Proof-of-concept of optical debris detection with SiFAP2 for Space Situational Awareness and optical signals characterization

Chiral detection plays a crucial role in exploring the potential applications of chiral molecules in biomedicine, chemistry, and pharmaceutical industries. Gold nanoparticle dimers (GND) have unique plasmonic optical properties, offering a promising approach to enhance the sensitivity of chiral optical detection for biomolecules. Coupling these dimers with chiral molecules leads to substantial plasmon-coupled circular dichroism within the visible light spectrum. However, the plasmonic properties of these dimers are dependent on their geometric characteristics, emphasizing the critical need for selecting an effective structure to amplify the optical detection signal and develop high-sensitivity nanosensors. This study utilized the finite element method to explore how geometric factors influence the enhancement of CD in chiral molecules using typical gold nanoparticle dimer sensors. Our findings unveil the optimal radius for gold nanosphere dimers, maximizing the enhancement of CD. Additionally, we identified the optimal radius and aspect ratios for gold nanorod dimers. Furthermore, we investigated the ideal aspect ratio for gold nanobipyramid dimers, which demonstrate superior field enhancements and optical cross sections. In comparison to other nanoparticle dimers, gold nanobipyramid dimers show a 20-fold enhancement factor. Consequently, GND significantly enhance the sensitivity of chiral molecular optical detection and have resonance wavelengths at the optimal aspect ratio within the visible light range, enabling direct optical detection in practical applications. These findings offer valuable theoretical guidance for effectively utilizing gold nanoparticle dimer structures to enhance the sensitivity of chiral molecular optical detection.

Results of a detailed theoretical analysis on the detection of phase-modulated optical signals with an arbitrary modulation index (Delta >< 1) and modulation frequency (omega/omega(0) >< 1, where omega(0) is the characteristic cutoff frequency) by adaptive photodetectors that are based on photorefractive crystals and the nonsteady- state photo-electromotive force are presented. Besides general analytical results, numerical calculations of the output signal form and the amplitude and the phase of its first and second harmonics are given. The main predictions of the theoretical analysis are illustrated by the experimental data obtained for a high-sensitivity cubic photorefractive Bi(12)SiO(20) crystal at the wavelength (lambda = 488 nm) of an argon-ion laser.

The objective of this special issue is to document the current state-of-the-art in the area of Optical Detectors and highlight recent progress and trends in innovative optical detector technology and its applications. This issue combines 11 invited and 21 contributed papers, authored byworld-renowned research groups and promising scientists from around the world. The invited papers present extended reviews on recent progress and significant perspectives on future research directions. They cover lownoise linear-mode avalanche photodiodes (APDs) for fiber optic communications, single photon APDs (SPADs) and Si SPAD arrays for photon counting imagers, hybrid integration technologies, waveguide APDs, lowcapacitance nano-photodetector, integrated photodetectors for coherent receivers, and high-speed uni-traveling carrier (UTC) photodiodes (PDs) for THz applications. The contributed papers cover a broad variety of key research areas and emerging technologies in optical detectors including graphene-based detectors, plasmonic structures for current enhancement, phototransistors, and UTC-type PDs on silicon-on-insulator waveguide as well as a UTC PD with type II hybrid absorber. Several papers describe advances in SPADs and CMOS SPAD arrays for UV and visible detection and four papers cover quantum well and quantum dot devices. This special issue also illustrates a wide range of optical detector technology applications including UV, visible, and IR imaging, fiber optic communications, microwave and THz analog applications, and quantum communications. Furthermore, this special issue covers a range of advances on theory and simulation, design and fabrication, and optical detector characterization methodologies.

Detection of optical neuronal signals in the visual cortex using continuous wave near-infrared spectroscopy

Chapter 7 - Optical Characterization, Diagnosis, and Performance Monitoring for PON

This paper presents a detailed study of a binary optical direct detection optimum receiver for the use of optical space communication systems, where a long‐distance transmission makes optical received signals extremely week. At such a low‐power operation of the receiver, the quantum discreteness structure of the shot noise process is evident in the optical detector output.A statistical model for optical detection output is derived, considering this quantum discreteness, thermal noise at the receiver circuit system, dark current at optical detector, and random photomultiplication. Moreover, the optical detection system is considered as an ideal integration system, and optical detection output is expressed by using a time‐dependent sample value vector.A logarithmic likelihood function derived from this sample value vector on the basis of maximum likelihood testing is studied, and the optimum receiver structure for weak optical signals is clarified. That is, it is shown that, even under the existence of thermal noise and random photomultiplication, primary photon count plays an essential role in signal detection similar to that in the shot noise limited case and the primary photocount estimator‐maximum likelihood detection receiver is derived theoretically. Then the error rate performance is analyzed theoretically and compared with that of a conventional direct detection receiver. As a result, it is shown that by introducing a wide‐band optical detection system, a significant improvement in error rate performance is observed over conventional receivers.

