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Monolithically integrated 940 nm VCSELs on bulk Ge substrates

Optics Express 2024年第4期32卷 6609-6618页

作者： Wan, Zeyu Yang, Yun-Cheng Chen, Wei-Hsin Chiu, Chih-Chuan Zhao, Yunlong Feifel, Markus Chrostowski, Lukas Lackner, David Wu, Chao-Hsin Xia, Guangrui Department of Materials Engineering The University of British Columbia 6350 Stores Road VancouverBCV6 T 1Z4 Canada Graduate Institute of Electronics Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei106319 Taiwan Graduate Institute of Photonics and Optoelectronics National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei106319 Taiwan Graduate School of Advanced Technology National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei106319 Taiwan Heidenhofstrasse Freiburg79110 Germany Department of Electrical and Computer Engineering The University of British Columbia 5500-2332 Main Mall VancouverBCV6 T 1Z4 Canada

This research successfully developed an independent Ge-based VCSEL epitaxy and fabrication technology route, which set the stage for integrating AlGaAs-based semiconductor devices on bulk Ge substrates. This is the second successful Ge-based VCSEL technology reported worldwide and the first Ge-based VCSEL technology with key details disclosed, including Ge substrate specification, transition layer structure and composition, and fabrication process. Compared with the GaAs counterparts, after epitaxy optimization, the Ge-based VCSEL wafer has a 40% lower surface root-mean-square roughness and 72% lower average bow-warp. After device fabrication, the Ge-based VCSEL has a 10% lower threshold current density and 19% higher maximum optical differential efficiency than the GaAs-based VCSEL. © 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Gallium arsenide

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Deep learning for brain age estimation: A systematic review

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Information Fusion 2023年 96卷 130-143页

作者： Tanveer, M. Ganaie, M.A. Beheshti, Iman Goel, Tripti Ahmad, Nehal Lai, Kuan-Ting Huang, Kaizhu Zhang, Yu-Dong Del Ser, Javier Lin, Chin-Teng Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Biomedical Imaging Lab National Institute of Technology Silchar Assam 788010 India Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Data Science Research Center Duke Kunshan University China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom Derio48160 Spain Bilbao48013 Spain School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated with the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required to elicit accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. © 2023 Elsevier B.V.

关键词： Deep neural networks

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Task Offloading and Resource Management for IIoT with Satellite-Terrestrial Integrated Computing Power Network Based on D3QN

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IEEE Internet of Things Journal 2025年第12期12卷 21770-21783页

作者： Li, Meng Li, Meihui Wang, Kan Yu, F. Richard Wang, Zhuwei Si, Pengbo Beijing University of Technology School of Information Science and Technology Beijing100124 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

The management of computing resources through the computing power network (CPN) has gradually become a focal point of research. With the development of the 6th generation (6G) mobile networks, some promising technologies such as satellite-terrestrial integrated network (STIN) and smart endogenous network driven by artificial intelligence (AI) are increasingly being applied in Industrial Internet of Things (IIoT). However, several issues in current studies are worthy of attention: 1) the large number of devices powered by battery in IIoT, 2) the complex communication environments, 3) the finite computing resources for task data processing. To cope with these challenges, a satellite-terrestrial integrated computing power network (STICPN) framework is introduced in this article. Within this framework, a task offloading link selection scheme is proposed, which minimizes the delay and the consumption of energy. The task offloading optimization problem is modeled as a Markov Decision Process (MDP). Meanwhile, deep reinforcement learning (DRL) algorithm is employed to adapt to the dynamic states of environment. Specifically, a Dueling Double Deep Q Network (D3QN) is used to make optimal decisions and delay as well as energy consumption can be reduced significantly. Moreover, the D3QN-based scheme extends the usage time of IIoT devices. The simulation results indicate that the proposed scheme outperforms the comparison schemes significantly. © 2025 IEEE.

关键词： Energy utilization

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Development of measurement system for subsurface scattering light of skin and analysis of its age-related changes

Development of measurement system for subsurface scattering ...

