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The integration of non-terrestrial networks into the 5G ecosystem is mainly driven by the possibility of provisioning service in remote areas. In this context, the advent of flying base stations at the low Earth orbit (LEO) will enable anywhere and anytime connectivity. To materialize this vision, it is of utmost importance to improve radio protocols with the aim of allowing direct satellite access. Bearing this aspect in mind, we present a new random access signal, which is based on the filter bank multicarrier (FBMC) waveform, and a computationally efficient detection scheme. The proposed solution outperforms the standardized access scheme based on single-carrier frequency division multiplexing (SC-FDM), by reducing out-of-band (OOB) emissions and reducing the missed detection probability in presence of very high carrier frequency offset (CFO), which is inherent to LEO satellite systems. The improvement is related to the fine frequency resolution of the detector and the use of pulse shaping techniques. Interestingly, the FBMC-based random access signal achieves a high level of commonality with 5G new radio, as the preamble generation method and the time-frequency allocation pattern can be kept unchanged. Concerning the practical implementation aspects, the complexity of the detector is similar in both SC-FDM and FBMC. This paper is part of the R+D+i project (PID2020-115323RB-C31) funded by MCIN/AEI/ 10.13039/501100011033. This work is supported by the grant from Spanish Ministry of Economic Affairs and Digital Transformation and the European union - NextGenerationEU (UNICO-5G I+D/AROMA3D-Space (TSI-063000-2021-70))

The introduction of low-cost sensors (LCSs) in air quality Internet of Things (IoT) monitoring platforms presents the challenge of improving the quality of the data that these sensors provide. In this article, we propose two algorithms to perform denoising and calibration for LCSs used in IoT monitoring platforms. Sensors are first calibrated in situ using linear or nonlinear machine learning models that only take into account instantaneous measurements. The best calibration model is used to estimate the values measured by the sensor during the sensor deployment. To improve the values of the estimates produced by the in situ calibration model, we propose to take into account the temporal patterns present in signals, such as temperature or tropospheric ozone that have regular patterns, e.g., daily. The first method, which we call temporal pattern-based denoising (TPB-D), performs signal denoising by projecting the daily signals of the in situ calibrated LCS onto a subspace generated by the daily signals stored in a database taken by reference instruments. The second method, which we call temporal pattern-based calibration (TPB-C), considers that if we also have a reference instrument colocated to the LCSs over a period of time, we can correct with a linear mapping with regularization the daily LCS signals projected in the subspace produced by the reference database to be as similar as possible to the projected signals of the colocated reference instrument. The results show that the TPB-D improves the estimates made by in situ calibration by up to 10%–20%, while the TPB-C improves the estimates made by in situ calibration by up to 20%–40%. This work was supported in part by the National Spanish Funding under Grant PID2019-107910RB-I00 and in part by the Regional Project under Grant 2021 SGR 01059.

The concept of structure engineering has been proposed for exploring the next generation of radiation detectors with improved performance. A TOF-PET geometry with heterostructured scintillators with a pixel size of 3.0x3.1x15 mm3 was simulated using Monte Carlo. The heterostructures consisted of alternating layers of BGO as a dense material with high stopping power and plastic (EJ232) as a fast light emitter. The detector time resolution was calculated as a function of the deposited and shared energy in both materials on an event-by-event basis. While sensitivity was reduced to 32% for 100 μm thick plastic layers and 52% for 50 μm, the CTR distribution improved to 204±49 ps and 220±41 ps respectively, compared to 276 ps that we considered for bulk BGO. The complex distribution of timing resolutions was accounted for in the reconstruction. We divided the events into three groups based on their CTR and modeled them with different Gaussian TOF kernels. On a NEMA IQ phantom, the heterostructures had better contrast recovery in early iterations. On the other hand, BGO achieved a better contrast to noise ratio (CNR) after the 15th iteration due to the higher sensitivity. The developed simulation and reconstruction methods constitute new tools for evaluating different detector designs with complex time responses.

Long Range (LoRa) is an emerging low-power wide-area network technology offering long-range wireless connectivity to Internet of Things (IoT) devices. For energy efficiency reasons, LoRa end nodes implement a nonslotted ALOHA multiple access scheme to transmit packets to the gateway. Due to the lack of synchronization between end nodes, collisions between uplink packets have been identified as the main obstacle to the scaling of dense LoRa networks. To tackle this issue, we present in this article a LoRa receiver that is capable of decoding colliding packets from two interfering end nodes. The proposed two-user detector is derived from the maximum-likelihood principle using a detailed model of two colliding LoRa packets. As the complexity of the maximum-likelihood sequence estimation is prohibitive, complexity-reduction techniques are introduced to enable practical implementations of the receiver. An in-depth performance analysis highlights that the proposed two-user detector inherently leverages the differences in received power, time offsets, and frequency offsets between the users to separate and demodulate their respective signals. To demonstrate the practicality of the proposed detector, an interference-robust synchronization algorithm is then designed and evaluated. Simulation results indicate that a LoRa receiver combining the proposed synchronization algorithm and two-user detector is capable of detecting and demodulating two interfering users with satisfactorily error rates.

Single-ion implantation as a technique for fabricating future quantum devices requires high precision in placing individual ions in the semiconductor material. One way to achieve this precision is to use focused ion beams and verify the implantation of each ion by detecting the charge it induces in the semiconductor. This method has already been demonstrated for silicon substrates but has yet to be successfully performed for wide-bandgap materials. One of the most promising materials for future quantum devices is a diamond. Quantum centers in diamond, such as nitrogen vacancies, can serve as information carriers in quantum computers—qubits— and are stable at room temperature. This work focuses on understanding the critical parameters required for the successful detection of shallow, low-energy ions in diamonds using commercially available diamond crystals and front-end electronics. Depth profiling of the electric field in the diamond crystal was performed using ions of different species and energy. State-of-the-art front-end electronics were also used to achieve high energy resolution. Using the XGLab CUBE PRE031 preamplifier, an energy resolution of 6 keV was achieved for the detection of 400-keV protons. Detection of 140-keV copper ions, penetrating an average of 100 nm into the diamond, was also demonstrated. The optimal position of implantation sites was determined from the spatial distribution of charge collection efficiency.

This work presents a novel InGaAs/InP SPAD structure fabricated using a selective area growth (SAG) method. The surface topography of the selectively grown film deposited within the 70 mu m diffusion apertures is used to engineer the Zn diffusion profile to suppress premature edge breakdown. The device achieves a highly uniform active area without the need for shallow diffused guard ring (GR) regions that are inherent in standard InGaAs/InP SPADs. We have obtained 33% and 43% photon detection probability (PDP) at 1550 nm, with 5 V and 7 V excess bias, respectively. These measurements were performed at 300 K and 225 K. The dark count rate (DCR) per unit area at room temperature and at 5 V excess bias is 430 cps/mu m(2) and it decreases to 5 cps/mu m(2) at 225 K. Timing jitter is measured with passive quenching at 1550nm as 149 ps at full-width-at-half-maximum (FWHM), (300 K, 5 V excess bias). The proposed technology is suitable for a number of applications, including optical time-domain reflectometry (OTDR), quantum information, and light detection and ranging (LiDAR).

ispartof: IEEE PHOTONICS JOURNAL vol:14 issue:4 status: published

In recent years, several new applications of SiC (both 4H and 3C polytypes) have been proposed in different papers. In this review, several of these emerging applications have been reported to show the development status, the main problems to be solved and the outlooks for these new devices. The use of SiC for high temperature applications in space, high temperature CMOS, high radiation hard detectors, new optical devices, high frequency MEMS, new devices with integrated 2D materials and biosensors have been extensively reviewed in this paper. The development of these new applications, at least for the 4H-SiC ones, has been favored by the strong improvement in SiC technology and in the material quality and price, due to the increasing market for power devices. However, at the same time, these new applications need the development of new processes and the improvement of material properties (high temperature packages, channel mobility and threshold voltage instability improvement, thick epitaxial layers, low defects, long carrier lifetime, low epitaxial doping). Instead, in the case of 3C-SiC applications, several new projects have developed material processes to obtain more performing MEMS, photonics and biomedical devices. Despite the good performance of these devices and the potential market, the further development of the material and of the specific processes and the lack of several SiC foundries for these applications are limiting further development in these fields.

