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.     

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.     

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 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     

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     

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.     

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。     

Mean likelihood frequency estimation

Estimation of signals with nonlinear as well as linear parameters in noise is studied. Maximum likelihood estimation has been shown to perform the best among all the methods. In such problems, joint maximum likelihood estimation of the unknown parameters reduces to a separable optimization problem, where first, the nonlinear parameters are estimated via a grid search, and then, the nonlinear parameter estimates are used to estimate the linear parameters. We show that a grid search can be avoided by using the mean likelihood estimator for estimating the unknown nonlinear parameters and how its performance can be made equivalent to that of the maximum likelihood estimator (MLE). The mean likelihood estimator requires computation of a multidimensional integral. However, using the concepts of importance sampling, we obtain the mean likelihood estimate without using integration. The technique is computationally far less burdensome than the direct maximum likelihood method but performs just as well. Simulation examples for estimating frequencies of multiple sinusoids in noise are given. The general technique can be applied to a large class of nonlinear regression problems     

Generalized likelihood signal resolution

This paper defines anM-ary generalized likelihood ratio test (MGLRT) that overcomes Root's early objection to the application of generalized likelihood ratio testing to the resolution of correlated signals. The proposed test extends the form of a conventional binary generalized likelihood ratio test (GLRT) in a manner that permits a generalization of the minimax properties of the binary test to theM- hypotheses case. When the estimated signals are orthogonal, the test reduces to a sequence of conventional binary tests duplicating the performance of a narrow-band matched filter envelope-detector receiver.     

Equalized near maximum likelihood detector

This paper presents new detector that is used to mitigate intersymbol interference introduced by bandlimited channels. This detector is named equalized near maximum likelihood detector which combines nonlinear equalizer and near maximum likelihood detector. Simulation results show that the performance of equalized near maximum likelihood detector is better than the performance of nonlinear equalizer but worse than near maximum likelihood detector.