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Biomedical Imaging and Sensing Conference 2023

作者： Kikuchi, K. Katsuyama, M. Shibata, T. Hardeberg, J.Y. Yuasa, T. Aizu, Y. Shiseido Co. Ltd. MIRAI Technology Institute 1-2-11 Takashima Nishiku Yokohama220-0011 Japan Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Teknologivegen 22 Gjøvik2815 Norway Division of Production Systems Engineering Muroran Institute of Technology 27-1 Mizumoto Muroran Hokkaido050-8585 Japan

ISBN: (纸本)9781510663435

We have developed a system that separates and measures the optical properties of skin, i.e., the surface reflection, diffuse reflection, and sub-surface scattering components of the skin. This system includes two polarization filters that separate light from the skin into a surface reflection component image and a diffuse reflection component image. Furthermore, by using a projector as a light source and irradiating the skin using a high-frequency binary illumination pattern, the subsurface scattering component image alone can be separated and generated. Using the proposed system, we performed a survey of 154 Japanese women aged from their 20s to their 70s and analyzed age-related changes in the optical properties of their skin. The results revealed the following. First, the luminance value Y of the surface reflection from the cheek and its standard deviation within the analysis area increase with age. Second, the Y value of diffuse reflection from the skin decreases with age. Third, the amount of light in the sub-surface scattering components also decreases with age. The proposed system is expected to have a wide range of applications in the medical and cosmetic fields. © 2023 SPIE.

关键词： Surface scattering

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Data analysis and interpretable machine learning for HVAC predictive control: A case-study based implementation

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Science and technology for the Built Environment 2023年第7期29卷 698-718页

作者： Mao, Jianqiao Grammenos, Ryan Karagiannis, Konstantinos Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom General Technology Ltd Athens15233 Greece

Energy efficiency and thermal comfort levels are key attributes to be considered in the design and implementation of a Heating, Ventilation and Air Conditioning (HVAC) system. With the increased availability of Internet of Things (IoT) devices, it is now possible to continuously monitor multiple variables that influence a user’s thermal comfort and the system’s energy efficiency, thus acting preemptively to optimize these factors. To this end, this paper reports on a case study with a two-fold aim;first, to analyze the performance of a conventional HVAC system through data analytics;secondly, to explore the use of interpretable machine learning techniques for HVAC predictive control. A new Interpretable Machine Learning (IML) algorithm called Permutation Feature-based Frequency Response Analysis (PF-FRA) is also proposed. Results demonstrate that the proposed model can generate accurate forecasts of Room Temperature (RT) levels by taking into account historical RT information, as well as additional environmental and time-series features. Our proposed model achieves 0.4017 °C and 0.9417 °C of Mean Absolute Error (MAE) for 1-h and 8-h ahead RT prediction, respectively. Tools such as surrogate models and Shapley graphs are employed to interpret the model’s global and local behaviors with the aim of increasing trust in the model. © Copyright © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： Energy efficiency

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Self-Immolative Electrochemical Redox Substrates: Emerging Artificial Receptors in Sensing and Biosensing

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ACS Measurement Science Au 2024年第2期4卷 163-183页

作者： Manibalan, Kesavan Arul, Ponnusamy Wu, Hsin-Jay Huang, Sheng-Tung Mani, Veerappan Department of Materials Science and Engineering National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan Institute of Biochemical and Biomedical Engineering Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei 10608 Taiwan Advanced Membranes and Porous Materials Center (AMPMC) Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia High-Value Biomaterials Research and Commercialization Center National Taipei University of Technology No. 1 Sec. 3 Zhongxiao E. Rd. Taipei 10608 Taiwan

