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Background Measurement of the Chinese burden of disease with disability-adjusted life-years (DALYs) requires disability weight (DW) that quantify health losses for all non-fatal consequences of disease and injury. The Global Burden of Disease (GBD) 2013 DW study indicates that it is limited by lack of geographic variation in DW data and by the current measurement methodology. We aim to estimate DW for a set of health states from major diseases in the Wuhan population. Methods We conducted the DW measurement study for 206 health states through a household survey with computer-assisted face-to-face interviews and a web-based survey. Based on GBD 2013 DW study, paired comparison (PC) and Population health equivalence (PHE) method was used and different PC/PHE questions were randomly assigned to each respondent. In statistical analysis, the PC data was analyzed by probit regression. The probit regression results will be anchored by results from the PHE data analyzed by interval regression on the DW scale units between 0 (no loss of health) and 1 (loss equivalent to death). Results A total of 2610 and 3140 individuals were included in the household and web-based survey, respectively. The results from the total pooled data showed health state “mild anemia” (DW = 0.005, 95% UI 0.000–0.027) or “allergic rhinitis (hay fever)” (0.005, 95% UI 0.000–0.029) had the lowest DW and “heroin and other opioid dependence, severe” had the highest DW (0.699, 95% UI 0.579–0.827). A high correlation coefficient (Pearson’s r = 0.876; P Conclusions This set of DWs could be used to calculate local diseases burden for health policy-decision in Wuhan population. The DW differences between the GBD’s survey and Wuhan’s survey suggest that there might be some contextual or culture factors influencing assessment on the severity of diseases.

In recent years, unmanned aerial vehicles (UAVs) have attracted wide attention in both military and civilian fields because of their versatility, flexibility, and low-cost characteristics. In this letter, a UAV network architecture oriented to the virtual cloud processing center (VCPC) is considered, where the highly dynamic UAV network can transmit real-time data from sensors on the ground as well as data observed by the UAV nodes to the VCPC. Due to the high dynamic characteristics of UAVs, the topology of networks and traffic load within the coverage of the VCPC change frequently, especially when the priority of the information is different, and it is necessary to design a novel media access control (MAC) protocol to reduce the transmission delay of high priority and improve throughput of UAV networks. Therefore, an information priority oriented adaptive (IPOA-MAC) protocol is presented in this letter for high dynamic UAV networks. The relative length of control channel interval and service channel interval can be adaptively adjusted to improve network throughput in high dynamic application scenarios. Also, priority flag bits are set in data to ensure real-time transmission of high-priority information. Finally, the simulation results show the proposed IPOA-MAC can improve network performance effectively than other existing protocols.

The received signal of passive radar based on mobile communication signals contains direct and multipath interference (DMI) signals from multiple co-channel base stations (CCBS). The direct signal spatial spectrum of each CCBS should be obtained first to eliminate the co-channel interference. The performance of the traditional spatial spectrum estimation algorithms based on uniform linear array (ULA) is related to the number of array elements. In the complex co-channel interference environment, the array requires an ultra-large number of array elements and the spatial spectrum estimation resolution is poor. This paper proposes a method for estimating the direct signal spatial spectrum of the CCBS by fusing coprime array and compressive sensing. Firstly, an augmented virtual array signal is constructed by using the second-order statistics of the received signals of the coprime array and then the compressive sensing algorithm is used to estimate the spatial spectrum of the direct signal of the CCBS. The proposed method can achieve higher resolution and higher-degrees-of-freedom (DOFs) performance than traditional ULA by using the same number of array elements. The effectiveness of the proposed method is verified by numerical simulation analysis and experimental data.

In this paper, a new approach without iteration is proposed for direct position determination (DPD) with Doppler shifts in the presence of unknown direction-dependent mutual coupling (DDMC). The special banded symmetric Toeplitz structure of mutual coupling matrix and Doppler shift containing location information can be exploited for constructing a space-time transformation matrix, and the spectral function which is utilized to obtain the locations of sources is established via the orthogonality between space-time transformation matrix and noise subspace. Simulation results show that our solution can accurately determine the emitter position in a single-step without a priori information of the DDMC.

Deep learning has been widely investigated for radio applications. The classification performance of the deep learning greatly depends on the quality of dataset. However, the deficiency of the training data is a critical issue limiting the classification accuracy in practical scenarios. In this paper, we proposed to use the generative adversarial network (GAN) as a data augmentation tool to solve the problem of inadequate training issue under the lack of sufficient data samples. The data augmentation process could be realized by Nash equilibrium of generator and discriminator. The result shows that the accuracy of the classifier is increased by nearly 4 percentage in the signal to noise ratio range of 0 to 20 dB after data augmentation.

Recently, in order to increase the degrees of freedom, sparse arrays are widely studied for circular signals. However, there are many applications of non-circular signals in practice, but the characteristics of non-circular signals are rarely considered in array design. A new extended array configuration is designed for non-circular signals in this paper. The configuration is designed by incorporating the extended subarray into the maximum inter-element spacing constraint (MISC) to enlarge the number of continuous virtual array elements in the sum-difference co-array. The closed form expressions of the structure and the number of uniform DOFs are given in this paper. Simulation results show that the proposed geometry provide better accuracy for DOA estimation in the presence of unknown mutual coupling while preserving high degrees of freedom.

