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PbS Colloidal Quantum Dots/ZnO/Si Hybrid Photodiode with Various Reverse Bias Voltages

IEEJ Transactions on Sensors and Micromachines 2022年第1期142卷 8-12页

作者： Miyazawa, Norihiro Wang, Haibin Usami, Naoto Kubo, Takaya Segawa, Hiroshi Mita, Yoshio Higo, Akio Department of Electrical Engineering and Information Systems The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan 3-1-1 Yoshinodai Chuo-ku Sagamihara252-5210 Japan Systems Design Lab School of Engineering The University of Tokyo 2-11-16 Yayoi Bunkyo-ku Tokyo113-0032 Japan

We have realized an infrared (IR) photodiode with silicon for the future beyond-VLSI photonics. Colloidal quantum dot (CQD) integration with Si is a simple process. We previously reported a PbS CQD/ZnO/Si hybrid IR photodiode. The device had an absorption peak at 1230 nm. To improve the quantum efficiency, we investigated the dose levels of Si substrates and improved the external quantum efficiency (EQE) by using a structural approach, such as using a higher doping level of Si. We achieved a 1230 nm absorption peak with an ITO/Au/PbS-EDT/PbS-I CQDs/ZnO/Si hybrid IR photodiode with an EQE close to 7%, at a reverse bias of −0.5 V. © 2022 The Institute of Electrical Engineers of Japan.

关键词： Lead compounds

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Decision-based black-box attack against vision transformers via patch-wise adversarial removal 22

Decision-based black-box attack against vision transformers ...

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Proceedings of the 36th International Conference on Neural information Processing systems

作者： Yucheng Shi Yahong Han Yu-an Tan Xiaohui Kuang College of Intelligence and Computing and Tianjin Key Lab of Machine Learning Tianjin University Tianjin China and Engineering Research Center of City Intelligence and Digital Governance Ministry of Education of the People's Republic of China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China National Key Laboratory of Science and Technology on Information System Security Beijing China

ISBN: (纸本)9781713871088

Vision transformers (ViTs) have demonstrated impressive performance and stronger adversarial robustness compared to Convolutional Neural Networks (CNNs). On the one hand, ViTs' focus on global interaction between individual patches reduces the local noise sensitivity of images. On the other hand, the neglect of noise sensitivity differences between image regions by existing decision-based attacks further compromises the efficiency of noise compression, especially for ViTs. Therefore, validating the black-box adversarial robustness of ViTs when the target model can only be queried still remains a challenging problem. In this paper, we theoretically analyze the limitations of existing decision-based attacks from the perspective of noise sensitivity difference between regions of the image, and propose a new decision-based black-box attack against ViTs, termed Patch-wise Adversarial Removal (PAR). PAR divides images into patches through a coarse-to-fine search process and compresses the noise on each patch separately. PAR records the noise magnitude and noise sensitivity of each patch and selects the patch with the highest query value for noise compression. In addition, PAR can be used as a noise initialization method for other decision-based attacks to improve the noise compression efficiency on both ViTs and CNNs without introducing additional calculations. Extensive experiments on three datasets demonstrate that PAR achieves a much lower noise magnitude with the same number of queries.

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The Main Progress of Perovskite Solar Cells in 2020-2021

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Nano-Micro Letters 2021年第10期13卷 113-130页

作者： Tianhao Wu Zhenzhen Qin Yanbo Wang Yongzhen Wu Wei Chen Shufang Zhang Molang Cai Songyuan Dai Jing Zhang Jian Liu Zhongmin Zhou Xiao Liu Hiroshi Segawa Hairen Tan Qunwei Tang Junfeng Fang Yaowen Li Liming Ding Zhijun Ning Yabing Qi Yiqiang Zhang Liyuan Han State Key Laboratory of Metal Matrix Composites School of Material Science and EngineeringShanghai Jiao Tong UniversityShanghai 200240People’s Republic of China Key Laboratory for Advanced Materials and Feringa Nobel Prize Scientist Joint Research Centre Shanghai Key Laboratory of Functional Materials ChemistryJoint International Research Laboratory of Precision Chemistry and Molecular EngineeringSchool of Chemistry and Molecular EngineeringEast China University of Science and TechnologyMeilong Road 130Shanghai 200237People’s Republic of China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyLuoyu Road 1037Wuhan 430074People’s Republic of China College of Materials Science and Engineering Nanjing University of Science and TechnologyNanjing 210094People’s Republic of China Beijing Key Laboratory of Novel Thin-Film Solar Cells and State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources North China Electric Power UniversityBeijing 102206People’s Republic of China Department of Microelectronic Science and Engineering Ningbo UniversityZhejiang 315211People’s Republic of China College of Chemical Engineering Jiangsu Key Lab of Biomass-Based Green Fuels and ChemicalsNanjing Forestry UniversityNanjing 210037People’s Republic of China College of Chemistry and Molecular Engineering Qingdao University of Science and TechnologyQingdao 266042People’s Republic of China Special Division of Environmental and Energy Science Komaba Organization for Educational Excellence(KOMEX)College of Arts and SciencesUniversity of TokyoTokyo 153-8902Japan National Laboratory of Solid State Microstructures Jiangsu Key Laboratory of Artificial Functional MaterialsCollege of Engineering and Applied SciencesNanjing UniversityNanjing 210093People’s Republic of China College of Information Science and Technology Jinan UniversityGuangzhou 510632People’s Republic of China School of Physics and Electronic Science Engineering Research Center of Nan

