Conventional Signal Processing Research Articles

Maximum‐likelihood‐based approach for single‐pass synthetic aperture radar tomography over urban areas

Synthetic aperture radar (SAR) tomography (TomoSAR) is one of the key techniques in remote sensing for a sophisticated, three-dimensional (3D) analysis of complex scenes such as forests or urban areas. During recent years, much progress has been made in order to deal with unfavourable data conditions and to further enhance the achievable resolutions. Since TomoSAR has essentially be seen as a spectral estimation problem in the context of array signal processing, most of the methods investigated until now rely on a relatively large tomographic aperture and a relatively high number of receiving antennas in order to be able to separately reconstruct scatterers below the Rayleigh resolution limit. In contrast to that, this study proposes a maximum-likelihood-based approach for tomographic SAR inversion, which provides a better height resolution than conventional array signal processing approaches even if the number of antennas or the overall baseline size is limited. The algorithm is tested on real multi-baseline SAR data acquired over an urban area by an airborne single-pass four-antenna system operating in the millimetre-wave domain. First results show the feasibility of the method both with respect to 3D scatterer reconstruction and 3D SAR focusing.

Signal subspace analysis for decoherent processes during interferometric fiber-optic gyroscopes using synchronous adaptive filters.

Conventional signal processing methods for improving the random walk coefficient and the bias stability of interferometric fiber-optic gyroscopes are usually implemented in one-dimension sequence. In this paper, as a comparison, we allocated synchronous adaptive filters with the calculations of correlations of multidimensional signals in the perspective of the signal subspace. First, two synchronous independent channels are obtained through quadrature demodulation. Next, synchronous adaptive filters were carried out in order to project the original channels to the high related error channels and the approximation channels. The error channel signals were then processed by principal component analysis for suppressing coherent noises. Finally, an optimal state estimation of these error channels and approximation channels based on the Kalman gain coefficient was operated. Experimental results show that this signal processing method improved the raw measurements' variance from 0.0630 [(°/h)2] to 0.0103 [(°/h)2].

Asymptotic Frequency-Shift Properizer for Block Processing of Improper-Complex Second-Order Cyclostationary Random Processes

In this paper, the block processing of a discrete-time (DT) improper-complex second-order cyclostationary (SOCS) random process is considered. In particular, it is of interest to find a preprocessing operation that enables the adoption of conventional signal processing techniques and algorithms developed for the filtering of proper-complex signals and that leads to computationally efficient near-optimal postprocessing. An invertible linear-conjugate linear (LCL) operator named the DT frequency shift (FRESH) properizer is first proposed. It is shown that the DT FRESH properizer converts a DT improper-complex SOCS random process input to an equivalent DT proper-complex SOCS random process output by utilizing the information only about the cycle period of the input. An invertible LCL block processing operator named the asymptotic FRESH properizer is then proposed that mimics the operation of the DT FRESH properizer but processes a finite number of consecutive samples of a DT improper-complex SOCS random process. It is shown that the output of the asymptotic FRESH properizer is not proper but asymptotically proper and that its frequency-domain covariance matrix converges to a highly structured block matrix with diagonal blocks as the block size tends to infinity. Two representative estimation and detection problems are presented to demonstrate that asymptotically optimal low-complexity postprocessors can be easily designed by exploiting these asymptotic second-order properties of the output of the asymptotic FRESH properizer.

16NM BULK CMOS DOCCCII BASED CONFIGURABLE ANALOG BLOCK DESIGN FOR FIELD PROGRAMMABLE ANALOG ARRAY

Field programmable analog array (FPAA) is a rapidly growing technology for fast prototyping of analog signal processing systems facilitating the quick innovation at industry level with saving in terms of both time and cost. Analog signal processing is much faster and consumes less power than conventional digital signal processing, justifying the need for FPAA. Here we propose a Dual output current controlled current conveyor (DOCCCII) in 16nm bulk CMOS technology using PTM (High Performance 16nm Metal Gate / High-K / Strained-Si parameter) and discuss its characteristic. DOCCCII is superior in design among Current Conveyors because it requires no additional resistance for activation, it has it’s own parasitic resistance tunable through biasing current and has high bandwidth (GHz range). A configurable analog block (CAB) is also proposed which mainly consists of the DOCCCII readily configurable to realize an application. This cab is used here for the realization of 2 nd order filters.

