A Signal Processing Algorithm Based on 2D Matched Filtering for SSAR

This study discusses a smart radar antenna scanning mode that combines features of both the sector-scan mode used for conventional radar and the line-scan mode used for synthetic aperture radar (SAR) and achieves an application of the synthetic aperture technique in the conventional sector-scan (mechanically scanned) radar, and we refer to this mode as sector-scan synthetic aperture radar (SSAR). The mathematical model is presented based on the principle of SSAR, and a signal processing algorithm is proposed based on the idea of two-dimensional (2D) matched filtering. The influences of the line-scan range and speed on the SSAR system are analyzed, and the solution to the problem that the target velocity is very high is given. The performance of the proposed algorithm is evaluated through computer simulations. The simulation results indicate that the proposed signal processing algorithm of SSAR can gather the signal energy of targets, thereby improving the ability to detect dim targets

Impact of corporate credit scoring on construction contractors in China

In an attempt to enhance the trustworthiness of contractors and reduce corruption, the China Government has launched a construction contractor credit scoring (CCCS) scheme in Beijing for evaluating the compliance and integrity of contractors registered in the construction market. The contribution of this paper to the Body of Knowledge is to analyze how the incorporation of CCCS may affect general contractors’ present and future competitiveness through a case study in China. The paper analyzes the procurement of 158 building projects tendered in Beijing, involving 2071 local general contractors active in the market. The results show that (1) the contractors’ CCCS scores are important for being awarded large and mega project contracts; (2) CCCS scores have a generally positive effect on future corporate financial income; and (3) that, contrary to expectations, the policy does not increase the CCCS of companies. Finally, it is observed how the changing trend in contractors’ CCCS scores is highly correlated with their initial values (the scores of higher CCCS scoring companies increase faster on average than other companies). Final remarks concern ways to better implement CCCS schemes in the future and avoid the potential risks involved in their use

Early growing season anomalies in vegetation activity determine the large-scale climate-vegetation coupling in Europe

The climate-vegetation coupling exerts a strong control on terrestrial carbon budgets and will affect the future evolution of global climate under continued anthropogenic forcing. Nonetheless, the effects of climatic conditions on such coupling at specific times in the growing season remain poorly understood. We quantify the climate-vegetation coupling in Europe over 1982–2014 at multiple spatial and temporal scales, by decomposing sub-seasonal anomalies of vegetation greenness using a grid-wise definition of the growing season. We base our analysis on long-term vegetation indices (Normalized Difference Vegetation Index and two-band Enhanced Vegetation Index), growing conditions (including 2m temperature, downwards surface solar radiation, and root-zone soil moisture), and multiple teleconnection indices that reflect the large-scale climatic conditions over Europe. We find that the large-scale climate-vegetation coupling during the first two months of the growing season largely determines the full-year coupling. The North Atlantic Oscillation and Scandinavian Pattern phases one-to-two months before the start of the growing season are the dominant and contrasting drivers of the early growing season climate-vegetation coupling over large parts of boreal and temperate Europe. The East Atlantic Pattern several months in advance of the growing season exerts a strong control on the temperate belt and the Mediterranean region. The strong role of early growing season anomalies in vegetative activity within the growing season emphasizes the importance of a grid-wise definition of the growing season when studying the large-scale climate-vegetation coupling in Europe

Statistical Methods for Clustering and High Dimensional Time Series Analysis

Thesis (Ph.D.)--University of Washington, 2022This dissertation mainly explores two statistical tasks, namely clustering and analysis of high-dimensional time series. Clustering, a very important unsupervised learning problem, studies the structure of unlabeled datasets. The goal of clustering is to partition the data points into subsets such that data points in the same subset are similar and different from those in other subsets. Mode-clustering is a clustering analysis method that partitions the data into groups by the local modes of the underlying density function. Sometimes, finding clusters is not the ultimate goal. The connectivity among clusters may yield valuable information for scientists. This dissertation presents a new clustering method inspired by mode-clustering that not only finds clusters but also assigns each cluster with an attribute label. Clusters obtained from our method show connectivity of the underlying distribution. We also design a local two-sample test based on the clustering result that has more power than a conventional method. We apply our method to the Astronomy and GvHD data and show that our method finds meaningful clusters. In addition, we derive the statistical and computational theory of our method. Motivated by the challenges of modeling time series data sets that exhibit non-linear patterns, especially in high dimensions, this dissertation also considers the threshold Auto-Regressive (TAR) process. The TAR process provides a family of non-linear auto-regressive time series models in which the process dynamics are specific step functions of a thresholding variable. While estimation and inference for low-dimensional TAR models have been investigated, high-dimensional TAR models have received less attention. In this dissertation, we develop a new framework for estimating high-dimensional TAR models and propose two different sparsity-inducing penalties. The first penalty corresponds to a natural extension of the classical TAR model to high-dimensional settings, where the same threshold is enforced for all model parameters. Our second penalty develops a more flexible TAR model, where different thresholds are allowed for different auto-regressive coefficients. We show that both penalized estimation strategies can be utilized in a three-step procedure that consistently learns both the thresholds and the corresponding auto-regressive coefficients. However, our theoretical and empirical investigations show that the direct extension of the TAR model is not appropriate for high-dimensional settings and is better suited for moderate dimensions. In contrast, the more flexible extension of the TAR model leads to consistent estimation and superior empirical performance in high dimensions. In addition to the three-step procedure, the dynamic programming approach can successfully handle high dimensions with diverging number of thresholds as well. In particular, extensive numerical analysis and theoretical results demonstrate the advantages of the dynamic programming approach. Finally, we also discuss a method to select the optimal thresholding variable automatically

