Aiming at the technology of target detection based on broadcasting satellite passive radar. This paper presents the characteristic of broadcasting satellite signals and its ambiguity function. Calculates the maximum d...
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ISBN:
(纸本)9781467391672
Aiming at the technology of target detection based on broadcasting satellite passive radar. This paper presents the characteristic of broadcasting satellite signals and its ambiguity function. Calculates the maximum detection distance by analyzing the power loss and the gain of signalprocessing. Demonstrates the necessity of depressing the direct satellite signals. The conclusion of this paper is: The broadcasting satellite signal is suitable for passive radar target detection. The detection distance can achieve 100km with 36MHz signal bandwidth, 60dB gain of antenna, Is integrated time. Appropriate methods is necessary to depress the direct wave received from the side lobe of echo antenna. The possibility of this target detection method is proved by the experiment results in this paper.
High accuracy of electroencephalogram (EEG) classification can hardly be achieved if the signals are contaminated by severe artefacts. One helpless way to avoid such artefacts is usually to directly discard the severe...
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High accuracy of electroencephalogram (EEG) classification can hardly be achieved if the signals are contaminated by severe artefacts. One helpless way to avoid such artefacts is usually to directly discard the severely disturbed EEG segments. This study considers a more elegant way that tries to recover the disturbed segments from other undisturbed segments. The possible artefacts in EEG are treated as missing values. A Bayesian tensor factorization (BTF) based method is proposed to implement EEG completion for artefact removal. By specifying a sparsity-inducing hierarchical prior, the underlying low-rank tensor is discovered from incomplete EEG tensor with automatically inferred model parameters. The EEG missing values are effectively predicted with robustness to overfitting. Effectiveness of the BTF algorithm is demonstrated on EEG data recorded from seven subjects in a brain-computer interface paradigm based on event-related potentials.
Non-small cell lung cancer (NSCLC) is a malignant tumor, and contains three major subtypes which are difficult to be distinguished at early stages of NSCLC. Many pathways work together to perform certain functions in ...
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ISBN:
(纸本)9781509016129
Non-small cell lung cancer (NSCLC) is a malignant tumor, and contains three major subtypes which are difficult to be distinguished at early stages of NSCLC. Many pathways work together to perform certain functions in cells. One might expect the high level of co-appearance or repression of pathways to distinguish different subtypes of NSCLC. However, it is difficult to detect coordinated regulations of pathways by existing methods. In our work, the coordinated regulations of pathways are detected using modified higher logic analysis of gene expression data. Specifically, we identify the genes whose regulation obeys a logic function by the modified higher logic analysis, which focuses on the relationships among the gene triplets that are not evident when genes are examined in a pairwise fashion. Then, the relationships among genes are mapped to pathways to predict the coordinated regulated relationships among pathways. By comparing coordinated regulations of pathways, we find that the regulation patterns of pathways which are associated with cell death are different in three subtypes of NSCLC. This method allows us to uncover co-appearance or repression of pathways in high level, and it has a potential to distinguish the subtypes for complex diseases.
This paper presents a new approach, called convex cone volume analysis (CCVA), which can be considered as a partially constrained-abundance (abundance non-negativity constraint) technique to find endmembers. It can be...
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In this paper,we introduce the Auto-Annotation LDA models(aa LDA),a statistical model of non-labeled *** model generates the annotation of LDA *** derive the annotation of LDA using a k-means methods combined with a p...
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In this paper,we introduce the Auto-Annotation LDA models(aa LDA),a statistical model of non-labeled *** model generates the annotation of LDA *** derive the annotation of LDA using a k-means methods combined with a pre-processing of the *** this paper,we use aa LDA models to categorize "zhongwenshilei" corpus,which is a famous Chinese *** we make a compare with the traditional LDA methods.
In this paper, we propose a tensor-based non-local filtering technique for image and MRI denoising using Bayesian CP factorization (BCPF). This approach simply groups together similar sub-tensors (e.g., 3D tensors) se...
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In this paper, we propose a tensor-based non-local filtering technique for image and MRI denoising using Bayesian CP factorization (BCPF). This approach simply groups together similar sub-tensors (e.g., 3D tensors) selected from a noisy tensor and forms a 4D stack, then decomposes this stack into latent factors by employing BCPF, resulting in a filtered group of 3D sub-tensors. This procedure is repeated across the entire tensor in sliding window fashion to obtain the denoised result of original tensor. Our Bayesian CP factorization can learn CP-rank as well as noise variance solely from the observed noisy tensor data, which can also avoid overfitting problem by employing a fully Bayesian treatment for latent factor inference. The main advantage of our method is that the standard deviation of Gaussion noise can be automatically inferred and not necessary to be fixed. The experimental results on image and MRI denoising demonstrate the superiorities of our method in terms of flexibility and performance, as compared to other tensor-based denoising methods.
Random numbers and sequences are widely used in various areas, especially the cryptography for information security. Statistical randomness test is used to evaluate the deviation from randomness. There are lots of sta...
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ISBN:
(纸本)9781467391672
Random numbers and sequences are widely used in various areas, especially the cryptography for information security. Statistical randomness test is used to evaluate the deviation from randomness. There are lots of statistical randomness tests. However, most of the randomness tests focused on the number of occurrences of pre-specified pattern, and lost the information of the distribution of occurrence position. Hence, in this paper, I proposed a new test focusing on the distribution of occurrence position of pre-specified pattern, and proved it by mathematical method. The experimental results show that there has a consistency between the new test and NIST test suite, and in the sense of the distribution of occurrence position of the pre-specified pattern, the new test has advantage over the NIST test suite.
Overlapping signal separation in spectrum is a difficult problemWe use Gamma Mixture Model to formulate the distribution of signal power in each frequency bin of digital phosphor technology(DPX) spectrumThen Expectati...
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Overlapping signal separation in spectrum is a difficult problemWe use Gamma Mixture Model to formulate the distribution of signal power in each frequency bin of digital phosphor technology(DPX) spectrumThen Expectation Maximization(EM) Algorithm is used to solve the modelSimulation results show that when CIR is greater than 2.7d B, parameters' estimation error rate of this algorithm is less than 1e-5.
The distributed radar system is a solution to detect small targets in the *** how to effectively accumulate the received signals from different radar stations is a challenging *** signals influenced by different time-...
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ISBN:
(纸本)9781510805750
The distributed radar system is a solution to detect small targets in the *** how to effectively accumulate the received signals from different radar stations is a challenging *** signals influenced by different time-delay,Doppler frequency and reflection coefficient will have different phases before *** this thesis,we study the phase compensation methods to eliminate the effects caused by these factors.
Due to the decrease of azimuth resolution and array gain, the performance of small aperture over the horizon radar (OTHR) is not as good as the conventional OTHR. Therefore, it is necessary to find a new method to imp...
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ISBN:
(纸本)9781467390996
Due to the decrease of azimuth resolution and array gain, the performance of small aperture over the horizon radar (OTHR) is not as good as the conventional OTHR. Therefore, it is necessary to find a new method to improve array performance of small aperture OTHR to satisfy the requirements of target detection. In this paper, conclusions on the performance losses are obtained by deducing the expression of signal to clutter ratio (SCR) under small aperture OTHR receiving condition. Based on the conclusions, Hyper Beam, which is derived from sonar array processing, are applied to improve the performance of small aperture OTHR. Eventually, experimental simulations are given to verify our conclusions.
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