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Conference on Lasers and Electro-Optics (CLEO)

作者： Haowen Shu Bitao Shen Lin Chang Yuansheng Tao Weiqiang Xie Ming Jin Andrew Netherton Xuguang Zhang Jun Qin Ruixuan Chen Shaohua Yu Xingjun Wang John. E. Bowers State Key Laboratory of Advanced Optical Communications System and Networks Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara USA Peng Cheng Laboratory Shenzhen China Frontiers Science Center for Nano-Optoelectronics Peking University Beijing China

We achieve 100 Gbps/Lane PAM4 transmission by employing an AlGaAsOI microcomb source and silicon modulators. With 20 parallel wavelength channels utilized at C-band, an aggregate data rate of 2 Tbit/s is achieved.

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： C-band Modulation Lasers and electrooptics Electro-optic modulators Data aggregation Silicon photonics

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Removing Image Artifacts From Scratched Lens Protectors

Removing Image Artifacts From Scratched Lens Protectors

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Yufei Wang Renjie Wan Wenhan Yang Bihan Wen Lap-Pui Chau Alex C. Kot School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Computer Science Hong Kong Baptist University Hong Kong Distributed High Performance Software Fundamental Research Laboratory Pengcheng National Lab China Department of Electronic and Information Engineering Hong Kong Polytechnic University Hong Kong

A protector is placed in front of the camera lens for mobile devices to avoid damage, while the protector itself can be easily scratched accidentally, especially for plastic ones. The artifacts appear in a wide variety of patterns, making it difficult to see through them clearly. Removing image artifacts from the scratched lens protector is inherently challenging due to the occasional flare artifacts and the co-occurring interference within mixed artifacts. Though different methods have been proposed for some specific distortions, they seldom consider such inherent challenges. In our work, we consider the inherent challenges in a unified framework with two cooperative modules, which facilitate the performance boost of each other. We also collect a new dataset from the real world to facilitate training and evaluation purposes. The experimental results demonstrate that our method outperforms the baselines qualitatively and quantitatively. The code and datasets will be released at https://***/wyf0912/flare-removal

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An Ensemble Approach for Brain Tumor Segmentation and Synthesis

arXiv

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arXiv 2024年

作者： Rivera, Juampablo Heras E. Chopra, Agamdeep S. Ren, Tianyi Oswal, Hitender Pan, Yutong Sordo, Zineb Walters, Sophie Henry, William Mohammadi, Hooman Olson, Riley Rezayaraghi, Fargol Lam, Tyson Jaikanth, Akshay Kancharla, Pavan Ruzevick, Jacob Ushizima, Daniela Kurt, Mehmet Department of Mechanical Engineering University of Washington SeattleWA United States Paul G. Allen School of Computer Science University of Washington SeattleWA United States Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Bioengineering University of Washington SeattleWA United States Department of Electrical and Computer Engineering University of Washington SeattleWA United States Watauga High School BooneNC United States Department of Neurological Surgery University of Washington SeattleWA United States

The integration of machine learning in magnetic resonance imaging (MRI), specifically in neuroimaging, is proving to be incredibly effective, leading to better diagnostic accuracy, accelerated image analysis, and data-driven insights, which can potentially transform patient care. Deep learning models utilize multiple layers of processing to capture intricate details of complex data, which can then be used on a variety of tasks, including brain tumor classification, segmentation, image synthesis, and registration. Previous research demonstrates high accuracy in tumor segmentation using various model architectures, including nn-UNet and Swin-UNet. U-Mamba, which uses state space modeling, also achieves high accuracy in medical image segmentation. To leverage these models, we propose a deep learning framework that ensembles these state-of-the-art architectures to achieve accurate segmentation and produce finely synthesized images. © 2024, CC BY.

