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14th International Conference on Digital Image Processing, ICDIP 2022

作者： Tesema, Fiseha B. Lin, Zheyuan Zhu, Shiqiang Song, Wei Gu, Jason Wu, Hong Interdisciplinary Innovation Research Institute Zhejiang Lab Kechuang Avenue Zhongtai Sub-District Yuhang District Hangzhou China Electrical and Computer Engineering Dalhousie University HalifaxNSB3J 2X4 Canada School of Computer Science and Engineering University of Electronic Science and Technology of China Shahe Campus:No.4 Section 2 North Jianshe Road Chengdu China Ocean College Zhejiang University 866 Yuhangtang Rd Xihu Hangzhou China

ISBN: (纸本)9781510657564

Active speaker detection plays a vital role in human-machine interaction. Recently, a few end-to-end audiovisual frameworks emerged. However, these models' inference time was not explored and are not applicable for real-time applications due to their complexity and large input size. In addition, they explored a similar feature extraction strategy that employs the ConvNet on audio and visual inputs. This work presents a novel two-stream end-to-end framework fusing features extracted from images via VGG-M with raw Mel Frequency Cepstrum Coefficients features extracted from the audio waveform. The network has two BiGRU layers attached to each stream to handle each stream's temporal dynamic before fusion. After fusion, one BiGRU layer is attached to model the joint temporal dynamics. The experiment result on the AVA-ActiveSpeaker dataset indicates that our new feature extraction strategy shows more robustness to noisy signals and better inference time than models that employed ConvNet on both modalities. The proposed model predicts within 44.41 ms, which is fast enough for real-time applications. Our best-performing model attained 88.929% accuracy, nearly the same detection result as state-of-the-art work. © 2022 SPIE.

关键词： Feature extraction

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Towards Modular and Accessible AUV Systems

Towards Modular and Accessible AUV Systems

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Symposium on Autonomous Underwater Vehicle Technology (AUV)

作者： Mingxi Zhou Farhang Naderi Yuewei Fu Tony Jacob Lin Zhao Manavi Panjnani Chengzhi Yuan William McConnell Emir Cem Gezer Graduate School of Oceanography University of Rhode Island Narragansett RI USA Department of Ocean Engineering University of Rhode Island Narragansett RI USA Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Department of Mechanical Industrial and Systems Engineering University of Rhode Island Kingston RI USA Department of Marine Technology Norwegian University of Science and Technology Norway

ISBN: (数字)9798331542238

ISBN: (纸本)9798331542245

This paper reports the development of a new open-access modular framework, called Marine Vehicle Packages (MVP), for Autonomous Underwater Vehicles. The framework consists of both software and hardware designs allowing easy construction of AUV for research with increased customizability and sufficient payload capacity. This paper will present the scalable hardware system design and the modular software design architecture. New features, such as articulated thruster integration and high-level Graphic User Interface will be discussed. Both simulation and field experiments results are shown to highlight the performance and compatibility of the MVP.

关键词： Autonomous underwater vehicles Software design Attitude control computer architecture Hardware Software Marine vehicles System analysis and design Payloads Graphical user interfaces

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Private adaptive gradient methods for convex optimization

arXiv

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arXiv 2021年

作者： Asi, Hilal Duchi, John Fallah, Alireza Javidbakht, Omid Talwar, Kunal Department of Electrical Engineering Stanford University Departments of Electrical Engineering and Statistics Stanford University Apple Department of Electrical Engineering & Computer Science Massachusetts Institute of Technology

We study adaptive methods for differentially private convex optimization, proposing and analyzing differentially private variants of a Stochastic Gradient Descent (SGD) algorithm with adaptive stepsizes, as well as the AdaGrad algorithm. We provide upper bounds on the regret of both algorithms and show that the bounds are (worst-case) optimal. As a consequence of our development, we show that our private versions of AdaGrad outperform adaptive SGD, which in turn outperforms traditional SGD in scenarios with non-isotropic gradients where (non-private) Adagrad provably outperforms SGD. The major challenge is that the isotropic noise typically added for privacy dominates the signal in gradient geometry for high-dimensional problems;approaches to this that effectively optimize over lower-dimensional subspaces simply ignore the actual problems that varying gradient geometries introduce. In contrast, we study non-isotropic clipping and noise addition, developing a principled theoretical approach;the consequent procedures also enjoy significantly stronger empirical performance than prior approaches. Copyright © 2021, The Authors. All rights reserved.