Frequency Characteristics and Conversion of Microwave Photons

The microwave frequency conversion technology, also known as mixers, RF and microwave system is the transmitter and the receiver have the basic functions. Microwave photon system, instead of the traditional high-speed optoelectronic devices electronic device to overcome the conventional signal processing electronics in the electronic bottleneck. In this paper, respectively, modulators and optical devices based on nonlinear effects of microwave photon frequency conversion methods are summarized, research and verify the bandpass filter can achieve frequency conversion scheme.

Digital coherent superposition of optical OFDM subcarrier pairs with Hermitian symmetry for phase noise mitigation.

Digital coherent superposition (DCS) provides an approach to combat fiber nonlinearities by trading off the spectrum efficiency. In analogy, we extend the concept of DCS to the optical OFDM subcarrier pairs with Hermitian symmetry to combat the linear and nonlinear phase noise. At the transmitter, we simply use a real-valued OFDM signal to drive a Mach-Zehnder (MZ) intensity modulator biased at the null point and the so-generated OFDM signal is Hermitian in the frequency domain. At receiver, after the conventional OFDM signal processing, we conduct DCS of the optical OFDM subcarrier pairs, which requires only conjugation and summation. We show that the inter-carrier-interference (ICI) due to phase noise can be reduced because of the Hermitain symmetry. In a simulation, this method improves the tolerance to the laser phase noise. In a nonlinear WDM transmission experiment, this method also achieves better performance under the influence of cross phase modulation (XPM).

Segmentation and classification of shallow subbottom acoustic data, using image processing and neural networks

Subbottom acoustic profiler provides acoustic imaging of the subbottom structure constituting the upper sediment layers of the seabed, which is essential for geological and offshore geo-engineering studies. Delineation of the subbottom structure from a noisy acoustic data and classification of the sediment strata is a challenging task with the conventional signal processing techniques. Image processing techniques utilise the spatial variability of the image characteristics, known for their potential in medical imaging and pattern recognition applications. In the present study, they are found to be good in demarcating the boundaries of the sediment layers associated with weak acoustic reflectivity, masked by noisy background. The study deals with application of image processing techniques, like segmentation in identification of subbottom features and extraction of textural feature vectors using grey level co-occurrence matrix statistics. And also attempted classification using Self Organised Map, an unsupervised neural network model utilising these feature vectors. The methodology was successfully demonstrated in demarcating the different sediment layers from the subbottom images and established the sediments constituting the inferred four subsurface sediment layers differ from each other. The network model was also tested for its consistency, with repeated runs of different configuration of the network. Also the ability of simulated network was tested using a few untrained test images representing the similar environment and the classification results show a good agreement with the anticipated.

Least-squares fitting of time-domain signals for Fourier transform mass spectrometry.

To advance Fourier transform mass spectrometry (FTMS)-based molecular structure analysis, corresponding development of the FTMS signal processing methods and instrumentation is required. Here, we demonstrate utility of a least-squares fitting (LSF) method for analysis of FTMS time-domain (transient) signals. We evaluate the LSF method in the analysis of single- and multiple-component experimental and simulated ion cyclotron resonance (ICR) and Orbitrap FTMS transient signals. Overall, the LSF method allows one to estimate the analytical limits of the conventional instrumentation and signal processing methods in FTMS. Particularly, LSF provides accurate information on initial phases of sinusoidal components in a given transient. For instance, the phase distribution obtained for a statistical set of experimental transients reveals the effect of the first data-point problem in FT-ICR MS. Additionally, LSF might be useful to improve the implementation of the absorption-mode FT spectral representation for FTMS applications. Finally, LSF can find utility in characterization and development of filter-diagonalization method (FDM) MS.

