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AlGaN-Based Deep-Ultraviolet Light-Emitting Diodes With Varied Thickness of Sidewall Passivation via Atomic Layer Deposition

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IEEE Transactions on Electron Devices 2023年第11期70卷 5727-5731页

作者： Peng, Kangwei Lai, Shouqiang Shen, Mengchun Li, Saijun Zheng, Lijie Dai, Yurong Chen, Jilan Zhu, Lihong Chen, Guolong Wang, Shuli Kuo, Hao-Chun Lu, Yijun Chen, Zhong Wu, Tingzhu Xiamen University Fujian Engineering Research Center for Solid-State Lighting Department of Electronic Science Xiamen361005 China Sanan Optoelectronics Company Ltd. Xiamen361005 China National Yang Ming Chiao Tung University College of Electrical and Computer Engineering Department of Photonics and Graduate Institute of Electro-Optical Engineering Hsinchu30010 Taiwan Hon Hai Research Institute Semiconductor Research Center Taipei11492 Taiwan Xiamen361005 China

In this study, we fabricated deep-ultraviolet light-emitting diodes (DUV-LEDs) with Al2O3 films of 0-, 130-, and 255-nm via atomic layer deposition (ALD). In addition, the effects of the passivation layers on the DUV-LEDs were systematically investigated. The current-voltage characteristics of these samples revealed the passivating effect of the Al2O3 ALD films on the DUV-LEDs. By analyzing the external quantum efficiency (EQE) at room temperature using the ABC + f,(n) model, the differences in the physical mechanisms of the Al2O3 ALD film on DUV-LEDs were determined. The measurements of the electroluminescence (EL) and far-field radiation patterns (EQE, light output power (LOP), and radiation patterns) showed that ALD can enhance the performance of DUV-LEDs, particularly DUV-LEDs with a 130-nm Al2O3 ALD passivation layer. Moreover, the aging test and failure ratio of these DUV-LEDs under humidity exposure (85 °C/85% RH) indicated the specific effect of these Al2O3 ALD films on the reliability of DUV-LEDs. © 1963-2012 IEEE.

关键词： Light emitting diodes

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A 26-Gb/s CMOS optical receiver with a reference-less CDR in 65-nm CMOS

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Journal of Semiconductors 2022年第7期43卷 68-77页

作者： Quan Pan Xiongshi Luo Zhenghao Li Zhengzhe Jia Fuzhan Chen Xuewei Ding C.Patrick Yue School of Microelectronics Engineering Research Center of Integrated Circuits for Next-Generation CommunicationsMinistry of EducationSouthern University of Science and TechnologyShenzhen 518055China Department of Electronic and Computer Engineering Hong Kong University of Science and TechnologyHong Kong SARChina ZTE Microelectronics Shenzhen 518055China

This paper presents a 26-Gb/s CMOS optical receiver that is fabricated in 65-nm technology. It consists of a tripleinductive transimpedance amplifier(TIA), direct current(DC) offset cancellation circuits, 3-stage gm-TIA variable-gain amplifiers(VGA), and a reference-less clock and data recovery(CDR) circuit with built-in equalization technique. The TIA/VGA frontend measurement results demonstrate 72-dB? transimpedance gain, 20.4-GHz-3-dB bandwidth, and 12-dB DC gain tuning range. The measurements of the VGA’s resistive networks also demonstrate its efficient capability of overcoming the voltage and temperature variations. The CDR adopts a full-rate topology with 12-dB imbedded equalization tuning range. Optical measurements of this chipset achieve a 10-12 BER at 26 Gb/s for a 2;-1 PRBS input with a-7.3-dBm input sensitivity. The measurement results with a 10-dB @ 13 GHz attenuator also demonstrate the effectiveness of the gain tuning capability and the built-in equalization. The entire system consumes 140 mW from a 1/1.2-V supply.

