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Intelligent electronic-optical sensor for information-measurement system of detection and identification of ground and aerodynamic objects

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SEMICONDUCTOR PHYSICS QUANTUM ELECTRONICS & OPTOELECTRONICS 2022年第2期25卷 219-226页

作者： Lepikh, Ya, I Santoniy, V., I Budiyanskaya, L. M. Yanko, V., I Balaban, A. P. Odesa II Mechnikov Univ Minist Educ & Sci Interdept Sci & Educ Physicotech Ctr Odesa Ukraine Odesa II Mechnikov Univ Natl Acad Sci Ukraine Odesa Ukraine

This work describes an original intelligent electronic-optical (laser) sensor (EOS) operating in the infrared range for systems that detect ground and aerodynamic objects and send control commands to the executive devices of portable information-measurement systems using specified algorithms. EOS proposed here differs from the known analogues by its ability of early detection and recognition of high-speed special machinery objects and small-sized unmanned aircrafts. EOS generates and sends probing optical pulses in the direction of a monitored high- speed object, processes the reflected location signal and retrieves the object coordinates and motion parameters. The classification features of the object are determined from the parameters of optical location signal used for object identification with the account the specific nature of optical location information systems.

关键词： electronic-optical sensor optical location signal algorithm information-measurement system

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High-Speed PAM8 Silicon Photonics IM/DD System with Artificial Neural Network Equalization 13

High-Speed PAM8 Silicon Photonics IM/DD System with Artifici...

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Optoelectronic devices and Integration XiiI 2024

作者： Lu, Junde Sun, Yu Qin, Jun Tan, Junxiong Wang, Qian Li, Yueqin Sun, Jian Li, Zhensong Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100192 China Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing100192 China

ISBN: (纸本)9781510682009

The intensity modulation direct detection (IM/DD) system based on silicon photonic devices stands out as a leading contender for the next generation of short-reach optical communication due to its cost-effectiveness, low power consumption, and compact physical footprint. Nonetheless, its direct representation of digital information through amplitude variations renders them acutely susceptible to transmission impairments. To improve the signal quality at the receiver, digital signal processing (DSP) based equalization plays a pivotal role due to its programmability, flexibility and stability. Among different kinds of equalization methods, neural network (NN)-based equalization algorithms have attracted considerable attention, surpassing traditional algorithms such as feed-forward equalization (FFE), decision feedback equalization (DFE) and Volterra series-based nonlinear equalization (VNLE) et al. This increased attention is attributed to their robust capability for modeling both linear and nonlinear systems. In this paper, by employing a novel NN-based equalization with eight saturation regions activation function, we successfully transmit a 60 GBaud 8-ary pulse amplitude modulation (PAM8) signal with the bit error rate (BER) below high-definition forward error correction (HD-FEC) threshold of 3.8×10-3 and a 70 GBaud PAM8 signal with BER below soft-decision forward error correction (SD-FEC) threshold 2×10-2 using a 4-layer network architecture. Compared to the traditional activation function such as sigmoid and tanh, 1∼3 orders of magnitude of BER can be decreased. The results show that the proposed innovative NNbased equalization has the potential to significantly enhance the performance of the next generation silicon photonics based short-range optical communication systems. © 2024 SPIE. All rights reserved.

关键词： Silicon photonics

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Numerical Performance Evaluation of 1 − 2 Sparse Reconstruction Using optical Analog Circuit

Numerical Performance Evaluation of 1 − 2 Sparse Reconstruc...