With the recent increase in the threat posed by unmanned aerial vehicles (UAVs) operating in environments where conventional detection systems such as radar, optical, or acoustic detection are impractical, attention is paid to methods for detecting low-flying UAVs with small radar cross-section (RCS). The most commonly used detection methods are radar detection, which is susceptible to electromagnetic (EM) interference, and optical detection, which is susceptible to weather conditions and line-of-sight. This research aims to demonstrate the possibility of using passive optical fiber Bragg grating (FBG) as a sensitive element array for low-flying UAV detection and localization. The principle is as follows: an optical signal that propagates through an optical fiber can be modulated due to the FBG reaction on the air pressure caused by a low-flying (even hovering) UAV. As a result, a small target—the DJI Avata drone can be detected and tracked via intensity surge determination. In this paper, the experimental setup of the proposed FBG-based UAV detection system, measurement results, as well as methods for analyzing UAV-caused downwash are presented. High-speed data reading and processing were achieved for low-flying drones with the possible presence of EM clutter. The proposed system has shown the ability to, on average, detect an overpassing UAV’s flight height around 85 percent and the location around 87 percent of the time. The key advantage of the proposed approach is the comparatively straightforward implementation and the ability to detect low-flying targets in the presence of EM clutter.

We propose an underwater optical signal detection system based on multi-dimensional integral imaging with spatially distributed multiple light sources and four-dimensional (4D) spatial-temporal correlation. We demonstrate our system for the detection of optical signals in turbid water. A 4D optical signal is generated from a three-dimensional (3D) spatial distribution of underwater light sources, which are temporally encoded using spread spectrum techniques. The optical signals are captured by an array of cameras, and 3D integral imaging reconstruction is performed, followed by multi-dimensional correlation to detect the optical signal. Inclusion of multiple light sources located at different depths allows for successful signal detection at turbidity levels not feasible using only a single light source. We consider the proposed system under varied turbidity levels using both Pseudorandom and Gold Codes for temporal signal coding. We also compare the effectiveness of the proposed underwater optical signal detection system to a similar system using only a single light source and compare between conventional and integral imaging-based signal detection. The underwater signal detection capabilities are measured through performance-based metrics such as receiver operating characteristic (ROC) curves, the area under the curve (AUC), and the number of detection errors. Furthermore, statistical analysis, including Kullback-Leibler divergence and Bhattacharya distance, shows improved performance of the proposed multi-source integral imaging underwater system. The proposed integral-imaging based approach is shown to significantly outperform conventional imaging-based methods.

We report on the optical characterization of a micromachined gyroscope prototype for automotive applications, by means of feedback interferometry. In order to directly detect the rotation-induced Coriolis force, we have developed a compact and stable interferometric setup, which has been positioned inside a small vacuum bell, mounted on a rotating table. By this setup, which has a noise limit of the order of 10/sup -11/ m/(Hz) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> 2/, we have measured the gyro responsivity curve, demonstrating the feasibility of the optical interferometric detection of the in-plane response of a MEMS sensor. In addition, we have carried out the full mechanical characterization of the device at different pressures, and we have performed the matching of the gyro resonance frequencies by the interferometric monitoring. Our gyro had a resonance frequency of 3986 Hz for both axes after tuning; at a pressure of 7 10/sup -2/ torr, the quality factor were Q=18000 for the driving axis and Q=1800 for the sensing axis, while the measured responsivity was 7 10/sup -10/ m/(°/s). The optical characterization represents an important feedback to the designer and is especially powerful in the case of prototypes for which the on-board electronics is not yet available.

Optical signal detection in turbid and occluded environments is a challenging task due to the light scattering and beam attenuation inside the medium. Three-dimensional (3D) integral imaging is an imaging approach which integrates two-dimensional images from multiple perspectives and has proved to be useful for challenging conditions such as occlusion and turbidity. In this manuscript, we present an approach for the detection of optical signals in turbid water and occluded environments using multidimensional integral imaging employing temporal encoding with deep learning. In our experiments, an optical signal is temporally encoded with gold code and transmitted through turbid water via a light-emitting diode (LED). A camera array captures videos of the optical signals from multiple perspectives and performs the 3D signal reconstruction of temporal signal. The convolutional neural network-based bidirectional Long Short-Term Network (CNN-BiLSTM) network is trained with clear water video sequences to perform classification on the binary transmitted signal. The testing data was collected in turbid water scenes with partial signal occlusion, and a sliding window with CNN-BiLSTM-based classification was performed on the reconstructed 3D video data to detect the encoded binary data sequence. The proposed approach is compared to previously presented correlation-based detection models. Furthermore, we compare 3D integral imaging to conventional two-dimensional (2D) imaging for signal detection using the proposed deep learning strategy. The experimental results using the proposed approach show that the multidimensional integral imaging-based methodology significantly outperforms the previously reported approaches and conventional 2D sensing-based methods. To the best of our knowledge, this is the first report on underwater signal detection using multidimensional integral imaging with deep neural networks.

