2,799 research outputs found

    A new multi-factor multi-objective strategy based on a factorial presence-absence design to determine polymer additive residues by means of head space-solid phase microextraction-gas chromatography-mass spectrometry

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    A new multi-factor multi-objective strategy to approach the joint assessment of the effect of six experimental factors in the determination by head space-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) of eight different additives commonly used in the plastic packaging manufacturing is proposed in this work. Five HS-SPME experimental factors, both qualitative and quantitative, are explored: the type of fiber, addition of salt, extraction and desorption time, and extraction temperature. The effect of these factors is studied through a factorial presence-absence model, that include interactions, using a D-optimal design. As a result, the number of experiments is reduced from 128, full factorial design, to 14. The effect of carrying out the measurements in different experimental sessions is considered by including a blocking factor. The response for each compound is estimated in the experimental domain and then the best experimental conditions are chosen by using Pareto front. Parallel coordinates are employed to show the conflicting conditions intrinsic to a multiobjective analysis when compounds of different nature are extracted by HS-SPME. Parallel factor analysis 2 (PARAFAC2) decomposition is used because it makes the determination of target compounds in the presence of unknown interferents possible, which enables the unequivocal identification of target compounds according to official regulations. The developed method is applied to determine 2,6-di-tert-butyl-4-methyl-phenol (BHT), benzophenone (BP), bis(2-ethylhexyl) adipate (DEHA), diethyl phthalate (DEP), diisobutyl phthalate (DiBP), dibutyl phthalate (DBP), benzyl butyl phthalate (BBP) and bis(2-ethylhexyl) phthalate (DEHP). The level of these compounds found in nine types of bottled natural still and sparkling mineral waters is very low, so the compounds were not present in quantities that may be injurious to human health.The authors thank the financial support provided by Consejería de la Junta de Castilla y Leon ´ (JCyL) through project BU052P20 co-financed with European FEDER funds. Lucía Valverde-Som thanks JCyL for her postdoctoral contract through this project

    Signal Design and Machine Learning Assisted Nonlinearity Compensation for Coherent Optical Fibre Communication Links

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    This thesis investigates low-complexity digital signal processing (DSP) for signal design and nonlinearity compensation strategies to improve the performance of single-mode optical fibre links over different distance scales. The performance of a novel ML-assisted inverse regular perturbation technique that mitigates fibre nonlinearities was investigated numerically with a dual-polarization 64 quadrature amplitude modulation (QAM) link over 800 km distance. The model outperformed the heuristically-optimised digital backpropagation approach with <5 steps per span and mitigated the gain expansion issue, which limits the accuracy of an untrained model when the balance between the nonlinear and linear components becomes considerable. For short reach links, the phase noise due to low-cost, high-linewidth lasers is a more significant channel impairment. A novel constellation optimisation algorithm was, therefore, proposed to design modulation formats that are robust against both additive white Gaussian noise (AWGN) and the residual laser phase noise (i.e., after carrier phase estimation). Subsequently, these constellations were numerically validated in the context of a 400ZR standard system, and achieved up to 1.2 dB gains in comparison with the modulation formats which were optimised only for the AWGN channel. The thesis concludes by examining a joint strategy to modulate and demodulate signals in a partially-coherent AWGN (PCAWGN) channel. With a low-complexity PCAWGN demapper, 8- to 64-ary modulation formats were designed and validated through numerical simulations. The bit-wise achievable information rates (AIR) and post forward error correction (FEC) bit error rates (BER) of the designed constellations were numerically validated with: the theoretically optimum, Euclidean (conventional), and low-complexity PCAWGN demappers. The resulting constellations demonstrated post-FEC BER shaping gains of up to 2.59 dB and 2.19 dB versus uniform 64 QAM and 64-ary constellations shaped for the purely AWGN channel model, respectively. The described geometric shaping strategies can be used to either relax linewidth and/or carrier phase estimator requirements, or to increase signal-to-noise ratio (SNR) tolerance of a system in the presence of residual phase noise

