基于先验信息辅助的8PSK信号信噪比估计

基于最大似然序列估计准则,提出了3种先验信息辅助的8PSK调制信号的信噪比估计方法.与现有硬判决辅助的信噪比估计方法进行了比较,解决了现有方法在低信噪比时估计误差较大的问题.分析了3种方法在Turbo均衡系统中初始迭代以及迭代进行过程中的性能,根据不同的迭代次数,在不同的方法之间进行切换会提高信噪比的估计精度,并降低计算复杂度.新方法只需要利用很少的数据符号就能得到准确的结果,并且能适用较大的信噪比范围.     

紫外非视距通信的MLSE均衡器

针对无线非视距紫外通信中的大气信道模拟问题,提出将最大似然序列估计均衡器应用于无线非视距紫外通信中.推导紫外通信中接收信号的概率密度函数,以此近似研究均衡器的输出误码率.理论分析与仿真结果表明,在短距离通信时,大气衰减系数与信道带宽的变化不影响均衡器性能.在接收信噪比不变的较长距离通信时,两者的变化对均衡器性能影响明显.信道记忆长度等因素对均衡器性能影响显著,而对调制方式不敏感.     

Δ∑模数转换器中数字抽取滤波器的设计

抽取滤波器是过采样模数转换器中的重要组成部分。低字率、高采样频率的数字调制信号被转换成高字率、奈奎斯特频率采样的信号。该文介绍了应用于不同过采样率的通用数字抽取滤波器的设计,适用于一阶到七阶的Δ∑调制器,输入字长从1bit到32bit。设计和实现了一个过采样率为256的数字抽取滤波器,应用于三阶级联的Δ∑调制器。该抽取滤波器包括:级联积分梳状滤波器、补偿滤波器和一个窄带有限冲击响应半带滤波器。滤波器系数都采用CSD(Canonic SignedDigit)码实现。多级多采样率信号处理电路被用来实现补偿和半带滤波。整个滤波器经过了FPGA验证,输出正弦波的信噪比达到了110dB。     

胎儿心电的提取方法分析

介绍了胎儿心电的提取方法,提取过程中要克服母体工频的干扰,以及母体心电本身的干扰,对胎儿心电的提取要尽可能降低这些噪声,由此通过相干平均、相关平均、自适应滤波3种主流的信号处理方法,建立了3种方法的电路原理图,从信号处理上分析了各种方法的优缺点,并给出了理论分析。根据不同的临床目的,可以采取不同的方法,达到最佳的胎儿心率监护。     

智能天线及其性能度量方法研究

智能天线是第三代移动通信不可缺少的空域信号处理技术。本文叙述了智能天线的基本原理,并给出了几种智能天线的性能度量准则。     

Network Sorting Algorithm of Multi-Frequency Signal with Adaptive SNR

An signal noise ratio (SNR) adaptive sorting algorithm using the time-frequency (TF) sparsity of frequency-hopping (FH) signal is proposed in this paper.Firstly,the Gabor transformation is used as TF transformation in the system and a sorting model is established under undetermined condition;then the SNR adaptive pivot threshold setting method is used to find the TF single source.The mixed matrix is estimated according to the TF matrix of single source.Lastly,signal sorting is realized through improved subspace projection combined with relative power deviation of source.Theoretical analysis and simulation results show that this algorithm has good effectiveness and performance.     

