Real-time modulation classification based on maximum likelihood

This paper describes a likelihood test based modulation classification method for identifying the modulation scheme of a software-defined radio (SDR) in real-time without pilot symbols between transmitters and receivers. Unlike the prior art, the paper converts an unknown signal symbol to an address of the look-up table (LUT), loads the pre-calculated values of the test functions for the likelihood ratio test, and produces the estimated modulation scheme in real-time. The statistical performance of the LUT based classifier is studied. Simulation results are presented to confirm the theoretical analysis.     

Efficient maximum likelihood classification for imagingspectrometer data sets

A simplified maximum likelihood classification technique for handling remotely sensed image data is proposed which reduces, significantly, the processing time associated with traditional maximum likelihood classification when applied to imaging spectrometer data, and copes with the training of geographically small classes. Several wavelength subgroups are formed from the complete set of spectral bands in the data, based on properties of the global correlation among the bands. Discriminant values are computed for each subgroup separately and the sum of discriminants is used for pixel labeling. Several subgrouping methods are investigated and the results show that a compromise among classification accuracy, processing time, and available training pixels can be achieved by using appropriate subgroup sizes     

Fast and scalable packet classification

Emerging Internet applications create the need for advanced packet classifiers. We propose a novel multifield classification scheme, called P/sup 2/C, which exploits the strengths of state-of-the-art memory technologies to provide wire-speed classification performance for OC-192 and beyond, in combination with very high storage efficiency and the support of fast incremental updates. Key features of the new scheme are its ability to adapt to the complexity of a classification rule set, whereas the storage requirements and update dynamics can be tuned at the granularity of individual rules. This makes P/sup 2/C suitable for a broad spectrum of applications.     

Fast maximum likelihood for blind identification of multiple FIRchannels

This paper develops a fast maximum likelihood method for estimating the impulse responses of multiple FIR channels driven by an arbitrary unknown input. The resulting method consists of two iterative steps, where each step minimizes a quadratic function. The two-step maximum likelihood (TSML) method is shown to be high-SNR efficient, i.e., attaining the Cramer-Rao lower bound (CRB) at high SNR. The TSML method exploits a novel orthogonal complement matrix of the generalized Sylvester matrix. Simulations show that the TSML, method significantly outperforms the cross-relation (CR) method and the subspace (SS) method and attains the CRB over a wide range of SNR. This paper also studies a Fisher information (FI) matrix to reveal the identifiability of the M-channel system. A strong connection between the FI-based identifiability and the CR-based identifiability is established     

Fast classification algorithm using table-look-up method

In this paper, a fast classification algorithm which uses a table-look-up method is proposed. Both theoretical and experimental results are shown: (1) in two dimensional case the computation time can be reduced to the same order of time needed for the data I/O; (2) in the case of more than two dimensions more than half a computation time can be saved. The classification accuracy is not affected by the use of the table-look-up method. The algorithm is most suitable for computers without a floating-point processor.     

On a relation between maximum likelihood classification and minimum relative-entropy classification (Corresp.)

Maximum likelihood (ML) and minimum relative-entropy (MRE) (minimum cross-entropy) classification of samples from an unknown probability density when the hypotheses comprise an exponential family are considered. It is shown that ML and MRE lead to the same classification nde, and the result is illustrated in terms of a method for estimating covariance matrices recently developed by Burg, Luenberger, and Wenger, MRE classification applies to the general case in which it cannot be assumed that the samples were generated by one of the hypothesis densities. The common use of ML in this case is technically incorrect, but the equivalence of MRE and ML provides a theoretical justification.     

基于NetFlow流记录的高速应用流量分类方法

针对目前应用流量分类算法效率不高的现状,提出一种以NetFlow统计的IP流记录信息作为输入的高速应用流量分类(FATC,fast application-level traffic classification)算法。该算法采用基于简单相关系数的测度选择算法衡量测度变量间的相关关系,删除对分类无用或相互冗余的测度,而后使用基于Bayes判别法的分类算法将网络流量分至误判损失最小的应用类别中。理论分析及实验表明,FATC算法在具有超过95%的分类准确率基础上,极大降低了当前应用流量分类方法在训练和分类过程的时空复杂度,满足实时准确分类当前10Gbit/s主干信道网络流量的需求。     

基于最大似然比准则的MPSK信号分类方法

本文提出了AWGN环境下一种基于最大似然比的MPSK信号识别方法,推导出了用于区分MPSK信号的特征参数通用表达式,提出了载频估计误差、信号功率对该参数影响的修正方法,本方法对实际BPSK,QPSK,8PSK,16PSK信号的正确识别率达到80%以上.     

