Learning-Based Hardware Design for Data Acquisition Systems

This multidisciplinary research work aims to investigate the optimized information extraction from signals or data volumes and to develop tailored hardware implementations that trade-off the complexity of data acquisition with that of data processing, conceptually allowing radically new device designs. The mathematical results in classical Compressive Sampling (CS) support the paradigm of Analog-to-Information Conversion (AIC) as a replacement for conventional ADC technologies. The AICs simultaneously perform data acquisition and compression, seeking to directly sample signals for achieving specific tasks as opposed to acquiring a full signal only at the Nyquist rate to throw most of it away via compression. Our contention is that in order for CS to live up its name, both theory and practice must leverage concepts from learning. This work demonstrates our contention in hardware prototypes, with key trade-offs, for two different fields of application as edge and big-data computing. In the framework of edge-data computing, such as wearable and implantable ecosystems, the power budget is defined by the battery capacity, which generally limits the device performance and usability. This is more evident in very challenging field, such as medical monitoring, where high performance requirements are necessary for the device to process the information with high accuracy. Furthermore, in applications like implantable medical monitoring, the system performances have to merge the small area as well as the low-power requirements, in order to facilitate the implant bio-compatibility, avoiding the rejection from the human body. Based on our new mathematical foundations, we built different prototypes to get a neural signal acquisition chip that not only rigorously trades off its area, energy consumption, and the quality of its signal output, but also significantly outperforms the state-of-the-art in all aspects. In the framework of big-data and high-performance computation, such as in high-end servers application, the RF circuits meant to transmit data from chip-to-chip or chip-to-memory are defined by low power requirements, since the heat generated by the integrated circuits is partially distributed by the chip package. Hence, the overall system power budget is defined by its affordable cooling capacity. For this reason, application specific architectures and innovative techniques are used for low-power implementation. In this work, we have developed a single-ended multi-lane receiver for high speed I/O link in servers application. The receiver operates at 7 Gbps by learning inter-symbol interference and electromagnetic coupling noise in chip-to-chip communication systems. A learning-based approach allows a versatile receiver circuit which not only copes with large channel attenuation but also implements novel crosstalk reduction techniques, to allow single-ended multiple lines transmission, without sacrificing its overall bandwidth for a given area within the interconnect's data-path

Optical Gas Sensing: Media, Mechanisms and Applications

Optical gas sensing is one of the fastest developing research areas in laser spectroscopy. Continuous development of new coherent light sources operating especially in the Mid-IR spectral band (QCL—Quantum Cascade Lasers, ICL—Interband Cascade Lasers, OPO—Optical Parametric Oscillator, DFG—Difference Frequency Generation, optical frequency combs, etc.) stimulates new, sophisticated methods and technological solutions in this area. The development of clever techniques in gas detection based on new mechanisms of sensing (photoacoustic, photothermal, dispersion, etc.) supported by advanced applied electronics and huge progress in signal processing allows us to introduce more sensitive, broader-band and miniaturized optical sensors. Additionally, the substantial development of fast and sensitive photodetectors in MIR and FIR is of great support to progress in gas sensing. Recent material and technological progress in the development of hollow-core optical fibers allowing low-loss transmission of light in both Near- and Mid-IR has opened a new route for obtaining the low-volume, long optical paths that are so strongly required in laser-based gas sensors, leading to the development of a novel branch of laser-based gas detectors. This Special Issue summarizes the most recent progress in the development of optical sensors utilizing novel materials and laser-based gas sensing techniques

Learning Tissue Geometries for Photoacoustic Image Analysis

Photoacoustic imaging (PAI) holds great promise as a novel, non-ionizing imaging modality, allowing insight into both morphological and physiological tissue properties, which are of particular importance in the diagnostics and therapy of various diseases, such as cancer and cardiovascular diseases. However, the estimation of physiological tissue properties with PAI requires the solution of two inverse problems, one of which, in particular, presents challenges in the form of inherent high dimensionality, potential ill-posedness, and non-linearity. Deep learning (DL) approaches show great potential to address these challenges but typically rely on simulated training data providing ground truth labels, as there are no gold standard methods to infer physiological properties in vivo. The current domain gap between simulated and real photoacoustic (PA) images results in poor in vivo performance and a lack of reliability of models trained with simulated data. Consequently, the estimates of these models occasionally fail to match clinical expectations. The work conducted within the scope of this thesis aimed to improve the applicability of DL approaches to PAI-based tissue parameter estimation by systematically exploring novel data-driven methods to enhance the realism of PA simulations (learning-to-simulate). This thesis is part of a larger research effort, where different factors contributing to PA image formation are disentangled and individually approached with data-driven methods. The specific research focus was placed on generating tissue geometries covering a variety of different tissue types and morphologies, which represent a key component in most PA simulation approaches. Based on in vivo PA measurements (N = 288) obtained in a healthy volunteer study, three data-driven methods were investigated leveraging (1) semantic segmentation, (2) Generative Adversarial Networks (GANs), and (3) scene graphs that encode prior knowledge about the general tissue composition of an image, respectively. The feasibility of all three approaches was successfully demonstrated. First, as a basis for the more advanced approaches, it was shown that tissue geometries can be automatically extracted from PA images through the use of semantic segmentation with two types of discriminative networks and supervised training with manual reference annotations. While this method may replace manual annotation in the future, it does not allow the generation of any number of tissue geometries. In contrast, the GAN-based approach constitutes a generative model that allows the generation of new tissue geometries that closely follow the training data distribution. The plausibility of the generated geometries was successfully demonstrated in a comparative assessment of the performance of a downstream quantification task. A generative model based on scene graphs was developed to gain a deeper understanding of important underlying geometric quantities. Unlike the GAN-based approach, it incorporates prior knowledge about the hierarchical composition of the modeled scene. However, it allowed the generation of plausible tissue geometries and, in parallel, the explicit matching of the distributions of the generated and the target geometric quantities. The training was performed either in analogy to the GAN approach, with target reference annotations, or directly with target PA images, circumventing the need for annotations. While this approach has so far been exclusively conducted in silico, its inherent versatility presents a compelling prospect for the generation of tissue geometries with in vivo reference PA images. In summary, each of the three approaches for generating tissue geometry exhibits distinct strengths and limitations, making their suitability contingent upon the specific application at hand. By opening a new research direction in the form of learning-to-simulate approaches and significantly improving the realistic modeling of tissue geometries and, thus, ultimately, PA simulations, this work lays a crucial foundation for the future use of DL-based quantitative PAI in the clinical setting

