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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Arnold, Kellen P. Allen, Joshua A. Halimi, Sami I. Ryder, Landen D. Afzal, Francis O. Bian, Yusheng Aboketaf, Abdelsalam Hirokawa, Takako Dezfulian, Kevin Rakowski, Michal Augur, Rod Nummy, Karen Weiss, Sharon M. Interdisciplinary Materials Science Program Vanderbilt University 2201 West End Ave NashvilleTN37235 United States Department of Electrical and Computer Engineering Vanderbilt University 2201 West End Ave NashvilleTN37235 United States NASA Goddard Space Flight Center Code 561 GreenbeltMD20771 United States GlobalFoundries 400 Stone Break Rd. Ext. MaltaNY12020 United States

We report scalable foundry fabrication and characterization of photonic crystal nanobeam waveguides incorporating subwavelength-scale dielectric antislot unit cells. This work enables enhanced light-matter interaction and three-fold improvement in VπL when incorporated in Mach-Zehnder modulators. © Optica Publishing Group 2024

关键词： Silicon photonics

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Subwavelength-engineered Antislot Photonic Crystals in a Silicon Photonics Foundry for On-chip Communications

Subwavelength-engineered Antislot Photonic Crystals in a Sil...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Mach-Zehnder interferometers

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Multi-Instance Task in Swarm Robotics: Sorting Groups of Robots or Objects into Clusters with Minimalist Controllers

Multi-Instance Task in Swarm Robotics: Sorting Groups of Rob...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Adilson Krischanski Yuri K. Lopes Andre B. Leal Ricardo F. Martins Roberto S.U. Rosso Department of Computer Science Santa Catarina State University (UDESC) Joinville Brazil Graduate Program in Applied Computing (PPGCAP) UDESC Brazil Department of Electrical Engineering Santa Catarina State University (UDESC) Joinville Brazil

Relying only on behaviors that emerge from simple responsive controllers; swarms of robots have been shown capable of autonomously aggregate themselves or objects into clusters without any form of communication. We push these controllers to the limit, requiring robots to sort themselves or objects into different clusters. Based on a responsive controller that maps the current reading of a line-of-sight sensor to a pair of speeds for the robots' differential wheels, we demonstrate how multiple tasks instances can be accomplished by a robotic swarm. Using the dividing rectangles approach and physics simulation, a training step optimizes the parameters of the controller guided by a fitness function. We conducted a series of systematic trials in physics-based simulation and evaluate the performance in terms of dispersion and the ratio of clustered robots/objects. Across 20 trials where 30 robots cluster themselves into 3 groups, an average of 99.83% of them were correctly clustered into their group after 300 s. Across 50 trials where 15 robots cluster 30 objects into 3 groups, an average of 61.20%, 82.87%, and 97.73% of objects were correctly clustered into their group after 600 s, 900 s, and 1800 s, respectively. The object cluster behavior scales well while the aggregation does not, the latter due to the requirement of control tuning based on the number of robots.

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T*$\boldsymbol{\varepsilon}$—Bounded-Suboptimal Efficient Motion Planning for Minimum-Time Planar Curvature-Constrained Systems

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IEEE Robotics and Automation Letters 2022年第2期7卷 4102-4109页

作者： Doron Pinsky Petr Váňa Jan Faigl Oren Salzman Technion Autonomous Systems Program (TASP) Technion Haifa Israel Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Department of Computer Science Technion Haifa Israel

We consider the problem of finding collision-free paths for curvature-constrained systems in the presence of obstacles while minimizing execution time. Specifically, we focus on the setting where a planar system can travel at some range of speeds with unbounded acceleration. This setting can model many systems, such as fixed-wing drones. Unfortunately, planning for such systems might require evaluating many (local) time-optimal transitions connecting two close-by configurations, which is computationally expensive. Existing methods either pre-compute all such transitions in a preprocessing stage or use heuristics to speed up the search, thus foregoing any guarantees on solution quality. Our key insight is that computing all the time-optimal transitions is both (i) computationally expensive and (ii) unnecessary for many problem instances. We show that by finding bounded-suboptimal solutions (solutions whose cost is bounded by $\boldsymbol{1+\varepsilon}$ times the cost of the optimal solution for any user-provided $\boldsymbol{\varepsilon}$ ) and not time-optimal solutions, one can dramatically reduce the number of time-optimal transitions used. We demonstrate using empirical evaluation that our planning framework can reduce the runtime by several orders of magnitude compared to the state-of-the-art while still providing guarantees on the quality of the solution.

