Recently, the joint design of opticalsystems and downstream algorithms is showing significant potential. However, existing rays-described methods are limited to optimizing geometric degradation, making it difficult t...
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Recently, the joint design of opticalsystems and downstream algorithms is showing significant potential. However, existing rays-described methods are limited to optimizing geometric degradation, making it difficult to fully represent the optical characteristics of complex, miniaturized lenses constrained by wavefront aberration or diffraction effects. In this work, we introduce a precise optical simulation model, and every operation in pipeline is differentiable. This model employs a novel initial value strategy to enhance the reliability of intersection calculation on high aspherics. Moreover, it utilizes a differential operator to reduce memory consumption during coherent point spread function calculations. To efficiently address various degradation, we design a joint optimization procedure that leverages field information. Guided by a general restoration network, the proposed method not only enhances the image quality, but also successively improves the optical performance across multiple lenses that are already in professional level. This joint optimization pipeline offers innovative insights into the practical design of sophisticated opticalsystems and post-processingalgorithms.
A secret sharing scheme is an important cryptographic procedure that enables the secure distribution of secret information, such as private images, in an untrusted network. However, in all secret sharing schemes, the ...
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A secret sharing scheme is an important cryptographic procedure that enables the secure distribution of secret information, such as private images, in an untrusted network. However, in all secret sharing schemes, the sizes of the shares increase in proportion to the size of the secret information, because they involve computationally expensive polynomials of degree n - 1 (n is total number of shares) and increasingly large modulus of modulo operations for security. Moreover, with large share sizes, it is not easy in practice to encrypt them quickly using block cipher algorithms. Therefore, ensuring the security of secret sharing for large-scale visual data with a reasonable share length and efficiently encrypting n shares properly represents formidable challenges. To overcome these challenges in secret sharing schemes, we propose new multiparty random phase wrapping secret sharing systems for visual datasets. Computational optical imaging enables the acquisition of wrapped phase information, ranging from-pi to pi, to be expressed in the form of a complex sinusoidal waveform. The proposed scheme allows for large-scale secret data-such as confidential digital images and visual data, including optical images-to be securely and efficiently shared and distributed to multiple parties or agencies utilizing a digital representation of a complex sinusoidal waveform of the secret information. The proposed scheme can be useful in cryptographic key escrow systems and in use with large-scale secret data.
Given the ubiquity of optical fiber networks in both terrestrial and submarine environments, leveraging these facilities for sensing anomalous conditions alongside telecommunications can provide significant added valu...
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Given the ubiquity of optical fiber networks in both terrestrial and submarine environments, leveraging these facilities for sensing anomalous conditions alongside telecommunications can provide significant added value. In this context, distributed acoustic sensing (DAS) systems have been widely employed and discussed due to their sensitivity and ability to locate events. However, integrating them within existing networks is complex and expensive. On the other hand, the received state of polarization (SOP) is also sensitive to external factors, and it can be used for sensing: in this case, no extra hardware would be required since the SOP is already estimated in coherent receivers for data demodulation. The sensing information is provided "for free" by the already installed hardware, potentially requiring only a software upgrade. In this work, we analyze the feasibility of using polarization-based sensing to detect anomalous conditions in metropolitan environments. A polarimeter was used to evaluate SOP noise induced by urban factors, while a commercial coherent transceiver was employed to assess SOP estimation noise. We propose two algorithms for processing polarization data: a time-based method called SOP angular speed (SOPAS) and an adaptive, frequency-based approach named SOP-power spectral density gap (SOP-PSDG). These algorithms were compared by processing Stokes vector samples from the polarimeter when different sinusoidal vibrations are applied to the fiber through a mechanical shaker. Results demonstrate that a sampling rate of just a few tens of Hz is sufficient to effectively identify various hazardous conditions, with SOP-PSDG consistently outperforming SOPAS. Additionally, preliminary findings on the performances of these algorithms using SOP samples from a commercial coherent receiver are discussed.
This paper provides a comprehensive overview of the process for information retrieval from invoices. Invoices serve as proof of purchase and contain important information, including the date, description, quantity, an...
