Showing papers in "Optical Engineering in 2022"

Quantum games and interactive tools for quantum technologies outreach and education

Abstract: Abstract. We provide an extensive overview of a wide range of quantum games and interactive tools that have been employed by the quantum community in recent years. We present selected tools as described by their developers, including “Hello Quantum, Hello Qiskit, Particle in a Box, Psi and Delta, QPlayLearn, Virtual Lab by Quantum Flytrap, Quantum Odyssey, ScienceAtHome, and the Virtual Quantum Optics Laboratory.” In addition, we present events for quantum game development: hackathons, game jams, and semester projects. Furthermore, we discuss the Quantum Technologies Education for Everyone (QUTE4E) pilot project, which illustrates an effective integration of these interactive tools with quantum outreach and education activities. Finally, we aim at providing guidelines for incorporating quantum games and interactive tools in pedagogic materials to make quantum technologies more accessible for a wider population.

Indium phosphide and black phosphorus employed surface plasmon resonance sensor for formalin detection: numerical analysis

Abstract: A hybrid Kretschmann configuration-based surface plasmon resonance biosensor is investigated to detect formalin in water was proposed. The modification is done in the conventional sensor by adding the indium phosphide (InP) and black phosphorus (BP) material layer. The silver (Ag) metal thickness is 45 nm, the optimized thickness of the metal for the proposed design. The thickness of the InP and BP materials 2 and 0.34 nm are considered. For three InP layers and one BP layer, the maximum sensitivity of 250.2 deg / RIU is achieved. The BP layer is used to improve the biorecognition ability of the sensor. The performance of the sensor is analyzed using the angular interrogation method. The proposed sensor is investigated for the aqueous sensing medium. The InP is an air-stable semiconductor material and has applications in chemical, medical, and biological fields.

Quantum games and interactive tools for quantum technologies outreach and education

Abstract: We provide an extensive overview of a wide range of quantum games and interactive tools that have been employed by the quantum community in recent years. We present selected tools as described by their developers, including “Hello Quantum, Hello Qiskit, Particle in a Box, Psi and Delta, QPlayLearn, Virtual Lab by Quantum Flytrap, Quantum Odyssey, ScienceAtHome, and the Virtual Quantum Optics Laboratory.” In addition, we present events for quantum game development: hackathons, game jams, and semester projects. Furthermore, we discuss the Quantum Technologies Education for Everyone (QUTE4E) pilot project, which illustrates an effective integration of these interactive tools with quantum outreach and education activities. Finally, we aim at providing guidelines for incorporating quantum games and interactive tools in pedagogic materials to make quantum technologies more accessible for a wider population.

Defining the quantum workforce landscape: a review of global quantum education initiatives

Abstract: Abstract. Rapid advances in quantum technology have exacerbated the shortage of a diverse, inclusive, and sustainable quantum workforce. National governments and industries are developing strategies for education, training, and workforce development to accelerate the commercialization of quantum technologies. We report the existing state of the quantum workforce as well as several learning pathways to nurture the talent pipeline between academia and industry. We provide a comprehensive guide to various educational initiatives accessible throughout the world, such as online courses, conferences, seminars, games, and community-focused networks, that facilitate quantum training and upskill the talent needed to develop a better quantum future.

InP and graphene employed surface plasmon resonance sensor for measurement of sucrose concentration: a numerical approach

Abstract: A surface plasmon resonance (SPR) biosensor utilizing an indium phosphide (InP) semiconductor and graphene material layer and placed on an Ag metal-based biosensor is proposed to detect the sucrose concentration in water. The SPR biosensor works on the principle of attenuated total reflection. The transverse matrix method has been utilized for the reflectance calculation. The thickness of the Ag layer, InP, and graphene are taken as 45 nm, 2 nm, and 2 nm, respectively. When three layers of InP and a single layer of graphene are taken, and the maximum sensitivity of the sensor 506 deg / RIU is obtained. The desired values of performance parameters, such as full-width half maximum and quality factor were computed and obtained as 6.94 deg and 72.86 deg RIU − 1 respectively, which recommend the significance of the proposed work. Graphene is used to enhance the bio-compatibility of the analyte with the sensing layer. The refractive index of the sensing medium is considered as 1.33. Indium phosphide is an air-stable optical material. SPR biosensors have wide applications in detecting and analyzing biomolecules and biochemicals.