Perovskite solar cells(PSCs)emerging as a promising photovoltaic technology with high efficiency and low manufacturing cost have attracted the attention from all over the *** the efficiency and stability of PSCs have increased steadily in recent years,and the research on reducing lead leakage and developing eco-friendly lead-free perovskites pushes forward the commercialization of PSCs step by *** review summarizes the main progress of PSCs in 2020 and 2021 from the aspects of efficiency,stability,perovskite-based tandem devices,and lead-free ***,a brief discussion on the development of PSC modules and its challenges toward practical application is provided.

关键词： Perovskite solar cells Stability Solar module Perovskite-based tandem devices Lead-free perovskite

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A Generic Fundus Image Enhancement Network Boosted by Frequency Self-supervised Representation Learning

arXiv

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arXiv 2023年

作者： Li, Heng Liu, Haofeng Fu, Huazhu Xu, Yanwu Shu, Hai Niu, Ke Hu, Yan Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Singapore School of Future Technology South China University of Technology Guangzhou China Pazhou Lab Guangzhou China Department of Biostatistics School of Global Public Health New York University New YorkNY United States Computer School Beijing Information Science and Technology University Beijing China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

Fundus photography is prone to suffer from image quality degradation that impacts clinical examination performed by ophthalmologists or intelligent systems. Though enhancement algorithms have been developed to promote fundus observation on degraded images, high data demands and limited applicability hinder their clinical deployment. To circumvent this bottleneck, a generic fundus image enhancement network (GFE-Net) is developed in this study to robustly correct unknown fundus images without supervised or extra data. Levering image frequency information, self-supervised representation learning is conducted to learn robust structure-aware representations from degraded images. Then with a seamless architecture that couples representation learning and image enhancement, GFE-Net can accurately correct fundus images and meanwhile preserve retinal structures. Comprehensive experiments are implemented to demonstrate the effectiveness and advantages of GFE-Net. Compared with state-of-the-art algorithms, GFE-Net achieves superior performance in data dependency, enhancement performance, deployment efficiency, and scale generalizability. Follow-up fundus image analysis is also facilitated by GFE-Net, whose modules are respectively verified to be effective for image enhancement. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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MVI-Wise GAN: Synthetic MRI to Improve Microvascular Invasion Prediction in Hepatocellular Carcinoma

MVI-Wise GAN: Synthetic MRI to Improve Microvascular Invasio...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Jing Liu Yulin Yang Yang Ai Titinunt Kitrungrotsakul Fang Wang Lanfen Lin Ruofeng Tong Yen-Wei Chen Jingsong Li Zhejiang Lab Research Center for Healthcare Data Science Hangzhou China College of Information Science and Engineering Ritsumeikan University Shiga Japan Department of Radiology Sir Run Run Shaw Hospital Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China

MRI is crucial for the diagnosis of HCC patients, especially when combined with CT images for MVI prediction, richer complementary information can be learned. Many studies have shown that whether hepatocellular carcinoma is accompanied by vascular invasion can be evidenced by imaging examinations such as CT or MR, so they can be used as a multimodal joint prediction to improve the prediction accuracy of MVI. However, it is high-risk, time-consuming and expensive in current clinical diagnosis due to the use of gadolinium-based contrast agent (CA) injection. If MRI could be synthesized without CA injection, there is no doubt that it would greatly optimize the diagnosis. Based on this, this paper proposes a high-quality image synthesis network, MVI-Wise GAN, that can be used to improve the prediction of microvascular invasion in HCC. It starts from the underlying imaging perspective, introduces K-space and feature-level constraints, and combines three related networks (an attention-aware generator, a convolutional neural network-based discriminator and a region-based convolutional neural network detector) Together, precise tumor region detection by synthetic tumor-specific MRI. Accurate MRI synthesis is achieved through backpropagation, the feature representation and context learning of HCC MVI are enhanced, and the performance of loss convergence is improved through residual learning. The model was tested on a dataset of 256 subjects from Run Run Shaw Hospital of Zhejiang University. Experimental results and quantitative evaluation show that MVI-Wise GAN achieves high-quality MRI synthesis with a tumor detection accuracy of 92.3%, which is helpful for the clinical diagnosis of liver tumor MVI.

