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From Spins to Beams: Antenna Array Synthesis and Optimization using Simulated Bifurcations of Parametric Oscillators

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IEEE Transactions on Antennas and Propagation 2025年

作者： Lim, Qi Jian Peng, Zhen University of Illinois at Urbana-Champaign Center for Computational Electromagnetics Department of Electrical and Computer Engineering UrbanaIL61801 United States

The rapid evolution of wireless communication, including the deployment of 5G and the advent of 6G networks, has dramatically increased the complexity of antenna system design and optimization. Traditional optimization methods often show limitations in addressing the high dimensionality, discrete configuration spaces, and complex scattering environments involved in advanced array design and beamforming. This paper presents a versatile and novel framework for antenna array synthesis and optimization using simulated bifurcations of nonlinear parametric oscillators. The discrete design variables are mapped onto spin states in the Ising model. The optimal antenna configuration is found by solving the ground-state solution of the Hamiltonian function through a quantum-inspired simulated bifurcation algorithm. We demonstrate the proposed work across a range of antenna synthesis scenarios. These include beamforming with conformal arrays, array thinning, maximization of beam efficiency, and sum-rate optimization. The results highlight the potential of the proposed work for addressing complex optimization problems in antenna arrays. © 1963-2012 IEEE.

关键词： Hamiltonians

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Controlling Biomolecular Timekeeping via Regulated Gene Product Degradation 63

Controlling Biomolecular Timekeeping via Regulated Gene Prod...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Rezaee, Sayeh Nieto, Cesar Singh, Abhyudai University of Delaware Department of Electrical and Computer Engineering NewarkDE United States University of Delaware Biomedical Engineering Mathematical Sciences Center of Bioinformatic and Computational Biology Department of Electrical and Computer Engineering NewarkDE United States

ISBN: (纸本)9798350316339

Numerous biological timing mechanisms are associated with the duration required for key regulatory molecules to reach critical threshold levels. Recent studies have mainly explored how random fluctuations affect the timing of events as molecules accumulate to reach a high threshold. This study examines the complementary scenario: decrease in protein levels until they reach a low critical threshold. We aim to study the timing precision resulting from different degradation pathways and their inherent stochastic nature, especially under low-copy number conditions. More specifically, we explore how integer-valued protein molecular counts decrease stochastically following Michaelis-Menten kinetics. This approach encompasses both zero-order (where the net degradation rate is independent of protein levels) and first-order (where the rate is proportional to protein levels) decay processes. Our results show that, while zero-order decay can be sluggish in terms of mean timing, it provides the highest precision in timing with the lowest noise in threshold-crossing time. Conversely, upon considering randomness in the initial protein levels, first-order decay demonstrates better precision compared to zero-order. Interestingly, in the presence of multiple noise sources, timing stochasticity can be minimized when Michaelis-Menten decay kinetics operate under sub-saturation conditions. This characterization of timing driven by decreasing copy numbers has implications for improving the precision of biological clocks and understanding the timing of cell-cycle events. © 2024 IEEE.

关键词： Stochastic systems

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Feedforward Regulation of Interneuronal Communication 63

Feedforward Regulation of Interneuronal Communication

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Gambrell, Oliver Vahdat, Zahra Singh, Abhyudai University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States University of Delaware Biomedical Engineering Mathematical Sciences Center for Bioinformatics and Computational Biology Department of Electrical and Computer Engineering NewarkDE19716 United States

ISBN: (纸本)9798350316339

We formulate a mechanistic model capturing the dynamics of neurotransmitter release in a chemical synapse. The proposed modeling framework captures key aspects such as the random arrival of action potentials (AP) in the presynaptic (input) neuron, probabilistic docking and release of neurotransmitter-filled vesicles, and clearance of the released neurotransmitter from the synaptic cleft. Analysis of the model results in analytical expressions for the statistical moments of docked vesicle count and the number of neurotransmitters in the cleft as a function of model parameters, and the frequency of presynaptic APs. We next consider a postsynaptic (output) neuron that fires an AP based on integrating upstream neurotransmitter activity. Our results show the existence of an optimal presynaptic AP frequency that maximizes the probability of a postsynaptic AP firing. We extend these results to consider feedforward regulation where in addition to a direct excitatory synapse, the input neuron also impacts the output indirectly via an inhibitory interneuron, and identify parameter regimes where the feedforward neuronal networks result in band-pass filtering, i.e., the output neuron frequency is maximized at intermediate input neuron frequencies. © 2024 IEEE.

