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All-silicon integrated platform for light generation, manipulation, and detection—and its application to refractive index sensors

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Optica 2025年第5期12卷 577-584页

作者： de Cea, Marc Kramnik, Danielius Stojanovic, Vladimir Ram, Rajeev J. Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Sciences UC Berkeley BerkeleyCA94720 United States

Traditional silicon photonic platforms offer powerful capabilities for light manipulation and detection but cannot realize an integrated light source. This results in systems with increased cost and complexity due to the need for external light generation and coupling. In this work we overcome this limitation and experimentally demonstrate the generation, manipulation, and detection of waveguide-coupled light in a commercial silicon photonics foundry with monolithically integrated electronics—without any modification to the fabrication process and no additional post-processing steps. We propose the use of our platform to realize fully integrated, monolithic optical refractometric sensors and theoretically show that performance comparable to commercial, state of the art bench-top systems can be achieved. Our work lays the foundation for the realization of truly monolithic, full-functionality silicon photonic electro-optical systems with increasing levels of miniaturization and cost reduction. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Silicon photonics

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Counterfactual brain amyloid pathology image synthesis

Counterfactual brain amyloid pathology image synthesis

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Medical Imaging 2025: Image Processing

作者： Bilgel, Murat Zuo, Lianrui Resnick, Susan M. Carass, Aaron Laboratory of Behavioral Neuroscience National Institute on Aging BaltimoreMD21224 United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37235 United States Image Analysis and Communications Laboratory Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States

ISBN: (纸本)9781510685901

We propose a U-Net-based MRI-to-PET image translation approach to synthesize distribution volume ratio (DVR) images for the amyloid PET radiotracer 11C-Pittsburgh compound B (PiB). Given a structural MRI and a binary map indicating the spatial presence of amyloid pathology, our network can generate PiB DVR images to reflect the specified spatial distribution-based on the binary map-of amyloid pathology. This approach allows for the synthesis of counterfactual PiB DVR images. Compared to using a separate U-Net per PiB status, our method enables the synthesis of PiB DVR images with improved SSIM (among PiB+, 0.818 vs. 0.783, p −6) and PSNR (among PiB- & PiB+, 32.475 vs. 31.729, p = 5.83 × 10−6). It also generates counterfactual PiB- DVR images that are more difficult to identify as counterfactual vs. synthetic "factual" (classification accuracy = 0.657 vs. 0.714). We show that the counterfactual PiB- images can be used for personalized monitoring of global brain amyloid burden and demonstrate that our proposed measure correlates highly with mean cortical DVR, an established index that requires anatomical labeling (Pearson correlation = 0.856 for testing set PiB+ scans). © 2025 SPIE.

关键词： 3D reconstruction

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Learning to see speckle in the weak laser field through multimode fiber

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Optoelectronics Letters 2025年

作者： JI Yunqi SONG Binbin LI Xueqing LI Yonghui The Engineering Research Center of Learning-Based Intelligent System (Ministry of Education)， the Key Laboratory of Computer Vision and Systems (Ministry of Education)， and the School of Computer Science and Engineering， Tianjin University of Technology

Multimode fibers (MMFs) have great potential for endoscopic imaging due to the high number of modes and a small core diameter. Deep learning based on neural networks has received increasing attention in the field of scattering image reconstruction. However， most studies focus on designing complex network architectures to improve reconstruction， but these network models struggle to reconstruct images in a weak laser field. In the paper， a lightweight generative adversarial network model combined with a histogram specification algorithm is designed to reconstruct speckles in the weak laser field through MMF. Experimental results show that the reconstruction results of our algorithm have better metrics. Moreover， the model demonstrates excellent cross-domain generalization ability with regards to the Fashion-MNIST dataset. It is worth mentioning that we found that the speckles after inactivation still retain the ability to be reconstructed， which enhances the robustness of the model

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Grid Services Demonstration Using Service-Oriented DERMS

Grid Services Demonstration Using Service-Oriented DERMS

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2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025

作者： Adham, Midrar Keene, Sean J. Slay, Tylor Kolln, Jaime T. Bass, Robert B. Portland State University Department of Electrical & Computer Engineering PortlandOR97201 United States Pacific Northwest National Laboratory RichlandWA99354 United States

ISBN: (纸本)9798350352528

This work demonstrates four grid service use cases using a service-oriented DER Management System within an Energy Grid of Things network. Imposed by a set of rules referred to as the Energy Service Interface, the DER Management System uses IEEE 2030.5 as its primary communication protocol. To simulate the efforts of Grid Operators, Grid Service Providers, and consumers to provide grid services, a co-simulation environment that leverages the GridAPPS-D simulation platform was developed. The results show that a service-oriented DER Management System provides a means of achieving reliable and impactful grid services while protecting customers' privacy and ensuring their agency over their Distributed Energy Resources. © 2025 IEEE.

