Advances in Sonar Technology

The demand to explore the largest and also one of the richest parts of our planet, the advances in signal processing promoted by an exponential growth in computation power and a thorough study of sound propagation in the underwater realm, have lead to remarkable advances in sonar technology in the last years.The work on hand is a sum of knowledge of several authors who contributed in various aspects of sonar technology. This book intends to give a broad overview of the advances in sonar technology of the last years that resulted from the research effort of the authors in both sonar systems and their applications. It is intended for scientist and engineers from a variety of backgrounds and even those that never had contact with sonar technology before will find an easy introduction with the topics and principles exposed here

Compressed sensing current mapping spatial characterization of photovoltaic devices

In this work a new measurement technique for current mapping of photovoltaic (PV) devices is developed, utilising the compressed sensing (CS) sampling theory. Conventional current mapping measurements of PV devices are realised using the light beam induced current (LBIC) measurement method. For its realization, a light beam scans a PV device and the induced current is measured for every point, generating the final current map of the device. Disadvantages of the LBIC method are the low measurement speed, the complicated and usually expensive measurement layouts and the impractical application of the method on PV modules. With the development of CS current mapping in this work, the above issues can be mitigated. Instead of applying a raster scan, a series of illumination patterns are projected onto the PV sample, acquiring fewer measurements than the pixels of the final current map. The final reconstruction of the current map is achieved by means of an optimisation algorithm. Spatially resolved electrical simulations of CS current mapping demonstrate that theoretically the proposed method is feasible. In addition, it is shown that current maps can be acquired with even 40% of the measurements a standard LBIC system would require, saving a significant amount of measurement time. The performance of CS current mapping is the same, regardless of the features a sample may contain and measurements can be applied to any type of photovoltaic device. The ability of the method to provide current maps of PV modules is demonstrated. The performance of several reconstruction algorithms is also investigated. An optical measurement setup for CS current mapping of small area PV devices was built at the National Physical Laboratory (NPL), based on a digital micromirror device (DMD). Accurate current maps can be produced with only 40% of the measurements a conventional point by point scan would need, confirming simulation results. The measurement setup is compact, straightforward to realise and uses a small number of optical elements. It can measure a small area of 1cm by 1cm, making it ideal for current mapping of small research samples. A significant signal amplification is achieved, since the patterns illuminate half of the sample. This diminishes the use of lock-in techniques, reducing the cost for current mapping of PV devices. Current maps of an optical resolution up to 27μm are acquired, without the use of any demagnification elements of the projected pattern that the DMD generates. v A scale up of this new current mapping method is demonstrated using Digital Light Processing (DLP) technology, which is based on DMD chips. A commercial DLP projector is utilised for building a proof of concept CS current mapping measurement system at the Centre of Renewable Energy Systems Technology (CREST). Current maps of individual PV cells in encapsulated modules can be acquired, something that is extremely difficult to achieve with conventional LBIC systems. Direct current mapping of a PV module with by-pass diodes is successfully applied for the first time. Specific shading strategies are developed for this purpose in order to isolate the cell under test. Due to the application of compressive sampling, current maps are acquired even if the signal-to-noise-ratio levels are so low that a point by point scan is not possible. Through the above implementations of CS current mapping of this work, the proposed technique is studied and evaluated. The results demonstrate that this novel method can offer a realistic alternative approach for current mapping of PV cells and modules that can be cost effective and straightforward to implement. In addition, this work introduces the application of the CS theory and DLP technology to PV metrology in general

Review of photoacoustic imaging plus X

Photoacoustic imaging (PAI) is a novel modality in biomedical imaging technology that combines the rich optical contrast with the deep penetration of ultrasound. To date, PAI technology has found applications in various biomedical fields. In this review, we present an overview of the emerging research frontiers on PAI plus other advanced technologies, named as PAI plus X, which includes but not limited to PAI plus treatment, PAI plus new circuits design, PAI plus accurate positioning system, PAI plus fast scanning systems, PAI plus novel ultrasound sensors, PAI plus advanced laser sources, PAI plus deep learning, and PAI plus other imaging modalities. We will discuss each technology's current state, technical advantages, and prospects for application, reported mostly in recent three years. Lastly, we discuss and summarize the challenges and potential future work in PAI plus X area

