Signal processing approach to electromagnetic sub-wavelength imaging

In this work, we show how we can improve the image resolution capabilities of a Phase Conjugating (PC) lens as well as the angular resolution of Luneburg lens antennas by employing signal processing techniques such as the Correlation Method (CM), the Minimum Residual Power Search Method (MRPSM), the sparse reconstruction method, and the Singular-Value-Decomposition (SVD)based basis matrix method. In the first part, we apply these techniques for sub-wavelength imaging in the microwave regime by combining them with the well-known phase conjugation principle. We begin by considering a one-dimensional microwave sub-wavelength imaging problem handled by using three signal processing methods, and then we move on to two- or three-dimensional problems by using the SVD-based basis matrix method. Numerical simulation results show that we can enhance the resolution significantly by using these methods, even if the measurement plane is not located in the very nearfield region of the source. We describe these proposed algorithms in detail and study their abilities to resolve at the sub-wavelength level. Next, we investigate the sparse reconstruction method for a normal Luneburg lens antenna and the Correlation Method and the SVD-based basis matrix method for a flat-base Luneburg lens antenna to estimate the Direction-of-Arrival (DOA). Numerical simulation results show that the signal processing techniques are capable of enhancing the angular resolution of the Luneburg lens antenna enabling the lens to locate multiple targets with different scattering cross-sections and achieving higher angular resolution.

This chapter mainly focuses on the signal processing algorithms used in the physical layer of a global navigation satellite system (GNSS) receiver. It provides a general introduction of all the subsystems of a GNSS receiver. The chapter describes the first operation that a receiver performs when it is switched on: the acquisition of the satellites in view and a first estimate of certain signal in space (SIS) parameters. These estimates derive from the approximation of some results from maximum likelihood (ML) theory. Such estimates are solid, but coarse, and need to be refined by other blocks, known as tracking systems or null seekers, based on different signal processing techniques. The chapter provides a description of the gradient theory and its application in the null seekers. It shows that the estimation of the pseudoranges can be obtained after the tracking outputs are converted into navigation data, that is, when each satellite channel is in navigation mode.

Brain-computer interface (BCI) devices are designed to bypass neuromuscular pathways enabling an individual to operate an external device using neural activity instead of motor activity. The use of BCIs in robotics control has important applications of assisting patients with severe motor disabilities, such as exploration, home assistance, and control of an electric wheelchair. This paper presents a development of a practical implementation of a BCI by utilizing low-cost technologies and asynchronous signal processing techniques for EEG signal acquisition and processing to navigate a quadcopter. A wireless EEG headset was used to acquire the occipital alpha and the frontal muscular artifacts from a user along with the gyroscope signals from the headset. These signals were transmitted wirelessly to a PC for further processing. Signal-processing algorithms were developed and implemented in the PC to extract the patterns from the acquired signals. These patterns, representing the intentions of the user, were then used to control a quadcopter. The design was successfully tested in a virtual environment and a physical device. The results from this preliminary pilot study proved that the design could be used with high accuracy to control a robotic device.

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

A comparative study between two types of tool wear monitoring systems for milling processes is introduced in this paper. The suggested sensory fusion approach includes the implementation of an infrared camera, in addition to force, vibration, sound and acoustic emission sensors. The majority of the research work available in literature and industry focuses on using one dimensional signals, such as force, vibration. Two dimensional data, such as infrared and visual images, are limited in literature in relation to machining operations. This work compares between one dimensional and two dimensional data for the development of a tool condition monitoring system for milling processes. The paper presents a comparative study between the performance of signal and image processing algorithms using neural networks. Fourier Transformation and Wavelets analysis are used to process one dimensional and Two dimensional data respectively. The results indicate that two dimensional data obtained from infrared images has significant capability in comparison to one dimensional data for the detection of tool wear for the selected image and signal processing algorithms.

