In this paper, we propose the random interval sub-Nyquist-sampling method for array radar. Under the assumption that the number of targets is much smaller than the number of receiver antennas, the received signal sati...
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Forward calculations and inverse calculations for magnetic targets are two important components of magnetic dataprocessing. Detection of magnetic targets is the premise of parameter inversion. By done some studies on...
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In biomedicine, it is typical to find studies discriminating between types of pathology or stages of disease based on as few as 30 sample points and tens of thousands of genes. Unfortunately, out-of-the-box classifica...
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ISBN:
(纸本)9781479903566
In biomedicine, it is typical to find studies discriminating between types of pathology or stages of disease based on as few as 30 sample points and tens of thousands of genes. Unfortunately, out-of-the-box classification and error estimation rules come with no small-sample performance guarantees, which has greatly contributed to the crisis in biomarker reproducibility. Recent work addresses this by supplementing the data with expert biological knowledge via a prior distribution over an uncertainty class of feature-label distributions, and uses the resulting probabilistic framework to define minimum mean-square-error (MMSE) estimators for the misclassification rate of any fixed classifier, as well as the sample-conditioned MSE itself for arbitrary error estimators. Here, we use the same framework to also define minimum expected error (MEE) classifiers, completing a Bayesian optimized theory of classification. We also present examples on real genomic data resulting in classifiers that greatly outperform popular rules.
Phenotype discrimination problems in biomedicine typically classify between types of pathology, stages of disease, response to treatment or survivability. In contrast to the usual heuristic classifier and error estima...
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ISBN:
(纸本)9781479936878
Phenotype discrimination problems in biomedicine typically classify between types of pathology, stages of disease, response to treatment or survivability. In contrast to the usual heuristic classifier and error estimate computed from small sample data, recent work proposes a Bayesian modeling framework over an uncertainty class of feature-label distributions, which when combined with data facilitates optimal MMSE error estimation, optimal classifier design and a sample-conditioned MSE for error estimation analysis, all relative to uncertainty in the underlying distributions conditioned on the sample. Here we address application of the conditional MSE to non-linear classifiers and present an example with optimal Bayesian classification and censored sampling, an economical sampling procedure in which data are collected incrementally until desired criteria are met.
Both maximum likelihood estimation as well as minimum mean optimal subpattern assignment (MMOSPA) estimation have been shown to provide meaningful estimates in instances of target identity uncertainty when the number ...
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ISBN:
(纸本)9780819495365
Both maximum likelihood estimation as well as minimum mean optimal subpattern assignment (MMOSPA) estimation have been shown to provide meaningful estimates in instances of target identity uncertainty when the number of targets present is known. Maximum likelihood measurement to track association (2D assignment) has been widely studied and is reviewed in this paper. However, it is widely believed that approximate MMOSPA estimation can not be performed in real time except when considering a very small number of targets. This paper demonstrates the MMOSPA estimator arises as a special case of a minimum mean Wasserstein metric estimator when the number of targets is unknown. Additionally, it is shown that approximate MMOSPA estimates can be calculated in microseconds to miliseconds without extensive optimization, making MMOSPA estimation a practicable alternative to more traditional estimators.
Since the concept of reversible data hiding technique was introduced, many researchers have applied it for authentication of uncompressed images. In this paper, an algorithm is introduced to compress JPEG files again ...
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ISBN:
(纸本)9789869000604
Since the concept of reversible data hiding technique was introduced, many researchers have applied it for authentication of uncompressed images. In this paper, an algorithm is introduced to compress JPEG files again without any loss in image quality. The proposed method can modify an entire segment of VLC codeword sequence to embed a bit of data. The modified codewords may destroy the correlation, or the smoothness, between neighboring pixels of the recovered image. The data extractor utilizes the smoothness change to know the hidden data. For this, a novel smoothness measurement function which uses both inter-and intra-MAD values is proposed. When the smoothness change is small, two consecutive segments are concatenated to extract correct data with higher smoothness sensitivity. As a result, compression ratio or embedding capacity is increased in most natural images.