Refined Mode-Clustering via the Gradient of Slope

In this paper, we propose a new clustering method inspired by mode-clustering that not only finds clusters, but also assigns each cluster with an attribute label. Clusters obtained from our method show connectivity of the underlying distribution. We also design a local two-sample test based on the clustering result that has more power than a conventional method. We apply our method to the Astronomy and GvHD data and show that our method finds meaningful clusters. We also derive the statistical and computational theory of our method

Contractor Recommendation Model Using Credit Networking and Collaborative Filtering

The credit of contractors in the construction market directly affects the cooperative intentions of owners. Although previous scholars have attempted to use credit to select appropriate contractors, they have rarely considered the trust relationship between decision-making and former owners. This work introduces and verifies a credit network recommendation model based on a collaborative filtering algorithm. The contractor’s credit established based on this model serves as a viable method for owners to select efficient contractors. The application of the model includes relevant information collection, neighbor set formation, contractor’s credit evaluation, and recommendation list formation, among which the neighbor set of the owner is used to calculate the comprehensive trust degree of the decision-making owner to the former owner. A time decay function is adopted to correct the difference in the trust relationship between an owner and a contractor introduced over time. To verify the feasibility of this model, an actual scenario was simulated, and the results obtained via simulations were compared and found to be consistent. Thus, a contractor with a high credit can be recommended to the decision-making owner. This approach is crucial for promoting contractors’ credit and conducive to the healthy development of the construction market

Contractor Recommendation Model Using Credit Networking and Collaborative Filtering

The credit of contractors in the construction market directly affects the cooperative intentions of owners. Although previous scholars have attempted to use credit to select appropriate contractors, they have rarely considered the trust relationship between decision-making and former owners. This work introduces and verifies a credit network recommendation model based on a collaborative filtering algorithm. The contractor’s credit established based on this model serves as a viable method for owners to select efficient contractors. The application of the model includes relevant information collection, neighbor set formation, contractor’s credit evaluation, and recommendation list formation, among which the neighbor set of the owner is used to calculate the comprehensive trust degree of the decision-making owner to the former owner. A time decay function is adopted to correct the difference in the trust relationship between an owner and a contractor introduced over time. To verify the feasibility of this model, an actual scenario was simulated, and the results obtained via simulations were compared and found to be consistent. Thus, a contractor with a high credit can be recommended to the decision-making owner. This approach is crucial for promoting contractors’ credit and conducive to the healthy development of the construction market

Multiple-Parameter Estimation Method Based on Spatio-Temporal 2-D Processing for Bistatic MIMO Radar

A novel spatio-temporal 2-dimensional (2-D) processing method that can jointly estimate the transmitting-receiving azimuth and Doppler frequency for bistatic multiple-input multiple-output (MIMO) radar in the presence of spatial colored noise and an unknown number of targets is proposed. In the temporal domain, the cross-correlation of the matched filters’ outputs for different time-delay sampling is used to eliminate the spatial colored noise. In the spatial domain, the proposed method uses a diagonal loading method and subspace theory to estimate the direction of departure (DOD) and direction of arrival (DOA), and the Doppler frequency can then be accurately estimated through the estimation of the DOD and DOA. By skipping target number estimation and the eigenvalue decomposition (EVD) of the data covariance matrix estimation and only requiring a one-dimensional search, the proposed method achieves low computational complexity. Furthermore, the proposed method is suitable for bistatic MIMO radar with an arbitrary transmitted and received geometrical configuration. The correction and efficiency of the proposed method are verified by computer simulation results

Size-dependent cytotoxicity of silver nanoparticles to Azotobacter vinelandii: Growth inhibition, cell injury, oxidative stress and internalization.

The influence of nanomaterials on the ecological environment is becoming an increasingly hot research field, and many researchers are exploring the mechanisms of nanomaterial toxicity on microorganisms. Herein, we studied the effect of two different sizes of nanosilver (10 nm and 50 nm) on the soil nitrogen fixation by the model bacteria Azotobacter vinelandii. Smaller size AgNPs correlated with higher toxicity, which was evident from reduced cell numbers. Flow cytometry analysis further confirmed this finding, which was carried out with the same concentration of 10 mg/L for 12 h, the apoptotic rates were20.23% and 3.14% for 10 nm and 50 nm AgNPs, respectively. Structural damage to cells were obvious under scanning electron microscopy. Nitrogenase activity and gene expression assays revealed that AgNPs could inhibit the nitrogen fixation of A. vinelandii. The presence of AgNPs caused intracellular reactive oxygen species (ROS) production and electron spin resonance further demonstrated that AgNPs generated hydroxyl radicals, and that AgNPs could cause oxidative damage to bacteria. A combination of Ag content distribution assays and transmission electron microscopy indicated that AgNPs were internalized in A. vinelandii cells. Overall, this study suggested that the toxicity of AgNPs was size and concentration dependent, and the mechanism of antibacterial effects was determined to involve damage to cell membranes and production of reactive oxygen species leading to enzyme inactivation, gene down-regulation and death by apoptosis