Defining the quantum workforce landscape: a review of global quantum education initiatives

Abstract: Rapid advances in quantum technology have exacerbated the shortage of a diverse, inclusive, and sustainable quantum workforce. National governments and industries are developing strategies for education, training, and workforce development to accelerate the commercialization of quantum technologies. In this paper, we report the existing state of the quantum workforce as well as several learning pathways to nurture the talent pipeline between academia and industry. We provide a comprehensive guide of various educational initiatives accessible throughout the world, such as online courses, conferences, seminars, games, and community-focused networks, that facilitate quantum training and upskill the talent needed to develop a better quantum future.

Photonic crystal fiber supporting 394 orbital angular momentum modes with flat dispersion, low nonlinear coefficient, and high mode quality

Abstract: A photonic crystal fiber (PCF) with an SSK2 dense crown glass ring is designed and analyzed. After optimization of the radius of the central air hole and thickness of the annular region, 394 orbital angular momentum (OAM) modes in the range of 1.48 to 1.62 μm can be transmitted stably. The effective refractive index, effective index difference, dispersion, effective mode area, nonlinear coefficient, numerical aperture (NA), mode quality, and confinement loss are analyzed numerically by the finite element method. The results show that the effective index difference of all the OAM modes is greater than 1 × 10 − 4 and they can propagate stably in the PCF. Besides, the fiber shows flat dispersion with a minimum variation of 8.55 ps / ( km · nm ) , very small nonlinear coefficient of less than 0.232 W − 1 · km − 1, as well as high mode quality bigger than 94.57%. This high-performance PCF has immense application potential in optical fiber communication.

Indium phosphide and black phosphorus employed surface plasmon resonance sensor for formalin detection: numerical analysis

Abstract: Abstract. A hybrid Kretschmann configuration-based surface plasmon resonance biosensor is investigated to detect formalin in water was proposed. The modification is done in the conventional sensor by adding the indium phosphide (InP) and black phosphorus (BP) material layer. The silver (Ag) metal thickness is 45 nm, the optimized thickness of the metal for the proposed design. The thickness of the InP and BP materials 2 and 0.34 nm are considered. For three InP layers and one BP layer, the maximum sensitivity of 250.2 deg / RIU is achieved. The BP layer is used to improve the biorecognition ability of the sensor. The performance of the sensor is analyzed using the angular interrogation method. The proposed sensor is investigated for the aqueous sensing medium. The InP is an air-stable semiconductor material and has applications in chemical, medical, and biological fields.

Photonic crystal fiber supporting 394 orbital angular momentum modes with flat dispersion, low nonlinear coefficient, and high mode quality

Abstract: Abstract. A photonic crystal fiber (PCF) with an SSK2 dense crown glass ring is designed and analyzed. After optimization of the radius of the central air hole and thickness of the annular region, 394 orbital angular momentum (OAM) modes in the range of 1.48 to 1.62 μm can be transmitted stably. The effective refractive index, effective index difference, dispersion, effective mode area, nonlinear coefficient, numerical aperture (NA), mode quality, and confinement loss are analyzed numerically by the finite element method. The results show that the effective index difference of all the OAM modes is greater than 1 × 10 − 4 and they can propagate stably in the PCF. Besides, the fiber shows flat dispersion with a minimum variation of 8.55 ps / ( km · nm ) , very small nonlinear coefficient of less than 0.232 W − 1 · km − 1, as well as high mode quality bigger than 94.57%. This high-performance PCF has immense application potential in optical fiber communication.

