Design of Wideband Multifunction Antenna Array Based on Multiple Interleaved Subarrays

A new Modified Iterative Fourier Technique (MIFT) is proposed for the design of interleaved linear antenna arrays which operate at different frequencies with no grating lobes, low-sidelobe levels, and wide bandwidths. In view of the Fourier transform mapping between the element excitations and array factor of uniform linear antenna array, the spectrum of the array factor is first acquired with FFT and its energy distributions are investigated thoroughly. The relationship between the carrier frequency and the element excitation is obtained by the density-weighting theory. In the following steps, the element excitations of interleaved subarrays are carefully selected in an alternate manner, which ensures that similar patterns can be achieved for interleaved subarrays. The Peak Sidelobe Levels (PSLs) of the interleaved subarrays are further reduced by the iterative Fourier transform algorithm. Numerical simulation results show that favorable design of the interleaved linear antenna arrays with different carrier frequencies can be obtained by the proposed method with favorable pattern similarity, low PSL, and wide bandwidths

Decomposing the Intergenerational Disparity in Income and Obesity

Intergenerational disparity in income and health violates the norm of equal opportunity and deserves the attention of researchers and policy makers. To understand changes in intergenerational disparity, we created the intergenerational mobility index (IMI), which can simultaneously measure changes in income rankings and in health outcomes across two generations. We selected obesity as one health outcome to illustrate the application of IMI due to its severe health and financial consequences for society and the significant changes in the distribution of obesity across income groups. Although obesity has increased in all income groups in the last four decades, higher income groups have tended to have a faster increase in obesity, which has reduced the disparity in obesity across income groups. The strength of our intergenerational approach within families is to control the genetic influence, which is one of the strongest determinants of obesity. The decomposition of the IMI illustrates that it captures changes in obesity distribution (holding constant income rankings between generations) and changes in income rankings (holding constant the obesity distribution across generations), simultaneously. We used the data of the Panel Study of Income Dynamics (PSID), which have been collected since 1967, is the longest longitudinal survey in the U.S. The PSID surveyed respondents’ height and weight were recorded in 1986 and from 1999 to 2007. We selected respondents from 1986 as the parental generation and respondents from 2007 as the adult children’s generation. To make the adult children’s body weight status and income comparable to their parents’, we stratified the analysis by gender. For the pairs of fathers and adult sons, we found the intergenerational disparity in overweight, a less severe indicator of excessive fatness, across income was decreasing. This was partially due to the up-swing in the adult children’s income status. For the pairs of mothers and adult daughters, we found a similar decrease in socioeconomic disparity in obesity. However, decomposition of the IMI indicated that changes in income distributions between mothers and adult daughters contributed smaller effects than that between fathers and adult sons. Our study has demonstrated that the IMI and its decomposition are useful tools for analyzing intergenerational disparity in income and health

Core-sheath structured electrospun nanofibrous membranes for oil-water separation

In recent years, both the increasing frequency of oil spill accidents and the urgency to deal seriously with industrial oil-polluted water, encouraged material scientists to design highly efficient, cost effective oil-water separation technologies. We report on electrospun nanofibrous membranes which are composed of core-sheath structured cellulose-acetate (CA)-polyimide (PI) nanofibers. On the surface of the CA-PI fibers a fluorinated polybenzoxazine (F-PBZ) functional layer, in which silica nanoparticles (SNPs) were incorporated, has been applied. Compared with F-PBZ/SNP modified CA fibers reported before for the separation of oil from water, the PI-core of the core-shell F-PBZ/SNP/CA-PI fibers makes the membranes much stronger, being a significant asset in their use. Nanofibrous membranes with a tensile strength higher than 200 MPa, a high water contact angle of 160 degrees and an extremely low oil contact angle of 0 degrees were obtained. F-PBZ/SNP/CA-PI membranes seemed very suitable for gravity-driven oil-water separation as fast and efficient separation (>99%) of oil from water was achieved for various oil-water mixtures. The designed core-sheath structured electrospun nanofibrous membranes may become interesting materials for the treatment of industrial oil-polluted water

