The first demonstration of indirect drive fusion in the National Ignition Facility (NIF) has ushered in a new era of laser-based fusion work. Before every experiment in NIF all 192 laser beams passing through various laser sub-sections must be aligned to the target chamber center within 30 minutes with a 50-micron accuracy at the target chamber center. NIF uses a CCD camera-based imaging system to identify the beam location; the laser beams are then aligned using motorized mirrors within a feedback control system. The success of the automatic alignment (AA) is contingent upon calculating the exact location of the laser beam in the CCD image of the beam. When an algorithm fails due to poor imaging, gradient illumination, or high noise content, the automated operation for the affected alignment loop stops, and the alignment enters a manual mode with operator intervention. The operator/shot director must decide if the imaging condition needs to be improved to successfully pass alignment or if the beam has to be dropped from participating in the shot thus reducing the total energy delivered to the target. To minimize such failures where a legitimate but challenging beam image is present, additional alternate algorithms are added to the original algorithm. When the first algorithm fails, this alternate algorithm is executed to process the image even though it may have a slightly higher uncertainty. Although the second algorithm may report a higher uncertainty, this system design allows the loop to continue to align the laser without operator intervention. The implication of this approach may be far-reaching, for example, in the application of driver-less cars, where the original algorithm developed by training on a large set of data, may sometimes fail to account for the presence of a pedestrian and lead to a fatal accident. Here we hypothesize that a back-up algorithm based on a second approach will minimize such risks in the real-world driver-less driving scenarios.

The National Ignition Facility (NIF) employs 192 laser beams to achieve inertial confinement fusion by irradiating a mm scale fusion target. Automatic alignment (AA) image processing algorithms are used to align 192 beams to the NIF target chamber center. Cameras placed along the beam path supply the images that are analyzed by AA algorithms to provide beam location and alignment information. NIF has the capability of using beam-specific database parameters. This allows beam line images to be processed using optimized algorithms tailored to individual beam alignment needs. For a given segment of alignment in the NIF beam path, 192 different versions of the algorithm can be run simply by changing data base parameters. This capability is vital to alignment precision in a system as complex and mature as NIF. Since optical components and devices age, laser parameters and beam alignment quality can and do change. Constant beam-by-beam monitoring of alignment performance is needed in order to mitigate any issues caused by such changes. The objective of this work is to evaluate how periodic AA algorithm beam parameter changes might better maintain alignment requirements over time in the NIF facility. We show examples from final optics assembly (FOA) and harmonic generator (THG, SHG) loops.

This special feature issue covers the intersection of topical areas in artificial intelligence (AI)/machine learning (ML) and optics. The papers broadly span the current state-of-the-art advances in areas including image recognition, signal and image processing, machine inspection/vision and automotive as well as areas of traditional optical sensing, interferometry and imaging.

The National Ignition Facility (NIF) employs 192 laser beams to achieve inertial confinement fusion by irradiating a mm scale fusion target. The optical Thomson scattering (OTS) laser is being deployed to probe the target and understand the target implosion physics. Centroid based approach is one of the common approaches for detecting the position of normal Gaussian beams within the OTS laser for beam alignment. Recently, we reported some results of aligning such a beam in 2021, where a pattern matching technique such as matched filtering was used. However, when we defined the template, it included a very high background noise. The correlated noise resulted in an artificial stability when the template was applied on a set of images taken at the same position in quick succession. However, when applied for alignment on different days, the presence of noise had a lesser effect as it made the noise more uncorrelated. In this paper, we re-evaluate this same dataset as published in 2021. We show that the performance of a matched filtering followed by a weighted centroid can overcome distortions appearing in the beam image and is capable of tracking the pattern motion reliably. This paper aims to explain some of the conclusions reached in the previous work while presenting a better approach.

