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Subwavelength imaging using a solid‑immersion diffractive optical processor

eLight 2024年第1期4卷 203-222页

作者： Jingtian Hu Kun Liao Niyazi Ulas Dinc Carlo Gigli Bijie Bai Tianyi Gan Xurong Li Hanlong Chen Xilin Yang Yuhang Li Cağatay Işıl Md Sadman Sakib Rahman Jingxi Li Xiaoyong Hu Mona Jarrahi Demetri Psaltis Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California NanoSystems Institute(CNSI) University of CaliforniaLos AngelesCA 90095USA Department of Physics Peking UniversityBeijing 100871China School of Engineering Ecole Polytechnique Federale de Lausanne(EPFL)LausanneSwitzerland David Geffen School of Medicine University of CaliforniaLos AngelesCA 90095USA

Phase imaging is widely used in biomedical imaging,sensing,and material characterization,among other ***,direct imaging of phase objects with subwavelength resolution remains a ***,we demonstrate subwavelength imaging of phase and amplitude objects based on all-optical diffractive encoding and *** resolve subwavelength features of an object,the diffractive imager uses a thin,high-index solid-immersion layer to transmit high-frequency information of the object to a spatially-optimized diffractive encoder,which converts/encodes high-frequency information of the input into low-frequency spatial modes for transmission through *** subsequent diffractive decoder layers(in air)are jointly designed with the encoder using deep-learning-based optimization,and communicate with the encoder layer to create magnified images of input objects at its output,revealing subwavelength features that would otherwise be washed away due to diffraction *** demonstrate that this all-optical collaboration between a diffractive solid-immersion encoder and the following decoder layers in air can resolve subwavelength phase and amplitude features of input objects in a highly compact *** experimentally demonstrate its proof-of-concept,we used terahertz radiation and developed a fabrication method for creating monolithic multi-layer diffractive *** these monolithically fabricated diffractive encoder-decoder pairs,we demonstrated phase-to-intensity(P→I)transformations and all-optically reconstructed subwavelength phase features of input objects(with linewidths of~λ/3.4,whereλis the illumination wavelength)by directly transforming them into magnified intensity features at the *** solid-immersion-based diffractive imager,with its compact and cost-effective design,can find wide-ranging applications in bioimaging,endoscopy,sensing and materials characterization.

关键词： Diffractive processors Solid immersion imaging Phase-to-intensity transformations

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AMMONIA, METHANE, HYDROGEN AND METHANOL PRODUCED IN REMOTE RENEWABLE ENERGY HUBS: A COMPARATIVE QUANTITATIVE ANALYSIS 37

AMMONIA, METHANE, HYDROGEN AND METHANOL PRODUCED IN REMOTE R...

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37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2024

作者： Larbanois, Antoine Dachet, Victor Dubois, Antoine Fonteneau, Raphaël Ernst, Damien Department of Computer Science and Electrical Engineering Liege University Liege Belgium LTCI Telecom Paris Institut Polytechnique de Paris Paris France

ISBN: (纸本)9798331307660

Remote renewable energy hubs (RREHs) for synthetic fuel production are engineering systems harvesting renewable energy where it is particularly abundant. They produce transportable synthetic fuels for export to distant load centers. This article aims to evaluate the production costs of different energy carriers, and includes a discussion on advantages and disadvantages in terms of technical performance. To do so, we extend the study of Berger et al. (2021) which focuses on methane (CH4) as energy carrier and introduce three new carriers: ammonia (NH3), hydrogen (H2) and methanol (CH3OH). The four different RREHs are located in the Algerian Sahara desert and must serve to the load center, Belgium, a constant electro-fuel demand of 10 TWh per year. The modelling and optimisation of these systems are performed using the modelling language GBOML (Graph-Based Optimisation Modelling Language). Our findings reveal that the three new RREHs, each with its respective carrier (ammonia, hydrogen, and methanol), are all more cost-effective than the methane-based system. Ammonia demonstrates the most favourable cost-to-energy exported ratio. © 2024 37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2024. All rights reserved.

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Deep Learning Meets Sparse Regularization: A Signal Processing Perspective

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arXiv 2023年第6期40卷 63页

作者： Parhi, Rahul Nowak, Robert D. The Biomedical Imaging Group École Polytechnique Fédérale de Lausanne Lausanne Switzerland The Department of Electrical and Computer Engineering University of Wisconsin–Madison MadisonWI United States

Deep learning has been wildly successful in practice and most state-of-the-art machine learning methods are based on neural networks. Lacking, however, is a rigorous mathematical theory that adequately explains the amazing performance of deep neural networks. In this article, we present a relatively new mathematical framework that provides the beginning of a deeper understanding of deep learning. This framework precisely characterizes the functional properties of neural networks that are trained to fit to data. The key mathematical tools which support this framework include transform-domain sparse regularization, the Radon transform of computed tomography, and approximation theory, which are all techniques deeply rooted in signal processing. This framework explains the effect of weight decay regularization in neural network training, the use of skip connections and low-rank weight matrices in network architectures, the role of sparsity in neural networks, and explains why neural networks can perform well in high-dimensional problems. Copyright © 2023, The Authors. All rights reserved.

