Due to existence of different environments and noises, the existing method is difficult to ensure the recognition accuracy of animal sound in low Signal-to-noise (SNR) conditions. To address these problems, we propose a double feature, which consists of projection feature and Local binary pattern variance (LBPV) feature, combined with Random forest (RF) for animal sound recognition. In feature extraction, an operation of projecting is made on spectrogram to generate the projection feature. Meanwhile, LBPV feature is generated by means of accumulating the corresponding variances of all pixels for every Uniform local binary pattern (ULBP) in the spectrogram. Short-time spectral estimation algorithm is used to enhance sound signals in severe mismatched noise conditions. In the experiments, we classify 40 kinds of common animal sounds under different SNRs with rain noise, traffic noise, and wind noise. As the experimental results show, the proposed framework consisting of shorttime spectrum estimation, double feature, and RF, can recognize a wide range of animal sounds and still remains a recognition rate over 80% even under 0dB SNR.

Cyclic codes as a subclass of linear codes have wide applications in communication systems, consumer electronics and data storage systems, due to their efficient encoding and decoding algorithms. We construct three classes of optimal ternary cyclic codes, which meet some certain bound. The weight distributions of their duals are also completely determined. The results show that their duals have few nonzero weights.

Leakage of private information including private key has become a threat to the security of computing systems. It has become a common security requirement that a cryptographic scheme should withstand various leakage attacks, including continuous leakage attacks. In order to obtain an Identity-based encryption (IBE) scheme which can keep its original security in the continuous leakage setting, we propose a new construction method of IBE scheme with Chosen-ciphertext attacks (CCA) security, which can tolerate continuous leakage attacks on many private keys of each identity, and whose security is proved based on the hardness of the classical Decisional bilinear Diffie-Hellman (DBDH) assumption in the standard model. The leakage parameter is independent of the plaintext space and has the constant size.

In a revocable broadcast encryption scheme, the group manager can flexibly set revoked users who cannot decrypt the ciphertext. Many applications of the revocable broadcast encryption have been found in the secure cloud data sharing. An adaptively secure revocable broadcast encryption system with constant ciphertext and private key size under standard assumptions is more suitable for use in the cloud environment. Few existing revocable broadcast encryption schemes meet such a requirement. We propose a revocable broadcast encryption scheme with constant size ciphertext and private key by combining the RSA cryptographic accumulator with an efficient identity based encryption system. We prove it to be adaptively secure under standard assumptions using dual system encryption techniques.

Keccak is the final winner of SHA-3 competition and it can be used as message authentic codes as well. The basic and balanced divide-and-conquer attacks on Keccak-MAC were proposed by Dinur et al. at Eurocrypt 2015. The idea of cube attacks is used in the two attacks to divide key bits into small portions. By carefully analysing the mappings used in Keccak-MAC, it is found that some cube variables could divide key bits into smaller portions and so better divide-and-conquer attacks are obtained. In order to evaluate the resistance of Keccak-MAC against divide-and-conquer attacks based on cubes, we theoretically analyse the lower bounds of the complexities of divide-and-conquer attacks. It is shown that the lower bounds of the complexities are still not better than those of the conditional cube tester proposed by Senyang Huang et al.. This indicates that KeccakMAC can resist the divide-and-conquer attack better than the conditional cube tester. We hope that these techniques still could provide some new insights on the future cryptanalysis of Keccak.

Linear codes with few weighs have many applications in secret sharing. Determining the access structure of the secret sharing scheme based on a linear code is a very difficult problem. We provides a method to construct a class of two-weight torsion codes over finite non-chain ring. We determine the minimal codewords of these torsion codes over the finite non-chain ring. Based on the two-weight codes, we find the access structures of secret sharing schemes.

Public crisis has the characteristics of suddenness and uncertainty, and it is necessary to combine the knowledge with the experience of other similar situations to make decisions effectively and quickly. This work combines artificial intelligent theory with information technology and brings case-based reasoning to build models consisting of the features of public crisis. We explore the case-representation approach and build a case-based retrieval algorithm. Combining the specificness of Case-based reasoning (CBR) technology in the monitoring of public crisis events, a new case retrieval algorithm for public crisis cases, named as Combined multi-similarity with set of simi-larity matching algorithm based on sememe (CMSBS), is proposed to analyze the cases with high similarity to current case. The CMSBS algorithm considers the structural and semantic similarities between two public crisis cases comprehensively. Simulation experiments are performed to validate the representation method of the knowledge, and the simulation results demonstrate that the CMSBS algorithm has superior performance in the average number of matching cases and matching accuracy rate and can work well in providing reference cases for subsequent events.