Detection of Optical Signals provides a comprehensive overview of important technologies for photon detection, from the X-ray through ultraviolet, visible, infrared to far-infrared spectral regions. It uniquely combines perspectives from many disciplines, particularly within physics and electronics, which are necessary to have a complete understanding of optical receivers. This interdisciplinary textbook aims to: Guide readers into more detailed and technical treatments of readout optical signals Give a broad overview of optical signal detection including terahertz region and two-dimensional material Help readers further their studies by offering chapter-end problems and recommended reading. This is an invaluable resource for graduate students in physics and engineering, as well as a helpful refresher for those already working with aerospace sensors and systems, remote sensing, thermal imaging, military imaging, optical telecommunications, infrared spectroscopy, and light detection.

Underwater optical signal detection in severely degraded environments remains a challenging task. In this work, we propose what we believe to be a novel dynamic vision-based underwater optical signal detection system. The signal detection performance is evaluated in a degraded underwater environment, including partial occlusion and turbidity. The system utilizes dynamic vision sensors from an event camera array, multi-dimensional integral imaging, and deep learning networks to achieve signal detections. In the experiment, optical signals are transmitted using a modulated light-emitting diode. The optical signals, after propagating through the degraded underwater environment, are captured by the event camera array in the form of event sequences. The event sequences are preprocessed as multi-dimensional event videos. The videos are classified by the vision transformer and gated recurrent unit network (ViT-GRU). The proposed system is compared to other relevant state-of-the-art frame-based approaches in terms of the detection performance evaluated by the Matthew correlation coefficient and the number of error symbols. For the experiments we conducted, the proposed dynamic vision-based underwater optical signal detection system with multi-dimensional integral imaging and ViT-GRU network outperforms other frame-based counterparts in degraded underwater environments. To the best of our knowledge, this is the first report on dynamic vision-based underwater optical signal detection using multidimensional integral imaging.