P2P Botnet Detection Based on Nodes Correlation by the Mahalanobis Distance

Botnets are a common and serious threat to the Internet. The search for the infected nodes of a P2P botnet is affected by the number of commonly connected nodes, with a lower detection accuracy rate for cases with fewer commonly connected nodes. However, this paper calculates the Mahalanobis distance—which can express correlations between data—between indirectly connected nodes through traffic with commonly connected nodes, and establishes a relationship evaluation model among nodes. An iterative algorithm is used to obtain the correlation coefficient between the nodes, and the threshold is set to detect P2P botnets. The experimental results show that this method can effectively detect P2P botnets with an accuracy of >85% when the correlation coefficient is high, even in cases with fewer commonly connected nodes

A Feature Extraction Method for P2P Botnet Detection Using Graphic Symmetry Concept

A DDoS (Distributed Denial of Service) attack makes use of a botnet to launch attacks and cause node congestion of wireless sensor networks, which is a common and serious threat. Due to the various kinds of features required in a Peer-to-Peer (P2P) botnet for DDoS attack detection via current machine learning methods and the failure to effectively detect encrypted botnets, this paper extracts the data packet size and the symmetric intervals in flow according to the concept of graphic symmetry. Combined with flow information entropy and session features, the frequency domain features can be sorted so as to obtain features with better correlations, which solves the problem of multiple types of features required for detection. Information entropy corresponding to the flow size can distinguish an encrypted botnet. This method is implemented through machine learning techniques. Experimental results show that the proposed method can detect the P2P botnet for DDoS attack and the detection accuracy is higher than that of traditional feature detection

An algorithm for fragment-aware virtual network reconfiguration.

In view of the fact that the current online virtual network embedding algorithms do not consider the fragment resources generated in the embedding process deeply enough, resulting in the problem that the acceptance ratio and the revenue to cost ratio are both low, a mathematical model for virtual network reconfiguration is constructed and a heuristic algorithm for fragment-aware virtual network reconfiguration (FA-VNR) is proposed. The FA-VNR algorithm selects the set of virtual nodes to be migrated according to the fragment degrees of the physical nodes, and selects the best virtual node migration scheme according to the reduction of the fragment degrees of the physical nodes as well as the reduction of the embedding cost of the embedded virtual networks. Extensive simulation results show that the proposed FA-VNR algorithm not only can obviously improve the acceptance ratio and the revenue to cost ratio of the current online virtual network embedding algorithm, but also has better optimization effect than the existing virtual network reconfiguration algorithm

Thinned Virtual Array for Cramer Rao Bound Optimization in MIMO Radar

By transmitting multiple independent waveforms at the transmit side and processing echoes of spatial targets at the receive side, Multiple Input Multiple Output (MIMO) radar enjoys virtual array aperture expansion and more degree of freedom (DOF), both of which favors the application of direction finding or estimation of direction of arrival (DOA). The expanded virtual aperture provides higher angular resolution which also promotes the precision of DOA estimation, and the extra DOF brought by waveform diversity can be leveraged to focus energy in certain spatial region for better direction-finding capacity. However, beamspace methods which match certain beampatterns suffer from deteriorated performance and complexity in implementation, and the advantage of virtual array aperture is limited by its virtual element redundancy. As an important performance indicator of DOA estimation, Cramer–Rao Bound (CRB) is closely connected to the array configuration of the system. To reduce the complexity of the system and improve CRB performance at the same time, in this paper, the virtual array of MIMO radar is designed directly by selecting outputs from matched filters at the receive side. For the sake of fair comparison, both scenarios with and without priori directions are considered to obtain optimized virtual array configuration, respectively. The original combinatorial problems are approximated by sequential convex approximations methods which produce solutions with efficiency. Numerical results demonstrate that the proposed method can provide thinned virtual arrays with excellent CRB performance