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Quantum dot coherent comb lasers for B5G/6G mobile and terrestrial communication systems 28

Quantum dot coherent comb lasers for B5G/6G mobile and terre...

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Integrated Optics: Devices, Materials, and Technologies XXVIII 2024

作者： Lu, Z.G. Liu, G.C. Liu, J.R. Poole, P.J. Mao, Y.X. Zeb, K. Xie, X.R. Rahim, M. Vachon, M. Song, C.Y. Qi, Y. Weber, J. Barrios, P. Poitras, D. Sabourin, N. Chen, S. Zhao, P. Huang, L. Askarian, A. Zhang, X.P. Yao, J.P. Wu, K. Advanced Elec. and Photonics Research Centre National Research Council OttawaON Canada Depart. Electrical and Computer Engineering Concordia University MontrealQC Canada School of Elec. Engineering and Computer Science University of Ottawa OttawaON Canada Department of Electrical Engineering Ecole Polytechnique Montreal MontrealQC Canada

ISBN: (纸本)9781510670389

Future mobile and terrestrial communication systems B5G/6G are strongly expected to heterogeneously realize typically diversified performances, i.e. high-data-rate, high-mobility, low-latency, high-capacity, massive-connectivity and low-energy in order to satisfy the highly diversified application requirements. To achieve those goals the operation band of B5G/6G should be primarily in the millimeter-wave (mmW) range. Generation and distribution of mmW with traditional methods is limited by elec.ronic bottleneck and associated complexity. Consequently broad bandwidth, simple, efficient, and cost-effective photonic mmW-over-fiber (mmWoF) transmission systems are solutions for B5G/6G. The spectral purity of mmW carriers is necessary. Numerous approaches have been proposed to generate pure mmW signals. Compared with other technologies, quantum dash or dot (QD) coherent comb lasers (QD CCLs) have great advantages for mmW generation because QD-CCLs with low power consumption and chip-scale integration capacity with silicon can provide multiple highly correlated and low noise optical channels. In this paper we will present our developed InAs/InP QD-CCLs around 1550 nm with the channel spacing from 10 GHz to 1000 GHz and the output power up to 50 mW. By using a C-band QD CCL and based on the single- and dual-optical carrier modulation schemes, an up to 16-Gb/s mmWoF optical heterodyne wireless signal at 28 GHz through a 25-km single mode fiber before the mmW carrier is optically synthesized remotely for detection over a 2-m free space. The data capacity and performance of the proposed mmWoF link can be significantly increased by utilizing a duplex mmWoF link with MIMO and WDM technique, which provides a cost-efficient and promising solution for Terabit/s capacity mmWoF fronthaul systems of B5G/6G networks. © 2024 SPIE.

关键词： Radio-over-fiber

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ATADA: Adaptive Time Aware Anomaly Detection Approach for Real-Time Intelligent Transportation Systems

ATADA: Adaptive Time Aware Anomaly Detection Approach for Re...

引用

2023 IEEE International Conference on Big Data, BigData 2023

作者： Zhang, Ke Liu, Hengchang Clarke, Siobhan Trinity College Dublin School of Computer Science and Statistics Dublin Ireland Univ. of Elec. Sci. & Tech. of China School of Computer Science Chengdu China

ISBN: (纸本)9798350324457

Accurately detecting passenger traffic flow in public transportation, e.g., for the purpose of identifying congested stations, and detecting anomalies in that flow, are both important for enhancing passenger satisfaction and safety. However, current methods for traffic flow detection and prediction train their models in offline modes using potentially outdated data, which can't be refreshed with new data. In this paper, we introduce a two-step, continuously updating framework aimed at real-time prediction of anomalies within transportation networks, which we name Adaptive Time-Aware Anomaly Detection Approach (ATADA). Specifically, the first step filters out regular traffic patterns and balances the data set, while the second step employs a sequence-to-sequence attention model, a type of deep learning model, to further detect the traffic anomalies. We propose a dynamic online time-aware learning mechanism which enables our models to continuously train on incoming data and to adapt predictive strategies based on the most recent traffic patterns. The proposed method is validated using real subway AFC data from Suzhou, China and Hangzhou, China. Experimental results demonstrate that our framework significantly improves efficiency while maintaining high accuracy in real-time traffic anomaly detection. © 2023 IEEE.

关键词： adaptive anomaly detection intelligent transportation neural networks

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How Green Are elec.ric Vehicles in Developing Countries? 60

How Green Are Electric Vehicles in Developing Countries?