Defect detection method for specular surfaces based on deflectometry and deep learning

Abstract: Abstract. Defect detection for specular surfaces plays a vital role in precision manufacturing. However, traditional defect detection methods are unsuitable for specular surfaces because of their specular reflection property. The defect detection on specular surfaces is usually performed by inspectors, which makes the defect detection a time-consuming and unstable task. Deflectometry has been widely used in defect detection for specular surfaces combined with machine learning. Nevertheless, conventional deflectometry methods use the local curvature deviation map based on the unwrapped phase, which can only detect geometrical defects. Moreover, hand-crafted features need to be defined for each specific task. We present a method based on deflectometry and deep learning. Deflectometry provides the input images for the network, and the deep learning network completes the identification and location of defects. In deflectometry, the proposed method uses the light intensity contrast map to replace the local curvature map, which can detect both geometrical and textural defects. Based on conventional networks, depthwise separable convolution kernel is applied to reduce parameters, and residual convolution block is utilized to alleviate vanishing or exploding gradients. A subnet for feature aggregation is used to obtain multiscale information of defect features. Performance evaluation based on experiment results proved the effectiveness of the proposed method.

Quantum technician skills and competencies for the emerging Quantum 2.0 industry

Abstract: Abstract. Quantum-based technologies have been instrumental in the development of a whole range of devices that are used these days (such as lasers, transistors, LiDAR, GPS, MRI, and many more). These technologies that emanated from the Quantum 1.0 Revolution have become ubiquitous in modern civilization over the past 50 years. The product commercialization and mass production were enabled by scientists, researchers, engineers, and most importantly, by the skilled technological workforce. This workforce played a critical support role in transforming inventions into high-volume, marketable products. We are currently at the heart of the Second Quantum Revolution, which is fueled by the research in quantum computing, quantum communication, quantum cryptography, and quantum sensing. The scientific progress that is currently taking place in these areas is going to fundamentally change the way we sense the world around us, approach our security, and process critical information. Governments and private entities across the world have recognized the strategic importance of quantum research-enabled technologies and have invested a significant amount of money to support graduate programs and research institutions where scientists, engineers, and other professionals are earning advanced degrees and immediately being engaged in active Quantum 2.0 research. To the best of our knowledge, and despite considerable investment in quantum research, no active efforts or programs exist that would train a quantum technological workforce at the technician level to support Quantum 2.0. The quantum industry, on the other side, has clearly identified the need for highly skilled quantum technicians that are able to support the commercialization of the new products and inventions. The lack of a trained quantum technician workforce is a major shortcoming that may have a profound negative impact on the long-term prospects and sustainability of the emerging quantum industry. The EdQuantum project, funded through the National Science Foundation (NSF) Advanced Technological Education (ATE) program, is an effort to close this shortcoming and propose a well-defined curriculum through which the incumbent photonic and laser technicians in the United States will be upskilled with the new skills and competencies from quantum research-enabled technologies. This paper presents the results of the first phase of the EdQuantum project—the quantum industry survey. Over the last few months, we sought input from the quantum industry as to what skills and competencies should a future quantum technician possess to support its emerging needs. The results collected during the survey are presented in this paper along with their impact on the proposed educational curriculum. This paper also elaborates on the alignment of the proposed curriculum with a few ongoing initiatives in the skilled technical workforce education (such as NSB Vision 2030, Convergence Accelerators Initiative, and Skilled Technical Workforce Initiative).

Defect detection method for specular surfaces based on deflectometry and deep learning

Abstract: Defect detection for specular surfaces plays a vital role in precision manufacturing. However, traditional defect detection methods are unsuitable for specular surfaces because of their specular reflection property. The defect detection on specular surfaces is usually performed by inspectors, which makes the defect detection a time-consuming and unstable task. Deflectometry has been widely used in defect detection for specular surfaces combined with machine learning. Nevertheless, conventional deflectometry methods use the local curvature deviation map based on the unwrapped phase, which can only detect geometrical defects. Moreover, hand-crafted features need to be defined for each specific task. We present a method based on deflectometry and deep learning. Deflectometry provides the input images for the network, and the deep learning network completes the identification and location of defects. In deflectometry, the proposed method uses the light intensity contrast map to replace the local curvature map, which can detect both geometrical and textural defects. Based on conventional networks, depthwise separable convolution kernel is applied to reduce parameters, and residual convolution block is utilized to alleviate vanishing or exploding gradients. A subnet for feature aggregation is used to obtain multiscale information of defect features. Performance evaluation based on experiment results proved the effectiveness of the proposed method.