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This paper provides a comprehensive overview of the process for information retrieval from invoices. Invoices serve as proof of purchase and contain important information, including the date, description, quantity, and the price of goods or services, as well as the terms of payment. Companies must process invoices quickly and accurately to maintain proper financial records. To automate this workflow, commercial systems have been developed. Despite the complexity involved, realizing automated processing of invoices necessitates the harmonious integration of a wide range of techniques and methods. While several surveys have shed light on different aspects of this workflow, our objective in this paper is to present a synthetic view of the process and emphasize the most pertinent challenges. We discuss the digitalization of invoices and the use of natural language processing techniques to extract relevant information. We also review machine learning and deep learning techniques that are widely used to handle the variability of layouts, minimize end-user tasks, and train and adapt to new contexts. The purpose of this overview is not to evaluate various systems and algorithms, but rather to propose a survey that reviews a wide scope of techniques for different data extraction tasks, addressing both information extraction and structure recognition for invoice processing. Specifically, we focus on table processing, paying particular attention to graph-based approaches.
Accurate characterization of optical fibers is crucial for numerous applications in telecommunications, sensing, and medical diagnostics. In this study, a novel method of sizing of step-index fibers is presented on th...
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Accurate characterization of optical fibers is crucial for numerous applications in telecommunications, sensing, and medical diagnostics. In this study, a novel method of sizing of step-index fibers is presented on the basis of the analysis of data on light scattering. This approach integrates mathematical modeling of light scattering by step-index fibers with signal processing and correlation algorithms to extract information on the layered structure of the fiber under test. Practical measurements use of a novel optical system for laboratory-level tests. The results show a clear route to improve non-destructive and efficient fiber characterization in online industrial process control.
Subject of study. This study investigates multisensor opticalsystems developed for biomedical research, focusing on methodologies for applying a multisensor approach to optical biomedical diagnostics. Aim of study. T...
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Subject of study. This study investigates multisensor opticalsystems developed for biomedical research, focusing on methodologies for applying a multisensor approach to optical biomedical diagnostics. Aim of study. The study aims to design and perform a comprehensive analysis of a multichannel optical system for collecting, transmitting, and analyzing diagnostic data. Additionally, it seeks to develop an effective algorithm for preprocessing large volumes of optical signals that characterize the state of biological objects using data mining techniques. Method. The study applies multidimensional data mining techniques to implement a multisensor approach to ranking optical spectroscopy signals. Main results. A compact optical multisensor system designed for biomedical diagnostics is introduced. This system features an array of 18 photodiode-sensitive elements with selective sensitivity to optical radiation in the visible and infrared ranges (410-940 nm). The study outlines the analytical stages for processing multidimensional information obtained from the system, incorporating principal component analysis and cluster analysis algorithms. Experimental studies involving human participants validated the efficacy of the proposed methodologies. Using data mining techniques, the study visualized ranked subject data, uncovering hidden patterns in the functional states of microcirculatory tissue systems based on sensor array readings. Practical significance. The findings have significant practical implications for the development of automated systems incorporating optical multisensor technologies. These systems can address challenges associated with identifying and analyzing the functional states of complex multicomponent biological tissues and fluids in the human body. (c) 2024 Optica Publishing Group
With the increasing capacity and complexity of optical fiber communication systems, both academic and industrial requirements for the essential tasks of transmission systems simulation, digital signal processing (DSP)...
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With the increasing capacity and complexity of optical fiber communication systems, both academic and industrial requirements for the essential tasks of transmission systems simulation, digital signal processing (DSP) algorithms verification, system performance evaluation, and quality of transmission (QoT) optimization are becoming significantly important. However, due to the intricate and nonlinear nature of optical fiber communication systems, these tasks are generally implemented in a divide -and -conquer manner, which necessitates a profound level of expertise and proficiency in software programming from researchers or engineers. To lower this threshold and facilitate professional research easy -to -start, a GPT-based versatile research assistant named OptiComm-GPT is proposed for optical fiber communication systems, which flexibly and automatically performs system simulation, DSP algorithms verification, performance evaluation, and QoT optimization with only natural language. To enhance OptiComm-GPT's abilities for complex tasks in optical fiber communications and improve the accuracy of generated results, a domain information base containing rich domain knowledge, tools, and data as well as the comprehensive prompt engineering with well -crafted prompt elements, techniques, and examples is established and performs under a LangChain-based framework. The performance of OptiComm-GPT is evaluated in multiple simulation, verification, evaluation, and optimization tasks, and the generated results show that OptiComm-GPT can effectively comprehend the user's intent, accurately extract system parameters from the user's request, and intelligently invoke domain resources to solve these complex tasks simultaneously. Moreover, the statistical results, typical errors, and running time of OptiComm-GPT are also investigated to illustrate its practical reliability, potential limitations, and further improvements. (c) 2024 Optica Publishing Group under the terms of the Optica O
Agricultural water accounts for more than 70 % of the total global water usage, and the scarcity of global freshwater resources will largely limit global agricultural production. Precision irrigation is the key to imp...
