Author: raoam488

NASA’s Goddard Space Flight Center’s optical navigation software, GIANT, initially instrumental in guiding the OSIRIS-REx mission to asteroid Bennu, is being further developed for broader applications in future space explorations. The team behind GIANT is enhancing its functionality and usability for both robotic and crewed missions.

GIANT, short for Goddard Image Analysis and Navigation Tool, operates by processing images from cameras and sensors to navigate spacecraft, similar to human navigation using visual cues. It identifies surface landmarks on targets like Bennu, calculates precise distances, and constructs 3D maps of landing zones and potential hazards. The software can also analyze spinning objects to determine their mass and center, crucial information for orbital maneuvers.

According to Andrew Liounis, the lead developer for GIANT, onboard autonomous optical navigation offers significant advantages. It reduces reliance on Earth-based tracking by processing navigation data onboard the spacecraft. This minimizes data downlink requirements and communication costs, particularly beneficial for smaller missions, while also potentially freeing up bandwidth for more science data in larger missions. Moreover, it decreases the need for extensive ground-based navigation teams.

During OSIRIS-REx’s mission at Bennu, GIANT played a critical role in identifying particles ejected from the asteroid’s surface. The team used the software to analyze these particles’ movements and masses, confirming they posed no major threat to the spacecraft.

Continuing development has led to an open-source version of GIANT, making the technology accessible to the public. Recent advancements also include celestial navigation capabilities, enabling deep space travel by analyzing images of stars, the Sun, and solar system objects. A streamlined package of GIANT is being developed to support autonomous operations throughout mission lifecycles.

Researchers are exploring further applications, including processing Cassini data to study Saturn’s interactions with its moons in collaboration with the University of Maryland. Goddard engineer Alvin Yew is also adapting GIANT to assist rovers and future human explorers on planetary surfaces like the Moon.

A refined open-source version of GIANT was released shortly after OSIRIS-REx departed Bennu, focusing on improved user-friendliness and operational efficiency. Intern-led modifications have enabled graphics processor utilization for ground operations, enhancing image processing speed. A simplified version, cGIANT, is being integrated with Goddard’s autonomous Navigation, Guidance, and Control software for enhanced autonomous capabilities in various mission scenarios.

Furthermore, GIANT’s celestial navigation feature, developed by Liounis and Chris Gnam, reduces dependence on NASA’s Deep Space Network by using star, planet, and asteroid images for spacecraft steering in deep space. The team is now focused on incorporating planning capabilities into GIANT, aiming to streamline mission design significantly, potentially reducing trajectory planning time from months to approximately a week. These ongoing innovations have garnered continuous support from Goddard’s Internal Research and Development program, individual missions, and NASA’s Space Communications and Navigation program, underscoring optical navigation’s growing importance as a vital tool for increasingly complex future missions.

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Samsung’s Galaxy S24 Ultra has swapped out the 10X optical zoom for a 5X optical zoom, a change noted in camera performance comparisons. While the sharpness is nearly matched using the S24 Ultra’s 10X digital zoom, there is a slight decrease in clarity. Color reproduction, contrast, and white balance also show minor discrepancies when compared to the S23 Ultra’s 10X optical zoom capabilities. Despite this change, the 5X optical zoom on the S24 Ultra still delivers sharp images, often outperforming the 5X zoom on the Google Pixel 8 Pro. Samsung continues its trend of vibrant color enhancement in its photos.

In other camera aspects, the Google Pixel 8 Pro’s ultrawide camera is preferred for its sharper details and more natural colors with improved white balance over the S24 series ultrawide lens. The 3X zoom on the S24 series appears to offer a marginal sharpness improvement over previous Samsung models. The S24 series continues to boast high-quality selfie cameras.

Video recording on the S24 series remains excellent, with a standout software addition being Instant Slow-Mo. This feature, accessible within the Gallery app, allows users to convert any part of a video into slow motion by simply pressing and holding. It generates interpolated frames to create a smooth slow-motion effect, suitable for social media content when regular slow-motion recording is missed, though the quality is not on par with footage originally shot in slow motion.