关键词： Costs Planning Robots Image edge detection Vehicle dynamics Runtime Heuristic algorithms

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TEAM: Task-Clustering and Enhanced Adaptability for Meta-Reinforcement Learning on Robotics Through Multi-Task Diffusion and Optimization

TEAM: Task-Clustering and Enhanced Adaptability for Meta-Rei...

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IEEE International Conference on Robotic Computing (IRC)

作者： Joshua Ho Chien-Min Wang Chung-Ta King Chi-Wei Feng Shih-Yun Sunkuan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

ISBN: (数字)9798331521554

ISBN: (纸本)9798331521561

The ability of robots to imitate human learning strategies-rapidly adapting to new tasks without large datasets-has garnered significant attention in meta-learning. Meta-reinforcement learning seeks to enhance robotic agent flexibility across diverse tasks and contexts, offering promise where single-task learning often fails. Despite advancements like multi-task diffusion models and task-weighted optimization mechanisms, effectively training tasks with varying complexities simultaneously remains a major challenge. This paper introduces a novel meta-reinforcement learning method that addresses this issue by clustering the training tasks of robotic arms based on semantic and trajectory similarities, while leveraging adaptive learning rates and task-specific weights proposed by the multitask optimization techniques. Our approach, TEAM, emphasizes performance-driven semantic clustering, optimizing based on robotic task similarity, complexity, and convergence objectives. We also integrate fast adaptive and multi-task optimization of the diffusion model to enhance computational efficiency and adaptability. More specifically, we introduce a cluster-specific optimization technique, using specialized parameters for each group to allow more refined task handling. The experimental validation demonstrates the effectiveness of this scalable method in improving performance, adaptability, and efficiency in real-world, heterogeneous robotic tasks, further advancing robotic computing in meta-reinforcement learning.

关键词： Training Metalearning program processors Semantics Multitasking Diffusion models Complexity theory Trajectory Robots Optimization

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Parallel Approximations for High-Dimensional Multivariate Normal Probability Computation in Confidence Region Detection Applications

Parallel Approximations for High-Dimensional Multivariate No...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Xiran Zhang Sameh Abdulah Jian Cao Hatem Ltaief Ying Sun Marc G. Genton David E. Keyes Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Statistics Program King Abdullah University of Science and Technology Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Mathematics The University of Houston Houston Texas USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. For example, the critical task of detecting confidence regions where a process probability surpasses a specific threshold is essential in diverse applications, such as pinpointing tumor locations in magnetic resonance imaging (MRI) scan images, determining hydraulic parameters in groundwater flow issues, and forecasting regional wind power to optimize wind turbine placement, among numerous others. One common way to compute high-dimensional MVN probabilities is the Separation-of-Variables (SOV) algorithm. This algorithm is known for its high computational complexity of O(n 3 ) and space complexity of O(n 2 ), mainly due to a Cholesky factorization operation for an n×n covariance matrix, where n represents the dimensionality of the MVN problem. This work proposes a high-performance computing framework that allows scaling the SOV algorithm and, subsequently, the confidence region detection algorithm. The framework leverages parallel linear algebra algorithms with a task-based programming model to achieve performance scalability in computing process probabilities, especially on large-scale systems. In addition, we enhance our implementation by incorporating Tile Low-Rank (TLR) approximation techniques to reduce algorithmic complexity without compromising the necessary accuracy. To evaluate the performance and accuracy of our framework, we conduct assessments using simulated data and a wind speed dataset. Our proposed implementation effectively handles high-dimensional multivariate normal (MVN) probability computations on shared and distributed-memory systems using finite precision arithmetics and TLR approximation computation. Performance results show a significant speedup of up to 20X in solving the MVN problem using TLR approximation compared to the reference dense solution without sacrificin

关键词： Accuracy Tensors Wind speed Magnetic resonance imaging Linear algebra Wind power generation Approximation algorithms

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Morphological imaging of 3D cultured kidney mesangial cells using gradient light interference microscopy

Morphological imaging of 3D cultured kidney mesangial cells ...