关键词： Network architecture

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Multi-Layer Multi-Objective Optimization for Smart Home Energy Management System with Integration of User Satisfaction Concept 3

Multi-Layer Multi-Objective Optimization for Smart Home Ener...

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3rd International Conference on Sustainable Mobility Applications, Renewables and Technology, SMART 2024

作者： Khezzane, Khaled Doumbia, Mamadou Lamine Khoucha, Farid Hissem, Simon Department of Electrical and Computer Engineering Université du Québec à Trois-Rivières Québec Canada Department of Electrical Engineering École Militaire Polytechnique Alger Algeria Mechatronics Engineering Department Higher College of Technology Abudhabi United Arab Emirates

ISBN: (纸本)9798331532925

A transition to a smart grid requires a significant role on the part of the electricity consumer, not only at the level of big users (industrial and institutional) but also at the residential level. For that, homes are also becoming smart home, and consumers can manage their consummation optimally due to the energy management system which allows energy to be used while respecting several economic, technical, and social objectives for getting more benefits and incentives in return. This paper proposes a smart home energy management system for residential customer to optimally schedule the demand responsive appliance. The main contribution is the application of multi-layer multi-objective optimization technique for optimally solving the scheduling problem of the demand appliances in smart home to minimize electricity consumption cost as well as peak-to-average ratio and maximize the satisfaction of users. The effectiveness of the proposed technique is proven with the comparison with different single-objective optimizations with distinct types of loads. The comparative results show that the objective functions are effectively optimized using the proposed algorithm. © 2024 IEEE

关键词： Smart homes

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Learning to Remove Cuts in Integer Linear Programming 41

Learning to Remove Cuts in Integer Linear Programming

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41st International Conference on Machine Learning, ICML 2024

作者： Puigdemont, Pol Skoulakis, Stratis Chrysos, Grigorios G. Cevher, Volkan LIONS École Polytechnique Fédérale de Lausanne Switzerland Spain Department of Electrical and Computer Engineering University of Wisconsin-Madison United States

Cutting plane methods are a fundamental approach for solving integer linear programs (ILPs). In each iteration of such methods, additional linear constraints (cuts) are introduced to the constraint set with the aim of excluding the previous fractional optimal solution while not affecting the optimal integer solution. In this work, we explore a novel approach within cutting plane methods: instead of only adding new cuts, we also consider the removal of previous cuts introduced at any of the preceding iterations of the method under a learnable parametric criteria. We demonstrate that in fundamental combinatorial optimization settings such cut removal policies can lead to significant improvements over both human-based and machine learning-guided cut addition policies even when implemented with simple models. Copyright 2024 by the author(s)

关键词： Integer linear programming

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WASTE HEAT RECOVERY IN REMOTE RENEWABLE ENERGY HUBS 37

WASTE HEAT RECOVERY IN REMOTE RENEWABLE ENERGY HUBS

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37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2024

作者： Dauchat, Liam Dachet, Victor Fonteneau, Raphaël Ernst, Damien Department of Electrical Engineering and Computer Science University of Liège Liège Belgium Telecom Paris Institut Polytechnique de Paris Palaiseau France

ISBN: (纸本)9798331307660

This paper delves into the investigation of the potential valorisation of waste heat generated inside a Remote Renewable Energy Hub (RREH).The RREH concept involves harvesting renewable energy where it is most abundant and producing synthetic fuels for export to energy demand *** case study explored in this work is an RREH located in the Sahara Desert, exporting 10 TWh of synthetic methane per year to *** primary aim of this study is to examine the impacts on costs resulting from the incorporation of waste heat recovery techniques into the *** results suggest a cost reduction of up to 3.88% by using waste heat recovery techniques, confirming their potential in reducing infrastructure size and optimizing the cost efficiency of the overall power-to-gas supply chain in RREH. © 2024 37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2024. All rights reserved.