关键词： DER DERMS Energy Service Interface Grid Services GridAPPS-D

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Grid Sweep: Active and Passive Signal Processing

Grid Sweep: Active and Passive Signal Processing

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2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025

作者： Yop, Philip Shehada, Halah Lawrence Livermore National Laboratory LivermoreCA94550 United States Virginia Tech Electrical and Computer Engineering Department BlacksburgVA24060 United States

ISBN: (纸本)9798350352528

The GridSweep device is an actively probing wave-form measurement unit developed by McEachern Laboratories in conjunction with Lawrence Livermore National Lab and Lawrence Berkeley National Lab. The active probing of the device combined with high sensitivity, and GPS time synchronization allow coordinated measurements of distribution system frequency response and other grid characteristics from common electrical outlets. This paper presents an initial review of the signal processing techniques applied to the collected data, a summary of interesting results from several different processing modalities including the active signal response, machine learning techniques, and ambient signal correlation. The early results indicate the measurements from the GridSweep device have potential for use to detect local grid properties like system impedance, and characteristics and identification of distributed energy resources connected on the system. © 2025 IEEE.

关键词： Signal analysis

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Impact of defects on the spectral characteristics of PureB avalanche-mode light emitting diodes 29

Impact of defects on the spectral characteristics of PureB a...

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Light-Emitting Devices, Materials, and Applications XXIX 2025

作者： Jelavić, Eva Nanver, Lis K. Knežević, Tihomir Ruder Boskovic Institute Division of Materials Physics Laboratory for Semiconductors Zagreb Croatia University of Twente Faculty of Electrical Engineering Mathematics & Computer Science Enschede Netherlands

ISBN: (纸本)9781510685208

Single photon avalanche diodes (SPADs) fabricated in PureB silicon technology offer exceptional versatility, functioning both as light emitting diodes and detectors sensitive down to a single photon. In PureB technology, nanometer-thin layers of boron are deposited on the silicon surface, creating defect-free ultra-shallow junctions. In this paper, PureB diodes with a junction depth of a few tens of nanometers are studied as avalanche-mode light-emitting diodes (AMLEDs) with the advantage that optical absorption was minimal. They were systematically analyzed to investigate the impact of defects on their optical and electrical characteristics. Bulk defects were unintentionally introduced by contamination during the processing of the n+ buried layer. These defects cause the increase in dark currents and the formation of light-emitting spots, observable in light emission spectra taken at reverse biases below the breakdown voltage. An increase in relative intensity for wavelengths from 450 nm to 550 nm was observed in spectral characteristics of light emitting spots compared to the light emission of the active-region breakdown. Optical and electrical characterization was conducted on diodes with different geometry, implantation doses in the multiplication region, and distance to an implicit guard ring to examine the impact of these parameters on the light emission spectrum. Low-temperature electrical characteristics and light emission patterns of the devices were obtained by measurements at cryogenic temperatures. © 2025 SPIE.

关键词： Particle beams

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Calculating Critical Inertia of a Power System

Calculating Critical Inertia of a Power System

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Sneha, Fariha Hakim Liu, Yilu Martinez, Rocio Uria Dong, Jiaojiao University of Tennessee Dept. of Electrical Engineering and Computer Science Knoxville United States Oak Ridge National Laboratory United States

ISBN: (纸本)9798331504847

The increasing integration of renewable energy sources in modern power systems has led to a decline in system inertia, raising concerns about frequency stability following large disturbances. Determining critical inertia is essential to prevent excessive frequency decline and ensure grid stability. This paper evaluates four different methods for calculating critical inertia, using the Electric Reliability Council of Texas (ERCOT) system as a test case. The results highlight the importance of employing multiple methodologies to capture the full spectrum of inertia requirements. © 2025 IEEE.