The electrification of transportation, and aircraft electrification in particular, is experiencing rapid development due to the more efficient use of energy. Since the dielectric strength of air decreases at the cruising altitudes of commercial aircraft due to the reduced pressure environment, there is a need to control and minimize the risks associated with electrical discharges. This paper shows from experimental and computational data that there is a relationship between the electrical and optical phenomena involved in the discharge process. To this end, this paper analyzes corona discharges generated using different electrode geometries under a wide range of pressures from 100 kPa to 10 kPa. It is shown that the densities of charged particles or charge carriers generated during the discharge process are positively correlated with the intensity of corona images acquired with a digital imaging sensor sensitive to the near UV and visible wavelength ranges. Therefore, the intensity of the images can be used as a reliable and accurate indicator of the corona activity, the values of which are related to the ionization processes involved in the discharges. The results presented in this paper can be applied in various physical and engineering fields, such as high voltage engineering, power line monitoring, or ozone generation, among others. This research was funded by Ministerio de Ciencia e Innovación de España, grant number PID2020-114240RB-I00 and by the Generalitat de Catalunya, grant number 2021 SGR 00392.

In this PhD, I have advanced the study of ground-based photometric observations in the near-infrared. Specifically, I have worked in the context of robotic exoplanet transit surveys in collaboration with the SPECULOOS Southern Observatory (SSO). Here, I targeted optimising the photometric precision of observing late M, L type stars by developing correction methods and a new instrument called SPIRIT. My first original contribution was the development of a correction method for the induced effects from varying precipitable water vapour (PWV) in our atmosphere, specifically on differentially resolved light curves from SSO. This work succeeded in reducing false variability of time-series data from late M, L type stars on both long and short timescales, to the extent of removing false transit features. In parallel, I performed a feasibility study of introducing new near-infrared instrumentation to SSO. One which would permit better photometric precision than the existing CCD Si based instrumentation, and likewise minimise the induced effects of PWV variability. An InGaAs based instrument, sensitive up to 1.62 µm was identified, and a custom wide-pass filter called zYJ was designed and manufactured to form SPeculoos' Infra-Red photometric Imager for Transits (SPIRIT). It proved to be a significantly lower-cost alternative to the traditionally used HgCdTe based instrumentation, as well as being better suited to robotic observatories. On sky results of SPIRIT at SSO successfully demonstrated better photometric precision for stars below 2550 K than the existing instrumentation. It similarly demonstrated the benefit of seeing further into the infrared for minimising the observed variability of M, L type stars. Finally, the custom designed wide-pass filter, zYJ, successfully demonstrated a significantly lower sensitivity to PWV variability. These results pave a new avenue for ground-based near-infrared robotic exoplanet transit surveys, as well as similar time-series focused astronomy. I conclude my work by suggesting viable routes to further improve the photometric precision of such new instrumentation.

El proyecto tiene por objetivo la implementación de un módulo de detección y seguimiento de personas que permite el guiado de un robot móvil dotado de un sensor LIDAR. El proyecto está implementado en ROS y consta de dos partes principales, el primero es el módulo de detección de personas y el segundo es el módulo de navegación que permite al robot seguir a la persona que ha detectado. El primer módulo toma la nube de puntos que proporciona el sensor LIDAR y lo procesa en tres etapas. La primera consiste en delimitar la nube de puntos, retirando aquellos puntos cercanos al suelo y techo. En la segunda etapa se agrupan aquellos puntos que representan a objetos candidatos y se extraen sus características. En la última etapa se clasifican los objetos candidatos, según sus características, en humanos y no humanos haciendo uso de un clasificador basado en máquina de soporte vectorial (SVM). El segundo módulo calcula la orientación de la persona detectada, y planifica la trayectoria que seguirá el robot en función de la posición y orientación de la persona detectada.

Context. The Medium Resolution Spectrometer (MRS) is one of the four observing modes of JWST/MIRI. Using JWST in-flight data of unresolved (point) sources, we can derive the MRS absolute spectral response function (ASRF) starting from raw data. Spectral fringing, caused by coherent reflections inside the detector arrays, plays a critical role in the derivation and interpretation of the MRS ASRF. The fringe corrections implemented in the current pipeline are not optimal for non-extended sources, and a high density of molecular features particularly inhibits an accurate correction. Aims. In this paper, we present an alternative way to calibrate the MIRI/MRS data. Firstly, we derive a fringe correction that accounts for the dependence of the fringe properties on the MIRI/MRS pupil illumination and detector pixel sampling of the point spread function. Secondly, we derive the MRS ASRF using an absolute flux calibrator observed across the full 5- 28 µm wavelength range of the MRS. Thirdly, we apply the new ASRF to the spectrum of a G dwarf and compare it with the output of the JWST/MIRI default data reduction pipeline. Finally, we examine the impact of the different fringe corrections on the detectability of molecular features in the G dwarf and K giant. Methods. The absolute flux calibrator HD 163466 (A-star) was used to derive tailored point source fringe flats at each of the default dither locations of the MRS. The fringe-corrected point source integrated spectrum of HD 163466 was used to derive the MRS ASRF using a theoretical model for the stellar continuum. A cross-correlation was run to quantify the uncertainty on the detection of CO, SiO, and OH in the K giant and CO in the G dwarf for different fringe corrections. Results. The point-source-tailored fringe correction and ASRF are found to perform at the same level as the current corrections, beating down the fringe contrast to the sub-percent level in the G dwarf in the longer wavelengths, whilst mitigating the alteration of real molecular features. The same tailored solutions can be applied to other MRS unresolved targets. Target acquisition is required to ensure the pointing is accurate enough to apply this method. A pointing repeatability issue in the MRS limits the effectiveness of the tailored fringe flats is at short wavelengths. Finally, resulting spectra require no scaling to make the sub-bands match, and a dichroic spectral leak at 12.2 µm is removed., Astronomy & Astrophysics, 673, ISSN:0004-6361, ISSN:1432-0746

This paper investigates the secure control problem for cyber-physical systems when the malicious data is injected into the cyber realm which is directly connecting to the actuators. Based on moving target defense and reinforcement learning, we propose a novel proactive and reactive defense control scheme. First, the system $(A,B)$ is modeled as a switching system consisting of several controllable pairs $(A,\mathcal{B}_{l})$ to facilitate the construction of the moving target defense control scheme. The controllable pairs $(A,\mathcal{B}_{l})$ can be altered to update system dynamics under certain unpredictable switching probabilities for each subsystem, which can prevent the adversaries from effective attacks. Second, both attack detection and isolation schemes are designed to accurately locate and exclude the compromised actuators from a switching sequence. Third, a reinforcement learning algorithm based on the zero-sum game theory is proposed to design the defense control scheme when there exist no controllable subsystems to switch. To demonstrate the effectiveness of the defense control scheme, a three-tank system under unknown cyber attacks is illustrated.

