Face Recognition Based on Gabor Feature Extraction Followed by FastICA and LDA

Over the past few decades, face recognition has become the most effective biometric technique in recognizing people’s identity, as it is widely used in many areas of our daily lives. However, it is a challenging technique since facial images vary in rotations, expressions, and illuminations. To minimize the impact of these challenges, exploiting information from various feature extraction methods is recommended since one of the most critical tasks in face recognition system is the extraction of facial features. Therefore, this paper presents a new approach to face recognition based on the fusion of Gabor-based feature extraction, Fast Independent Component Analysis (FastICA), and Linear Discriminant Analysis (LDA). In the presented method, first, face images are transformed to grayscale and resized to have a uniform size. After that, facial features are extracted from the aligned face image using Gabor, FastICA, and LDA methods. Finally, the nearest distance classifier is utilized to recognize the identity of the individuals. Here, the performance of six distance classifiers, namely Euclidean, Cosine, Bray-Curtis, Mahalanobis, Correlation, and Manhattan, are investigated. Experimental results revealed that the presented method attains a higher rank-one recognition rate compared to the recent approaches in the literature on four benchmarked face datasets: ORL, GT, FEI, and Yale. Moreover, it showed that the proposed method not only helps in better extracting the features but also in improving the overall efficiency of the facial recognition system.     

基于MIMO-OFDM系统的盲源分离算法研究

为了实现使用OFDM调制的MIMO系统的盲源分离问题,提出了在接收端使用Nyquist频率对接收到的信号进行采样的,从而将原始的MIMO-OFDM系统盲源分离问题转变为N组的独立分量分离的问题.通过将FastICA算法扩展到复数域,在频域中对每一组相同子载波上的信号进行独立分量分析,最终实现信号的盲分离.仿真实验获得的结果证实了该方案具有良好的恢复效果,且算法收敛的迭代次数只需要8～10次.     

一种改进的基于多用户检测的独立分量分析算法

为了降低FastICA算法的计算复杂度,提出了一种基于多用户检测串行干扰抵消的新型独立分量分析算法MUD_FastICA。该算法结合了盲信号分离和多用户检测串行干扰抵消两种信号处理技术,利用减法和低维特征值分解来保证每次分离出不同独立分量和达到降低算法复杂度的目的。通过分析和仿真可以看出,所提算法在不影响分离性能的前提下,显著降低了算法的迭代次数和每次迭代的计算复杂度。在信噪比0 dB和4个源信号混合情况下,分离第二个信号的迭代次数和所需计算单元分别下降了14%和37%,分离第三个信号的迭代次数和所需计算单元分别下降了22%和58%,因此更加适合对实时性要求高的通信系统。     

基于快速独立分量分析的谐波/间谐波频谱分离算法

在非同步采样条件下,若电网采样信号中谐波和间谐波相邻,会出现严重的频谱干涉问题,且无法识别出信号中实际频率成分。针对以上问题,提出了一种基于快速独立分量分析(Fast ICA)的频谱分离算法测量谐波和间谐波参数。首先构建了多频率成分模型,将频谱中的谱线表示为多个频率成分分量的叠加,然后利用Fast ICA算法和最小二乘法得到频率成分参数,最终实现了对相邻多频率成分的测量。仿真结果表明,该算法可以在需求谱线数较少的情况下准确识别频率成分并保持较好的测量精度,且具有一定的抗噪能力。     

独立成分分析及其在纺纱锭子噪音测试中的应用

纺纱锭子性能的优劣对生产效率和纱线质量具有决定性的影响。以纺纱锭子运转中的噪音信号为考察对象,对纺纱锭子运转过程进行分析。利用快速独立成分分析FastICA算法对工作状态中的锭子声压信号进行噪音源数据分离分析,得到工作状态下源信号的主频率。将工作状态下源信号的主频率与锭子不运转下的噪音主频率进行对比,38.88%分离主频率与锭子不运转下的噪音主频率一致,44.44%分离主频率与锭子不运转下的噪音主频率相近,最小误差为0.05%。实验与分析结果表明,FastICA算法用于噪音源信号主频率分离的有效性和正确性。     

基于盲源分离的雷达信号分选方法

传统的信号分选算法建立在脉冲描述字（PDW）参数分析的基础上,对同频或频谱混叠的雷达信号可能无法分选.鉴于越发明显的常规雷达信号处理方法的局限性,通过仿真手段,用盲源分离的方法对混合后的雷达信号进行分选.仿真结果表明,该方法可对雷达盲信号进行有效的分离,且不需要其它信号处理方法所要求的任何先验知识作为条件.     

基于FPGA的微电流传感器相位差自校正系统

微电流传感器是提取弱电流信号的关键设备,但在实际应用中由于温度、湿度、现场强电磁干扰、冲击电流、运行时间漂移等因素会使其相位特性发生变化,从而给对相位敏感的应用带来很严重的影响,比如表征电力设备绝缘状况的重要特征量——介质损耗tanδ的测量。文中引入负熵极大的FastICA算法,利用现场可编程门阵列(FPGA)设计实现了一种微电流传感器相位差自校正系统。设计中采用单精度浮点数运算单元,并针对算法的特点对硬件结构进行了优化。实验结果表明,所设计系统的测量精度、速度、抗噪声能力以及抗频漂能力满足工程要求。     

Noise separation of the yarn tension signal on twister using FastICA

Some unknown noise will influence the wave appearance of the twist yarn tension signal in different factory environments or production processes. It is difficult to analyse the tension signal pattern and recognize unusual defects by the on-line yarn quality control system. In this paper, the independent component analysis (FastICA: fixed-point independent component analysis) is applied to separate the unknown noise signal from the unusual tension signal on the yarn twist machine. Different from the traditional low-pass filter (e.g., Butterworth filter), FastICA can not only successfully separate the noise with different types but also remain the main tension signal information. Firstly, FastICA algorithm is introduced, and then simulation experiments and on-line tests are carried out to evaluate the performance of this method and traditional low-pass filter.     

基于NSCT的独立分量分析方法

为了提高独立分量分析（ICA）算法的分离性能,提出了基于非下采样轮廓波变换（NSCT）的ICA方法。首先对混合图像进行NSCT,使混合图像的非高斯性在NSCT域得以增强,然后在变换域求解分离矩阵。理论分析和仿真实验均表明,该方法可以获取更好的分离精度和更快的收敛速度。     

FSS-kernel与FastICA融合的盲源分离算法研究

Fast ICA算法有着比传统ICA算法更快、更稳健的收敛速度,但由于其选用的非线性函数不能很好地符合源信号的统计特性,恢复结果并不理想。针对该问题,提出了一种有限支持样本核函数(FSS-kernel)与Fast ICA融合的盲源分离算法。该方法是通过FSS-kernel算法估计得出源信号概率密度函数,结合Fast ICA算法,实现混合信号的盲分离。仿真结果表明,该方法能够有效地完成混叠信号的分离,通过与传统ICA算法及Fast ICA算法比较,证明了该方法具有更高的分离精度和自适应能力。