Fast algorithms for phone classification and recognition usingsegment-based models

Methods for reducing the computation requirements of joint segmentation and recognition of phones using the stochastic segment model are presented. The approach uses a fast segment classification method that reduces computation by a factor of two to four, depending on the confidence of choosing the most probable model. A split-and-merge segmentation algorithm is proposed as an alternative to the typical dynamic programming solution of the segmentation and recognition problem, with computation savings increasing proportionally with model complexity. Although the current recognizer uses context-independent phone models, the results reported for the TIMIT database for speaker-independent joint segmentation and recognition are comparable to those of systems that use context information     

基于朴素贝叶斯分类的DVC-HEVC快速转码

分布式视频编码(DVC)与传统视频编码之间的转码为移动终端设备之间的低功耗视频通信提供了一种有效的实现思路。以DVC与HEVC转码为研究对象,利用DVC解码端信息,针对高效视频编码(HEVC)中复杂度极高的编码单元(CU)划分过程进行复杂度优化研究。在DVC解码端提取与CU划分相关的纹理复杂度、运动矢量及预测残差3种特征信息;在HEVC编码端基于朴素贝叶斯原理建立CU快速划分模型,模型生成后便可以通过输入特征信息对当前CU划分进行快速决策,避免大量率失真(RD)代价计算过程。实验结果表明,本方案在编码比特率略有上升的情况下大幅缩短了HEVC编码时间,平均下降幅度达到58.26%,且几乎不影响视频质量。     

Fast maximum likelihood DOA estimation in the two-target case with applications to automotive radar

Direction-of-arrival (DOA) estimation of two targets using a single snapshot plays an important role in automotive radar for advanced driver assistance systems. Conventional Fourier methods have a limited resolution and generally yield biased estimates. Subspace methods involve a numerically complex eigendecomposition and require multiple snapshots or a suboptimal pre-processing for reliable estimation. We therefore consider the maximum likelihood (ML) DOA estimator, which is applicable with a single snapshot and shows good statistical properties. To reduce the computational burden, we propose a grid search procedure with a simplified calculation of the objective function. The required projection operators are pre-calculated off-line and stored. To save storage space and computations, we further propose a rotational shift of the field-of-view such that the relevant angular sector, which has to be evaluated, is delimited and centered with respect to broadside. The final estimates are obtained using a quadratic interpolation. The developed method is demonstrated with an example. Simulations are designed to assess the performance of the considered ML estimator with grid search and interpolation, and to compare it among selected representative methods. We further present results obtained with experimental data from a typical application in automotive radar.     

The effect of zero feature correlation assumption on maximum likelihood based classification of chromosomes

As a first stage in the classification of chromosomes in a cell, the computation of the maximum class likelihood is widely used. This is a relatively expensive method, and here we investigate the effect on accuracy and the resulting reduction in computational cost of two techniques: (i) assumption of zero feature correlations, and (ii) a method of automatic selection of feature sub-sets, and the consequent dependence of the classification accuracy on the size of the training set.     

Fast road classification and orientation estimation using omni-viewimages and neural networks

This paper presents the results of integrating omnidirectional view image analysis and a set of adaptive backpropagation networks to understand the outdoor road scene by a mobile robot. Both the road orientations used for robot heading and the road categories used for robot localization are determined by the integrated system, the road understanding neural networks (RUNN). Classification is performed before orientation estimation so that the system can deal with road images with different types effectively and efficiently. An omni-view image (OVI) sensor captures images with 360 degree view around the robot in real-time. The rotation-invariant image features are extracted by a series of image transformations, and serve as the inputs of a road classification network (RCN). Each road category has its own road orientation network (RON), and the classification result (the road category) activates the corresponding RON to estimate the road orientation of the input image. Several design issues, including the network model, the selection of input data, the number of the hidden units, and learning problems are studied. The internal representations of the networks are carefully analyzed. Experimental results with real scene images show that the method is fast and robust.     