Review on all-optical logic gates: design techniques and classifications – heading toward high-speed optical integrated circuits

Abstract: All-optical logic gates (AO-LGs) are the key elements that play a pivotal role in the development of future all-optical networks and all-optical computing. A complete overview of the seven all-optical logic gates (i.e., AND, OR, NOT, XOR, XNOR, NAND, and NOR) based on their design techniques and applications are covered, including the latest technologies, such as topological photonics and artificial intelligence-based designs techniques. In addition, we have further categorized the AO-LGs as reconfigurable gates, simultaneous gates, reversible gates, modulation-based gates, and data rate-based gates. The techniques to implement these different classes of gates are reviewed and their limitations are discussed. We also discuss in brief the various simulation tools used to design and analyze the AO-LGs. Finally, the most feasible techniques for constructing optical integrated circuits based on the existing fabrication technologies and available resources are explored, and future prospects are outlined.

Near-surface pedestrian detection method based on deep learning for UAVs in low illumination environments

Abstract: Abstract. With the development of unmanned aerial vehicles (UAVs) and computer vision, target detection methods based on UAVs have been increasingly applied in military and civilian fields. Considering the adaptability requirements of low illumination environments such as rain, fog, and night, visible and infrared (IR) sensors are often installed on UAVs to perform in all-weather and all-day conditions. To improve the near-surface detection performance of UAVs in low illumination environments, a pedestrian detection method using image fusion and deep learning is proposed. Visible and IR pedestrian images are collected by the UAV. The corresponding aerial images are registered and annotated. These two different types of aerial images are aligned at the time sequence and matched using the scale invariant feature transform. A U-type generative adversarial network (GAN) is first developed to fuse visible and IR images. A convolutional block attention module is introduced to strengthen the pedestrian target information in the GAN. The spatial domain and channel domain attention mechanisms are proposed to generate color fusion images with rich details and solve the problems of feature extraction as well as fusion rules designed manually in the existing image fusion methods. Then, You Only Look Once Version 3 (YOLOv3)-spatial pyramid pooling combined with transfer learning is adopted using the fused images to train the model on our aerial dataset to verify the pedestrian detection performance. In addition, comparison experiments are carried out. The experimental results demonstrate that the YOLOv3 model is successfully transferred to the target dataset. The performance of the proposed detection model using the fused images for transfer training is the best among the different methods. Finally, the accuracy P, recall R, mean average precision, and F1 score reach 0.804, 0.923, 0.928, and 0.859, respectively.

Review on all-optical logic gates: design techniques and classifications – heading toward high-speed optical integrated circuits

Abstract: Abstract. All-optical logic gates (AO-LGs) are the key elements that play a pivotal role in the development of future all-optical networks and all-optical computing. A complete overview of the seven all-optical logic gates (i.e., AND, OR, NOT, XOR, XNOR, NAND, and NOR) based on their design techniques and applications are covered, including the latest technologies, such as topological photonics and artificial intelligence-based designs techniques. In addition, we have further categorized the AO-LGs as reconfigurable gates, simultaneous gates, reversible gates, modulation-based gates, and data rate-based gates. The techniques to implement these different classes of gates are reviewed and their limitations are discussed. We also discuss in brief the various simulation tools used to design and analyze the AO-LGs. Finally, the most feasible techniques for constructing optical integrated circuits based on the existing fabrication technologies and available resources are explored, and future prospects are outlined.

