Effect of sparsity-aware time–frequency analysis on dynamic hand gesture classification with radar micro-Doppler signatures

Dynamic hand gesture recognition is of great importance in human-computer interaction. In this study, the authors investigate the effect of sparsity-driven time-frequency analysis on hand gesture classification. The time-frequency spectrogram is first obtained by sparsity-driven time-frequency analysis. Then three empirical micro-Doppler features are extracted from the time-frequency spectrogram and a support vector machine is used to classify six kinds of dynamic hand gestures. The experimental results on measured data demonstrate that, compared to traditional time-frequency analysis techniques, sparsity-driven time-frequency analysis provides improved accuracy and robustness in dynamic hand gesture classification

Dynamic Hand Gesture Classification Based on Radar Micro-Doppler Signatures

Dynamic hand gesture recognition is of great importance for human-computer interaction. In this paper, we present a method to discriminate the four kinds of dynamic hand gestures, snapping fingers, flipping fingers, hand rotation and calling, using a radar micro-Doppler sensor. Two micro-Doppler features are extracted from the time-frequency spectrum and the support vector machine is used to classify these four kinds of gestures. The experimental results on measured data demonstrate that the proposed method can produce a classification accuracy higher than 88.56%

Multi-source adversarial transfer learning based on similar source domains with local features

Transfer learning leverages knowledge from other domains and has been successful in many applications. Transfer learning methods rely on the overall similarity of the source and target domains. However, in some cases, it is impossible to provide an overall similar source domain, and only some source domains with similar local features can be provided. Can transfer learning be achieved? In this regard, we propose a multi-source adversarial transfer learning method based on local feature similarity to the source domain to handle transfer scenarios where the source and target domains have only local similarities. This method extracts transferable local features between a single source domain and the target domain through a sub-network. Specifically, the feature extractor of the sub-network is induced by the domain discriminator to learn transferable knowledge between the source domain and the target domain. The extracted features are then weighted by an attention module to suppress non-transferable local features while enhancing transferable local features. In order to ensure that the data from the target domain in different sub-networks in the same batch is exactly the same, we designed a multi-source domain independent strategy to provide the possibility for later local feature fusion to complete the key features required. In order to verify the effectiveness of the method, we made the dataset "Local Carvana Image Masking Dataset". Applying the proposed method to the image segmentation task of the proposed dataset achieves better transfer performance than other multi-source transfer learning methods. It is shown that the designed transfer learning method is feasible for transfer scenarios where the source and target domains have only local similarities.Comment: Submitted to Information Fusio

Multi-source adversarial transfer learning for ultrasound image segmentation with limited similarity

Lesion segmentation of ultrasound medical images based on deep learning techniques is a widely used method for diagnosing diseases. Although there is a large amount of ultrasound image data in medical centers and other places, labeled ultrasound datasets are a scarce resource, and it is likely that no datasets are available for new tissues/organs. Transfer learning provides the possibility to solve this problem, but there are too many features in natural images that are not related to the target domain. As a source domain, redundant features that are not conducive to the task will be extracted. Migration between ultrasound images can avoid this problem, but there are few types of public datasets, and it is difficult to find sufficiently similar source domains. Compared with natural images, ultrasound images have less information, and there are fewer transferable features between different ultrasound images, which may cause negative transfer. To this end, a multi-source adversarial transfer learning network for ultrasound image segmentation is proposed. Specifically, to address the lack of annotations, the idea of adversarial transfer learning is used to adaptively extract common features between a certain pair of source and target domains, which provides the possibility to utilize unlabeled ultrasound data. To alleviate the lack of knowledge in a single source domain, multi-source transfer learning is adopted to fuse knowledge from multiple source domains. In order to ensure the effectiveness of the fusion and maximize the use of precious data, a multi-source domain independent strategy is also proposed to improve the estimation of the target domain data distribution, which further increases the learning ability of the multi-source adversarial migration learning network in multiple domains.Comment: Submitted to Applied Soft Computing Journa

Combined Toxicity Evaluation of Ochratoxin A and Aflatoxin B1 on Kidney and Liver Injury, Immune Inflammation, and Gut Microbiota Alteration Through Pair-Feeding Pullet Model

Ochratoxin A (OTA) and aflatoxin B1 (AFB1) are often co-contaminated, but their synergistic toxicity in poultry is limitedly described. Furthermore, the traditional ad libitum feeding model may fail to distinguish the specific impact of mycotoxins on the biomarkers and the indirect effect of mildew on the palatability of feed. A pair-feeding model was introduced to investigate the specific effect and the indirect effect of the combined toxicity of OTA and AFB1, which were independent and dependent on feed intake, respectively. A total of 180 one-day-old pullets were randomly divided into 3 groups with 6 replicates, and each replicate contained 10 chicks. The control group (Group A) and the pair-feeding group (Group B) received the basal diet without mycotoxin contamination. Group C was administrated with OTA- and AFB1-contaminated feed (101.41 μg/kg of OTA + 20.10 μg/kg of AFB1). The scale of feeding in Group B matched with the feed intake of Group C. The trial lasted 42 days. Compared with the control group, co-contamination of OTA and AFB1 in feed could adversely affect the growth performance (average daily feed intake (ADFI), body weight (BW), average daily weight gain (ADG), feed conversion ratio (FCR), and shank length (SL)), decrease the relative weight of the spleen (p < 0.01), and increase the relative weight of the kidney (p < 0.01). Moreover, the reduction of feed intake could also adversely affect the growth performance (BW, ADG, and SL), but not as severely as mycotoxins do. Apart from that, OTA and AFB1 also activated the antioxidative and inflammation reactions of chicks, increasing the level of catalase (CAT), reactive oxygen species (ROS), and interleukin-8 (IL-8) while decreasing the level of IL-10 (p < 0.01), which was weakly influenced by the feed intake reduction. In addition, OTA and AFB1 induced histopathological changes and apoptosis in the kidney and liver as well as stimulated the growth of pernicious bacteria to cause toxic effects. There were no histopathological changes and apoptosis in the kidney and liver of the pair-feeding group. The combined toxicity of OTA and AFB1 had more severe effects on pullets than merely reducing feed supply. However, the proper reduction of the feed intake could improve pullets’ physical health by enriching the bacteria Lactobacillus, Phascolarctobacterium, Bacteroides, Parabacteroides, and Barnesiella

The Generalized Quadratic Gauss Sum and Its Fourth Power Mean

In this article, our main purpose is to introduce a new and generalized quadratic Gauss sum. By using analytic methods, the properties of classical Gauss sums, and character sums, we consider the calculating problem of its fourth power mean and give two interesting computational formulae for it