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Surface phase diagram of CsSnI3 from first-principles calculations

Physical Review Materials 2024年第9期8卷 093401页

作者： Kejia Li Chadawan Khamdang Mengen Wang Department of Electrical and Computer Engineering Materials Science and Engineering Program

CsSnI3 is widely studied as an environmentally friendly Pb-free perovskite material for optoelectronic device applications. To further improve material and device performance, it is important to understand the surface structures of CsSnI3. We generate surface structures with various stoichiometries, perform density functional theory calculations to create phase diagrams of the CsSnI3 (001), (110), and (100) surfaces, and determine the most stable surfaces under a wide range of Cs, Sn, and I chemical potentials. Under I-rich conditions, surfaces with Cs vacancies are stable, which lead to partially occupied surface states above the valence band maximum. Under I-poor conditions, we find the stoichiometric (100) surface to be stable under a wide region of the phase diagram, which does not have any surface states and can contribute to long charge-carrier lifetimes. Consequently, the I-poor (Sn-rich) conditions will be more beneficial to improve the device performance.

关键词： Electronic structure Surface reconstruction Surface states Perovskites Semiconductors Density functional theory

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Snapshot multispectral imaging using a diffractive optical network

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Light(Science & Applications) 2023年第5期12卷 789-808页

作者： Deniz Mengu Anika Tabassum Mona Jarrahi Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California NanoSystems Institute University of CaliforniaLos AngelesCA 90095USA

Multispectral imaging has been used for numerous applications in e.g.,environmental monitoring,aerospace,defense,and ***,we present a diffractive optical network-based multispectral imaging system trained using deep learning to create a virtual spectral filter array at the output image *** diffractive multispectral imager performs spatially-coherent imaging over a large spectrum,and at the same time,routes a pre-determined set of spectral channels onto an array of pixels at the output plane,converting a monochrome focal-plane array or image sensor into a multispectral imaging device without any spectral filters or image recovery ***,the spectral responsivity of this diffractive multispectral imager is not sensitive to input polarization *** numerical simulations,we present different diffractive network designs that achieve snapshot multispectral imaging with 4,9 and 16 unique spectral bands within the visible spectrum,based on passive spatially-structured diffractive surfaces,with a compact design that axially spans ~72λ_(m),where λ_(m) is the mean wavelength of the spectral band of ***,we experimentally demonstrate a diffractive multispectral imager based on a 3D-printed diffractive network that creates at its output image plane a spatially repeating virtual spectral filter array with 2×2=4 unique bands at terahertz *** to their compact form factor and computation-free,power-efficient and polarization-insensitive forward operation,diffractive multispectral imagers can be transformative for various imaging and sensing applications and be used at different parts of the electromagnetic spectrum where high-density and wide-area multispectral pixel arrays are not widely available.

关键词： spectrum spectral spatially

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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Distance Measurement of Tunnel Facilities for Monocular Camera-based Localization

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Journal of Institute of Control, Robotics and Systems 2023年第1期29卷 7-14页

作者： Kim, Jongkwang Kim, Hyoungrae Oh, Jangsub Li, Xingyou Jang, Kyeongjun Kim, Hakil Department of Electrical and Computer Engineering Inha University Korea Republic of Program in Future Vehicle Engineering Inha University Korea Republic of Department of Smart Mobility Engineering Inha University Korea Republic of

The GNSS signal used for vehicle localization exhibits a shaded area such as a tunnel. To accurately estimate the location in the shaded area, the accumulated position error must be compensated with information obtained from a separate sensor. When a monocular camera is selected as an additional sensor, the process of measuring the relative distance after detecting objects in a tunnel should be performed before using these data as an indicator. This paper presents a modified inverse perspective transformation model that projects image pixels to the plane parallel to the ground. The image pixels are projected to the plane accounting for the distance and the height of the facility in the tunnel. Then, through the transformation model, the distance of the facility in the tunnel can be measured using a single camera sensor. The Samsungsan Tunnel located on National Highway 110 in South Korea was chosen for the experiment. The estimated distance results by the proposed method were compared with the LiDAR measurements obtained real-time time by computing the position errors for the longitudinal and lateral directions, separately. This study demonstrates the possibility of estimating a distance with only a monocular camera for precise positioning even in a tunnel. © ICROS 2023.

