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arXiv 2023年

作者： Théate, Thibaut Sutera, Antonio Ernst, Damien Department of Electrical Engineering and Computer Science University of Liège Liège Belgium Haulogy Intelligent Systems Solutions Braine-le-Comte Belgium Information Processing and Communications Laboratory Institut Polytechnique de Paris Paris France

The energy transition is expected to significantly increase the share of renewable energy sources whose production is intermittent in the electricity mix. Apart from key benefits, this development has the major drawback of generating a mismatch between power supply and demand. The innovative dynamic pricing approach may significantly contribute to mitigating that critical problem by taking advantage of the flexibility offered by the demand side. At its core, this idea consists in providing the consumer with a price signal which is evolving over time, in order to influence its consumption. This novel approach involves a challenging decision-making problem that can be summarised as follows: how to determine a price signal maximising the synchronisation between power supply and demand under the constraints of maintaining the producer/retailer’s profitability and benefiting the final consumer at the same time? As a contribution, this research work presents a detailed formalisation of this particular decision-making problem. Moreover, the paper discusses the diverse algorithmic components required to efficiently design a dynamic pricing policy: different forecasting models together with an accurate statistical modelling of the demand response to dynamic prices. © 2023, CC BY.

关键词： Decision making

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Automatic Modulation Classification of Frequency-Hopping signals Using High-Dimensional Phase Diagrams

Automatic Modulation Classification of Frequency-Hopping Sig...

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2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2023

作者： Chen, Qibo Yan, Kun Wu, Hsiao-Chun Chang, Shih Yu Yan, Xiao Wu, Yiyan Chang, Haonan Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing GuilinGuangxi China Guilin University of Electronic Technology Department of Information and Telecommunication Guangxi Guilin541004 China Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States San Jose State University Department of Applied Data Science San JoseCA95192 United States University of Electronic Science and Technology of China School of Aeronautics and Astronautics Chengdu China Communications Research Centre Canada OttawaK2H 8S2 Canada Haonan Chang Is with Xi'an University Xi'an China

ISBN: (纸本)9798350321524

Automatic modulation recognition for frequency-hopping (FH) signals remains very challenging to researchers due to the signals' time-varying spectral characteristics. In this work, a novel robust automatic modulation recognition scheme is investigated for FH signals using the phase-space topological features represented by the embedded phase diagrams. As such embedded phase diagrams are often high-dimensional, it is necessary to formulate the phase-space features as tensors. In the training process, the phase-space tensor features will be utilized to establish the regression models as linear encoders for the individual modulations. The aforementioned linear encoders are constructed using the support vector machine (SVM);the phase-space feature-tensors of the training signals of all modulations will be projected by their corresponding regression models (or linearly encoded) to produce the representative code-vectors, respectively. In the test stage, the phase-space feature-tensor produced from a test signal will be projected by each individual trained regression model (or linearly encoded) to generate the respective code-vectors. Then, the code-vectors resulting from the test stage will be compared with the representative code-vectors to find which modulation will lead to the smallest Euclidean distance in between and such a modulation will be picked as the modulation type of the test signal. Monte Carlo simulation results have demonstrated that the average recognition accuracy of our proposed new approach is more than 90% when the signal-to-noise ratio is no less than 0 dB for additive white Gaussian noise. © 2023 IEEE.

关键词： Support vector machines

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40 Gb/s High-speed mode-division multiplexing transmission employing NRZ modulation format

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Journal of Optical communications 2023年第s1期44卷 S1157-S1165页

作者： Sabitu, Rabiu Imam Khan, Nafizah Goriman Malekmohammadi, Amin Affiliated to the George Green Institute for Electromagnetics Research Nottingham United Kingdom Photonics Communication Systems and Signal Processing Research Group Kano University of Science and Technology Wudil KanoPMB 3244 Nigeria Semenyih Malaysia Department of Electrical and Computer Engineering California State University BakersfieldCA93311 United States Photonics Communication Systems and Signal Processing Research Group Kano University of Science and Technology Wudil KanoPMB 3244 Nigeria

This report examines the performance of a high-speed MDM transmission system supporting four nondegenerate spatial modes at 10 Gb/s. The analysis adopts the NRZ modulation format to evaluate the system performance in terms of a minimum power required (PN) and the nonlinear threshold power (PTH) at a BER of 10−9. The receiver sensitivity, optical signal-to-noise ratio, and the maximum transmission distance were investigated using the direct detection by employing a multimode erbium-doped amplifier (MM-EDFA). It was found that by properly optimizing the MM-EDFA, the system performance can significantly be improved. © 2023 Walter de Gruyter GmbH. All rights reserved.