Characterizing plasmas generated in the world’s largest and most energetic laser facility, the National Ignition Facility (NIF), is an important capability for experimentalists working to achieve fusion ignition in a laboratory setting. The optical Thompson scattering (OTS) laser has been de-veloped to understand the target implosion physics, espe-cially for under-dense plasma conditions. A 5w probe beams can be set up for diagnosing various plasma densi-ties. Just as the NIF laser with 192 laser beams are precisely aligned, the OTS system also requires precision alignment using a series of automated closed loop control steps. CCD images from the OTS laser (OTSL) beams are analyzed using a suite of image processing algorithms. The algorithms provide beam position measurements that are used to control motorized mirrors that steer beams to their defined desired location. In this paper, several alignment algorithms will be discussed with details on how they utilize various types of fiducials such as diffraction rings, con-trasting squares and circles, octagons and very faint 5w laser beams.

Welcome and Introduction to SPIE Photonics West LASE conference 11666: High Power Lasers for Fusion Research VI

The world’s largest and most energetic laser facility, the National Ignition Facility (NIF) is dedicated to understanding inertial confinement fusion by irradiating a mm scale fusion target using 192 precisely aligned laser beams. The optical Thompson scattering (OTS) laser is being commissioned to understand the target implosion physics, especially for under-dense plasma conditions. The OTS will enable the diagnosis of the plasma performance leading to better control of the symmetry and efficiency of target implosion. Both 3w and 5w probe beams can be selectively chosen for diagnosing various plasma densities. Just as the NIF laser, the OTS system is aligned using several closed loop controls. CCD images of the laser beams are analyzed using automatic alignment image processing algorithm to provide beam position and motorized mirrors are mobilized to steer those beams to the desired path and location. One such control loop of the OTS uses a pair of far field diffraction pattern generated by a spherical object connected to a shaft. During the control loop alignment movements, the diffraction rings may be partially missing due to obstruction by the aperture. This paper describes the systematic process of algorithm development for detection of the diffraction rings and the necessary modifications as the requirements change. It describes the template design and algorithm for position detection from such images under challenging illumination and obstructive conditions. Among other novelties of this work is the use of a single computational neuron, inspired by a bipolar binary associative memory, employed to evaluate the quality and uncertainty of the location measurement, and avoid false detection.

This chapter discusses the smearing of the displayed image is demonstrated by means of simulation by considering the appropriate temporal response for the imaging condition under analysis. It shows that the phosphor type as well as the magnitude and direction of the relative motion between the target and the sensor play significant roles in image degradation. Imaging faithfully in a dynamic environment is an important task for an electrooptical imaging system. An environment is referred to as dynamic when there exists a finite relative motion between the object and the image-gathering system. To comprehend the extent of image degradation in an electro-optical display, a particular image intensifier tube, such as that used in night vision goggles, a cathode-ray tube display under motion, and display under vibration are considered. To restore the degraded image, one needs to know the phosphor decay time constant and the magnitude and direction of relative velocity between the sensor and the target.

The characterization of nondiscrete displays mainly focuses on the displays in both static and dynamic modes of operation. The main purpose of a display system is to faithfully reproduce the visual information as perceived by its image-gathering end. A display system is merely the end interface of a complete information transmission system that transfers visual information from a distant location acquired by optical, opto-electronic, or some other means to the eye of an observer. The average standard deviation of the pixel intensity levels from the intensity levels of the original image across several pixels is taken as the pixel error measure. A visual display may have excellent high-frequency response, but human perception is limited to a certain maximum frequency. A review of display specifications will reveal that square waves were more popular in the early days and sine wave responses have gained popularity only recently, owing to the advances in linear systems and communications theory and its interaction with optics.

In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others. This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network (DNN). The survey goes on to cover Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). Additionally, we have discussed recent developments, such as advanced variant DL techniques based on these DL approaches. This work considers most of the papers published after 2012 from when the history of deep learning began. Furthermore, DL approaches that have been explored and evaluated in different application domains are also included in this survey. We also included recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches. There are some surveys that have been published on DL using neural networks and a survey on Reinforcement Learning (RL). However, those papers have not discussed individual advanced techniques for training large-scale deep learning models and the recently developed method of generative models.

