FAST COMPUTATION OF WIDEBAND RCS USING CHARACTERISTIC BASIS FUNCTION METHOD AND ASYMPTOTIC WAVEFORM EVALUATION TECHNIQUE

The Asymptotic Waveform Evaluation (AWE) technique is an extrapolation method that provides a reduced-order model of linear system and has already been successfully used to analyze wideband electromagnetic scattering problems. As the number of unknowns increases, the size of Method Of Moments (MOM) impedance matrix grows very rapidly, so it is a prohibitive task for the computation of wideband Radar Cross Section (RCS) from electrically large object or multi-objects using the traditional AWE technique that needs to solve directly matrix inversion. In this paper, an AWE technique based on the Characteristic Basis Function (CBF) method, which can reduce the matrix size to a manageable size for direct matrix inversion, is proposed to analyze electromagnetic scattering from multi-objects over a given frequency band. Numerical examples are presented to il-lustrate the computational accuracy and efficiency of the proposed method.

Strong Convergence of Empirical Distribution for a Class of Random Matrices

Let {vij}, i, j = 1, 2, …, be i.i.d, random variables with Ev11 = 0, Ev11^2 = 1 and a1 = （ai1,…, aiM） be random vectors with {aij} being i.i.d, random variables. Define XN =（x1,…, xk） and SN =XNXN^T,where xi=ai×si and si=1/√N（v1i,…, vN,i）^T. The spectral distribution of SN is proven to converge, with probability one, to a nonrandom distribution function under mild conditions.

Speech Endpoint Detection in Noisy Environments Using EMD and Teager Energy Operator

Accurate endpoint detection is a necessary capability for speech recognition. A new energy measure method based on the empirical mode decomposition （EMD） algorithm and Teager energy operator （TEO） is proposed to locate endpoint intervals of a speech signal embedded in noise. With the EMD, the noise signals can be decomposed into different numbers of sub-signals called intrinsic mode functions （IMFs）, which is a zero-mean AM-FM component. Then TEO can be used to extract the desired feature of the modulation energy for IMF components. In order to show the effectiveness of the proposed method, examples are presented to show that the new measure is more effective than traditional measures. The present experimental results show that the measure can be used to improve the performance of endpoint detection algorithms and the accuracy of this algorithm is quite satisfactory and acceptable.

Wave separation for the great Sumatra-Andaman earthquake with regional seismic array

We analyzed the seismic waveforms from the December 26, 2004 Sumatra-Andaman earthquake recorded at broadband seismic stations in western Europe. Previous studies involving of the beam-forming technique and high frequency analysis suggest that the earthquake ruptured with a duration of around 500 s. This very long duration makes P wave overlap with later arrivals such as PP wave, which follows P in about 200 s. Since P waves are crucial for modeling earthquake processes, we propose an iterative method to separate P and PP waveforms. The separated P waveform confirms a second large energy release around 300 s after the initial rupture. The iterative signal separation technique is particularly useful for mixed signals that are not independent and the number of recording stations far exceeds number of mixed signal sources.

Study on moving target detection to passive radar based on FM broadcast transmitter

Target detection by a noncooperative illuminator is a topic of general interest in the electronic warfare field. First of all, direct-path interference （DPI） suppression which is the technique of bottleneck of moving target detection by a noncooperative frequency modulation（FM） broadcast transmitter is analyzed in this article; Secondly, a space-time-frequency domain synthetic solution to this problem is introduced： Adaptive nulling array processing is considered in the space domain, DPI cancellation based on adaptive fractional delay interpolation （AFDI） technique is used in planned time domain, and long-time coherent integration is utilized in the frequency domain; Finaily, an experimental system is planned by considering FM broadcast transmitter as a noncooperative illuminator, Simulation results by real collected data show that the proposed method has a better performance of moving target detection.

Bayesian-based Wavelet Shrinkage for SAR Image Despeckling Using Cycle Spinning

A novel and efficient speckle noise reduction algorithm based on Bayesian wavelet shrinkage using cycle spinning is proposed. First, the sub-band decompositions of non-logarithmically transformed SAR images are shown. Then, a Bayesian wavelet shrinkage factor is applied to the decomposed data to estimate noise-free wavelet coefficients. The method is based on the Mixture Gaussian Distributed （MGD） modeling of sub-band coefficients. Finally, multi-resolution wavelet coefficients are reconstructed by wavelet-threshold using cycle spinning. Experimental results show that the proposed despeclding algorithm is possible to achieve an excellent balance between suppresses speckle effectively and preserves as many image details and sharpness as possible. The new method indicated its higher performance than the other speckle noise reduction techniques and minimizing the effect of pseudo-Gibbs phenomena.

Single Channel Speech Enhancement by De-noising Using Stationary Wavelet Transform

A method of single channel speech enhancement is proposed by de-noising using stationary wavelet transform. The approach developed herein processes multi-resolution wavelet coefficients individually and then recovery signal is reconstructed. The time invariant characteristics of stationary wavelet transform is particularly useful in speech de-noising. Experimental results show that the proposed speech enhancement by de-noising algorithm is possible to achieve an excellent balance between suppresses noise effectively and preserves as many target characteristics of original signal as possible. This de-noising algorithm offers a superior performance to speech signal noise suppress.