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60th IEEE/IAS Industrial and Commercial Power Systems Technical Conference, I and CPS 2024

作者： Ayaz, Muhammad Ashar Ehsani, Mehrdad Rahman, Syed Khan, Irfan Su, Chun-Lien Texas A&m University Dept. of Elec. & Comp. Engr. College StationTX77840 United States National Kaohsiung University of Science & Technology Department of Electrical Engineering Taiwan

ISBN: (纸本)9798350345308

This paper addresses the urgent challenges of escalating global warming, rapid ozone layer depletion, and persistent greenhouse gas (GHG) emissions exacerbated by nations' reluctance to limit industrial emissions for economic growth. This has prompted a global shift towards exploring alternative strategies for pollution mitigation, including transitioning from conventional power generation to sustainable energy resources and a move from gasoline-based transportation to elec.ric vehicles (EVs). Particularly critical is the question of whether this transition genuinely yields environmental benefits, or if it potentially exacerbates existing challenges, especially in underdeveloped or developing countries heavily reliant on fossil fuel-generated elec.ricity. The paper presents a concise comparative analysis between elec.ric Vehicles (EVs) and Internal Combustion Engine Vehicles (ICEVs), examining GHG emissions throughout their lifetimes, during manufacturing, and in terms of fuel consumption. Additionally, it explores the energy mix of developing countries compared to the global scenario, presenting results for the comparison between ICEVs and EVs using elec.ricity from coal, gas, nuclear, and a balanced energy mix-dominated power system. The study identifies opportunities for improvement in EV adoption and emission mitigation strategies, offering valuable insights into sustainable practices and environmental conservation. © 2024 IEEE.

关键词： Gasoline

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Analyzing the Impact of Temperature Variations on the Performance of Thermoelec.ric Generators 2

Analyzing the Impact of Temperature Variations on the Perfor...

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2nd International engineering Conference on elec.rical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Yahya, Adel E. M. Sarreb, Rebha Daw Yahya, Khalid Attar, Hani Aldababsa, Mahmoud Hafez, Mohamed Sabratah University Dept of Computer Engineering Regdalen Libya Sabratah University Dept of Electrical Engineering Regdalen Libya Istanbul Gelisim University Dept of Elec. and Electr. Engineering Istanbul Turkey Zarqa University Jordan College of Engineering University of Business and Technology Dept of Energy Engineering Jeddah21448 Saudi Arabia Nisantasi University Dept of Elec. and Electr. Engineering Istanbul Turkey INTI-IU-University Faculty of Engineering Feqs Nilai Malaysia

ISBN: (纸本)9798350373363

Recent advancements in renewable energy technologies have brought Thermoelec.ric Generators (TEGs) to the forefront, primarily due to their ability to efficiently convert waste thermal energy into elec.rical power across multiple power levels. This study delves into the environmental benefits and operational efficiencies of TEGs, highlighting their zero-emission, silent operation, and low maintenance requirements. A significant portion of this research is dedicated to exploring the influence of temperature differential (ΔT) on the efficacy of TEGs, as temperature is a crucial factor in the energy conversion process. The elec.rical representation of TEGs is modeled as a voltage source in series with an internal resistance, while its thermal aspect comprises parallel-connected p- and n-type thermocouples. The study aims to meticulously analyze the behavior of TEG models under various thermal gradients and to scrutinize their elec.rical characteristics under different load conditions. This is achieved through comprehensive experimental methodologies, with the findings underscoring the impact of temperature variations on both hot and cold sides of TEGs on all elec.rical parameters. It is observed that an increase in the temperature difference results in a corresponding rise in both the maximum power output and the open-circuit voltage. In essence, the efficiency of TEGs is noted to improve with a higher and more stable temperature differential. © 2023 IEEE.

关键词： Thermocouples

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Key Success Factor of Marketing Intelligence in Higher Education: Systematic Literature Review 8

Key Success Factor of Marketing Intelligence in Higher Educa...

引用

8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Radhia, Fathy Christian, Albert Budi Putra, Edi Purnomo Hartono, Sugiarto Triantono, Henricus Bambang School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Coll. of Elec. Engineering and Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)9798350326093

Marketing Intelligence (MI) is the process of collecting and applying information to formulate effective marketing strategies. Currently, the information on MI at XYZ University still does not fully describe the user's needs and it can be improved so that its use can be successfully used by end-users. The research question is what key success factors are needed to build marketing intelligence in higher education. The objective of this study is to ascertain the critical success factors of marketing intelligence needed by universities. This study uses a qualitative research method, namely SLR conducting a Systematic Literature Review (SLR) that involves a comprehensive analysis of marketing intelligence. This research produces several factors that determine the success of marketing intelligence, consisting of Technology;Strategic, Tactical, Operational;Small and Medium Enterprise (SM E)'s Performance;Predictors and Consequences;Customer Satisfaction and Loyalty;Environment;Concept, Overview;Sales Forces;External Intelligence;Internal Information;Talent Management;Individual Sources;Information Sources;Personal Factors;and Organizational. The impact of this research is that universities that aim to develop and implement marketing intelligence can pay attention to technology, personal factors, and strategic/tactical/operational variables which are the 3 critical variables for building marketing intelligence. © 2023 IEEE.