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Digital Holography and Three-Dimensional Imaging, DH 2024

作者： Butola, Ankit Ghosh, Biswajoy Park, Jaena Kwon, Minsung De la Cadena, Alejandro Mukherjee, Sudipta Bhargava, Rohit Boppart, Stephen A. Agarwal, Krishna Department of Physics and Technology UiT The Arctic University of Norway Tromsø9037 Norway 405 N. Mathews Ave. UrbanaIL61801 United States Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign 405 N Mathews Avenue UrbanaIL61801 United States Department of Bioengineering University of Illinois at Urbana-Champaign 1406 W Green Street UrbanaIL61801 United States Department of Chemical and Biomolecular Engineering University of Illinois Urbana-Champaign UrbanaIL United States Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign UrbanaIL United States Cancer Center at Illinois University of Illinois Urbana-Champaign UrbanaIL United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign 306 N Wright Street UrbanaIL61801 United States Carle Illinois College of Medicine University of Illinois at Urbana-Champaign 506 S Mathews Avenue UrbanaIL61801 United States

We present a gradient light interference microscopy system to visualize 3D quantitative imaging of kidney mesangial cells. We used the system to obtain the morphology of 3D cultured kidney cells of thickness 200 µ... 详细信息

关键词： Light interference

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Q-learning based Path Planning Method for UAVs using Priority Shifting

Q-learning based Path Planning Method for UAVs using Priorit...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Kevin B. de Carvalho Iure Rosa L. de Oliveira Daniel K. D. Villa Alexandre G. Caldeira Má rio Sarcinelli-Filho Alexandre S. Brandã o Department of Electrical Engineering Graduate Program in Computer Science Nucleus of Specialization in Robotics Universidade Federal de Vi&#x00E7 osa Vi&#x00E7 osa MG Brazil Department of Electrical Engineering Graduate Program in Electrical Engineering Universidade Federal do Esp&#x00ED rito Santo Vit&#x00F3 ria ES Brazil

ISBN: (数字)9781665405935

ISBN: (纸本)9781665405942

Path planning is a crucial part of autonomous navigation when regarding autonomous aerial vehicles, often demanding different priorities such as the length, safety or energy consumption. Dynamic programming and geometric methods have been applied to solve this problem, but in recent years, more work has been developed using artificial intelligence approaches, such as reinforcement learning. In this paper we propose an offline path planning method for static environments using Q-learning. An optimal policy is found weighting three important factors: path length, safety and energy consumption. Due to a well balanced exploring/exploiting ratio, the proposed method can lead the agent to the desired destination starting from anywhere in the map. Simulations are done in different scenarios to address the performance of the proposed method and it showcased that the algorithm is able to find feasible paths in each scenario while regarding different set of priorities.

关键词： Energy consumption Q-learning Atmospheric modeling Autonomous aerial vehicles Path planning Safety Dynamic programming

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Subwavelength-Engineered Antislot Photonic Crystals in a Silicon Photonics Foundry for On-chip Communications

Subwavelength-Engineered Antislot Photonic Crystals in a Sil...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Kellen P. Arnold Joshua A. Allen Sami I. Halimi Landen D. Ryder Francis O. Afzal Yusheng Bian Abdelsalam Aboketaf Takako Hirokawa Kevin Dezfulian Michal Rakowski Rod Augur Karen Nummy Sharon M. Weiss Interdisciplinary Materials Science Program Vanderbilt University Nashville TN USA Department of Electrical and Computer Engineering Vanderbilt University Nashville TN USA NASA Goddard Space Flight Center Greenbelt MD US GlobalFoundries Malta NY USA

ISBN: (纸本)9798350369311

We report scalable foundry fabrication and characterization of photonic crystal nanobeam waveguides incorporating subwavelength-scale dielectric antislot unit cells. This work enables enhanced light-matter interaction and three-fold improvement in $\mathrm{V}_{\pi}\mathrm{L}$ when incorporated in Mach-Zehnder modulators.

关键词： Mach-Zehnder interferometers Optical device fabrication Photonic crystals Waveguide lasers Lasers and electrooptics Electro-optic modulators Silicon photonics Foundries System-on-chip Electro-optical waveguides

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Comparative Analysis of Real-Time Multi-Person Pose Detection in electrical Industrial Safety Scenarios Using YOLOv8-Pose and OpenPose

Comparative Analysis of Real-Time Multi-Person Pose Detectio...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Shang-Te Tsai Zong-Rong Wu Pin-Hsuan Lin Chin-Hsiung Chen Wei Chien Yu-Cheng Chang School of Art and Design Sanming University Fujian China Department of Technical and Vocational Program in Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan Department of Business Administration National Central University Taoyuan Taiwan Department of Electrical Engineering New Taipei Municipal Tamsui Commercial Industrial Vocational Senior High School New Taipei Taiwan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

We reviewed the application of modern technology for rapid and accurate multi-person real-time pose detection in the hazardous field of electrical engineering. We focused on two leading pose detection technologies: YOLOv8 and OpenPose. To optimize performance, we integrated these two techniques into the LSTM model for training and investigated frame rates and accuracy.

关键词： Training Accuracy Microcontrollers Neural networks Real-time systems Safety Internet of Things

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