关键词： Convex optimization

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A self-supervised CNN for image watermark removal

arXiv

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arXiv 2024年

作者： Tian, Chunwei Zheng, Menghua Jiao, Tiancai Zuo, Wangmeng Zhang, Yanning Lin, Chia-Wen the School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China the School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China the Peng Cheng Laboratory Laboratory Guangdong Shenzhen518055 China the School of Computer Science Northwestern Polytechnical University the National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China the Department of Electrical Engineering the Institute of Communications Engineering National Tsing Hua University Hsinchu300 Taiwan

Popular convolutional neural networks mainly use paired images in a supervised way for image watermark removal. However, watermarked images do not have reference images in the real world, which results in poor robustness of image watermark removal techniques. In this paper, we propose a self-supervised convolutional neural network (CNN) in image watermark removal (SWCNN). SWCNN uses a self-supervised way to construct reference watermarked images rather than given paired training samples, according to watermark distribution. A heterogeneous U-Net architecture is used to extract more complementary structural information via simple components for image watermark removal. Taking into account texture information, a mixed loss is exploited to improve visual effects of image watermark removal. Besides, a watermark dataset is conducted. Experimental results show that the proposed SWCNN is superior to popular CNNs in image watermark removal. Codes can be obtained at https://***/hellloxiaotian/SWCNN. Copyright © 2024, The Authors. All rights reserved.

关键词： Textures

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Wideband Reconfigurable Reflectarray based on Reflector-backed Second-order Bandpass Frequency Selective Surface

TechRxiv

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TechRxiv 2022年

作者： Li, Huan Qi, Xiaokang Zhou, Tianyi Xu, Zhiwei Denidni, Tayeb A. The Ocean College Zhejiang University Zhoushan316021 China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Montreal Canada

In this communication, a wideband reconfigurable reflectarray based on reflector-backed active second-order bandpass frequency selective surface (FSS) is presented. The reflector is composed of periodic short-circuited parallel plate waveguide (PPW) and the FSS is composed of stacked non-resonant metallic elements separated by thin dielectric substrates. By integrating microwave varactors in the capacitive layers of FSS, more than 270o continuous phase tunability is achieved within a factional bandwidth of 14%. A one-dimensional reflectarray prototype operating at C band is fabricated and measured. The experimental results show that it can achieve ±55o beam scanning coverage. Symmetric beam steering is observed due to the center-fed configuration. With advantages of low cost and simple structure, the proposed reflectarray can be potentially used in wideband wireless communication and radar systems. © 2022, CC BY.

关键词： Frequency selective surfaces

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A Study of Enclosed Magnetic Shielding Room by Simulation

A Study of Enclosed Magnetic Shielding Room by Simulation

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2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2020

作者： Xu, Zihan Zhang, Zhidan Wu, Bao Wang, Hai Kong, Xiangyan Wang, Meiling Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China College of Engineering Science and Technology Shanghai Ocean University Shanghai China

ISBN: (纸本)9781728152158

MSR (magnetically shielded room) is one of the most effective methods of shielding the magnetic field of the environment. The design of a cuboid enclosed magnetic shielding room (MSR), which mainly made up of permalloy and aluminum, is optimized by an electromagnetic simulation software called ANSYS Maxwell. And the effects of the magnetic shielding room's parameters on the shielding effectiveness (SE) are studied. As far as we know, the presented results show that SE is inversely proportional to the size of the shielding space, but proportional to the thickness of each layer, and the interval between two layers exists an optimal value. An equivalent circuit model demonstrates the corresponding interpretations of the above results. © 2020 IEEE.

关键词： Magnetic shielding

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Control of Transverse Motion for Quantum Gates on Individually Addressed Atomic Qubits

引用

PRX Quantum 2022年第1期3卷 010334-010334页

作者： M. Cetina L.N. Egan C. Noel M.L. Goldman D. Biswas A.R. Risinger D. Zhu C. Monroe Joint Quantum Institute Center for Quantum Information and Computer Science and Departments of Physics and Electrical and Computer Engineering University of Maryland College Park Maryland 20742 USA Department of Physics Duke University Durham North Carolina 27708 USA Department of Electrical and Computer Engineering Duke University Durham North Carolina 27708 USA IonQ Inc 4505 Campus Drive College Park Maryland 20740 USA

Individual trapped atomic qubits represent one of the most promising technologies to scale quantum computers, owing to their low idle errors and the ability to implement a full set of reconfigurable gate operations via focused optical fields. However, the fidelity of quantum gate operations can be limited by weak confinement of the atoms transverse to the laser. We present measurements of this effect by performing individually addressed entangling gates in chains of up to 25 trapped atomic ions. We present a semiclassical model that accurately describes the observed decoherence from the residual heating of the ions caused by noisy electric fields. We suppress these effects by cotrapping ancilla ions for sympathetically cooling the qubit ions throughout a quantum circuit.

