Submitted by (edna.saturno@ufrpe.br) on 2016-07-27T14:57:21Z No. of bitstreams: 1 Rodrigo Barbosa Acioli de Oliveira.pdf: 1281699 bytes, checksum: af3b5b3f4c17c790ad47750c27fe13be (MD5) / Made available in DSpace on 2016-07-27T14:57:21Z (GMT). No. of bitstreams: 1 Rodrigo Barbosa Acioli de Oliveira.pdf: 1281699 bytes, checksum: af3b5b3f4c17c790ad47750c27fe13be (MD5) Previous issue date: 2009-02-27 / The objective of this study was to obtain information regarding tuna fish quality captured in the northwestern coast of Brazil from December 2007 until December 2008. The levels of histamine in export type tuna caught in the coast of Rio Grande do Norte by the technique of pelagic trawl were determined. It was investigated the efficiency of two objective parameters of quality, levels of bioactive amines and color evaluation, which were related to the classification performed by a trained evaluator. The influence of the capture of live or dead fish on the quality of meat was also investigated. Among the 180 samples of fresh tuna analyzed, 95% did not contain histamine. Only nine samples contained histamine at levels that ranged between 4.92 and 6.90 mg/kg. The results indicated that the practices of handling and fishing gear used by companies of Rio Grande do Norte, Brazil, ensured the achievement of good quality fish. The levels of bioactive amines in 68 samples of tuna categorized into six levels of quality by experienced painelist were determined. The polyamines(spermine and spermidine), were found, however, the levels did not differ suggesting that this parameter objective in itself was not sufficient to distinguish the different levels of quality of yellowfin tuna and bigeye tuna. No significant differences were found in the values of the colorimetric coordinates (CIEL*, CIEa*, CIEb* and CIEC*) in samples of tuna classification 1, 2+, 2H, 2, 2- and 3. The conditions dead or alive at the time of slaughter showed no influence on the production of bioactive amines indicating that other factors may be involved. However, the samples were in excellent state of freshness, because were found significant levels of spermine and spermidine, rather low levels of cadaverine, putrescine, histamine, tyramine, phenylethylamine, agmatine, serotonin and tryptamine. / Este trabalho teve como objetivo obter informações sobre a qualidade dos atuns capturados na região nordeste do Brasil. Os teores de histamina de atuns tipo exportação capturados no litoral do Rio Grande do Norte pela técnica de espinhel pelágico foram determinados de dezembro de 2007 a dezembro de 2008. Investigou-se a eficiência de dois parâmetros objetivos de qualidade, teores de aminas bioativas e avaliação cor, que foram relacionados com a classificação realizada por um avaliador treinado. A influência da captura de peixes vivos ou mortos sobre a qualidade da carne também foi investigada. A presença de histamina não foi detectada em 95% das 180 amostras de atum fresco analisadas. Apenas nove amostras continham histamina em teores que variaram de 4,92 a 6,90 mg/kg. Estes resultados indicaram que as práticas de manipulação e a arte de pesca utilizadas pelas empresas do Rio Grande do Norte, Brasil, asseguraram a obtenção de peixes de boa qualidade. Os teores de aminas bioativas em 68 amostras de atum categorizadas em seis níveis de qualidade por avaliador experiente foram determinados. Foram encontradas as poliaminas espermina e espermidina, no entanto, os teores não diferiram entre si indicando que esse parâmetro objetivo por si só não era suficiente para distinguir os diferentes níveis de qualidade da albacora laje e albacora bandolim. Também não foram encontradas diferenças significativas nos valores das coordenadas colorimétricas (CIE L*, CIE a*, CIE b* e CIE C*) nas amostras de atum da classificação 1, 2+, 2H, 2, 2- e 3. As condições vivo ou morto no momento de abate demonstraram não exercer influência sobre a produção de aminas bioativas indicando que outros fatores podem estar envolvidos, porém, as amostras encontravam-se em excelente estado de frescor, pois foram encontrados teores significativos de espermina e espermidina, em detrimento de baixos teores decadaverina, putrescina, histamina, tiramina, feniletilamina, agmatina, serotonina e triptamina.

