La prévision en temps réel des charges de polluants dans un réseau d'assainissement urbain

L'objectif principal du présent travail est la prévision en temps réel des charges de polluants dans un réseau d'assainissement urbain. La méthodologie préconisée dans cette étude se base sur deux outils. En premier lieu, le modèle de la courbe de tarage a été utilisé afin d'exprimer la corrélation entre les charges de polluants et les débits de ruissellement. Ce modèle a été sélectionné en raison de sa simplicité et de la disponibilité des paramètres nécessaires pour sa mise en œuvre. L'hypothèse de synchronisme systématique entre les pointes de l'hydrogramme et du pollutogramme dans ce modèle constitue une des faiblesses que nous proposons de surmonter dans le cadre du présent travail. Ainsi, le modèle de la courbe de tarage a été modifié par l'introduction d'un terme de déphasage qu'on identifie en temps réel. D'autre part, la constance des paramètres mis en jeu dans le modèle classique de la courbe de tarage constitue un autre obstacle pour la reproductibilité des phénomènes au cours du même événement et d'un événement à l'autre. Afin de surmonter cette deuxième faiblesse, le filtre de Kalman a été utilisé pour identifier les paramètres d'un modèle dynamique en fonction des erreurs de prévision constatées à chaque pas de temps. La méthodologie a été testée avec succès sur le secteur I de la ville de Verdun du Québec. Le modèle établi a été validé à l'aide de trois critères de performance, à savoir, le coefficient de Nash, le rapport des pointes mesurées/prévues et leur déphasage. Selon ces critères, les résultats trouvés par le modèle dynamique concordent bien avec les mesures.It is normally unrealistic to send the total combined water volume generated during a rainfall event to a wastewater treatment plant and this approach is not retained as a viable solution when physical and economic constraints need to be accounted for. It becomes therefore pertinent to reduce the pollution from a given area by limiting water treatment to the most polluted portion of the runoff volume. For this purpose, various municipalities have expressed an urgent need for an automated system that could dynamically manage all the hydraulic components of their urban drainage basins. However, such a system of management in real time requires short-term forecasting of the water quality in the drainage basins. The main object of this work is the development of tools for the real-time forecasting of pollutant loads in an urban sewer network. The method used in this study is based on two tools: the rating curve model and the Kalman filter.The rating curve model is used to explain the correlation between pollutant loads and runoff. This model was selected because of its simplicity and the availability of the parameters necessary for its implementation. The rating curve model has several important characteristics. First of all, the formulation of the model is independent of the accumulation phase and the load accumulated over the basin is assumed to be unlimited. A second characteristic consists in the normalized form in which runoff is present in the model as a flow rate, so that the rating curve model can integrate the quantitative and qualitative aspects of urban runoff in a simple formulation, which requires parameters available in real time.The assumption of systematic overlap between the hydrograph and pollutograph peaks constitutes the main weakness of this model, which we propose to overcome within the framework of this work. Thus, the rating curve model was modified by the introduction of a lag term identified in real time. In order to define the time lag parameter in real time, a mobile window has been programmed to scan the two observation vectors of flow rates and loads. Theoretically speaking, the time lag corresponds to the maximum of the cross correlation function between flow rate and load vectors observed in real time. Three cases are therefore possible. In the first case, an increase of the pollutograph precedes that of the hydrograph and the time lag is positive. In this case and in a context of real-time management, loads are determined using a forecast model for flow rates. Measured flow rates are considered in this work as forecasted flow rates. If the hydrograph precedes the pollutograph, the time lag "d" is negative and the loads are related to the flow rate measured at an instant that precedes forecast time by "d" times the time step. When, finally, the two curves are perfectly synchronous, the "d" parameter is equal to zero and the flow rates are forecasted on the basis of the flow rates measured at the time of forecasting. The model is thus sufficiently flexible and adapted to the various foreseeable conditions.In addition, the constancy of the parameters concerned in the classic rating curve model constitutes another weakness with respect to the reproducibility of the phenomena during the same event and from one event to another. In order to overcome this second weakness, the Kalman filter was used to identify the parameters of a dynamic model according to the forecast errors noted with each time step. Use of the Kalman filter also allowed us to eliminate the calibration procedure required by the static model. With this filter, the dynamic model continuously readjusts its parameters to satisfy the non-stationary behaviour of hydrological phenomena.The methodology was tested successfully on the sector I of the town of Verdun (Quebec). The established model was validated using three performance criteria, namely, the Nash coefficient, the peak ratio and the lag between measured and forecasted values. According to these criteria, the results obtained with the dynamic model agree well with measurements