The development of artificial receptors has great significance in measurement science and technology. The need for a robust version of natural receptors is getting increased attention because the cost of natural receptors is still high along with storage difficulties. Aptamers, imprinted polymers, and nanozymes are some of the matured artificial receptors in analytical chemistry. Recently, a new direction has been discovered by organic chemists, who can synthesize robust, activity-based, self-immolative organic molecules that have artificial receptor properties for the targeted analytes. Specifically designed trigger moieties implant selectivity and sensitivity. These latent electrochemical redox substrates are highly stable, mass-producible, inexpensive, and eco-friendly. Combining redox substrates with the merits of electrochemical techniques is a good opportunity to establish a new direction in artificial receptors. This Review provides an overview of electrochemical redox substrate design, anatomy, benefits, and biosensing potential. A proper understanding of molecular design can lead to the development of a library of novel self-immolative redox molecules that would have huge implications for measurement science and technology. © 2024 The Authors. Published by American Chemical Society.

关键词： alkaline phosphatase artificial receptors electrochemical mediators electrochemical sensors and biosensors galactosidase hydrogen peroxide neuraminidase organic electrochemistry ratiometric sensors self-immolative sensors

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LIAS: A Lightweight Incentive Authentication Scheme for Forensic Services in IoV

LIAS: A Lightweight Incentive Authentication Scheme for Fore...

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作者： Zhang, Mingyue Zhou, Junlong Cong, Peijin Zhang, Gongxuan Zhuo, Cheng Hu, Shiyan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing100190 China Zhejiang University Department of Information Science and Electronic Engineering Hangzhou310027 China University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

Internet of Vehicles (IoV) has become an indispensable data sensing and processing platform in Internet of Things (IoT) for intelligent transportation. The mounted cameras on the vehicles along with the fixed roadside cameras are utilized to provide pictorial services for IoV users and law enforcement agencies. For such forensic services, ensuring the security and privacy of vehicles while guaranteeing the efficiency of data transmission among vehicles is important. In this paper, we propose a lightweight incentive authentication scheme (LIAS) for forensic services in IoV. LIAS is developed on a three-tier architecture containing cloud layer, fog layer, and user layer. LIAS uses pairing-free certificateless signcryption, pseudonym update mechanism, and incentive mechanism to realize a secure anonymous authentication efficiently. We conduct correctness and security analysis, as well as performance analysis and evaluation to validate the high security and efficiency of LIAS. Experimental results reveal that, the communication and computation overheads as well as the message delay and packet loss of LIAS are much lower than those of state-of-the-art techniques. Note to Practitioners - This paper is motivated by the security and privacy issues of forensic services in IoV for intelligent transportation. Our goal is to improve the security and privacy of vehicles while guaranteeing the lightweight and incentive of data transmission among the vehicles. Fog-assisted IoV is introduced to fully utilize the capacities of near-user edge devices as well as the connections between fog nodes and devices. However, it still faces the difficulties in ensuring vehicles' security and privacy. Moreover, vehicles' information dissemination could be easily monitored because of the unavoidable defect of wireless communication. Thereby, it is essential to guarantee the security and privacy of vehicles while enhancing the efficiency of vehicles' data transmission during the forensic service.

关键词： Authentication

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SOSO: Symbiotic Organisms Search Optimization based Faster RCNN for Secure Data Storage in Cloud

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IETE Journal of Research 2024年第2期70卷 1196-1208页

作者： J. Thresa Jeniffer A. Chandrasekar S. Jothi 1 Research Scholar Department of Information Technology St. Joseph's College of Engineering Anna University Chennai India 2 Department of Computer Science and Engineering St. Joseph’s College of Engineering Chennai India

Because of the Internet of Things (IoT) and wearable devices, information obtained is subject to cyber attacks, making intrusion detection an essential component. The increased number of attacks on IoT devices exposes them to ongoing exploitation and data theft. Mirai, denial of Service (DoS), scan,etcare the major types of attacks conducted on IoT. Traditional attack detection algorithms have more disadvantages like lack of adaptability in all scenarios, low local minima computational complexity,etc. Thus developing a method that is suitable for all environments and has good convergence speed is taken as the motivation of this study. This paper presents a novel Symbiotic Organisms Search Algorithm (SOSA) optimized Faster region-based convolutional neural network (FRCNN) for attack detection and data type classification. The data to be stored in the cloud is analyzed whether they have restricted access, confidential, or unclassified. Depending upon the variety of data, the Optimal homomorphic encryption (OHE) approach is provided for security. The key selection process is done by an Elastic Collision Seeker Optimization Algorithm (ECSOA) which offers secure access to the data owner and prevents the data from unauthorized access. The model is optimized using the algorithm which improves the feature extraction performance. The effectiveness of the proposed model in attack detection is evaluated using two datasets namely the CSE-CIC-ID2018 dataset and the IoTID20 dataset. When compared to the existing techniques, the proposed model offers optimal performance in terms of multiclass attack detection.