关键词： Image segmentation

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Design and exploration of vertically stacked complementary tunneling field-effect transistors

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Applied Physics Express 2024年第1期17卷 014001-014001页

作者： Thoti, Narasimhulu Li, Yiming Parallel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Biomedical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

The purpose of this letter is to study the design and explore vertically stacked complementary tunneling field-effect transistors (CTFETs) using CFET technology for emerging technology nodes. As a prior work, the CTFET’s device-level simulations are implemented and deliberated in strict compliance with the experimental settings. This work comprises the study of physical and DC analyses by scaling the p- to n-CTFET separation (D pn), being a significant factor in CFET/CTFET design for its process difficulty. By utilizing the 50% benefit in footprint, the work is further extended to CTFET static random access memory implementation and characterization with hold/read noise margin analysis. © 2023 The Author(s). Published on behalf of The Japan Society of Applied Physics by IOP Publishing Ltd

关键词： Si-Ge alloys

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Social Group Optimization Based Cluster Head Identification in Underwater Acoustic Sensor Networks 5

Social Group Optimization Based Cluster Head Identification ...

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5th International Conference on Future Networks and Distributed Systems: The Premier Conference on Smart Next Generation Networking Technologies, ICFNDS 2021

作者： Bhattacharya, Trilok Patel, Pratul Jasmine Jena, Junali Acharya, Biswaranjan Sagayam, K. Martin Abd Wahab, Mohd Helmy Ambar, Radzi School of Computer Science and Engineering Kalinga Institute of Industrial Technology Deemed to Be University Odisha Bhubaneswar India Karunya Institute of Technology and Sciences Department of ECE India Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Advanced Telecommunication Research Centre Malaysia Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronic Engineering Malaysia

ISBN: (纸本)9781450387347

Due to the advancements in ocean monitoring and underwater exploration, it has become increasingly important to incorporate a method of underwater sensor network that would be cost effective and energy efficient, reducing maintenance efforts and increasing longevity. Underwater Sensor Network applications based on acoustic, optical and RF communication are gaining popularity, especially acoustic communication in this area for applications such as offshore oil and gas extraction, military surveillance, mine detection, tsunami, and hurricane forecasts to name a few. One of the simpler architectural implementations of the sensor network is the 2D - Underwater Sensor Network, where a group of sensor networks are anchored underwater to the surface, with a cluster head nominated for communication within the cluster nodes and the surface buoyant nodes. There exist certain optimization algorithms that address the problem of optimal cluster head selection to minimize resources consumed and increase the lifespan of the cluster. One such technique that we use in our research is Social Group Optimization (SGO). It is based on the human behavior of learning and solving complex problems. This report uses this SGO technique to choose the cluster head among a set of clusters, in which number of solutions are taken and calculated to yield a robust solution where the sacrifices of time and complexity are minimized. © 2021 ACM.

关键词： Offshore oil well production

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Removing Image Artifacts From Scratched Lens Protectors

arXiv

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arXiv 2023年

作者： Wang, Yufei Wan, Renjie Yang, Wenhan Wen, Bihan Chau, Lap-Pui Kot, Alex C. School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Computer Science Hong Kong Baptist University Hong Kong Distributed High Performance Software Fundamental Research Laboratory Pengcheng National Lab China Department of Electronic and Information Engineering Hong Kong Polytechnic University Hong Kong

关键词： Machine learning

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Development of a Modular and Submersible Soft Robotic Arm and Corresponding Learned Kinematics Models

Development of a Modular and Submersible Soft Robotic Arm an...

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IEEE International Conference on Soft Robotics (RoboSoft)

作者： W. David Null James Menezes Y Z Department of Electrical and Computer Engineering University of Illinois at Urbana-Chamnaign Urbana IL USA Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Chamnaign Urbana IL USA Department of Nuclear Plasma and Radiological Engineering University of Illinois at Urbana-Chamnaign. Urbana IL USA Department of Nuclear Engineering and Radiological Sciences University of Michigan Ann Arbor MI USA

Many soft-body organisms found in nature flourish underwater. Similarly, soft robots are potentially well-suited for underwater environments partly because the problematic effects of gravity, friction, and harmonic oscillations are less severe underwater. However, it remains a challenge to design, fabricate, waterproof, model, and control underwater soft robotic systems. Furthermore, submersible robots usually do not have configurable components because of the need for sealed electronics and mechanical elements. This work presents the development of a modular and submersible soft robotic arm driven by hydraulic actuators which consists of mostly 3D printable parts and can be assembled or modified in a relatively short amount of time. Its modular design enables multiple shape configurations and easy swapping of soft actuators. As a first step to exploring machine learning control algorithms on this system, we also present preliminary forward and inverse kinematics models developed using deep neural networks.