The author outlines the performance assessment process currently used for development of Jindalee Australia Air Force over-the-horizon radar signal processing and tracking algorithm. The associated software tools provide an automated testing facility for new signal processing algorithms with numerical figure-of-merit output. Users can automatically test new algorithms over a large standard set of input replay tapes to assess the performance of the signal processing sequence over a large set of environmental and target conditions. >

Diffraction and holography are fertile areas for application of signal theory and processing. Recent work on 3DTV displays has posed particularly challenging signal processing problems. Various procedures to compute Rayleigh-Sommerfeld, Fresnel and Fraunhofer diffraction exist in the literature. Diffraction between parallel planes and tilted planes can be efficiently computed. Discretization and quantization of diffraction fields yield interesting theoretical and practical results, and allow efficient schemes compared to commonly used Nyquist sampling. The literature on computer-generated holography provides a good resource for holographic 3DTV related issues. Fast algorithms to compute Fourier, Walsh-Hadamard, fractional Fourier, linear canonical, Fresnel, and wavelet transforms, as well as optimization-based techniques such as best orthogonal basis, matching pursuit, basis pursuit etc., are especially relevant signal processing techniques for wave propagation, diffraction, holography, and related problems. Atomic decompositions, multiresolution techniques, Gabor functions, and Wigner distributions are among the signal processing techniques which have or may be applied to problems in optics. Research aimed at solving such problems at the intersection of wave optics and signal processing promises not only to facilitate the development of 3DTV systems, but also to contribute to fundamental advances in optics and signal processing theory.

This paper describes an investigation of the discrimination of heavy and light space objects based on infrared (IR) multi-spectral surveillance data. A time-sequence model of sensor measurements was used to produce test data, upon which the signal processing and discrimination algorithms were to be tested. The signal processing algorithms are based primarily on the estimation of the sinusoid that modulates the signal in the three IR bands, this frequency being one useful discrimination feature. Both frequency domain and novel-time domain techniques were investigated. The time domain technique employs binary median filtering of the original time sequence of measurements with its quadratically modeled trend removed. A second feature for discrimination is also proposed, based upon the quality of fit of the estimated sinusoid to the original time sequence. This combination of features from multiple IR bands was fused using the back-propagation neural network (BPNN) and the polynomial neural network (PNN), which were shown to provide excellent discrimination of the two target classes of interest.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Advances in millimeter wave solid-state power amplifier technology over the past decade have led to the development of a new generation of compact, lightweight cloud and precipitation radars. Solid-state power amplifiers (SSPAs) have lower peak power than traditional vacuum tube transmitters, but generally are capable of operating at much higher duty cycle. Taking advantage of the high duty cycle of SSPAs usually requires some signal processing and/or pulse compression technique to achieve similar sensitivity at comparable range resolution. FMCW (frequency modulated continuous wave) radars take full advantage of SSPAs capable of CW (continuous wave) operation, but the usual requirement for separate transmit and receive antennas is often impractical. Radars that combine a pulse compression waveform with a short pulse offset in frequency are now commonly used to operate at duty cycles as high as 10–20%. This paper reviews the latest solid-state millimeter wave radar designs, benefits, and limitations and describes operating modes and signal processing techniques that are especially well suited for single antenna operation. High PRF (pulse repetition frequency) operation using coherent power (single lag covariance) provides automatic noise cancellation without requiring explicit noise sampling. A novel phase coding technique employing Chu-codes with quadratically-increasing phase can be used to separate ambiguous signals when running at MHz-level PRFs. Weather data measured using the quadratic phase coding (QPC) technique with a solid-state Ka-band radar running at 3.1 MHz PRF is presented. This data was gathered at a transmitter duty cycle of approximately 50 percent and exhibits a 20 dB sensitivity improvement when compared to conventional short pulse data gathered at the same range resolution.

Numerical linear algebra, digital signal processing, and parallel algorithms are three disciplines with a great deal of activity in the last few years. The interaction between them has been growing to a level that merits an Advanced Study Institute dedicated to the three areas together. This volume gives an account of the main results in this interdisciplinary field. The following topics emerged as major themes of the meeting: - Singular value and eigenvalue decompositions, including applications, - Toeplitz matrices, including special algorithms and architectures, - Recursive least squares in linear algebra, digital signal processing and control, - Updating and downdating techniques in linear algebra and signal processing, - Stability and sensitivity analysis of special recursive least squares problems, - Special architectures for linear algebra and signal processing. This book contains tutorials on these topics given by leading scientists in each of the three areas. A consider- able number of new research results are presented in contributed papers. The tutorials and papers will be of value to anyone interested in the three disciplines.

