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Generalization-Enhanced Few-Shot Object Detection in Remote Sensing

IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5445-5460页

作者： Lin, Hui Li, Nan Yao, Pengjuan Dong, Kexin Guo, Yuhan Hong, Danfeng Zhang, Ying Wen, Congcong China Academy of Electronics and Information Technology Beijing100846 China China Meteorological Administration National Satellite Meteorological Center Innovation Center for FengYun Meteorological Satellite Beijing100081 China Tsinghua University State Key Laboratory of Hydroscience and Engineering Department of Hydraulic Engineering Beijing100084 China Chinese Academy of Sciences Aerospace Information Research Institute Beijing100094 China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100049 China University of Science and Technology Beijing School of Automation and Electrical Engineering Beijing100083 China New York University Abu Dhabi Department of Electrical and Computer Engineering Abu Dhabi United Arab Emirates University of Science and Technology of China Anhui China

Object detection is a fundamental task in computer vision that involves accurately locating and classifying objects within images or video frames. In remote sensing, this task is particularly challenging due to the high resolution, multi-scale features, and diverse ground object characteristics inherent in satellite and UAV imagery. These challenges necessitate more advanced approaches for effective object detection in such environments. While deep learning methods have achieved remarkable success in remote sensing object detection, they typically rely on large amounts of labeled data. Acquiring sufficient labeled data, particularly for novel or rare objects, is both challenging and time-consuming in remote sensing scenarios, limiting the generalization capabilities of existing models. To address these challenges, few-shot learning (FSL) has emerged as a promising approach, aiming to enable models to learn new classes from limited labeled examples. Building on this concept, few-shot object detection (FSOD) specifically targets object detection challenges in data-limited conditions. However, the generalization capability of FSOD models, particularly in remote sensing, is often constrained by the complex and diverse characteristics of the objects present in such environments. In this paper, we propose the Generalization-Enhanced Few-Shot Object Detection (GE-FSOD) model to improve the generalization capability in remote sensing FSOD tasks. Our model introduces three key innovations: the Cross-Level Fusion Pyramid Attention Network (CFPAN) for enhanced multi-scale feature representation, the Multi-Stage Refinement Region Proposal Network (MRRPN) for more accurate region proposals, and the Generalized Classification Loss (GCL) for improved classification performance in few-shot scenarios. GE-FSOD demonstrates superior robustness and accuracy in remote sensing FSOD tasks through these enhancements. Extensive experiments on the DIOR and NWPU VHR-10 datasets show that our

关键词： Labeled data

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On a novel tracking differentiator design based on iterative learning in a moving window

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Control Theory and Technology 2023年第1期21卷 46-55页

作者： Xiangyang Li Rafal Madonski Zhiqiang Gao Senping Tian Key Laboratory of Autonomous Systems and Network Control Ministry of EducationSchool of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510641China Energy and Electricity Research Center Jinan UniversityZhuhai 519070China Department of Electrical and Computer Science Cleveland State UniversityClevelandOH 44115USA

Differential signals are key in control engineering as they anticipate future behavior of process variables and therefore are critical in formulating control laws such as proportional-integral-derivative(PID).The practical challenge,however,is to extract such signals from noisy measurements and this difficulty is addressed first by *** in the form of linear and nonlinear tracking differentiator(TD).While improvements were made,TD did not completely resolve the conflict between the noise sensitivity and the accuracy and timeliness of the *** two approaches proposed in this paper start with the basic linear TD,but apply iterative learning mechanism to the historical data in a moving window(MW),to form two new iterative learning tracking differentiators(IL-TD):one is a parallel IL-TD using an iterative ladder network structure which is implementable in analog circuits;the other a serial IL-TD which is implementable digitally on any computer *** algorithms are validated in simulations which show that the proposed two IL-TDs have better tracking differentiation and de-noise performance compared to the existing linear TD.