关键词： Feedforward neural networks

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Quantifying statistics of gene product copy-number fluctuations: A stochastic hybrid systems approach 63

Quantifying statistics of gene product copy-number fluctuati...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Vahdat, Zahra Singh, Abhyudai University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States University of Delaware Department of Electrical and Computer Engineering Biomedical Engineering Mathematical Sciences Center for Bioinformatics and Computational Biology NewarkDE19716 United States

ISBN: (纸本)9798350316339

Across diverse cell types, genes are often expressed at low levels resulting in significant stochastic fluctuations (noise) in intracellular copy number of mRNAs and proteins over time. Motivated by single-cell experiments, we capture this stochasticity by considering a given gene that toggles between transcriptionally active and inactive states with the time spent in each state being an arbitrary random variable. mRNAs are synthesized from the active state as per a Poisson process and each molecule degrades after a random lifespan (i.e., random time from birth to death). Modeling this process using the Stochastic Hybrid System (SHS) formalism we derive exact analytical results for the statistical moments of molecular counts. Our results show that for fixed mean mRNA numbers, fluctuations in mRNA levels are amplified with decreasing noise in mRNA lifespan. Relaxing the Poisson process assumption, we next consider a scenario where transcription events occur such that the time between successive events is an arbitrarily distributed random variable. In this case, we show that decreasing noise in mRNA lifespan can both increase/decrease mRNA count fluctuations depending on the underlying transcriptional process. Finally, we extend these results to the protein level, where increasing noise in mRNA counts is sometimes associated with decreasing noise in protein copy numbers. © 2024 IEEE.

关键词： Stochastic systems

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RayProNet: A Neural Point Field Framework for Radio Propagation Modeling in 3D Environments

arXiv

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arXiv 2024年

作者： Cao, Ge Peng, Zhen Center for Computational Electromagnetics Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States

The radio wave propagation channel is central to the performance of wireless communication systems. In this paper, we introduce a novel machine learning-empowered methodology for wireless channel modeling. The key ingredients include a point-cloud-based neural network and a Spherical Harmonics encoder with light probes. Our approach offers several significant advantages, including the flexibility to adjust antenna radiation patterns and transmitter/receiver locations, the capability to predict radio power maps, and the scalability of large-scale wireless scenes. As a result, it lays the groundwork for an end-toend pipeline for network planning and deployment optimization. The proposed work is validated in various outdoor and indoor radio environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Directional patterns (antenna)

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Stochastic dynamics of the logistic growth model subjected to environmental perturbations

Stochastic dynamics of the logistic growth model subjected t...

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2023 IEEE Conference on Control Technology and Applications, CCTA 2023

作者： Rezaee, Sayeh Nieto, Cesar Vahdat, Zahra Singh, Abhyudai University of Delaware Department of Electrical and Computer Engineering NewarkDE United States University of Delaware Biomedical Engineering Mathematical Sciences Center of Bioinformatic and Computational Biology Department of Electrical and Computer Engineering NewarkDE United States

ISBN: (纸本)9798350335446

The stochastic logistic model is widely used to capture random fluctuations arising from birth-death processes in ecological populations. We use this model to study the impact of environmental perturbations that may occur naturally or as a consequence of population harvesting. In our model formulation, environmental perturbations occur randomly as per a Poisson process, and the perturbations result in each individual dying with a certain probability of death. Moment closure schemes are employed to derive expressions for the mean and variability in population numbers. Moreover, to quantify the impact of population extinction in our model we compute the probability of extinction using the Finite State Projection (FSP) numerical method. Our analysis shows that rare environmental perturbations with a high probability of death lead to overall larger random fluctuations and extinction risk as compared to frequent perturbations with a low probability of death. Finally, we formulate the problem in the context of population harvesting to find the optimal harvesting rate that maximizes the cumulative yield. © 2023 IEEE.

关键词： Harvesting

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Optimizing Precision in Cellular Clocks Through Self-Regulated Accumulation Of Molecules 27

Optimizing Precision in Cellular Clocks Through Self-Regulat...

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27th International Conference on System Theory, Control and Computing, ICSTCC 2023

作者： Rezaee, Sayeh Nieto, César Singh, Abhyudai Department of Electrical and Computer Engineering University of Delaware NewarkDE United States Department of Electrical and Computer Engineering Biomedical Engineering Mathematical Sciences Center of Bioinformatic and Computational Biology University of Delaware NewarkDE United States

ISBN: (纸本)9798350337983

The timing of cellular events is often determined by the instant when the level of a chemical species crosses a threshold from an initial value of zero. To attenuate the inherent random fluctuations in this first-passage time, the production rate can be a function of the current species level by implementing a feedback or self-regulation. In this study, we aim to identify the self-regulation strategy that minimizes timing noise around a specified mean first-passage time. Previous results showed that no-feedback is the optimal strategy in the absence of molecule degradation. Based on this result, we study the case where the species may undergo degradation. First, we set the feedback to be a linear function of the number of molecules. We derive analytical formulas concluding that the optimal strategy involves positive feedback, whereby the production rate increases with the number of molecules. When feedback constraints are removed, our numerical methods suggest that a mixed self-regulation scheme is optimal. This strategy involves an initial positive feedback phase, followed by a sharp transition to a negative feedback scheme in proximity to the threshold. In summary, this research quantifies the fundamental limits of timing noise reduction, considering different strategies of self-regulation in situations where time-keeping species may be subjected to degradation. © 2023 IEEE.