Deep learning-based hyperspectral image reconstruction from emulated and real computed tomography imaging spectrometer data

Abstract: The computed tomography imaging spectrometer (CTIS) is a hyperspectral imaging (HSI) approach where spectral and spatial information of a scene is mixed during the imaging process onto a monochromatic sensor. This mixing is due to a diffractive optical element integrated into the underlying optics and creates a set of diffraction orders. To reconstruct a three-dimensional hyperspectral cube from the CTIS sensor image, iterative algorithms were applied. Unfortunately, such methods are highly sensitive to noise and require high computational time for reconstruction thus hindering their applicability in real-time and high frame-rate applications. To overcome such limitations, we propose a lightweight and efficient deep convolutional neural network for hyperspectral image reconstruction from CTIS sensor images. Compared with classical approaches our model delivers considerably better reconstruction results on synthetic as well as real CTIS images in under 0.17 s, which is over 60 times faster compared with the standard iterative approach. In addition, the reshaping method we have developed enables a lightweight network architecture with over 100 times fewer parameters than previously reported.

Silicon microring resonator based on all-optical reversible modified Fredkin gate: design and analysis

Abstract: The conventional logic circuits dissipate energy into the environment due to the loss of information; hence these are termed irreversible logic circuits. The reversible logic (RL) circuits are capable of minimizing the power dissemination, quantum cost, and garbage outputs. The RL circuits based on all-optical technology have been adopted by many researchers in recent. The Fredkin gate is a significant RL gate. A modified Fredkin gate (MFG) made of a silicon all-optical microring resonator is realized in MATLAB. The MFG is capable of designing 16-Boolean functions with very small complexity. The figure of merit of the proposed MFG is achieved through numerical simulation. The parameters are selected from the determined specifications, and this could provide a practicable implementation of the proposed design.

Low-light image enhancement based on deep learning: a survey

Abstract: Abstract. Images taken under low light or dim backlight conditions usually have insufficient brightness, low contrast, and poor visual quality of the image, which leads to increased difficulty in computer vision and human recognition of images. Therefore, low illumination enhancement is very important in computer vision applications. We mainly provide an overview of existing deep learning enhancement algorithms in the low-light field. First, a brief overview of the traditional enhancement algorithms used in early low-light images is given. Then, according to the neural network structure used in deep learning and its learning algorithm, the enhancement methods are introduced. In addition, the datasets and common performance indicators used in the deep learning enhancement technology are introduced. Finally, the problems and future development of the deep learning enhancement method for low-light images are described.

Displacement calculation method for homodyne interferometers based on spatial phase delay of beams

Abstract: Abstract. Based on the spatial phase delay of beams, a displacement measurement method is proposed for homodyne interferometers. In this method, two detectors are arranged in the interference spot to collect signals. Based on the algorithm of least squares fitting the parameters of the ellipse equation, the relative phase of the signals is solved to calculate the displacement. The proposed method uses optical fiber bundles for beam reception, which is more compact in structure than the traditional quadrature phase calculation method, and eliminates polarization mixing. The spatial delay phase in the method can be any unknown constant, which makes it more applicable in practice. The feasibility of this method is verified by ZEMAX simulations and experiments. The experimental results show that the 3σ of errors without the effect of power noise and vibration is 7.2 nm.

Low-light image enhancement based on deep learning: a survey

Abstract: Images taken under low light or dim backlight conditions usually have insufficient brightness, low contrast, and poor visual quality of the image, which leads to increased difficulty in computer vision and human recognition of images. Therefore, low illumination enhancement is very important in computer vision applications. We mainly provide an overview of existing deep learning enhancement algorithms in the low-light field. First, a brief overview of the traditional enhancement algorithms used in early low-light images is given. Then, according to the neural network structure used in deep learning and its learning algorithm, the enhancement methods are introduced. In addition, the datasets and common performance indicators used in the deep learning enhancement technology are introduced. Finally, the problems and future development of the deep learning enhancement method for low-light images are described.

Convolutional networks for speckle-based orbital angular momentum modes classification

Abstract: Machine learning has emerged as a powerful tool for physicists for building empirical models from the data. We exploit two convolutional networks, namely Alexnet and wavelet scattering network for the classification of orbital angular momentum (OAM) beams. We present a comparative study of these two methods for the classification of 16 OAM modes having radial and azimuthal phase profiles and eight OAM superposition modes with and without atmospheric turbulence effects. Instead of direct OAM intensity images, we have used the corresponding speckle intensities as an input to the model. Our study demonstrates a noise and alignment-free OAM mode classifier having maximum accuracy of >94 % and >99 % for with and without turbulence, respectively. The main advantage of this method is that the mode classification can be done by capturing a small region of the speckle intensity having a sufficient number of speckle grains. We also discuss this smallest region that needs to be captured and the optimal resolution of the detector required for mode classification.