This work investigates the spectral quantum efficiency and inter-pixel crosstalk of a MWIR-LWIR dual band, HgCdTe-based focal plane array (FPA) photodetector (MWIR and LWIR stand for mid- and long-wavelength infrared bands). Pixels are $\text{10}\,\mu$ m-wide with truncated pyramid geometry and separated by deep trenches. Three-dimensional combined full-wave electromagnetic and electrical simulations in the drift-diffusion approximation allowed to describe the complex, standing-wave-like spectral features resulting from the light interference and diffraction due to the pixels and illuminating beam aperture. The inter-pixel crosstalk for the MWIR operation demonstrated to be very sensitive to the trench depth, in contrast to the LWIR electrooptical response, left almost unchanged. The present work also investigates the causes of performance worsening in the two IR bands when pixel pitch is reduced to $\text{5}\,\mu$ m, hence well below typical LWIR wavelengths and close to the diffraction limited operation.

This article presents a low-jitter and low-spur charge-sampling phase-locked loop (CSPLL). A charge-domain sub-sampling phase detector is introduced to achieve a high phase-detection gain and to reduce the PLL in-band phase noise. Even without employing any power-hungry isolation buffers, the proposed phase detector dramatically suppresses the reference spurs by both minimizing the modulated capacitance seen by the voltage-controlled oscillator (VCO) tank and by reducing the duty cycle of the sampling clock. A 50μW RF-dividerless frequency-tracking loop is also introduced to lock the CSPLL robustly when the VCO faces a sudden frequency disturbance. Fabricated in a 40-nm CMOS process, the prototype CSPLL occupies a core area of 0.13 mm 2 and synthesizes 9.6-to-12-GHz tones using a 100-MHz reference. At 11.2 GHz, it achieves a reference spur of −77.3 dBc and an RMS jitter of 48.6 fs while consuming 5 mW.

Many sweat-based wearable monitoring systems have been recently proposed, but the data provided by those systems often lack a reliable and meaningful relation to standardized blood values. One clear example is lactate, a relevant biomarker for both sports and health sectors, with a complex sweat–blood bioequivalence. This limitation decreases its individual significance as a sweat-based biomarker. Taking into account the insights of previous studies, a multiparametric methodology has been proposed to predict blood lactate from non-invasive independent sensors: sweat lactate, sweat rate, and heart rate. The bioequivalence study was performed with a large set of volunteers (>30 subjects) in collaboration with sports institutions (Institut Nacional d’Educació Física de Catalunya, INEFC, and Centre d’Alt Rendiment, CAR, located in Spain). A neural network algorithm was used to predict blood lactate values from the sensor data and subject metadata. The developed methodology reliably and accurately predicted blood lactate absolute values, only adding 0.3 mM of accumulated error when compared to portable blood lactate meters, the current gold standard for sports clinicians. The approach proposed in this work, along with an integrated platform for sweat monitoring, will have a strong impact on the sports and health fields as an autonomous, real-time, and continuous monitoring tool The authors kindly acknowledge the support from the Spanish Ministerio de Indústria, Energía y Turismo (AEI Clusters Program, AEI2009L1CA011), Ministerio de Ciencia y Innovació n (PID2020-114070RB-I00), Agencia Estatal de Investigación (RED2018-102829-T and CPP2021-009021), and AGAUR (2019 DI 18 and 2021PROD00064)

The European Space Agency's Rosetta mission to comet 67P/Churyumov-Gerasimenko has offered scientists the opportunity to study a comet in unprecedented detail. Four instruments of the Rosetta orbiter, namely, the Micro-Imaging Dust Analysis System (MIDAS), the Grain Impact Analyzer and Dust Accumulator (GIADA), the COmetary Secondary Ion Mass Analyser (COSIMA), and the Rosetta Orbiter Spectrometer for Ion and Neutral Analysis (ROSINA) have provided information on cometary dust particles. Cross-instrument comparisons are crucial to characterize cometary dust particles beyond the capabilities of individual sensors, as they are sensitive to different dust components. We present the first comparison between detections of the ROSINA COmet Pressure Sensor (COPS) and GIADA. These two instruments are complementary as the former is sensitive solely to volatiles of icy particles, while the latter measured the dust particle as a whole, including refractories and condensed (semi)volatiles. Our goal is to correlate the particles detected by COPS and GIADA and to assess whether they belong to a common group. We statistically analyzed the in situ data of COPS and GIADA by calculating Pearson correlation coefficients. Among the several types of particles detected by GIADA, we find that COPS particles are significantly correlated solely with GIADA fluffy agglomerates (Pearson correlation coefficient of 0.55 and p-value of $4.6\cdot 10^{-3}$). This suggests that fluffy particles are composed of both refractories and volatiles. COPS volatile volumes, which may be represented by equivalent spheres with a diameter in the range between 0.06 $\mu$m and 0.8 $\mu$m, are similar to the sizes of the fractal particle's subunits identified by MIDAS (i.e., 0.05-0.18 $\mu$m)., Comment: 6 pages, 3 figures, accepted for publication in A&A

A comprehensive summary of the measurements made to characterize test mass charging due to the space environment during the LISA Pathfinder mission is presented. Measurements of the residual charge of the test mass after release by the grabbing and positioning mechanism, show that the initial charge of the test masses was negative after all releases, leaving the test mass with a potential in the range $-12$ mV to $-512$ mV. Variations in the neutral test mass charging rate between $21.7$ e s$^{-1}$ and $30.7$ e s$^{-1}$ were observed over the course of the 17-month science operations produced by cosmic ray flux changes including a Forbush decrease associated with a small solar energetic particle event. A dependence of the cosmic ray charging rate on the test mass potential between $-30.2$ e s$^{-1}$ V$^{-1}$ and $-40.3$ e s$^{-1}$ V$^{-1}$ was observed and this is attributed to a contribution to charging from low-energy electrons emitted from the gold surfaces of the gravitational reference sensor. Data from the on-board particle detector show a reliable correlation with the charging rate and with other environmental monitors of the cosmic ray flux. This correlation is exploited to extrapolate test mass charging rates to a 20-year period giving useful insight into the expected range of charging rate that may be observed in the LISA mission., Comment: 17 pages, 10 figures

Sending and receiving information with electromagnetic radiation is fundamental to imaging and communications. Humankind’s ability to utilise this radiation is dependent on how efficiently we can detect it, and improving detectors will advance these technologies. Visible light and X-rays make up two regions of the electromagnetic spectra, both key bands for imaging in different modalities. The visible spectrum represents the energies we can detect with our eyes, while X-rays are highly penetrative and allow the inside of opaque objects to be imaged. While their applications are highly complementary, their detector technologies also share commonalities, allowing the development of both simultaneously. In this thesis, the unique properties of metal halide perovskites are utilised to advance both visible light and Xray detectors. However, other properties of perovskites, such as ion migration, can provide challenges when characterising performance, and so techniques to accurately measure their detecting ability are also developed. Metal halide perovskites are exploited to design two new detector device structures, increasing the functionality of photodetectors and overcoming the existing limitations of direct X-ray detectors. A unique photodetector, utilising the band gap tunability of perovskites, is developed to produce a multiband response that can controllably detect different regions of the visible spectrum. The resulting device is employed in a method to send communications with added encryption. Additionally, a concept for a novel X-ray detecting device is developed, using the ability of perovskites to retain impressive properties after low-temperature solution deposition. The device structure decouples the dimensions of photon absorption and charge carrier collection to retain performance across the X-ray spectrum, overcoming the limitations currently preventing the commercial success of direct X-ray detectors. The potential of perovskites as scintillators for indirect X-ray detection is investigated. The published performances are contextualised with a detailed analysis of the operating mechanism. This mechanistic insight highlights the advantages this material could bring, and we propose the applications that would benefit most from perovskite scintillators, as well as the origins of the remaining limitations. The concurrent understanding of perovskites in other optoelectronic devices is utilised to suggest pathways to overcome the remaining challenges and bring the material closer to commercialisation. These suggestions are applied, and impressive scintillation performance is demonstrated from an emerging Cs2ZrBr6 nanocrystal scintillator system. This work also highlights the specific considerations required when characterising perovskitebased detectors. The large defect density in these materials is shown to be a double-edged sword; making measurements under low light intensities prone to errors, but also acting as another lever to control detection performance. The challenges of characterising direct X-ray detectors are also discussed, alongside the development of experimental procedures to robustly measure halide perovskite devices. Overall, this thesis utilises the unique properties of perovskites to develop detectors with new functionality, whilst ensuring the same properties do not reduce the accuracy when characterising their performance. The work brings perovskite detectors one step closer to a commercial reality.