Compressed sensing on terahertz imaging

Most terahertz (THz) time-domain (pulsed) imaging experiments that have been performed by raster scanning the object relative to a focused THz beam require minutes or even hours to acquire a complete image. This slow image acquisition is a major limiting factor for real-time applications. Other systems using focal plane detector arrays can acquire images in real-time, but they are too expensive or are limited by low sensitivity in the THz range. More importantly, such systems cannot provide spectroscopic information of the sample. To develop faster and more efficient THz time-domain (pulsed) imaging systems, this research used random projection approach to reconstruct THz images from the synthetic and real-world THz data based on the concept of compressed/compressive sensing/sampling (CS). Compared with conventional THz time-domain (pulsed) imaging, no raster scanning of the object is required. The simulation results demonstrated that CS has great potential for real-time THz imaging systems because its use can dramatically reduce the number of measurements in such systems. We then implemented two different CS-THz systems based on the random projection method. One is a compressive THz time-domain (pulsed) spectroscopic imaging system using a set of independent optimized masks. A single-point THz detector, together with a set of 40 optimized two-dimensional binary masks, was used to measure the THz waveforms transmitted through a sample. THz time- and frequency-domain images of the sample comprising 20×20 pixels were subsequently reconstructed. This demonstrated that both the spatial distribution and the spectral characteristics of a sample can be obtained by this means. Compared with conventional THz time-domain (pulsed) imaging, ten times fewer THz spectra need to be taken. In order to further speed up the image acquisition and reconstruction process, another hardware implementation - a single rotating mask (i.e., the spinning disk) with random binary patterns - was utilized to spatially modulate a collimated THz. After propagating through the sample, the THz beam was measured using a single detector, and a THz image was subsequently reconstructed using the CS approach. This demonstrated that a 32×32 pixel image could be obtained from 160 to 240 measurements. This spinning disk configuration allows the use of an electric motor to rotate the spinning disk, thus enabling the experiment to be performed automatically and continuously. To the best of our knowledge, this is the first experimental implementation of a spinning disk configuration for high speed compressive image acquisition. A three-dimensional (3D) joint reconstruction approach was developed to reconstruct THz images from random/incomplete subsets of THz data. Such a random sampling method provides a fast THz imaging acquisition and also simplifies the current THz imaging hardware implementation. The core idea is extended in image inpainting to the case of 3D data. Our main objective is to exploit both spatial and spectral/temporal information for recovering the missing samples. It has been shown that this approach has superiority over the case where the spectral/temporal images are treated independently. We first proposed to learn a spatio-spectral/temporal dictionary from a subset of available training data. Using this dictionary, the THz images can then be jointly recovered from an incomplete set of observations. The simulation results using the measured THz image data confirm that this 3D joint reconstruction approach also provides a significant improvement over the existing THz imaging methods

Active Wavelength Selection for Chemical Identification Using Tunable Spectroscopy