The interest in parallel binary and non-binary computer arithmetic in digital computing was initiated with the pioneering works of Avizienis. 1 The parallel nature of such modified binary systems prompted researchers to adopt the modified signed-digit~MSD! algorithm for optical computing. 2 Around the same time, non-binary systems such as multiple valued logic~MVL ! ~Ref. 3! also achieved prominence both in optics and digital computing. Since then a large number of papers have been published in optical computer arithmetic. This special section is an attempt to capture current research in computer arithmetic for optical computing. The five major areas that are presented in this section are MSD-based algorithm and systems, optimization of MVL, novel architectures for binary optical computing, high accuracy analog optical system implementations, and system studies for fault-tolerance and accuracy. Some of the papers may have overlap of two or more areas with one primary focus; they are pointed out in the following discussion. The largest cluster of papers appears in the area of signed-digit arithmetic and its implementation. A number of different techniques for addition, multiplication and division are proposed by several authors. The number systems addressed include redundant binary, MSD binary, negabinary, MSD trinary, recoded trinary and MSD quaternary. In terms of number of steps, addition/subtraction in single, dual and triple step has been proposed. While the MSD number system leads to higher information density, if the number of steps is reduced, the truth tables may become humongous, which may impose challenging requirements on the actual implementation. Techniques for reducing the cost of such implementations have been addressed by some authors. Proposed implementations include space-variant logic array, correlator ~composite and pseudo-inverse filter ! and non-holographic content addressable memory~CAM! using electron-trapping material. Several authors have proposed novel algorithms and their possible optical implementations while others have suggested implementations and/or optimization on known algorithms. The first paper in the area of signed-digit arithmetic by Li et al. presents negabinary arithmetic operations for addition, subtraction and multiplication and implements them using electron trapping material. A carry free addition technique in signed-digit negabinary ~SDN! is presented with a conversion technique from SDN to normal negabinary. Zhang and Karim propose modified two-step, one-step, canonical and three-input algorithms for addition of redundant binary numbers and provide architecture and encoding for corresponding optical space-variant implementation. Cherri demonstrates single step trinary and quaternary signed-digit circuits. In general, the reduction in step increases the complexity of the truth table. However, Cherri overcomes the problem by smart digit grouping to reduce the number of rules and an intelligent pixel encoding to implement the system within a certain space-bandwidth product. Huang, Itoh and Yatagai propose a new technique for high-speed 2-D data array addition and multiplication based on binary MSD addition and digit-decomposition-plane representation. Huang, Itoh and Yatagai generate all the partial products in parallel and propose to add them using an MSD adder tree. It is interesting to note that they perform multiplication operation using five elementary operations such as bitwise product, duplication, shifting, masking and magnification. In the next paper, Alam introduces trinary division technique based on recoded trinary addition and multiplication. The proposed implementation uses a pseudo-inverse filter correlator. The last two papers in this group by Ahmed, Awwal and Power and by Zhang and Karim propose novel implementations of trinary and binary MSD algorithm. Ahmed, Awwal and Power implement an MSD trinary adder using composite phase-only-filter correlator architecture. In this framework, the truth table rules are Special Section Guest Editorial

At the National Ignition Facility (NIF), the world's largest most energetic laser facility with over 40,000 optics, image processing algorithms are essential to position 192 laser beams onto a mm scale fusion target. In its most general sense, an image processing algorithm consists of a series of steps which produce a desired outcome. When humans set out to solve specific tasks, they often exploit their own experience and intuition by decomposing the problem into subproblems. They then apply and develop techniques which are woven together in the end to maximize performance and minimize uncertainty. The fact that many options are available in each of these steps, can lead to a large variety of recipes from which one can choose. Consequently, the chosen solution is not unique, but multiple solutions evolve when combining the multiple choices from each step. The fact that some of the steps are nonlinear gives rise to interesting performance tradeoffs in arriving to the final solution. This paper will explore and illustrate various approaches to solving a specific image processing problem.

Deep learning has demonstrated tremendous success in variety of application domains in the past few years. This new field of machine learning has been growing rapidly and applied in most of the application domains with some new modalities of applications, which helps to open new opportunity. There are different methods have been proposed on different category of learning approaches, which includes supervised, semi-supervised and un-supervised learning. The experimental results show state-of-the-art performance of deep learning over traditional machine learning approaches in the field of Image Processing, Computer Vision, Speech Recognition, Machine Translation, Art, Medical imaging, Medical information processing, Robotics and control, Bio-informatics, Natural Language Processing (NLP), Cyber security, and many more. This report presents a brief survey on development of DL approaches, including Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) including Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). In addition, we have included recent development of proposed advanced variant DL techniques based on the mentioned DL approaches. Furthermore, DL approaches have explored and evaluated in different application domains are also included in this survey. We have also comprised recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches. There are some surveys have published on Deep Learning in Neural Networks [1, 38] and a survey on RL [234]. However, those papers have not discussed the individual advanced techniques for training large scale deep learning models and the recently developed method of generative models [1].