Adding to its software capabilities, the Galaxy S24 series incorporates a Generative Edit feature, akin to Google’s Magic Editor. This tool enables users to manipulate photo subjects by resizing and repositioning them, with AI intelligently filling in the altered areas.

Overall, the Galaxy S24 series merges premium Android hardware with intelligent software features, drawing comparisons to Google’s Pixel phones. While Google’s Pixel devices are perceived to have more immediately useful software features, Samsung excels in hardware. The pricing for the S24 ($800) and S24+ ($1,000) is considered reasonable, but the $1,300 price point for the Ultra model is seen as less justified. Potential buyers may find better value by waiting for sales on the S24 Ultra.

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Researchers at King Abdullah University of Science and Technology (KAUST) have developed a new artificial intelligence method called DeepLens that dramatically speeds up and simplifies the design of optical lenses for imaging systems. This innovative approach automates the complex process, significantly reducing the time and cost associated with lens development, particularly for mobile phone cameras, while also promising enhanced image quality.

DeepLens utilizes a technique known as “curriculum learning,” inspired by human learning processes, to iteratively refine lens designs. Instead of requiring human-designed templates, as with traditional methods, the AI autonomously generates designs for compound optical systems. These systems comprise multiple refractive lens elements, each specifically shaped to achieve optimal performance.

According to Xinge Yang, one of the developers, traditional automated methods offered only minor improvements to existing optical designs. DeepLens, however, offers a substantial leap forward, potentially shrinking months of work by experienced engineers down to a single day.

The effectiveness of DeepLens has been demonstrated in creating both conventional optical designs and advanced computational lenses with extended depth-of-field. For example, it has been used to design a mobile phone lens system with a wide field of view, incorporating highly aspheric lens elements and a short back focal length.

Currently, DeepLens is designed for refractive lenses, but the KAUST team is working to expand its capabilities to include hybrid optical systems. This would integrate refractive lenses with diffractive optics and metalenses, allowing for further miniaturization and new imaging functionalities such as spectral cameras and joint-color depth imaging. The researchers believe this technology will have a far-reaching impact, setting new performance standards for imaging systems across various applications, from mobile phones to advanced imaging technologies. The research findings have been published in Nature Communications.

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Tellabs, a leading provider of Passive Optical Network (PON) solutions, has announced the release of the industry’s first Building Information Modeling (BIM) family for their Optical LAN solution. This new BIM library, built using intelligent 3D Revit, offers architects, engineers, and integrators a set of tools to design fiber-based networks in a 3D modeling environment.

The BIM family includes smart 3D Revit objects for Optical Line Terminals and Optical Network Terminals, along with crucial specifications such as dimensions, clearances, weight, temperature requirements, power needs, thermal characteristics, connector types, and hyperlinks to Tellabs datasheets for detailed information. Beyond Tellabs’ specific products, the library also features generic families for fiber and power components, as well as buildable families for IT, network, wireless, smart building, and Internet of Things related objects. Embedded intelligence within the family components allows for connection value assignments, and the system can automatically generate smart schedules for all connected products, streamlining the design process.

Tellabs President and CEO, Rich Schroder, emphasized the importance of investing in 3D Revit to allow professionals to visualize the benefits of fiber-based networks through 3D modeling. He highlighted the automatic generation of smart schedules as a key advantage, simplifying design work and improving speed and accuracy.

The new BIM library is designed to help architects, engineers, designers, integrators, and consultants reduce both costs and time in the design of innovative fiber networks. It also facilitates informed decision-making through spatial awareness offered by 3D visualization. Furthermore, the software aims to enhance change management throughout project lifecycles and improve design accuracy by mitigating errors commonly associated with traditional 2D drawings. Those interested in utilizing this tool can access it by submitting a contact request form.

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Scientists at the Max Planck Institute for the Science of Light (MPL) have developed a new artificial intelligence (AI) framework named XLuminA that can autonomously discover new experimental designs in microscopy, particularly super-resolution microscopy. This innovation addresses the challenge of the vast number of possible optical configurations for microscopes, making it difficult for human researchers to find optimal setups through traditional methods based on experience and intuition.

