TIME-DIFFERENCE CIRCUITS: METHODOLOGY, DESIGN, AND DIGITAL REALIZATION

This thesis presents innovations for a special class of circuits called Time Difference (TD) circuits. We introduce a signal processing methodology with TD signals that alters the target signal from a magnitude perspective to time interval between two time events and systematically organizes the primary TD functions abstracted from existing TD circuits and systems. The TD circuits draw attention from a broad range of application fields. In addition, highly evolved complementary metal-oxide-semiconductor (CMOS) technology suffers from various problems related to voltage and current amplitude signal processing methods. Compared to traditional analog and digital circuits, TD circuits bring several compelling features: high-resolution, high-throughput, and low-design complexity with digital integration capability. Further, the fabrication technology is advancing into the nanometer regime; the reduction in voltage headroom limits the performance of traditional analog/mixed-signal designs. All-digital design of time-difference circuit needs to be stressed to adapt to the low-cost, low-power, and high-portability applications. We focus on Time-to-Digital Converters (TDC), one of the crucial building blocks in TD circuits. A novel algorithmic architecture is proposed based on a binary search algorithm and validated with both simulation and fabricated silicon. An all-digital structure Time-difference Amplifier (TDA) is designed and implemented to make FPGA and other all-digital implementations for TDC and related TD circuits feasible. Besides, we propose an all-digital timing measurement circuit based on the process variation from CMOS fabrication: PVTMC, which achieves a high measurement resolution:

New methods for deep tissue imaging

Microscopes play vital role biological science and medicine. For single photon microscopies, the scattering of photons makes regions of interest located a few hundred microns beneath the surface inaccessible. Multi-photon microscopes are widely used for minimally invasive in vivo brain imaging due to their increased imaging depth. However, multi-photon microscopes are hampered by limited dynamic range, preventing weak sample features from being detected in the presence of strong features, or preventing the capture of unpredictable bursts in sample strength. In the first part of the thesis, I present a solution to vastly improve the dynamic range of a multi-photon microscope while limiting potential photodamage. Benefits are shown in both structural and in-vivo functional mouse brain imaging applications. In the second section of the thesis work, I explore a completely different approach towards deep tissue imaging by changing the type of radiation from light to ultrasound. Inspired by an optical phase contrast technique invented in the lab, I developed an unprecedented ultrasound imaging system that can visualize the ultrasound phase contrast in the sample. The ultrasound phase contrast technique is able to visualize local sound speed variations instead of local reflectivity. Compared with existing sound speed tomography systems, our technique eliminates the cumbersome sound speed reconstruction process. The research work in this section contains three parts. In the first part, we designed a low-cost single element scanning system as proof of concept. In the second part, we implemented the ultrasound phase contrast imaging system on a commercial linear phased transducer array and an imaging apparatus designed for samples with finite thickness. In the third part, we studied the feasibility of ultrasound phase contrast imaging in arbitrarily thick tissue. We presented a complete workflow of theoretical study, simulation, prototyping and experimental testing for all three parts.2020-02-28T00:00:00

UWB Pulse Radar for Human Imaging and Doppler Detection Applications

We were motivated to develop new technologies capable of identifying human life through walls. Our goal is to pinpoint multiple people at a time, which could pay dividends during military operations, disaster rescue efforts, or assisted-living. Such system requires the combination of two features in one platform: seeing-through wall localization and vital signs Doppler detection. Ultra-wideband (UWB) radar technology has been used due to its distinct advantages, such as ultra-low power, fine imaging resolution, good penetrating through wall characteristics, and high performance in noisy environment. Not only being widely used in imaging systems and ground penetrating detection, UWB radar also targets Doppler sensing, precise positioning and tracking, communications and measurement, and etc. A robust UWB pulse radar prototype has been developed and is presented here. The UWB pulse radar prototype integrates seeing-through imaging and Doppler detection features in one platform. Many challenges existing in implementing such a radar have been addressed extensively in this dissertation. Two Vivaldi antenna arrays have been designed and fabricated to cover 1.5-4.5 GHz and 1.5-10 GHz, respectively. A carrier-based pulse radar transceiver has been implemented to achieve a high dynamic range of 65dB. A 100 GSPS data acquisition module is prototyped using the off-the-shelf field-programmable gate array (FPGA) and analog-to-digital converter (ADC) based on a low cost solution: equivalent time sampling scheme. Ptolemy and transient simulation tools are used to accurately emulate the linear and nonlinear components in the comprehensive simulation platform, incorporated with electromagnetic theory to account for through wall effect and radar scattering. Imaging and Doppler detection examples have been given to demonstrate that such a “Biometrics-at-a-glance” would have a great impact on the security, rescuing, and biomedical applications in the future