关键词： Optical radar

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MUMR: Mask-UnMask Regions Framework for AMD Grades Classification Based on Inter-regional Interactions 27th

MUMR: Mask-UnMask Regions Framework for AMD Grades Classifi...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Abdelhalim, Ibrahim Elsharkawy, Mohamed Nadmid, Namuunaa Ghazal, Mohammed Mahmoud, Ali El-Baz, Ayman Department of Bioengineering University of Louisville LouisvilleKY United States Electrical and Computer Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031781032

The early diagnosis and effective treatment of age-related macular degeneration (AMD), a leading cause of vision impairment, is contingent upon accurate grading. This paper introduces a novel framework, named Mask-UnMask Regions (MUMR), designed to distinguish between normal retina, intermediate AMD, geographic atrophy (GA), and wet AMD using retinal fundus images, with the input resolution standardized to 1024×1024 pixels. The framework begins by downscaling images to a quarter of their size using a Preserving High-Frequency Information (PHFI) module, which maintains critical details essential for furthure analysis. Furthermore, we developed a simple, lightweight, yet effective ResNet-like network for efficient feature extraction and introduced a Region Interaction (RI) module, which consists of Adaptive Mask and UnMask Sub-Modules. This module identifies significant regions while reconstructing the insignificant ones using a direction-constrained self-attention mechanism to ensure the learning of global structural cues of AMD grades. The proposed method was evaluated on a dataset of 864 retinal fundus images. Our model consistently achieves superior results compared to other state-of-the-art models, with mean accuracy, mean F1-score, and mean Cohen’s Kappa of 92.55%, 92.59%, and 89.97%, respectively. Additionally, we demonstrate that these results are statistically significant compared to other models based on F1-score, indicating that our proposed framework achieves robust and improved AMD grading performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ophthalmology

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A ShuffleNet Architecture-Based Multi-Layer Feature Scaling and Fusion Approach for Underwater Net Cage Damage Detection in Aquaculture

A ShuffleNet Architecture-Based Multi-Layer Feature Scaling ...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Huang, Xiang-Rui Chen, Yi-Cheng Chen, Liang-Bi Hu, Wu-Chih National Penghu University of Science and Technology Master Program in Electrical Engineering and Computer Science Department of Electrical Engineering Penghu Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan

ISBN: (纸本)9798331530839

In offshore aquaculture operations, personnel equipped with diving gear are often necessary to inspect the underwater net cages for damage, particularly on the sea floor. This manual inspection process is time-consuming and complex. To overcome this problem, this paper proposes a computer vision solution for identifying damage in underwater net cages to address the inefficiencies and challenges of traditional manual inspections. The proposed scheme utilizes a high-performance multi-branch computational architecture designed based on ShuffleNet architecture to detect net cage damage more efficiently. Experimental results demonstrate that this work performs well on the ImageNet ILSVRC-2010 dataset and achieves an accuracy of 88.54% in underwater net damage detection. © 2024 IEEE.

关键词： Aquaculture

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Universal linear intensity transformations using spatially incoherent diffractive processors

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Light(Science & Applications) 2023年第9期12卷 1830-1856页

作者： Md Sadman Sakib Rahman Xilin Yang Jingxi Li Bijie Bai Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California NanoSystems Institute(CNSI) University of CaliforniaLos AngelesCA 90095USA

Under spatially coherent light,a diffractive optical network composed of structured surfaces can be designed to perform any arbitrary complex-valued linear transformation between its input and output fields-of-view(FOVs)if the total number(N)of optimizable phase-only diffractive features is≥~2N_(i)N_(o),where Ni and No refer to the number of useful pixels at the input and the output FOVs,*** we report the design of a spatially incoherent diffractive optical processor that can approximate any arbitrary linear transformation in time-averaged intensity between its input and output *** spatially incoherent monochromatic light,the spatially varying intensity point spread function(H)of a diffractive network,corresponding to a given,arbitrarily-selected linear intensity transformation,can be written as H(m,n;m′,n′)=|h(m,n;m′,n′)|^(2),where h is the spatially coherent point spread function of the same diffractive network,and(m,n)and(m′,n′)define the coordinates of the output and input FOVs,*** numerical simulations and deep learning,supervised through examples of input-output profiles,we demonstrate that a spatially incoherent diffractive network can be trained to all-optically perform any arbitrary linear intensity transformation between its input and output if N≥~2N_(i)N_(o).We also report the design of spatially incoherent diffractive networks for linear processing of intensity information at multiple illumination wavelengths,operating ***,we numerically demonstrate a diffractive network design that performs all-optical classification of handwritten digits under spatially incoherent illumination,achieving a test accuracy of>95%.Spatially incoherent diffractive networks will be broadly useful for designing all-optical visual processors that can work under natural light.

