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Investigation of operation and degradation mechanisms in ZnTeSe blue quantum-dot light-emitting diodes by identifying recombination zone

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Nano research 2024年第7期17卷 6527-6533页

作者： Oul Cho Sujin Park Hogeun Chang Jiwhan Kim Jaekwon Kim Sungwoo Kim Taehyung Kim Jeonghun Kwak Department of Electrical and Computer Engineering Inter-university Semiconductor Research Centerand Soft Foundry InstituteSeoul National UniversitySeoul 08826Republic of Korea Material Research Center Samsung Advanced Institute of TechnologySuwonGyeonggi-do 16419Republic of Korea

ZnTeSe quantum dots(QDs),recognized as promising eco-friendly blue electroluminescent emitters,remain under-explored in light-emitting diode(LED)***,to elucidate the operation and degradation mechanisms of ZnTeSe blue QD-LEDs,stacked ZnTeSe QD layers with discernable luminescence are designed by varying Te doping concentrations,and the recombination zones(RZs)of the blue QD-LEDs are *** RZs are identified near the hole-transport layer(HTL),confirmed by angular-dependent electroluminescence measurements and optical *** addition,in order to investigate carrier dynamics in the process of recombination,the transient electroluminescence(tr-EL)signals of the dichromatic QD-LEDs are *** a result,it is inferred that the RZ initially formed near the electron-transport layer(ETL)due to the high injection barriers of ***,due to the fast electron mobility,the RZ shifts toward the HTL as the operating current *** the device lifetime tests,the RZ remains stationary while the photoluminescence(PL)corresponding to the RZ undergoes a substantial decrease,indicating that the degradation is accelerated by the concentrated *** this study contributes to a deeper understanding of the operational mechanisms of ZnTeSe blue QD-LEDs.

关键词： quantum dots light-emitting diodes degradation recombination zones carrier dynamics

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Solar Cell Defect Detection using Deep-Learning Segmentation with Two-Fold Training 9

Solar Cell Defect Detection using Deep-Learning Segmentation...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Narayanan, Mathiyazhagan Mohan, Ramya Rajinikanth, Venkatesan Center for Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India

ISBN: (纸本)9798350377972

Recently, the applications of Deep Learning (DL) methodologies have been extensively utilized across numerous areas to extract critical solutions from selected databases. The DL Segmentation Tool (DLST) is frequently utilized to extract and assess selected segments from digital images captured using various modalities. This study intends to propose a DLST to analyze the selected database for the identification of defects in solar cells. The proposed DLST encompasses several phases: (i) image acquisition and resizing, (ii) application of an image preprocessing technique to improve defect visibility, (iii) execution of the segmentation process utilizing a selected DL-model to extract the defect, and (iv) comparison of the segmented region with a binary mask and calculation of performance metrics. The created DLST incorporates the VGG-UNet, with VGG19 serving as the model's backbone. This work implemented a two-fold training approach for improved outcomes. The experimental result is validated using selected image metrics, demonstrating that the proposed DLST yields superior values of Jaccard (91.31%), Dice (95.38%), and Accuracy (96.18%) in comparison to other chosen DL models. © 2024 IEEE.

关键词： Image acquisition

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Deep Learning and Features Fusion for Colorectal Cancer Detection from Histopathology Images 9

Deep Learning and Features Fusion for Colorectal Cancer Dete...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Rajinikanth, Venkatesan Mohan, Ramya Narayanan, Mathiyazhagan Center for Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India

ISBN: (纸本)9798350377972

Modern hospitals have extensively adopted the automatic disease examination methods to detect diseases from biomedical images of selected modalities. These methods reduce the diagnostic burden and enhance detection accuracy. This study seeks to create a Deep-Learning (DL) tool for the analysis of histopathology images related to colorectal cancer (CC). This DL-tool consists of the following stages: image collection and resizing, feature extraction using a selected DL-scheme, feature reduction through 50% dropout and serial feature fusion to create the Fused-Features-Vector (FFV), followed by binary classification and 3-fold cross-validation to validate the results. This study initially examines traditional DL-models for classification using SoftMax. Based on the results, the two best models are selected to generate the FFV. This study examines the features of VGG16 and ResNet101 to produce the FFV, thereby validating the effectiveness of the proposed scheme. The CC detection utilizing the FFV achieved an accuracy exceeding 95%, confirming that the developed scheme yields improved results on the selected data. © 2024 IEEE.