    Development of a method for biomarkers characterization by mass spectrometry techniques

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    The purpose of this study is to define an extractive approach for the detection of the low-molecule peptide fraction from human plasma or serum and the subsequent analysis and interpretation of the obtained data, with the ultimate aim of developing a standardised protocol for the identification of potential biomarkers. The extraction of the low molecular protein fraction was developed thanks to a series of standard peptides solutions and using silica magnetic beads techniques differently functionalised with the purpose to bind target molecules with a different type of intermolecular force. The treatment of the samples, plasma or serum, took place without the use of proteases, as trypsin, to generate digested lysates, or electrophoresis and gel separation techniques, to avoid creating additional complexity in subsequent steps of data interpretation and to use the lower quantity of sample as possible. Both the peptides contained in the standard solution and those in the low molecular weight fraction of the pre-treated biological sample were separated and characterized through high performance liquid chromatography (HPLC) coupled to full scan and tandem mass spectrometry equipped with an electrospray ion source (ESI-MS/MS). Samples from biological sources were subsequently analysed using the mass spectrometry MALDI-TOF technique. In this project the development of the extraction method was followed by its application to real samples. The presence of low-molecular-weight peptides in plasma samples, from dialysis nephrotic patients at various stages of Sars-COV2 infection, and in plasma from healthy donors was evaluated with the aim to find significant differences between groups, especially in terms of qualitative/quantitative differences in the m/z ratios present in MS spectra. A bioinformatics approach to data processing has also been implemented, either by using statistical tools such as the Venn diagram or the Meaning Analysis of Microarrays (SAM) or by developing a series of codes in Python, for processing spectral data combined with algorithms with silico fragmentation rules. Outputs were compared with information from peptide databases to obtain significant correspondences between the theoretical and experimental spectrum

    Achievable information rates for nonlinear frequency division multiplexed fibre optic systems

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    Fibre optic infrastructure is critical to meet the high data rate and long-distance communication requirements of modern networks. Recent developments in wireless communication technologies, such as 5G and 6G, offer the potential for ultra-high data rates and low-latency communication within a single cell. However, to extend this high performance to the backbone network, the data rate of the fibre optics connection between wireless base stations may become a bottleneck due to the capacity crunch phenomena induced by the signal dependent Kerr nonlinear effect. To address this, the nonlinear Fourier transform (NFT) is proposed as a solution to resolve the Kerr nonlinearity and linearise the nonlinear evolution of time domain pulses in the nonlinear frequency domain (NFD) for a lossless and noiseless fibre. Nonlinear frequency division multiplexing (NFDM), which encodes information on NFD using the discrete and continuous spectra revealed by NFT, is also proposed. However, implementing such signalling in an optical amplifier noise-perturbed fibre results in complicated, signal-dependent noise in NFD, the signal-dependent statistics and unknown model of which make estimating the capacity of such a system an open problem. In this thesis, the solitonic part of the NFD, the discrete spectrum is first studied. Modulating the information in the amplitude of soliton pulse, the maximum time-scaled mutual information is estimated. Such a definition allows us to directly incorporate the dependence of soliton pulse width to its amplitude into capacity formulation. The commonly used memoryless channel model based on noncentral chi-squared distribution is initially considered. Applying a variance normalising transform, this channel is approximated by a unit-variance additive white Gaussian noise (AWGN) model. Based on a numerical capacity analysis of the approximated AWGN channel, a general form of capacity-approaching input distributions is determined. These optimal distributions are discrete comprising a mass point at zero (off symbol) and a finite number of mass points almost uniformly distributed away from zero. Using this general form of input distributions, a novel closed-form approximation of the capacity is determined showing a good match to numerical results. A mismatch capacity bounds are developed based on split-step simulations of the nonlinear Schro¨\rm \ddot{o}dinger equation considering both single soliton and soliton sequence transmissions. This relaxes the initial assumption of memoryless channel to show the impact of both inter-soliton interaction and Gordon-Haus effects. Our results show that the inter-soliton interaction effect becomes increasingly significant at higher soliton amplitudes and would be the dominant impairment compared to the timing jitter induced by the Gordon-Haus effect. Next, the intrinsic soliton interaction, Gordon Haus effect and their coupled perturbation on the soliton system are visualised. The feasibility of employing an artificial neural network to resolve the inter-soliton interaction, which is the dominant impairment in higher power regimes, is investigated. A method is suggested to improve the achievable information rate of an amplitude modulated soliton communication system using a classification neural network against the inter-soliton interaction. Significant gain is demonstrated not only over the eigenvalue estimation of nonlinear Fourier transform, but also the continuous spectrum and eigenvalue correlation assisted detection scheme in the literature. Lastly, for the nonsolitonic radiation of the NFT, the continuous spectrum is exploited. An approximate channel model is proposed for direct signalling on the continuous spectrum of a NFDM communication system, describing the effect of noise and nonlinearity at the receiver. The optimal input distribution that maximises the mutual information of the proposed approximated channel under peak amplitude constraint is then studied. We present that, considering the input-dependency of the noise, the conventional amplitude-constrained constellation designs can be shaped geometrically to provide significant mutual information gains. However, it is observed that further probabilistic shaping and constellation size optimisation can only provide limited additional gains beyond the best geometrically shaped counterparts, the 64 amplitude phase shift keying. Then, an approximated channel model that neglects the correlation between subcarriers is proposed for the matched filtered signalling system, based on which the input constellation is shaped geometrically. We demonstrate that, although the inter-subcarrier interference in the filtered system is not included in the channel model, shaping of the matched filtered system can provide promising gains in mismatch capacity over the unfiltered scenario