关键词： Tracking differentiator(TD) Iterative learning Iterative learning tracking differentiator(IL-TD) Active disturbance rejection control(ADRC)-Two-dimensional system(2-D system)

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Estimating ECG Intervals from Lead-I Alone: External Validation of Supervised Models

arXiv

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arXiv 2024年

作者： Alam, Ridwan Stultz, Collin M. the Research Laboratory of Electronics the Department of Electrical Engineering Computer Science the Massachusetts Institute of Technology United States

The diagnosis, prognosis, and treatment of a number of cardiovascular disorders rely on ECG interval measurements, including the PR, QRS, and QT intervals. These quantities are measured from the 12-lead ECG, either manually or using automated algorithms, which are readily available in clinical settings. A number of wearable devices, however, can acquire the lead-I ECG in an outpatient setting, thereby raising the potential for out-of-hospital monitoring for disorders that involve clinically significant changes in ECG intervals. In this work, we therefore developed a series of deep learning models for estimating the PR, QRS, and QT intervals using lead-I ECG. From a corpus of 4.2 million ECGs from patients at the Massachusetts General Hospital, we train and validate each of the models. At internal holdout validation, we achieve mean absolute errors (MAE) of 6.3 ms for QRS durations and 11.9 ms for QT intervals, and a MAE of 9.2 ms for estimating PR intervals. Moreover, as a well-defined P-wave does not always exist in ECG tracings – for example, when there is atrial fibrillation – we trained a model that can identify when there is a P-wave, and consequently, a measurable PR interval. We validate our models on three large external healthcare datasets without any finetuning or retraining - 3.2 million ECG from the Brigham and Women’s Hospital, 668 thousand from MIMIC-IV, and 20 thousand from PTB-XL - and achieve similar performance. Also, our models significantly outperform two publicly available baseline algorithms. This work demonstrates that ECG intervals can be tracked from only lead-I ECG using deep learning, and highlights the potential for out-of-hospital applications. © 2024, CC BY-NC-ND.

关键词： Electrocardiography

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Distributed Consensus Multi-Agent System Control of AC Microgrid Under Various Communication Disturbances

Distributed Consensus Multi-Agent System Control of AC Micro...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Ibtissam Kharchouf Mahmoud S. Abdelrahman Hossam M. Hussein S M Sajjad Hossain Rafin Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami USA

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

The resilience of AC microgrids (MGs) in the face of communication disturbances, including variable delays, packet loss, packet corruption (noise), and limited bandwidth, is studied. A Controller Hardware-in-the-Loop (CHIL) platform is used to investigate the ramifications of these disturbances comprehensively. The MG cyber-physical system (CPS) integrates a real-time islanded AC microgrid deployed on RT-LAB, distributed secondary consensus controllers on single-board computers, and a real communication network infrastructure. Through meticulous analysis and experimentation, this research highlights the vulnerabilities of AC MGs to communication disturbances and their detrimental effects on the system resiliency, stability, and efficiency. The experiments emphasize the need for designing robust and adaptive control strategies that can effectively mitigate the effects of communication disruptions and enhance the overall reliability and performance of AC microgrid systems.

关键词： Noise Packet loss Microgrids Bandwidth Cyber-physical systems Stability analysis Real-time systems Delays Resilience Multi-agent systems

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LSTM-Based Digital Twin Modeling for LVDC Microgrid Operation with Pulsed Load

LSTM-Based Digital Twin Modeling for LVDC Microgrid Operatio...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Mahmoud S. Abdelrahman Mustafa Esoofally Hossam Hussein Ibtissam Kharchouf Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami USA

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

Proper control and energy management is crucial for the effective, practical, and reliable operation of DC microgrids. In DC microgrids, the high certainties of renewables and the dynamic behavior of particular loads propose different modeling, operation, and control challenges. With recent advancements in information and communication technologies offering energy solution services relying on data analytics, machine learning (ML), and IoT technologies, the concept of digital twins (DT) has emerged as a feasible and active model to enhance the operation and resilience of microgrids. This paper proposes a long short-term memory (LSTM) neural networkbased strategy to construct a data-driven digital twin (DT) model of DC microgrids. The model is tested and validated with a standalone DC MG containing Renewable Energy Sources (RES), Energy Storage Sources (ESSs), and different load types. The accuracy of the created twin model is tested and evaluated under different scenarios of the microgrid operation, such as a sudden decrease in renewable generation and energizing pulsed load to the MG.