关键词： Frequency stability

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A variable structure model of neurons’ application in computer vision

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Multimedia Tools and Applications 2025年 1-11页

作者： Sridi, Alaeddine Attia, Mouna Bouallegue, Kais Electronics and Micro-Electronics Laboratory Faculty of Sciences of Monastir University of Monastir Monastir Tunisia The Higher Institute of Computer Science and Communication Technologies of Hammam Sousse University of Sousse Sousse Tunisia Laboratory of Computer Science Parallelism and Intelligent Communicating Systems Faculty of Sciences of Tunisia University of Tunis ELManar Tunis Tunisia Department of Electrical Engineering Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia

The neuron doctrine defines the neuron as the basic unit of the nervous system, which drives the dynamic behavior of our organs. This has led to neurons becoming the focus of modern neuroscience research and to the rise of neurocomputing, in which we try to digitally simulate human behavior. The goal being to better understand the human nervous system and gain more control over neuron-related diseases, helping doctors establish faster and more reliable medical diagnoses. In this context, we aim to simulate eye diseases and vision problems by convolving input images using a new class of neural networks based on a variable structure model of neurons presented in [1]. In this work, we focus on recreating the common convolution filters as a first step. This is the first time in neurocomputing literature that dendritic neurons have been used for dynamic convolution kernels generation and eye diseases simulation. We start with a presentation of our upgrades to the model’s flexibility and accuracy. Then, we propose a novel kernel generator algorithm to convolve the input images. Finally, we show its promising results in creating the outline, pixelization, and night convolution kernels, as well as control some metrics, including brightness and data retention. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurons

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Automobile Entry System Based on Face Recognition

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 573-585页

作者： Fang, Weidong Yu, Zuoxi Pan, Jeng-Shyang School of Electronic Electrical Engineering and Physics Fujian Provincial Key Laboratory of Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Chaoyang University of Technology Taichung413310 Taiwan

With the development of technology, the automobile has become an indispensable part of people’s daily lives. People’s needs for automobile entry systems have also changed, in automobile safety and ease of use have become more and more important. In recent years, face recognition technology has made significant progress, and face recognition technology has been widely used in various fields, especially face recognition based on deep learning has great advantages in accuracy, recognition speed, and security, and can provide a more secure and reliable way of identity verification. Traditional automobile entry systems usually use mechanical keys and remote control keys, which do not remove the key and have certain shortcomings in security and user experience. Face recognition-based car entry systems can make up for these shortcomings and provide a more convenient and intuitive user experience. Combining face recognition with automobiles is also a hot topic in current scientific research. In this paper, a set of automobile entry systems with high efficiency and security is designed according to the face recognition method research, using three deep learning models: face detection, live body detection, and face recognition. In face detection, the RetinaFace lightweight model is used the network structure is improved, and the detection speed is increased by 14.9%. For face live detection and face recognition, the MobileFaceNet lightweight network is used as the base network for live detection and face recognition, achieving a 98.9% accuracy rate on the CelebA Spoof live detection dataset. In face recognition, feature extraction is performed on the detected faces after face detection, and the recognition results are output by comparing with the recorded faces. Improvements to its network improved the recognition accuracy by 0.18%, 0.77%, and 0.73% on the LFW, CFP FP, and AgeDB30 datasets, respectively. The model was deployed on Raspberry Pi and connected to CANoe via CAN bu

关键词： Face recognition

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Learning to Communicate Among Agents for Large-Scale Dynamic Path Planning With Genetic Programming Hyperheuristic

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1269-1283页

作者： Liao, Xiao-Cheng Hu, Xiao-Min Chen, Xiang-Ling Mei, Yi Jia, Ya-Hui Chen, Wei-Neng Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Hanyang University Department of Electrical and Electronic Engineering Ansan15588 Korea Republic of South China University of Technology School of Future Technology Guangzhou510006 China Pazhou Lab Guangzhou510005 China South China University of Technology School of Computer Science and Engineering State Key Laboratory of Subtropical Building and Urban Science Guangzhou510006 China

Genetic programming hyperheuristic (GPHH) has recently become a promising methodology for large-scale dynamic path planning (LDPP) since it can produce reusable heuristics rather than disposable solutions. However, in this methodology, the extracted local and decentralized heuristic for agents that lack a global systemic view sometimes may be problematic. Therefore, a new challenge is to strike a balance between conciseness to improve generalization ability and incorporation of more global information to obtain better performance. In this work, we target the LDPP problem and propose a communication learning mechanism (ComLGP) for GPHH to address the above difficulties. In ComLGP, a communication function is introduced to serve as a communication protocol and exist in the form of an extra terminal in GPHH. Compared to the classic terminals which are fixed in genetic programing, this communication function undergoes optimization along with the evolutionary process of GPHH. In this way, the communication function can be learned which enables agents to communicate without a predefined communication protocol. Then, a caching and lazy updating mechanism for ComLGP is presented to accelerate the calculation of communication content. Last, we verified our method on 22 scenarios including two real world road networks. The experimental results demonstrate that the proposed ComLGP can successfully learn to communicate. Although in the absence of any manually designed communication features, ComLGP is capable of achieving performance competitive to the state-of-the-art method that employs a predefined communication protocol and outperforms the remaining compared methods in most scenarios. © 2020 IEEE.

关键词： Genetic programming

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