关键词： clock and data recovery equalizer optical receiver transimpedance amplifier variable-gain amplifier

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DiffMix: Diffusion Model-based Data Synthesis for Nuclei Segmentation and Classification in Imbalanced Pathology Image Datasets

arXiv

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arXiv 2023年

作者： Oh, Hyun-Jic Jeong, Won-Ki Korea University College of Informatics Department of Computer Science and Engineering Korea Republic of

Nuclei segmentation and classification is a significant process in pathology image analysis. Deep learning-based approaches have greatly contributed to the higher accuracy of this task. However, those approaches suffer from the imbalanced nuclei data composition, which shows lower classification performance on the rare nuclei class. In this paper, we propose a realistic data synthesis method using a diffusion model. We generate two types of virtual patches to enlarge the training data distribution, which is for balancing the nuclei class variance and for enlarging the chance to look at various nuclei. After that, we use a semantic-label-conditioned diffusion model to generate realistic and high-quality image samples. We demonstrate the efficacy of our method by experiment results on two imbalanced nuclei datasets, improving the state-of-the-art networks. The experimental results suggest that the proposed method improves the classification performance of the rare type nuclei classification, while showing superior segmentation and classification performance in imbalanced pathology nuclei datasets. © 2023, CC BY-NC-ND.

关键词： Classification (of information)

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Eye Disease Classification Based on Fundus Images Using Convolutional Neural Network

Eye Disease Classification Based on Fundus Images Using Conv...

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International electronics Symposium (IES)

作者： Rafi Denandra Arna Fariza Yanuar Risah Prayogi Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

The eye is a vital organ in the human body. Through the eyes, humans can absorb over 80% of visual information used to carry out various daily activities. However, in Indonesia, there are many cases of vision impairments that, if not properly addressed, can lead to blindness. Examples of such impairments include cataracts, glaucoma, and diabetic retinopathy. Currently, there are many research studies on eye diseases that are aided by technologies like Artificial Intelligence (AI). One of the AI technologies that is commonly developed is Convolutional Neural Network (CNN). CNN is a deep learning framework that excels in solving image or picture classification problems because its architecture applies convolution layers, which can break down images to extract features and easily reduce the high dimensionality without losing the image’s information itself. In this study, the author used various CNN models to find the most suitable model for classifying eye diseases, including EfficientNet, ResNet, Inception V3, and many others. Through various model training experiments, the accuracy for the ResNet50 model was found to be 95% with a loss of 17%, followed by Xception with an accuracy of 95% and a loss of 21%, and EfficientNetV2B2 with an accuracy of 92% and a loss of 25%.

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Multi-Channel Multi-Frame Integration Detection Method for Weak Target with Coherent MIMO Radar

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IEEE Transactions on Aerospace and electronic Systems 2025年第3期61卷 6518-6536页

作者： Gao, Longji Li, Xiaolong Wang, Mingxing Sun, Zhi Cui, Guolong Yeo, Tat Soon University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China National University of Singapore Department of Electrical and Computer Engineering Singapore119077 Singapore

Coherent multiple-input multiple-output (MIMO) radar could significantly improve the weak moving target detection ability by accumulating multi-channel and multi-frame echo signal. However, due to the target motion and the geometric configuration differences of each node relative to the target, the multi-channel multi-frame accumulation of target echoes by coherent MIMO radar faces several difficulties, i.e., range migration (RM) and Doppler frequency migration (DFM) issues within each channel, spatial variations of target signal envelope and phase across different channels, time variations of target signal envelope and phase across different frames. To tackle these problems, two contributions are made in this paper. Firstly, by applying the generalized Radon Fourier transform (GRFT) to correct the RM/DFM and obtain the signal integration within each channel, we propose a multi-channel accumulation algorithm to compensate the spatial variations of target signal and realize the target energy accumulation across different channels, which takes full advantage of the GRFT output characteristics and the radar system topological structure. Secondly, a multi-frame accumulation algorithm is developed to compensate the time variations of target signal and achieve the further integration of target energy distributed in different frames. We also provide explicit expressions and analysis for performance metrics, including the output response, SNR improvement performance. The proposed method's effectiveness is confirmed through simulation experiments. © 1965-2011 IEEE.

关键词： Topology

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Improving Maintainability in GraphQL Authorization using Dynamic Authorization 24

Improving Maintainability in GraphQL Authorization using Dyn...