关键词： Erbium doped fiber amplifiers

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Epileptic Seizure Detection Using a Hybrid 1D CNN-Machine Learning Approach from EEG Data

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Journal of Healthcare engineering 2022年第1期2022卷 9579422页

作者： Hassan, Fatima Hussain, Syed Fawad Qaisar, Saeed Mian Faculty of Computer Science and Engineering G. I. K. Institute Topi Pakistan School of Computer Science University of Birmingham Birmingham United Kingdom Electrical and Computer Engineering Department Effat University Jeddah22332 Saudi Arabia Communication and Signal Processing Lab Energy and Technology Research Center Effat University Jeddah22332 Saudi Arabia

Electroencephalography (EEG) is a widely used technique for the detection of epileptic seizures. It can be recorded in a noninvasive manner to present the electrical activity of the brain. The visual inspection of nonlinear and highly complex EEG signals is both costly and time-consuming. Therefore, an effective automatic detection system is needed to assist in the long-term evaluation and treatment of patients. Traditional approaches based on machine learning require feature extraction, while deep learning approaches are time-consuming and require more layers for effective feature learning and processing of complex EEG waveforms. Deep learning-based approaches also have weak generalization ability. This paper proposes a solution based on the combination of convolution neural networks (CNN) and machine learning classifiers. It preprocesses the EEG signal using the Butterworth filter and performs feature extraction using CNN. From the extracted set of features, the approach selects only the relevant features using mutual information-based estimators to reduce the curse of dimensionality and improve classification accuracy. The selected features are then passed as input to different machine learning classifiers. The suggested solution is evaluated on the University of Bonn dataset and CHB-MIT datasets. Our model effectively predicts 2, 3, 4, and 5 classes with accuracy of 100%, 99%, 94.6%, and 94%, respectively, for the Bonn dataset and 98% for CHB-MIT datasets. © 2022 Fatima Hassan et al.

关键词： Electroencephalography

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Talking Detection in Collaborative Learning Environments 19th

Talking Detection in Collaborative Learning Environments

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19th International Conference on computer Analysis of Images and Patterns, CAIP 2021

作者： Shi, Wenjing Pattichis, Marios S. Celedón-Pattichis, Sylvia LópezLeiva, Carlos Image and Video Processing and Communications Lab Department of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM United States Departtment of Language Literacy and Sociocultural Studies University of New Mexico AlbuquerqueNM United States

ISBN: (纸本)9783030891305

We study the problem of detecting talking activities in collaborative learning videos. Our approach uses head detection and projections of the log-magnitude of optical flow vectors to reduce the problem to a simple classification of small projection images without the need for training complex, 3-D activity classification systems. The small projection images are then easily classified using a simple majority vote of standard classifiers. For talking detection, our proposed approach is shown to significantly outperform single activity systems. We have an overall accuracy of 59% compared to 42% for Temporal Segment Network (TSN) and 45% for Convolutional 3D (C3D). In addition, our method is able to detect multiple talking instances from multiple speakers, while also detecting the speakers themselves. © 2021, Springer Nature Switzerland AG.

关键词： computer aided instruction

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Joint inference of multiple graphs from matrix polynomials

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 3302-3336页

作者： Madeline Navarro Yuhao Wang Antonio G. Marques Caroline Uhler Santiago Segarra Department of Electrical and Computer Engineering Rice University Houston TX Institute for Interdisciplinary Information Sciences Tsinghua University and Shanghai Qi Zhi Institute Haidian District Beijing China Department of Signal Theory and Communications King Juan Carlos University Madrid Spain Department of Electrical Engineering & Computer Science Department of Institute for Data Systems and Society Massachusetts Institute of Technology Cambridge MA

Inferring graph structure from observations on the nodes is an important and popular network science task. Departing from the more common inference of a single graph, we study the problem of jointly inferring multiple graphs from the observation of signals at their nodes (graph signals), which are assumed to be stationary in the sought graphs. Graph stationarity implies that the mapping between the covariance of the signals and the sparse matrix representing the underlying graph is given by a matrix polynomial. A prominent example is that of Markov random fields, where the inverse of the covariance yields the sparse matrix of interest. From a modeling perspective, stationary graph signals can be used to model linear network processes evolving on a set of (not necessarily known) networks. Leveraging that matrix polynomials commute, a convex optimization method along with sufficient conditions that guarantee the recovery of the true graphs are provided when perfect covariance information is available. Particularly important from an empirical viewpoint, we provide high-probability bounds on the recovery error as a function of the number of signals observed and other key problem parameters. Numerical experiments demonstrate the effectiveness of the proposed method with perfect covariance information as well as its robustness in the noisy regime.