Camera–Lidar sensor fusion for drivable area detection in winter weather using convolutional neural networks

Abstract: Abstract. Autonomous vehicles rely on perceiving the road environment through a perception pipeline fed by a variety of sensor modalities, including camera, lidar, radar, infrared, gated camera, and ultrasonic. The vehicle computer must perform sensor fusion reliably in various sensor signals degrading environments, which can be performed parametrically or using machine learning. We study sensor fusion of a forward-facing camera and a lidar sensor using convolutional neural networks (CNNs), for drivable area detection in winter driving, where the roads are partially covered with snow, tire tracks obscuring lane markers. Seven fusion models are developed and evaluated for their ability to classify pixels into two classes: drivable and nondrivable. A total of seven models are designed and tested, including camera only, lidar only, early fusion, (three types of) intermediate fusion, and late fusion. The models have accuracies between 84% and 89%, with runtimes between 23 and 61 ms. To select the best model from this group, we introduce a unique metric, named normalized accuracy runtime (NAR) score. A network in a group has a higher NAR score, the more accurate it is, and the shorter its runtime is. The models are evaluated on a winter driving DENSE dataset. Camera and lidar fog noise are synthesized to examine model robustness.

Silicon microring resonator based on all-optical reversible modified Fredkin gate: design and analysis

Abstract: Abstract. The conventional logic circuits dissipate energy into the environment due to the loss of information; hence these are termed irreversible logic circuits. The reversible logic (RL) circuits are capable of minimizing the power dissemination, quantum cost, and garbage outputs. The RL circuits based on all-optical technology have been adopted by many researchers in recent. The Fredkin gate is a significant RL gate. A modified Fredkin gate (MFG) made of a silicon all-optical microring resonator is realized in MATLAB. The MFG is capable of designing 16-Boolean functions with very small complexity. The figure of merit of the proposed MFG is achieved through numerical simulation. The parameters are selected from the determined specifications, and this could provide a practicable implementation of the proposed design.

Joint modulation format recognition and optical performance monitoring for efficient fiber-optic communication links using ensemble deep transfer learning

Abstract: Abstract. We propose an ensemble deep transfer learning (EDTL) method, which is a more refined multilayer feature extraction achieved by aggregating the convolutional layers of pretrained convolutional neural network models for joint optical modulation format recognition (MFR) and optical performance monitoring (OPM) in fiber-optic communication links. Modulation formats, such as quadrature phase shift keying, 16 quadrature amplitude modulation (QAM), and 64 QAM, are monitored for the optical signal-to-noise ratio (OSNR) range of 20 to 30 dB by considering the dispersive effects of chromatic dispersion from 0 to 1200 ps / nm and polarization mode dispersion from 10 to 70 ps in the fiber-optic transmission path. First, the generated constellation diagrams affected by the impairments are used to optimize and evaluate the pretrained models based on classification targets. Then the proposed EDTL model is designed by aggregating the feature extractor parts of the pretrained models; it is implemented in three phases, and the results are comprehensively studied. Further, data augmentation and aggregation methods are introduced to enhance the performance of joint MFR and OPM. The results obtained prove that the proposed model provides faster convergence of MFR and better identification accuracy of OSNR toward optical signal diagnostics in optical networks for efficient optical link monitoring.