Measuring Thickness and Pretilt in Reflective Vertically Aligned Nematic Liquid Crystal Displays

Pretilt angle is a parameter of the utmost importance in the ultimate performance of vertically-aligned negative nematic LC displays. When these devices work in reflective mode, as is the LCOS microdisplays, accurate measurement of pretilt angles becomes a difficult problem, since usual experimental setups based on retardation of the polarization components of the impinging light are proportional to the product effective birefringence (neff - no) times thickness, and any attempt to separate these variables is cancelled out by symmetry. This work shows a relatively simple method capable of separating both variables. An experimental setup specifically aimed at vertically aligned reflective cells has been prepared. At the same time, a simulation model has been developed taking into account the properties of actual reflective displays. Comparison between experimental and theoretical results shows some discrepancies that can be explained assuming that the LC profile contains a residual twist. Including that twist in the model, an excellent agreement between theory and experiment has been achieved. Matching of simulations and measurements yields to the separate determination of pretilt angle and thickness and gives good estimates for the residual twist angle

Inter-Operator Spectrum Sharing from a Game Theoretical Perspective

International audienceWe address the problem of spectrum sharing where competitive operators coexist in the same frequency band. First, we model this problem as a strategic non-cooperative game where operators simultaneously share the spectrum according to theNash Equilibrium (NE). Given a set of channel realizations, several Nash equilibria exist which renders the outcome of the game unpredictable. Then, in a cognitive context with the presence of primary and secondary operators, the inter-operator spectrum sharing problem is reformulated as a Stackelberg game using hierarchy where the primary operator is the leader. The Stackelberg Equilibrium (SE) is reached where the best response of the secondary operator is taken into account upon maximizing the primary operator's utility function. Moreover, an extension to the multiple operators spectrum sharing problem is given. It is shown that the Stackelberg approach yields better payoffs for operators compared to the classical water-filling approach. Finally, we assess the goodness of the proposed distributed approach by comparing its performance to the centralized approach

Deep Learning-Enabled Text Semantic Communication under Interference: An Empirical Study

At the confluence of 6G, deep learning (DL), and natural language processing (NLP), DL-enabled text semantic communication (SemCom) has emerged as a 6G enabler by promising to minimize bandwidth consumption, transmission delay, and power usage. Among text SemCom techniques, \textit{DeepSC} is a popular scheme that leverages advancements in DL and NLP to reliably transmit semantic information in low signal-to-noise ratio (SNR) regimes. To understand the fundamental limits of such a transmission paradigm, our recently developed theory \cite{Getu'23_Performance_Limits} predicted the performance limits of DeepSC under radio frequency interference (RFI). Although these limits were corroborated by simulations, trained deep networks can defy classical statistical wisdom, and hence extensive computer experiments are needed to validate our theory. Accordingly, this empirical work follows concerning the training and testing of DeepSC using the proceedings of the European Parliament (Europarl) dataset. Employing training, validation, and testing sets \textit{tokenized and vectorized} from Europarl, we train the DeepSC architecture in Keras 2.9 with TensorFlow 2.9 as a backend and test it under Gaussian multi-interferer RFI received over Rayleigh fading channels. Validating our theory, the testing results corroborate that DeepSC produces semantically irrelevant sentences as the number of Gaussian RFI emitters gets very large. Therefore, a fundamental 6G design paradigm for \textit{interference-resistant and robust SemCom} (IR$^2$ SemCom) is needed

