Mean Shift-Based Mobile Localization Method in Mixed LOS/NLOS Environments for Wireless Sensor Network

Mobile localization estimation is a significant research topic in the fields of wireless sensor network (WSN), which is of concern greatly in the past decades. Non-line-of-sight (NLOS) propagation seriously decreases the positioning accuracy if it is not considered when the mobile localization algorithm is designed. NLOS propagation has been a serious challenge. This paper presents a novel mobile localization method in order to overcome the effects of NLOS errors by utilizing the mean shift-based Kalman filter. The binary hypothesis is firstly carried out to detect the measurements which contain the NLOS errors. For NLOS propagation condition, mean shift algorithm is utilized to evaluate the means of the NLOS measurements and the data association method is proposed to mitigate the NLOS errors. Simulation results show that the proposed method can provide higher location accuracy in comparison with some traditional methods

Single-Tooth Modeling for 3D Dental Model

An integrated single-tooth modeling scheme is proposed for the 3D dental model acquired by optical digitizers. The cores of the modeling scheme are fusion regions extraction, single tooth shape restoration, and single tooth separation. According to the “valley” shape-like characters of the fusion regions between two adjoining teeth, the regions of the 3D dental model are analyzed and classified based on the minimum curvatures of the surface. The single tooth shape is restored according to the bioinformation along the hole boundary, which is generated after the fusion region being removed. By using the extracted boundary from the blending regions between the teeth and soft tissues as reference, the teeth can be separated from the 3D dental model one by one correctly. Experimental results show that the proposed method can achieve satisfying modeling results with high-degree approximation of the real tooth and meet the requirements of clinical oral medicine

Visualizing the Invisible: Occluded Vehicle Segmentation and Recovery

In this paper, we propose a novel iterative multi-task framework to complete the segmentation mask of an occluded vehicle and recover the appearance of its invisible parts. In particular, to improve the quality of the segmentation completion, we present two coupled discriminators and introduce an auxiliary 3D model pool for sampling authentic silhouettes as adversarial samples. In addition, we propose a two-path structure with a shared network to enhance the appearance recovery capability. By iteratively performing the segmentation completion and the appearance recovery, the results will be progressively refined. To evaluate our method, we present a dataset, the Occluded Vehicle dataset, containing synthetic and real-world occluded vehicle images. We conduct comparison experiments on this dataset and demonstrate that our model outperforms the state-of-the-art in tasks of recovering segmentation mask and appearance for occluded vehicles. Moreover, we also demonstrate that our appearance recovery approach can benefit the occluded vehicle tracking in real-world videos

Haar Graph Pooling

Deep Graph Neural Networks (GNNs) are useful models for graph classification and graph-based regression tasks. In these tasks, graph pooling is a critical ingredient by which GNNs adapt to input graphs of varying size and structure. We propose a new graph pooling operation based on compressive Haar transforms -- HaarPooling. HaarPooling implements a cascade of pooling operations; it is computed by following a sequence of clusterings of the input graph. A HaarPooling layer transforms a given input graph to an output graph with a smaller node number and the same feature dimension; the compressive Haar transform filters out fine detail information in the Haar wavelet domain. In this way, all the HaarPooling layers together synthesize the features of any given input graph into a feature vector of uniform size. Such transforms provide a sparse characterization of the data and preserve the structure information of the input graph. GNNs implemented with standard graph convolution layers and HaarPooling layers achieve state of the art performance on diverse graph classification and regression problems.Comment: 14 pages, 4 figures, 7 tables; Published in ICML202

Degradation Data-Driven Remaining Useful Life Estimation in the Absence of Prior Degradation Knowledge

Recent developments in prognostic and health management have been targeted at utilizing the observed degradation signals to estimate residual life distributions. Current degradation models mainly focus on a population of “identical” devices or an individual device with population information, not a single component in the absence of prior degradation knowledge. However, the fast development of science and technology provides us with many kinds of new systems, and we just have the real-time monitoring information to analyze the reliability for them. The fusion algorithm presented herein addresses this challenge by combining the excellent modeling ability of Bayesian updating method for the multilevel data and the prominent estimation ability of ECM algorithm for incomplete data. Residual life distributions and posterior distributions are first calculated through the Bayesian updating method based on random initial a priori distributions. Then the a priori distributions are revised and improved for future predictions by the ECM algorithm. Once a new signal is observed, we can reuse the fusion algorithm to improve the accuracy of residual life distributions. The applicability of this fusion algorithm is validated by a set of simulation experiments