Signal processing method based on group delay calculation for distributed Bragg wavelength shift in optical frequency domain reflectometry

A signal processing method based on group delay calculations is introduced for distributed measurements of long-length fiber Bragg gratings (FBGs) based on optical frequency domain reflectometry (OFDR). Bragg wavelength shifts in interfered signals of OFDR are regarded as group delay. By calculating group delay, the distribution of Bragg wavelength shifts is obtained with high computational efficiency. We introduce weighted averaging process for noise reduction. This method required only 3.5% of signal processing time which was necessary for conventional equivalent signal processing based on short-time Fourier transform. The method also showed high sensitivity to experimental signals where non-uniform strain distributions existed in a long-length FBG.

Photonic spin Hall effect in hyperbolic metamaterials for polarization-controlled routing of subwavelength modes

The routing of light in a deep subwavelength regime enables a variety of important applications in photonics, quantum information technologies, imaging and biosensing. Here we describe and experimentally demonstrate the selective excitation of spatially confined, subwavelength electromagnetic modes in anisotropic metamaterials with hyperbolic dispersion. A localized, circularly polarized emitter placed at the boundary of a hyperbolic metamaterial is shown to excite extraordinary waves propagating in a prescribed direction controlled by the polarization handedness. Thus, a metamaterial slab acts as an extremely broadband, nearly ideal polarization beam splitter for circularly polarized light. We perform a proof of concept experiment with a uniaxial hyperbolic metamaterial at radio-frequencies revealing the directional routing effect and strong subwavelength λ/300 confinement. The proposed concept of metamaterial-based subwavelength interconnection and polarization-controlled signal routing is based on the photonic spin Hall effect and may serve as an ultimate platform for either conventional or quantum electromagnetic signal processing.

Spherical-bubble-based four-sensor probe signal processing algorithm for two-phase flow measurement

This paper describes a newly-developed complete four-sensor probe signal processing algorithm for local instantaneous 3-dimensional bubble velocity vector, local instantaneous bubble diameter, local instantaneous interfacial normal unit vector, local interfacial area concentration (IAC) in a multi-dimensional two-phase flow by utilizing the spherical bubbles in the flow. The newly-developed algorithm has opened a way for the measurement of 3-dimensional bubble velocity vector and bubble diameter with a four-sensor probe. The newly-developed algorithm also has overcome the weakness of the conventional four-sensor probe signal processing algorithm, in which the interfaces passing through the tips of the probe sensors are supposed to be planar, even in the small bubble measurement for the local IAC. The newly-developed algorithm can keep a consistency with the conventional algorithm in measuring large bubbles since the newly-developed algorithm is reduced to the conventional algorithm for the local IAC, the local instantaneous interfacial normal unit vector and the local instantaneous 3-dimensional interfacial displacement velocity vector when the bubble size becomes to be much larger than the size of the four-sensor probe and the interface of the bubble can be locally viewed as 2 tangent planes. The bubbles in practical two-phase flows are classified into spherical bubbles and non-spherical bubbles according to a newly-introduced bubble deviation coefficient from spherical shape (namely an aspheric shape factor). Based on the spherical bubbles, the newly-developed signal processing algorithm can perform the measurement for the local parameters in two-phase flows. The newly-developed signal processing algorithm was applied to the measurements in a vertical air–water multi-dimensional two-phase flow in a large-diameter pipe as an example and was checked against the other measurement methods. The comparisons were very satisfactory and showed that the newly-developed four-sensor probe signal processing algorithm can perform the local measurements for the practical multi-dimensional two-phase flow.

Ultrasonic velocity profiler for very low velocity field

An ultrasonic velocity profile measurement system has been realized that can measure velocities in a very slow flow. The system implements a novel phase difference method that overcomes the low velocity limitation in a conventional signal processing algorithm for the Doppler method. The measurement system consists of pulser/receiver, transducer and digitizer only with all signal processing carried out on a personal computer. The measurement limitation at a very low velocity was examined using signals reflected from a wall which are collected by moving transducer at speeds less than 10−2mm/s. For velocity profiling, real-time profile measurements were performed, which demonstrated successful results for the flow inside a rotating cylinder.