关键词： Atom & ion cooling Atom & ion trapping & guiding Entanglement production Quantum gates Quantum information with trapped ions Trapped ions Coherent control Rabi model Semiclassical physics

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Achieving Risk Control in Online Learning Settings

arXiv

引用

arXiv 2022年

作者： Feldman, Shai Ringel, Liran Bates, Stephen Romano, Yaniv Department of Computer Science Technion IIT Haifa Israel Departments of Statistics and of EECS UC Berkeley CA United States Department of Electrical and Computer Engineering Technion IIT Haifa Israel

To provide rigorous uncertainty quantification for online learning models, we develop a framework for constructing uncertainty sets that provably control risk—such as coverage of confidence intervals, false negative rate, or F1 score—in the online setting. This extends conformal prediction to apply to a larger class of online learning problems. Our method guarantees risk control at any user-specified level even when the underlying data distribution shifts drastically, even adversarially, over time in an unknown fashion. The technique we propose is highly flexible as it can be applied with any base online learning algorithm (e.g., a deep neural network trained online), requiring minimal implementation effort and essentially zero additional computational cost. We further extend our approach to control multiple risks simultaneously, so the prediction sets we generate are valid for all given risks. To demonstrate the utility of our method, we conduct experiments on real-world tabular time-series data sets showing that the proposed method rigorously controls various natural risks. Furthermore, we show how to construct valid intervals for an online image-depth estimation problem that previous sequential calibration schemes cannot handle. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Large-Scale Distributed Multidisciplinary Design Optimization of the NASA Lift-Plus-Cruise Air Taxi Concept

Large-Scale Distributed Multidisciplinary Design Optimizatio...

引用

AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： van Schie, Sebastiaan P. C. Ruh, Marius L. Fletcher, Andrew Warner, Michael Sperry, Mark Scotzniovsky, Luca Orndorff, Nicholas C. Xiang, Ru Yan, Jiayao Zhao, Han Krokowski, Joshua J. Chen, J.S. Sarojini, Darshan Gill, Hyunjune Lee, Seongkyu Tagg, Andrew Anderson, Ryan Green, Eric Joseph, Cibin Ning, Andrew Cheng, Zeyu Cao, Zhi Mi, Chunting Guibert, Alexandre T. R. Cronk, Ashley Kim, H. Alicia Meng, Y. Shirley Silva, Christopher Hwang, John T. University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Department of Mechanical and Aerospace Engineering University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Department of Structural Engineering University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Aerospace and Ocean Engineering Department Virginia Tech 1600 Innovation Dr BlacksburgVA24060 United States Department of Mechanical and Aerospace Engineering University of California Davis 1 Shields Avenue DavisCA95616 United States Mechanical Engineering Brigham Young University ProvoUT84602 United States Department of Electrical & Computer Engineering San Diego State University 5500 Campanile Dr San Diego92182 United States Structural Engineering Department University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Materials Science & Engineering Program University of California San Diego 9500 Gilman Dr La Jolla CA92093 United States Pritzker School of Molecular Engineering The University of Chicago ChicagoIL60607 United States NASA Ames Research Center Moffett FieldCA94035 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Large-scale gradient-based Multidisciplinary Design Optimization (MDO) can aid in the exploration of high-dimensional design spaces for novel air vehicle concepts, thereby leading to more efficient and economic designs. This paper builds on past works where we demonstrated large-scale physics-based MDO capabilities and applied these to NASA’s lift-plus-cruise electric air taxi concept. We extend this comprehensive mid-fidelity system-level optimization problem with high(er)-fidelity subsystem-level optimizations of various aircraft systems and mission phases, with the aim of further enhancing the accuracy and scope of the aforementioned system-level optimization problem: 1) Power minimization during the transition mission phase;2) electrical powertrain topology optimization;3) Cell chemistry modeling and thermomechanical battery pack topology optimization;4) Shell-based coupled aero-elastic wing structure optimization. An Analytical Target Cascading-like distributed MDO architecture allows us to couple the system- and subsystem-level optimizations in order to arrive at a consistent and feasible design. We find that the system-level design is most heavily impacted by the reduced battery pack-level energy densities that stem from power peaks in the mission profile. These power peaks seem to result from the inefficient lift rotor blade designs that are needed to satisfy noise constraints. We draw conclusions based on trends in our results and give recommendations for future MDO studies of electrical air taxi vehicle concepts. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Shape optimization

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Improved Sample Complexity Bounds for Branch-And-Cut 28

Improved Sample Complexity Bounds for Branch-And-Cut

引用

28th International Conference on Principles and Practice of Constraint Programming, CP 2022

作者： Balcan, Maria-Florina Prasad, Siddharth Sandholm, Tuomas Vitercik, Ellen Computer Science and Machine Learning Departments Carnegie Mellon University PittsburghPA United States Computer Science Department Carnegie Mellon University PittsburghPA United States Optimized Markets Inc. PittsburghPA United States Strategic Machine Inc. PittsburghPA United States Strategy Robot Inc. PittsburghPA United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA United States

ISBN: (纸本)9783959772402

The branch-and-cut algorithm for integer programming has a wide variety of tunable parameters that have a huge impact on its performance, but which are challenging to tune by hand. An increasingly popular approach is to use machine learning to configure these parameters based on a training set of integer programs from the application domain. We bound how large the training set should be to ensure that for any configuration, its average performance over the training set is close to its expected future performance. Our guarantees apply to parameters that control the most important aspects of branch-and-cut: node selection, branching constraint selection, and cut selection, and are sharper and more general than those from prior research. © Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm, and Ellen Vitercik.

关键词： Integer programming

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