polymerization temperature for the ortho-amide benzoxazine in comparison with its para-isomer. Density functional theory (DFT) calculations suggested that the intramolecular hydrogen bond between the oxazine ring and the adjacent amide softens the C–O bond, resulting in a reduced activation energy and thus a low ring-opening polymerization temperature. In addition, the polymerization kinetics of both para- and ortho-amide functional benzoxazines were investigated using the Starink method, which confirmed a relatively lower activation energy for the ortho-amide functional benzoxazine compared with its para-isomer. Our work suggests that softening chemical bonds by intramolecular hydrogen bonding may become a new strategy for the design of high-performance polybenzoxazine thermosets with low processing temperatures. Graphical abstract: [Figure not available: see fulltext.

Extraction and characterization of phenolic compounds and their potential antioxidant activities

AbstractFor thousands of years, plant has been widely applied in the medical area and is an important part of human diet. A high content of nutrients could be found in all kinds of plants, and the most outstanding group of nutrients that attracts scientists’ attention is the high level of phenolic compounds. Due to the relationship between high phenolic compound content and high antioxidant capacity, plant extracts are expected to become a potential treatment for oxidation stress diseases including diabetes and cancer. However, according to the instability of phenolic compounds to light and oxygen, there are certain difficulties in the extraction of such compounds. But after many years of development, the extraction technology of phenolic compounds has been quite stable, and the only problem is how to obtain high-quality extracts with high efficiency. To further enhance the value of plant extracts, concentration and separation methods are often applied, and when detailed analysis is required, characterization methods including HPLC and LC/GC–MS will be applied to evaluate the number and type of phenolic compounds. A series of antioxidant assays are widely performed in numerous studies to test the antioxidant capacity of the plant extracts, which is also an important basis for evaluating value of extracts. This paper intends to provide a view of a variety of methods used in plants’ phenolic compound extraction, separation, and characterization. Furthermore, this review presents the advantages and disadvantages of techniques involved in phenolic compound research and provides selected representative bibliographic examples

Uncertainty-Aware Multiview Deep Learning for Internet of Things Applications

As an essential approach in many Internet of Things (IoT) applications, multiview learning synthesizes multiple features to achieve more comprehensive descriptions of data items. Most of the previous studies on multiview learning have been dedicated to increasing the prediction accuracy, while ignoring the reliability of the decision. This would limit their deployment in high-risk IoT and industrial applications such as the automated vehicle. Although a trusted multiview classification model has been proposed recently, it cannot well deal with the highly complementary multiview data. In this work, we present an evidential multiview deep learning (EMDL) method to make reliable decisions. EMDL first seeks view-specific evidence of each category, which could be termed as the amount of support to each category collected from data. It then dynamically fuses different views at the evidence level to construct the multiview common evidence and makes reliable prediction accordingly (strong evidence indicates high prediction confidence). In particular, we establish a degradation layer to learn the mappings from the common evidence (comprehensive information) to view-specific evidences (partial information) for evidence fusion. It aims to explicitly model consistent and complementary relations in multiview data at the evidence level. We apply EMDL on a synthetic toy dataset and five real-world datasets (three datasets are related to industrial scenarios). Experiments show that EMDL outperforms state-of-the-art baseline methods

User On-demand Driven MEC Servers Deployment from Collaborative Device-Edge-Cloud Network

User On-demand Driven MEC Servers Deployment from Collaborative Device-Edge-Cloud Networ

Experimental study on the damping property of aluminum alloy latticed shells with gusset joints