Biomedical Photoacoustic Imaging and Sensing Using Affordable Resources

The overarching goal of this book is to provide a current picture of the latest developments in the capabilities of biomedical photoacoustic imaging and sensing in an affordable setting, such as advances in the technology involving light sources, and delivery, acoustic detection, and image reconstruction and processing algorithms. This book includes 14 chapters from globally prominent researchers , covering a comprehensive spectrum of photoacoustic imaging topics from technology developments and novel imaging methods to preclinical and clinical studies, predominantly in a cost-effective setting. Affordability is undoubtedly an important factor to be considered in the following years to help translate photoacoustic imaging to clinics around the globe. This first-ever book focused on biomedical photoacoustic imaging and sensing using affordable resources is thus timely, especially considering the fact that this technique is facing an exciting transition from benchtop to bedside. Given its scope, the book will appeal to scientists and engineers in academia and industry, as well as medical experts interested in the clinical applications of photoacoustic imaging

Experimental And Computational Analyses Of Locomotor Rhythm Generation And Modulation In Caenorhabditis Elegans

Neural circuits coordinate with muscles and sensory feedback to generate motor behaviors appropriate to its natural environment. Studying mechanisms underlying complex organism locomotion has been challenging, partly due to the complexity of their nervous systems. Here, I used the roundworm C. elegans to understand the locomotor circuit. With its well-mapped nervous system, easily-measurable movements, genetic manipulability, and many human homologous genes, C. elegans has been commonly used as a model organism for dissecting the circuit, cellular, and molecular principles of locomotion. My work introduces two separate approaches to probe the mechanisms by which the C. elegans motor circuit generates and modulates undulations. First, I quantified C. elegans movements during free locomotion and during transient muscle inhibition. Undulations were asymmetrical with respect to the duration of bending and unbending per cycle. Phase response curves induced by brief optogenetic head muscle inhibitions showed gradual increases and rapid decreases as a function of phase at which the perturbation was applied. A relaxation oscillator model was developed based on proprioceptive thresholds that switch the active muscle moment. It quantitatively agrees with data from free movement, phase responses, and previous results for gait adaptation to mechanical loads. Next, I characterized a proprioception-mediated compensatory behavior during C. elegans forward locomotion: the anterior body bending amplitude compensates for the change in midbody bending amplitude by an opposing homeostatic response. I demonstrated that curvature compensation requires dopamine signaling driven by PDE neurons. Calcium imaging experiments suggested a proprioceptive functionality for PDE in sensing midbody curvature. Downstream of PDE dopamine signaling, curvature compensation requires D2-like dopamine receptor DOP-3 in the interneurons AVK. FMRFamide-like neuropeptide FLP-1, released by AVK, regulates SMB motor neurons via receptor NPR-6 to modulate anterior bending amplitude. These results revealed a mechanism whereby proprioception works with dopamine and neuropeptide signaling to mediate homeostatic locomotor control. Together, through a consolidation of experimental and computational approaches, I found C. elegans utilizes its circuitry not only to act motor behaviors but to adjust/correct its ongoing behaviors in its natural contexts

Dichotomic role of NAADP/two-pore channel 2/Ca2+ signaling in regulating neural differentiation of mouse embryonic stem cells

Poster Presentation - Stem Cells and Pluripotency: abstract no. 1866The mobilization of intracellular Ca2+stores is involved in diverse cellular functions, including cell proliferation and differentiation. At least three endogenous Ca2+mobilizing messengers have been identified, including inositol trisphosphate (IP3), cyclic adenosine diphosphoribose (cADPR), and nicotinic adenine acid dinucleotide phosphate (NAADP). Similar to IP3, NAADP can mobilize calcium release in a wide variety of cell types and species, from plants to animals. Moreover, it has been previously shown that NAADP but not IP3-mediated Ca2+increases can potently induce neuronal differentiation in PC12 cells. Recently, two pore channels (TPCs) have been identified as a novel family of NAADP-gated calcium release channels in endolysosome. Therefore, it is of great interest to examine the role of TPC2 in the neural differentiation of mouse ES cells. We found that the expression of TPC2 is markedly decreased during the initial ES cell entry into neural progenitors, and the levels of TPC2 gradually rebound during the late stages of neurogenesis. Correspondingly, perturbing the NAADP signaling by TPC2 knockdown accelerates mouse ES cell differentiation into neural progenitors but inhibits these neural progenitors from committing to the final neural lineage. Interestingly, TPC2 knockdown has no effect on the differentiation of astrocytes and oligodendrocytes of mouse ES cells. Overexpression of TPC2, on the other hand, inhibits mouse ES cell from entering the neural lineage. Taken together, our data indicate that the NAADP/TPC2-mediated Ca2+signaling pathway plays a temporal and dichotomic role in modulating the neural lineage entry of ES cells; in that NAADP signaling antagonizes ES cell entry to early neural progenitors, but promotes late neural differentiation.postprin