V-Shape Liquid Crystal-Based Retromodulator Air to Ground Optical Communications

This paper describes the use of a 2D liquid crystal retro-modulator as a free space, wireless, optical link. The retro-modulator is made up of a retro-reflecting cornercube onto which 2 cascaded V-shape smectics liquid crystal modulators are mounted. The communication link differs with respect to more conventional optical links in not using amplitude (nor frequency) modulation, but instead state-of-polarisation (SOP) modulation known as Polarisation Shift Keying (PolSK). PolSK has the advantage over amplitude modulation, that it is less sensitive to changes in the visibility of the atmosphere, and increases inherently the bandwidth of the link. The implementation of PolSK both in liquid crystal based and in retro-modulated communication are novelties

Federated Learning and Meta Learning: Approaches, Applications, and Directions

Over the past few years, significant advancements have been made in the field of machine learning (ML) to address resource management, interference management, autonomy, and decision-making in wireless networks. Traditional ML approaches rely on centralized methods, where data is collected at a central server for training. However, this approach poses a challenge in terms of preserving the data privacy of devices. To address this issue, federated learning (FL) has emerged as an effective solution that allows edge devices to collaboratively train ML models without compromising data privacy. In FL, local datasets are not shared, and the focus is on learning a global model for a specific task involving all devices. However, FL has limitations when it comes to adapting the model to devices with different data distributions. In such cases, meta learning is considered, as it enables the adaptation of learning models to different data distributions using only a few data samples. In this tutorial, we present a comprehensive review of FL, meta learning, and federated meta learning (FedMeta). Unlike other tutorial papers, our objective is to explore how FL, meta learning, and FedMeta methodologies can be designed, optimized, and evolved, and their applications over wireless networks. We also analyze the relationships among these learning algorithms and examine their advantages and disadvantages in real-world applications

Performance Limits of a Deep Learning-Enabled Text Semantic Communication under Interference

A deep learning (DL)-enabled semantic communication (SemCom) has emerged as a 6G enabler while promising to minimize power usage, bandwidth consumption, and transmission delay by minimizing irrelevant information transmission. However, the benefits of such a semantic-centric design can be limited by radio frequency interference (RFI) that causes substantial semantic noise. The impact of semantic noise due to interference can be alleviated using an interference-resistant and robust (IR$^2$) SemCom design. Nevertheless, no such design exists yet. To shed light on this knowledge gap and stimulate fundamental research on IR$^2$ SemCom, the performance limits of a text SemCom system named DeepSC are studied in the presence of (multi-interferer) RFI. By introducing a principled probabilistic framework for SemCom, we show that DeepSC produces semantically irrelevant sentences as the power of (multi-interferer) RFI gets very large. We also derive DeepSC's practical limits and a lower bound on its outage probability under multi-interferer RFI. Toward a fundamental 6G design for an IR$^2$ SemCom, moreover, we propose a generic lifelong DL-based IR$^2$ SemCom system. Eventually, we corroborate the derived performance limits with Monte Carlo simulations and computer experiments, which also affirm the vulnerability of DeepSC and DL-enabled text SemCom to a wireless attack using RFI

Terahertz-Band Integrated Sensing and Communications: Challenges and Opportunities

The sixth generation (6G) wireless networks aim to achieve ultra-high data transmission rates, very low latency and enhanced energy-efficiency. To this end, terahertz (THz) band is one of the key enablers of 6G to meet such requirements. The THz-band systems are also quickly merging as high-resolution sensing devices because of their ultra-wide bandwidth and very narrow beamwidth. As a means to efficiently utilize spectrum and thereby save cost and power, THz integrated sensing and communications (ISAC) paradigm envisages a single integrated hardware platform with common signaling mechanism. However, ISAC at THz-band entails several design challenges such as beam split, range-dependent bandwidth, near-field beamforming, and distinct channel model. This article examines the technologies that have the potential to bring forth ISAC and THz transmission together. In particular, it provides an overview of antenna and array design, hybrid beamforming, integration with reflecting surfaces and data-driven techniques such as machine learning. These systems also provide research opportunities in developing novel methodologies for channel estimation, near-field beam split, waveform design and beam misalignment.Comment: 7pages, submitted to IEE