A 4800 Bit/s Microprocessor Data Modem

The development of a new 4800 bit/s data modem, based on real-time digital signal processing by program controlled microprocessors, is described. The modem is fully compatible with the CCITT Recommendations V.27 bis and V.27 ter. In the microprocessor modem, all the digital signals are processed by one general purpose microprocessor unit only and no hardware multiplier is employed. A series of processing cycle reduction tactics have allowed an off-the-shelf microprocessor to handle all the modem functions in a real-time environment. Particularly noteworthy, among these tactics, is the use of a decision feedback equalization algorithm. Numbers of multiplications, which are required for a conventional linear equalization, are shown to be obviated with the DFE algorithm. It is also shown that the DFE facilitates a certain timing phase control method. The microprocessor modem is extremely flexible because of its software controlled nature, and through programming it will provide a variety of additional functions.     

基于星座修正聚类的双模式盲均衡算法

针对时变信道下,调制类型未知的MPSK/MQAM类信号快速均衡问题,本文提出了一种新的基于星座聚类的双模式均衡算法。初始阶段均衡器采用稳健的恒模算法,一段时间后,用低复杂度的减法聚类算法处理缓存的均衡器输出。然后利用MPSK/MQAM类信号星座图的几何旋转不变性,采用C均值模糊聚类算法修正初次聚类的结果,恢复信号星座,由此获得精确符号信息。最后依据修复后的聚类效果选择切换时机,均衡器切换到符号匹配算法,完成信号均衡。与已有算法相比,本文提出的新算法克服了调制类型未知的不利因素,具有收敛速度快,剩余码间干扰小,均衡器模式切换时性能平稳等优点。仿真验证了新算法对4PSK和16QAM信号的有效性。     

Joint blind equalization, carrier recovery and timing recovery forhigh-order QAM signal constellations

Two existing blind equalization tap update recursions for 64-point and greater QAM (quadrature amplitude modulation) signal constellations are studied, along with existing and novel carrier and timing recovery techniques. It is determined that the superior tap update recursion is the one known as the constant modulus algorithm. Carrier recovery requires a modified second-order decision-directed digital phase-locked loop. An all-digital implementation of band-edge timing recovery is used. With 14.4 kb/s outbound transmission using CCITT V.33 trellis-coded 128-QAM signals having 12.5% excess bandwidth, a prototype blind retrain procedure is developed to demonstrate the feasibility of the techniques for high-speed multipoint modems. A WE DSP32-based real-time digital signal processor was employed to test the retrain over a set of severely impaired channels. For each channel in the set, the retrain succeeded at least 90% of the time     

A 600-MHz VLIW DSP

A 600-MHz VLIW digital signal processor (DSP) delivers 4800 MIPS, 2400 (16 b) or 4800 (8 b) million multiply accumulates (MMACs) at 0.3 mW/MMAC (16 b). The chip has 64M transistors and dissipates 719 mW at 600 MHz and 1.2 V, and 200 mW at 300 MHz and 0.9 V. It has an eight-way VLIW DSP core, a two-level memory system, and an I/O bandwidth of 2.4 GB/s. The chip integrates a c64X DSP core with Viterbi and turbo decoders. Architectural and circuit design approaches to achieve high performance and low power using a semi-custom standard cell methodology, while maintaining backward compatibility, are described. The chip is implemented in a 0.13-/spl mu/m CMOS process with six layers of copper interconnect.     

DCME的V.29 9600 bit/s传真解调/再调制技术

简述了DCME中符合CCITT V.29建议的传真解调再调制方案,着重研究了V.29建议9600bit/s速率的传真信号的定时、载波恢复和均衡算法的实现,最后给出了系统仿真结果.     

面向癫痫脑电图信号识别的径向基最小最大概率分类树

脑电图(EEG)信号检测和识别是癫痫病的重要诊断手段。径向基函数神经网络具有出色的逼近能力和泛化性能,能直接识别出不同状态的脑电信号,但其透明性和可解释性差,忽视了不同类别数据间可分性的不同。对此,该文提出一种基于径向基函数神经网络和最小最大概率决策技术的分类树,采用一对一策略和排除法,更多考虑了类间可分性的不同。针对脑电信号识别的实验表明,所提方法结构清晰,分类能力强,可解释性更好。     

光接入网络中的数字信号处理技术

采用先进的数字信号处理(DSP)技术,在发射机和接收机分别引入预处理和后处理,以提高光接入网络的频谱效率并延长传输距离。研究了一种基于光超奈奎斯特(Super-Nyquist)滤波的类9状态正交振幅调制(9QAM)信号多模均衡(MMEQ)后端DSP算法,使用这种方案,能够有效提高频谱效率,实现了频谱效率高达4 bps/Hz的正交移相键控(QPSK)信号传输;还研究了一种基于数字SuperNyquist信号前端预处理的方案,此方案的优点是不需要光预滤波即可达到相同的频谱效率。使用一个采用直接调制激光器(DML)、直接探测和数字均衡技术的高速无载波幅度相位-64状态正交振幅调制(CAP-64QAM)系统,在20 km标准单模光纤(SSMF)上实现了创纪录的60 Gbit/s CAP-64QAM信号传输;使用相干探测,实现了速率高达100 Gbit/s的64状态正交振幅调制-正交频分复用(16QAM-OFDM)实时传输系统,解决了实时OFDM信号处理中的关键问题。     