La majorité des méthodes d’analyse et de contrôle actuelles dans l’agroalimentaire sont basées sur une approche ciblée, c'est-à-dire avec une définition en amont des contaminants recherchés, et présentent ainsi le risque de ne pas détecter certaines fraudes ou sources potentielles de contaminations de produits authentiques. C’est autour de cette problématique que le projet Agrifood GPS (Global Protection System), dont fait partie cette thèse, a été initié. Celui-ci a pour objectif principal la mise en place de nouvelles méthodes analytiques de criblage (non-ciblées) afin de garantir l’intégrité des produits analysés. Cette thèse regroupe ainsi les différents résultats obtenus sur des matrices semi-solides (poisson principalement), par RMN Haute Résolution en Rotation à l’Angle Magique (HR-MAS), et sur des extraits de caviar par RMN liquide haute résolution (HR). Ce manuscrit présente, tout d’abord, une méthode de détermination rapide de la fraîcheur et la qualité du poisson basée sur la mesure de deux indicateurs chimiques (le TMA-N et la valeur K) ainsi que les résultats portant sur l’évaluation de la texture du poisson à travers l’étude du temps de relaxation transversale (T2) de l’eau contenue dans les tissus musculaires, ces deux études étant réalisées par RMN HR-MAS. Puis, les résultats des travaux réalisés sur la détermination de l’origine géographique du caviar à l’aide du profil métabolique enregistré par spectroscopie RMN liquide haute résolution et d’analyses statistiques multivariées, dans le cadre de la démarche d’obtention d’une IGP (Indication Géographique Protégée) des producteurs de l’Aquitaine, et sur l’étude de la dégradation du caviar au cours de sa conservation au réfrigérateur sont alors présentés. / Most of the current analytical and quality control methods in the food industry are based on a targeted approach, with an upstream definition of the intended contaminants, and may fail to detect some frauds or contaminations of genuine products. It is around this issue that the Agrifood GPS (Global Protection System), of which this thesis is part of, has been initiated. This project aims at developing new holistic analytical methods (untargeted) in order to ensure the integrity of the foodstuff analyzed. This thesis manuscript gathers the results obtained on semi-solid foodstuffs (mainly fish), by High Resolution Magic Angle Spinning NMR, and on caviar extracts by high resolution liquid-state NMR (HR NMR). First of all, it presents a rapid method to evaluate fish freshness and quality based on the determination of two chemical indicators (the TMA-N and the K-value) and the results of a fish texture study through the measurement of the transverse relaxation time (T2) of water in muscle tissues, both by HR-MAS NMR spectroscopy. Thereafter, are presented the results of the work carried out on the determination of the geographical origin of caviar using the metabolic profile acquired by liquid-state NMR spectroscopy and multivariate statistical analysis in the context of the PGI (Protected Geographical Indication) status for the Aquitaine producers, and on the degradation study of caviar during its storage in a fridge.

A typical model for real-time status update systems consists of a transmitter node that generates real-time status updates about some physical process(es) of interest and sends them through a communication network to a destination node. Such a model can be used to analyze the performance of a plethora of emerging Internet of Things (IoT)-enabled real-time applications including healthcare, factory automation, autonomous vehicles, and smart homes, to name a few. The performance of these applications highly depends upon the freshness of the information status at the destination node about its monitored physical process(es). Because of that, the main design objective of such real-time status update systems is to ensure timely delivery of status updates from the transmitter node to the destination node. To measure the freshness of information at the destination node, the Age of Information (AoI) has been introduced as a performance metric that accounts for the generation time of each status update (which was ignored by conventional performance metrics, specifically throughput and delay). Since then, there have been two main research directions in the AoI research area. The first direction aimed to analyze/characterize AoI in different queueing-theoretic models/disciplines, and the second direction was focused on the optimization of AoI in different communication systems that deal with time-sensitive information. However, the prior queueing-theoretic analyses of AoI have mostly been limited to the characterization of the average AoI and the prior studies developing AoI/age-aware scheduling/transmission policies have mostly ignored the energy constraints at the transmitter node(s). Motivated by these limitations, this dissertation develops new queueing-theoretic methods that allow the characterization of the distribution of AoI in several classes of status updating systems as well as novel AoI-aware scheduling policies accounting for the energy constraints at the transmitter nodes (for several settings of communication networks) in the process of decision-making using tools from optimization theory and reinforcement learning. The first part of this dissertation develops a stochastic hybrid system (SHS)-based general framework to facilitate the analysis of characterizing the distribution of AoI in several classes of real-time status updating systems. First, we study a general setting of status updating systems, where a set of source nodes provide status updates about some physical process(es) to a set of monitors. For this setting, the continuous state of the system is formed by the AoI/age processes at different monitors, the discrete state of the system is modeled using a finite-state continuous-time Markov chain, and the coupled evolution of the continuous and discrete states of the system is described by a piecewise linear SHS with linear reset maps. Using the notion of tensors, we derive a system of linear equations for the characterization of the joint moment generating function (MGF) of an arbitrary set of age processes in the network. Afterwards, we study a general setting of gossip networks in which a source node forwards its measurements (in the form of status updates) about some observed physical process to a set of monitoring nodes according to independent Poisson processes. Furthermore, each monitoring node sends status updates about its information status (about the process