Design of a low cross‐talk micro‐coil array for micro‐scale MRI

Design of magnetic resonance micro‐coil arrays with low cross‐talk among the coils can be the main challenge to improve the effectiveness of magnetic resonance micro‐imaging because the electrical cross‐talk which is mainly due to the inductive coupling perturbs the sensitivity profile of the array and causes image artifacts. In this work, a capacitive decoupling network with N(M ? 1) + (N ? 1)(M ? 2) capacitors is proposed to reduce the inductive coupling in an N × M array. A 3 × 3 array of optimized micro‐coils is designed using the finite element simulations and all the needed elements for the array equivalent circuit are extracted in order to evaluate the effectiveness of the proposed decoupling method by assessing the reduction of the coupled signals after employing the capacitive network on the circuit. The achieved results for the designed array show that the high cross‐talk level is reduced by the factor of 2.2–3.4 after employing the capacitive network. By employing this method of decoupling, the adjacent coils in each row and inner columns can be decoupled properly while the minimum decoupling belongs to the outer columns because of the lack of all necessary decoupling capacitances for these columns. The main advantages of the proposed decoupling method are its efficiency and design easiness which facilitates the design of dense arrays with the properly decoupled coils, especially the inner coils which are more coupled due to their neighbors. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 353–359, 2013     

Adaptive filtering of EEG/ERP through Bounded Range Artificial Bee Colony (BR-ABC) algorithm

In this paper, the Artificial Bee Colony (ABC) algorithm is applied to construct Adaptive Noise Canceller (ANC) for electroencephalogram (EEG)/Event Related Potential (ERP) filtering with modified range selection, described as Bounded Range ABC (BR-ABC). ERP generated due to hand movement is filtered through Adaptive Noise Canceller (ANC) from the EEG signals. ANCs are also implemented with Least Mean Square (LMS) and Recursive Least Square (RLS) algorithm. Performance of the algorithms is evaluated in terms of Signal-to-Noise Ratio (SNR) in dB, correlation between resultant and template ERP, and mean value difference. Testing of their noise attenuation capability is done on contaminated ERP with white noise at different SNR levels. A comparative study of the performance of conventional gradient based methods like LMS, RLS, and ABC algorithm is also made which reveals that ABC algorithm gives better performance in highly noisy environment.     

Reducing over- and under-estimation of the a priori SNR in speech enhancement techniques

Most speech enhancement methods based on short-time spectral modification are generally expressed as a spectral gain depending on the estimate of the local signal-to-noise ratio (SNR) on each frequency bin. Several studies have analyzed the performance of a priori SNR estimation algorithms to improve speech quality and to reduce speech distortions. In this paper, we concentrate on the analysis of over- and under estimation of the a priori SNR in speech enhancement and noise reduction systems. We first show that conventional approaches such as the decision-directed approach proposed by Ephraïm and Malah lead to a biased estimator for the a priori SNR. To reduce this bias, our strategy relies on the introduction of a correction term in the a priori SNR estimate depending on the current state of both the available a posteriori SNR and the estimated a priori one. The proposed solution leads to a bias-compensated a priori SNR estimate, and allows to finely estimating the output speech signal to be very close to the original one on each frequency bin. Such refinement procedure in the a priori SNR estimate can be inserted in any type of spectral gain function to improve the output speech quality. Objective tests under various environments in terms of the Normalized Covariance Metric (NCM) criterion, the Coherence Speech Intelligibility Index (CSII) criterion, the segmental SNR criterion and the Perceptual Evaluation of Speech Quality (PESQ) measure are presented showing the superiority of the proposed method compared to competitive algorithms.     

Noise variance estimation based on dual-channel phase difference for speech enhancement

In this paper, we propose a method for estimating a signal-to-noise ratio (SNR) in order to improve the performance of a dual-microphone speech enhancement algorithm. The proposed method is able to reliably estimate both a priori and a posteriori SNRs by exploring a direction-of-arrival (DOA)-based local SNR that is defined by using spatial cues obtained from dual-microphone signals. The estimated a priori and a posteriori SNRs are then incorporated into a Wiener filter. Consequently, it is shown from an objective perceptual evaluation of speech quality (PESQ) comparison and a subjective listening test that a speech enhancement algorithm employing the proposed SNR estimate outperforms those using conventional single- or dual-microphone speech enhancement algorithms such as the Wiener filter, beamformer, or phase error-based filter under different noise conditions ranging from 0 to 20 dB.