关键词： Lung cancer

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Electromechanically Tunable Metasurface for Guided Spoof Surface Plasmon Polaritons

Electromechanically Tunable Metasurface for Guided Spoof Sur...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Franke, Lars Klingel, Steffen Rahm, Marco Department of Electrical and Computer Engineering Research Center OPTIMAS University of Kaiserslautern-Landau Germany

We report an electromechanically tunable metasurface for active manipulation of spoof surface plasmon polaritons (SSPPs). We designed a dynamic SSPP low pass filter that can be actively controlled by application of an... 详细信息

关键词： Low pass filters

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Cancer Region Segmentation in Pre-Processed Breast Ultrasound Image Using VGG16 Based UNet/SegNet 9

Cancer Region Segmentation in Pre-Processed Breast Ultrasoun...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Mohan, Ramya Narayanan, Mathiyazhagan Rajinikanth, Venkatesan Department of Computer Science and Engineering Center for Research and Innovation TN Chennai602105 India

ISBN: (纸本)9798350377972

Breast Cancer (BC) is a predominant cause of mortality among women globally, with its incidence progressively rising due to various causes. Early detection of BC is crucial to plan and execute appropriate treatment to cure the disease. The clinical level examination of the BC involves in;personal check by the doctor, screening using non-invasive medical imaging modality, and biopsy test to confirm the BC and its stage. Compared to the radiological imaging modalities, the breast-ultrasound (BU) based screening is widely preferred due to its simplicity. The abnormality recorded with BU is then examined to classify the tumor as benign/malignant, an essential process during BC detection. This research aims to propose a deep-learning (DL) tool to segment the tumor from the chosen BU-image. To enhance the segmentation accuracy, every image is preprocessed using tri-level thresholding with Tsallis's Entropy and Hummingbird-Algorithm (TE+HA). The performance of proposed UNet/SegNet is verified on both unprocessed and preprocessed images. The experimental outcome of this research confirms that the VGG-UNet offered an improved mean accuracy (>97%) when the preprocessed BU is considered for examination. © 2024 IEEE.

关键词： Ultrasonic imaging

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Recombination Operators for the Multi-Objective Team Formation Problem in Social Networks 13

Recombination Operators for the Multi-Objective Team Formati...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Juárez, Julio Brizuela, Carlos A. Terashima-Marín, Hugo Coello Coello, Carlos A. School of Engineering and Sciences Tecnologico de Monterrey Monterrey Mexico Cicese Research Center Department of Computer Science Ensenada Mexico CINVESTAV-IPN Computer Science Department Mexico City Mexico

ISBN: (纸本)9798350308365

The Team Formation Problem in Social Networks (TFP-SN) describes the process of finding an effective group of people, drawn from a network of experts, to perform a particular task. For a team to be considered as effective, it requires to comply with a task-specific skills set while also showing a high degree of cohesiveness. Although team effectiveness is subject to multiple criteria, the study of the problem from a multi-objective (MO) perspective is still scarce. In this paper, we focus on an MO TFP-SN whose objective is to maximize the team's level of expertise and the team's density, simultaneously. To solve this problem, we introduce two novel recombination operators to be used within the framework of the well-known NSGA-II. Our proposed crossover operators act as heuristics that compute the parents' unique and shared information, which is then combined for generating potentially improved offspring. Our experiments show that each of the two proposed crossover operators lead to significantly better results when compared to a naive crossover operator taken from the specialized literature. Particularly, the results consistently show higher hypervolume values when compared to the use of an adaptation of a simple recombination operator commonly used for this problem. The good performance of our proposed operators may be attributed to the incorporation of knowledge that exploits the structure of the problem. © 2024 IEEE.

关键词： Combinatorial optimization Multi-objective optimization Recombination operators Team formation problem

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Electromechanically Tunable Metasurface for Guided Spoof Surface Plasmon Polaritons

Electromechanically Tunable Metasurface for Guided Spoof Sur...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Franke, Lars Klingel, Steffen Rahm, Marco Department of Electrical and Computer Engineering Research Center OPTIMAS University of Kaiserslautern-Landau Germany

ISBN: (纸本)9781957171395

关键词： Low pass filters

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Extending Online 4D Situational Awareness in Connected and Automated Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年第8期9卷 1-19页

作者： Piperigkos, Nikos Anagnostopoulos, Christos Lalos, Aris S. Berberidis, Kostas Computer Engineering and Informatics Department University of Patras Greece Industrial Systems Institute ATHENA Research Center Patras Greece