L’appareil de radiochirurgie CyberKnife performe des traitements avancés de radiothérapie qui offrent des avantages nets pour certains types de cancer. Or, cet appareil produit uniquement des petits faisceaux circulaires ce qui complexifie les procédures de dosimétrie en milieu clinique. En effet, en conditions de petits champs, les diverses perturbations au niveau du détecteur peuvent être très grandes. Ainsi, l’utilisation de la méthode Monte Carlo est nécessaire lors de l’étalonnage et la caractérisation de faisceaux. Ces processus, lors desquels des valeurs de dose de référence et relative sont mesurées et entrées dans les systèmes de planification de traitement, assurent l’efficacité des traitements ainsi que la sécurité des patients. Cette thèse porte sur la modélisation Monte Carlo du CyberKnife M6 et étudie diverses applications à la dosimétrie de petits champs de radiothérapie. En premier lieu, une nouvelle méthode permettant la correction de la dépendance au débit de dose des diodes au silicium est proposée. Cette dernière est validée puis appliquée à des mesures relatives effectuées au CyberKnife du Centre hospitalier de l’Université de Montréal (CHUM). Les résultats illustrent la correction de l’erreur systématique induite dans les mesures due à la dépendance au débit de dose de la diode considérée. La méthode proposée fournit alors une solution efficace à cette problématique. En second lieu, une méthode pour l’optimisation des paramètres sources requis en entrée lors de la modélisation Monte Carlo de faisceaux de radiothérapie est introduite. Cette dernière est basée sur une approche probabiliste portant sur la comparaison de mesures et de simulations pour divers détecteurs, et permet la détermination de l’énergie du faisceau d’électrons incident sur la cible d’un appareil, ainsi que de la largeur à mi-hauteur de sa distribution radiale. La méthode proposée, qui est appliquée au CyberKnife du CHUM, fournit une nouvelle approche permettant l’optimisation d’un modèle Monte Carlo d'un faisceau ainsi que l’estimation des incertitudes sur ses paramètres sources. En troisième lieu, le modèle de faisceau du CyberKnife développé est utilisé afin d’estimer l’impact des incertitudes des paramètres sources sur diverses fonctions dosimétriques couramment utilisées en milieu clinique, ainsi que sur des distributions de dose obtenues par simulation de plans de traitement. Les résultats illustrent l’augmentation de l’impact des incertitudes du modèle de faisceau avec la réduction de la taille de champ, et fournissent une nouvelle perspective sur la précision de calcul atteignable pour ce type de calcul de dose Monte Carlo en petits champs. En quatrième lieu, les protocoles de dosimétrie TG-51 (version adaptée du manufacturier) et TRS-483 sont respectivement appliqués et comparés pour l’étalonnage du CyberKnife M6 se trouvant au CHUM. Il est observé que le TRS-483 est cohérent avec le TG-51. Des facteurs de correction de la qualité et corrigeant pour les effets de moyenne sur le volume propres au CyberKnife du CHUM sont estimés par simulations Monte Carlo pour une chambre à ionisation Exradin A12. Les résultats illustrent que la valeur générique fournie dans le TRS-483 pourrait être surestimée en comparaison à notre modèle de CyberKnife et que cette surestimation pourrait être due à la composante de moyenne sur le volume., The CyberKnife radiosurgery system performs advanced radiotherapy treatments that offer clear benefits for certain types of cancer. However, this device produces small circular fields only, which complicates dosimetry procedures in a clinical environment. Indeed, under small field conditions, the various perturbations at the detector level can become very large. Thus, the use of the Monte Carlo method is necessary when calibrating and characterizing beams. Such processes, during which reference and relative dose values are measured and entered into treatment planning systems, ensure the validity of treatments as well as patient safety. This thesis focuses on the Monte Carlo modeling of the CyberKnife M6 and studies various applications to small photon fields dosimetry. Firstly, a new method for the correction of the dose rate dependency of silicon diode detectors is proposed. The latter is validated and applied to relative measurements performed at the CyberKnife of the Centre hospitalier de l’Université de Montréal (CHUM). Results illustrate the correction of the systematic error induced in the measurements due to the dose rate dependency of the considered diode. The proposed method provides an efficient solution to this issue. Secondly, a method for the optimization of the source parameters required as input during Monte Carlo beam modeling is introduced. The latter is based on a probabilistic approach and on the comparison of measurements and simulations for various detectors. The method allows the determination of the energy of the electron beam incident on the target of a linac, as well as the full width at half-maximum of its radial distribution. The proposed method, which is applied to the CyberKnife unit of the CHUM, provides a new approach for the optimization of a Monte Carlo beam model and a way to estimate the uncertainties on its source parameters. Thirdly, the developed CyberKnife beam model is used to estimate the impact of source parameter uncertainties on various dosimetric functions commonly used in the clinic environment, and on dose distributions obtained by simulation of treatment plans. Results illustrate the increase of the impact of beam modeling uncertainties with the decrease of the field size, and provide insights on the reachable calculation accuracy for this type of Monte Carlo dose calculation in small fields. Lastly, the TG-51 (manufacturer’s adapted version) and TRS-483 dosimetry protocols are respectively applied and compared for the calibration of the CHUM’s CyberKnife. We observe that TRS-483 is consistent with TG-51. Beam quality and volume averaging correction factors specific to the CHUM's CyberKnife are estimated using Monte Carlo simulations for an Exradin A12 ionization chamber. Results illustrate that the generic value provided in the TRS-483 could be overestimated in comparison to our CyberKnife model and that this overestimation could be due to the volume averaging component.

System operators around the world are building new high-voltage power lines to meet the growing electricity demand. Since there are no indications that it is going to stop in the near future the operators are researching new ways to increase the supply avoiding the expensive and time-consuming constructions. Amongst the solutions proposed the implementation of a dynamic thermal line rating (DTLR) stands out for its large potential. A DTLR is a method to update in real time the maximum working current of the line, known as ampacity, based on the weather conditions and the conductor temperature. As the ampacity is determined by the maximum allowable temperature of the conductor, on colder or windier days a larger power could be transported through the line, as opposed to the prevalent static line rating (SLR) which is always calculated assuming the worst conditions. Existing DTLR models and on-line sensors have faced issues due to the variations in temperature within the conductor. This thesis presents the results of the study of said variations. The research has consisted of temperature rise tests with wind and solar radiation to quantify the temperature gradient inside the conductor, the variations in surface temperature along the perimeter and the variations in temperature along the conductor itself. Moreover, the ac resistance of the conductor has been studied in order to model the temperature evolution in time. Finally, the age effect on a conductor has been researched by observing the differences between two conductors of the same model but at opposite points of their lifespan.