关键词： Molecules

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A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

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computers, Materials & Continua 2023年第4期75卷 697-714页

作者： Samra Rehman Muhammad Attique Khan Majed Alhaisoni Ammar Armghan Usman Tariq Fayadh Alenezi Ye Jin Kim Byoungchol Chang Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakakahSaudi Arabia Department of Management Information Systems CoBAPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Identifying fruit disease manually is time-consuming, expertrequired,and expensive;thus, a computer-based automated system is widelyrequired. Fruit diseases affect not only the quality but also the *** a result, it is possible to detect the disease early on and cure the fruitsusing computer-based techniques. However, computer-based methods faceseveral challenges, including low contrast, a lack of dataset for training amodel, and inappropriate feature extraction for final classification. In thispaper, we proposed an automated framework for detecting apple fruit leafdiseases usingCNNand a hybrid optimization algorithm. Data augmentationis performed initially to balance the selected apple dataset. After that, twopre-trained deep models are fine-tuning and trained using transfer ***, a fusion technique is proposed named Parallel Correlation Threshold(PCT). The fused feature vector is optimized in the next step using a hybridoptimization algorithm. The selected features are finally classified usingmachine learning algorithms. Four different experiments have been carriedout on the augmented Plant Village dataset and yielded the best accuracy of99.8%. The accuracy of the proposed framework is also compared to that ofseveral neural nets, and it outperforms them all.

关键词： Convolutional neural networks deep learning features fusion features optimization classification

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Nonlinear dynamic response of bimorph piezoelectric energy harvester with functionally graded porous core

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Noise and Vibration Worldwide 2023年第2-3期54卷 145-157页

作者： Akbar, Narges Soltani, Madjid Department of Mechanical Engineering Alzahra University Tehran Iran Department of Mechanical Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical and Computer Engineering University of Waterloo WaterlooON Canada University of Waterloo WaterlooON Canada Advanced Bioengineering Initiative Center Computational Medicine Center K. N. Toosi University of Technology Tehran Iran

This study deals with piezoelectric energy harvesters carrying the magnet at the free end while a magnifier connected them to the base. The harvester consists of a cantilever beam with Functionally Graded (FG) porous foam core and two piezoelectric faces while taking into account the von-Karman strains and the magnetic interaction of two magnets. The governing equation has been developed using Hamilton’s principle and reduced-order via the Galerkin method. Frequency response of vibrations and voltage derived using harmonic balance method and the adjusted model has been confirmed by parametric studies of the lumped parameter model. A parametric study is also performed to expose the effects of porosity coefficient and pattern, magnifier ratios, and excitation level on the responses. Results show that by correct selection of the magnifier parameters, the proposed harvester can provide a higher output over a wider frequency band. Also, the core porosity increases the flexibility of the beam and raised the voltage by a factor of 1.8. © The Author(s) 2023.

关键词： Porosity

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Evaluation of Deep Neural Operator Models Toward Ocean Forecasting

Evaluation of Deep Neural Operator Models Toward Ocean Forec...

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2023 MTS/IEEE U.S. Gulf Coast, OCEANS 2023

作者： Rajagopal, Ellery Babu, Anantha N. S. Ryu, Tony Haley, Patrick J. Mirabito, Chris Lermusiaux, Pierre F. J. Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA United States Center for Computational Science and Engineering Massachusetts Institute of Technology Department of Mechanical Engineering CambridgeMA United States

ISBN: (纸本)9798218142186

Data-driven, deep-learning modeling frameworks have been recently developed for forecasting time series data. Such machine learning models may be useful in multiple domains including the atmospheric and oceanic ones, and in general, the larger fluids community. The present work investigates the possible effectiveness of such deep neural operator models for reproducing and predicting classic fluid flows and simulations of realistic ocean dynamics. We first briefly evaluate the capabilities of such deep neural operator models when trained on a simulated two-dimensional fluid flow past a cylinder. We then investigate their application to forecasting ocean surface circulation in the Middle Atlantic Bight and Massachusetts Bay, learning from high-resolution data-assimilative simulations employed for real sea experiments. We confirm that trained deep neural operator models are capable of predicting idealized periodic eddy shedding. For realistic ocean surface flows and our preliminary study, they can predict several of the features and show some skill, providing potential for future research and applications. © 2023 The Marine Technology Society (MTS).

关键词： Flow of fluids

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