Electronics for Sensors

The aim of this Special Issue is to explore new advanced solutions in electronic systems and interfaces to be employed in sensors, describing best practices, implementations, and applications. The selected papers in particular concern photomultiplier tubes (PMTs) and silicon photomultipliers (SiPMs) interfaces and applications, techniques for monitoring radiation levels, electronics for biomedical applications, design and applications of time-to-digital converters, interfaces for image sensors, and general-purpose theory and topologies for electronic interfaces

Extraction of anthropological data with ultrasound

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.Human body scanners used to extract anthropological data have a significant drawback, the subject is required to undress or wear tight fitting clothing. This thesis demonstrates an ultrasonic based alternative to the current optical systems, that can potentially operate on a fully clothed subject. To validate the concept several experiments were performed to determine the acoustic properties of multiple garments. The results indicated that such an approach was possible. Beamforming is introduced as a method by which the ultrasonic scanning area can be increased, the concept is thoroughly studied and a clear theoretical analysis is performed. Additionally, Matlab has been used to demonstrate graphically, the results of such analysis, providing an invaluable tool during the simulation, experimental and results stages of the thesis. To evaluate beamfoming as a composite part of ultrasonic body imaging, a hardware solution was necessary. During the concept phase, both FPGA and digital signal processors were evaluated to determine their suitability for the role. An FPGA approach was finally chosen, which allows highly parallel operation, essential to the high acquisition speeds required by some beamforming methodologies. In addition, analogue circuitry was also designed to provide an interface with the ultrasonic transducers, which, included variable gain amplifiers, charge amplifiers and signal conditioning. Finally, a digital acquisition card was used to transfer data between the FPGA and a desktop computer, on which, the sampled data was processed and displayed in a coherent graphical manner. The beamforming results clearly demonstrate that imaging multiple layers in air, with ultrasound, is a viable technique for anthroplogical data collection. Furthermore, a wavelet based method of improving the axial resolution is also proposed and demonstrated

Measurements, Models, Systems and Design

531 s.

Data Acquisition Applications

Data acquisition systems have numerous applications. This book has a total of 13 chapters and is divided into three sections: Industrial applications, Medical applications and Scientific experiments. The chapters are written by experts from around the world, while the targeted audience for this book includes professionals who are designers or researchers in the field of data acquisition systems. Faculty members and graduate students could also benefit from the book

Digital CMOS ISFET architectures and algorithmic methods for point-of-care diagnostics

Over the past decade, the surge of infectious diseases outbreaks across the globe is redefining how healthcare is provided and delivered to patients, with a clear trend towards distributed diagnosis at the Point-of-Care (PoC). In this context, Ion-Sensitive Field Effect Transistors (ISFETs) fabricated on standard CMOS technology have emerged as a promising solution to achieve a precise, deliverable and inexpensive platform that could be deployed worldwide to provide a rapid diagnosis of infectious diseases. This thesis presents advancements for the future of ISFET-based PoC diagnostic platforms, proposing and implementing a set of hardware and software methodologies to overcome its main challenges and enhance its sensing capabilities. The first part of this thesis focuses on novel hardware architectures that enable direct integration with computational capabilities while providing pixel programmability and adaptability required to overcome pressing challenges on ISFET-based PoC platforms. This section explores oscillator-based ISFET architectures, a set of sensing front-ends that encodes the chemical information on the duty cycle of a PWM signal. Two initial architectures are proposed and fabricated in AMS 0.35um, confirming multiple degrees of programmability and potential for multi-sensing. One of these architectures is optimised to create a dual-sensing pixel capable of sensing both temperature and chemical information on the same spatial point while modulating this information simultaneously on a single waveform. This dual-sensing capability, verified in silico using TSMC 0.18um process, is vital for DNA-based diagnosis where protocols such as LAMP or PCR require precise thermal control. The COVID-19 pandemic highlighted the need for a deliverable diagnosis that perform nucleic acid amplification tests at the PoC, requiring minimal footprint by integrating sensing and computational capabilities. In response to this challenge, a paradigm shift is proposed, advocating for integrating all elements of the portable diagnostic platform under a single piece of silicon, realising a ``Diagnosis-on-a-Chip". This approach is enabled by a novel Digital ISFET Pixel that integrates both ADC and memory with sensing elements on each pixel, enhancing its parallelism. Furthermore, this architecture removes the need for external instrumentation or memories and facilitates its integration with computational capabilities on-chip, such as the proposed ARM Cortex M3 system. These computational capabilities need to be complemented with software methods that enable sensing enhancement and new applications using ISFET arrays. The second part of this thesis is devoted to these methods. Leveraging the programmability capabilities available on oscillator-based architectures, various digital signal processing algorithms are implemented to overcome the most urgent ISFET non-idealities, such as trapped charge, drift and chemical noise. These methods enable fast trapped charge cancellation and enhanced dynamic range through real-time drift compensation, achieving over 36 hours of continuous monitoring without pixel saturation. Furthermore, the recent development of data-driven models and software methods open a wide range of opportunities for ISFET sensing and beyond. In the last section of this thesis, two examples of these opportunities are explored: the optimisation of image compression algorithms on chemical images generated by an ultra-high frame-rate ISFET array; and a proposed paradigm shift on surface Electromyography (sEMG) signals, moving from data-harvesting to information-focused sensing. These examples represent an initial step forward on a journey towards a new generation of miniaturised, precise and efficient sensors for PoC diagnostics.Open Acces

