Identifying the most influential roads based on traffic correlation networks

Prediction of traffic congestion is one of the core issues in the realization of smart traffic. Accurate prediction depends on understanding of interactions and correlations between different city locations. While many methods merely consider the spatio-temporal correlation between two locations, here we propose a new approach of capturing the correlation network in a city based on realtime traffic data. We use the weighted degree and the impact distance as the two major measures to identify the most influential locations. A road segment with larger weighted degree or larger impact distance suggests that its traffic flow can strongly influence neighboring road sections driven by the congestion propagation. Using these indices, we find that the statistical properties of the identified correlation network is stable in different time periods during a day, including morning rush hours, evening rush hours, and the afternoon normal time respectively. Our work provides a new framework for assessing interactions between different local traffic flows. The captured correlation network between different locations might facilitate future studies on predicting and controlling the traffic flows. © 2019, The Author(s)

V-Matrix-Based Scalable Data Aggregation Scheme in WSN

Data aggregation is one of the most important functions provided by wireless sensor networks (WSNs). Among a variety of data aggregation schemes, the coding-based approaches (such as Compressive sensing (CS) and other similar programs) can significantly reduce traffic quantity by encoding the raw sensed data using weight vectors. The critical feature to design a coding-based data aggregation protocol is to construct a weight/measurement matrix for the application scenario. After that, the sink node assigns the column of the matrix, which is treated as the weight vector during the encoding process, to each sensor node respectively. However, for a dynamic scenario where the number of sensor nodes changes frequently, the existing approaches have to reconfigure the network by regenerating the measurement matrix and allocating the new weight vectors for all the existing nodes, which causes a considerable energy consumption and affects the regular monitoring tasks. To solve this problem, we propose a Vandermonde matrix-based scalable data aggregation protocol (VSDA), which preserves the advantages of coding-based schemes and addresses the issues mentioned above. In VSDA, as new nodes join into the scaled-up network, the original weight vectors owned by the original nodes do not need to regenerate the weight vectors entirely but add some new entries by itself at all. It outperforms the existing schemes by saving the energy in network scaling-up. Besides, we propose a concise hardware framework to quantify the data encoding process of VSDA, which provides a performance analysis process that is closer to practical application. The numeric tests validate the performance of VSDA compared with the existing schemes in several aspects, such as, the number of transmissions, energy consumption, and storage space showing the outperformance of VSDA scheme

Performance analysis of multi-rate signal processing digital filters on FPGA

Abstract Multi-rate signal processing, an important part of the design of a digital frequency converter, is realized mainly based on interpolation and decimation, which match the sampling rate between the baseband and high-frequency processing side, especially in down conversion. However, the design of a digital filter is important for realizing multi-rate interpolation and decimation, which is highlighted in this paper. To analyze the digital filter performance in multi-rate signal processing, the ordinary finite impulse response (FIR) filter and more efficient digital filter are discussed respectively. The ordinary FIR filters use a Hamming window to design, while a more efficient digital filter includes a cascaded integrate comb (CIC) and half-band filter. Sampling rate transformation factor is 12 in this design, which is cascaded by three stages. Each stage corresponding to the conversion factor is 3, 2, and 2. Both of these design methods are implemented on the FPGA development board. The hardware resource occupancy and the error rate of the signal amplitude in decimation show that the efficient digital filter is superior to the digital filter designed by the Hamming window in the real-time processing

Compressive Sensing-Based Data Aggregation Approaches for Dynamic WSNs

Among various data aggregation approaches proposed for wireless sensor networks (WSNs), the one based on compressive sensing (CS) has the merit of low traffic cost. The key step to design a CS-based data aggregation protocol is to construct a measurement matrix {\Phi } based on the network structure and to assign each node a unique column vector of {\Phi }. Assuming an expanded scenario, where some new nodes join in the network, the data aggregation scheme has to entirely re-generate a new matrix {\Phi ^{\prime }} with a larger size to meet the node number and CS property simultaneously. Apparently, it is energy-consuming to reallocate the weight vectors from the new measurement matrix to all the nodes. Thus, we propose an approach which aims to keep the weight vectors of existing sensors unchanged but assign only optimized measurement vectors to the newly added nodes. In order to solve the relevant non-convex optimization problem, two efficient methods with good data aggregation performance are proposed. Numeric experiments validate the effectiveness of our proposed methods

Improved Resnet Model Based on Positive Traffic Flow for IoT Anomalous Traffic Detection