The XLuminA framework operates as an AI-driven optics simulator capable of exploring the entire space of potential optical configurations much faster than conventional computational methods, achieving optimizations up to 10,000 times quicker. Researchers emphasize that this technology can significantly accelerate the discovery of new super-resolution microscopy techniques which are crucial for biological sciences, allowing scientists to overcome the diffraction limit of light and visualize cellular structures at a nanoscale level.

The team validated XLuminA by demonstrating its ability to independently rediscover three established microscopy techniques, including the Nobel Prize-winning STED microscopy. Furthermore, XLuminA has proven its capacity for genuine discovery by devising a novel super-resolution design that integrates principles from existing methods and outperforms them.

The open-source XLuminA framework is modular and adaptable to various microscopy and imaging techniques. The researchers are working on expanding its capabilities to include nonlinear interactions, light scattering, and time information, which would enable simulations for an even wider range of microscopy systems. The team believes that XLuminA holds great promise for advancing scientific discovery in optics and is expected to be a valuable tool for interdisciplinary research collaborations.

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The James Webb Space Telescope, renowned for its groundbreaking exploration of the universe, relied heavily on advanced computer modeling throughout its development due to the unprecedented scale and complexity of the project. Engineers faced the challenge of a telescope too large and intricate to fully test on Earth under space-like conditions, including extreme cold. Instead, they turned to software simulations to predict the telescope’s behavior in space, a process that significantly advanced the field of integrated computer modeling.

Erin Elliott, an optical engineer at Ansys, Inc., highlighted the intense demands placed on simulation technology during the Webb project. Ansys Zemax OpticStudio, a software suite she worked with, was crucial in designing both the telescope’s hardware and software. Over the past two decades, simulation technology has seen remarkable progress thanks to increased computing power and cloud-based access. However, Elliott noted that specific improvements in software capabilities directly stemmed from the Webb telescope’s unique challenges.

Working on the Webb project for NASA contractors in the early 2000s, before joining Zemax and later Ansys, Elliott witnessed firsthand how the telescope’s demands shaped software development. She recalled that Zemax customized OpticStudio to handle the coordinate systems of the Webb’s 18 hexagonal mirror segments. Furthermore, the need for better communication between OpticStudio and other programs led to the introduction of an API, enhancing customization and interoperability.

Joseph Howard, an optical engineer at NASA’s Goddard Space Flight Center, where the Webb telescope was assembled, emphasized the importance of multiple software companies in driving innovation. He stated that competition amongst these companies fostered improvements in modeling capabilities and provided valuable cross-checking.

The development of OpticStudio’s Structural, Thermal, Analysis, and Results (STAR) module in 2021 exemplifies the direct benefits derived from the Webb project. This module streamlines the integration of thermal and structural analyses into optical models, improving efficiency particularly for complex designs like telescopes and aerospace systems. This improvement is also increasingly relevant for terrestrial applications such as autonomous vehicles and cell phone lenses operating in challenging environments.

Looking ahead, future space telescopes and spacecraft are expected to incorporate elements of the Webb design, including segmented, self-assembling structures. The development of these advanced technologies will further depend on sophisticated modeling software. Howard emphasized that the next generation of large observatories will be even more reliant on such software than Webb was.

The advancements made during the Webb project are already benefiting terrestrial technologies. Improved simulation software is being used to design precision endoscopes, thermal imagers for crowd monitoring, augmented reality displays, laser thrusters for nanosatellites, and new generations of telescopes. Moreover, the Webb project served as a crucial training ground for a new generation of engineers. Elliott pointed out that experienced engineers from the Hubble Space Telescope project guided the Webb effort, and now, the younger engineers who gained experience on Webb are poised to lead future complex projects in high-tech fields. She concluded that the training of this new cohort of engineers alone made the Webb project a worthwhile endeavor.

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Scientists have introduced a new open-source software package designed to simplify the analysis of functional optical brain imaging data. This software, named Vobi One, addresses the growing need for standardized and accessible tools in this complex field of neuroscience.