Empowering each vehicle with four dimensional (4D) situational awareness, i.e., accurate knowledge of neighboring vehicles' 3D locations over time in a cooperative manner (instead of focusing only on self-localization), is fundamental for improving autonomous driving performance in diverse traffic conditions. For this task, identification, localization and tracking of nearby road users is critical for enhancing safety, motion planning and energy consumption of automated vehicles. Advanced perception sensors as well as communication abilities, enable the close collaboration of moving vehicles and other road users, and significantly increase the positioning accuracy via multi-modal sensor fusion. The challenge here is to actually match the extracted measurements from perception sensors with the correct vehicle ID, through data association. In this paper, two novel and distributed Cooperative Localization or Awareness algorithms are formulated, based on linear least-squares minimization and the celebrated Kalman Filter. They both aim to improve ego vehicle's 4D situational awareness, so as to be fully location aware of its surrounding and not just its own position. For that purpose, ego vehicle forms a star like topology with its neighbors, and fuses four types of multi-modal inter-vehicular measurements (position, distance, azimuth and inclination angle) via the linear Graph Laplacian operator and geometry capturing differential coordinates. Moreover, a data association strategy has been integrated to the algorithms as part of the identification process, which is shown to be much more beneficial than traditional Hungarian algorithm. An extensive experimental study has been conducted in CARLA, SUMO and Artery simulators, highlighting the benefits of the proposed methods in a variety of experimental scenarios, and verifying increased situational awareness ability. The proposed distributed approaches offer high positioning accuracy, outperforming other state-of-the-art c

关键词： Laplace equation

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Watch You under Low-Resolution and Low-Illumination: Face Enhancement via Bi-Factor Degradation Decoupling

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IEEE Transactions on Circuits and Systems for Video Technology 2024年第5期34卷 3481-3495页

作者： Ding, Xin Wang, Zheng Fang, Jing Shu, Zhenyu Hu, Ruimin Lin, Chia-Wen NingboTech University School of Computer and Data Engineering Ningbo315100 China Wuhan University National Engineering Research Center for Multimedia Software School of Computer Wuhan430072 China National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu300044 Taiwan

Face enhancement aims to improve low-quality face images to a higher-quality level. However, in real-world nighttime scenes, complex degradation factors often affect these images, making it challenging to preserve important facial details. Existing image enhancement algorithms typically focus on independently conducting image super-resolution and brightness enhancement, assuming a fixed degradation level based on simulated training datasets. Nonetheless, real nighttime scenes involve complex degradation processes, where degradation factors dynamically and variably manifest. Therefore, achieving effective face enhancement in such scenarios is particularly daunting. This work analyzes and unveils the multiple factors of low resolution and low illumination during degradation. Based on this analysis, we propose a Bi-factor Degradation Decoupling network. Our method leverages a decoupling network to generate qualitative and quantitative features corresponding to each factor's degradation degree in the low-quality environment. These features are then combined with robust facial feature constraints to recover the details of low-quality faces. Extensive experiments demonstrate that our method surpasses state-of-the-art approaches in both enhancement and face super-resolution. © 1991-2012 IEEE.

关键词： Image reconstruction

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Design of a High-Efficiency Temporal Engine for Real-Time Spatial Satellite Image Classification Using Augmented Incremental Transfer Learning for Crop Analysis

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SN computer Science 2024年第5期5卷 585页

作者： Dhande, Akshay Pramodrao Malik, Rahul Saini, Dipen Garg, Rachit Jha, Sudan Nazeer, Jabeen Ahmad, Sultan Department of Electronics and Telecommunication Engineering Lovely Professional University Punjab Phagwara India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering Kathmandu University Dhulikhel Nepal Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India

Satellite image processing is a multidomain task which involves design of image capturing, denoising, segmentation, feature extraction, feature reduction, classification, and post-processing tasks. A wide variety of satellite image processing models are proposed by researchers, and each of them has different data and process requirements. For instance, the image capturing module might obtain images in layered form, while feature extraction module might require data in 2D or 3D forms. Moreover, performance of these models also varies due to changes in internal process parameters and dataset parameters, which limits their accuracy and scalability when applied to real-time scenarios. To reduce the probability of these limitations, a novel high-efficiency temporal engine for real-time satellite image classification using augmented incremental transfer learning is proposed and discussed in this text. The model initially captures real-time satellite data using Google’s Earth Engine and processes it using a transfer learning-based convolutional neural network (CNN) via backscatter coefficient analysis. These coefficients indicate average intensity value of Precision Image (PRI) when evaluated over a distributed target. Due to extraction of backscattering coefficients, the model is capable of representing crop images in VV (vertical transmit, vertical receive), and HV (horizontal transmit vertical receive) modes. Thereby assisting the CNN model to extract a wide variety of features from input satellite image, which classifies these datasets (original, VV, and VH) into different crop categories. The classified images are further processed via an incremental learning layer, which assists in visual identification of affected regions. Due to use of incremental learning and CNN for classification, the proposed TRSAITL model is capable of achieving an average accuracy of 97.8% for crop type and severity of damage detection, with an average PSNR (Peak Signal-to-Noise Ratio) of 29.

关键词： Accuracy Classification Crop Damage Deep learning Earth Incremental Precision Recall Satellite Transfer Type

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