Deep-learning methods are gaining popularity because of their state-of-the-art performance in image classification tasks. In this paper, we explore classification of laser-beam images from the National Ignition Facility (NIF) using a novel deeplearning approach. NIF is the world's largest, most energetic laser. It has nearly 40,000 optics that precisely guide, reflect, amplify, and focus 192 laser beams onto a fusion target. NIF utilizes four petawatt lasers called the Advanced Radiographic Capability (ARC) to produce backlighting X-ray illumination to capture implosion dynamics of NIF experiments with picosecond temporal resolution. In the current operational configuration, four independent short-pulse ARC beams are created and combined in a split-beam configuration in each of two NIF apertures at the entry of the pre-amplifier. The subaperture beams then propagate through the NIF beampath up to the ARC compressor. Each ARC beamlet is separately compressed with a dedicated set of four gratings and recombined as sub-apertures for transport to the parabola vessel, where the beams are focused using parabolic mirrors and pointed to the target. Small angular errors in the compressor gratings can cause the sub-aperture beams to diverge from one another and prevent accurate alignment through the transport section between the compressor and parabolic mirrors. This is an off-normal condition that must be detected and corrected. The goal of the off-normal check is to determine whether the ARC beamlets are sufficiently overlapped into a merged single spot or diverged into two distinct spots. Thus, the objective of the current work is three-fold: developing a simple algorithm to perform off-normal classification, exploring the use of Convolutional Neural Network (CNN) for the same task, and understanding the inter-relationship of the two approaches. The CNN recognition results are compared with other machine-learning approaches, such as Deep Neural Network (DNN) and Support Vector Machine (SVM). The experimental results show around 96% classification accuracy using CNN; the CNN approach also provides comparable recognition results compared to the present feature-based off-normal detection. The feature-based solution was developed to capture the expertise of a human expert in classifying the images. The misclassified results are further studied to explain the differences and discover any discrepancies or inconsistencies in current classification.

Every summer in the National Ignition Facility (NIF) at Lawrence Livermore National Laboratory, students are brought in to gain interesting research and development experience. In this work, we will review some case studies of past research experiences with students inside and outside NIF, that led to successful journal and conference publications. Several of these works will be reviewed to demonstrate how problems were chosen and defined so that meaningful results could be obtained within a limited time frame. It is anticipated that success with such projects will go a long way in motivating students in their future graduate career. Projects from laser measurement, optical computing and application of matched filtering in laser beam alignment will be reviewed to demonstrate this approach.

The Automatic Alignment system in the National Ignition Facility is responsible for aligning 192 laser beams using camera sensor images. This paper reviews some of the image processing algorithms that generate the crucial alignment positions.

The Advance Radiographic Capability (ARC) at the National Ignition Facility (NIF) is a laser system that employs up to four petawatt (PW) lasers to produce a sequence of short pulses that generate X-rays which backlight high-density inertial confinement fusion (ICF) targets. ARC is designed to produce multiple, sequential X-ray images by using up to eight back lighters. The images will be used to examine the compression and ignition of a cryogenic deuterium-tritium target with tens-of-picosecond temporal resolution during the critical phases of an ICF shot. Multi-frame, hard-X-ray radiography of imploding NIF capsules is a capability which is critical to the success of NIF's missions. As in the NIF system, ARC requires an optical alignment mask that can be inserted and removed as needed for precise positioning of the beam. Due to ARC's split beam design, inserting the nominal NIF main laser alignment mask in ARC produced a partial blockage of the mask pattern. Requirements for a new mask design were needed. In this paper we describe the ARC mask requirements, the resulting mask design pattern, and the image analysis algorithms used to detect and identify the beam and reference centers required for ARC alignment.