The deficiencies of existing polyp detection methods remain: i) They primarily depend on the manually extracted features and require considerable amounts of preprocessing. ii) Most traditional methods cannot specify the location of the polyps in colonoscopy images, especially for the polyps with variable size. In order to derive the improvement and lift the accuracy, we propose a novel and scalable detection algorithm based on deep neural networks-an improved Faster Regionbased Convolutional neural networks (Faster R-CNN)-by increasing the fusion of feature maps at different levels. It can be employed to detect and locate polyps, and even achieve a multi-object task for polyps in the future. The experimental consequences demonstrate that the best version among improved algorithms achieves 97.13% accuracy on the CVC-ClinicDB database, overtaking the previous methods.

Attribute reduction, also known as feature selection, is a vital application of rough set theory in areas such as machine learning and data mining. With several information systems constantly and dynamically changing in reality, the method of continuing the incremental attribute reduction for these dynamic information systems is the focus of this research. In an incomplete information system, the increasing form of attribute sets is an important form of dynamic change. In this paper, the definition of conditional entropy is first introduced in the incomplete information system, and for the circumstances of the dynamic change of the attribute sets, two types of incremental mechanisms of the matrix and non-matrix forms based on conditional entropy are subsequently proposed. In addition, on the basis of the two incremental mechanisms, the incremental attribute reduction algorithm is given when the attribute set increases dynamically. Finally, the experimental results of the UCI (University of California Irvine) datasets verify that the two proposed incremental algorithms exhibit a superior performance with regard to attribute reduction when compared with the non-incremental attribute reduction algorithm, which in turn is superior to other relative incremental algorithms.

For the shortcomings of the basic flower pollination algorithm, this paper proposes a differential evolution flower pollination algorithm with dynamic switch probability based on the Weibull distribution. This new algorithm improved the convergence rate and precision. The switch probability is improved by Weibull distribution function combined with the number of iterations. It can balance the relationship between the global pollination and the local pollination to improve the overall optimization performance of the algorithm. Random mutation operator is merged into the global pollination process to increase diversity of the population, enhance the ability of the algorithm's global search and avoid premature convergence. In the process of local pollination, directed mutation and crossover operation of the differential evolution are incorporated, it makes the individual flower position update with the memory function, which can choose the direction of variation reasonably. The use of cross-operation can avoid new solutions crossing the boundary. Convergence rate is improved and the algorithm can approach the global optimal solution continuously. Theoretical analysis proved the convergence and time complexity of the improved algorithm. The simulation results based on the function optimization problem show that the improved algorithm has better performance of optimization, faster convergence speed and higher convergence accuracy.

There are generally zero drift, sensitivity drift and nonlinear error in silicon piezoresistive pressure sensors due to the inherent characteristics of semiconductor materials. It is necessary to compensate and correct the errors produced so as to meet the requirements of measurement accuracy. In order to further improve the compensation precision, based on the research of various basic software compensation methods, a surface fitting compensation algorithm based on least square method is designed, and the software is implemented on the Visual Basic platform. The experimental results show that the zero drift, sensitivity drift and nonlinear error is effectively eliminated, and the output precision of the sensor is greatly improved.

How to classify incredible messages has attracted great attention from academic and industry nowadays. The recent work mainly focuses on one type of incredible messages (a.k.a rumors or fake news) and achieves some success to detect them. The existing problem is that incredible messages have different types on social media, and rumors or fake news cannot represent all incredible messages. Based on this, in the paper, we divide messages on social media into five types based on three dimensions of information evaluation metrics. And a novel method is proposed based on deep learning for classifying the five types of incredible messages on social media. More specifically, we use attention mechanism to obtain deep text semantic features and strengthen emotional semantics features, meanwhile, construct universal metadata as auxiliary features, concatenating them for incredible messages classification. A series of experiments on two representative real-world datasets demonstrate that the proposed method outperforms the state-of-the-art methods.