Spectrometers are the cornerstone of analytical chemistry. Recent advances in microoptics manufacturing provide lightweight and portable alternatives to traditional spectrometers. In this dissertation, we developed a spectrometer based on Fabry-Perot interferometers (FPIs). A FPI is a tunable (it can only scan one wavelength at a time) optical filter. However, compared to its traditional counterparts such as FTIR (Fourier transform infrared spectroscopy), FPIs provide lower resolution and lower signal-noiseratio (SNR). Wavelength selection can help alleviate these drawbacks. Eliminating uninformative wavelengths not only speeds up the sensing process but also helps improve accuracy by avoiding nonlinearity and noise. Traditional wavelength selection algorithms follow a training-validation process, and thus they are only optimal for the target analyte. However, for chemical identification, the identities are unknown. To address the above issue, this dissertation proposes active sensing algorithms that select wavelengths online while sensing. These algorithms are able to generate analytedependent wavelengths. We envision this algorithm deployed on a portable chemical gas platform that has low-cost sensors and limited computation resources. We develop three algorithms focusing on three different aspects of the chemical identification problems. First, we consider the problem of single chemical identification. We formulate the problem as a typical classification problem where each chemical is considered as a distinct class. We use Bayesian risk as the utility function for wavelength selection, which calculates the misclassification cost between classes (chemicals), and we select the wavelength with the maximum reduction in the risk. We evaluate this approach on both synthesized and experimental data. The results suggest that active sensing outperforms the passive method, especially in a noisy environment. Second, we consider the problem of chemical mixture identification. Since the number of potential chemical mixtures grows exponentially as the number of components increases, it is intractable to formulate all potential mixtures as classes. To circumvent combinatorial explosion, we developed a multi-modal non-negative least squares (MMNNLS) method that searches multiple near-optimal solutions as an approximation of all the solutions. We project the solutions onto spectral space, calculate the variance of the projected spectra at each wavelength, and select the next wavelength using the variance as the guidance. We validate this approach on synthesized and experimental data. The results suggest that active approaches are superior to their passive counterparts especially when the condition number of the mixture grows larger (the analytes consist of more components, or the constituent spectra are very similar to each other). Third, we consider improving the computational speed for chemical mixture identification. MM-NNLS scales poorly as the chemical mixture becomes more complex. Therefore, we develop a wavelength selection method based on Gaussian process regression (GPR). GPR aims to reconstruct the spectrum rather than solving the mixture problem, thus, its computational cost is a function of the number of wavelengths. We evaluate the approach on both synthesized and experimental data. The results again demonstrate more accurate and robust performance in contrast to passive algorithms

Front-end receiver for miniaturised ultrasound imaging

Point of care ultrasonography has been the focus of extensive research over the past few decades. Miniaturised, wireless systems have been envisaged for new application areas, such as capsule endoscopy, implantable ultrasound and wearable ultrasound. The hardware constraints of such small-scale systems are severe, and tradeoffs between power consumption, size, data bandwidth and cost must be carefully balanced. To address these challenges, two synthetic aperture receiver architectures are proposed and compared. The architectures target highly miniaturised, low cost, B-mode ultrasound imaging systems. The first architecture utilises quadrature (I/Q) sampling to minimise the signal bandwidth and computational load. Synthetic aperture beamforming is carried out using a single-channel, pipelined protocol in order to minimise system complexity and power consumption. A digital beamformer dynamically apodises and focuses the data by interpolating and applying complex phase rotations to the I/Q samples. The beamformer is implemented on a Spartan-6 FPGA and consumes 296mW for a frame rate of 7Hz. The second architecture employs compressive sensing within the finite rate of innovation (FRI) framework to further reduce the data bandwidth. Signals are sampled below the Nyquist frequency, and then transmitted to a digital back-end processor, which reconstructs I/Q components non-linearly, and then carries out synthetic aperture beamforming. Both architectures were tested in hardware using a single-channel analogue front-end (AFE) that was designed and fabricated in AMS 0.35μm CMOS. The AFE demodulates RF ultrasound signals sequentially into I/Q components, and comprises a low-noise preamplifier, mixer, programmable gain amplifier (PGA) and lowpass filter. A variable gain low noise preamplifier topology is used to enable quasi-exponential time-gain control (TGC). The PGA enables digital selection of three gain values (15dB, 22dB and 25.5dB). The bandwidth of the lowpass filter is also selectable between 1.85MHz, 510kHz and 195kHz to allow for testing of both architectural frameworks. The entire AFE consumes 7.8 mW and occupies an area of 1.5×1.5 mm. In addition to the AFE, this thesis also presents the design of a pseudodifferential, log-domain multiplier-filter or “multer” which demodulates low-RF signals in the current-domain. This circuit targets high impedance transducers such as capacitive micromachined ultrasound transducers (CMUTs) and offers a 20dB improvement in dynamic range over the voltage-mode AFE. The bandwidth is also electronically tunable. The circuit was implemented in 0.35μm BiCMOS and was simulated in Cadence; however, no fabrication results were obtained for this circuit. B-mode images were obtained for both architectures. The quadrature SAB method yields a higher image SNR and 9% lower root mean squared error with respect to the RF-beamformed reference image than the compressive SAB method. Thus, while both architectures achieve a significant reduction in sampling rate, system complexity and area, the quadrature SAB method achieves better image quality. Future work may involve the addition of multiple receiver channels and the development of an integrated system-on-chip.Open Acces