Wideband DOA Estimation via Sparse Bayesian Learning over a Khatri-Rao Dictionary

This paper deals with the wideband direction-of-arrival (DOA) estimation by exploiting the multiple measurement vectors (MMV) based sparse Bayesian learning (SBL) framework. First, the array covariance matrices at different frequency bins are focused to the reference frequency by the conventional focusing technique and then transformed into the vector form. Then a matrix called the Khatri-Rao dictionary is constructed by using the Khatri-Rao product and the multiple focused array covariance vectors are set as the new observations. DOA estimation is to find the sparsest representations of the new observations over the Khatri-Rao dictionary via SBL. The performance of the proposed method is compared with other well-known focusing based wideband algorithms and the Cramer-Rao lower bound (CRLB). The results show that it achieves higher resolution and accuracy and can reach the CRLB under relative demanding conditions. Moreover, the method imposes no restriction on the pattern of signal power spectral density and due to the increased number of rows of the dictionary, it can resolve more sources than sensors

Research progress on detection methods of N-dimethylnitrosamine in foods

N-dimethylnitrosamine is one of the most toxic nitrosamine compounds and can be produced in the process of food processing or storage. The detection methods are various with tedious operation and low accuracy. QuEChERS pretreatment combined with GC/LC-MS has been widely used in the determination of N-dimethylnitrosamine in food due to its advantages of simple operation， good extraction and purification， high sensitivity， stable recovery and effective improvement of detection rate and throughput. The pretreatment methods， detection equipment and detection parameters of N-dimethylnitrosamine in food were compared to analyze the advantages and disadvantages of different methods

Validity and Reproducibility of a Revised Semiquantitative Food Frequency Questionnaire (SQFFQ) for Women of Age-group 12-44 Years in Chengdu

To find a credible nutritional screening tool for evaluating relationship between nutritional status and diseases in Chengdu female residents, the reliability and validity of a revised semi-quantitative food frequency questionnaire (SQFFQ) were tested. The validity was assessed by comparing the SQFFQ with the \u2018standard\u2019 method of 3 days\u2019 dietary recall, and the reliability was assessed by comparing the first SQFFQ with the second SQFFQ at 4 weeks interval. Correlation analysis showed that, for reliability, the average correlation coefficient (CC) of 22 kinds of nutrients was 0.66 and reduced to 0.60 after adjusting for energy; the average of intra-class correlation coefficients (ICC) was 0.65. For validity, the average CC was 0.35 and remained stable after adjusting for CC of energy or nutrients. Validity of 17 nutrients in SQFFQ survey had correlation with result of 3 days\u2019 dietary recall. The results showed that the revised SQFFQ can be used for investigating the role of nutrients in development of disease in Chengdu female residents

Structural controllability and controlling centrality of temporal networks.

Temporal networks are such networks where nodes and interactions may appear and disappear at various time scales. With the evidence of ubiquity of temporal networks in our economy, nature and society, it's urgent and significant to focus on its structural controllability as well as the corresponding characteristics, which nowadays is still an untouched topic. We develop graphic tools to study the structural controllability as well as its characteristics, identifying the intrinsic mechanism of the ability of individuals in controlling a dynamic and large-scale temporal network. Classifying temporal trees of a temporal network into different types, we give (both upper and lower) analytical bounds of the controlling centrality, which are verified by numerical simulations of both artificial and empirical temporal networks. We find that the positive relationship between aggregated degree and controlling centrality as well as the scale-free distribution of node's controlling centrality are virtually independent of the time scale and types of datasets, meaning the inherent robustness and heterogeneity of the controlling centrality of nodes within temporal networks

The statistical relationship between node's aggregated degree and the average controlling centrality.

<p>(a) HT09 (b) SG-Infectious (c) FudanWIFI. All the temporal networks are the same as those in Fig. 6. Each point in this figure is an average controlling centrality of nodes with the same aggregated degree, and there's a positive relationship between the aggregated degree and its controlling centrality, even with some structural destructions or time evolutions.</p

<b>Notations in the paper.</b>

<p><b>Notations in the paper.</b></p

The sequence of graphs representation of the contacts in Table I.

<p>In each discrete time point, the network has a different formation shown as .</p

The illustration of information propagation on a temporal network.

<p>(a), (b), (c) and (d) denote different networks at different time points, respectively. Red (gray) time points on edges denote the elapsed time, and the black (dark) time points denote the forthcoming time.</p

The specific relationship between node's aggregated degree and controlling centrality.

<p>(a) and (b) Temporal networks generated by the dataset of 'SG-Infectious' (c) and (d) Temporal networks generated by the dataset of 'Fudan WIFI'. Although big nodes (node with larger aggregated degree) tend to own larger controlling centralities, there exist many nodes with larger (smaller) aggregated degree but smaller (larger) controlling centrality, such as circled points in (a), (b) and (d).</p