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2023 Asia Pacific Signal and information processing Association Annual Summit and Conference, APSIPA ASC 2023

作者： Furusawa, Soma Hayashi, Kazunori Kameda, Kaito Hayakawa, Ryo Graduate School of Informatics Kyoto University Kyoto Japan Graduate School of Engineering Science Osaka University Osaka Japan

ISBN: (纸本)9798350300673

Iterative shrinkage thresholding algorithm (ISTA), fast ISTA (FISTA) and alternating direction method of multipliers (ADMM) based algorithm are well known convex optimization algorithms based on proximal splitting for 1 − 2 optimization problem in compressed sensing. In our previous study, aiming at the implementation with optical analog devices, we have proposed constant inertial FISTA (CIFISTA) and an approximated ADMM based algorithm, which do not require the operation of division. In this paper, we consider to realize those algorithms with optical analog devices and show concrete configurations of analog circuits using optical devices. We have conducted computer experiments to evaluate the performance of each algorithm, taking into account the effects of the additive noise introduced by optical amplifiers assuming the implementation using optical devices for common optical fiber communications systems. From the numerical results, it has been confirmed that both the proposed CIFISTA and the approximated ADMM based algorithm can achieve faster convergence than ISTA, while the degradation of the steady state mean squared error (MSE) due to the circuit noise is much smaller than that of the conventional ADMM algorithm. © 2023 IEEE.

关键词： Iterative methods

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First principle and deep learning based switching property prediction of optical bio-molecular switch

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MICROSYSTEM TECHNOLOGIES-MICRO-AND NANOsystems-information STORAGE AND processing systems 2024年第7期30卷 871-882页

作者： Roy, Debarati Dey Roy, Pradipta De, Debashis BP Poddar Inst Management & Technol Dept Elect & Commun Engn 137 VIP Rd Kolkata 700052 West Bengal India Maulana Abul Kalam Azad Univ Technol Dept Comp Sci & Engn NH 12Old NH 34PO Simhat Nadia 741249 West Bengal India Dr BC Roy Acad Profess Courses Dept Comp Applicat Jemua Rd Fuljhore Durgapur 713206 West Bengal India Univ Western Australia Dept Phys M01335 Stirling Highway Perth WA 6009 Australia

Electronic characterization of bio-molecular nanoscale devices is important in the new edge of nanotechnology and the field of nanoelectronics. In this new era, the molecular transmission properties of the Adenine bio-molecular optical switch are predicted using Density Functional Theory along with Non-Equilibrium Green's Function-based First Principle approach and this phenomenon is supported with Machine Learning based algorithms. The photo-induced switching characteristics are observed for the bi-directional bio-molecular switch for both forward and reverse bias conditions. This switching behaviour converts the position of the bio-molecular switch to its straightened and 60 degrees twisted form. The electrical doping process plays an important role in generating p and n regions at the two ends of the switch. In this experiment, gold electrodes are used as the supporting anchor of the Adenine molecules. The HOMO-LUMO gap and I-V characteristics of the bio-molecular switch are analyzed using first principle formalisms and compared with the Machine learning approach. This ML approach helps design the future-generation prediction model for the nano-scale electronic device simulation process. This prediction model can be obtained without knowing the switching characteristics and also without knowing all-time sequence generating waveforms but only observing the waveform prediction for up to a certain period sequence.

关键词： Density functional theory

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Self-Powered Photonic Synapses with Rapid optical Erasing Ability for Neuromorphic Visual Perception

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RESEARCH 2024年第11期7卷 0526页

作者： Li, Mingchao Li, Chen Ye, Kang Xu, Yunzhe Song, Weichen Liu, Cihui Xing, Fangjian Cao, Guiyuan Wei, Shibiao Chen, Zhihui Di, Yunsong Gan, Zhixing Nanjing Normal Univ Ctr Future Optoelect Funct Mat Sch Comp & Elect Informat Sch Artificial Intelligence Nanjing 210023 Peoples R China Southeast Univ Sch Elect Sci & Engn Joint Int Res Lab Informat Display & Visualizat Nanjing 210096 Peoples R China Shenzhen Univ Nanophoton Res Ctr Shenzhen Key Lab Microscale Opt Informat Technol Shenzhen 518060 Peoples R China Taiyuan Univ Technol Coll Elect Informat & Opt Engn Key Lab Adv Transducers & Intelligent Control Syst Minist Educ & Shanxi Prov Taiyuan 030024 Peoples R China