Concave-convex local binary features for automatic target recognition in infrared imagery

This paper presents a novel feature extraction algorithm based on the local binary features for automatic target recognition (ATR) in infrared imagery. Since the inception of the local binary pattern (LBP) and local ternary pattern (LTP) features, many extensions have been proposed to improve their robustness and performance in a variety of applications. However, most attentions were paid to improve local feature extraction with little consideration on the incorporation of global or regional information. In this work, we propose a new concave-convex partition (CCP) strategy to improve LBP and LTP by dividing local features into two distinct groups, i.e., concave and convex, according to the contrast between local and global intensities. Then two separate histograms built from the two categories are concatenated together to form a new LBP/LTP code that is expected to better reflect both global and local information. Experimental results on standard texture images demonstrate the improved discriminability of the proposed features and those on infrared imagery further show that the proposed features can achieve competitive ATR results compared with state-of-the-art methods.Peer reviewedElectrical and Computer Engineerin

Toll-Like Receptor 4 Reduces Oxidative Injury via Glutathione Activity in Sheep

Toll-like receptor 4 (TLR4) is an important sensor of Gram-negative bacteria and can trigger activation of the innate immune system. Increased activation of TLR4 can lead to the induction of oxidative stress. Herein, the pathway whereby TLR4 affects antioxidant activity was studied. In TLR4-overexpressing sheep, TLR4 expression was found to be related to the integration copy number when monocytes were challenged with lipopolysaccharide (LPS). Consequently, production of malondialdehyde (MDA) was increased, which could increase the activation of prooxidative stress enzymes. Meanwhile, activation of an antioxidative enzyme, glutathione peroxidase (GSH-Px), was increased. Real-time PCR showed that expression of activating protein-1 (AP-1) and the antioxidative-related genes was increased. By contrast, the expression levels of superoxide dismutase 1 (SOD1) and catalase (CAT) were reduced. In transgenic sheep, glutathione (GSH) levels were dramatically reduced. Furthermore, transgenic sheep were intradermally injected with LPS in each ear. The amounts of inflammatory infiltrates were correlated with the number of TLR4 copies that were integrated in the genome. Additionally, the translation of γ-glutamylcysteine synthetase (γ-GCS) was increased. Our findings indicated that overexpression of TLR4 in sheep could ameliorate oxidative injury through GSH secretion that was induced by LPS stimulation. Furthermore, TLR4 promoted γ-GCS translation through the AP-1 pathway, which was essential for GSH synthesis

Evolution of the Mass-Metallicity Relation from Redshift z≈8z\approx8 to the Local Universe

A tight positive correlation between the stellar mass and the gas-phase metallicity of galaxies has been observed at low redshifts. The redshift evolution of this correlation can strongly constrain theories of galaxy evolution. The advent of JWST allows probing the mass-metallicity relation at redshifts far beyond what was previously accessible. Here we report the discovery of two emission-line galaxies at redshifts 8.15 and 8.16 in JWST NIRCam imaging and NIRSpec spectroscopy of targets gravitationally lensed by the cluster RXJ2129.4$+$0005. We measure their metallicities and stellar masses along with nine additional galaxies at $7.2 < z_{\rm spec} < 9.5$ to report the first quantitative statistical inference of the mass-metallicity relation at $z\approx8$. We measure $\sim 0.9$ dex evolution in the normalization of the mass-metallicity relation from $z \approx 8$ to the local Universe; at fixed stellar mass, galaxies are 8 times less metal enriched at $z \approx 8$ compared to the present day. Our inferred normalization is in agreement with the predictions of the FIRE simulations. Our inferred slope of the mass-metallicity relation is similar to or slightly shallower than that predicted by FIRE or observed at lower redshifts. We compare the $z \approx 8$ galaxies to extremely low metallicity analog candidates in the local Universe, finding that they are generally distinct from extreme emission-line galaxies or "green peas" but are similar in strong emission-line ratios and metallicities to "blueberry galaxies". Despite this similarity, at fixed stellar mass, the $z \approx 8$ galaxies have systematically lower metallicities compared to blueberry galaxies.Comment: Published in Ap