The development of algorithms to solve Many-objective optimization problems (MaOPs) has attracted significant research interest in recent years. Solving various types of Pareto front (PF) is a daunting challenge for evolutionary algorithm. A Research mode based evolutionary algorithm (RMEA) is proposed for many-objective optimization. The archive in the RMEA is used to store non-dominated solutions that can reflect the shape of the PF to guide the reference vector adaptation. Information concerning the population is collected, once the number of non-dominated solutions reaches its limit after many generations without exceeding a given threshold, RMEA introduces a research mode that generates more reference vectors to search through the solutions. The proposed algorithm showed competitive performance with four state-of-the-art evolutionary algorithms in a large number of experiments.

Characteristics of long-running applications in cloud and big data environment are various and significantly influence the performance of cache systems. The gap between existing cache systems and the increasing performance requirements motivates us to propose the Application-oriented cache allocation and prefetching method (ACAP) to improve data access performance. An application-oriented cache allocation approach is designed based on hit count growth rates for a higher overall hit rate. Two application-oriented sequential prefetching approaches are proposed to improve the hit rate and prefetching accuracy by learning average read sizes of long-running applications. Based on correlation of data accesses, a parallelized correlated-directed prefetching approach is proposed to further increase the hit rate. Above approaches are intergrated to obtain the maximized hit rate and prefetching accuracy. Experimental results on 12 public real system traces show that ACAP achieves 14.03% (up to 33.82%) higher prefetching accuracy and 2.01% (up to 7.54%) higher hit rate compared with the best combination of baselines.

Duffing oscillator is one of the classic nonlinear system that can generate chaotic motion. Given the sensitivity to regular signals but immunity to noise of its chaotic attractor, the Duffing oscillator can be used for weak signal detection. Our recent study on other attractors of Duffing oscillator showed that the state transition of its steady attractor not only has the two major advantages of the chaotic attractor, but also has a specific advantage, that is, it has no transitional zone. In the nearby area of the steady attractor, noise may even cause stochastic resonance, which significantly increases the output signaltonoise ratio. For the first time, we present a measure function for the state transition of the steady attractor of Duffing oscillator and then proposed a novel estimation method for weak sinusoidal signal buried in strong noise. Simulations were conducted to show the efficiency of the proposed method, and results indicate that the proposed method can achieve estimation of amplitude and frequency for sinusoidal signal. Moreover, the proposed method has a higher estimation accuracy and a stronger anti-noise performance than the classical spectrum and maximum likelihood estimation method.

This paper proposed Quaternion locality preserving projection (QLPP) for multi-feature multimodal biometric recognition. Multi-features fill the real part or the three imaginary parts of quaternion to constitute the quaternion fusion features. In quaternion division ring, QLPP extracts the local information and finds essential manifold structure of the quaternion fusion features. Deferent from Quaternion principal component analysis (QPCA) and Quaternion fisher discriminant analysis (QFDA), QLPP takes advantage of the optimal linear approximations to find the nonlinear manifold structures. Two experiments are designed: one fuses four features from two biometric modalities, and the other fuses three features from three biometric modalities. The experimental results show the proposed algorithm achieves much better performance than the unimodal biometric algorithms, the traditional feature level fusion methods(weighted sum rule and series rule) and two quaternion representation methods(QPCA and QFDA).

To facilitate the search of rapidly growing biomedical knowledge in literature, we developed a Biomedical entity-relationship search tool (BERST). It is also a biomedical knowledge integration framework, which presently contains six popular databases represented in terms of a network of concepts and relations extracted from these knowledge sources. Users search the integrated knowledge network by entering keywords, and BERST returns a sub-network matching and representing the keywords and their relationships. The resulting graph can be navigated interactively allowing users to explore specific paths between any two nodes representing potentially interesting relationships between them. A graphical UI was developed to provide a more intuitive and overall view of the information being searched and studied. BERST framework can be naturally expanded to integrate other biomedical knowledge sources. BERST is implemented as a Java web application.

Ultrasound computed tomography (USCT) is considered to have great potential for breast cancer screening. Compared with ray-based methods, Waveform inversion (WI) methods obtain high spatial resolution images because they consider higher-order diffraction effects. For the WI method, considering more properties of the medium in a forward model can estimate more accurate images. However, longer reconstruction time is required. Therefore, to reduce the reconstruction time, three hypotheses are set in this work to develop the medium under different conditions. We compare the reconstructed images using the four forward models to analyze the effects of the various considered medium properties, which include the sound speed, density of the medium, acoustic absorption and dispersion. To reduce the difficulty of hardware manufacturing, a square border ultrasonic transducer array is adopted in the USCT data acquisition system. Penalized leastsquares optimization problems are constructed to obtain numerical solutions of the sound speed and bulk modulus distributions. The reconstruction of the bulk modulus makes the reconstructed sound speed images more accurate. Computer simulations are conducted to compare reconstructed images using the four forward models under different noise conditions. A numerical breast phantom is used to evaluate the performance. The results suggest that for breast imaging, the forward model (which only considers the heterogeneous sound speed) is a compromise option between image accuracy and computational time.