关键词： Customer satisfaction

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Edge Computing-based Intelligent Garbage Classification and Recognition Application 4

Edge Computing-based Intelligent Garbage Classification and ...

引用

4th International Conference on computers and Artificial Intelligence Technology, CAIT 2023

作者： Yu, Liang Mo, Fu Lai, Yangbing Tian, Liwei Guangdong University of Science and Technology College of Computer Science and AIoT Edge Computing Engineering Technology Research Center of Dongguan City DongGuan China Guangdong University of Science and Technology Coll. of Mech. and Elec. Eng. and Intelligent Mfg. and Environ. Monitoring Engineering Technology Research Center of Dongguan City DongGuan China

ISBN: (纸本)9798350326710

As the standard of living improves, there is a corresponding increase in the variety of materials, leading to a rise in household garbage. Dealing with the challenge of classifying numerous types of garbage can be a daunting task. The accuracy of waste separation cannot be effectively improved solely by relying on individuals' common sense perception, knowledge propaganda, and training. A more comprehensive and efficient approach is needed. This paper proposes an intelligent waste classification and recognition application based on edge computing to address the aforementioned issues. The edge mobile intelligent waste classification and recognition application utilizes a small-sample lightweight machine learning model for training compressed quantization on the Edge Impulse Platform (EI). The application is built using the MobileNetV2 model and migration learning theory. When a user scans a trash image by running the application through a mobile edge device, the tiny machine learning model is invoked to perform edge computation and identify the class of trash. System testing on the EI platform and field applications demonstrates that the average response time for intelligent waste classification and recognition is 0.66 seconds. Additionally, the average inference time is less than 5 milliseconds, and the application achieves an average recognition accuracy of 97% or higher. This intelligent waste classification and recognition system exhibits excellent overall performance and offers great convenience for individuals in their daily waste classification tasks. © 2023 IEEE.

关键词： Edge computing

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Geometric-Aware Facial Landmark Emotion Recognition 6

Geometric-Aware Facial Landmark Emotion Recognition

引用

6th International Conference on Software engineering and computer science, CSECS 2023

作者： Zhang, Qixuan Wang, Zhifeng Liu, Yang Qin, Zhenyue Zhang, Kaihao Gedeon, Tom College of Engineering and Computer Science Australian National University CanberraACT Australia School of Elec Eng Comp and Math Sci Curtin University PerthWA Australia

ISBN: (纸本)9798350306378

Emotion recognition is crucial for enhancing human-computer interaction. While traditional approaches largely depend on RGB images, our proposed Geometric-Aware Facial Landmark Emotion Recognition framework harnesses the geometric and spectral attributes of facial landmarks for emotion recognition. This work unfolds three key contributions: utilizing Graph Convolutional Networks to grasp the natural spatial relationships among facial landmarks, introducing distance-aware graph operations to accentuate the relational geometry, and employing spectral encodings to comprehend the frequency-based attributes of landmark positions. Through rigorous experiments on recognized datasets RAF-DB and KDEF, Our method surpasses baseline methods, demonstrating its effectiveness especially in high-resolution scenarios where detailed facial landmarks are apparent. The datasets curated for Facial Landmark Emotion Recognition will also be shared publicly as part of this work, offering a significant resource for the community. The encouraging results highlight the potential of geometric-aware analysis in propelling emotion recognition systems forward, opening avenues for further research in this evolving domain. © 2023 IEEE.

关键词： Emotion Recognition

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Autonomous Drone Landing on a Moving Naval Base using Vision-Based Robot Control

Autonomous Drone Landing on a Moving Naval Base using Vision...

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2023 International Conference on elec.rical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Subamanian, Senthil Pon Vignesh Subramanian, Suvetha Meenaa Muthiah, Padmapriya Shajahan, Jalaluddin Mohd Ansari Saveetha University Simats School of Engineering Dept. of Elec. and Comm. Engg. Chennai India City University of New York City College of New York Dept. of Computer Science New York United States Monroe College King Graduate School Dept. of Computer Science New York United States City University of New York City College of New York Dept. of Electrical Engineering New York United States

