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Spectral period doubling and encoding of dissipative optical solitons via gain control

PhotoniX 2024年第1期5卷 290-305页

作者： Kangwen Yang Yi Zhou Yuqing Ling Kevin K.Tsia Heping Zeng Kenneth K.Y.Wong Shanghai Key Laboratory of Modern Optical System and Engineering Research Center of Optical Instrument and SystemMinistry of EducationSchool of Optical Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Department of Electrical and Electronic Engineering The University of Hong KongHong KongChina Advanced Biomedical Instrumentation Centre Hong Kong Science ParkHong KongChina State Key Laboratory of Precision Spectroscopy East China Normal UniversityShanghai 200062China

Period-doubling bifurcation,as an intermediate state between order and chaos,is ubiquitous in all disciplines of nonlinear ***,previous experimental observations of period doubling in ultrafast fiber lasers are mainly restricted to self-sustained steady state,controllable manipulation and dynamic switching between period doubling and other intriguing dynamical states are still largely ***,we propose to expand the vision of dissipative soliton periodic doubling,which we illustrate experimentally by reporting original spontaneous,collisional,and controllable spectral period doubling in a polarization-maintaining ultrafast fiber ***,the spontaneous period doubling can be observed in both single-and *** mechanism of the switchable state and periodic doubling was revealed by numerical ***,state transformation of individual solitons can be resolved during the collision of triple solitons involving stationary,oscillating,and period ***,controllable deterministic switching between period doubling and other dynamical states,as well as exemplifying the application of period-doubling-based digital encoding,is achieved under programmable pump *** results open a new window for unveiling complex Hopf bifurcation in dissipative systems and bring useful insights into nonlinear science and applications.

关键词： Period doubling Mode locking Ultrafast fiber lasers

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Joint Trajectory Optimization for Redundant Manipulators with Constant Path Speed

Joint Trajectory Optimization for Redundant Manipulators wit...

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American Control Conference (ACC)

作者： Jonathan Fried Santiago Paternain Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

In this work, we present an approach to minimizing the time necessary for the end-effector of a redundant robot manipulator to traverse a given trajectory by optimizing the trajectory of its joints, under a number of restrictions. Each joint has limits in the ranges of position, velocity and acceleration, the latter making jerks in joint space undesirable. Furthermore, for the applications involved, the end-effector must traverse the path with high accuracy and at constant path velocity, i.e. the tip of the manipulator must cover equal distances in equal amounts of time. The proposed approach takes this nonlinear optimization problem that has two variables (path speed and joint trajectory) and solves it in two steps - First, we solve an inner subproblem that considers a fixed joint trajectory and maximizes path speed, for which we obtain a closed form solution that considers all joint velocity and acceleration restrictions. Moreover, we establish that the value of the inner subproblem is convex. Then, we solve an outer subproblem that takes a subgradient of the inner subproblem's value to update the trajectory with a Primal-Dual optimization method that considers all path accuracy and joint position restrictions. We show the efficacy of our proposed approach with simulations.

关键词： Accuracy Closed-form solutions Tracking Optimization methods Spraying End effectors Trajectory

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Blood Pressure Estimation through Photoplethysmography and Machine Learning Models 9

Blood Pressure Estimation through Photoplethysmography and M...

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9th International Conference on Control, Instrumentation and Automation, ICCIA 2023

作者： Mohammadi, Hanieh Tarvirdizadeh, Bahram Alipour, Khalil Ghamari, Mohammad Laboratory Faculty of New Sciences and Technologies Department of Mechatronics Engineering Tehran Iran Kettering University Department of Electrical and Computer Engineering FlintMI United States

ISBN: (纸本)9798350366013

Blood pressure (BP) is a crucial health element, the fluctuations of which may have profound implications for an individual's well-being. Traditional methods of measuring BP, such as cuff-based and invasive devices, are not only uncomfortable but also unable to provide continuous monitoring. In response to this need, we propose a cuffless, continuous, and noninvasive BP measurement system utilizing photoplethysmograph (PPG) signals and machine learning (ML) models. PPG, an optical volumetric measurement technique, can identify alterations in blood volume within the tissue's vascular network. In this study, PPG signals obtained from diverse individuals underwent preprocessing and feature extraction. Subsequently, a feature selection technique was employed to identify suitable features. These selected features were then employed to train and evaluate ML models. Ultimately, we identified optimal regression models for independent estimation of diastolic blood pressure (DBP) and systolic blood pressure (SBP). Our findings indicate that the random forest (RF) model, in combination with the SelectFromModel feature selection method, outperformed other models. This model yielded significant outcomes, achieving a root mean square error (RMSE) of 6.61 for DBP and 10.06 for SBP, highlighting its superior performance in BP estimation. © 2023 IEEE.