In order to investigate the damping property of aluminum alloy latticed shells with gusset joints, as well as to fill a blank in the existing design codes which do not give clear damping ratio value of this kind of structures, experimental tests were carried out on an aluminum alloy latticed shell with gusset joints. The structural dynamic responses were aroused by hammer impact, and were recorded by vibration sensors. The acceleration response frequency spectra at nodes were obtained through FFT method, and the structural damping ratio was calculated by half-power bandwidth method. In the experiments, the different strength of hummer impact, different locations of vibration sensors and different excitation places were considered, a total number of 57 test load cases were designed and executed, and a series of damping ratios were gotten from the experiments. A statistic study was carried out on the data given by the tests, then an average damping ratio was suggested for this kind of structures, that is, ξ=3.3%. A finite element model of the tested structure was established using the suggested damping ratio, and the nodal dynamic responses given by numerical analysis show good consistency with those given by the tests. It is demonstrated that the damping ratio given in the paper could serve the purpose of the dynamic response analysis and engineer design of aluminum alloy latticed shells with gusset joints

Underwater image enhancement using adaptive color restoration and dehazing

Underwater images captured by optical cameras can be degraded by light attenuation and scattering, which leads to deteriorated visual image quality. The technique of underwater image enhancement plays an important role in a wide range of subsequent applications such as image segmentation and object detection. To address this issue, we propose an underwater image enhancement framework which consists of an adaptive color restoration module and a haze-line based dehazing module. First, we employ an adaptive color restoration method to compensate the deteriorated color channels and restore the colors. The color restoration module consists of three steps: background light estimation, color recognition, and color compensation. The background light estimation determines the image is blueish or greenish, and the compensation is applied in red-green or red-blue channels. Second, the haze-line technique is employed to remove the haze and enhance the image details. Experimental results show that the proposed method can restore the color and remove the haze at the same time, and it also outperforms several state-of-the-art methods on three publicly available datasets. Moreover, experiments on an underwater object detection dataset show that the proposed underwater image enhancement method is able to improve the accuracy of the subsequent underwater object detection framework

Unbalanced Multi-view Deep Learning

Most existing multi-view learning methods assume that the dimensions of different views are similar. In real-world applications, it is often the case that the dimension of a view may be extremely small compared with these of other views, resulting in an unbalanced multi-view learning problem. Previous methods for this problem have at least one of the following drawbacks: (1) despising the information of low dimensional views; (2) constructing balanced view-specific inter-instance similarity graphs or employing decision-level fusion, which cannot well learn multi-level inter-view correlations and is limited to category-related tasks such as clustering. To eliminate all these drawbacks, we present an Unbalanced Multi-view Deep Learning (UMDL) method. Considering a low dimensional view usually contains multiple patterns, we construct an overcomplete dictionary with its atoms exceeding the dimension of the original data. We transfer the original data into a combination of atoms and obtain a higher dimensional representation. We propose a sparse multi-view fusion paradigm to explicitly capture the complementarity of multi-view data in a flexible manner. Moreover, we construct positive and negative examples via balanced similarity graphs and employ contrastive learning to train UMDL in a self-supervised manner. Experiments conducted on a toy example and 7 balanced/unbalanced datasets show that UMDL outperforms baseline methods and can be well applied to downstream classification and segmentation tasks. The code is released at https://github.com/xdmvteam/UMDL

Underwater image enhancement using adaptive color restoration and dehazing

Underwater images captured by optical cameras can be degraded by light attenuation and scattering, which leads to deteriorated visual image quality. The technique of underwater image enhancement plays an important role in a wide range of subsequent applications such as image segmentation and object detection. To address this issue, we propose an underwater image enhancement framework which consists of an adaptive color restoration module and a haze-line based dehazing module. First, we employ an adaptive color restoration method to compensate the deteriorated color channels and restore the colors. The color restoration module consists of three steps: background light estimation, color recognition, and color compensation. The background light estimation determines the image is blueish or greenish, and the compensation is applied in red-green or red-blue channels. Second, the haze-line technique is employed to remove the haze and enhance the image details. Experimental results show that the proposed method can restore the color and remove the haze at the same time, and it also outperforms several state-of-the-art methods on three publicly available datasets. Moreover, experiments on an underwater object detection dataset show that the proposed underwater image enhancement method is able to improve the accuracy of the subsequent underwater object detection framework