In this work, we propose an optical OFDM system using phase modulation followed by optical filtering and direct detection. A fiber Bragg grating is used as an optical filter for phase to amplitude conversion. The perf...
In this work, we propose an optical OFDM system using phase modulation followed by optical filtering and direct detection. A fiber Bragg grating is used as an optical filter for phase to amplitude conversion. The performance of the proposed system is investigated for both 16 QAM- and 64 QAM-OFDM signals considering different numbers of training signals for frequency-domain channel estimation. With adequate choice of the training sequence length, BER results below 10 −4 are reported for the 16 QAM based signal.
This paper presents a proposal of a computer vision tool based on Open CV that allows the pre-visual identification of non-uniform constellation levels, channel coding, and Signal-to-Noise Ratio (SNR) estimation on AT...
This paper presents a proposal of a computer vision tool based on Open CV that allows the pre-visual identification of non-uniform constellation levels, channel coding, and Signal-to-Noise Ratio (SNR) estimation on ATSC. Nowadays, the challenge of digital television systems is to transmit high quality videos employing the new technologies, such as Ultra High Definition (UHD). Thus, the new standards as ATSC 3.0 have incorporated several modifications on their physical layers. Among them, it is possible to highlight the use of non-uniform constellations, advanced channel error coding and layer division multiplexing (LDM). However, the pre-visual understanding of the received constellations has become hard since the complex symbols are not distributed evenly in the complex plane and there are different layers to be seen simultaneously. So, the techniques of computer vision have a great potential to analyze and to extract an initial information from the images related to the received constellation, to identify the modulation level, channel coding rate and SNR to each layer, without the necessity of complete demodulation.
Metasurfaces offer remarkable control over different characteristics of the electromagnetic waves. They can be used to modify the phase, amplitude, polarization, and direction of reflection associated with an incoming...
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ISBN:
(数字)9789463968119
ISBN:
(纸本)9798350359497
Metasurfaces offer remarkable control over different characteristics of the electromagnetic waves. They can be used to modify the phase, amplitude, polarization, and direction of reflection associated with an incoming incident field. This behavior can be mathematically represented using the generalized sheet transition conditions (GSTCs) (K. Achouri and C. Caloz, Electromagnetic Metasurfaces: Theory and Applications, Wiley, 2021). GSTCs connect the electromagnetic fields on the two sides of the sheet using equivalent bianisotropic electric and magnetic susceptibility tensors. These tensors account for the cumulative electric and magnetic polarization density effect of the unit-cell configurations on the electromagnetic fields.
Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances in likelihood-free inference with neur...
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Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances in likelihood-free inference with neural Bayes estimators, that is, neural networks that approximate Bayes estimators, we develop highly efficient estimators for censored peaks-over-threshold models that use augmented data to encode censoring information in the neural network input. Our new method provides a paradigm shift that challenges traditional censored likelihood-based inference methods for spatial extremal dependence models. Our simulation studies highlight significant gains in both computational and statistical efficiency, relative to competing likelihood-based approaches, when applying our novel estimators to make inference with popular extremal dependence models, such as max-stable, r-Pareto, and random scale mixture process models. We also illustrate that it is possible to train a single neural Bayes estimator for a general censoring level, precluding the need to retrain the network when the censoring level is changed. We illustrate the efficacy of our estimators by making fast inference on hundreds-of-thousands of high-dimensional spatial extremal dependence models to assess extreme particulate matter 2.5 microns or less in diameter (PM2:5) concentration over the whole of Saudi Arabia.
Recent research has shown that a small perturbation to an input may forcibly change the prediction of a machine learning (ML) model. Such variants are commonly referred to as adversarial examples. Early studies have f...
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This paper explores the application of system identification to a lubrication system found in heavy-duty diesel engines. These engines are equipped with a variable oil pump and a cooling piston jet. The objective is t...
This paper explores the application of system identification to a lubrication system found in heavy-duty diesel engines. These engines are equipped with a variable oil pump and a cooling piston jet. The objective is to establish a dynamic model that accurately captures the relationship between the duty cycle of the valves and the resulting pressure values under normal road operating conditions to be used as a digital twin of the system. Additionally, the study aims to determine whether a simple recursive model can sufficiently describe the system with enough precision. Different linear and nonlinear models were evaluated and validated to identify the best fit for the system. Ultimately, the system was described using a Hammerstein-Wiener model, resulting in an 83.86% Normalized Root Mean Squared Error (NRMSE) for main gallery pressure and 82.69% for piston cooling jet gallery pressure.
Metasurfaces are expected to revolutionize wireless communications due to their ability to enhance different characteristics of electromagnetic wave propagation channels. The response of a metasurface to an electromag...
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ISBN:
(数字)9789463968119
ISBN:
(纸本)9798350359497
Metasurfaces are expected to revolutionize wireless communications due to their ability to enhance different characteristics of electromagnetic wave propagation channels. The response of a metasurface to an electromagnetic excitation is determined by the geometry, the material composition, and the spatial arrangement of its sub-wavelength unit cells. This response can be considered as a spatiotemporal discontinuity in the field and can be mathematically described using the so-called generalized sheet transition conditions (GSTCs) (K. Achouri and C. Caloz, Electromagnetic Metasurfaces: Theory and Applications, Wiley, 2021). The GSTCs connect the electromagnetic fields on two sides of the metasurface using the electric and magnetic bianisotropic susceptibility tensors which effectively represent the metasurface.
We demonstrate Purcell enhancement of a single T center integrated in a silicon photonic crystal cavity, increasing the fluorescence decay rate by a factor of 6.89 and achieving a photon outcoupling rate of 73.3 kHz. ...
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