Photonic synapses combining photosensitivity and synaptic function can efficiently perceive and memorize visual information, making them crucial for the development of artificial vision systems. However, the development of high-performance photonic synapses with low power consumption and rapid optical erasing ability remains challenging. Here, we propose a photon-modulated charging/discharging mechanism for self-powered photonic synapses. The current hysteresis enables the devices based on CsPbBr3/solvent/carbon nitride multilayer architecture to emulate synaptic behaviors, such as excitatory postsynaptic currents, paired-pulse facilitation, and long/short-term memory. Intriguingly, the unique radiation direction-dependent photocurrent endows the photonic synapses with the capability of optical writing and rapid optical erasing. Moreover, the photonic synapses exhibit exceptional performance in contrast enhancement and noise reduction owing to the notable synaptic plasticity. In simulations based on artificial neural network (ANN) algorithms, the pre-processing by our photonic synapses improves the recognition rate of handwritten digit from 11.4% (200 training epochs) to 85% (similar to 60 training epochs). Furthermore, due to the excellent feature extraction and memory capability, an array based on the photonic synapses can imitate facial recognition of human retina without the assistance of ANN.

关键词： Face recognition

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Application of machine learning to optical sensing and imaging

Application of machine learning to optical sensing and imagi...

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作者： Zhou, Chen The Pennsylvania State University

学位级别：博士

Recent years have witnessed significant advancements in optical sensing and imaging techniques. To effectively interpret complex data acquired through these techniques and accurately extract information from detectors, machine learning has emerged as a promising solution. Machine learning enables automatic learning of the relationship between raw data and desired outputs, without the need for complete and explicit physics-based models. This data-driven approach presents opportunities for making inferences on material properties, solving inverse problems in the area of optical sensing and imaging. However, the current majority of machine learning methods applied in optical systems primarily serve as post-processing tools to enhance automation and improve the signal-to-noise ratio after data acquisition with standard optical systems. This approach often utilizes precision optics that can be bulky and expensive, along with typical machine learning algorithms that may not fully exploit the underlying physics. The separation of optics and algorithms in design and optimization limits the potential for integration and performance improvement. Consequently, an area of fruitful research lies in deeply integrating machine learning into the design of efficient optical hardware systems, optimizing and streamlining their performance. This dissertation aims to demonstrate how machine learning can contribute to the design of cost-effective and portable optical devices by leveraging minimal optical components in conjunction with powerful learning models. The proposed approach adopts a holistic perspective in designing optical sensing systems. By relieving the burden on optics, simpler and more affordable optical components can be utilized. Moreover, optical domain knowledge can be effectively employed to custom design efficient machine learning algorithms. The dissertation is divided into three main parts, exploring different spaces: spectral, polarization, spatial-temporal, and mo

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Influence of Histogram Equalization on Multi-Classification of Retinal Diseases in OCT B-scans 27

Influence of Histogram Equalization on Multi-Classification ...

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27th IEEE Signal processing: algorithms, Architectures, Arrangements, and Applications, SPA 2024

作者： Marciniak, Tomasz Stankiewicz, Agnieszka Poznan University of Technology Institue of Automatic Control and Robotics Department of Automatic Control Robotics and Electrical Engineering Poznan Poland

ISBN: (纸本)9788362065486

The aim of this article is to analyze the impact of adaptive histogram equalization of the human eye OCT B-scans on the effectiveness of the classification of pathological changes. Tests were performed on two datasets. The first dataset contains 4 class B-scans (CNV, DME, DRUSEN, and NORMAL) taken with the use of Spectralis Heidelberg device, while the second dataset contains images from two devices (Spectralis Heidelberg OCT and Avanti Optovue OCT) and additionally contains the VMT (i.e., vitreomacular traction) class. The images were subjected to standard histogram equalization and contrast limited adaptive histogram equalization (CLAHE) with various parameters. The classification process was carried out using VGG16 artificial neural network. It can be observed that histogram equalization has no significant impact on improving classification performance. © 2024 Division of Signal processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： optical coherence tomography

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Object detection algorithm based on residual deformable convolution pyramid 4

Object detection algorithm based on residual deformable conv...