Fast fluorescence lifetime imaging and sensing via deep learning

Error on title page – year of award is 2023.Fluorescence lifetime imaging microscopy (FLIM) has become a valuable tool in diverse disciplines. This thesis presents deep learning (DL) approaches to addressing two major challenges in FLIM: slow and complex data analysis and the high photon budget for precisely quantifying the fluorescence lifetimes. DL's ability to extract high-dimensional features from data has revolutionized optical and biomedical imaging analysis. This thesis contributes several novel DL FLIM algorithms that significantly expand FLIM's scope. Firstly, a hardware-friendly pixel-wise DL algorithm is proposed for fast FLIM data analysis. The algorithm has a simple architecture yet can effectively resolve multi-exponential decay models. The calculation speed and accuracy outperform conventional methods significantly. Secondly, a DL algorithm is proposed to improve FLIM image spatial resolution, obtaining high-resolution (HR) fluorescence lifetime images from low-resolution (LR) images. A computational framework is developed to generate large-scale semi-synthetic FLIM datasets to address the challenge of the lack of sufficient high-quality FLIM datasets. This algorithm offers a practical approach to obtaining HR FLIM images quickly for FLIM systems. Thirdly, a DL algorithm is developed to analyze FLIM images with only a few photons per pixel, named Few-Photon Fluorescence Lifetime Imaging (FPFLI) algorithm. FPFLI uses spatial correlation and intensity information to robustly estimate the fluorescence lifetime images, pushing this photon budget to a record-low level of only a few photons per pixel. Finally, a time-resolved flow cytometry (TRFC) system is developed by integrating an advanced CMOS single-photon avalanche diode (SPAD) array and a DL processor. The SPAD array, using a parallel light detection scheme, shows an excellent photon-counting throughput. A quantized convolutional neural network (QCNN) algorithm is designed and implemented on a field-programmable gate array as an embedded processor. The processor resolves fluorescence lifetimes against disturbing noise, showing unparalleled high accuracy, fast analysis speed, and low power consumption.Fluorescence lifetime imaging microscopy (FLIM) has become a valuable tool in diverse disciplines. This thesis presents deep learning (DL) approaches to addressing two major challenges in FLIM: slow and complex data analysis and the high photon budget for precisely quantifying the fluorescence lifetimes. DL's ability to extract high-dimensional features from data has revolutionized optical and biomedical imaging analysis. This thesis contributes several novel DL FLIM algorithms that significantly expand FLIM's scope. Firstly, a hardware-friendly pixel-wise DL algorithm is proposed for fast FLIM data analysis. The algorithm has a simple architecture yet can effectively resolve multi-exponential decay models. The calculation speed and accuracy outperform conventional methods significantly. Secondly, a DL algorithm is proposed to improve FLIM image spatial resolution, obtaining high-resolution (HR) fluorescence lifetime images from low-resolution (LR) images. A computational framework is developed to generate large-scale semi-synthetic FLIM datasets to address the challenge of the lack of sufficient high-quality FLIM datasets. This algorithm offers a practical approach to obtaining HR FLIM images quickly for FLIM systems. Thirdly, a DL algorithm is developed to analyze FLIM images with only a few photons per pixel, named Few-Photon Fluorescence Lifetime Imaging (FPFLI) algorithm. FPFLI uses spatial correlation and intensity information to robustly estimate the fluorescence lifetime images, pushing this photon budget to a record-low level of only a few photons per pixel. Finally, a time-resolved flow cytometry (TRFC) system is developed by integrating an advanced CMOS single-photon avalanche diode (SPAD) array and a DL processor. The SPAD array, using a parallel light detection scheme, shows an excellent photon-counting throughput. A quantized convolutional neural network (QCNN) algorithm is designed and implemented on a field-programmable gate array as an embedded processor. The processor resolves fluorescence lifetimes against disturbing noise, showing unparalleled high accuracy, fast analysis speed, and low power consumption