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Extracting a justification for OWL ontologies by critical axioms

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Frontiers of Computer science 2020年第4期14卷 55-64页

作者： Yuxin YE Xianji CUI Dantong OUYANG College of Computer Science and Technology Jilin UniversityChangchun130012China Key Lab of Symbolic Computation and Knowledge Engineering(Jilin University) Ministry of EducationChangchun130012China College of Information and Communication Engineering Dalian Minzu UniversityDalian116600China

Extracting justifications for web ontology language(OWL)ontologies is an important mission in ontology *** this paper,we focus on black-box techniques which are based on ontology *** creating a recursive expansion procedure,all elements which are called critical axioms in the justification are explored one by *** this detection procedure,an axiom selection function is used to avoid testing irrelevant *** addition,an incremental reasoning procedure has been proposed in order to substitute series of standard reasoning tests *** is implemented by employing a pseudo model to detect“obvious”satisfiability *** experimental results show that our proposed strategy for extracting justifications for OWL ontologies by adopting incremental expansion is superior to traditional Black-box methods in terms of efficiency and performance.

关键词： description logics automated reasoning ontology engineering justification

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Evolutionary Morphology Towards Overconstrained Locomotion via Large-Scale, Multi-Terrain Deep Reinforcement Learning

Evolutionary Morphology Towards Overconstrained Locomotion v...

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ASME/IFToMM International Conference on Reconfigurable Mechanisms and Robots, ReMAR

作者： Yenan Chen Chuye Zhang Pengxi Gu Jianuo Qiu Jiayi Yin Nuofan Qiu Guojing Huang Bangchao Huang Zishang Zhang Hui Deng Wei Zhang Fang Wan Chaoyang Song Department of Mechanical and Energy Engineering Southern University of Science and Technology (SUSTech) Shenzhen China SUSTech School of Design Department of System Design and Intelligent Manufacturing SUSTech. Shenzhen Key Lab of Intelligent Robotics & Flexible Manufacturing Systems SUSTech. SUSTech. SUSTech Institute of Robotics

ISBN: (数字)9798350395969

ISBN: (纸本)9798350395976

While the animals' Fin-to-Limb evolution has been well-researched in biology, such morphological transformation remains under-adopted in the modern design of advanced robotic limbs. This paper investigates a novel class of overconstrained locomotion from a design and learning perspective inspired by evolutionary morphology, aiming to integrate the concept of ‘intelligent design under constraints’–hereafter referred to as constraint-driven design intelligence–in developing modern robotic limbs with superior energy efficiency. We propose a 3D-printable design of robotic limbs parametrically reconfigurable as a classical planar 4-bar linkage, an overconstrained Bennett linkage, and a spherical 4- bar linkage. These limbs adopt a co-axial actuation, identical to the modern legged robot platforms, with the added capability of upgrading into a wheel-legged system. Then, we implemented a large-scale, multi-terrain deep reinforcement learning framework to train these reconfigurable limbs for a comparative analysis of overconstrained locomotion in energy efficiency. Results show that the overconstrained limbs exhibit more efficient locomotion than planar limbs during forward and sideways walking over different terrains, including floors, slopes, and stairs, with or without random noises, by saving at least 22% mechanical energy in completing the traverse task, with the spherical limbs being the least efficient. It also achieves the highest average speed of 0.85m/s on flat terrain, which is 20% faster than the planar limbs. This study paves the path for an exciting direction for future research in overconstrained robotics leveraging evolutionary morphology and reconfigurable mechanism intelligence when combined with state-of-the-art methods in deep reinforcement learning.