Fast human action classification and VOI localization with enhanced sparse coding

Sparse coding which encodes the natural visual signal into a sparse space for visual codebook generation and feature quantization, has been successfully utilized for many image classification applications. However, it has been seldom explored for many video analysis tasks. In particular, the increased complexity in characterizing the visual patterns of diverse human actions with both the spatial and temporal variations imposes more challenges to the conventional sparse coding scheme. In this paper, we propose an enhanced sparse coding scheme through learning discriminative dictionary and optimizing the local pooling strategy. Localizing when and where a specific action happens in realistic videos is another challenging task. By utilizing the sparse coding based representations of human actions, this paper further presents a novel coarse-to-fine framework to localize the Volumes of Interest (VOIs) for the actions. Firstly, local visual features are transformed into the sparse signal domain through our enhanced sparse coding scheme. Secondly, in order to avoid exhaustive scan of entire videos for the VOI localization, we extend the Spatial Pyramid Matching into temporal domain, namely Spatial Temporal Pyramid Matching, to obtain the VOI candidates. Finally, a multi-level branch-and-bound approach is developed to refine the VOI candidates. The proposed framework is also able to avoid prohibitive computations in local similarity matching (e.g., nearest neighbors voting). Experimental results on both two popular benchmark datasets (KTH and YouTube UCF) and the widely used localization dataset (MSR) demonstrate that our approach reduces computational cost significantly while maintaining comparable classification accuracy to that of the state-of-the-art methods.     

Fast evaluation of the likelihood of an HMM: ion channel currentswith filtering and colored noise

Hidden Markov models (HMMs) have been used in the study of single-channel recordings of ion channel currents for restoration of idealized signals from noisy recordings and for estimation of kinetic parameters. A key to their effectiveness from a computational point of view is that the number of operations to evaluate the likelihood, posterior probabilities and the most likely state sequence is proportional to the product of the square of the dimension of the state space and the length of the series. However, when the state space is quite large, computations can become infeasible. This can happen when the record has been lowpass filtered and when the noise is colored. In this paper, we present an approximate method that can provide very substantial reductions in computational cost at the expense of only a very small error. We describe the method and illustrate through examples the gains that can be made in evaluating the likelihood     

Fast convergence caching replacement algorithm based on dynamic classification for content-centric networks

One of the key research fields of content-centric networking （CCN） is to develop more efficient cache replacement policies to improve the hit ratio of CCN in-network caching. However, most of existing cache strategies designed mainly based on the time or frequency of content access, can not properly deal with the problem of the dynamicity of content popularity in the network. In this paper, we propose a fast convergence caching replacement algorithm based on dynamic classification method for CCN, named as FCDC. It develops a dynamic classification method to reduce the time complexity of cache inquiry, which achieves a higher caching hit rate in comparison to random classification method under dynamic change of content popularity. Meanwhile, in order to relieve the influence brought about by dynamic content popularity, it designs a weighting function to speed up cache hit rate convergence in the CCN router. Experimental results show that the proposed scheme outperforms the replacement policies related to least recently used （LRU） and recent usage frequency （RUF） in cache hit rate and resiliency when content popularity in the network varies.     

Expected likelihood support for deterministic maximum likelihood DOA estimation

In this paper, a solution quality assessment method referred to as the “expected likelihood” (EL) approach, previously introduced for the stochastic (unconditional) Gaussian model, is extended over the deterministic (conditional) Gaussian model. This model is applied for arbitrary temporally correlated (narrowband) waveforms, emitted by point sources impinging upon an antenna array. Performance of direction of arrival (DOA) estimation is then examined.     

光盘表面缺陷的快速分类与质量评价

本文介绍一种利用多通道脉冲宽度鉴别技术直接对光盘表面或光盘盘片表面缺陷进行快速分类和统计的方法和实验结果。通过对聚焦激光束扫描盘面获得的缺陷信号的脉冲宽度分类,统计各类缺陷个数,计算缺陷密度值,有效地评价光盘表面的性能质量。