observed by the source) to the other monitoring nodes according to independent Poisson processes. For this setup, we develop SHS-based methods that allow the characterization of higher-order marginal/joint moments of the age processes in the network. Finally, our SHS-based framework is applied to derive the stationary marginal and joint MGFs for several queueing disciplines and gossip network topologies, using which we derive closed-form expressions for marginal/joint high-order statistics of age processes, such as the variance of each age process and the correlation coefficients between all possible pairwise combinations of age processes. In the second part of this dissertation, our analysis is focused on understanding the distributional properties of AoI in status updating systems powered by energy harvesting (EH). In particular, we consider a multi-source status updating system in which an EH-powered transmitter node has multiple sources generating status updates about several physical processes. The status updates are then sent to a destination node where the freshness of each status update is measured in terms of AoI. The status updates of each source and harvested energy packets are assumed to arrive at the transmitter according to independent Poisson processes, and the service time of each status update is assumed to be exponentially distributed. For this setup, we derive closed-form expressions of MGF of AoI under several queueing disciplines at the transmitter, including non-preemptive and source-agnostic/source-aware preemptive in service strategies. The generality of our analysis is demonstrated by recovering several existing results as special cases. A key insight from our characterization of the distributional properties of AoI is that it is crucial to incorporate the higher moments of AoI in the implementation/optimization of status updating systems rather than just relying on its average (as has been mostly done in the existing literature on AoI). In the third and final part of this dissertation, we employ AoI as a performance metric for several settings of communication networks, and develop novel AoI-aware scheduling policies using tools from optimization theory and reinforcement learning. First, we investigate the role of an unmanned aerial vehicle (UAV) as a mobile relay to minimize the average peak AoI for a source-destination pair. For this setup, we formulate an optimization problem to jointly optimize the UAV's flight trajectory as well as energy and service time allocations for packet transmissions. This optimization problem is subject to the UAV's mobility constraints and the total available energy constraints at the source node and UAV. In order to solve this non-convex problem, we propose an efficient iterative algorithm and establish its convergence analytically. A key insight obtained from our results is that the optimal design of the UAV's flight trajectory achieves significant performance gains especially when the available energy at the source node and UAV is limited and/or when the size of the update packet is large. Afterwards, we study a generic system setup for an IoT network in which radio frequency (RF)-powered IoT devices are sensing different physical processes and need to transmit their sensed data to a destination node. For this generic system setup, we develop a novel reinforcement learning-based framework that characterizes the optimal sampling policy for IoT devices with the objective of minimizing the long-term weighted sum of average AoI values in the network. Our analytical results characterize the structural properties of the age-optimal policy, and demonstrate that it has a threshold-based structure with respect to the AoI values for different processes. They further demonstrate that the structures of the age-optimal and throughput-optimal policies are different. Finally, we analytically characterize the structural properties of the AoI-optimal joint sampling and updating policy for wireless powered communication networks while accounting for the costs of generating status updates in the process of decision-making. Our results demonstrate that the AoI-optimal joint sampling and updating policy has a threshold-based structure with respect to different system state variables. / Doctor of Philosophy / A typical model for real-time status update systems consists of a transmitter node that generates real-time status updates about some physical process(es) of interest and sends them through a communication network to a destination node. Such a model can be used to analyze the performance of a plethora of emerging Internet of Things (IoT)-enabled real-time applications including healthcare, factory automation, autonomous vehicles, and smart homes, to name a few. The performance of these applications highly depends upon the freshness of the information status at the destination node about its monitored physical process(es). Because of that, the main design objective of such real-time status update systems is to ensure timely delivery of status updates from the transmitter node to the destination node. To measure the freshness of information at the destination node, the Age of Information (AoI) has been introduced as a performance metric that accounts for the generation time of each status update (which was ignored by conventional performance metrics, specifically throughput and delay). Since then, there have been two main research directions in the AoI research area. The first direction aimed to analyze/characterize AoI in different queueing-theoretic models/disciplines, and the second direction was focused on the optimization of AoI in different communication systems that deal with time-sensitive information. However, the prior queueing-theoretic analyses of AoI have mostly been limited to the characterization of the average AoI and the prior studies developing AoI/age-aware scheduling/transmission policies have mostly ignored the energy constraints at the transmitter node(s). Motivated by these limitations, this dissertation first develops new queueing-theoretic methods that allow the characterization of the distribution of AoI in several classes of status updating systems. Afterwards, using tools from optimization theory and reinforcement learning, novel AoI-aware scheduling policies are developed while accounting for the energy constraints at the transmitter nodes for several settings of communication networks, including unmanned aerial vehicles (UAVs)-assisted and radio frequency (RF)-powered communication networks, in the process of decision-making. In the