This thesis aims to study the characteristics of two sensors implemented in a pedal made using strain gauges and barometric sensors. We study the range, linearity, repeatability and dependency on time and temperature to establish if this sensors will be suitable or not to develop a torque sensor to control an ebike. Esta tesis tiene como objetivo estudiar las características de dos sensores implementados en un pedal fabricados mediante galgas extensométricas y sensores barométricos. Estudiamos el rango, la linealidad, la repetibilidad y dependencia en tiempo y temperatura para establecer si estos sensores serán adecuados o no para desarrollar un sensor de par para controlar una ebike. Aquesta tesi té com a objectiu estudiar les característiques de dos sensors implementats en un pedal fabricats utilitzant extensometres i sensors baromètrics. Estudiem el rang, la linealitat, la repetibilitat i dependència del temps i temperatura per establir si aquests sensors seran adequats o no per desenvolupar un sensor de parell per controlar una bicicleta elèctrica.

La detección de daños en estructuras es un reto para la ingeniería. Hasta ahora en el sector aeronáutico la metodología a seguir era llevar a cabo mantenimientos preventivos periódicos para la detección de estos daños. Algunos daños son perceptibles mediante una simple inspección visual, pero otros requieren desmontar partes de la aeronave y ser analizada mediante ensayos de detección no destructivos (NDT). Esto supone un alto coste económico, ya que, a parte del propio coste de estas inspecciones, se le tiene que sumar el tiempo que la aeronave está inoperativa. Algunos defectos son difíciles de detectar y una vez son detectados ya pueden haber causado un daño severo en la estructura. Para conseguir detectar estos fallos en sus primeros estados y además reducir o eliminar el tiempo de que la nave esté fuera de servicio, se están implementando una red de sensores para monitorizar la salud de la estructura en tiempo real. Estos sistemas de monitorización y detección de defectos en estructuras, es conocido como SHMS por sus siglas en inglés Structural Health Monitoring Systems. Para implementar esta red de sensores en la estructura, es importante determinar el número de sensores y la localización de éstos para detectar cualquier daño en la estructura. Una buena optimización de la red de sensores permite detectar cualquier fallo en la estructura y además reducir costes tanto en la instalación de los sensores como en el equipamiento para procesar los datos. Un alto número de sensores daría un conjunto de datos demasiado grande, que tendría un gran coste computacional que repercute en el tiempo de procesamiento de los datos y los requerimientos del equipo. En el presente proyecto se propone e implemente una metodología para determinar qué sensores son los que nos dan más información para después mediante pruebas poder determinar si esta metodología funciona. Este método se basa en hacer uso de un método de reducción de dimensiones y calcular ciertos índices estadísticos, para después calcular las contribuciones de cada sensor a estos índices. Con las contribuciones tenemos un criterio de selección para determinar qué sensores son más importantes y finalmente mediante un algoritmo de clasificación se valida si esta nueva metodología es efectiva. La detecció de defectes en estructures és un repte per l’enginyeria. Fins ara en el sector aeronàutic la metodologia a seguir era portar a terme manteniments preventius periòdicament per la detecció d’aquests defectes. Alguns defectes són perceptibles mitjançant una simple inspecció visual, però d’ altres requereixen desmuntar part de l’aeronau i ser analitzada mitjançant assajos de detecció no destructius (NDT). Això suposa un alt cost econòmic, ja que, a part de propi cost d’aquestes inspeccions, s’ha de sumar el temps que l’aeronau està inoperativa. Alguns defectes són difícils de detectar i una vegada son detectats ja poden haver causat un dany sever a l’estructura. Per aconseguir detectar aquests defectes en els seus primers estats i a més reduir o eliminar el temps que l’aeronau està inoperativa, s’està implementant una xarxa de sensors per monitoritzar la salut de l’estructura en temps real. Aquests sistemes de monitorització i detecció de defectes en estructures, són coneguts com SHS per les seves sigles en anglès Structural Health Monitoring Systems. Per implementar aquesta xarxa de sensors a l’estructura és important determinar el nombre de sensors i la localització d’aquests per detectar qualssevol defecte a l’estructura. Una bona optimització de la xarxa de sensors permet detectar qualssevol defecte a l’estructura i a més reduir costos tant en la instal·lació dels sensors com en l’equipament per a processar les dades. Un alt nombre de sensors donaria un conjunt de dades massa gran, que tindria un gran cost computacional que repercutiria en el temps de processament d’aquestes dades i els requeriments de l’equip. En el present projecte es proposa i s’implementa una metodologia per determinar quins sensors son els que ens donen més informació per després mitjançant probes poder determinar si aquesta metodologia funciona. Aquest mètode es basa en fer ús d’un mètode de reducció de dimensions i calcular uns índexs estadístics per després calcular les contribucions de cada sensor a aquests índexs estadístics. Amb les contribucions tindrem un criteri de selecció per determinar quins sensors són els més importants i així, finalment, mitjançant un algoritme de classificació validar si aquest mètode proposat és efectiu. The detection of damage in structures is a challenge for engineering. Until now, in the aeronautical sector, the methodology to follow was to carry out periodic preventive maintenance to detect these damages. Some damage is perceptible by simple visual inspection, but others require disassembling parts of the aircraft and being analyzed by non-destructive detection testing (NDT). This implies a high economic cost, since, apart from the cost of these inspections, the time that the aircraft is inoperative must be added. Some defects are difficult to detect and once detected they may already have caused severe damage to the structure. In order to detect these failures in their early stages and also reduce or eliminate the time the ship is out of service, a network of sensors is being implemented to monitor the health of the structure in real time. These systems for monitoring and detecting defects in structures are known as SHMS for its acronym Structural Health Monitoring Systems. To implement this sensor network in the structure, it is important to determine the number of sensors and their location to detect any damage to the structure. A good optimization of the sensor network allows detecting any failure in the structure and also reduces costs both in the installation of the sensors and in the equipment to process the data. A high number of sensors would give a data set that is too large, which would have a large computational cost that affects the data processing time and equipment requirements. In the present project, a methodology is proposed and implemented to determine which sensors are the ones that give us more information so that later, through tests, we can determine if this methodology works. This method is based on making use of a dimension reduction method and calculating certain statistical parameters, to later calculate the contributions of each sensor to these parameters. With the contributions we have a selection criteria to determine which sensors are most important and finally, through a classification algorithm, it is validated if this new methodology is effective.

Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2022-2023, Tutor: Manel Puig i Vidal, In this project an Arduino IoT based board connected to some sensors, is used to control the temperature, the humidity and the concentration of CO2 and combustible gases. The device will be installed at the top of an autonomous moving robot that will be in clean hospital rooms. The user will be able to consult the data via smartphone or web

Anomaly Detection systems based on Machine and Deep learning are the most promising solutions to detect cyberattacks in the industry. However, these techniques are vulnerable to adversarial attacks that downgrade prediction performance. Several techniques have been proposed to measure the robustness of Anomaly Detection in the literature. However, they do not consider that, although a small perturbation in an anomalous sample belonging to an attack, i.e., Denial of Service, could cause it to be misclassified as normal while retaining its ability to damage, an excessive perturbation might also transform it into a truly normal sample, with no real impact on the industrial system. This paper presents a methodology to calculate the robustness of Anomaly Detection models in industrial scenarios. The methodology comprises four steps and uses a set of additional models called support models to determine if an adversarial sample remains anomalous. We carried out the validation using the Tennessee Eastman process, a simulated testbed of a chemical process. In such a scenario, we applied the methodology to both a Long-Short Term Memory (LSTM) neural network and 1-dimensional Convolutional Neural Network (1D-CNN) focused on detecting anomalies produced by different cyberattacks. The experiments showed that 1D-CNN is significantly more robust than LSTM for our testbed. Specifically, a perturbation of 60% (empirical robustness of 0.6) of the original sample is needed to generate adversarial samples for LSTM, whereas in 1D-CNN the perturbation required increases up to 111% (empirical robustness of 1.11).