Medical X-Ray Image Enhancement Based on Kramer's PDE Model

The purpose of this study is to present an application of a novel enhancement technique for enhancing medical images generated from X-rays. The method presented in this study is based on a nonlinear partial differential equation （PDE） model, Kramer＇s PDE model. The usefulness of this method is investigated by experimental results. We apply this method to a medical X-ray image. For comparison, the X-ray image is also processed using classic Perona-Malik PDE model and Catte PDE model. Although the Perona-Malik model and Catte PDE model could also enhance the image, the quality of the enhanced images is considerably inferior compared with the enhanced image using Kramer＇s PDE model. The study suggests that the Kramer＇s PDE model is capable of enhancing medical X-ray images, which will make the X-ray images more reliable.

Improving Transferability Reversible Adversarial Examples Based on Flipping Transformation

Adding subtle perturbations to an image can cause the classification model to misclassify,and such images are called adversarial examples.Adversar-ial examples threaten the safe use of deep neural networks,but when combined with reversible data hiding(RDH)technology,they can protect images from being correctly identified by unauthorized models and recover the image lossless under authorized models.Based on this,the reversible adversarial example(RAE)is ris-ing.However,existing RAE technology focuses on feasibility,attack success rate and image quality,but ignores transferability and time complexity.In this paper,we optimize the data hiding structure and combine data augmentation technology,whichflips the input image in probability to avoid overfitting phenomenon on the dataset.On the premise of maintaining a high success rate of white-box attacks and the image’s visual quality,the proposed method improves the transferability of reversible adversarial examples by approximately 16%and reduces the com-putational cost by approximately 43%compared to the state-of-the-art method.In addition,the appropriateflip probability can be selected for different application scenarios.

Multiple-Instance Learning with Instance Selection via Constructive Covering Algorithm

Multiple-Instance Learning （MIL） is used to predict the unlabeled bags＇ label by learning the labeled positive training bags and negative training bags.Each bag is made up of several unlabeled instances.A bag is labeled positive if at least one of its instances is positive,otherwise negative.Existing multiple-instance learning methods with instance selection ignore the representative degree of the selected instances.For example,if an instance has many similar instances with the same label around it,the instance should be more representative than others.Based on this idea,in this paper,a multiple-instance learning with instance selection via constructive covering algorithm （MilCa） is proposed.In MilCa,we firstly use maximal Hausdorff to select some initial positive instances from positive bags,then use a Constructive Covering Algorithm （CCA） to restructure the structure of the original instances of negative bags.Then an inverse testing process is employed to exclude the false positive instances from positive bags and to select the high representative degree instances ordered by the number of covered instances from training bags.Finally,a similarity measure function is used to convert the training bag into a single sample and CCA is again used to classification for the converted samples.Experimental results on synthetic data and standard benchmark datasets demonstrate that MilCa can decrease the number of the selected instances and it is competitive with the state-of-the-art MIL algorithms.

MICkNN:Multi-Instance Covering kNN Algorithm

Mining from ambiguous data is very important in data mining. This paper discusses one of the tasks for mining from ambiguous data known as multi-instance problem. In multi-instance problem, each pattern is a labeled bag that consists of a number of unlabeled instances. A bag is negative if all instances in it are negative. A bag is positive if it has at least one positive instance. Because the instances in the positive bag are not labeled, each positive bag is an ambiguous. The mining aim is to classify unseen bags. The main idea of existing multi-instance algorithms is to find true positive instances in positive bags and convert the multi-instance problem to the supervised problem, and get the labels of test bags according to predict the labels of unknown instances. In this paper, we aim at mining the multi-instance data from another point of view, i.e., excluding the false positive instances in positive bags and predicting the label of an entire unknown bag. We propose an algorithm called Multi-Instance Covering kNN （MICkNN） for mining from multi-instance data. Briefly, constructive covering algorithm is utilized to restructure the structure of the original multi-instance data at first. Then, the kNN algorithm is applied to discriminate the false positive instances. In the test stage, we label the tested bag directly according to the similarity between the unseen bag and sphere neighbors obtained from last two steps. Experimental results demonstrate the proposed algorithm is competitive with most of the state-of-the-art multi-instance methods both in classification accuracy and running time.

Hierarchical Covering Algorithm

The concept of deep learning has been applied to many domains, but the definition of a suitable problem depth has not been sufficiently explored. In this study, we propose a new Hierarchical Covering Algorithm （HCA） method to determine the levels of a hierarchical structure based on the Covering Algorithm （CA）. The CA constructs neural networks based on samples＇ own characteristics, and can effectively handle multi-category classification and large-scale data. Further, we abstract characters based on the CA to automatically embody the feature of a deep structure. We apply CA to construct hidden nodes at the lower level, and define a fuzzy equivalence relation R on upper spaces to form a hierarchical architecture based on fuzzy quotient space theory. The covering tree naturally becomes from R. HCA experiments performed on MNIST dataset show that the covering tree embodies the deep architecture of the problem, and the effects of a deep structure are shown to be better than having a single level.