关键词： Blood pressure

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Random Policy Enables In-Context Reinforcement Learning within Trust Horizons

arXiv

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arXiv 2024年

作者： Chen, Weiqin Paternain, Santiago Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute

Pretrained foundation models (FMs) have exhibited extraordinary in-context learning performance, allowing zero-shot (or few-shot) generalization to new environments/tasks not encountered during the pretraining. In the case of reinforcement learning (RL), in-context RL (ICRL) emerges when pretraining FMs on decision-making problems in an autoregressive-supervised manner. Nevertheless, the current state-of-the-art ICRL algorithms, such as Algorithm Distillation, Decision Pretrained Transformer and Decision Importance Transformer, impose stringent requirements on the pretraining dataset concerning the behavior (source) policies, context information, and action labels, etc. Notably, these algorithms either demand optimal policies or require varying degrees of well-trained behavior policies for all pretraining environments. This significantly hinders the application of ICRL to real-world scenarios, where acquiring optimal or well-trained policies for a substantial volume of real-world training environments can be prohibitively expensive or even intractable. To overcome this challenge, we introduce a novel approach, termed State-Action Distillation (SAD), that allows to generate an effective pretraining dataset guided solely by random policies. In particular, SAD selects query states and corresponding action labels by distilling the outstanding state-action pairs from the entire state and action spaces by using random policies within a trust horizon, and then inherits the classical autoregressive-supervised mechanism during the pretraining. To the best of our knowledge, this is the first work that enables effective ICRL under (e.g., uniform) random policies and random contexts. We also establish the quantitative analysis of the trustworthiness as well as the performance guarantees of our SAD approach. Moreover, our empirical results across multiple popular ICRL benchmark environments demonstrate that, on average, SAD outperforms the best baseline by 236.3% in the offline

关键词： Reinforcement learning

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Edge Computing for Real Time Botnet Propagation Detection

Edge Computing for Real Time Botnet Propagation Detection

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2022 IEEE International Conference and Expo on Real Time Communications at IIT, RTC 2022

作者： Gromov, Mikhail Arnold, David Saniie, Jafar Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (数字)9781665475037

ISBN: (纸本)9781665475037

Continued growth and adoption of the Internet of Things (IoT) has greatly increased the number of dispersed resources within both corporate and private networks. IoT devices benefit the user by providing more local access to computation and observation compared to dedicated servers within a centralized data center. However, years of lax or nonexistent cybersecurity standards leave IoT devices as easy prey for hackers looking for easy targets. Further, IoT devices normally operate at the edge of the network, far from sophisticated cyberattack detection and network monitoring tools. When hacked, IoT can be used as a launching point to attack more sensitive targets or can be collected into a larger botnet. These botnets are frequently utilized for targeted Distributed Denial of Service (DDoS) attacks against service providers and servers, decreasing response time or overwhelming the system. In order to protect these vulnerable resources, we propose an edge computing system for detecting active threats against local IoT devices. Our system will utilize deep learning, specifically a Convolutional Neural Network (CNN) for detecting attacks. Incoming network traffic will be converted into an image before beings supplied to the CNN for classification. The network will be trained using the N-BaIoT dataset. Since the system is designed to operate at the edge of the network, it will run on the Jetson Nano for real-time attack detection. © 2022 IEEE.

关键词： Botnet

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The good, the bad and the ugly sides of data augmentation: an implicit spectral regularization perspective

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 4470-4554页

作者： Chi-Heng Lin Chiraag Kaushik Eva L. Dyer Vidya Muthukumar School of Electrical & Computer Engineering Georgia Institute of Technology Atlanta GA Coulter Department of Biomedical Engineering School of Electrical & Computer Engineering Georgia Institute of Technology Atlanta GA School of Electrical & Computer Engineering School of Industrial & Systems Engineering Georgia Institute of Technology Atlanta GA

Data augmentation (DA) is a powerful workhorse for bolstering performance in modern machine learning. Specific augmentations like translations and scaling in computer vision are traditionally believed to improve generalization by generating new (artificial) data from the same distribution. However, this traditional viewpoint does not explain the success of prevalent augmentations in modern machine learning (e.g. randomized masking, cutout, mixup), that greatly alter the training data distribution. In this work, we develop a new theoretical framework to characterize the impact of a general class of DA on underparameterized and overparameterized linear model generalization. Our framework reveals that DA induces implicit spectral regularization through a combination of two distinct effects: a) manipulating the relative proportion of eigenvalues of the data covariance matrix in a training-data-dependent manner, and b) uniformly boosting the entire spectrum of the data covariance matrix through ridge regression. These effects, when applied to popular augmentations, give rise to a wide variety of phenomena, including discrepancies in generalization between over-parameterized and under-parameterized regimes and differences between regression and classification tasks. Our framework highlights the nuanced and sometimes surprising impacts of DA on generalization, and serves as a testbed for novel augmentation design.

关键词： data augmentation generalization analysis overparameterized models spectral analysis regression classification

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Photonic Indistinguishability of the Tin-Vacancy Center in Diamond

Photonic Indistinguishability of the Tin-Vacancy Center in D...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Arjona, Jesús Parker, Ryan Chen, Kevin Purser, Carola Li, Linsen Michaels, Cathryn Stramma, Alexander Debroux, Remain Harris, Isaac Hayhurst, Martin Nichols, Eleanor Trusheim, Matthew Gangloff, Dorian Englund, Dirk Atature, Mete Cavendish Laboratory University of Cambridge Cambridge United Kingdom Department of Electrical Engineering and Computer Science MIT Cambridge United States

Tin-vacancy centres in diamond are spin-photon interfaces with intrinsic environmental noise insensitivity. We reveal their high optical coherence in a nanostructured environment and generate single photons with a 99.... 详细信息

ISBN: (纸本)9781957171258

关键词： Photons

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Real-Time Services Robot with an Intelligent System Based on a Radio Frequency Identification Unit 17

Real-Time Services Robot with an Intelligent System Based on...