Various signal processing techniques have been used for the enhancement of defect detection and defect characterisation. Cross-correlation, filtering, autoregressive analysis, deconvolution, neural network, wavelet transform and sparse signal representations have all been applied in attempts to analyse ultrasonic signals. In ultrasonic nondestructive evaluation (NDE) applications, a large number of materials have multilayered structures. NDE of multilayered structures leads to some specific problems, such as penetration, echo overlap, high attenuation and low signal-to-noise ratio. The signals recorded from a multilayered structure are a class of very special signals comprised of limited echoes. Such signals can be assumed to have a sparse representation in a proper signal dictionary. Recently, a number of digital signal processing techniques have been developed by exploiting the sparse constraint. This paper presents a review of research to date, showing the up-to-date developments of signal processing techniques made in ultrasonic NDE. A few typical ultrasonic signal processing techniques used for NDE of multilayered structures are elaborated. The practical applications and limitations of different signal processing methods in ultrasonic NDE of multilayered structures are analysed.

Flow separation and its control is a persistent topic of fluid dynamic research. The paper describes the development and implementation of a novel optical signal processing system to measure liquid flow velocities with a high spatial resolution in the order of 20 μm. The principles of Laser Doppler Anemometry coupled with signal processing algorithms are used to quantify the fluid flow velocities in the vicinity of a flow controlling device called Lorentz Force Actuator. The signal processing routines are tested on the bench using a Field Programmable Gate Array (FPGA) using LabVIEW from National Instruments. The LabVIEW code is ported to a PC based Data Acquisition Card for real time signal processing and correlation measurements in highly turbulent flows. The aim is to achieve the same performance as commercial systems, which are not applicable in conjunction with the novel laser Doppler sensors, while maintaining the high precision and flexibility of the propriety signal processing techniques.

In this letter, an improved phase correlation (PC) method based on 2-D plane fitting and the maximum kernel density estimator (MKDE) is proposed, which combines the idea of Stone's method and robust estimator MKDE. The proposed PC method first utilizes a vector filter to minimize the noise errors of the phase angle matrix and then unwraps the filtered phase angle matrix by the use of the minimum cost network flow unwrapping algorithm. Afterward, the unwrapped phase angle matrix is robustly fitted via MKDE, and the slope coefficients of the 2-D plane indicate the subpixel shifts between images. The experiments revealed that the improved method can effectively avoid the impact of outliers on the phase angle matrix during the plane fitting and is robust to aliasing and noise. The matching accuracy can reach 1/50th of a pixel using simulated data. The real image sequence tracking experiment was also undertaken to demonstrate the effectiveness of the proposed PC method with a registration accuracy of root-mean-square error better than 0.1 pixels.

Data acquisition and Signal Processing is an important part of frequency modulated continuous wave (FMCW) radar system. In recent year P2ET-LIPI was developing FMCW navigation radar system in portable size. The main component of data acquisition for navigation radar is highspeed Analog to Digital Conversion (ADC) USB-2020 board and signal processing algorithm has been developed using Visual C# 2015 Community Edition. USB-2020 board has two channel ADC on board with 12 bits resolution and operated in a burst-io mode to get maximum sampling rate at 20 MS/s. A timing generator for synchronization between ADC and linear frequency modulation (LFM) chirp generator is developed using Complex Programmable Logic Device (CPLD) Max II series based on VHSIC hardware description language (VHDL). Signal processing unit consists of windowing function to reduce side- lobe suppression, Fast Fourier Transform (FFT) function to get range information and adaptive threshold using three algorithms of Constant False Alarm Rate (CFAR): Cell Averaging (CA), Order Statistic (OS) and Variable Index (VI) CFAR. The experiment shows that the data acquisition module can capture In-phase and Quadrature (I/Q) beat signal and signal processing algorithm can detect object/target from the noise.

Chapter 14 - Edge algorithms for wearables: an overview of a truly multi-disciplinary problem