    Multipath assisted positioning using machine learning

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    The multipath propagation of the radio signal was considered a problem for positioning systems that had to be eliminated. However, a groundbreaking new approach called multipath assisted positioning caused a paradigm shift, where multipath propagation improves the positioning performance. Moreover, the multipath assisted positioning algorithm called Channel-SLAM shows the possibility of using a single physical transmitter in a multipath environment for positioning. In this thesis, I open a discussion on some problems that have vital importance for multipath assisted positioning algorithms with a focus on pedestrian positioning. Using the idea of multipath assisted positioning, I present a single frequency network positioning algorithm. I evaluated the single frequency network-based positioning algorithm for positioning in a real scenario using a terrestrial digital video broadcasting transmission. I propose a novel pedestrian transition model utilizing the inertial measurements from a handheld inertial measurement unit. The proposed pedestrian transition model improves the precision and reliability of the Channel-SLAM. Comparing the proposed transition model with the Rician transition model previously used in Channel-SLAM quantifies the performance improvement. This thesis proposes a joint data association technique that overcomes the strong dependence on the radio channel estimation algorithm used in Channel-SLAM. The joint data association allows reusing the previously observed virtual transmitters after an outage of multipath component tracking. The evaluation based on the walking pedestrian scenario shows that the joint data association algorithm provides superior positioning precision. The virtual transmitter position estimation yields a significant computational load in Channel-SLAM. I propose a method that represents the virtual transmitter by a Gaussian mixture model and learns its parameters. The evaluation shows that the proposed method outperforms the previous approach while decreasing the computational load. Also, the current methods for radio channel estimation yield a considerable computational load that prohibits a real-time deployment. The thesis investigates the possibility of using artificial neural networks trained to estimate the number of multipath components and corresponding delays in a noisy measurement of a channel impulse response. The artificial neural network-based delay estimator provides a superresolution performance and faster runtime than the classical approaches. The precision of the trained artificial neural network architecture is evaluated and compared to the Cramer-Rao lower bound theoretical limit and classical channel estimation algorithms
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