关键词： Deep learning Intrusion detection IoT Machine learning Optimal homomorphic encryption Secure storage

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IoT networks attacks detection using multi-novel features and extra tree random - voting ensemble classifier (ER-VEC)

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16637-16651页

作者： Sarwar, Ayesha Mushtaq, Muhammad Faheem Akram, Urooj Rustam, Furqan Hamza, Ameer Rupapara, Vaibhav Ullah, Saleem Department of Information Technology Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan64200 Pakistan Department of Artificial Intelligence The Islamia University of Bahawalpur Bahawalpur63100 Pakistan School of Computer Science University College Dublin DublinD04 V1W8 Ireland School of Computing and Information Sciences Florida International University MiamiFL United States

Internet usage is increasing day by day all over the world and as a result, technology is also developing to make daily life appliances as smart as possible. Millions of devices are connected using IoT technology, and the vulnerabilities of these devices are still exploitable by attackers. Having access to IoT devices through a Bot Master allows the Bot Master to attack a targeted server with these devices. To detect malicious traffic in IoT networks, there is a need for an intelligent mechanism. Although there have been many studies on the detection of botnet malware, accuracy and efficiency remain a gap. This study focuses on an automatic system that can detect botnet malware with high accuracy. A new ensemble model has been proposed in this study, known as the Extra Tree Random Voting Ensemble Classifier (ER-VEC), which is a combination of two tree-based models called Extra Tree and Random Forest. The proposed model is tested on several malicious traffic in the IoT networks datasets such as IoTID20, MedBIoT, UNSW-NB15, N-BaIoT, and ER-VEC achieving 99.99%, 99.91%, 95.64%, and 100% accuracy scores, respectively. In comparison with the proposed model, other machine learning models were also employed, and ER-VEC significantly outperformed them in terms of accuracy, precision, recall, F1-score, and error rate across all datasets. In addition, we performed K-Fold cross-validation and found that ER-VEC achieved an accuracy score of 98% and a standard deviation of 0.04±. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Botnet

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A New Belief-Based Incomplete Pattern Unsupervised Classification Method : Extended Abstract 39

A New Belief-Based Incomplete Pattern Unsupervised Classific...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Zhang, Zuowei Liu, Zhe Ma, Zongfang Zhang, Yiru Wang, Hao University of Rennes 1 Institut de Recherche en Informatique et Systèmes Aléatoires France Xi'an University of Architecture and Technology School of Information and Control Engineering China Norwegian University of Science and Technology Department of Computer Science Norway

ISBN: (纸本)9798350322279

Imputing the incomplete patterns in clustering tasks is a common but risky procedure, because the estimated values may affect the real distribution of the data and deteriorate the results. To address this problem, a new belief-based incomplete pattern unsupervised classification method (BPC) with uncertainty and imprecision reasoning is proposed in this paper. First, the complete patterns are grouped into a few clusters to obtain the corresponding reliable centers, and thereby are divided into reliable patterns and unreliable ones by an optimization method. Second, a basic classifier trained by reliable patterns is employed to classify unreliable patterns and incomplete patterns edited by the neighbors. Finally, some imprecise patterns are carefully reassigned again by a new distance-based rule depending on the obtained reliable centers and belief functions theory. The simulation results show that the BPC has the potential to deal with real datasets. © 2023 IEEE.

关键词： Uncertainty analysis

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