The detection of objects on the seafloor is a complex task. The domain of the detection and classification of naval mines is additionally complicated by the high risk nature of the task. Autonomous underwater vehicles (AUVs) have been used in naval mine countermeasures (MCM) operations to search large areas using sensors such as sidescan or synthetic aperture sonars. These sensors generally have a high coverage rate, while sacrificing spatial resolution. Conversely, sensors with higher resolution but lower coverage (such as forward-looking sonars and electro-optical cameras) are employed for the later classification and identification stages of the MCM mission. However, to autonomously execute a target reacquisition mission, it is important to be able to collect and process data automatically and, in near real time, onboard an AUV. For this purpose, an automatic target recognition (ATR) system is required. This article proposes an ATR, which can be used onboard an autonomous vehicle, capable of detecting mine-like objects in forward-looking sonar data. The ATR combines a detector and a classifier, based on convolutional neural network models, with a probabilistic grid map that filters out false positives and combines reported detections at nearby locations. A strategy, combining a survey pattern with target-mapping maneuvers automatically activated by the ATR, has been designed to maximize the performance of this ATR. The whole system has been tested in simulation as well as using data from previous MCM exercises, the results of which are presented here This document is the results of the research project funded by the EU project Open Cooperation for European mAritime awareNess (OCEAN2020)

An on-wafer micro-detector for in situ EUV (wavelength of 13.5 nm) detection featuring FinFET CMOS compatibility, 1 T pixel and battery-less sensing is demonstrated. Moreover, the detection results can be written in the in-pixel storage node for days, enabling off-line and non-destructive reading. The high spatial resolution micro-detectors can be used to extract the actual parameters of the incident EUV on wafers, including light intensity, exposure time and energy, key to optimization of lithographic processes in 5 nm FinFET technology and beyond.

This paper presents the design and bit error rate (BER) analysis of a phase-independent non-orthogonalmultiple access (NOMA) system. The proposed NOMA system can utilize amplitude-coherent detection(ACD) which requires only the channel amplitude for equalization purposes. In what follows, threedifferent designs for realizing the detection of the proposed NOMA are investigated. One is based on themaximum likelihood (ML) principle, while the other two are based on successive interference cancellation(SIC). Closed-form expressions for the BER of all detectors are derived and compared with the BER ofthe coherent ML detector. The obtained results, which are corroborated by simulations, demonstrate that,in most scenarios, the BER is dominated by multiuser interference rather than the absence of the channelphase information. Consequently, the BER using ML and ACD are comparable for various cases ofinterest. The paper also shows that the SIC detector is just an alternative approach to realize the MLdetector, and hence, both detectors provide the same BER performance.

На первом этапе были синтезированы объекты исследования - диоксид кремния методом Штобера, где в качестве прекурсора использовали тетраэтоксисилан, и нанокомпозит ZnO - Au золь-гель методом с использованием в качестве прекурсора 2 - водного ацетата цинка. На втором этапе, микроструктуру и морфологию полученных образцов исследовали методом растровой электронной микроскопии на сканирующем электронном микроскопе «MIRA-LMH» фирмы «Tescan» с применением как классического детектора вторичных электронов, так и дополнительных детекторов - внутрилинзового детектора вторичных электронов и детектора отраженных электронов. В результате исследований установлено, что при использовании детектора вторичных электронов получаются изображения с топографическим контрастом и практически без шумов. При использовании внутрилинзового детектора вторичных электронов создаются изображения только материального контраста, без влияния рельефа поверхности. Также использование данного детектора позволило получить высококачественные изображения с большим разрешением на расстоянии от образца 5 мм. При использовании детектора отраженных электронов с рабочим расстоянием до образца 8 мм и увеличении разрешающей способности микроскопа, полученные изображения имеют низкий контраст границ, но представляют композиционную информацию с высокой чувствительностью. Таким образом, установлено, что внутрилинзовый детектор вторичных электронов, с рабочим расстоянием до образца 5 мм, является оптимальным для получения четких изображений микроструктры поверхности наноматериалов при многократном увеличении. At the first stage, the objects of study were synthesized - silicon dioxide by the Stober method, where tetraethoxysilane was used as a precursor, and a nanocomposite ZnO - Au by the sol-gel method using the aqueous zinc acetate dihydrate as a precursor. At the second stage, the microstructure and morphology of the obtained samples were investigated by scanning electron microscopy on a «MIRA-LMH» scanning electron microscope (Tescan company) using both a classical secondary electron detector and additional detectors - intralens secondary electron detector and back-scattered electrons detector. As a result of the research, it was found that when using the secondary electron detector, practically no noise images with topographic contrast are obtained. When using the intralens secondary electron detector, images of only material contrast are created, without the influence of the surface relief. Also, the use of this detector made it possible to obtain high-quality images with a high resolution at a distance of 5 mm from the sample. When using a back-scattered electrons detector with a working distance to the sample of 8 mm and increasing the resolution of the microscope, the resulting images have low border contrast, but represent compositional information with high sensitivity. Thus, it was found that the intralens secondary electron detector with a working distance of 5 mm to the sample is optimal for obtaining clear images of the microstructure of the surface of nanomaterials at multiple magnifications.

Optoacoustic signals are typically reconstructed into images using inversion algorithms applied in the time-domain. However, time-domain reconstructions can be computationally intensive and therefore slow when large amounts of raw data are collected from an optoacoustic scan. Here we considered a fast weighted ${\omega }-{k}$ (FWOK) algorithm operating in the frequency domain to accelerate the inversion in raster-scan optoacoustic mesoscopy (RSOM), while seamlessly incorporating impulse response correction with minimum computational burden. We investigated the FWOK performance with RSOM measurements from phantoms and mice in vivo and obtained 360-fold speed improvement over inversions based on the back-projection algorithm in the time-domain. This previously unexplored inversion of in vivo optoacoustic data with impulse response correction in frequency domain reconstructions points to a promising strategy of accelerating optoacoustic imaging computations, toward video-rate tomography.

In the electrical power and energy (EPE) area, the traditional concept in designing the synchronization system of power converters is using the phase-locked loop (PLL) concept. An alternative way is using the frequency-locked loop (FLL) concept, which is also known as the adaptive notch filter, adaptive noise cancelation technique, gradient descent estimator, adaptive observer, D-estimation method, and transformation angle detector, among others names. Along with the increased popularity and application of FLLs in EPE systems, the misconception about this design concept is growing. To be more exact, most EPE research communities introduce FLLs as synchronization systems of different properties compared to PLLs. This article aims to explain the main reasons behind this misconception and contribute to dispelling it.

In this paper, a method to develop embroidered textile strain resistive sensors is presented. The method is based on two overlapped zigzag conductive yarn patterns embroidered in an elastic textile. To demonstrate the functionality of the proposed configuration, a textile sensor embroidered with a conductor yarn composed of 99% pure silver-plated nylon yarn 140/17 dtex has been experimentally characterised for an elongation range from 0% to 65%. In order to show the sensor applicability, a second test with the sensor embroidered in a knee-pad has been done to evaluate the flexion knee angle from 180° to 300°. The experimental results show the usefulness of the proposed method to develop fabric strain sensors that can help to manufacture commercial applications on the healthcare sector. This work was supported by Spanish Government-MINECO under Project TEC2016-79465-R and AGAUR-UPC(2020 FI-B 00028).