Geolocalization and navigation by visible light communication to address automated logistics control

Abstract: Abstract. Global positioning system uses satellite signals to infer position. In buildings, however, these signals are attenuated and scattered by walls and other objects, making it impossible to measure an exact position inside them. Using the location information supplied by the lighting infrastructure, we propose an indoor navigation system based on visible light communication (VLC). The application presented relates the use of robotic solutions in a modern, efficient warehouse. As warehouses and distribution centers compete for a competitive advantage, automated guided vehicles (AGVs) are becoming increasingly popular. Our work reports a VLC-based geolocalization and navigation system to address automated logistics control. The proposed system includes VLC links and a space layout connecting RGB lamps and AGVs. The controlling flowchart, methods, and the data frame content required to support bidirectional communication between the infrastructure and AGVs are also discussed. The communication network is supported by VLC emitters using trichromatic RGB white LEDs and dedicated receivers based on a-SiC:H/a-Si:H photodiodes with selective spectral sensitivity. The downlink channel establishes the infrastructure-to-vehicle link and transmits information through the modulation of the red and blue emitters of the white RGB LEDs. The decoding strategy is based on accurate calibration of the output signal. Synchronization of the transmitted frames is used to ensure the identification of the start and end of each message. The uplink channel is used for the communication from the vehicle-to-infrastructure. This link is established using a single optical signal. The communication flowchart model was defined to establish the different communication modes and types of messages transmitted by each of the system entities. We present basic system requirements, give details on the network topology, define the communication flowchart model, and discuss the methodology used to decode the multiplexed signal transmitted by simultaneous emitters.

Fabrication techniques and applications of two-dimensional photonic crystal: history and the present status

Abstract: Abstract. The idea of photonic crystal came to be when E. Yablonovitch and S. John recommended a structure in 1987 having periodic alteration of refractive index in one or multiple directions. Photonic crystals have the potential to manipulate and tailor the flow of light across the crystal, which gives advantage to invent many photonic devices on the microscopic scale having very small footprints. In recent years several articles have been published on the advances of photonic crystal-based structures. This review article mainly focuses on the several fabrication techniques of photonic crystals and their applications in different fields. Several photonic crystal-based structures such as logic gates, optical sensors, polarization beam splitters, and absorbers for solar cells are reviewed in this paper to show recent advancements on this trending topic. Also, a brief review of the different steps of fabrication of photonic crystals and different fabrication methods proposed by researchers is articulated in this paper.

Comparison of fiber-optic linear beam shaping designs for laparoscopic laser sealing of vascular tissues

Abstract: Infrared lasers may provide faster and more precise sealing of blood vessels and with lower device jaw temperatures than ultrasonic and electrosurgical devices during surgery. Our study explores three beam shaping methods using optical fibers for transformation of a circular laser beam into a linear beam, necessary for integration into a standard 5-mm-diameter laparoscopic device, and for uniform irradiation perpendicular to the vessel length. In the first design, a servo motor connected to a side-firing, 550-μm-core fiber, provided linear translation of a 2.0-mm-diameter circular beam, back, and forth, over either 5 or 11 mm scan lengths for sealing of small or large vessels. The second design used external beam splitters to divide laser power equally into three side-firing fibers, stacked side-by-side, producing a linear beam of 4 × 2 mm. The third design used external beam splitters with three forward-firing fibers and a slanted jaw surface, to produce a linear beam of 5 × 1.5 mm. Laser seals were performed, ex vivo, on 41 porcine renal arteries of 1- to 6-mm diameter (n ≥ 10 samples for each design). Each vessel was compressed to a fixed 0.4-mm-thickness, matching the optical penetration depth at 1470 nm. Vessels were irradiated with fluences of 636 to 800 J/cm2, which, based on previous studies, is sufficient for sealing, but not cutting. A burst pressure setup was used to evaluate vessel seal strength. Reciprocating fiber and fiber bundles produced mean burst pressures of 554 ± 142, 524 ± 132, 429 ± 99, and 390 ± 140 mmHg, respectively. All designs consistently sealed blood vessels, with burst pressures above hypertensive (180 mmHg) blood pressures. The reciprocating fiber produced the most uniform linear beam profile and aspect ratio but will require integration of the servo motor into a handpiece. Fiber bundle designs produced shorter, less uniform beams, but enable optical components to be assembled outside the handpiece.