The adaptive spectral estimation method IAA provides better performance than the periodogram at the cost of higher computational complexity. Current fast IAA algorithms reduce the computational complexity using Toepli...
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ISBN:
(纸本)9781479903566
The adaptive spectral estimation method IAA provides better performance than the periodogram at the cost of higher computational complexity. Current fast IAA algorithms reduce the computational complexity using Toeplitz/Vandermonde structures, but are not efficient for missing data cases when the number of missing samples is small. We considerably reduce the computational complexity compared to the state-of-the-art by using a low rank completion to transform the problem to a Toeplitz/Vandermonde structured problem.
This paper investigates the role of noise in speaker-adaptation of HMM-based text-to-speech (TTS) synthesis and presents a new evaluation procedure. Both a new listening test based on ITU-T recommendation 835 and a pe...
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ISBN:
(纸本)9781479903566
This paper investigates the role of noise in speaker-adaptation of HMM-based text-to-speech (TTS) synthesis and presents a new evaluation procedure. Both a new listening test based on ITU-T recommendation 835 and a perceptually motivated objective measure, frequency-weighted segmental SNR, improve the evaluation of synthetic speech when noise is present. The evaluation of voices adapted with noisy data show that the noise plays a relatively small but noticeable role in the quality of synthetic speech: Naturalness and speaker similarity are not affected in a significant way by the noise, but listeners prefer the voices trained from cleaner data. Noise removal, even when it degrades natural speech quality, improves the synthetic voice.
The true potential of the orthogonal frequency divisional multiple access (OFDMA)coordinated multipoint (CoMP) is in turning the adverse effects of the inter-cell interference (ICI) into the system performance gains. ...
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The true potential of the orthogonal frequency divisional multiple access (OFDMA)coordinated multipoint (CoMP) is in turning the adverse effects of the inter-cell interference (ICI) into the system performance gains. The small ultra-dense deployments of data-hungry users in the forthcoming generations of wireless mobile access systems, namely the long term evolution (LTE) A as 4G and the subsequent 5G, will be pushing for the revolutionary innovations to accommodate the required peak rates and the reliability in the cell edge. CoMP has been identified as a competent solution to exploit the interference in the cell-edge. To this end, we consider the non-coherent joint processing (JP) mode of CoMP in the downlink (DL) of an LTE system. Here, we formulate the problem of the joint cooperative scheduling and transmit power allocationas a hybrid mixed integer non-convex optimization problem. In particular, the transmit power allocation is based on the allocation of the time-frequency resources according to a proportionally fair (PF) utility function with certain CoMPspecific constraints on the *** problem formulation serves a two-fold purpose of both the interference coordination under a full frequency reuse regime and the inherent interference mitigation capabilities.
Genetic transformation of maize is highly dependent on the development of embryonic calli from the dedifferentiated immature embryo. To better understand the regulatory mechanism of immature embryo dedifferentiation, ...
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Genetic transformation of maize is highly dependent on the development of embryonic calli from the dedifferentiated immature embryo. To better understand the regulatory mechanism of immature embryo dedifferentiation, we generated four small RNA and degradome libraries from samples representing the major stages of dedifferentiation. More than 186 million raw reads of small RNA and degradome sequence data were generated. We detected 102 known miRNAs belonging to 23 miRNA families. In total, we identified 51, 70 and 63 differentially expressed miRNAs (DEMs) in the stage I, II, III samples, respectively, compared to the control. However, only 6 miRNAs were continually up-regulated by more than fivefold throughout the process of dedifferentiation. A total of 87 genes were identified as the targets of 21 DEM families. This group of targets was enriched in members of four significant pathways including plant hormone signal transduction, antigen processing and presentation, ECM-receptor interaction, and alpha-linolenic acid metabolism. The hormone signal transduction pathway appeared to be particularly significant, involving 21 of the targets. While the targets of the most significant DEMs have been proved to play essential roles in cell dedifferentiation. Our results provide important information regarding the regulatory networks that control immature embryo dedifferentiation in maize. (C) 2013 Elsevier Inc. All rights reserved.
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