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The TITAN ground system solution will provide multi-domain integrated data directly to the front lines

AZUSA, Calif. March 7, 2024 Northrop Grumman Corporation (NYSE: NOC) is partnering with Palantir USG, Inc. on the newly awarded Tactical Intelligence Targeting Access Node (TITAN) ground system for the U.S. Army. The program supports one of the Armys key modernization imperatives by using artificial intelligence (AI) and machine learning (ML) to enhance the automation of target recognition and geolocation and integrate data from multiple sensors to reduce sensor-to-shooter timelines.

Northrop Grumman will partner to:

The TITAN ground system will enable faster decision making on the frontlines by providing actionable intelligence to reduce sensor-to-shooter timelines and maximize effectiveness of long-range fires. (Photo Credit: Palantir)

Expert:

Aaron Dann, vice president, strategic force programs, Northrop Grumman: Northrop Grummans extensive experience in large-scale system integration will help enable mission success and provide information superiority for our warfighters in complex operating environments.Our work on TITAN continues our long history of supporting our nations need for actionable intelligence when and where it matters most.

Details:

TITAN is a ground system that has access to space, high altitude, aerial, and terrestrial sensors to provide actionable targeting information for enhanced mission command. TITAN will enable the Army to fuse, correlate, and integrate intelligence data from a rapidly expanding series of sensors providing operational forces a full picture of their surroundings. This robust capability allows real-time decision making that will substantially increase the accuracy, precision, and effects of long-range precision fires.

Northrop Grumman is a leading global aerospace and defense technology company. Our pioneering solutions equip our customers with the capabilities they need to connect and protect the world, and push the boundaries of human exploration across the universe. Driven by a shared purpose to solve our customers toughest problems, our employees define possible every day.

The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the U.S. Government.

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Northrop Grumman Partners to Advance Deep Sensing for the US Army | Northrop Grumman - Northrop Grumman Newsroom

NEW DELHI: The global number of cellular Internet of Things (IoT) devices will rise 90%, from 3.4 billion in 2024 to 6.5 billion by 2028, according to the latest report released by Juniper Research on Monday. The study predicts the growth in connection will require the deployment of new services enabling the efficient automation of IoT device management and security.

As per the research firm, intelligent infrastructure management solutions, which enable IoT users to automate the configuration of devices, security processes and connectivity in real-time, is the key to handling the large increase in cellular data.

At present, the majority of machine learning models are trained via data sources that are stored in a single location, making opportunities for fraudulent players a simpler task.

Juniper Research said that telecom operators can transition to federated learning models, a subset of machine learning that leverages a decentralised data approach to minimise the chances of data fraud over IoT networks.

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Global cellular IoT connections to grow 90% to 6.5 bn by 2028: Juniper Research - ETTelecom

At opposite ends of Illinois, two leaders in emerging construction technologies are collaborating to demonstrate how AI, ML (machine learning) and CV (computer vision) can validate constructions digital-data mandate: Save time, save money, save lives.

For the fourteenth consecutive year, U.S. construction valuation has grown and is now at $1.98 trillion, noted Mani Golparvar, professor of civil engineering, computer science and technology entrepreneurship at the Grainger College of Engineering/University of Illinois. But, hes reported, 53% of construction projects are behind schedule, 66% are over budget and nearly all carry cost overruns.

Golparvar estimated that $280 billion in potential added value exists through improved coordination. As an industry, weve significantly improved the way we plan projects. But the way we plan the job, execute, monitor our execution and use whatever we monitor to update a planthese four problems all contribute to why projects are behind schedule and over budget. If youre looking to these issues, 80% are preventable: Project team members do not have visibility early enough to be able to come up with a remedy.

To that end, Golparvar is founder and chief strategy officer of Reconstruct, a Visual Command Center that uploads schedules, 2D and 3D models, and reality capture so users can track progress as well as coordination and communications problems. Reconstruct says its benefits include a 30% reduction in time reporting field progress, and 25%-40% in improved schedule management and proactive risk mitigation. Eighty-five companies and several universities now partner with it.

Burcin Kaplanoglu mirrored Golparvars remarks. Speaking from the Oracle Industry Lab he heads just outside Chicago, he noted that U.S. infrastructure is graded as a C- by the American Society of Civil Engineers. At the same time, he said, we [would] need to hire 500,000 people a year, on top of regular hiring, to meet demand.

Consequently, he added, we need to automate processes, gain efficiency and improve safety with technology. Talking to your data is going to have tremendous benefits for the construction engineer. Getting that response really quickly is going to change how we interact with technology.

Golparvar and Kaplanoglu recently joined forces around a Reality Mapping Experiment (see Constructing the Future sidebar, pg. 46) at the Oracle lab to explore what combination of tools, processes and teams can best conduct reality capture and mapping on-site, reducing time and cost and ensuring safety.

