69 research outputs found
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Aiding the conservation of two wooden Buddhist sculptures with 3D imaging and spectroscopic techniques
The conservation of Buddhist sculptures that were transferred to Europe at some point during their lifetime raises numerous questions: while these objects historically served a religious, devotional purpose, many of them currently belong to museums or private collections, where they are detached from their original context and often adapted to western taste.
A scientific study was carried out to address questions from Museo d'Arte Orientale of Turin curators in terms of whether these artifacts might be forgeries or replicas, and how they may have transformed over time. Several analytical techniques were used for materials identification and to study the production technique, ultimately aiming to discriminate the original materials from those added within later interventions
1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface
A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance
High Frequency Physiological Data Quality Modelling in the Intensive Care Unit
Intensive care medicine is a resource intense environment in which technical and clinical decision making relies on rapidly assimilating a huge amount of categorical and timeseries physiologic data. These signals are being presented at variable frequencies and of variable quality. Intensive care clinicians rely on high frequency measurements of the patient's physiologic state to assess critical illness and the response to therapies. Physiological waveforms have the potential to reveal details about the patient state in very fine resolution, and can assist, augment, or even automate decision making in intensive care. However, these high frequency time-series physiologic signals pose many challenges for modelling. These signals contain noise, artefacts, and systematic timing errors, all of which can impact the quality and accuracy of models being developed and the reproducibility of results. In this context, the central theme of this thesis is to model the process of data collection in an intensive care environment from a statistical, metrological, and biosignals engineering perspective with the aim of identifying, quantifying, and, where possible, correcting errors introduced by the data collection systems. Three different aspects of physiological measurement were explored in detail, namely measurement of blood oxygenation, measurement of blood pressure, and measurement of time. A literature review of sources of errors and uncertainty in timing systems used in intensive care units was undertaken. A signal alignment algorithm was developed and applied to approximately 34,000 patient-hours of simultaneously collected electroencephalography and physiological waveforms collected at the bedside using two different medical devices
Micro/Nano Devices for Blood Analysis, Volume II
The development of micro- and nanodevices for blood analysis continues to be a growing interdisciplinary subject that demands the careful integration of different research fields. Following the success of the book “Micro/Nano Devices for Blood Analysis”, we invited more authors from the scientific community to participate in and submit their research for a second volume. Researchers from different areas and backgrounds cooperated actively and submitted high-quality research, focusing on the latest advances and challenges in micro- and nanodevices for diagnostics and blood analysis; micro- and nanofluidics; technologies for flow visualization and diagnosis; biochips, organ-on-a-chip and lab-on-a-chip devices; and their applications to research and industry
Biosensors for Diagnosis and Monitoring
Biosensor technologies have received a great amount of interest in recent decades, and this has especially been the case in recent years due to the health alert caused by the COVID-19 pandemic. The sensor platform market has grown in recent decades, and the COVID-19 outbreak has led to an increase in the demand for home diagnostics and point-of-care systems. With the evolution of biosensor technology towards portable platforms with a lower cost on-site analysis and a rapid selective and sensitive response, a larger market has opened up for this technology. The evolution of biosensor systems has the opportunity to change classic analysis towards real-time and in situ detection systems, with platforms such as point-of-care and wearables as well as implantable sensors to decentralize chemical and biological analysis, thus reducing industrial and medical costs. This book is dedicated to all the research related to biosensor technologies. Reviews, perspective articles, and research articles in different biosensing areas such as wearable sensors, point-of-care platforms, and pathogen detection for biomedical applications as well as environmental monitoring will introduce the reader to these relevant topics. This book is aimed at scientists and professionals working in the field of biosensors and also provides essential knowledge for students who want to enter the field
Microplastics Degradation and Characterization
In the last decade, issues related to pollution from microplastics in all environmental compartments and the associated health and environmental risks have been the focus of intense social, media, and political attention worldwide. The assessment, quantification, and study of the degradation processes of plastic debris in the ecosystem and its interaction with biota have been and are still the focus of intense multidisciplinary research. Plastic particles in the range from 1 to 5 mm and those in the sub-micrometer range are commonly denoted as microplastics and nanoplastics, respectively. Microplastics (MPs) are being recognized as nearly ubiquitous pollutants in water bodies, but their actual concentration, distribution, and effects on natural waters, sediments, and biota are still largely unknown. Contamination by microplastics of agricultural soil and other environmental areas is also becoming a matter of concern. Sampling, separation, detection, characterization and evaluating the degradation pathways of micro- and nano-plastic pollutants dispersed in the environment is a challenging and critical goal to understand their distribution, fate, and the related hazards for ecosystems. Given the interest in this topic, this Special Issue, entitled “Microplastics Degradation and Characterization”, is concerned with the latest developments in the study of microplastics
Intelligent Biosignal Processing in Wearable and Implantable Sensors
This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine
Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging
Significance: Hyperspectral reflectance imaging can be used in medicine to identify tissue types, such as tumor tissue. Tissue classification algorithms are developed based on, e.g., machine learning or principle component analysis. For the development of these algorithms, data are generally preprocessed to remove variability in data not related to the tissue itself since this will improve the performance of the classification algorithm. In hyperspectral imaging, the measured spectra are also influenced by reflections from the surface (glare) and height variations within and between tissue samples.Aim: To compare the ability of different preprocessing algorithms to decrease variations in spectra induced by glare and height differences while maintaining contrast based on differences in optical properties between tissue types.Approach: We compare eight preprocessing algorithms commonly used in medical hyperspectral imaging: standard normal variate, multiplicative scatter correction, min-max normalization, mean centering, area under the curve normalization, single wavelength normalization, first derivative, and second derivative. We investigate conservation of contrast stemming from differences in: blood volume fraction, presence of different absorbers, scatter amplitude, and scatter slope-while correcting for glare and height variations. We use a similarity metric, the overlap coefficient, to quantify contrast between spectra. We also investigate the algorithms for clinical datasets from the colon and breast.Conclusions: Preprocessing reduces the overlap due to glare and distance variations. In general, the algorithms standard normal variate, min-max, area under the curve, and single wavelength normalization are the most suitable to preprocess data used to develop a classification algorithm for tissue classification. The type of contrast between tissue types determines which of these four algorithms is most suitable
Magnetoencephalography for the investigation and diagnosis of Mild Traumatic Brain Injury
Mild Traumatic Brain Injury (mTBI), (or concussion), is the most common type of brain injury. Despite this, it often goes undiagnosed and can cause long term disability—most likely caused by the disruption of axonal connections in the brain. Objective methods for diagnosis and prognosis are needed but clinically available neuroimaging modalities rarely show structural abnormalities, even when patients suffer persisting functional deficits. In the past three decades, new powerful techniques to image brain structure and function have shown promise in detecting mTBI related changes. Magnetoencephalography (MEG), which measures electrical brain activity by detecting magnetic fields outside the head generated by neural currents, is particularly sensitive and has therefore gained interest from researchers. Numerous studies are proposing abnormal low-frequency neural oscillations and functional connectivity—the statistical interdependency of signals from separate brain regions—as potential biomarkers for mTBI. However, typically small sample sizes, the lack of replication between groups, the heterogeneity of the cohorts studied, and the lack of longitudinal studies impedes the adoption of MEG as a clinical tool in mTBI management. In particular, little is known about the acute phase of mTBI.
In this thesis, some of these gaps will be addressed by analysing MEG data from individuals with mTBI, using novel as well as conventional methods. The potential future of MEG in mTBI research will also be addressed by testing the capabilities of a wearable MEG system based on optically pumped magnetometers (OPMs).
The thesis contains three main experimental studies. In study 1, we investigated the signal dynamics underlying MEG abnormalities, found in a cohort of subjects scanned within three months of an mTBI, using a Hidden Markov Model (HMM), as growing evidence suggests that neural dynamics are (in part) driven by transient bursting events. Applying the HMM to resting-state data, we show that previously reported findings of diminished intrinsic beta amplitude and connectivity in individuals with mTBI (compared to healthy controls) can be explained by a reduction in the beta-band content of pan-spectral bursts and a loss in the temporal coincidence of bursts respectively. Using machine learning, we find the functional connections driving group differences and achieve classification accuracies of 98%. In a motor task, mTBI resulted in reduced burst amplitude, altered modulation of burst probability during movement and decreased connectivity in the motor network.
In study 2, we further test our HMM-based method in a cohort of subjects with mTBI and non-head trauma—scanned within two weeks of injury—to ensure specificity of any observed effects to mTBI and replicate our previous finding of reduced connectivity and high classification accuracy, although not the reduction in burst amplitude. Burst statistics were stable over both studies—despite data being acquired at different sites, using different scanners. In the same cohort, we applied a more conventional analysis of delta-band power. Although excess low-frequency power appears to be a promising candidate marker for persistently symptomatic mTBI, insufficient data exist to confirm this pattern in acute mTBI. We found abnormally high delta power to be a sensitive measure for discriminating mTBI subjects from healthy controls, however, similarly elevated delta amplitude was found in the cohort with non-head trauma, suggesting that excess delta may not be specific to mTBI, at least in the acute stage of injury.
Our work highlights the need for longitudinal assessment of mTBI. In addition, there appears to be a need to investigate naturalistic paradigms which can be tailored to induce activity in symptom-relevant brain networks and consequently are likely to be more sensitive biomarkers than the resting state scans used to date. Wearable OPM-MEG makes naturalistic scanning possible and may offer a cheaper and more accessible alternative to cryogenic MEG, however, before deploying OPMs clinically, or in pitch-side assessment for athletes, for example, the reliability of OPM-derived measures needs to be verified. In the third and final study, we performed a repeatability study using a novel motor task, estimating a series of common MEG measures and quantifying the reliability of both activity and connectivity derived from OPM-MEG data. These initial findings—presently limited to a small sample of healthy controls—demonstrate the utility of OPM-MEG and pave the way for this technology to be deployed on patients with mTBI
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