first part of this dissertation, a stochastic hybrid system (SHS)-based general framework is first developed to facilitate the analysis of characterizing the distribution of AoI in several classes of real-time status updating systems. Afterwards, this framework is applied to derive the stationary marginal and joint moment generating functions (MGFs) for several queueing disciplines and gossip network topologies, using which we derive closed-form expressions for marginal/joint high-order statistics of age processes, such as the variance of each age process and the correlation coefficients between all possible pairwise combinations of age processes. In the second part of this dissertation, our analysis is focused on understanding the distributional properties of AoI in status updating systems powered by energy harvesting (EH). In particular, we consider a multi-source status updating system in which an EH-powered transmitter node has multiple sources generating status updates about several physical processes. The status updates are then sent to a destination node where the freshness of each status update is measured in terms of AoI. For this setup, we derive closed-form expressions of MGF of AoI under several queueing disciplines at the transmitter. The generality of our analysis is demonstrated by recovering several existing results as special cases. A key insight from our characterization of the distributional properties of AoI is that it is crucial to incorporate the higher moments of AoI in the implementation/optimization of status updating systems rather than just relying on its average (as has been mostly done in the existing literature on AoI). In the third and final part of this dissertation, we employ AoI as a performance metric for several settings of communication networks, and develop novel AoI-aware scheduling policies using tools from optimization theory and reinforcement learning. First, we investigate the role of a UAV as a mobile relay to minimize the average peak AoI for a source-destination pair. For this setup, we formulate an optimization problem to jointly optimize the UAV's flight trajectory as well as energy and service time allocations for packet transmissions. This optimization problem is subject to the UAV's mobility constraints and the total available energy constraints at the source node and UAV. A key insight obtained from our results is that the optimal design of the UAV's flight trajectory achieves significant performance gains especially when the available energy at the source node and UAV is limited and/or when the size of the update packet is large. Afterwards, we study a generic system setup for an IoT network in which RF-powered IoT devices are sensing different physical processes and need to transmit their sensed data to a destination node. For this generic system setup, we develop a novel reinforcement learning-based framework that characterizes the optimal sampling policy for IoT devices with the objective of minimizing the long-term weighted sum of average AoI values in the network. Our analytical results characterize the structural properties of the age-optimal policy, and demonstrate that it has a threshold-based structure with respect to the AoI values for different processes. They further demonstrate that the structures of the age-optimal and throughput-optimal policies are different. Finally, we analytically characterize the structural properties of the AoI-optimal joint sampling and updating policy for wireless powered communication networks while accounting for the costs of generating status updates in the process of decision-making. Our results demonstrate that the AoI-optimal joint sampling and updating policy has a threshold-based structure with respect to different system state variables.

<p>It is predicted that there would be 41.6 billion IoT devices by 2025, which has kindled new interests on the timing coordination between sensors and controllers, i.e., how to use the waiting time to improve the performance. Sun et al. showed that a <i>controller</i> can strictly improve the data freshness, the so-called Age-of-Information (AoI), via careful scheduling designs. The optimal waiting policy for the <i>sensor</i> side was later characterized in the context of remote estimation. The first part of this work develops the jointly optimal sensor/controller waiting policy. It generalizes the above two important results in that not only do we consider joint sensor/controller designs, but we also assume random delay in both the forward and feedback directions. </p> <p> </p> <p>The second part of the work revisits and significantly strengthens the seminal results of Sun et al on the following fronts: (i) When designing the optimal offline schemes with full knowledge of the delay distributions, a new <i>fixed-point-based</i> method is proposed with <i>quadratic convergence rate</i>; (ii) When the distributional knowledge is unavailable, two new low-complexity online algorithms are proposed, which provably attain the optimal average AoI penalty; and (iii) the online schemes also admit a modular architecture, which allows the designer to <i>upgrade</i> certain components to handle additional practical challenges. Two such upgrades are proposed: (iii.1) the AoI penalty function incurred at the destination is unknown to the source node and must also be estimated on the fly, and (iii.2) the unknown delay distribution is Markovian instead of i.i.d. </p> <p> </p> <p>With the exponential growth of interconnected IoT devices and the increasing risk of excessive resource consumption in mind, the third part of this work derives an optimal joint cost-and-AoI minimization solution for multiple coexisting source-destination (S-D) pairs. The results admit a new <i>AoI-market-price</i>-based interpretation and are applicable to the setting of (i) general heterogeneous AoI penalty functions and Markov delay distributions for each S-D pair, and (ii) a general network cost function of aggregate throughput of all S-D pairs. </p> <p> </p> <p>In each part of this work, extensive simulation is used to demonstrate the superior performance of the proposed schemes. The discussion on analytical as well as numerical results sheds some light on designing practical network utility maximization protocols.</p>

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Telemetry system, Optimisation, Sensoric networks, Smart Grid, Internet of Things, Sensors, Information security, Cryptography, Cryptography algorithms, Cryptosystem, Confidentiality, Integrity, Authentication, Data freshness, Non-Repudiation.