关键词： Renewable energy sources Accuracy Simulation Microgrids Real-time systems Digital twins Reliability Long short term memory Load modeling Resilience

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Modelling individual head-related transfer function(HRTF)based on anthropometric parameters and generic HRTF amplitudes

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CAAI Transactions on Intelligence Technology 2023年第2期8卷 364-378页

作者： Rui Zhang Ruijie Meng Jinqiu Sang Yi Hu Xiaodong Li Chengshi Zheng Key Laboratory of Noise and Vibration Research Institute of AcousticsChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina Shanghai Institute of AI for Education East China Normal UniversityShanghaiChina Department of Electrical Engineering and Computer Science University of Wisconsin–MilwaukeeMilwaukeeWisconsinUSA

The head-related transfer function(HRTF)plays a vital role in immersive virtual reality and augmented reality technologies,especially in spatial audio synthesis for binaural *** article proposes a deep learning method with generic HRTF amplitudes and anthropometric parameters as input features for individual HRTF *** designing fully convolutional neural networks,the key anthropometric parameters and the generic HRTF amplitudes were used to predict each individual HRTF amplitude spectrum in the full-space directions,and the interaural time delay(ITD)was predicted by the transformer *** the amplitude prediction model,the attention mechanism was adopted to better capture the relationship of HRTF amplitude spectra at two distinctive directions with large angle differences in ***,with the minimum phase model,the predicted amplitude spectrum and ITDs were used to obtain a set of individual head-related impulse *** the separate training of the HRTF amplitude and ITD generation models,their joint training was also considered and *** root-mean-square error and the log-spectral distortion were selected as objective measurement metrics to evaluate the *** experiments further showed that the auditory source localisation performance of the proposed method was better than other methods in most cases.

关键词： audio databases augmented reality deep learning multimedia

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Neural Network Modeling Bias for Hafnia-based FeFETs 23

Neural Network Modeling Bias for Hafnia-based FeFETs

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Proceedings of the 18th ACM International Symposium on Nanoscale Architectures

作者： Osama Yousuf Imtiaz Hossen Andreu Glasmann Sina Najmaei Gina C. Adam Department of Electrical and Computer Engineering George Washington University USA DEVCOM Army Research Laboratory USA

ISBN: (纸本)9798400703256

Modeling bias – the difference between the test accuracy obtained by a reference network prototype and a simulated model of that prototype – is explored in the context of hafnia-based ferroelectric field effect transistor (FeFET) devices. Device operating conditions are investigated as a parameter for mitigating the impact of device-to-device variability on the underlying network performance. The computational framework includes a physics-based compact model with artificial variance to sample device data and a multivariate Kriging model to create jump table device models; this framework is a fast and efficient technique to model device populations for realistic neural network simulations. Neural network simulations elucidate optimal operating conditions for practical implementation of FeFET neuromorphic circuits. Future work will include experimental verification and performance comparison against other neuromorphic emerging-memory technologies such as resistive random-access memory (ReRAM) in terms of neural network performance.

关键词： Ferroelectric field effect transistor (FeFET) emerging device modeling hardware neural network neuromorphic hardware

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Polarization‑entangled photon‑pair source with van der Waals 3R‑WS_(2) crystal

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eLight 2024年第1期4卷 99-106页

作者： Jiangang Feng Yun‑Kun Wu Ruihuan Duan Jun Wang Weijin Chen Jiazhang Qin Zheng Liu Guang‑Can Guo Xi‑Feng Ren Cheng‑Wei Qiu Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore CAS Key Laboratory of Quantum Information University of Science and Technology of ChinaHefei 230026China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of ChinaHefei 230026China Hefei National Laboratory University of Science and Technology of ChinaHefei 230088China School of Materials Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Optical Science and Engineering and Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and PerceptionFudan UniversityShanghai 200433China

