Human Activity Recognition Using Deep Learning Networks with Enhanced Channel State Information

© 2018 IEEE. Channel State Information (CSI) is widely used for device free human activity recognition. Feature extraction remains as one of the most challenging tasks in a dynamic and complex environment. In this paper, we propose a human activity recognition scheme using Deep Learning Networks with enhanced Channel State information (DLN-eCSI). We develop a CSI feature enhancement scheme (CFES), including two modules of background reduction and correlation feature enhancement, for preprocessing the data input to the DLN. After cleaning and compressing the signals using CFES, we apply the recurrent neural networking (RNN) to automatically extract deeper features and then the softmax regression algorithm for activity classification. Extensive experiments are conducted to validate the effectiveness of the proposed scheme

Joint Blind Motion Deblurring and Depth Estimation of Light Field

Removing camera motion blur from a single light field is a challenging task since it is highly ill-posed inverse problem. The problem becomes even worse when blur kernel varies spatially due to scene depth variation and high-order camera motion. In this paper, we propose a novel algorithm to estimate all blur model variables jointly, including latent sub-aperture image, camera motion, and scene depth from the blurred 4D light field. Exploiting multi-view nature of a light field relieves the inverse property of the optimization by utilizing strong depth cues and multi-view blur observation. The proposed joint estimation achieves high quality light field deblurring and depth estimation simultaneously under arbitrary 6-DOF camera motion and unconstrained scene depth. Intensive experiment on real and synthetic blurred light field confirms that the proposed algorithm outperforms the state-of-the-art light field deblurring and depth estimation methods

Three-Dimensional Spectral-Domain Optical Coherence Tomography Data Analysis for Glaucoma Detection

Purpose: To develop a new three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) data analysis method using a machine learning technique based on variable-size super pixel segmentation that efficiently utilizes full 3D dataset to improve the discrimination between early glaucomatous and healthy eyes. Methods: 192 eyes of 96 subjects (44 healthy, 59 glaucoma suspect and 89 glaucomatous eyes) were scanned with SD-OCT. Each SD-OCT cube dataset was first converted into 2D feature map based on retinal nerve fiber layer (RNFL) segmentation and then divided into various number of super pixels. Unlike the conventional super pixel having a fixed number of points, this newly developed variable-size super pixel is defined as a cluster of homogeneous adjacent pixels with variable size, shape and number. Features of super pixel map were extracted and used as inputs to machine classifier (LogitBoost adaptive boosting) to automatically identify diseased eyes. For discriminating performance assessment, area under the curve (AUC) of the receiver operating characteristics of the machine classifier outputs were compared with the conventional circumpapillary RNFL (cpRNFL) thickness measurements. Results: The super pixel analysis showed statistically significantly higher AUC than the cpRNFL (0.855 vs. 0.707, respectively, p = 0.031, Jackknife test) when glaucoma suspects were discriminated from healthy, while no significant difference was found when confirmed glaucoma eyes were discriminated from healthy eyes. Conclusions: A novel 3D OCT analysis technique performed at least as well as the cpRNFL in glaucoma discrimination and even better at glaucoma suspect discrimination. This new method has the potential to improve early detection of glaucomatous damage. © 2013 Xu et al

The large terminase DNA packaging motor grips DNA with its ATPase domain for cleavage by the flexible nuclease domain

Many viruses use a powerful terminase motor to pump their genome inside an empty procapsid shell during virus maturation. The large terminase (TerL) protein contains both enzymatic activities necessary for packaging in such viruses: the adenosine triphosphatase (ATPase) that powers DNA translocation and an endonuclease that cleaves the concatemeric genome at both initiation and completion of genome packaging. However, how TerL binds DNA during translocation and cleavage remains mysterious. Here we investigate DNA binding and cleavage using TerL from the thermophilic phage P74-26. We report the structure of the P74-26 TerL nuclease domain, which allows us to model DNA binding in the nuclease active site. We screened a large panel of TerL variants for defects in binding and DNA cleavage, revealing that the ATPase domain is the primary site for DNA binding, and is required for nuclease activity. The nuclease domain is dispensable for DNA binding but residues lining the active site guide DNA for cleavage. Kinetic analysis of DNA cleavage suggests flexible tethering of the nuclease domains during DNA cleavage. We propose that interactions with the procapsid during DNA translocation conformationally restrict the nuclease domain, inhibiting cleavage; TerL release from the capsid upon completion of packaging unlocks the nuclease domains to cleave DNA

A perceptual quality metric for 3D triangle meshes based on spatial pooling

© 2018, Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature. In computer graphics, various processing operations are applied to 3D triangle meshes and these processes often involve distortions, which affect the visual quality of surface geometry. In this context, perceptual quality assessment of 3D triangle meshes has become a crucial issue. In this paper, we propose a new objective quality metric for assessing the visual difference between a reference mesh and a corresponding distorted mesh. Our analysis indicates that the overall quality of a distorted mesh is sensitive to the distortion distribution. The proposed metric is based on a spatial pooling strategy and statistical descriptors of the distortion distribution. We generate a perceptual distortion map for vertices in the reference mesh while taking into account the visual masking effect of the human visual system. The proposed metric extracts statistical descriptors from the distortion map as the feature vector to represent the overall mesh quality. With the feature vector as input, we adopt a support vector regression model to predict the mesh quality score.We validate the performance of our method with three publicly available databases, and the comparison with state-of-the-art metrics demonstrates the superiority of our method. Experimental results show that our proposed method achieves a high correlation between objective assessment and subjective scores

Ordered silicon nanocones arrays for label-free DNA quantitative analysis by surface-enhanced Raman spectroscopy

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Transmission efficiency and noise, vibration and harshness refinement of differential hypoid gear pairs

This article presents a combined multi-body dynamics and lubricated contact mechanics model of vehicular differential hypoid gear pairs, demonstrating the transient nature of transmission efficiency and noise, vibration and harshness performance under various driving conditions. The contact of differential hypoid gears is subjected to mixed thermo-elastohydrodynamic regime of lubrication. The coefficient of friction is obtained using an analytical approach for non-Newtonian lubricant shear and supplemented by boundary interactions for thin films. Additionally, road data and aerodynamic effects are used in the form of resisting torque applied to the output side of the gear pair. Sinusoidal engine torque variation is also included to represent engine order torsional input resident on the pinion gear. Analysis results are presented for New European Driving Cycle transience from low-speed city driving condition in second gear to steady-state cruising in fourth gear for a light truck. It is shown that the New European Driving Cycle captures the transmission efficiency characteristics of the differential hypoid gear pair under worst case scenario, with its underlying implications for fuel efficiency and emissions. However, it fails to address the other key attribute, being the noise, vibration and harshness performance. In the case of hypoid gears, the resultant noise, vibration and harshness characteristics can be particularly annoying. It is concluded that broader transient manoeuvres encompassing New European Driving Cycle are required for assessment, in order to obtain a balanced approach for transmission efficiency and noise, vibration and harshness performance. This approach is undertaken in this article, which is not hitherto reported in the literature