ISBN: (纸本)9798350322972

This paper proposes a novel technique of an Autonomous Drone landing on top of a moving Naval Vessel Base using Vision-Based Robot Control. To achieve an effective landing a camera is fixed under the drone which captures the images and detects the movement of the landing base. The landing base on top of the vessel is specially designed with a nested tag which is scanned by the fixed camera on the drone from a high altitude. This nested tag is a fiducial marker that is used by the imaging system of the drone as a point of reference in the Naval Vessel Base for precise landing. The controller is designed in the image coordinate frame captured by the imaging system on the drone with the help of the Jacobian Matrix. Whether the vessel remains stationary or moving, it always trembles, especially, when a vessel experiences bad weather at sea, it will be quite challenging for the drone to land on the trembling Naval Vessel Base. These unexpected movements cause the landing tag to disorient from the horizontal view of the captured frames, which makes it difficult to obtain the expected mid-point of the tag. To overcome this complication the translational and rotational motions of the drone are decoupled from the camera using the Virtual Camera Method where the mid-point of the tag is set as the feature point which helps to obtain the expected position during the horizontal plane disorientation of the tag image captured by the camera from the drone. Additionally, Kalman Filter is employed in the imaging system to reduce the observation noise so that precise estimation of the feature point can be achieved. © 2023 IEEE.

关键词： Cameras

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Development of an Ultra-Sensitive Magnetic-Based Biosensor;a Simulation Study 2

Development of an Ultra-Sensitive Magnetic-Based Biosensor;a...

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2nd International engineering Conference on elec.rical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Yahya, Khalid Husseini, Abbas Ali Dirican, Onur Attar, Hani Aldababsa, Mahmoud Hafez, Mohamed Sabratah University Dept of Electrical Engineering Regdalen Libya Istanbul Gelisim University Dept of Elec. and Electr. Engineering Istanbul Turkey Istanbul Gelisim University Life Science and Biomed. Center Istanbul Turkey Istanbul Gelisim University Dept. of Pathology Laboratory Techniques Istanbul Turkey Zarqa University Jordan College of Engineering University of Business and Technology Dept of Energy Engineering Jeddah21448 Saudi Arabia Nisantasi University Dept of Elec. and Electr. Engineering Istanbul Turkey INTI-IU-University Faculty of Engineering Feqs Nilai Malaysia

ISBN: (纸本)9798350373363

This study presents an advanced magnetic biosensor design incorporating an L-shaped ferromagnetic core with UL dimensions and an air gap replaced by highly porous aluminum or copper foam later-filled biological samples containing high-permeability ferromagnetic nanoparticles. The sensor detects specific biological molecules through magnetic field interactions. The system's elec.rical parameters were methodically optimized for enhanced performance. The research investigated the impact of various materials on the air gap's magnetic properties and assessed the relationships between permeability, output-induced voltage, input voltage, and input frequency. Findings indicate that using materials with higher magnetic permeability, such as Magnetite (Fe304) or Cobalt ferrite (CoFe2O4) ferrofluids, considerably improved the biosensor's performance by optimizing magnetic coupling between primary and secondary windings. This innovative magnetic biosensor holds potential for diverse applications, including medical diagnostics, environmental monitoring, and industrial process control. The study offers valuable insights into magnetic biosensor design and optimization, facilitating heightened sensitivity and selec.ivity in detecting target molecules. © 2023 IEEE.

关键词： Biosensors

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HRPE-Enhanced AI-Based 5G Indoor Localization in Presence of Specular and Dense Multipaths

HRPE-Enhanced AI-Based 5G Indoor Localization in Presence of...

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European Conference on Antennas and Propagation, EuCAP

作者： Yuchen Shi Xiaoxiao Yang Yuying Sun José Rodríguez-Piñeiro Xueming Hong Tomás Domínguez-Bolaño Xuefeng Yin College of Electronics and Information Engineering Tongji University Shanghai China School of Elec. Science and Engineering Xiamen University China School of Informatics Xiamen University China CITIC Research Center & Department of Computer Engineering University of A Coruña Spain

Fifth generation (5G) communication systems are expected to provide an ubiquitous solution for indoor positioning, and deep neural networks (DNNs) have been recently proposed for this purpose. However, DNN-based positioning solutions are in general very dependent on the training data. In this paper, an architecture for positioning based on wireless signals is proposed, namely the SAGE-Enhanced CEAP (SE-CEAP). The architecture considers firstly an enhancement of the acquired Channel State Information (CSI) data by means of a high-resolution parameter estimation (HRPE) method, namely the space-alternating generalized-expectation maximization (SAGE) algorithm, and secondly a specifically designed DNN for localization, namely the CNN-Enblock AI Positioning (CEAP). The provided results, considering a realistic 5G New Radio (NR) deployment in an indoor scenario, show that the proposed architecture outperforms other classical DNN-based localization approaches, not only in localization accuracy, but also in generalizability of the results, which is a common drawback of DNN-based solutions.

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