Depression is a neurophysiological disorder with recurrent dysregulations of self-mental states. Multiscale Approximate entropy (ApEn) and Sample entropy (SampEn) are employed to characterize nonlinear complexity of Magnetoencephalography (MEG) of depressive patients in our contribution. SampEn shares similarities with ApEn while has better distinctions between the MEGs of depression patients and normal people. Test results prove that nonlinear complexity of the depressive MEG is lower than that of the normal subjects, indicating weaker response of depression patients to emotional stimuli, and the optimum discriminations between the depressive and healthy people lie in frontal lobe of brain which is related to emotional regulation. Our .ndings provide valuable information about depression, highlight the loss of nonlinear complexity in MEG of depressive patient and can be used as clinical diagnostic aids.

If the glowworm individual has no memory during its movement, and the decision of next direction is limited to its current position. It is precisely these reasons mentioned above that make the basic Glowworm Swarm Optimization easy to trap into the local optimum. In order to solve the problem, this paper suggests a Shuffled mutation glowworm swarm optimization (SMGSO), which combines the thought of Shuffled Frog Leaping with Glowworm Swarm Optimization. Making use of a grouping idea of Shuffled Mutation, the glowworm swarm is divided into several subgroups. The location updating of each individual is not only influenced by the brightest node in neighbour scope, but also by the brightest one in their local subgroup, meanwhile the locations of those isolated nodes are updated by the difference mutation of the global optimum and local optimal. In group shuffling stage, an orthogonal strategy can guide the whole population to generate their offspring. The performance of this proposed approach is examined by well-known 10 benchmark functions, and its obtained results are compared with what other variants hold. The experimental analysis show that the Shuffled mutation glowworm swarm optimization is effective and outperforms other variants in terms of solving multi-modal function optimization problems, and the proposed approach can improve the positioning accuracy of the centroid localization.

To facilitate the integration of terrestrial and satellite mobile communication systems in the future, the new generation of satellite mobile communication system should be Long-term evolution (LTE)-based. In LTE, voice is carried by Internet protocol (IP) packets (voice over IP, VoIP). The feasibility of LTE-based Geosynchronous earth orbit (GEO) satellite mobile communication systems for VoIP is discussed. Detailed link budgets are created for typical handheld terminals. The effectiveness of Physical resource block (PRB) bundling and Transmission time interval (TTI) bundling for the capacity improvement of downlink and uplink are evaluated, and further uplink improvement for low level terminals by Narrowband resource block (NRB) scheme is also studied. The analysis results show that the downlink VoIP transmission is mainly power limited, and optimization of PRB or TTI bundling can maximize the capacity. For uplink, only highlevel terminals can support VoIP under the normal LTE frame structure, and TTI bundling and Resource block (RB) block techniques can help the low-level terminals support VoIP transmission.

A multi-user detection algorithm for multiple access interference suppression combined with the minimum mean squared error and an artificial fish swarm algorithm is proposed. By taking advantage of the artificial fish swarm algorithm, the proposed algorithm can quickly converge to the optimal solution. The variation in the minimum mean squared error based test results is assigned to the artificial fish swarm algorithm, which is regarded as the initial state of artificial fish. The simulation results show that the proposed algorithm has a better multi-user capacity performance and lower bit-error rate than the minimum mean squared error detector.

It is critical to design a reliable packet transmission scheme to improve throughput in Energy harvesting Wireless sensor network (EH-WSN) or Battery free Wireless sensor network (BF-WSN). We present the Optimal size and rate (OSR) scheme to improve throughput of communication links in the IEEE 802.15.4 based BF-WSN that harvests radio frequency energy. Based on the de.ned truncated geometrical distribution, we formulate the optimization problem that maximizes the e.ective throughput of communication link. The solution of the optimization problem yields the optimal triple, namely the optimal packet size, data rate, and Maximum number of transmission trials (MNTT), which is used in the OSR so that throughput is considerably improved. Simulation results show the OSR scheme can improve throughput.

