Discrete cosine harmonic wavelet transform and its application to signal compression and subband spectral estimation using modified group delay

This paper proposes a new harmonic wavelet transform (HWT) based on discrete cosine transform (DCTHWT) and its application for signal or image compression and subband spectral estimation using modified group delay (MGD). Further, the existing DFTHWT has also been explored for image compression. The DCTHWT provides better quality decomposed decimated signals, which enable improved compression and MGD processing. For signal/image compression, compared to the HWT based on DFT (DFTHWT), the DCTHWT reduces the reconstruction error. Compared to DFTHWT for the speech signal considered for a compression factor of 0.62, the DCTWHT provides a 30% reduction in reconstruction error. For an image, the DCTHWT algorithm due to its real nature, is computationally simple and more accurate than the DFTHWT. Further compared to Cohen–Daubechies–Feauveau 9/7 biorthogonal symmetric wavelet, the DCTHWT, with its computational advantage, gives a better or comparable performance. For an image with 6.25% coefficients, the reconstructed image by DFTHWT is significantly inferior in appearance to that by DCTHWT which is reflected in the error index as its values are 3.0 and 2.65%, respectively. For spectral estimation, DCTHWT reduces the bias both in frequency (frequency resolution) and spectral magnitude. The reduction in magnitude bias in turn improves the signal detectability. In DCTHWT, the improvement in frequency resolution and the signal detectability is not only due to good quality DCT subband signals but also due to their stretching (decimation) in the wavelet transform. The MGD reduces the variance while preserving the frequency resolution achieved by DCT and decimation. In view of these, the new spectral estimator facilitates a significant improvement both in magnitude and frequency bias, variance and signal detection ability; compared to those of MGD processing of both DFT and DCT fullband and DFT subband signals.     

Phase retrieval from local measurements: Improved robustness via eigenvector-based angular synchronization

We improve a phase retrieval approach that uses correlation-based measurements with compactly supported measurement masks [30]. Our approach admits deterministic measurement constructions together with a robust, fast recovery algorithm that consists of solving a system of linear equations in a lifted space, followed by finding an eigenvector (e.g., via an inverse power iteration). Theoretical reconstruction error guarantees from [30] are improved as a result for the new and more robust reconstruction approach proposed herein. Numerical experiments demonstrate robustness and computational efficiency that compete with other approaches on large problems. Along the way, we show that this approach also trivially extends to phase retrieval problems based on windowed Fourier measurements.     

尾流光学信号的处理方法

主要利用滤波器、离散傅里叶变换、以及统计信号处理方法等对实验室模拟的尾流信号进行分析和处理、计算.从能量的角度来看,光学信号在有无气泡幕情况下的区别是非常明显的;利用滤波器只能作为尾流光学信号的预处理,从实际应用来看,需要做进一步处理;离散傅里叶变换对不同气压下得到的处理结果存在的区别,必然也为判断不同航速、不同尾龄下的尾流提供了一种线索;以Chirp z变换的形式对尾流光学信号进行处理,使得这种分布具有相当直观的图形,结合适当的图形处理方式,将有可能确定探测尾流光学性质的特征信号.     

输入信号和噪声对单模激光随机共振的影响

采用色抽运噪声和实虚部间关联的量子噪声驱动的单模激光损失模型,运用线性化近似方法计算了周期性信号加性输入时激光系统的输出光强信噪比,发现用信噪比与量子噪声实虚部间关联系数的关系曲线描述的随机共振现象.在抽运噪声自关联为短时关联情况下,当信号振幅增大和频率增快、抽运噪声色关联时间增大时,系统的随机共振加强;而噪声强度的增加会削弱系统的随机共振.在抽运噪声自关联为长时关联情况下,当信号振幅增大和量子噪声强度减弱时,系统的随机共振加强;而信号频率、抽运噪声强度、抽运噪声色关联时间的变化对系统随机共振的影响很小.     