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4th International Conference on Computer Vision and Pattern Analysis, ICCPA 2024

作者： Huang, Bin Wang, Sanqian Li, Linjie Fu, Xiang Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang*** China School of Software Nanchang Hangkong University Nanchang*** China

ISBN: (纸本)9781510682528

Considering the challenges associated with robots in optoelectronic imaging applications, typically require real-time and accurate recognition and localization of targets, especially in complex environments. Due to the potential challenges of noise, blurring, or dense targets in the images obtained by optoelectronic imaging systems, traditional object detection algorithms may face issues such as semantic information loss and difficulty in detecting small targets. By introducing residual deformable convolution structures, feature representation can be enhanced in optoelectronic imaging systems, and the shape of the convolution kernel can be dynamically adjusted to adapt to the non rigid deformation of the target, thereby better capturing the detailed features of small targets. Experimental results exhibit that RAFPN enhances the mAP of object detection algorithms RetinaNet, Faster R-CNN, and GFL by 1.2%, 0.9%, and 1.1% respectively on the MS COCO dataset, effectively enhancing the performance of object detection algorithms in optoelectronic imaging. © 2024 SPIE.

关键词： Optoelectronic devices

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Scheduling algorithm for fidelity of quantum gate circuits 3

Scheduling algorithm for fidelity of quantum gate circuits

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3rd International Conference on Electronic information Engineering and Data processing, EIEDP 2024

作者： Liu, Xudong Xu, Jinchen Lian, Hang Information Engineering University Zhengzhou450001 China

ISBN: (纸本)9781510680531

As quantum computing technology matures, the availability and performance of quantum devices are steadily improving. However, in the NISQ (Noisy Intermediate-Scale Quantum) era, the quantum bit error rate caused by quantum noise remains a significant constraint on computational accuracy and reliability. In quantum operating systems, quantum task scheduling is a core technology crucial for fully leveraging the performance potential of quantum processors. Currently, most multi-processor scheduling algorithms focus on task load balancing and do not directly consider the characteristics of quantum circuits and the differentiated error rates of quantum computing backends. In quantum computing, the impact of noise errors in single-qubit gates, two-qubit gates, and measurement gates on the fidelity of quantum gate circuits is crucial. Therefore, intelligently matching quantum tasks with the characteristics of backend processors to optimize computational accuracy is an urgent problem to be addressed. This paper proposes a new quantum task scheduling algorithm, which matches tasks with backends based on the weight of single-qubit gates, two-qubit gates, and measurement gates in quantum circuits, as well as the error rate characteristics of each quantum computing backend. Experimental results show that the proposed scheduling algorithm for improving the fidelity of quantum gate circuits outperforms static load balancing scheduling algorithms, resulting in a 4.39% increase in the fidelity of quantum tasks. © 2024 SPIE.

关键词： Scheduling algorithms

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Double-exponential trapezoidal shaping filtering algorithm based on FPGA 4

Double-exponential trapezoidal shaping filtering algorithm b...

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4th International Conference on Advanced algorithms and Signal Image processing, AASIP 2024

作者： Wang, Chaoya Qu, Jinhui Li, Lang Yang, Hua Pan, Anyuan Wang, Shaofei Tang, Bin Zhang, Ying Zhi, Chao Chongqing University of Technology Chongqing Key Laboratory of Optical Fibre Sensing and Photodetection Chongqing400054 China East China University of Technology Nanchang China High-tech Enterprise Research and Development Centre for High-grade Servo Numerical Control System Zhejiang Province Jiaxing China

ISBN: (数字)9781510682818

ISBN: (纸本)9781510682801

With the rapid development of high-precision and high-speed ADC devices, traditional analog processing methods for nuclear pulse signals have gradually exited the historical stage. Digitizing nuclear pulse signals has become the main research direction in multi-channel nuclear energy spectrum measurement systems. The trapezoidal shaping filtering algorithm, as the most used nuclear pulse shaping algorithm, has a certain noise suppression capability. This paper studies the double-exponential trapezoidal shaping filtering method based on FPGA in practical applications and explores its role in further eliminating pile-up effects under high counting rates. © 2024 SPIE.

关键词： Wiener filtering

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