一种面向信号分类的匹配追踪新方法

匹配追踪(MP)的主要策略是通过每次迭代时选择一个局部最优解,从而逐步逼近原始信号。然而传统的MP系列算法进行原子匹配时,各类原子集间存在交集,从而影响了原子的表示能力以及相应的分类效果。基于此,该文提出一种适用于信号监督分类的匹配追踪新算法。其原子挑选的准则为:同类信号采用相同的原子集匹配,获取相同的类内表示结构;异类信号选择不同的原子集匹配,从而增强信号的类间差异。示例分析表明,使原子集间相互独立,能够减少异类信号间的共性因素,强化信号间的区分度,从而有利于提升分类识别效果。通过在标准图像库和实测雷达辐射源信号集上的实验表明,较之传统的MP系列方法,所提算法对噪声和遮挡具有更强的鲁棒性。     

Voiceband data speed discrimination

Some results of research to discriminate voiceband data signals of different speeds are reported. A discrimination technique based upon the autocorrelation envelope and second order moment of the complex lowpass data signal is introduced. Under ideal channel conditions, the discriminator, within a window of 128 ms of PCM data samples, classifies whether the signal is at 9600 bit/s, 4800 bit/s, 2400 bit/s, 1200 bit/s or 300 bit/s. Performance of the discriminator for signals in the presence of various analog impairments shows that this method is quite robust.     

An application of informational divergence to Huffman codes

A classification of all probability distributions over the finite alphabet of an information source is given, where the classes are the sets of distributions sharing the same binary Huffman code. Such a classification can be used in noiseless coding, when the distribution of the finite memoryless source varies in time or becomes gradually known. Instead of applying the Huffman algorithm to each new estimate of the probability distribution, if a simple test based on the above classification is passed, then the Huffman code used previously is optimal also for the new distribution.     

Properties and performance of the IEEE 802.11b complementary-code-key signal sets

We describe similarities and differences between complementary-code-key (CCK) modulation and modulation that is derived from biorthogonal signals, and we present performance results and other information that may be useful to those who have applications for CCK modulation that do not require IEEE 802.11b compliance. The properties and performance of the highrate IEEE CCK 802.11b modulation formats are investigated and compared with the properties and performance of alternative modulation formats that are based on biorthogonal signals. Several complementary properties are derived for the full-rate (11 Mb/s) CCK signal set, the half-rate (5.5 Mb/s) CCK signal set, a full-rate signal set obtained from biorthogonal signals, and a half-rate biorthogonal signal set. Each signal set is a complementary set, but each also has stronger complementary properties. We evaluate the performance of IEEE 802.11b standard CCK modulation, CCK with certain modifications that depart from the IEEE standard, and modulation that is derived from biorthogonal signals. Performance comparisons are presented for additive white Gaussian noise (AWGN) channels and for channels with specular multipath. In particular, for AWGN channels, we provide an accurate analytical approximation for the frame error probability for full-rate CCK modulation.     

Tunable gain equalization using a Mach-Zehnder optical filter in multistage fiber amplifiers

Tunable signal gain equalization is demonstrated in three-stage Er/sup 3+/-doped fiber amplifiers using a waveguide type Mach-Zehnder (MZ) optical fiber. A 29-channel multiplexed system is examined where signal wavelengths are positioned from 1.548 to 1.555 mu m. By adjusting the MZ transmittance with the external control current, tunable gain equalization is achieved at the output of each amplifier.<>     

Automatic modulation classification for cognitive radios using cyclic feature detection

Cognitive radios have become a key research area in communications over the past few years. Automatic modulation classification (AMC) is an important component that improves the overall performance of the cognitive radio. Most modulated signals exhibit the property of cyclostationarity that can be exploited for the purpose of classification. In this paper, AMCs that are based on exploiting the cyclostationarity property of the modulated signals are discussed. Inherent advantages of using cyclostationarity based AMC are also addressed. When the cognitive radio is in a network, distributed sensing methods have the potential to increase the spectral sensing reliability, and decrease the probability of interference to existing radio systems. The use of cyclostationarity based methods for distributed signal detection and classification are presented. Examples are given to illustrate the concepts. The Matlab codes for some of the algorithms described in the paper are available for free download at http://filebox.vt.edu/user/bramkum.     