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Agricultural water accounts for more than 70 % of the total global water usage, and the scarcity of global freshwater resources will largely limit global agricultural production. Precision irrigation is the key to improving water efficiency and achieving sustainable agriculture. Accurate and rapid access to crop water information is an essential prerequisite for precise irrigation decisions. Traditional moisture detection methods based on soil moisture and crop physiological parameters are faced with the problems of variable field conditions, low efficiency and lack of spatial information, which can be extremely limited in practical applications. By contrast, unmanned aerial vehicle (UAV) remote sensing has the advantages of low cost, small size, flexible data acquisition time, and easy acquisition of high-resolution image data. Therefore, UAV remote sensing has become an easy and efficient method for crop water information monitoring. This study systematically introduces the principles, methods and applications of crop water stress analysis using the UAV technology. First, the mechanism of crop water stress analysed by UAV is elaborated, focusing on the relationship between canopy temperature, evapotranspiration, sun-induced chlorophyll fluorescence (SIF) and crop water stress. Next, various UAV imaging technologies for crop water stress monitoring are presented, including optical sensing systems, red, green and blue (RGB) images, multi-spectral sensing systems, and hyper-spectral sensing systems. Subsequently, the application of machine learning algorithms in the field of UAV monitoring of crop water information is outlined, demonstrating their potential for data processing and analysis. Finally, new directions and challenges in UAV-based crop water information acquisition and processing are synthesised and discussed, with special emphasis on the prospects of data assimilation algorithms and non-stomatal restriction in monitoring crop water information in the futu
Low-complexity decoding algorithms and ultra-high-order modulation formats are necessary to meet data rate requirements in excess of 1 Tbps. The information bottleneck algorithm has been effectively applied to the LDP...
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Low-complexity decoding algorithms and ultra-high-order modulation formats are necessary to meet data rate requirements in excess of 1 Tbps. The information bottleneck algorithm has been effectively applied to the LDPC decoding algorithm in recent years, and its performance is comparable to that of the double-precision information propagation technique. However, the application of information bottleneck decoding algorithms in ultra-high order modulation forms has received little attention. Furthermore, the number of table lookups required for a single decoding loop is square to the node degree, which is undesirable for optical communications. We present a low-complexity LDPC decoding technique for ultra-high-order modulated signals in this study. First, the algorithm employs multivariate covariates to build an information bottleneck framework, which introduces the processing required for applying the information bottleneck algorithm to 1024-QAM signals and the requirement of combining higher-order modulation formats with LDPC codes. The technique makes use of a bidirectional recursive network and the symmetry of quantized information to reuse the same set of tables, considerably reducing the number of table lookup operations required in the decoding process. Constructing a coherent optical communication system with 1024-QAM signals proves that the proposed algorithm can operate effectively. The performance sacrifice of 0.2 similar to 0.3 dB reduces the number of table lookup operations required for decoding from square to linear magnitude, which greatly reduces the decoding time required in optical communication.
optical wireless communication (OWC) is in high demand due to its potential for high-speed data transmission and spectrum relief in congested radio frequencies. However, real-world implementations face significant cha...
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optical wireless communication (OWC) is in high demand due to its potential for high-speed data transmission and spectrum relief in congested radio frequencies. However, real-world implementations face significant challenges, particularly due to atmospheric turbulence, which distorts the optical signal, making reliable OWC difficult to achieve. In this study, we propose a novel methodology to mitigate the effects of turbulence by investigating the application of orbital angular momentum (OAM) and deep learning for robust OWC. In this study, we propagate information in the form of optical Laguerre-Gaussian (LG) beams through turbulent atmosphere and use the conjugate light field (CLF) method to retrieve the information. This process is further integrated into a deep learning framework to enhance the OWC systems performance. This integration reduces the computational load by minimizing the network classes to two CLF outcomes instead of using all LG beam modes as classes. The proposed method achieves faster processing time by reducing computational load, essential for real-time applications. An optical tabletop experiment also verifies the proposed technique, where we achieve a bit error rate (BER) of 2.44 x 10(-4), demonstrating a performance that surpasses baseline algorithms by over similar to 99% for a given set of parameters. To the best of the author's knowledge, this is the first time this concept has been presented for the use of OWC in turbulent atmospheric conditions.
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