Thats of great value to an industry facing an ever-growing tech stack, as Jennifer Suerth, senior vice president of Pepper Construction, which built the lab, said in an Oracle TV video. Autonomy allows us to continue to get the work done but be efficient with the resources we have.

Golparvar and Kaplanoglu offered quick definitions of ML, CV and AI.

Machine learning allows computers to learn from data to improve and streamline processes. Machine learning is continuing to improve the construction industry, Kaplanoglu said. We need to figure out how to optimize and how to forecast better.

Computer vision is a form of AI, Golparvar said. It involves the process of analyzing pictures and videos and generating actionable insights from them, doing image processing and understanding the geometry of the scene. Recognizing objects by automatically analyzing them can track progress, detect and recognize anomalies, and coordinate and optimize operations.

To solve that coordination issue requires everyone to be on the same page in terms of what was there versus what should be there, Golparvar continued. In construction, weve really taken advantage of design information that we call BIM, building information models, 3D representation of design. What if you tie them against the schedule? At any snapshot in time, you can click on any two points, you can measure the length, area and volume. The picture shows you what was promised to be done; the picture in the background shows you what has been achieved on the job. And the delta between the twowhat needs to be coordinated in terms of quality, safety and progresscan be color-coded in red and green: what is on the schedule, what is behind.

Generative AI is the third leg of this digital triad. AI involves machines mimicking human cognitive functions, but theres more to that than large language models. GenAI is going to help us do project management and planning, Kaplanoglu said. AI can inform the entire construction cycle, from estimating preliminary costs, to tracking quality and productivity, to the more tactical tasks of ordering and payment.

Kaplanoglu ventured predictions for AI, ML and CV in construction:

Unified, intelligent clouds will unite software, platforms and infrastructure within end-to-end solutions, easing management and accelerating change.

Oracle is a major player in the world of project management and design, and in 2018 Kaplanoglu co-founded the Oracle Industry Lab to advance the companys innovation in construction and engineering. He explained the rationale. When youre running day-to-day operations, its very hard to stop what youre doing and try these new technologies. So, by creating a testbed where we bring the technology and they bring their problems, we are creating a neutral space to try and learn from it.

To realize this, writing a data strategy around machine learning was followed by building an ecosystem because our products do scheduling, safety, risk management, cost. We had almost 2,000 engagements in the first two years, he said. The pandemic only spurred a concentration on autonomy. All of a sudden, we did remote site monitoring, cameras and drone flights, route inspections. We could work with architects and designers remotely; they didnt have to come to the site. Everything was video.

The original sandbox gave way to a much larger, industry-benchmarking construction space for investigatingrealistic job site situations. It opened in April 2022on time, on budget. With analogs now operating in the United Kingdom and Australia, it has passed 13,000 engagements. I think construction really got used to digital imagery, 3D point clouds, using drones, capturing data, Kaplanoglu said.

Construction has some relatively unique characteristics, from a constantly changing environment to wind and weather exposure. Its like building a prototype each time, Kaplanoglu added. Data can bridge that. For example, Komatsu, the massive construction and mining equipment manufacturer, is using Nvidias Jetson edge AI platform to impart intelligence to trucks, excavators and the like. AI and machine learning are critical to providing real value to the construction space, a Nvidia spokesman toldInside Unmanned Systems.

In terms of project management, Kaplanoglu said, theres tons of opportunities when we want to use machine learning, computer vision and GenAI.

The need to test construction innovation, validation and measurement in real-world situations led to a collaboration between Oracles. Smart Construction Platform ecosystem and founding partner tenant Reconstructs visualization tools. The goal was to create guidelines and tool utilizations best suited for particular use cases, improving documentation of and guidelines for progress, quality and assessment of as-built conditions through a structures lifecycle.

The Report explains costs, time spent on different tools, and what kind of results you should be expecting from each, Golparvar explained about the 2023 document.

Pepper Construction and Clayco offered project expertise on the construction side, with leading drone company Skydio and 3D measurement company FARO Technologies providing technical expertise.

Eight different ways of capturing and mapping data were scanned across resolutions, speed, cost and deliverables. These included manual and autonomous drones, 360 and smartphone cameras, and various stationary and mobile LiDAR techniques. Capture data was processed into Oracles platform and then into Reconstruct, which could process them for a consistent viewing experience. Post-processing, photogrammetry and 4D simulations could be viewed for actionable and exportable results.

Construction is said to be the number one civil market for drones, which can take pictures quickly in hard-to-access areas while their sensors allow for obstacle avoidance. For the experiment, a fully autonomous Skydio drone used its Indoor Capture vision-based autonomous software over several iterations to, as Solutions Engineering Manager Colin Romberger put it on Oracle TV, get total coverageto have the best data to put into photogrammetry software for Reconstruction.