The Internet of Things (IoT) has been highly appreciated by several nations and societies as a worldwide strategic developing sector. However, IoT security is seriously threatened by anomalous traffic in the IoT. Therefore, creating a detection model that can recognize such aberrant traffic is essential to ensuring the overall security of the IoT. We outline the main approaches that are used today to detect anomalous network traffic and suggest a Resnet detection model based on fused one-dimensional convolution (Conv1D) for this purpose. Our method combines one-dimensional convolution and a Resnet network to create a new network model. This network model improves the residual block by including Conv1D and Conv2D layers for two-dimensional convolution. This change enhances the model’s ability to identify aberrant traffic by enabling the network to extract feature information from one-dimensional linearity and two-dimensional space. The CIC IoT Dataset from the Canadian Institute for Cybersecurity Research was used to assess the effectiveness of the proposed enhanced residual network technique. The outcomes demonstrate that the algorithm performs better at identifying aberrant traffic in the IoT than the original residual neural network. The accuracy achieved can be as high as 99.9%

Profit and Financing Choice: An Empirical Comparison on the Basis of Traditional Empirical Model and Debt-equity Multi-model

Conference Name:2nd International Conference on Information Management, Innovation Management and Industrial Engineering. Conference Address: Xian, PEOPLES R CHINA. Time:DEC 26-27, 2009.In the research of relationship between enterprise profit and financing choice, the existing literatures mainly focus on the regression analysis using leverage ratio as the proxy of capital structure. This paper asserts that the dual effect of debt-equity financing has been incorporated into leverage ratio; therefore, regression analysis merely on leverage ratio fails to uncover the financing choice of enterprise. This article examines whether the financing behavior of listed company in China is consistent with the static trade-off theory using traditional empirical model with data of 619 listed companies in the Shanghai and Shenzhen stock exchange from 1998 to 2007. Under the guideline of static trade-off theory, a new empirical model called debt-equity financing multi-model has been deduced, with which we examine the impact that firm profit casts upon financing behavior of listed company in China

Correlation between Transit-Oriented Development (TOD), Land Use Catchment Areas, and Local Environmental Transformation

Transit-oriented development (TOD) has been recognised as a sustainable planning approach and that is typically designed for a whole city. Individual land use characteristics and the causations have often been ignored. Therefore, the primary objective of this study was to explore the factors that influence the land use catchment area (LCA) characteristics at a station neighborhood level. First, it contributes a methodology to measure the LCA by introducing a new concept. The density gradient was introduced to generate the scale and compactness degree of each station. Second, it provides a theoretical framework for understanding the causes of different LCAs. The partial least squares (PLS) regression model was employed to explore the accessibility effects. By analysing density gradient curves, it reveals that stations grew to fit the negative exponential function. Regarding the scale and form degree of LCAs, the impact of accessibility before and after a station construction have been corroborated. Moreover, the effects of facilities function before construction, distance from main roads, and elevated stations have been emphasized. The results provide support for a more sophisticated concept of catchment area relating to land use at the level of an individual TOD station, while shedding light on the benefits of those engaged in the future design of TOD with due consideration of the local physical environments

The complete mitochondrial genome of Reticulitermes ovatilabrum (Isoptera: Rhinotermitidae)

The mitochondrial genome of the Reticulitermes ovatilabrum is 15,913 bp in length and encodes 37 genes including 13 protein-coding genes (PCGs), 22 transfer RNA genes (tRNA), 2 ribosomal RNA genes (rRNA), and a non-coding control region (D-loop). The percentage of A/T (65.59%) is much higher than that of C/G (34.41%). The phylogenetic tree revealed that R. ovatilabrum was closest to R. kanmonensi and R. periflaviceps. The mitochondrial genome of the R. ovatilabrum provides a resource for evolutional analysis within termites especially Reticulitermes

Experimental Realization of 16-Pixel Terahertz Receiver Front-End Based on Bulk Silicon MEMS Power Divider and AlGaN/GaN HEMT Linear Detector Array

A 16-pixel terahertz (THz) receiver front-end working at room temperature was designed, built, and measured in this paper. The designed receiver front-end is based on the antenna-coupled AlGaN/GaN high-electron-mobility transistor (HEMT) THz linear detector array (TeraLDA) and a 16-way THz power divider. The local oscillator (LO) signal is divided by the power divider into 16 ways and transmits to the TeraLDA. Each detector contains a planar unified antenna printed on a 150 μm-thick sapphire substrate and a transistor fabricated on AlGaN/GaN heterostructure. There are 16 silicon hemispheric lenses located on the TeraLDA to increase the responsivity of the TeraLDA. The focus of each lens is aligned in the center of the TeraLDA pixels. Depending on different read out circuits, the receiver front-end could work in homodyne and heterodyne modes. The 16-way power divider is a four-stage power divider that consists of fifteen same 2-way dividers, and was fabricated by bulk silicon microelectromechanical systems (MEMS) technology to achieve low insertion loss (IL). This designed receiver front-end could be a key component of a THz coherent focal plane imaging radar system, that may play a crucial role in nondestructive 3D imaging application