A Novel Adaptive Spectrum Noise Cancellation Approach for Enhancing Heartbeat Rate Monitoring in a Wearable Device

This paper presents a novel approach, Adaptive Spectrum Noise Cancellation (ASNC), for motion artifacts removal in Photoplethysmography (PPG) signals measured by an optical biosensor to obtain clean PPG waveforms for heartbeat rate calculation. One challenge faced by this optical sensing method is the inevitable noise induced by movement when the user is in motion, especially when the motion frequency is very close to the target heartbeat rate. The proposed ASNC utilizes the onboard accelerometer and gyroscope sensors to detect and remove the artifacts adaptively, thus obtaining accurate heartbeat rate measurement while in motion. The ASNC algorithm makes use of a commonly accepted spectrum analysis approaches in medical digital signal processing, discrete cosine transform, to carry out frequency domain analysis. Results obtained by the proposed ASNC have been compared to the classic algorithms, the adaptive threshold peak detection and adaptive noise cancellation. The mean (standard deviation) absolute error and mean relative error of heartbeat rate calculated by ASNC is 0.33 (0.57) beats·min-1 and 0.65%, by adaptive threshold peak detection algorithm is 2.29 (2.21) beats·min-1 and 8.38%, by adaptive noise cancellation algorithm is 1.70 (1.50) beats·min-1 and 2.02%. While all algorithms performed well with both simulated PPG data and clean PPG data collected from our Verity device in situations free of motion artifacts, ASNC provided better accuracy when motion artifacts increase, especially when motion frequency is very close to the heartbeat rate

An adaptive ensemble approach to ambient intelligence assisted people search

Some machine learning algorithms have shown a better overall recognition rate for facial recognition than humans, provided that the models are trained with massive image databases of human faces. However, it is still a challenge to use existing algorithms to perform localized people search tasks where the recognition must be done in real time, and where only a small face database is accessible. A localized people search is essential to enable robot–human interactions. In this article, we propose a novel adaptive ensemble approach to improve facial recognition rates while maintaining low computational costs, by combining lightweight local binary classifiers with global pre-trained binary classifiers. In this approach, the robot is placed in an ambient intelligence environment that makes it aware of local context changes. Our method addresses the extreme unbalance of false positive results when it is used in local dataset classifications. Furthermore, it reduces the errors caused by affine deformation in face frontalization, and by poor camera focus. Our approach shows a higher recognition rate compared to a pre-trained global classifier using a benchmark database under various resolution images, and demonstrates good efficacy in real-time tasks

Prior knowledge-based deep learning method for indoor object recognition and application

Indoor object recognition is a key task for indoor navigation by mobile robots. Although previous work has produced impressive results in recognizing known and familiar objects, the research of indoor object recognition for robot is still insufficient. In order to improve the detection precision, our study proposed a prior knowledge-based deep learning method aimed to enable the robot to recognize indoor objects on sight. First, we integrate the public Indoor dataset and the private frames of videos (FoVs) dataset to train a convolutional neural network (CNN). Second, mean images, which are used as a type of colour knowledge, are generated for all the classes in the Indoor dataset. The distance between every mean image and the input image produces the class weight vector. Scene knowledge, which consists of frequencies of occurrence of objects in the scene, is then employed as another prior knowledge to determine the scene weight. Finally, when a detection request is launched, the two vectors together with a vector of classification probability instigated by the deep model are multiplied to produce a decision vector for classification. Experiments show that detection precision can be improved by employing the prior colour and scene knowledge. In addition, we applied the method to object recognition in a video. The results showed potential application of the method for robot vision

Structure and Function Analysis of Cultivated <i>Meconopsis integrifolia</i> Soil Microbial Community Based on High-Throughput Sequencing and Culturability

(1) Background: The structure, function, and community interactions of soil microbial communities of cultivated Meconopsis integrifolia were characterized by studying this alpine flower and traditional endangered Tibetan medicine. (2) Methods: Soil bacteria and fungi were studied based on high-throughput sequencing technology. Bacteria were isolated using culturomics and functionally identified as IAA-producing, organic phosphorus-dissolving, inorganic phosphorus-dissolving, and iron-producing carriers. (3) Results: The dominant bacterial phyla were found to be Proteobacteria and Acidobacteria, and unclassified_Rhizobiales was the most abundant genus. Ascomycota and Mortierellomycota were the dominant fungal phyla. The bacteria were mainly carbon and nitrogen metabolizers, and the fungi were predominantly Saprotroph—Symbiotroph. The identified network was completely dominated by positive correlations, but the fungi were more complex than the bacteria, and the bacterial keystones were unclassified_Caulobacteraceae and Pedobacter. Most of the keystones of fungi belonged to the phyla Ascomycetes and Basidiomycota. The highest number of different species of culturable bacteria belonged to the genus Streptomyces, with three strains producing IAA, 12 strains solubilizing organic phosphorus, one strain solubilizing inorganic phosphorus, and nine strains producing iron carriers. (4) Conclusions: At the cost of reduced ecological stability, microbial communities increase cooperation toward promoting overall metabolic efficiency and enabling their survival in the extreme environment of the Tibetan Plateau. These pioneering results have value for the protection of endangered Meconopsis integrifolia under global warming and the sustainable utilization of its medicinal value

