The aim of spatial sound or spatial audio is to reproduce the spatial information of sound, so as to recreate the desired spatial auditory events or perceptions. Recently, spatial sound becomes a hot topic in the fields of acoustics, signal processing, and communication. A series of spatial sound techniques have been developed and applied to a wide area of scientific research, engineering, and amusement. The history, principle, progress and applications of spatial sound technique are comprehensively reviewed in this article. Especially, spatial sound techniques based on different principles are united within the framework of spatial sampling and reconstruction theorem of sound field. The challenges and prospects of spatial sound are also addressed.

As a new security defense theory, Cyberspace mimic defense (CMD) provides an architecture named Dynamic heterogeneous redundancy (DHR) to enhance the defense level of system security. Due to the new dynamic defense mechanism DHR introduced in CMD systems, traditional security modelling and analysis methods can hardly be used for them. In this paper, we propose a Security ontology-based modelling method for CMD systems (SOCMD), which uses ontology to represent DHR components and to define their inner relationships. SOCMD also connects information components including DHRs with security vulnerabilities, threats and attackers in cyberspace. Next, attacking rules, multi-mode arbitration mechanism and combination rules are designed with SOCMD for CMD systems and a new logical-checking method is proposed to make judgement about the security state of SOCMD. Finally, different use cases and performance tests are developed to demonstrate the application process for the model and to verify the validity of our method.

We propose a novel video saliency detection method based on pairwise interaction learning in this paper. Different from the traditional video saliency detection methods, which mostly combine spatial and temporal features, we adopt Least squares Conditional random field (LS-CRF) to capture the interaction information of regions within a frame or between video frames. Specifically, dual graph-connection models are built on superpixels structure of each frame for training and testing, respectively. In order to extract the essential scene structure from video sequences, LS-CRF is introduced to learn the background texture, object components and the various relationships between foreground and background regions through the training set, and each region will be distributed an inferred saliency value in testing phase. Benefitting from the learned diverse relations among scene regions, the proposed approach achieves reliable results especially on multiple objects scenes or under highly complicated scenes. Further, we substitute weak saliency maps for pixel-wise annotations in training phase to verify the expansibility and practicability of the proposed method. Extensive quantitative and qualitative experiments on various video sequences demonstrate that the proposed algorithm outperforms conventional saliency detection algorithms.

To reduce the implementation complexity and excessive overhead caused by remote attestation, we propose a new remote attestation scheme based on trusted level measurement considering the limited system resources of the Intelligent electronic devices (IEDs) in smart grid. In this scheme, a lightweight trusted level measurement mechanism is designed, and a new dynamic weighted multi-classifier integration method based on Binary tree Support vector machine (BT-SVM) is proposed. The trusted level of configuration information and running state of an IED are measured through trusted third party. The measurement parameters and attestation period are dynamically adjusted according to different trusted levels of an IED, so that reducing computational overhead and communication delays in remote attestation process. Analysis and experiments show that the proposed scheme can enhance the security of IEDs in smart grid by occupying a small amount of computation and communication resources.

Group key agreement (GKA) is one of the key technologies for ensuring information exchange security among group members. While GKA is widely used in secure multi-party computation, safety of resources sharing, and distributed collaborative computing. It still has some security flaws and limitations. We proposes a Blockchain-based dynamic Group key agreement (BDGKA) protocol. In contrast to prior works, BDGKA differs in several significant ways: 1) anonymous identity authenticationit can prevent privacy leaks; 2) traceability-it can track illegal operating entities; 3) load balancing-it balances computation and communication to each node, avoiding the breakdown of single-point and network bottlenecks. This protocol is proven secure under the hardness assumption of decision bilinear DiffieHellman. The performance analysis shows that it is more efficient than the referred works.