An efficient analytical method is proposed to calculate the Shielding effectiveness (SE) of an enclosure with an oblique rectangular aperture. This enclosure was first decomposed into two ones with horizontal rectangular apertures through mirror image and Green function theories. The SE of each decomposed enclosure was calculated by the Robinson's method. The relationship between the SE of the original cavity and those of the decomposed ones was also derived analytically. The SE of the original cavity could be directly obtained from this analytical relationship. The accuracy of the proposed method was validated with measurements and the Transmission line matrix (TLM) solver. The results show that the efficiency was largely improved compared with the TLM solver.

Micro-motion characteristics play an important role in some applications of radar target classi.cation. In this paper, a classi.cation method of rigid targets in space using radar micro-Doppler signatures is proposed. Based on the attitude kinematics of rigid targets, we analyze feasibility of classi.cation using micro-Doppler signatures by the relationship among inertial properties of typical rigid targets, their micro-motion characteristics, and corresponding modulation to radar echoes. According to the micro-Doppler time-frequency distribution of echoes and the scale of training sample set, Convolutional neural network (CNN) based feature extraction method and softmax Classi.er are designed. Simulations are carried out to validate its e.ectiveness and discuss the impact of observation duration, composition of training data and size of convolutional kernels on its classi.cation robustness and computational cost.

Constant false-alarm rate Block-sparse Bayesian learning (CFAR-BSBL) algorithm is proposed to reduce the computational complexity and improve Direction of arrival (DOA) estimation accuracy of offgrid signals with coprime array. Firstly, a signal model with normalized noise is built to avoid the learning procedure of noise parameter. Secondly, a block sparse Bayesian framework is built with the introduction of a temporary correlation matrix in order to use t he temporal structure of incident signals. Then the algorithm uses CFAR detection to detect the grids close to the real DOA and relieve the dependence on the number of signals. Finally, an off-grid process based on the closest grids is adopted to deal with the off-grid problem. The proposed CFAR-BSBL algorithm can obtain high accuracy and low complexity DOA estimation of off-grid signals with coprime array.

Accurate on-wafer large signal characterization of RF transistor is crucial for the optimum design of wireless communication circuits. We report a novel and systematic measurement method for the accurate acquisition of input and output power of on-wafer transistors up to 40GHz. This method employs external couplers to extract the travelling waves, combined with a novel large signal calibration algorithm to calculate the power at on-wafer probe tip. The accuracy of this method was bench marked versus conventional approaches in a real measurement bench, and further been verified by characterizing the large signal response of a 0.25μm GaN HEMT device. It is concluded that the measurement uncertainty has been greatly decreased with this new method, especially at mm-wave frequencies.

A novel structure cavity filter, crosscoupled multi-cavity filter with double-layer structure is proposed. The transmission zero of cross coupling is analyzed qualitatively and quantitatively, while some of the design experience of simulation modeling are summed up and the magnetic coupling and electric coupling of synchronously tuned coupling resonators with double-layer structure are studied. Simulation and experimental results show that the cross-coupled multi-cavity filter with doublelayer structure not only can meet the high selectivity requirements, but also save space, which is conducive to miniaturization design. The feasibility, practicability and superior performance of the double-layer multi-cavity filter are fully verified by the experimental samples and the measurement results of the six-pole double-layer base station filter processed by the design model.

Degradation and self-recovery of polycrystalline Silicon (poly-Si) Thin film transistor (TFT) by using complementary metal oxide semiconductor (CMOS) inverter were investigated. Under DC stress, degradation mechanisms were clarified by comparing the Voltage transfer characteristics (VTC) of fresh and stressed inverters. It is determined that Negative bias temperature instability (NBTI) of p-TFT dominates the degradation of the inverter under zero bias DC stress. After removing the stress, the VTC continues to be degraded, because the interface trap-states and the grain boundary trapstates increase due to hydrogen species diffusion. It is found out that the VTC is shifted to its right side severely with negative bias stress of VIN. The NBTI of p-TFT is enhanced and the NBTI of n-TFT also plays a role on the degradation. When removing the negative bias stress, the self-recovery of NBTI of nTFT and the continuing degradation of NBTI of p-TFT become competing mechanisms, together controlling the VTC after-stress behavior. Consequently, the continuing degradation of NBTI of p-TFT is restrained by selfrecovery of NBTI of n-TFT.