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17th International Conference on Developments in esystems engineering, DeSE 2024

作者： Al Mashhadany, Yousif Al-Askari, Mohanad A. Al-Ani, Abdullah Alani, Taisir Ahmed Yaseen Dawod Algburi, Sameer Department of Electrical Engineering University of Anbar Anbar Iraq Information Systems Department University of Anbar Baghdad Iraq Computer Network Department University of Anbar Anbar Iraq College of Engineering Al-Kitab University Kirkuk Iraq

ISBN: (纸本)9798350368697

Technological developments have come a long way in developing service robots and employing them to serve human needs to the fullest extent. Communication between the robot and its user is the most important aspect, and developing a system to train robots and provide them with the necessary capabilities to accomplish specific tasks is important for creating a suitable service robot. This work provides solutions to most of the challenges in developing such systems using smart systems that can implement multiple and complex commands in a short and appropriate time, such as matching data outputs with what is examined by these systems. For this reason, (RFID) was used to compare data and give commands so that the robot can move in complex environments. Another challenge to consider is keeping the robot on the specified path without deviating from its specified path, so a special sensor was developed to track the robot's deviation and correct the path. Another challenge to consider is if the robot might collide with an object on the path, this problem has been solved by a wireless sensor, all of which is processed and controlled by the microprocessor and motor control unit. Several practical tasks were implemented for the proposed model, and these tasks were in complex and different environments and under difficult conditions. The proposed service robot model was able to implement them with very high accuracy, so it can be relied upon as an advanced service robot model. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Enhanced Grid-Forming Inverter Control Through Integral Sliding Mode Control in a Cascaded Framework 4

Enhanced Grid-Forming Inverter Control Through Integral Slid...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Rayane, Khaled Rabhi, Abdelhamid Oubbati, Brahim Khalil Benzoubir, Mohammed University of Laghouat LACoSERE Lab Laghouat Algeria Texas A and M University at Qatar Qatar Foundation Department of Electrical and Computer Engineering Doha Qatar University of Picardie Jules Verne Laboratory of Modelisation Information and Systems-MIS France

ISBN: (纸本)9798350306262

The microgrid (MG) relies heavily on the grid-forming inverter (GFI), making it a crucial component. Therefore, precise and adaptable control of the GFI is essential for microgrid operations due to its pivotal role in shaping voltage and regulating frequency. In this context, this paper introduces a Integral Sliding Mode Control (ISMC) based cascaded approach for controlling the GFI system. The primary goal of this control strategy is to produce a three-phase sinusoidal voltage with a specified amplitude while minimizing harmonic distortion in the output voltage under varying load conditions. The key advantage of the proposed GFI controller lies in its ability to deliver superior performance, characterized by low Total Harmonic Distortion (THD), rapid dynamic tracking capabilities, and robustness in the face of load fluctuations. A comparative assessment is conducted between the conventional repetitive controller and the newly proposed method, considering linear and nonlinear load scenarios. The simulation results provide strong validation for the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Sliding mode control

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The Impact of the Digital Transformation of Education on Literacy 7

The Impact of the Digital Transformation of Education on Lit...

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7th IEEE International Conference and Workshop Obuda on electrical and Power engineering, CANDO-EPE 2024

作者： Farkas, Imre Kovari, Attila Rajcsanyi-Molnar, Monika Institute of Computer Engineering University of Dunaujvaros Department of Computer Systems and Control Technology Dunaujvaros Hungary Eszterházy Károly Catholic University Institute of Digital Technology Faculty of Computer Science Eger Hungary Obuda University Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Social Sciences University of Dunaujvaros Department of Economics and Management Dunaujvaros Hungary

ISBN: (纸本)9798331531119

During the COVID19 epidemic, people of all ages from all walks of life around the world have become inevitably familiar with and almost dependent on the digital tools of the age and the opportunities they offer. A change had to take place very quickly that was unprecedented. This process has also affected education. During the time of the reading lock-in, there was a proliferation of digital curricular content. The second time around, online education came to the fore, thanks to less or well-functioning online platforms, and has since become part of everyday teaching. What is the effectiveness and usefulness of sharing knowledge in this way? Is literacy still needed at all? Can online and hybrid (a controlled mix of face-to-face and online) education replace the well-established contact and face-to-face education? The study show that the respondents believe that online education has had an impact on literacy and that it is perceived to have a positive impact. The results indicate that online education has a positive impact on the literacy levels of students, as it facilitates knowledge acquisition and enhances digital literacy skills. © 2024 IEEE.

关键词： digital education digital literacy digital-informatics literacy

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