This paper is devoted to the angle estimation problem for bistatic multiple-input multiple-output (MIMO) radar under strongly correlated targets and spatially colored noise scenes. Firstly, the cross-covariance matrix is established by using the independent noise vector after matched filtering in time domain, and the components of spatial colored noise are eliminated. Then, the cross-covariance matrix is rearranged into a third-order tensor, and the reconstructed tensor can be obtained by concatenating two third-order tensors. Finally, the Parallel Factor (PARAFAC) decomposition is utilized to resolve angle estimates. Owing to the reconstructed third-order tensor, angle estimation by the proposed method is more accurate than existing methods and has stronger robustness even though there is a strong correlation between targets. Simulation results verify the advantages of our solutions over its cutting-edge counterparts.

The direct position determination (DPD) approach confronts with many difficulties when determining the position of coherent signals in the presence of direction-dependent mutual coupling (DDMC). In this paper, to restore the rank of data covariance matrix, we extend enhanced spatial smoothing techniques. Then, we extract the middle subarray by using the banded symmetric Toeplitz structure of the mutual coupling matrix of uniform linear array to avoid the effect of DDMC. Finally, according to the orthogonality between the steering vector and the noise subspace, the spatial spectrum function can be derived to locate multiple coherent sources under DDMC. Simulation results show that our solution can accurately determine the emitter position without a priori information of the DDMC.

The adaptive beamformer is very sensitive to model mismatches. In order to improve the robustness of the adaptive beamformer against mismatch errors, this paper proposes a novel robust adaptive beamforming (RAB) method. Firstly, we use a subspace-based method to estimate the steering vectors of signal-of-interest (SOI) and interference signals. The essence of this method is that each steering vector (SV) can be derived from the vector located at the intersection of two subspaces. Meanwhile, the estimate of each SV is given in a closed-form expression. Secondly, a simplified power estimation method that is independent of the array structure is used to estimate the interference and noise powers. Then based on the estimated SVs of interference and the powers of interference and noise, a more accurate interference-plus-noise covariance matrix (INCM) is reconstructed according to its definition. Finally, the proposed beamformer is obtained based on the more accurate SOI SV and INCM, which has higher robustness to array model mismatches. Simulation results demonstrate the effectiveness of the proposed RAB method.

In this paper, a novel efficient algorithm is developed for direction of arrival (DOA) estimation of coherent signals under the direction-dependent mutual coupling (DDMC) based on weighted subspace fitting. DOAs are determined by applying the least square fitting between signal space and the modified array manifold at first. Subsequently, we put forward an approach to calculate the DDMC matrices and the complex fading coefficients by utilizing the estimated DOAs. Without any iterations, the proposed algorithm can identify the angular information of the coherent signals in a single step in the presence of DDMC. Numerical simulation results show the effectiveness of the proposed algorithm.

Full duplex radio acts as an important role in the future wireless networks since it can transmit and receive data simultaneously over the same channel. However, there are just a few medium access control (MAC) protocols designed for asymmetric full duplex radio networks, most of existed protocols are only suitable for fixed uplink and downlink nodes, which leads to a waste of channel resource. In this paper, we propose a novel MAC protocol for full duplex radio networks depend on the different transmission characteristics of uplink and downlink. To make an efficient use of channel and improve the network throughput, the proposed protocol is composed of two parts, uplink initiated link (UIL) based and downlink initiated (DIL) link based, respectively. In UIL model, access point (AP) can transmit data to different stations according to the UIL transmission slot design, vice versa in DIL model. The analysis and simulation results indicate that the proposed MAC protocol achieves throughput gains compared to existing protocol.

On the ground with complex electromagnetic environment, protecting the received satellite signals from interference is a key issue for the receiver. To effectively cope with the coexistence of jamming and spoofing interference, and accurately suppress both jamming and spoofing, in this paper, a hybrid interference suppression algorithm based on robust beamforming is proposed. The combination of subspace projection algorithm, despreading algorithm, multiple signal classification (MUSIC) algorithm and robust beamforming based on the linearly constrained minimum variance criterion can suppress both jamming and spoofing effectively, and ensure that the desired signal is undistorted. At the same time, it can overcome the problem of inaccurate direction estimation under low signal-to-noise ratio. Numerical examples show that the proposed algorithm can effectively suppress hybrid interference.

With the rapid proliferation of mobile wireless sensors networks (WSNs), which are widely deployed in the Internet of things, the Internet of vehicles, and flying ad hoc networks, more and more researchers are making the effort to improve energy efficiency and throughput of WSNs. In recent years, many protocols have been proposed to solve the energy shortage problem. Some of the protocols are based on cooperative or allocation schemes, some are based on contending schemes, and others are based on the foundation of hybrid schemes. However, it is still difficult to improve the energy efficiency and networks throughput simultaneously. In this article, we present a novel carrier sense multiple accesscollision avoidance and time division multiple access hybrid scheme protocol medium access control (MAC) on the MAC layer to improve the throughput and reduce energy consumption at the same time for mobile WSN with position prediction algorithms in 3-D space. Compared to existing protocols, the proposed protocol can efficiently reduce the energy consumption and significantly improve the throughput, especially for high-speed mobile WSN in the many-to-one communication paradigm. Extensive simulation results prove the performance of the protocol.