Functional optical imaging is a technique used to observe brain activity by measuring changes in light properties that are linked to neural activity. It’s particularly valuable for studying brain function at a mesoscopic scale, allowing researchers to examine activity within specific brain areas with high spatial and temporal precision. This technique is used in various settings, from examining tissue samples in the lab to non-invasively studying brain activity in humans.

However, the analysis of the large datasets produced by optical imaging has been hampered by a lack of uniformity in methods and software. Researchers have often had to rely on diverse, sometimes custom-built, scripts and tools, making it difficult to compare results across studies and ensure reproducibility.

Vobi One aims to overcome these obstacles by providing a comprehensive and user-friendly platform for processing mesoscopic functional optical imaging data. Developed as a toolbox within the BrainVISA software environment, Vobi One offers both a graphical user interface for ease of use and the option to write scripts in Python for more advanced analyses.

Key features of the software include standardized workflows for common data processing techniques, a built-in database to manage and organize data, compatibility with the Nifti neuroimaging data format for easy data sharing, and a range of analysis methods. These methods include a standard technique for removing background noise and a more advanced linear model-based method for detailed signal analysis. Vobi One also provides visualization tools to help researchers interpret their imaging data.

The developers emphasize that Vobi One is designed to be open source, encouraging collaboration and allowing researchers to contribute to its ongoing development. This open approach is expected to foster the sharing of best practices in data analysis and improve the reliability of research findings in functional optical imaging.

Currently, Vobi One supports data from specific optical imaging systems but is designed to be extensible to accommodate other data formats and analysis techniques. Future development plans include enhancing visualization capabilities and incorporating new methodological advancements to further refine the software’s analytical power and broaden its applicability to different types of optical imaging experiments. The software is freely available online, along with detailed documentation and example datasets, aiming to support a wider community of neuroscientists using functional optical imaging in their research.

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September 16, 2024 – Fujitsu has announced Virtuora® OLS-Designer, a new intelligent network tool designed for planning and designing open optical networks. Leveraging machine learning, OLS-Designer creates digital twin models from real-time optical network data, aiming to accelerate, simplify, and optimize multivendor network planning, design, and testing processes.

The tool addresses the increasing trend of disaggregated optical network infrastructure, where components from various vendors are combined. Current planning and design tools often struggle with these multivendor setups, leading to reliance on manual field measurements. This complicates network development, slows progress, and can result in static network models and underutilized network capacity.

Virtuora OLS-Designer, slated for release in early 2025 as part of Virtuora PD, builds a digital twin of the optical line system using live data, transponder configurations, and ROADM topology details. It facilitates intelligent network planning with assessments of transponder reachability and provides optimal modulation format and data rate suggestions for maximizing network performance and scalability. The tool’s machine learning capabilities continuously refine the accuracy of OLS network designs. Fujitsu states that this eliminates the need for manual calculations, thereby reducing operational costs and speeding up service delivery with maximum capacity.

Rod Naphan, senior vice president and head of the photonics systems business unit at Fujitsu, emphasized the company’s history in open networking and its commitment to providing service providers with the solutions needed to fully utilize open optical OLS architectures. He stated that Virtuora OLS-Designer uses digital twin models and machine learning to streamline OLS network planning and deployment, reduce operational costs, maximize network capacity, and accelerate service delivery to the market.

Fujitsu will be showcasing Virtuora OLS-Designer at the ECOC Exhibition 2024 in Frankfurt from September 23-25, and at SCTE TechExpo24 in Atlanta from September 24-26.

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Nashville, TN — Researchers at Vanderbilt University have developed a new method using computer simulations to optimize the design of optical waveguides, critical components for advanced technologies like quantum computing. The team utilizes Lumerical FDTD software to accurately model and analyze silicon nitride waveguides, enabling them to predict and enhance waveguide performance characteristics. Traditional waveguide design can be complex and time-intensive; however, these simulations offer a significantly faster and more precise approach to understanding light propagation within these structures. This advancement is expected to accelerate progress in integrated photonics and quantum technologies by allowing for rapid and efficient design iterations of these essential optical elements. The research underscores the growing importance of simulation tools in engineering cutting-edge optical devices.

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