This paper addresses the application of automatically adaptive data-driven and goal-driven software tools for the detection and environment caused characterization of abnormal space system behavior with a priori unknown signatures. Goal-driven abnormality detection determines when retraining is needed, what data to train on, what data to test on, how to test, whether additional training is needed, and whether to promote the updated software. We discuss the design of automated space weather (SpWx) attribution tools and show sample results on real data. The space weather attribution, context assessment, and visualization tools described in this paper are extendable for characterization of fused multiple source abnormal event tracks from new sources using the Smoking Gun (SG) and the Bayesian Fusion Node (BFN). SG identifies correlation relationships such as between space weather and satellite system abnormal events. The BFN is runtime configurable with regard to input formats, taxonomy specification, decision logic, and processing. BACKGROUND Situational Awareness forms the framework for operations, planning, and decision making. Space Situational Awareness (SSA) brings knowledge of the operational space environment, its supporting ground elements and links and the projection of its future status. To achieve effective space situational awareness, the SSA system architecture must provide the decision maker and user the right data, information, tools, and decision aids at the right time. Using a net-centric service-oriented data fusion approach will allow a rapid assessment of the situation, capitalize on many available data sources, and adapt to situations in a timely manner. Information will need to be gathered across a broad range of DOD, civil, and commercial sources. Once this data is identified, it will need to be developed into actionable information for the decision maker. Space assets are susceptible to numerous anomalous conditions including: space weather events, radio frequency interference (RFI), satellite payload jamming, dazzling, proximity operations, breakup, bus failures, and other satellite anomalies. Given this variety of problematic situations, decision makers need a distributed satellite resource management system to effectively accomplish their space access mission. Near real-time integrated Space Situational Awareness (SSA) methods are needed to: detect & distinguish between environmental, man-made, and unintentional acts; predict actor intent; and provide real-time response recommendations to evolving scenarios. Distributed satellite resource management promises continued access to space capabilities so as to maintain mission-critical information after space-based asset degradation. Currently, in the event that space-based assets suffer an outage or abnormality, the responsibility falls to the human-in-the-loop to follow checklist procedures to restore operations. Unfortunately, these procedural checklists are time-consuming and are not always optimized with consideration of the need to maintain SSA. Also, systems used to compensate for satellites suffering outages may not achieve the restoration of service with sufficient time to adequately support ongoing missions. A semi-automated satellite mission re-planning system capable of confirming and characterizing abnormalities and then recommending space-based asset responses will be integral to the future improved use of US space order of battle assets. This system will run continuously, monitoring the relationships between space asset events, the potential for satellite system outages, and the ongoing missions relying on space-based assets. The system will provide immediate input to the human-in-the-loop in the form of a series of satellite mission re-planning and response options based on the current SSA that will balance support to ongoing operations with the need to ensure US space missions. There are five functional levels of SSA. Namely, incident detection/ causality, event tracking/characterization, event relationship assessment, mission impact prediction, and process & context assessments) and five dual response management levels within which the automated response decision aids of interest reside. These five levels have been described based upon the Data Fusion & Resource Management (DF&RM) Dual Node Network (DNN) technical architecture which is an extension of the JDL fusion model [1 and 2]. Activities include development of space weather attribution technologies, performance assessments, and services that utilize real sources of space systems data (e.g., State of Health (SOH), Signal to Noise Ratio (SNR), signal strength, etc.) and authoritative space weather sources. Scope and Relationships of This Work The DF&RM DNN technical architecture has been applied to guide the system architecture development for numerous DF&RM capabilities developed for SSA. The divide & conquer techniques in the DF&RM DNN technical architecture begin with guidance to functionally partition by layer and at the applications layer by “DF&RM functional levels” that are extensions and duals of the Joint Director’s Lab (JDL) data fusion model from the 1980’s [1]. The five fusion levels are summarized as follows:  Signal/Feature Assessment -Level 0: estimation of entity feature states  Entity Assessment -Level 1: estimation of entity states  Situation Assessment -Level 2: estimation of entity relationship states  Impact Assessment -Level 3: estimation of the mission impact of fused states  Process Assessment -Level 4: estimation of the DF&RM system performance, context conformity, and distributed DF&RM consistency measures both internal and external to the baseline DF&RM system. At each fusion level the DF node is designed to perform: data preparation, data association, and state estimation. This decomposition of the data driven decision support problem into fusion levels and corresponding nodes allows relatively constrained and low-risk development of each processing step in a loosely coupled manner, where many of the nodes in the fusion node network can be used and tested independently. The DNN technical architecture engineering guidelines provide for building the SSA capability as process flows of fusion and management node networks. These process flows cleanly map onto services composing a Service Oriented Architecture (SOA) as described in the STP quoted above. At the output side, modular visualization UDOP components can provide graphical user interfaces. These modular services and components will provide reusable building blocks for supporting a more agile enterprise for rapid, cost-effective development of SSA mission capabilities. The SOA building-block approach to constructing mission-specific applications is conducive to the spiral development approach providing iterative, agile, incremental development to achieve SSA objective prototype systems. This work aimed to develop and demonstrate a data fusion application that could be used across multiple domains for higher level data fusion (primarily multiple source levels 1-3) to increase SSA and provide timely and highly summarized data driven decision support to analysts and operators.

ADIODE is a two-terminal, passive, non-linear device that can be used to control voltage and current in a circuit. Some diodes are used primarily to rectify alternating current, some are used as signal detectors and others are used as voltage references or voltage regulators. There are also optical diodes which are used as indicators (light-emitting diodes, or LEDs), signal sources (LEDs and laser diodes) or optical detectors (avalanche photodiodes and PIN diodes). The solar cell is a special type of optical diode which converts light energy directly into electricity and by tonnage is probably the most important use of diodes today. The shape of a diode’s current-voltage relationship determines its specific application, which in today’s solid-state devices depends on the way it is doped during manufacture. There are several classes of diode besides the ‘ordinary’ rectifier, but of these we shall examine only Schottky diodes, Zener diodes and light-emitting diodes. Optical signal source and detector diodes are discussed in Chapter 25.

In order to study the detection effect of optical detection on corona discharge in gas insulated switchgear (GIS), a photomultiplier tube (PMT) based optical detection method test platform is built. The needle-plate electrode is used to simulate corona discharge. An oscilloscope is used to collect the optical signal of corona discharge, a high-voltage probe is used to obtain the reference voltage, and the phase resolved partial discharge (PRPD) pattern of the optical signal is analyzed. The results show that with the development of partial discharge (PD), the PRPD pattern can obviously unfold in different stages, which indicates that the optical detection for GIS has high detection sensitivity and good discrimination of the severity of discharge development. By combining the results of the PRPD pattern and previous studies, it is inferred that the negative half cycle discharge of AC corona discharge mainly depends on the electron avalanche ($\alpha$-process) and the secondary electron avalanche on the cathode surface ($\gamma$-process), and the light radiation is mainly excitation radiation and bremsstrahlung. The positive half cycle mainly depends on spatial photoionization (anode streamer), and the light radiation mainly consists of recombination radiation, excitation radiation and bremsstrahlung. This paper can provide a reference for the application of photoelectric devices used in GIS.