Ultracompact entangled photon sources are pivotal to miniaturized quantum photonic *** der Waals(vdW)nonlinear crystals promise efficient photon-pair generation and on-chip monolithic integration with nanophotonic ***,it remains challenging to generate maximally entangled Bell states of photon pairs with high purity,generation rate,and fidelity required for practical ***,we realize a polarization-entangled photon-pair source based on spontaneous parametric down conversion in an ultrathin rhombohedral tungsten disulfide(3R-WS2)*** vdW entangled photonic source exhibits a high photon-pair purity with a coincidenceto-accidental ratio of above 800,a generation rate of 31 Hz,and two maximally polarization-entangled Bell states with fidelities exceeding 0.93 and entanglement degree over *** results stem from scalable optical nonlinearity,enhanced second-order susceptibility by electronic transitions,and a well-defined symmetry-enabled selection rule inherent in *** polarization entangled photon source can be integrated with photonic structures for generating more complex entangled states,thus paving an avenue for advanced quantum photonic systems toward computation and metrology.

关键词： purity maximal Polarization

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Entanglement Capacity Estimates and Throughput Measurements of Quantum Channels

Entanglement Capacity Estimates and Throughput Measurements ...

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2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Rao, Nageswara S. V. Alshowkan, Muneer Chapman, Joseph C. Peters, Nicholas A. Lu, Hsuan-Hao Lukens, Joseph M. Guha, Saikat Oak Ridge National Laboratory Computational Sciences and Engineering Division Oak Ridge United States Arizona State University Research Technology Office TempeAZ United States University of Maryland Department of Electrical and Computer Engineering College ParkMD United States

ISBN: (纸本)9798350355543

The throughput is an important performance metric of entangled qubit distribution quantum networks, and may be characterized by the number of distributed entangled qubit pairs per second (ebps). It is measured over physical quantum network connections using specialized instruments, including photonic entanglement sources and single photon detectors. Extensive theory has been developed to estimate the entangled qubit capacity of quantum channels using abstractions of physical connections. These two quantities both characterize the throughput performance but in different ways, and typically have been hard to relate to each other in concrete terms, in part due to the lack of precise measurements with matching analytical models and derivations. We describe measurements on a physical testbed with fiber connections of lengths 0-75 kilometers. We obtain the normalized analytic capacity estimates using the transmissivity approximations derived using single photon coincidence measurements, and convert them to bounds on throughput (measured in ebps) using a multiplier derived from co-located detector measurements. The results indicate consistent throughput measurements upper-bounded by their analytical capacity estimates across all connections. We show that previous capacity estimates using light measurements are below ebps measurements for some connections, due to the inclusion of non-representative decrease of light levels outside C-band with distance. © 2024 IEEE.

关键词： channel capacity ebit entangled bits per second quantum network testbed throughput estimates

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EMS-Based HESS Using an Adaptive Filter Approach with SoC Balancing for DCMG

EMS-Based HESS Using an Adaptive Filter Approach with SoC Ba...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Hossam M. Hussein Mahmoud S. Abdelrahman Ibtissam Kharchouf S M Sajjad Hossain Rafin Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami USA

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

Globally, microgrid-based renewable energy systems are increasingly adopted as part of the transition to sustainable energy sources. However, their intermittent nature and low inertia necessitate energy storage systems to maintain system stability. A highly effective solution involves employing hybrid energy storage systems combining batteries and supercapacitors. This combination leverages the unique characteristics of each component, particularly their energy and power densities, to effectively mitigate fluctuations in the DC-bus voltage under varying power conditions. To address these challenges, this paper proposes a novel energy management system-based hybrid energy storage system and a modified filter-based control technique to enhance the stability of islanded DC microgrids. Further, balance the state of charge of each storage device without breaching the stability state of the system. The effectiveness of the proposed system was validated through various study cases conducted in the MATLAB/SIMULINK environment. Additionally, this paper explores the topology of hybrid battery/supercapacitor systems, the dynamics of pulsed power loads, and the capability of the energy management system (EMS) to manage available resources across different operating modes to minimize their impact on DC-bus voltage stability.

关键词： Renewable energy sources Uncertainty Adaptive filters Microgrids Stability analysis Topology State of charge Energy management systems MATLAB Voltage control

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