Distributed Target Tracking and Synchronization in Wireless Sensor Networks

Wireless sensor networks provide useful information for various applications but pose challenges in scalable information processing and network maintenance. This dissertation focuses on statistical methods for distributed information fusion and sensor synchronization for target tracking in wireless sensor networks. We perform target tracking using particle filtering. For scalability, we extend centralized particle filtering to distributed particle filtering via distributed fusion of local estimates provided by individual sensors. We derive a distributed fusion rule from Bayes\u27 theorem and implement it via average consensus. We approximate each local estimate as a Gaussian mixture and develop a sampling-based approach to the nonlinear fusion of Gaussian mixtures. By using the sampling-based approach in the fusion of Gaussian mixtures, we do not require each Gaussian mixture to have a uniform number of mixture components, and thus give each sensor the flexibility to adaptively learn a Gaussian mixture model with the optimal number of mixture components, based on its local information. Given such flexibility, we develop an adaptive method for Gaussian mixture fitting through a combination of hierarchical clustering and the expectation-maximization algorithm. Using numerical examples, we show that the proposed distributed particle filtering algorithm improves the accuracy and communication efficiency of distributed target tracking, and that the proposed adaptive Gaussian mixture learning method improves the accuracy and computational efficiency of distributed target tracking. We also consider the synchronization problem of a wireless sensor network. When sensors in a network are not synchronized, we model their relative clock offsets as unknown parameters in a state-space model that connects sensor observations to target state transition. We formulate the synchronization problem as a joint state and parameter estimation problem and solve it via the expectation-maximization algorithm to find the maximum likelihood solution for the unknown parameters, without knowledge of the target states. We also study the performance of the expectation-maximization algorithm under the Monte Carlo approximations used by particle filtering in target tracking. Numerical examples show that the proposed synchronization method converges to the ground truth, and that sensor synchronization significantly improves the accuracy of target tracking

What you say and how you say it : joint modeling of topics and discourse in microblog conversations

This paper presents an unsupervised framework for jointly modeling topic content and discourse behavior in microblog conversations. Concretely, we propose a neural model to discover word clusters indicating what a conversation concerns (i.e., topics) and those reflecting how participants voice their opinions (i.e., discourse).1 Extensive experiments show that our model can yield both coherent topics and meaningful discourse behavior. Further study shows that our topic and discourse representations can benefit the classification of microblog messages, especially when they are jointly trained with the classifier

Source-independent elastic envelope inversion using the convolution method

Elastic full waveform inversion (EFWI) is a powerful technique. However, its strong non-linearity makes it susceptible to converging towards local extremes during the iterative process due to various factors like insufficient low-frequency information or an inadequate initial model. The existing elastic envelope inversion can offer a promising initial model for EFWI when low-frequency information is unavailable, reducing the dependence on both the initial model and low-frequency data. However, its accuracy is affected by the quality of the source wavelet, potentially causing the EFWI to run in the wrong direction if there is a discrepancy between the simulated wavelet and the field wavelet. To address these issues and enhance the reconstruction of large-scale information in the model, we propose a novel approach called source-independent elastic envelope inversion, employing the convolution method. By combining this method with source-independent multiscale EFWI, we effectively establish P- and S-wave velocity models even in situations with inaccurate wavelet information. The results of testing on a portion of the Marmousi2 model demonstrate the effectiveness of this technique for both full-band and low-frequency missing data scenarios

An overview of Old Tibetan synchronic phonology

Despite the importance of Old Tibetan in the Tibeto-Burman language family, little research has treated Old Tibetan synchronic phonology. This article gives a complete overview of the Old Tibetan phonemic system by associating sound values with the letters of the Tibetan alphabet and exploring the distribution of these sounds in syllable structure

CNS-LAND score: predicting early neurological deterioration after intravenous thrombolysis based on systemic responses and injury

ImportanceEarly neurological deterioration (END) is a critical complication in acute ischemic stroke (AIS) patients receiving intravenous thrombolysis (IVT), with a need for reliable prediction tools to guide clinical interventions.ObjectiveThis study aimed to develop and validate a rating scale, utilizing clinical variables and multisystem laboratory evaluation, to predict END after IVT.Design, setting, and participantsThe Clinical Trial of Revascularization Treatment for Acute Ischemic Stroke (TRAIS) cohort enrolled consecutive AIS patients from 14 stroke centers in China (Jan 2018 to Jun 2022).OutcomesEND defined as NIHSS score increase >4 points or death within 24 h of stroke onset.Results1,213 patients (751 in the derivation cohort, 462 in the validation cohort) were included. The CNS-LAND score, a 9-point scale comprising seven variables (CK-MB, NIHSS score, systolic blood pressure, LDH, ALT, neutrophil, and D-dimer), demonstrated excellent differentiation of END (derivation cohort C statistic: 0.862; 95% CI: 0.796–0.928) and successful external validation (validation cohort C statistic: 0.851; 95% CI: 0.814–0.882). Risk stratification showed END risks of 2.1% vs. 29.5% (derivation cohort) and 2.6% vs. 31.2% (validation cohort) for scores 0–3 and 4–9, respectively.ConclusionCNS-LAND score is a reliable predictor of END risk in AIS patients receiving IVT

Corrigendum: Hu H et al. (2023) Taxonomic and phylogenetic characterisations of six species of Pleosporales (in Didymosphaeriaceae, Roussoellaceae and Nigrogranaceae) from China. MycoKeys 100: 123–151. https://doi.org/10.3897/mycokeys.100.109423

Four new species, Xynobius azonius sp. nov., X. brevifemora sp. nov., X. duoferus sp. nov., and X. stipitoides sp. nov., are described and illustrated, and one species X. geniculatus (Thomson, 1895) is newly reported from South Korea. Xynobius geniculatus (Thomson, 1895) is redescribed and illustrated, and a new combination, Xynobius (Stigmatopoea) cubitalis (Fischer, 1959), comb. nov. is suggested. An identification key to the Xynobius species known from South Korea is provided

Taxonomic and phylogenetic characterisations of six species of Pleosporales (in Didymosphaeriaceae, Roussoellaceae and Nigrogranaceae) from China

Pleosporales comprise a diverse group of fungi with a global distribution and significant ecological importance. A survey on Pleosporales (in Didymosphaeriaceae, Roussoellaceae and Nigrogranaceae) in Guizhou Province, China, was conducted. Specimens were identified, based on morphological characteristics and phylogenetic analyses using a dataset composed of ITS, LSU, SSU, tef1 and rpb2 loci. Maximum Likelihood (ML) and Bayesian analyses were performed. As a result, three new species (Neokalmusia karka, Nigrograna schinifolium and N. trachycarpus) have been discovered, along with two new records for China (Roussoella neopustulans and R. doimaesalongensis) and a known species (Roussoella pseudohysterioides). Morphologically similar species and phylogenetically close taxa are compared and discussed. This study provides detailed information and descriptions of all newly-identified taxa