Kaplanoglu discussed gains from using autonomous drones. The time savings dropped significantly, because youre already preprogrammed. Human drone flights also improved with repetition. Theres still some autonomy, like it avoids obstacles and you can do flight plans.

But the biggest drop happens when its fully autonomous.

You always want to know about what youre trying to solve, what kind of tools you have and what kind of resources you can leverage to solve that problem, Golparvar said.

He and Kaplanoglu enumerated how AI and associated technologies can self-direct and break down planning and actions.

Site selection already involves machine learning, Kaplanoglu said, because you have certain parameters to make sure you comply with, like height restrictions, zoning. You can use machine learning to pick the optimal location.

Design can use machine learning to investigate bigger datasets and find helpful patterns. Golparvar: You want to capture the architects intent and transform it into a document that can be used as a base of design. GenAI can provide and reconcile design alternatives, incorporating everything from routing mechanical systems to complying with local codes. The way it works, Kaplanoglu added, you sketch things and then people try to visualize it and build models. Learning from them can offer significant time savings. Im talking about doing this in an hour versus doing it in months.

Construction has its own mini cycle. You define your scope, you hire engineers and architects, and now you need a contractor to build it Kaplanoglu said. You send an RFP for a contractor to build it.

RFPs, however, can be limited and schedulers may cut and paste. Kaplanoglu offered a solution: We can upload an RFP document to Oracle Cloud infrastructure. It reads the document and gives you a summary and then it asks you questions. Its going to show you an early-stage prototype, and it builds a schedule for you. Its going to be a good template for you to build your own schedule.

Now something that would have taken you three days is going to take you maybe two to three hours.

Operations can use computer vision. How many times does someone show up at their location and they dont even know what the specifications are or what theyre supposed to do, Kaplanoglu said. You can take a picture or video and then computer vision can tell you, Its this manufacturer, this model, or vision can process the video or image and say, Oh, theres rust in this corner.

Predictive maintenance can significantly reduce costs and downtime. Safety also can be empowered by AI. Many of the AI tools that we have are completely focusing on offering better awareness for our workers, Golparvar said.

Skydio has participated in the Oracle labs work, and Kaplanoglu offered an example of its drones adding value. We have a great relationship with Skydio. Instead of taking images that humans need to tag, locate and input, that data goes to our product, and then our Vision Services recognizes the rust and registers it to our work order system. But it doesnt just register; it tells you the lat[itude] and long[itude], the location, the severity of the damage, it helps you create a ticket.

Basically, we automate the whole thing.

Imprecise capture, incorrect inputting and lack of updating can undermine results. Still, the industry has significantly improved its use of cloud, Kaplanoglu said. The data is more accessible, you can put more line, compute is easier. There are a lot of benefits to it, and our industry really took that to heart.

Golparvar advocates a step-by-step process. We do a maturity assessment on the readiness of a company for adopting and adapting AI-driven products, and what we can do to make sure that these products fit into the existing workflows. Because people are resistant to change.

We also need to demonstrate the return on investment, at the project level and the individual levelquality, speed, time and money saved. So, the best strategy is to kind of introduce one next step to that project team so we can take them from where they are to that future where many of their steps will be fully automated, and make sure they see how AI is verifiable, to have that element of trust.

Kaplanoglu is bullish in terms of technology adoption. Machine learning, computer vision and GenAI are all going to have a big impact in the next three to five years.

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Inside AI: Talking to the Data - Inside Unmanned Systems

A new plugin from French developer Anemond utilizes machine learning to split sounds up into individual layers that can be remixed and randomized, generating thousands of rhythmic and melodic variations of any sample or loop.

Based on the same machine learning engine as Anemond's Factorsynth, a "fully-fledged sound design studio", Factoid 2 offers a simpler interface, reduced feature set and a lighter CPU load than its bigger and more expensive brother.

Described as a "loop revamper", Factoid's machine learning engine can extract between 2 and 8 layers from any sample. Anemond explains that Factoid isn't a stem separation tool, but instead a unique kind of processor that decomposes, or "factorizes" audio into a set of "temporal and spectral components".

This is "not intended to unmix instruments," Anemond says, "but to discover and extract unpredictable but interesting sound elements with a certain degree of structure", like notes, drum hits, and rhythmic or melodic motifs.

Once these layers are extracted, Factoid can remix and manipulate them in a variety of ways to create new ideas. You're able to randomize sample slices on a quantized grid to create glitchy patterns, transform melodies into textures, and solo, mute and adjust the levels for each factorized layer. After manipulating your sample, you can drag and drop the results from Factoid into your DAW.

Factoid 2 is priced at 29 and is available as a standalone app or VST3/AU plugin for macOS and Windows.

Find out more on Anemond's website.

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Anemond's Factoid 2 is an experimental sampler plugin that uses machine learning to "decompose", remix and ... - MusicRadar