Named entity recognition is a fundamental and crucial issue of biomedical data mining. For effectively solving this issue, we propose a novel approach based on Deep belief network (DBN). We select nine entity features, and construct feature vector mapping tables by the recognition contribution degree of different values of them. Using the mapping tables, we transform words in biomedical texts to feature vectors. The DBN will identify entities by reducing dimensions of vector data. The extensive experimental results reveal that the novel approach has achieved excellent recognition performance, with 69.96% maximum value of F-measure on GENIA 3.02 testing corpus. We propose a self-supervised DBN, which can decide whether to add supervised fine-tuning or not according to the recognition performance of each layer, can overcome the errors propagation problem, while the complexity of model is limited. Test analysis shows that the new DBN improves recognition performance, the F-measure increases to 72.12%.

Binary and quaternary sequences have received a lot of attention since they are easy to be implemented as multiple-access sequence in practical communication systems, radar, and cryptography. In this paper, the two complete binary sequence of period N are analyzed. For odd prime N, new balanced quaternaruy sequences of even period 2N are constructed using Gary mapping. Furthermore, we computer the complete autocorrelation distributions of this sequence.

In order to facilitate crowdsourcing-based task solving, complex tasks are decomposed into smaller subtasks that can be executed by individual workers. Decomposing task into sequential subtasks attracts a plenty of empirical explorations. The absence of formal studies makes it difficult to provide task requesters with explicit guidelines on task decomposition strategy. We formally present the vertical task decomposition model by specifying the positive quality dependencies among sequential subtasks. Our focus is on addressing solutions of low quality intentionally provided by selfinterested workers who are paid equally or based on their contributions. By combining the theoretical analysis on workers' strategic behaviors and experimental exploration on the efficiency of task decomposition, our study demonstrates the relationship between the incentive and the worker's performance, and gives the explicit instructions on vertical task decomposition, which show promise on improving the quality of the final outcome.

the Artificial intelligence (AI) has gradually changed from frontier technology to practical application with the continuous progress of deep learning technology in recent years. In this paper, the Random forest (RF) algorithm is adopted to preprocess and optimize the feature subset of ICU data sets. Then these optimized feature subsets are used as input of Long shortterm memory (LSTM) deep learning model, and the early disease prediction of ICU inpatients is carried out by the method of neural network deep learning. Experiments show that this prediction method has higher prediction accuracy compared with other machine learning and deep learning models.

Recommender system has been recognized as a superior way for solving personal information overload problem. More and more aspect-based models are leveraging user ratings and extracting information from review texts to support recommendation. Aspect-based latent factor model predicts user ratings relying on latent aspect inferred from user reviews. It usually constructs only a single global model for all users, which may be not sufficient to capture the diversity of users' preferences and leave some items or users be badly modeled. We propose a Hybrid aspect-based latent factor model (HALFM), which jointly optimizes the Global aspect-based latent factor model (GALFM) and the Local Aspect-based Latent Factor Models (LALFM), their user-specific combination, and the assignment of users to the LALFMs. HALFM makes prediction by combining user-specific of GALFM and many LALFMs. Experimental results demonstrate that the proposed HALFM outperforms most of aspectbased recommendation techniques in rating prediction.

Existing methods utilized single words as text features. Some words contain multiple meanings, and it is difficult to distinguish its specific classification according to a single word, which probably affects the accuracy of the text classification. Propose a framework based on Words in pairs neural networks (WPNN) for text classification. Words in pairs include all single word combinations which have a high mutual association. Mine the crucial explicit and implicit Words in pairs as text features. These words in pairs as a text feature are easily classified. The words in pairs are utilized as the input of the neural network, which provides a better classification ability to the model, because they are more recognizable than the single word. Experimental results show that our model outperforms five benchmark algorithms.

To track the bearings-only maneuvering target tracking accurately online, the soft measurement constraints are implemented into the Unscented Kalman filtering (UKF). To deal with the soft measurement constraints, the Lasso regularization is added as the obstacle function. In doing this, the sampled sigma points can be restricted into the feasible region. To enhance the sampling efficiency, the global optimal solution is acquired by a heuristic optimizer. To smooth the outliers, the posterior distribution is approximated by a Gaussian mixture consists of the original and the modified priors with the fuzzy weighted factor. Simulated results indicate the accuracy and the computational efficiency of the proposed method.