A geometrical attack resistant image watermarking algorithm based on histogram modification

In order to avoid geometric distortions in watermark embedding, a geometrical attack resistant image watermarking algorithm is proposed based on histogram modification. The watermark information is embedded into the host image by modifying the number of the gray samples of the image histogram. In a fixed gray range, every three consecutive bins are divided into a group for embedding one-bit watermark, through modifying the sample value of a bin to another of the adjacent bin to realize the watermark insertion. Moreover, to avoid duplicate modification and improve the transparency, only one bin is modified every two neighbor bins, the modified histogram is mapped into the watermarked image. For watermark detection, it is in fact an inverse process of watermark embedding. Watermark is extracted through calculating and judging the relationship of the number of samples of the three successive bins. The experimental results show that the algorithm is robust to both geometrical attacks and those conventional signal processing attacks.

Improved Signal Processing Methods for Velocity Selective Neural Recording Using Multi-Electrode Cuffs

This paper describes an improved system for obtaining velocity spectral information from electroneurogram recordings using multi-electrode cuffs (MECs). The starting point for this study is some recently published work that considers the limitations of conventional linear signal processing methods (`delay-and-add') with and without additive noise. By contrast to earlier linear methods, the present paper adopts a fundamentally non-linear velocity classification approach based on a type of artificial neural network (ANN). The new method provides a unified approach to the solution of the two main problems of the earlier delay-and-add technique, i.e., a damaging decline in both velocity selectivity and velocity resolution at high velocities. The new method can operate in real-time, is shown to be robust in the presence of noise and also to be relatively insensitive to the form of the action potential waveforms being classified.

Localization and de-noising seismic signals on SASW measurement by wavelet transform

Abstract SASW method is a nondestructive in situ testing method that is used to determine the dynamic properties of soil sites and pavement systems. Phase information and dispersion characteristics of a wave propagating through these systems have a significant role in the processing of recorded data. Inversion of the dispersive phase data provides information on the variation of shear-wave velocity with depth. However, in the case of sanded residual soil, it is not easy to produce the reliable phase spectrum curve. Due to natural noises and other human intervention in surface wave date generation deal with to reliable phase spectrum curve for sanded residual soil turn into the complex issue for geological scientist. In this paper, a time–frequency analysis based on complex Gaussian Derivative wavelet was applied to detect and localize all the events that are not identifiable by conventional signal processing methods. Then, the performance of discrete wavelet transform (DWT) in noise reduction of these recorded seismic signals was evaluated. Furthermore, in particular the influence of the decomposition level choice was investigated on efficiency of this process. This method is developed by various wavelet thresholding techniques which provide many options for controllable de-noising at each level of signal decomposition. Also, it obviates the need for high computation time compare with continuous wavelet transform. According to the results, the proposed method is powerful to visualize the interested spectrum range of seismic signals and to de-noise at low level decomposition.

A System Level Study of Two-Dimensional Magnetic Recording (TDMR)

Two-dimensional magnetic recording (TDMR) is a recently proposed recording scheme aimed at extending the life of conventional granular magnetic recording (CGMR) without a significant modification to the writer, reader or medium. This is atypical, as these are the usual sources of areal density gain in the history of magnetic recording. TDMR is the readback counterpart of shingled magnetic recording (SMR) that is currently being implemented by the hard drive industry today. SMR/TDMR gains are in parallel with those from HAMR and BPMR and the total gains from TDMR+HAMR or TDMR+HAMR+BPMR can be accumulated. TDMR was originally proposed as a very high-density contender for conventional granular media, using coding and signal processing to try to make up the gaps. Initial expectations have yet to be realized as the simulations yielded high error rates when the density is in the regime approaching single grain per bit. Nonetheless, TDMR still has something to offer when used in a closer-to-conventional regime, of around 5-10 grains per bit. However, the system architecture for SMR and TDMR has not been well studied and the effects of the block sizes and various other system parameters is not well known in an SMR/TDMR system. In this work, we perform a study on TDMR/SMR at the system level using micromagnetic simulations and the grain-flipping probability (GFP) model to calculate the final system error rates. We study the effect of variable shingle-block dimensions, aspect ratios and reader-offsets. The experiments in this work, carried out via micromagnetic simulations and the GFP model, yield insights into the SMR/TDMR architectures, and predict estimated gains of TDMR+SMR over SMR alone.