关键词： Actuators Three-dimensional displays Shape Neural networks Kinematics Soft robotics Manipulators

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Six-Body and Eight-Body Exciton States in Monolayer WSe2

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Physical Review Letters 2022年第7期129卷 076801-076801页

作者： Dinh Van Tuan Su-Fei Shi Xiaodong Xu Scott A. Crooker Hanan Dery Department of Electrical and Computer Engineering University of Rochester Rochester New York 14627 USA Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute Troy New York 12180 USA Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy New York 12180 USA Department of Physics University of Washington Seattle Washington 98195 USA Department of Materials Science and Engineering University of Washington Seattle Washington 98195 USA National High Magnetic Field Laboratory Los Alamos New Mexico 87545 USA Department of Physics and Astronomy University of Rochester Rochester New York 14627 USA

In the archetypal monolayer semiconductor WSe2, the distinct ordering of spin-polarized valleys (low-energy pockets) in the conduction band allows for studies of not only simple neutral excitons and charged excitons (i.e., trions), but also more complex many-body states that are predicted at higher electron densities. We discuss magneto-optical measurements of electron-rich WSe2 monolayers and interpret the spectral lines that emerge at high electron doping as optical transitions of six-body exciton states (“hexcitons”) and eight-body exciton states (“oxcitons”). These many-body states emerge when a photoexcited electron-hole pair interacts simultaneously with multiple Fermi seas, each having distinguishable spin and valley quantum numbers. In addition, we explain the relations between dark trions and satellite optical transitions of hexcitons in the photoluminescence spectrum.

关键词： Excitons Magneto-optics Trions Transition metal dichalcogenides

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Graph Confidence Intercalibration Network for Intracranial Aneurysm Lesion Instance Segmentation in Dsa

SSRN

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SSRN 2024年

作者： Ye, Haili Mo, Yancheng Tang, Chen Liao, Mingqian Zhang, Xiaoqing Dai, Limeng Li, Baihua Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Computer Science Loughborough University LoughboroughLE11 3TU United Kingdom School of Ophthalmology and Optometry Eye Hospital Wenzhou Medical University Wenzhou325027 China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen People’s Hospital Neurosurgery Department Shenzhen518020 China China

Intracranial aneurysm (IA) lesion segmentation is significant for its treatment and prognosis. Although exiting deep network-based instance methods have good IA lesion segmentation results based on digital subtraction angiography (DSA) images, they still face great challenges with instance confidence bias and imprecise boundary segmentation, which may negatively affect IA diagnosis. To tackle these problems, this paper proposes a novel graph confidence intercalibration network (GCINet) to automatically segment IA lesions from DSA images. To be specific, we design a graph confidence intercalibration (GCI) module to mitigate instance confidence bias by dynamically adjusting their confidence distributions. At the same time, we propose an edge space perception (ESP) module to correct ambiguous segmentation boundaries. Extensive experiments on a clinical IA-DSA and a publicly available LiTS dataset demonstrate that our GCINet outperforms state-of-the-art methods. Additionally, visual analysis and ablation studies are provided to verify the effectiveness of each module in GCINet. © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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Searching to exploit memorization effect in learning with noisy labels 37

Searching to exploit memorization effect in learning with no...

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37th International Conference on Machine Learning, ICML 2020

作者： Yao, Quanming Yang, Hansi Han, Bo Niu, Gang Kwok, James T. Hong Kong Department of Electrical Engineering Tshinghua University China Department of Computer Science Hong Kong Baptist University Hong Kong RIKEN Center for Advanced Intelligence Project Japan Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9781713821120

Sample selection approaches are popular in robust learning from noisy labels. However, how to properly control the selection process so that deep networks can benefit from the memorization effect is a hard problem. In this paper, motivated by the success of automated machine learning (AutoML), we model this issue as a function approximation problem. Specifically, we design a domain-specific search space based on general patterns of the memorization effect and propose a novel Newton algorithm to solve the bi-level optimization problem efficiently. We further provide theoretical analysis of the algorithm, which ensures a good approximation to critical points. Experiments are performed on benchmark data sets. Results demonstrate that the proposed method is much better than the state-of-the-art noisy-label-learning approaches, and also much more efficient than existing AutoML algorithms. Copyright 2020 by the author(s).

关键词： Approximation algorithms

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