For muti-scatter point target and undersampled data, tradintional imaging method has a poor precision for imaging result and spin angular velocity. In view of the above problems, this paper proposes a narrow-band Interferometric inverse synthetic aperture radar(InISAR) 3-dimensional(3D) Compressed sensing imaging method based on the joint spin angle velocity error parameter estimation. First, a sparse model of narrow-band spin target is constructed. Then, the image entropy is used as the criterion function to search the optimal angle velocity by the joint reconstruction iteration. Finally, the image of the target is obtained by interference processing. The simulation results show that compared with the traditional method, the estimation of spin angular velocity and the imaging result are more accurate in proposed method.

The necessary and sufficient condition for the quinary cyclic codes with generator polynomial (x + 1)m _α (x)m _α ^e (x) to have parameters [5 ^m - 1,5 ^m - 2m - 2,4] is provided by analyzing solutions of certain equations over the finite field 𝔽₅ ^m . And thus several classes of new optimal quinary cyclic codes with the same parameters and generator polynomial are constructed based on analyzing irreducible factors of certain polynomials with low degrees over finite fields.

Electrical contact degradation will affect the signal integrity resulting in poor communication performance. In the present work, the effect of electrical contact degradation on wide-band digital signal integrity was studied using both the theoretical analysis and experimental testing. The characteristics of digital signals with rise times of 200ps, 400ps, 600ps and 800ps through connectors with different degradation levels were studied. The maximum bandwidth of these signals can reach up to 1.75GHz. An equivalent model of the connector with degraded contact surface was developed and the experimental results are consistent with the model results approximately. The influence of capacitance characteristics caused by degraded contact surface on digital signal waveform was calculated and analyzed. The results of this paper are helpful in developing a better understanding of the characteristics of wide-band digital signal waveforms through the degraded contact surface and provide a theoretical support to identify failure features in fault diagnosis.

We propose a UnionPay payment scheme based on controlled quantum teleportation in which the whole process is realized through correlation of the three-particle entangled state, controlled quantum teleportation, and the physical property of quantum mechanics. Combined with some typical quantum key distribution protocols, the proposed UnionPay payment scheme with unconditional security (information theory security)overcomes the limitation of computational security generally existing in traditional UnionPay payment systems.

Non-orthogonal multiple access (NOMA) makes multiple users to overload the same wireless resources by utilizing the superposition coding principle, so that it can improve the system capacity and spectral efficiency. Various NOMA schemes are proposed, including the Power-domain NOMA (PD-NOMA). Most of them are using Successive interference cancellation (SIC) for detection, which limits the promised performance gain brought by NOMA due to the error propagation. An Iterative SIC and parallel interference cancellation (ISP) method is proposed for the PD-NOMA system. The MaxLog-MAP (MLM) algorithm (MAP is short for Maximum a posteriori) and SIC are used for performance comparison. Simulation results show that the Bit error rate (BER) performance of ISP is much better than that of SIC, and is close to that of MLM. It can perform even better than MLM at low signal to noise ratio for the user with lower power allocation. The ISP can obtain the promised performance gain brought by NOMA.

Arbitration mechanism is the core element of Cyberspace mimic defense (CMD) scheme, providing the security effect of exception perception and exception shielding for the protected system. The existing arbitration methods confront the problem of confidence skewing caused by attack changes when calculating confidence. This paper proposes a correction method for confidence calculation based on Logistic function (Lcorrection). The method makes a grading on influence for different historical periods and filters the noise of fioverheatingfl exception outputs so that the confidence calculation process can adapt to the attack changes in real applications. The evaluations show that the L-correction can adequately handle the confidence skewing and skewingbased confidence cheating, improving the security of mimic arbitration mechanism.