Eddy Current Detecting of Leak Hole in Pipeline by Wavelet Packet Signal Analysis Method

In the process of detecting leak hole in pipeline by intelligent pig with eddy current measuring method, the cut off effect of eddy current field in the closed cross section of leak hole in steel plate is weak due to geometric deformation of eddy current on the edge of the hole produced by destroyed drilling. As a result, the measured signal is very unobvious. Meanwhile, owing to the rough surface of steel plate, the periodic interference generated by movement of the detecting probe cannot be eliminated or be inhibited easily by conventional signal processing methods for its large amplitude and approximate frequency band with the leak hole signals, the signal to noise ratio (SNR) of measured signal is very low and the accurate identification of leak hole cannot be guaranteed. The wavelet transform, with characteristics of time-frequency localization and multiple scales, is a useful and effective method for identifying singularity of the signals and adapts to detect the transient signal or extract non-stationary information in the signals with strong periodic interference and noise. The reconstructing signal SNR will be increased greatly in eddy current detecting of leak hole in the pipeline with wavelet packet analysis of the signal by constructing the self-defined cost function based on maximum SNR to obtain optimal wavelet packet basis function. This ensures good detection and location of leak hole in the pipeline.

Detection of Mechanical Defects by Neural Networks “Experimental Analysis”

Various methods are implemented to identify the nature of a defect on a rotating machine, by using vibratory measures; they differ in their precision, simplicity of implementation and their sensitivity to errors measurement. The identification of several defects combination is still difficult to implement by conventional signal processing, as the vibration signal that emerges is disturbed, thus making any identification so hard. In this study, we proposed a method based on the neural networks to identify one defect or several combinations of mechanical defects. Thus we propose the neuronal method: the Radial Basis Function (RBF). We highlight their capacity to detect the defect and their sensibility with regard to a signal noise characterizing the other independent sources to the defects. This evaluation will be done with measurement will be carried out on a housing bearing and test bench made up of a toothed gearing on two floors, and without lubrication. Some provoked defects will be analyzed in this study.

Detection of Digital Amplitude-Phase Modulated Signals in Symmetric Alpha-Stable Noise

In this paper, we propose a scheme for the detection of digital amplitude-phase modulated signals in flat fading channels with non-Gaussian noise. The additive noise is modeled by a symmetric alpha-stable distribution, a well-known model of man-made and natural noise that appears in various environments. A major challenge in the design of signal detection schemes is that detectors typically operate without knowledge of the transmitted data, channel state, and noise statistics. The design becomes especially difficult for the considered noise model since conventional signal processing approaches are not applicable. For this reason, we propose a five-stage signal detection scheme that is based on the use of a matched myriad filter. An important contribution of this paper is the development of new algorithms for the estimation of noise distribution parameters in the presence of an unknown signal. Results are presented which show that the proposed detector outperforms a zero-memory non-linearity-based scheme.

Time frequency and array processing of non-stationary signals

Introduction and overview Conventional time-frequency analysis methods were extended to data arrays in many applications, and there is the potential for more synergistic development of new advanced tools by exploiting the joint properties of timefrequency methods and array signal processing methods. Conventional array signal processing assumes stationary signals and mainly employs the covariance matrix of the data array. This assumption is motivated by the crucial need in practice for estimating sample statistics by resorting to temporal averaging under the additional hypothesis of ergodic signals. When the frequency content of the measured signals is time varying (i.e., non-stationary signals), this class of approaches can still be applied. However, the achievable performances in this case are reduced with respect to those that would be achieved in a stationary environment. Instead of considering the nonstationarity as a shortcoming, time frequency array processing (TFAP) takes advantage of the nonstationarity by considering it as a source of information in the design of efficient algorithms in such nonstationary environments. Generally, this significantly improves performance. This improvement comes essentially from the fact that the effects of spreading the noise power while localizing the source energy in the time frequency domain increases the signal to noise ratio (SNR). Such approaches found applications in many important fields dealing with nonstationary signals and multi-sensor systems, such as biomedical, radar, seismic, telecommunications, and mechanical engineering.