关键词： spatially intensity arbitrary

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Correntropy-Based Data Selective Adaptive Filtering

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IEEE Transactions on Circuits and Systems I: Regular Papers 2024年第2期71卷 754-766页

作者： Chien, Ying-Ren Wu, Sheng-Teng Tsao, Hen-Wai Diniz, Paulo S.R. Adaptive and Autonomous Communication Laboratory Department of Electrical Engineering National Ilan University Yilan City260007 Taiwan College of Electrical Engineering and Computer Science Graduate Institute of Communication Engineering National Taiwan University Taipei10617 Taiwan Program of Electrical Engineering Department of Electronics and Computer Engineering COPPE/Poli Universidade Federal do Rio de Janeiro Rio de Janeiro68504 Brazil

Data selection can be used in conjunction with adaptive filtering algorithms to avoid unnecessary weight updating and thereby reduce computational overhead. This paper presents a novel correntropy-based data selection method as an alternative to conventional data selection mechanisms based on squared error values. We developed a variable correntropy sensing algorithm to maximize the instantaneous correntropy function for the Gaussian kernel function to mitigate the impact of impulse noise and other forms of noise that can be disregarded in data selection. The proposed data selection mechanism can be implemented with any adaptive filtering algorithm. In simulations, the proposed method (implemented with the least mean squared algorithm) outperformed comparable error-based data selection schemes in terms of hit rate and miss rate, and the resulting weight updating ratio was close to the expected weight updating ratio. © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

关键词： Adaptive filters

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Neural network-based processing and reconstruction of compromised biophotonic image data

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Light(Science & Applications) 2024年第10期13卷 2101-2113页

作者： Michael John Fanous Paloma Casteleiro Costa Gagatay lsi Luzhe Huang Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCAUSA Bioengineering Department University of CaliforniaLos AngelesCAUSA California NanoSystems Institute(CNSi) University of CaliforniaLos AngelesCAUSA Department of Surgery David Geffen School of MedicineUniversity of CaliforniaLos AngelesCAUSA

In recent years,the integration of deep learning techniques with biophotonic setups has opened new horizons in bioimaging.A compelling trend in this feld involves deliberately compromising certain measurement metrics to engineer better bioimaging tools in terms of e.g,cost,speed,and form-factor,followed by compensating for the resulting defects through the utilization of deep learning models trained on a large amount of ideal,superior or alternative *** strategic approach has found increasing popularity due to its potential to enhance various aspects of biophotonic *** of the primary motivations for employing this strategy is the pursuit of higher temporal resolution or increased imaging speed,critical for capturing fine dynamic biological ***,this approach offers the prospect of simplifying hardware requirements and complexities,thereby making advanced imaging standards more accessible in terms of cost and/or *** article provides an in-depth review of the diverse measurement aspects that researchers intentionally impair in their biophotonic setups,including the point spread function(PSF),signal-to-noise ratio(SNR),sampling density,and pixel *** deliberately compromising these metrics,researchers aim to not only recuperate them through the application of deep learning networks,but also bolster in return other crucial parameters,such as the field of view(FOV),depth of field(DOF),and space-bandwidth product(SBP).Throughout this article,we discuss various biophotonic methods that have successfully employed this strategic *** techniques span a wide range of applications and showcase the versatility and effectiveness of deep learning in the context of compromised biophotonic ***,by offering our perspectives on the exciting future possibilities of this rapidly evolving concept,we hope to motivate our readers from various disciplines to explore novel ways of balancing hardware compromises with compensatio

关键词： offering hardware return

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Trade Ease with Machine Learning and AWS

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IEEE Access 2023年 11卷 25893-25905页

作者： Teja, Kamurthi Ravi Liu, Chuan-Ming National Taipei University of Technology International Program of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

Global trading is undergoing significant changes, necessitating modifications to the trading strategies. This study presents a newly developed cloud-based trading strategy that uses Amazon Web Services (AWS), machine learning (ML), and data science to automate trading tasks. The study begins by creating a machine learning trading strategy using a customized deep neural network (DNN). The strategy was then tested using a trading station before deployment on a cloud machine. The execution of the strategy resulted in a total profit of 42.74526 USD, with a test accuracy score of 87.45% and a training accuracy score of 89.15% over 6047 epochs. The roadmap provides a step-by-step overview of the entire process from strategy development to execution. In addition, this study offers insights into related issues and solutions that can enhance the effectiveness of trading strategies. Overall, this study contributes significantly to the field of cloud-based trading strategies and opens avenues for future research. © 2013 IEEE.

关键词： Deep neural networks

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