Multiple channel detection of steady-state visual evoked potentials for brain-computer interfaces

In this paper, novel methods for detecting steady-state visual evoked potentials using multiple electroencephalogram (EEG) signals are presented. The methods are tailored for brain-computer interfacing, where fast and accurate detection is of vital importance for achieving high information transfer rates. High detection accuracy using short time segments is obtained by finding combinations of electrode signals that cancel strong interference signals in the EEG data. Data from a test group consisting of 10 subjects are used to evaluate the new methods and to compare them to standard techniques. Using 1-s signal segments, six different visual stimulation frequencies could be discriminated with an average classification accuracy of 84%. An additional advantage of the presented methodology is that it is fully online, i.e., no calibration data for noise estimation, feature extraction, or electrode selection is needed.     

Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms

In this paper, we propose a method for the analysis and classification of electroencephalogram (EEG) signals using EEG rhythms. The EEG rhythms capture the nonlinear complex dynamic behavior of the brain system and the nonstationary nature of the EEG signals. This method analyzes common frequency components in multichannel EEG recordings, using the filter bank signal processing. The mean frequency (MF) and RMS bandwidth of the signal are estimated by applying Fourier-transform-based filter bank processing on the EEG rhythms, which we refer intrinsic band functions, inherently present in the EEG signals. The MF and RMS bandwidth estimates, for the different classes (e.g., ictal and seizure-free, open eyes and closed eyes, inter-ictal and ictal, healthy volunteers and epileptic patients, inter-ictal epileptogenic and opposite to epileptogenic zone) of EEG recordings, are statistically different and hence used to distinguish and classify the two classes of signals using a least-squares support vector machine classifier. Experimental results, with 100 % classification accuracy, on a real-world EEG signals database analysis illustrate the effectiveness of the proposed method for EEG signal classification.     

利用累积量和似然特征的卫星单-混信号调制识别算

针对卫星通信中常用调制信号和PCMA类混叠信号的调制识别问题,提出了一种利用高阶累积量和似然特征的识别算法.算法先对接收数据进行预处理,在此基础上提取4个高阶累积量特征和1个似然特征,构造一个树形分类器进行调制识别.算法不需要知晓信号的定时等先验信息,对收发载频存在的频差不敏感.仿真结果表明,算法在正常通信所需的载噪比下具有较高的正确识别率.     

Distributed target classification and tracking in sensor networks

The highly distributed infrastructure provided by sensor networks supports fundamentally new ways of designing surveillance systems. In this paper, we discuss sensor networks for target classification and tracking. Our formulation is anchored on location-aware data routing to conserve system resources, such as energy and bandwidth. Distributed classification algorithms exploit signals from multiple nodes in several modalities and rely on prior statistical information about target classes. Associating data to tracks becomes simpler in a distributed environment, at the cost of global consistency. It may be possible to filter clutter from the system by embedding higher level reasoning in the distributed system. Results and insights from a recent field test at 29 Palms Marine Training Center are provided to highlight challenges in sensor networks.     

Familiar/unfamiliar face classification from EEG signals by utilizing pairwise distant channels and distinctive time interval

The aim of the study is to classify single trial electroencephalogram and to estimate active regions/locations on skull in unfamiliar/familiar face recognition task. For this purpose, electroencephalographic signals were acquired from ten subjects in different sessions. Sixty-one familiar and fifty-nine unfamiliar face stimuli were shown to the subjects in the experiments. Since channel responses are different for familiar and unfamiliar classes, the channels discriminating the classes were investigated. To do so, three distances and four similarity measures were employed to assess the most distant channel pairs between familiar and unfamiliar classes for a 1-s time duration; 0.6 s from the stimulus to 1.6 s in a channel selection process. It is experimentally observed that this time interval is maintaining the greatest distance between two categories. The electroencephalographic signals were classified using the determined channels and time interval to measure accuracy. The best classification accuracy was 81.30% and was obtained with the Pearson correlation as channel selection method. The most discriminative channel pairs were selected from prefrontal regions.     

Adaptive multichannel decision feedback equalization for volterra type nonlinear communication channels

A model-based approach for the decision feedback equalization of Volterra type nonlinear communication channels is proposed such that the linear model-based decision feedback equalization can be considered as a special case of the proposed approach. In designing the decision feedback equalizer, the nonlinear decision feedback equalization problem is visualized as a linear, multichannel equalization problem. A complete modified Gram–Schmidt orthogonalization of the input vector is achieved by using modified sequential processing multichannel lattice stages. The elements of the multichannel desired signal vector are then estimated from the new orthogonal set by using only scalar operations. The probability of error performance of the proposed equalizer is improved by the estimation of the elements of the desired signal vector through a sigmoid activation function so that a polynomial perceptron equalizer is realized. The comparative computational complexity calculations and performance results of the proposed decision feedback equalizer are also provided.