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24th International electronics Symposium, IES 2022

作者： Nafis, Muhammad Riza Sa'adah, Umi Ahsan, Ahmad Syauqi Fauzi, Willy Achmat Politeknik Elektronika Negeri Surabaya Informatics and Computer Engineering Department Surabaya Indonesia PT. Sinergi Dimensi Informatika Surabaya Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

GraphQL is the new way to build an API that Facebook has developed. The rising trend of GraphQL has intrigued developers as it solves the REST API problems like under-fetching and over-fetching. Still, at the same time, it also brings additional security issues like authorization. GraphQL authorization is one of the most challenging and hot issues. As a state-of-the-art method to solve GraphQL Authorization uses hard-coded authorization rules, it increases the burden for a developer to develop in a maintainable way. This paper proposes a platform that alleviates the authorization problem in GraphQL in a maintainable way. This platform will showcase how to solve this problem in a microservices system. Authorization services will decouple the application, increasing the separation of concern between the application and security contexts. The experiment on the proposed system tested with an identical system shows that dynamic authorization will increase software maintainability, proved by the class coupling decreasing by 21%. Although the proposed design uses GraphQL technology, the approach is helpful for any distributed computing technology. © 2022 IEEE.

关键词： Maintainability

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Enhancing Smart City Mobility Using Software Defined Networks

Enhancing Smart City Mobility Using Software Defined Network...

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International Conference on Mechatronics (ICOM)

作者： Zeinab E. Ahmed Aisha A. Hashim Rashid A. Saeed Mamoon M. Saeed Department of Computer Engineering University of Gezira Sudan Department of Electrical and Computer Engineering International Islamic University Malaysia Malaysia School of Electronic Engineering Sudan University of Science and Technology Khartoum Sudan Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Yemen

ISBN: (数字)9798350349788

ISBN: (纸本)9798350349795

Smart cities face challenges in mobility management, such as scalability, latency, resource allocation, security, and energy efficiency, which traditional methods struggle to address effectively. Software Defined Networking (SDN) is a rapidly evolving field that provides centralized control and programmability of network devices. SDN-based MM can advance sustainable urban development by centralizing control, adapting to changes, optimizing resource allocation, reducing energy consumption, and improving urban mobility. This paper investigates SDN's role in enhancing urban sustainability and analyzes its implications for sustainable cities. The paper categorizes the use of SDN technology to improve various aspects of smart cities into three categories: Emergency Services and Traffic Management, Security and DDoS Defense, and Network Performance. Results showed a 20% reduction in average travel times and a 33% improvement in emergency response times. Future research should focus on advanced real-time traffic management algorithms, integrating emerging technologies like 6G, and artificial intelligence to improve SDN-based systems' scalability and efficiency in smart cities.

关键词： Energy consumption Smart cities Scalability Transforms Emergency services Energy efficiency Security

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Novel Side-Polished Balloon Shaped Heterocore Structured Plastic Optical Fibre Ethanol Sensor

Novel Side-Polished Balloon Shaped Heterocore Structured Pla...

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2023 European Workshop on Optical Fibre Sensors, EWOFS 2023

作者： Memon, Sanober Farheen Wang, Ruoning Strunz, Bob Chowdhry, Bhawani Shankar Pembroke, J. Tony Lewis, Elfed Optical Fibre Sensors Research Centre University of Limerick Ireland Department of Electronic and Computer Engineering University of Limerick Ireland Key Laboratory of In-fiber Integrated Optics of Ministry of Education College of Physics and Optoelectronic Engineering Harbin Engineering University China NCRA-CMS Lab IICT Mehran University of Engineering and Technology Pakistan Department of Chemical Sciences Bernal Institute University of Limerick Ireland