基于高速时钟电路终端的信号完整性分析

本文分析了高速时钟电路的终端在确保信号完整性方面的重要作用,介绍了几种常用的终端方法,并用软件对采用上述终端方法的具体电路进行了仿真,最后从工程实现的角度选用了一种适宜的终端方法,得到了实测结果。     

一种新型的紧凑型DGS共模抑制滤波器设计

提出一种新的UH形DGS宽带共模滤波器,通过选择单个DGS结构合适尺寸和谐振点,利用每个谐振器间的相互耦合,达到了面积小(8mm×8mm),宽阻带(3.61GHz 10.7GHz)和低下限截止频率的效果。对于差模信号,阻带内衰减小于-1.5dB,保持了良好的信号完整性。同时采用一种新的减小宽带DGS结构滤波器面积的设计方法,通过改变图形的形状,用“C”形槽代替“U”形槽,使该DGS结构相比原有模型减小了10%,同时阻带范围增加了1.34GHz。     

Solar loading thermography: Time-lapsed thermographic survey and advanced thermographic signal processing for the inspection of civil engineering and cultural heritage structures

The experimental results from infrared thermography surveys over two buildings externally exposed walls are presented. Data acquisition was performed on a static configuration by recording direct and indirect solar loading during several days and was processed using advanced signal processing techniques in order to increase signal-to-noise ratio and signature contrast of the elements of interest. It is demonstrated that it is possible to detect the thermal signature of large internal structures as well as surface features under such thermographic scenarios. Results from a long-wave microbolometer compared favorably to those from a mid-wave cooled infrared camera for the detection of large subsurface features from unprocessed images. In both cases, however, advanced signal processing greatly improved contrast of the internal features.     

Joint signal alignment precoding and physical network coding for heterogeneous networks

Mobile traffic in cellular based networks is increasing exponentially, mainly due to the use of data intensive services like video. One effective way to cope with these demands is to reduce the cell-size by deploying small-cells along the coverage area of the current macro-cell system. The deployment of small-cells significantly improves the indoor coverage. Nevertheless, as additional spectrum licenses are difficult and expensive to acquire, it is expected that the macro and small-cells will coexist under the same spectrum. The coexistence of the two systems results in cross-tier/inter-system interference. In this context, we consider the application of joint signal alignment (SA) and physical network coding (PNC) for the uplink of heterogeneous networks, in order to cancel the interference generated from small-cells at the macro-cell user terminal. The joint design of SA and PNC allows to serve more users than the case where only PNC or interference alignment (IA) is employed individually. We compare our proposed joint SA-PNC schemes with the recently designed IA based techniques for the uplink heterogeneous systems. Simulation results show that the proposed SA-PNC is quite efficient to remove the inter-tier/system interference while allowing to increase the overall data rate, by serving more users, as compared with the IA based methods     

基于CC2431实现的无线传感器网络定位

无线传感器网络（WSN）中的定位技术是目前该领域研究的热点。对无线传感器网络中常用的定位技术进行了概述,着重分析了基于接收的信号强度指示（RSSI）定位技术的误差来源及减小误差的办法。重点讨论了利用CC2431内嵌的定位引擎实现的无线传感器网络定位功能及其原理,并通过实际测试验证了其定位准确度满足本应用需求,最后对该定位系统的各项性能指标进行了全面的分析总结。     

Possibilistic signal processing: How to handle noise?

We propose a novel approach for noise quantifier at each location of a signal. This method is based on replacing the conventional kernel-based approach extensively used in signal processing by an approach involving another kind of kernel: a possibility distribution. Such an approach leads to interval-valued resulting methods instead of point-valued ones. We propose a theoretical justification to this approach and we show, on real and artificial data sets, that the length of the obtained interval and the local noise level are highly correlated. This method is non-parametric and has an advantage over other methods since no assumption about the nature of the noise has to be made, except its local ergodicity. Besides, the propagation of the noise in the involved signal processing method is direct and does not require any additional computation.