关键词： Couplings Legged locomotion Technological innovation Education Noise Morphology Stairs

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EventTrojan: Manipulating Non-Intrusive Speech Quality Assessment via Imperceptible Events

arXiv

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arXiv 2023年

作者： Ren, Ying Shen, Kailai Ye, Zhe Yan, Diqun Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Shandong China Juphoon System Software Co. Ltd Ningbo China

Non-Intrusive speech quality assessment (NISQA) has gained significant attention for predicting speech’s mean opinion score (MOS) without requiring the reference speech. Researchers have gradually started to apply NISQA to various practical scenarios. However, little attention has been paid to the security of NISQA models. Backdoor attacks represent the most serious threat to deep neural networks (DNNs) due to the fact that backdoors possess a very high attack success rate once embedded. However, existing backdoor attacks assume that the attacker actively feeds samples containing triggers into the model during the inference phase. This is not adapted to the specific scenario of NISQA. And current backdoor attacks on regression tasks lack an objective metric to measure the attack performance. To address these issues, we propose a novel backdoor triggering approach (EventTrojan) that utilizes an event during the usage of the NISQA model as a trigger. Moreover, we innovatively provide an objective metric for backdoor attacks on regression tasks. Extensive experiments on four benchmark datasets demonstrate the effectiveness of the EventTrojan attack. Besides, it also has good resistance to several defense methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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A 3D Wideband Non-Stationary GBSM for RIS-Assisted Communications in Outdoor Scenarios 4

A 3D Wideband Non-Stationary GBSM for RIS-Assisted Communica...

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4th IEEE International Conference on Electronics and Communication engineering, ICECE 2021

作者： Yang, Yinghui Zhang, Wensheng Sun, Jian Wang, Cheng-Xiang Shandong Provincial Key Lab of Wireless Communication Technologies School of Information Science and Engineering Shandong University Qingdao China Shandong Provincial Key Lab of Wireless Communication Technologies Shandong University Qingdao China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing University of Aeronautics and Astronautics Nanjing China National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories Nanjing China

ISBN: (纸本)9781728194226

In this paper, we propose a novel three-dimensional (3D) wideband non-stationary geometry-based stochastic model (GBSM) for reconfigurable intelligent surface (RIS)-assisted communications in outdoor scenarios. To mimic a more realistic outdoor environment, we introduce a twin-cluster model and consider the evolutions of clusters in time and antenna domain. The proposed model is a combination of transmitter (Tx)-RISreceiver (Rx) reflection components and Tx-Rx components. Based on the proposed model, some key statistical properties like power delay profile (PDP), achievable rate, temporal autocorrelation functions (ACFs) are derived and analyzed. The simulation results demonstrate the correctness of the proposed model and the non-stationary property. Although imperfections in phase knowledge acquirement are existed in real world, the use of the RIS still makes appreciable improvements in the communication system performance according to the simulations. ©2021 IEEE

关键词： Antennas

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EMAHA-DB1: A New Upper Limb sEMG Dataset for Classification of Activities of Daily Living

arXiv

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arXiv 2023年

作者： Karnam, Naveen Kumar Turlapaty, Anish Chand Dubey, Shiv Ram Gokaraju, Balakrishna The Biosignal Analysis Lab The Indian Institute of Information Technology A.P. Sri City India The Computer Vision and Biometrics Laboratory Indian Institute of Information Technology U.P. Allahabad Prayagraj211015 India Department of Computational Data Science An Engineering North Carolina A and T State University GreensboroNC United States

In this paper, we present electromyography analysis of human activity - database 1 (EMAHA-DB1), a novel dataset of multi-channel surface electromyography (sEMG) signals to evaluate the activities of daily living (ADL). The dataset is acquired from 25 able-bodied subjects while performing 22 activities categorised according to functional arm activity behavioral system (FAABOS) (3 - full hand gestures, 6 - open/close office draw, 8 - grasping and holding of small office objects, 2 - flexion and extension of finger movements, 2 - writing and 1 - rest). The sEMG data is measured by a set of five Noraxon Ultium wireless sEMG sensors with Ag/Agcl electrodes placed on a human hand. The dataset is analyzed for hand activity recognition classification performance. The classification is performed using four state-of-the-art machine learning classifiers, including Random Forest (RF), Fine K-Nearest Neighbour (KNN), Ensemble KNN (sKNN) and Support Vector Machine (SVM) with seven combinations of time domain and frequency domain feature sets. The state-of-the-art classification accuracy on five FAABOS categories is 83.21% by using the SVM classifier with the third order polynomial kernel using energy feature and auto regressive feature set ensemble. The classification accuracy on 22 class hand activities is 75.39% by the same SVM classifier with the log moments in frequency domain (LMF) feature, modified LMF, time domain statistical (TDS) feature, spectral band powers (SBP), channel cross correlation and local binary patterns (LBP) set ensemble. The analysis depicts the technical challenges addressed by the dataset. The developed dataset can be used as a benchmark for various classification methods as well as for sEMG signal analysis corresponding to ADL and for the development of prosthetics and other wearable robotics. Copyright © 2023, The Authors. All rights reserved.

关键词： Local binary pattern

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