ISBN: (纸本)9781510665002

A unique side-polished balloon shaped heterocore structure plastic optical fibre (POF) sensor for real-time measurement of very low to high ethanol concentration in water is reported. The sensor is designed as a large core-small core-large core heterocore structure where small core fibre (SCF) acts as a sensing region, whereas large core fibre (LCFs) are used as input and output light waveguide s as well as to introduce the light leakage in the cladding of SCF at the heterocore structure’s input interface and hence generate the significant evanescent field. The principle of operation of the sensor is based on evanescent field interaction at the interface of modif ied SCF and the liquid boundary. The sensor is characterized for ethanol-water solutions in the ethanol concentration ranges of 20 %v/v to 80 %v/v, 1 %v/v to 10 %v/v, and 0.1 %v/v to 1 %v/v, demonstrating a maximum sensitivity of 54673 %/RIU. The experimentally evaluated high sensitivity of this sensor design for real-time measurement of ethanol concentration in water at different ranges makes it a potential candidate for implementation in the industry as a low-cost and real-time solution for ethanol sensing as well as other RI sensing applications. © 2023 SPIE.

关键词： Refractive index

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A Study of Localization in 5G Green Network (5G-GN) for Futuristic Cellular Communication 3rd

A Study of Localization in 5G Green Network (5G-GN) for Futu...

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3rd International Conference on Communication, Devices and Computing, ICCDC 2021

作者： Pradhan, Devasis Sahu, P.K. Rajeswari Tun, Hla Myo Department of Electronics & Communication Engineering Acharya Institute of Technology Karnataka Bengaluru India National Institute of Technology Odisha Rourkela India Department of Electronic Engineering Faculty of Electrical and Computer Engineering Yangon Technological University Yangon Myanmar

ISBN: (纸本)9789811691539

The fifth-generation (5G) remote environment will be fundamental for a horde of new applications dependent on precise area mindfulness and other logical data. Such a remote environment will be empowered by cutting-edge 5G remote innovations coordinated with existing advancements for the Internet-of-things (IoT) and the worldwide route satellite framework. With immense mm-Wave range and tight shaft reception apparatus innovation, exact position area is currently conceivable in 5G and future portable correspondence frameworks. As yet being a field being developed, a restriction is relied upon to be completely unavoidable in the following not many years. Albeit the improvement of such strategies is driven by the commercialization of area-based administrations (e.g., route), its application to help cell the executives are considered to be a vital methodology for improving its flexibility and execution. This paper gives a depth of the concept, requirements, and signal processing advancement in localization for accurate positioning. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Signal processing

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An Interpretable Transformer-Based Approach to Classify Malaria from Blood Cell Images

An Interpretable Transformer-Based Approach to Classify Mala...

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Sustainable Technology and engineering (i-COSTE), International Conference on

作者： Shakib Mahmud Dipto Mehafuza Islam S.M Abdulla Al Mamun Md. Kaviul Hossain A M Esfar-E-Alam Md. Ashraful Alam Department of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering BRAC University Dhaka Bangladesh Department of Science Engineering and Built Environment Deakin University Victoria Australia Department of informatics University of Oslo Norway

ISBN: (数字)9798331517335

ISBN: (纸本)9798331517342

Malaria is a disease that can be fatal, and it is spread through the bite of the female Anopheles mosquito. The life of the sufferer is put in jeopardy as a result of the presence of numerous plasmodium parasites, which spread throughout their blood cells. Malaria can potentially be fatal if it is not treated within the first few stages of the disease. A well-known method for diagnosing malaria, microscopy involves taking blood samples from the patient, calculating the number of parasites, and counting the victim's red blood cells. Nevertheless, the procedure of microscopy takes a lot of time, and, in certain circumstances, it can produce an incorrect result. When compared to the more conventional approach of microscopic examination, the recent successes of deep learning (DL) in the field of medical diagnosis make it quite conceivable to reduce the expenses associated with the diagnosis while simultaneously improving overall detection accuracy. This study proposes a transformer-based DL technique for diagnosing the malaria parasite using blood cell images. An explainable AI technique called Grad-CAM was applied in order to determine which aspects of an image the proposed model paid significantly more attention to in comparison to the other aspects of the image through saliency mapping. This was done in order to demonstrate the usefulness of the models. According to the findings of this research, the performance of the vision transformer and the VGG16 are identical. Both models have reached an accuracy score of approximately 96 %, which is very impressive.

关键词： Deep learning computer vision Accuracy Malaria Computational modeling Microscopy Cells (biology) Predictive models Transformers Medical diagnosis

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