关键词： network topology inference graph signal processing spectral graph theory multi-layer graphs network diffusion processes

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Rate-Splitting Multiple Access for Quantized Multiuser MIMO communications

arXiv

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arXiv 2022年

作者： Park, Seokjun Choi, Jinseok Park, Jeonghun Shin, Wonjae Clerckx, Bruno Department of Electrical Engineering Ulsan National Institute of Science and Technology Korea Republic of The School of Electronics Engineering Kyungpook National University Korea Republic of The Department of Electrical and Computer Engineering Ajou University Suwon16499 Korea Republic of The Communications and Signal Processing Group Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom

This paper investigates the sum spectral efficiency maximization problem in downlink multiuser multiple-input multiple-output (MIMO) systems with low-resolution quantizers at an access point (AP) and users. In particular, we consider rate-splitting multiple access (RSMA) to enhance spectral efficiency by offering opportunities to boost achievable degrees of freedom. Optimizing RSMA precoders, however, is highly challenging due to the minimum rate constraint when determining the rate of the common stream. The quantization errors coupled with the precoders further make the problem more complicated and difficult to solve. In this paper, we develop a novel RSMA precoding algorithm incorporating quantization errors for maximizing the sum spectral efficiency. To this end, we first obtain an approximate spectral efficiency in a smooth function. Subsequently, we derive the first-order optimality condition in the form of the nonlinear eigenvalue problem (NEP). We propose a computationally efficient algorithm to find the principal eigenvector of the NEP as a sub-optimal solution. Simulation results validate the superior spectral efficiency of the proposed method. The key benefit of using RSMA over spatial division multiple access (SDMA) comes from the ability of the common stream to balance between the channel gain and quantization error in multiuser MIMO systems with different quantization resolutions. Copyright © 2022, The Authors. All rights reserved.

关键词： Spectrum efficiency

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A Radiation Performance Improvement Method of Extremely Low Frequency Acoustics Promoted Antenna

A Radiation Performance Improvement Method of Extremely Low ...

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IEEE International Workshop on Electromagnetics, Applications and Student Innovation (iWEM)

作者： Junwei Qi Chenwei Zhang Yingsong Li Zhixiang Huang Tian Hong Loh Kwok L. Chung College of Information and Communications Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Electromagnetic and Eletrochemical Technologies Department National Physical Laboratory Middlesex UK School of Computer Science and Engineering Huizhou University P. R. China

In this paper, Q-value theory of conventional antennas is used to analyze reasons of low radiation performance of acoustics promoted antennas, where we modify the radiating structure of existing acoustics promoting antenna using the simulations to get the required improvement on radiation performance. The simulation experiments are carried out in the finite element simulation software Comsol Multiphysics to analyze the radiating performance.

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A Learning Assisted Approach for Electronic and Optical Characterization of Tin-Doped Titanium Oxide 2

A Learning Assisted Approach for Electronic and Optical Char...

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2nd IEEE International Conference on Artificial Intelligence in Everything, AIE 2022

作者： Vubangsi, Mercel Nforna, Edwin Akongnwi Kamsu, Brigitte Fotso Tchoffo, M. Al-Turjman, Fadi Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin Nicosia10 Turkey Htttc Bambili University of Bamenda Nwr Computational Materials Science Lab Department of Computer Science Cameroon Htttc Bambili University of Bamenda Materials Science Research Unit Department of Fundamental Sciences Cameroon Faculty of Science University of Dschang Condensed Matter Electronics and Signal Processing Research Unit Department of Physics Cameroon University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Mersin Kyrenia10 Cyprus

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

In this work, we evaluate the effect of variation of mole fraction of Tin in doped titanium oxide on band gap. To achieve this, we resort to Matminer's "dielectric_constant"dataset and apply an algorithm to featurize the chemical formulae of the compounds in the dataset. Using k-fold cross-validation of random forest, we obtain a Machine learning model with R2 value of 0.81 MAE=0.45. Applying our model to predict the band gap of doped Titanium oxide over a range of 6 compositions, we find that the 50% doping level has a remarkably lower band gap than all other compositions which makes it a more suitable candidate for optical applications such as in solar cells. © 2022 IEEE.

关键词： Smart contract

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Online Convex Optimization of Programmable Quantum computers to Simulate Time-Varying Quantum Channels

Online Convex Optimization of Programmable Quantum Computers...

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IEEE (first ITW conference of the year) Information Theory Workshop (ITW)

作者： Hari Hara Suthan Chittoor Osvaldo Simeone Leonardo Banchi Stefano Pirandola Department of Engineering of Kings College King’s Communications Learning and Information Processing (KCLIP) lab London UK Department of Physics and Astronomy University of Florence & INFN Sezione di Firenze Sesto Fiorentino (FI) Italy Department of Computer Science University of York York UK

Simulating quantum channels is a fundamental primitive in quantum computing, since quantum channels define general (trace-preserving) quantum operations. An arbitrary quantum channel cannot be exactly simulated using a finite-dimensional programmable quantum processor, making it important to develop optimal approximate simulation techniques. In this paper, we study the challenging setting in which the channel to be simulated varies adversarially with time. We propose the use of matrix exponentiated gradient descent (MEGD), an online convex optimization method, and analytically show that it achieves a sublinear regret in time. Through experiments, we validate the main results for time-varying dephasing channels using a programmable generalized teleportation processor.

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