The effects of vacuum-ultraviolet radiation on defects in low-k organosilicate glass (SiCOH) as measured with electron-spin resonance

Defect concentrations in SiCOH low-k dielectrics deposited on high-resistivity silicon substrates were measured with Electron Spin Resonance (ESR). CP4 and HF treatmentswere used in order to eliminate dangling bonds from the backside of the silicon substrate as well as the sample edges. Two kinds of defects with characteristic g = 2.0054–2.0050 and g=2.0018–2.0020were detected in pristine samples and quantified using Lorentzian fitting. The defect with the g factor of 2.0054–2.0050 is likely to be fromthe silicon-dangling bonds. The defect with the g factor of 2.0018–2.0020 is most likely from carbon-related centers. Upon exposure to VUV synchrotron radiation (hν = 12 eV), the concentration of the silicon-dangling bonds is found to increase significantly.status: publishe

Neutrophil Cyto-Pharmaceuticals Suppressing Tumor Metastasis via Inhibiting Hypoxia-Inducible Factor-1α in Circulating Breast Cancer Cells

Circulating tumor cells (CTCs) are reported as the precursor of tumor metastases, implying that stifling CTCs would be beneficial for metastasis prevention. However, challenges remain for the application of therapies that aim at CTCs due to lack of effective CTC-targeting strategy and sensitive therapeutic agents. Herein, a general CTC-intervention strategy based on neutrophil cyto-pharmaceuticals is proposed for suppressing CTC colonization and metastasis formation. Breast cancer 4T1 cells are infused as the mimic CTCs, and 4T1 cells trapped are first elucidated in neutrophil extracellular traps (NETs) expressing high levels of hypoxia-inducible factor-1α (HIF-1α) due to NET formation and thus promoting tumor cell colonization through enhanced migration, invasion and stemness. After verifying HIF-1α as a potential target for metastasis prevention, living neutrophil cyto-pharmaceuticals (CytPNEs) loaded with HIF-1α inhibitor are fabricated to therapeutically inhibit HIF-1α. It is demonstrated that CytPNEs can specially convey the HIF-1α inhibitor to 4T1 cells according to the inflammatory chemotaxis of neutrophils and down-regulate HIF-1α, thereby inhibiting metastasis and prolonging the median survival of mice bearing breast cancer lung metastasis. The research offers a new perspective for understanding the mechanism of CTC colonization, and puts forward the strategy of targeted intervention of CTCs as a meaningful treatment for tumor metastasis.Fil: Zhang, Ying. China Pharmaceutical University; ChinaFil: Wang, Cong. China Pharmaceutical University; ChinaFil: Li, Weishuo. China Pharmaceutical University; ChinaFil: Tian, Wei. China Pharmaceutical University; ChinaFil: Tang, Chunming. China Pharmaceutical University; ChinaFil: Xue, Lingjing. China Pharmaceutical University; ChinaFil: Lin, Ziming. China Pharmaceutical University; ChinaFil: Liu, Guilai. China Pharmaceutical University; ChinaFil: Liu, Dongfei. China Pharmaceutical University; ChinaFil: Zhou, Ying. China Pharmaceutical University; ChinaFil: Wang, Qianqian. China Pharmaceutical University; ChinaFil: Wang, Xu. China Pharmaceutical University; ChinaFil: Birnbaumer, Lutz. Pontificia Universidad Católica Argentina "Santa María de los Buenos Aires". Instituto de Investigaciones Biomédicas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas; ArgentinaFil: Yang, Yong. China Pharmaceutical University; ChinaFil: Li, Xianjing. China Pharmaceutical University; ChinaFil: Ju, Caoyun. China Pharmaceutical University; ChinaFil: Zhang, Can. China Pharmaceutical University; Chin