关键词： Clean energy

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Load Demand Forecasting in Smart Microgrid Using NWP via Feature engineering

Load Demand Forecasting in Smart Microgrid Using NWP via Fea...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Mohamed, Muamar Mahmood, Farhad E. Abd, Mehmmood A. Rezkallah, Miloud Hamadi, Abdelhamid Chandra, Ambrish Ecole de Technologie Superieure University of Quebec Department of Electrical Engineering MontrealQC Canada University of Mosul Department of Electrical Engineering Mosul Iraq Electrical and Computer Engineering Ibm United States

ISBN: (纸本)9781665471640

This study developed a machine learning model to forecast electrical demand for smart micro-grids with the existing of numerical weather forecasting (NWP). The model uses three techniques, linear regression (LR), Long Short-Term Memory (LSTM) and eXtreme Gradient Boosting (XGBoost), to harness insights from historical data and predict future load patterns. Notably, the XGBoost algorithm is highlighted as the most effective due to underscoring the importance of feature selection and model optimization. The utilization of these techniques results in accurate electrical demand predictions, which are crucial for enhancing micro-grid efficiency, practically in response to weather-induced load fluctuations. This paper suggests employing advanced feature selection methods based on high feature importance, ultimately refining the model's predictive capabilities for smart micro-grid applications. © 2023 IEEE.

关键词： forecasting techniques Load demand machine learning micro-grid NWP XGBoost algorithm

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Learning-Based MRI Response Predictions from OCT Microvascular Models to Replace Simulation-Based Frameworks 28th

Learning-Based MRI Response Predictions from OCT Microvascu...

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28th Annual Conference on Medical Image Understanding and Analysis, MIUA 2024

作者： Rustamov, Jaloliddin Rustamov, Zahiriddin Badawi, Nadia Lesage, Frederic Zaki, Nazar Damseh, Rafat Department of Computer Science and Software Engineering United Arab Emirates University Al Ain United Arab Emirates Biomedical Engineering Institute Ecole Polytechnique de Montreal MontrealQC Canada

ISBN: (纸本)9783031669545

Computational quantification of magnetic resonance imaging (MRI) response from neurovascular structures is used to investigate potential biomarkers for different types of cerebrovascular deteriorations at the microscopic scale. Simulation-based MRI requires fully resolved microvascular structures, with geometric and physiological parameters, from tissue volumes captured using microscopic imaging modalities, e.g., optical coherence tomography (OCT). The preparation of such input models hinders large cohort studies and requires extensive manual effort. Here, we propose using 3D neural networks as an alternative learning-based solution over MRI simulation schemes. We trained state-of-the-art 3D neural networks to predict the spin echo (SE) MRI response from OCT microvascular volumes. By validating against simulated signals, our result demonstrates that the 3D ResNet-based regression network achieves a high accuracy to predict MRI signals with an average mean square error (MSE) 2 of 82.8% and explained variance score of 82.9%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Forecasting

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Load Demand Forecasting Using eXtreme Gradient Boosting (XGboost)

Load Demand Forecasting Using eXtreme Gradient Boosting (XGb...

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2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

作者： Mohamed, Muamar Mahmood, Farhad E. Abd, Mehmmood A. Rezkallah, Miloud Hamadi, Abdelhamid Chandra, Ambrish Ecole de Technologie Superieure University of Quebec Department of Electrical Engineering QC Montreal Canada University of Mosul Department of Electrical Engineering Mosul Iraq Ibm Electrical and Computer Engineering United States

ISBN: (纸本)9798350320169

This study focuses on the development of an electrical demand forecasting model using machine learning techniques, specifically Long Short-Term Memory (LSTM) and eXtreme Gradient Boosting (XGBoost). The objective is to provide accurate forecasts for microgrids to prepare for upcoming loads due to temperature changes in the city. The study uses data collected from wireless sensors installed in the city. The LSTM and XG Boost models were trained and tested using historical data, and the results were compared to select the best-performing model. The chosen model was then used to forecast the electrical demand for the upcoming period. © 2023 IEEE.

关键词： Forecasting

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Instantaneous Frequency based estimation of time varying Directions of Arrival

Instantaneous Frequency based estimation of time varying Dir...

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2023 International Symposium on Signals, Circuits and Systems, ISSCS 2023

作者： Abbassen, Chafaa Lyes Belouchrani, Adel Abed-Meraim, Karim LDCCP Lab. Ecole Nationale Polytechnique Electrical Engineering Department Algiers Algeria University of Orléans PRISME Lab. France

ISBN: (纸本)9798350342031

This paper addresses the problem of estimating time-varying directions of arrival. It demonstrates how the concept of instantaneous frequency can be employed for this purpose. The proposed approach can localize more sources than the number of available sensors. It also theoretically allows the estimation of any angular time variation. Herein, we consider linear and hyperbolic time variations, which, in practice, take into account velocity and acceleration, respectively. Numerical experiments are conducted to validate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Direction of arrival

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