A fully-integrated 400MHz transceiver for high definition video transmission in Unmanned aerial vehicle (UAV) is presented. The transceiver integrates a Receiver (RX), a Transmitter (TX), a tunable multi-mode complex filter, two sigma-delta fractional-N PLL frequency synthesizers and some baseband calibration circuits, which is applicable for both Time division duplex (TDD) and Frequency division duplex (FDD) systems. In order to meet different data rate and sensitivity requirements for control signal and video signal transmission, the transceiver supports BPSK/QPSK/QAM modulation. The receiver sensitivity is -75dBm and the transmitter provides emission power up to 12dBm. Besides, the Error vector magnitude (EVM) of -35dB is measured under 64QAM modulation with 54Mbps data rate. Fabricated in a standard 130nm CMOS process and operated at 1.2V supply for most circuits excluding PA, 3.3V supply for PA, the receiver and the transmitter consume 68.2mW and 164.3mW, including the frequency synthesizer, respectively. The total chip area with pads is 6.25mm².

We study the impacts of non-ideal filters in Modulated wideband converter (MWC). An analog filter compensating method is presented. The limitations of the conventional back-end of the advanced MWC is analyzed and an improved back-end based on Fast Fourier transform (FFT) is proposed. By setting proper length of the digital signal processed each time, the proposed system increases the recovery Signal-to-noise ratio (SNR) and significantly reduces the computation loads. The compensating operation is accurate to each frequency point and the expanding operation is simplified to a splitting of the frequency points. Numerical simulations demonstrate our proposed system against the ideal MWC with different filters. The results indicate that the recovery SNR of over 70 decibel is achieved with almost only one-tenth the computation complexity of the conventional MWC backend.

The main problem in on-site radiated emission test of EMU (Electric multiple unit) is the excess ambient electromagnetic noise due to the complex electromagnetic environment. In order to solve this problem, this paper proposed a novel approach for on-site radiated emission measurement test. A joint algorithm comprise blind source separation and adaptive signal processing is used to suppress the ambient electromagnetic noise. The normalized estimation of ambient electromagnetic noise is obtained by blind source separation algorithm, then the normalized estimation of ambient electromagnetic noise is used as the reference signal to adaptively cancel the ambient noise in the mixed electromagnetic noise, so the estimation of radiated noise of EMU is obtained. The simulation and experiment results showed that the proposed method can effectively suppress the ambient electromagnetic noise, thus realize the accurate measurement of the radiated emission of the EMU.

The paper presents a new Z-source inverter with a low-stress reduction in the voltage of capacitors compared to the traditional Z-source inverter with the same boost factor. The proposed structure reduces costs and weight by reducing voltage capacitors. The main difference between this inverter and the traditional Z-source inverter lies in the arrangement of the components. In addition, the superiority of the proposed inverter over the L-Z-source inverter is related to the use of two capacitors instead of two diodes, which increases the boost power factor and obviates the need for the snubber circuit. Furthermore, another advantage of the proposed inverter can be extended to n cases in cascade form and the proposed inverter is continues current. The analysis, derivation of boost factor, capacitor voltages, voltages gain, and switch stress of the proposed inverter topology are carried out and are compared with the similar inverters. The advantages of the proposed structure are verified by simulation and implementation.

In the rail transit system, the running foundation of the computer control system is required to be highly reliable, available and safe. A redundant system structure of double one out of two with a hot standby applied to the safety computer platform of the train control system is proposed in this paper. Considering the common cause failure, fault diagnosis rate, repair rate and switching success rate, the Markovian approach is adopted for modeling four safety redundant structures, including two out of three, double one out of two, one out of two with a hot standby and double one out of two with a hot standby. The calculation of the failure rate and PFH values is carried out with MATLAB to evaluate the reliability, safety and influencing factors of each structure. The final conclusion is that double one out of two with a hot standby structure can be best in the aspect of both reliability and safety with appropriate DC and β parameters.