Falcon Neuro: an event-based sensor on the International Space Station

Abstract: Abstract. We report on the Falcon neuro event-based sensor (EBS) instrument that is designed to acquire data from lightning and sprite phenomena and is currently operating on the International Space Station. The instrument consists of two independent, identical EBS cameras pointing in two fixed directions, toward the nominal forward direction of flight and toward the nominal Nadir direction. The payload employs stock DAVIS 240C focal plane arrays along with custom-built control and readout electronics to remotely interface with the cameras. To predict the sensor’s ability to effectively record sprites and lightning, we explore temporal response characteristics of the DAVIS 240C and use lab measurements along with reported limitations to model the expected response to a characteristic sprite illumination time-series. These simulations indicate that with appropriate camera settings the instrument will be capable of capturing these transient luminous events when they occur. Finally, we include initial results from the instrument, representing the first reported EBS recordings successfully collected aboard a space-based platform and demonstrating proof of concept that a neuromorphic camera is capable of operating in the space environment.

Comparison of fiber-optic linear beam shaping designs for laparoscopic laser sealing of vascular tissues

Abstract: Abstract. Infrared lasers may provide faster and more precise sealing of blood vessels and with lower device jaw temperatures than ultrasonic and electrosurgical devices during surgery. Our study explores three beam shaping methods using optical fibers for transformation of a circular laser beam into a linear beam, necessary for integration into a standard 5-mm-diameter laparoscopic device, and for uniform irradiation perpendicular to the vessel length. In the first design, a servo motor connected to a side-firing, 550-μm-core fiber, provided linear translation of a 2.0-mm-diameter circular beam, back, and forth, over either 5 or 11 mm scan lengths for sealing of small or large vessels. The second design used external beam splitters to divide laser power equally into three side-firing fibers, stacked side-by-side, producing a linear beam of 4 × 2 mm. The third design used external beam splitters with three forward-firing fibers and a slanted jaw surface, to produce a linear beam of 5 × 1.5 mm. Laser seals were performed, ex vivo, on 41 porcine renal arteries of 1- to 6-mm diameter (n ≥ 10 samples for each design). Each vessel was compressed to a fixed 0.4-mm-thickness, matching the optical penetration depth at 1470 nm. Vessels were irradiated with fluences of 636 to 800 J/cm2, which, based on previous studies, is sufficient for sealing, but not cutting. A burst pressure setup was used to evaluate vessel seal strength. Reciprocating fiber and fiber bundles produced mean burst pressures of 554 ± 142, 524 ± 132, 429 ± 99, and 390 ± 140 mmHg, respectively. All designs consistently sealed blood vessels, with burst pressures above hypertensive (180 mmHg) blood pressures. The reciprocating fiber produced the most uniform linear beam profile and aspect ratio but will require integration of the servo motor into a handpiece. Fiber bundle designs produced shorter, less uniform beams, but enable optical components to be assembled outside the handpiece.

Visualizing quantum mechanics in an interactive simulation – Virtual Lab by Quantum Flytrap

Abstract: Abstract. Virtual Lab by Quantum Flytrap is a no-code online laboratory of an optical table, presenting quantum phenomena interactively and intuitively. It supports a real-time simulation of up to three entangled photons. Users can place typical optical elements (such as beam splitters, polarizers, Faraday rotators, and detectors) with a drag-and-drop graphical interface. Virtual Lab operates in two modes. The sandbox mode allows users to compose arbitrary setups. Quantum Game serves as an introduction to Virtual Lab features, approachable for users with no prior exposure to quantum mechanics. We introduce visual representation of entangled states and entanglement measures. It includes interactive visualizations of the ket notation and a heatmap-like visualization of quantum operators. These quantum visualizations can be applied to any discrete quantum system, including quantum circuits with qubits and spin chains. These tools are available as open-source TypeScript packages – Quantum Tensors and BraKetVue. Virtual Lab makes it possible to explore the nature of quantum physics (state evolution, entanglement, and measurement), to simulate quantum computing (e.g., the Deutsch-Jozsa algorithm), to use quantum cryptography (e.g., the Ekert protocol), to explore counterintuitive quantum phenomena (e.g., quantum teleportation and the Bell inequality violation), and to recreate historical experiments (e.g., the Michelson–Morley interferometer).