We present a field-programmable gate array (FPGA)-based technique for the on-line identification of highly ionizing particles in a liquid xenon (LXe) detector. The method was developed and successfully exploited to select $\alpha $ particles emitted by 241Am sources submerged in LXe in an overwhelming, mostly beam-related, $\gamma $ -ray background. After revising the main features of xenon and other liquid noble gases as ultra-violet (UV) scintillating media, we describe the algorithm idea and its firmware implementation. We then present the results in terms of efficiency and background suppression for the real-time $\alpha $ -particle tagging and the limits of the MEG trigger configuration. Finally, we show that in MEG II we are going to overcome the main issues and further improve the performances.

In this work, we deploy a one-day-ahead prediction algorithm using a deep neural network for a fast-response BESS in an intelligent energy management system (I-EMS) that is called SIEMS. The main role of the SIEMS is to maintain the state of charge at high rates based on the one-day-ahead information about solar power, which depends on meteorological conditions. The remaining power is supplied by the main grid for sustained power streaming between BESS and end-users. Considering the usage of information and communication technology components in the microgrids, the main objective of this paper is focused on the hybrid microgrid performance under cyber-physical security adversarial attacks. Fast gradient sign, basic iterative, and DeepFool methods, which are investigated for the first time in power systems e.g. smart grid and microgrids, in order to produce perturbation for training data.

A complex event processor (CEP) is a real-time processing system for extracting meaningful data from an input data stream. One potential application of the CEP system is for a network intrusion detection system (NIDS), which monitors packet data streams in a network to detect packets containing viruses. A complex event detector (CED) circuit performs pattern matching in the CEP system, where high throughput is required. To respond to high throughput, we designed a CED circuit using single-flux-quantum (SFQ) circuits, which have the potential to operate at a very high speed beyond 50 GHz with extremely low power consumption. The proposed SFQ CED circuit corresponds to regular expressions. It consists of a string-matching unit (SMU), which performs string matching, and a state transition unit (STU), a structure mimicking a finite automaton. We designed the fundamental component of the SFQ CED circuit and demonstrated an eight-symbol CED circuit at a maximum operating frequency of 52.9 GHz.

A woven capacitive sensor is presented as a novel automotive seat occupancy sensor. The woven capacitive sensor is developed as a response to one of the most difficult challenges that the automotive sector has faced the past few years: seat occupancy sensor false positives. The designed sensor demonstrates the error-free sitting detection of a person by detecting the change of the permittivity around it, providing a distinction between a person and an object with the same weight. This research was funded by Spanish Government “Ministerio de ciencia e innovacion” PID2021 124288OB I00 and AGAUR 2020FI-B 00028.

[eng] Although the first LED dates to the middle of the 20th century, it has not been until the last decade that the market has been flooded with high efficiency and high durability LED solutions compared to previous technologies. In addition, luminaires that include types of LEDs differentiated in hue or color have already appeared. These luminaires offer new possibilities to reach colorimetric or non-visual capabilities not seen to date. Due to the enormous number of LEDs on the market, with very different spectral characteristics, the use of the spectrometer as a measuring device for determining LEDs properties has become popular. Obtaining colorimetric information from a luminaire is a necessary step to commercialize it, so it is a tool commonly used by many LED manufacturers. This doctoral thesis advances the state-of-the-art and knowledge of LED technology at the level of combined spectral emission, as well as applying innovative spectral reconstruction techniques to a commercial multichannel colorimetric sensor. On the one hand, new spectral simulation algorithms that allow obtaining a very high number of results have been developed, being able to obtain optimized values of colorimetric and non-visual parameters in multichannel light sources. MareNostrum supercomputer has been used and new relationships between colorimetric and non-visual parameters in commercial white LED datasets have been found through data analysis. Moreover, the functional improvement of a multichannel colorimetric sensor has been explored by providing it with a neural network for spectral reconstruction. A large amount of data has been generated, which has allowed simulations and statistical studies on the error committed in the spectral reconstruction process using different techniques. This improvement has led to an increase in the spectral resolution measured by the sensor, allowing better accuracy in the calculation of colorimetric parameters. Prototypes of the light sources and the colorimetric sensor have been developed in order to experimentally demonstrate the theoretical framework generated. All the prototypes have been characterized and the errors generated with respect to the theoretical models have been evaluated. The results obtained have been validated through the application of different industry standards by comparison with calibrated commercial devices., [cat] Aquesta tesi doctoral realitza un avançament en l’estat de l’art i en el coneixement sobre la tecnologia LED a nivell d’emissió espectral combinada, a més d’aplicar tècniques innovadores de reconstrucció espectral a un sensor colorimètric multicanal comercial. Per una banda, s’han desenvolupat nous algoritmes de simulació espectral que permeten obtenir un nombre molt elevat de resultats, sent capaços d’obtenir valors optimitzats de paràmetres colorimètrics i no-visuals en fonts de llum multicanal. S’ha fet ús del supercomputador MareNostrum i s’han trobat noves relacions entre paràmetres colorimètrics i no visuals en conjunts de LEDs blancs comercials a través de l’anàlisi de dades. Per altra banda, s’ha explorat la millora funcional d’un sensor colorimètric multicanal, dotant-lo d’una xarxa neuronal per a la reconstrucció espectral. S’han generat una gran quantitat de dades que han permès realitzar simulacions i estudis estadístics sobre l’error comès en el procés de reconstrucció espectral utilitzant diferents tècniques. Aquesta millora ha implicat un augment de la resolució espectral mesurada pel sensor, permetent obtenir una millor precisió en el càlcul de paràmetres colorimètrics. S’han desenvolupat prototips de les fonts de llum i del sensor colorimètric amb l’objectiu de demostrar experimentalment el marc teòric generat. Tots els prototips han estat caracteritzats i s’han avaluat els errors generats respecte els models teòrics. Els resultats obtinguts s’han validat a través de l’aplicació de diferents estàndards de la indústria o a través de la comparativa amb dispositius comercials calibrats.

La calidad del aire ha sido un tema ampliamente tratado desde la revolución industrial, época donde los seres humanos empezaron a consumir grandes cantidades de combustible para el desarrollo tecnológico, emitiendo gases tóxicos en el centro de las ciudades donde habitaban. Pero no fue hasta los años cincuenta, cuando se empezó a realizar investigaciones de los problemas que podía generar este entorno y concienciar de la importancia de estos. Este proyecto se centra en la realización de un sistema automático de adquisición de datos y monitorización de la calidad del aire mediante sensores de la familia MQ. Los objetivos del trabajo son estudiar el funcionamiento de este tipo de sensores, integrar todos los componentes necesarios y realizar la conexión entre ellos para almacenar sus datos. Además de, realizar un método de adquisición y monitorización remota, sencilla e intuitiva de esta información. Para ello, se ha diseñado una PCB que conecta los sensores con una placa Arduino, que se encarga de recoger las señales analógicas proporcionadas y las transfiere a una placa ESP32. A través de ella, los datos son publicados a un servidor de datos que posee una dirección IP pública. Para la adquisición de estos datos almacenados, se hace uso de lenguaje SQL, mediante el cual, se realizan peticiones personalizadas que otorgan la parte requerida del servidor. Estos datos son mostrados mediante diferentes métodos según la plataforma seleccionada, compuesta de una página web y una aplicación para Android, que también es capaz de realizar controles básicos del sistema. Se ha conseguido realizar gran parte de los objetivos y se ha desarrollado un sistema que trabaja en armonía para obtener y otorgar los datos de la calidad del aire gráficamente. Consiguiendo de esta manera, ofrecer estos datos de manera visual a cualquiera que encuentre interés en ellos y pudiendo estudiar un historial completo de la calidad del aire y su cambio en el transcurso del tiempo. El trabajo ha contribuido en el aprendizaje del funcionamiento de este tipo de sensores, además de, otorgar los conocimientos necesarios para la realización de páginas web y aplicaciones Android. Todo ello, sustentando por las habilidades obtenidas con el lenguaje SQL y el trabajo con bases de datos, pudiendo publicar y recoger datos de ellas. Air quality has been a widely discussed issue since the industrial revolution, when human beings began to consume large amounts of fuel for technological development, emitting toxic gases in the cities and enveloping them in a gas clouds. But it was not until the 50s, when investigations of this environment started, raising awareness of the importance of this problem. This project focuses on the implementation of an automatic system for data acquisition and air quality monitoring using MQ family sensors. The objectives of the work are to study the operation of these sensors, integrate all necessary components and make the connection between them to store their data. In addition, to perform remote, simple and intuitive method for acquisition and monitoring of this information. For it, a PCB has been designed to connects the sensors to an Arduino board, which is responsible for collecting the analogic signals provided and transferring them to an ESP32 board. Through it, the data is published to a data server that has a public IP address. For the acquisition of these stored data, SQL has been used, through which custom requests are made that grant the required part of the server. These data are displayed using different methods depending on the selected platform, composed of a website and an Android application, which is also capable of performing basic system controls. A large part of the objectives has been achieved, managing to develop a system that works in harmony to obtain and provide air quality data graphically, offering these data visually to anyone who is interested in them and being able to study a complete history of air quality and its change over time. The work has contributed to the learning of the operation of this type of sensors, in addition to providing the necessary knowledge for the development of web pages and Android applications. All of this, supported by the skills obtained with SQL language and work with databases, being able to publish and collect data from them.

Robust and highly accurate position estimation in underground mines is investigated by considering a vehicle equipped with 2D laser scanners. A survey of available methods to process data from such sensors is performed with focus on feature extraction methods. Pros and cons of the usage of different methods for the positioning application with 2D laser data are highlighted, and suitable methods are identified. Three state-of-the-art feature extraction methods are adapted to the scenario of positioning in a predefined map and the methods are evaluated through experiments conducted in a simulated underground mine environment. Results indicate that feature extraction methods perform in parity with the method of matching each ray individually to the map, and better than the point cloud scan matching method of a pure ICP, assuming a highly accurate map is available. Furthermore, experiments show that feature extraction methods more robustly handle imperfections or regions of errors in the map by automatically disregarding these regions. Funding Agencies|Wallenberg AI, Autonomous Systems and Software Program (WASP); Knut and Alice Wallenberg Foundation

With a growing focus on properties of softness, miniaturization, and intelligence, extensive research has been focusing on constructing wearable electronic devices facilitating comfort, wearable health monitoring and diagnosis. Among recent progress in the development of wearable bioelectronics, wearable near-infrared spectroscopy (NIRS) devices demonstrate wide implementation possibilities in multiple health monitoring scenarios. Throughout the years, multiple design strategies have assisted in developing wearable NIRS devices with high wearing comfortability and miniaturized size. This review summarizes the principle of NIRS technology, recent advances in design strategies towards soft, wearable, miniaturized NIRS devices, and the future potential development directions. Based on the discussion of different design strategies, including modular device design, flexible hybrid electronics, and materials innovation, we also pinpoint some development directions for wearable NIRS. The reviewed and proposed research efforts may enhance the applicability and capability of NIRS as an important technology for digital health.

International audience; The high-luminosity phase of operation of the CERN Large Hadron Collider (HL-LHC) will pose new challenges to the detectors and their readout electronics. In particular, the Compact Muon Solenoid (CMS) barrel electromagnetic calorimeter will require a full redesign of the electronic readout chain in order to cope with the increase in luminosity and trigger rate. In this framework, a new application-specific integrated circuit (ASIC) integrating A/D conversion, lossless data compression, and high-speed transmission has been developed and tested. The ASIC, named Lisboa-Torino Ecal Data Transmission Unit (LiTE-DTU), is designed in a commercial CMOS 65-nm process and embeds two 12-bit, 160-MS/s analog-to-digital converters (ADCs), a data selection and compression logic, and a 1.28-Gb/s output serial link. The high-speed 1.28-GHz clock is generated internally from the 160-MHz input by a clock multiplication phase-locked loop (PLL). The circuit has been designed implementing radiation-tolerant techniques in order to work in the harsh environment of the HL-LHC upgrade. The LiTE-DTU is currently in the preproduction phase. A sample of 600 chips has been tested and incorporated into front-end (FE) boards for systems performance testing.

Intrusive sensors are widely employed in numerous industrial applications to determine the phase fraction in gas-liquid two-phase flows. Such applications include those in the chemical, nuclear, and oil industries. This study aims to investigate the use of three distinct intrusive sensor topologies, specifically intended for underwater applications, wherein the detailed characterization of the disperse phase is the primary objective. Examples of such applications include aeration diffusers in wastewater treatment plants (WWTPs) and fish farming facilities.

The existence of the CMB marks a big success of the lambda cold dark matter standard model, which describes the universe’s evolution with six free parameters. The inflationary theory was added to the picture in the ’80s to explain the initial conditions of the universe. Scalar perturbations from inflation seeded the formation of the large-scale structure and produced the curl-free E-mode polarization pattern in the CMB. On the other hand, tensor fluctuations sourced primordial gravitational waves (PGW), which could leave unique imprints in the CMB polarization: the gradient-free B-mode pattern. The amplitude of B modes is directly related to the tensor-to-scalar ratio r of the primordial fluctuations, which indicates the energy scale of inflation. The detection of the primordial B modes will be strong supporting evidence of inflation and give us opportunities to study physics at energy scales far beyond what can ever be accessed in laboratory experiments on the Earth. Currently, the main challenge for the B-mode experiments is to separate the primordial B modes from those sourced by matter between us and the last scattering surface: the galactic foregrounds and the gravitational lensing effect. The two most important foregrounds are thermal dust and synchrotron, which have very different spectral properties from the CMB. Thus the key to foreground cleaning is the high sensitivity data at multiple frequency bands and the accurate modeling of the foregrounds in data analyses and simulations. In this dissertation, I present my work on ISM and dust property studies which enriched our understanding of the foregrounds. The BICEP/Keck (BK) experiments build a series of polarization-sensitive microwave telescopes targeting degree-scale B-modes from the early universe. The latest publication from the collaboration with data taken through 2018 reported tensor-to-scalar ratio r0.05 < 0.036 at 95% C.L., providing the tightest constraint on the primordial tensor mode. BICEP Array is the latest generation of the series experiments. The final configuration of the BICEP Array has four BICEP3-class receivers spanning six frequency bands, aiming to achieve σ(r) ≾ 0.003. The first receiver of the BICEP Array is at 30 and 40 GHz, constraining the synchrotron foregrounds. In this dissertation, I cover the development of this new receiver focusing on the design and performance of the detectors. I report on the characterizing and diagnosing tests for the receiver during its first few observing seasons.

The PERCIVAL detector is a CMOS imager designed for the soft X-ray regime at photon sources. Although still in its final development phase, it has recently seen its first user experiments: ptychography at a free-electron laser, holographic imaging at a storage ring and preliminary tests on X-ray photon correlation spectroscopy. The detector performed remarkably well in terms of spatial resolution achievable in the sample plane, owing to its small pixel size, large active area and very large dynamic range; but also in terms of its frame rate, which is significantly faster than traditional CCDs. In particular, it is the combination of these features which makes PERCIVAL an attractive option for soft X-ray science.