In metal Additive Manufacturing (AM), monitoring the temperature of the Melt Pool (MP) is crucial for ensuring part quality, process stability, defect prevention, and overall process optimization. Traditional methods, are slow to converge and require extensive manual effort to translate data into actionable insights, rendering them impractical for real-time monitoring and control. To address this challenge, we propose an Artificial Intelligence (AI)-based solution aimed at reducing manual data processing reliance and improving the efficiency of transitioning from data to insight. In our study, we utilize a dataset comprising dual-wavelength real-time process monitoring data and corresponding temperature maps. We introduce a deep learning model called the "Binocular model," which exploits dual input observations to perform a precise analysis of MP temperature in Laser Powder Bed Fusion (L-PBF). Through advanced deep learning techniques, we seamlessly convert raw data into temperature maps, significantly streamlining the process and enabling batch processing at a rate of up to 750 frames per second, approximately 1000 times faster than conventional methods. Our Binocular model achieves high accuracy in temperature estimation, evidenced by a 0.95 R-squared score, while simultaneously enhancing processing efficiency by a factor of $\sim1000x$ times. This model directly addresses the challenge of real-time MP temperature monitoring and offers insights into the encountered constraints and the benefits of our Deep Learning-based approach. By combining efficiency and precision, our work contributes to the advancement of temperature monitoring in L-PBF, thus driving progress in the field of metal AM.

Deep learning-based methods have spearheaded the automatic analysis of echocardiographic images, taking advantage of the publication of multiple open access datasets annotated by experts (CAMUS being one of the largest public databases). However, these models are still considered unreliable by clinicians due to unresolved issues concerning i) the temporal consistency of their predictions, and ii) their ability to generalize across datasets. In this context, we propose a comprehensive comparison between the current best performing methods in medical/echocardiographic image segmentation, with a particular focus on temporal consistency and cross-dataset aspects. We introduce a new private dataset, named CARDINAL, of apical two-chamber and apical four-chamber sequences, with reference segmentation over the full cardiac cycle. We show that the proposed 3D nnU-Net outperforms alternative 2D and recurrent segmentation methods. We also report that the best models trained on CARDINAL, when tested on CAMUS without any fine-tuning, still manage to perform competitively with respect to prior methods. Overall, the experimental results suggest that with sufficient training data, 3D nnU-Net could become the first automated tool to finally meet the standards of an everyday clinical device.

Tiny Machine Learning Development Board Now Available for Building Low-Power Voice, Audio and Sensor Applications using Edge Impulse's Embedded ML Platform IRVINE, Calif., Sept. 29, 2021 (GLOBE NEWSWIRE) -- Syntiant Corp, a provider of deep learning solutions making edge AI a reality for always-on applications in battery-powered devices, today unveiled its TinyML Development Board, an easy-to-use developer kit aimed at both technical and non-technical users for building machine learning-powered applications in smart products, such as speech commands, wake word detection, acoustic event detection and other sensor use cases. Equipped with the ultra-low-power Syntiant NDP101 Neural Decision Processor, the TinyML board can enable speech and sensor applications to run at under 140 and 100 microwatts, respectively, delivering 20x more throughput and 200x efficiency improvement compared to traditional MCU-based systems. Sized at 24 mm x 28 mm, the Syntiant TinyML board is a small, self-contained system that allows trained models to be easily downloaded via Edge Impulse through a micro-USB connection without the need for any specialized hardware. The new board also is fully compatible with Arduino's open-source platform. "Syntiant's TinyML board is another example of how we are advancing AI pervasiveness by moving machine learning from the cloud to the edge," said Kurt Busch, CEO of Syntiant.

With computer vision and deep learning approaches, the Italian company Covision Quality wants to help the industry. The joint project with Alupress is intended to support this. We spoke with Franz Tschimben, CEO of Covision Quality, about his company's goals and challenges. What is Covision Quality about? Franz Tschimben: Covision Quality automates the industrial quality control process on metals through computer vision and deep learning technology.

Medical Imaging has been used in several applications in the healthcare industry. Deep Learning solutions have exceeded many healthcare tasks in detecting and diagnosing abnormalities in medical data. In January 2020, we noticed Google's DeepMind AI outperformed radiologists in detecting breast cancer, according to Nature's publication. Data management is one of the most critical steps in deep learning solutions. The size of healthcare data is reaching 2314 Exabytes of new data by 2020.

Curious how deep learning solutions are affecting your industry? On Wednesday, March 18th, join Upward, Connecticut Center for Advanced Technology (CCAT), and Connecticut Technology Council (CTC) for Hartford's first "teCTalk" focused on artificial intelligence/machine learning. Learn how machines are being taught to interact with the organic world around them and how this smart technology is working to elevate modern business. An esteemed panel of AI/ML experts across industries, including Upward Citizens GalaxE Solutions, VAANGO, and Saya Life, will navigate participants through the complex topic of deep learning. This event is designed to be interactive: pose your questions to the experts and engage with others in the crowd!

Artificial intelligence, machine learning, and deep learning solutions are some of the hottest buzzwords in today's corporate landscape. These technologies are reshaping the corporate landscape with their capability to provide innovative solutions to some long-standing problems. In today's quickly-evolving corporate landscape, companies must often engage in intense competition to secure users and customers. In the age of big data and in-depth analysis of customer behavior, artificial intelligence (AI) and machine learning (ML) solutions are emerging as the de facto way for companies to gain a competitive edge. Today, it is easier to harvest large amounts of data from the customer. The advancement of the AI field has resulted in the creation and adoption of machine learning. Machine learning was then discovered to be a good fit for the corporate landscape, providing cost-effective solutions to problems that previously required a lot of resources.

Every week, my team at Invector Labs publishes a newsletter to track the most recent developments in AI research and technology. You can find this week's issue below. You can sign up for it below. Data privacy is one of the biggest challenges of modern machine learning applications. In order to build machine learning models, researchers need to have complete access to datasets that often contain sensitive data.

Almost every day we read about companies and their Artificial Intelligence (AI) strategies. Sometimes it feels like an arms race where businesses feel they will get left behind if they can't claim to have AI and (usually) deep learning embedded somewhere in their product. We have seen this type of thing before, reminiscent of the social media and big data hypes of years gone by. It used to be that companies were tripping over themselves to be seen as "big data" now the focus is on "AI " as they try to position themselves as appealing to customers and investors – one report estimates as much as 40% of European startups classified as "AI" don't actually use AI in any material way! The hype around AI has largely been driven by substantial recent progress in the sub field of deep learning.

Due to the high demand in computation and memory, deep learning solutions are mostly restricted to high-performance computing units, e.g., those present in servers, Cloud, and computing centers. In pervasive systems, e.g., those involving Internet-of-Things (IoT) technological solutions, this would require the transmission of acquired data from IoT sensors to the computing platform and wait for its output. This solution might become infeasible when remote connectivity is either unavailable or limited in bandwidth. Moreover, it introduces uncertainty in the "data production to decision making"-latency, which, in turn, might impair control loop stability if the response should be used to drive IoT actuators. In order to support a real-time recall phase directly at the IoT level, deep learning solutions must be completely rethought having in mind the constraints on memory and computation characterizing IoT units. In this paper we focus on Convolutional Neural Networks (CNNs), a specific deep learning solution for image and video classification, and introduce a methodology aiming at distributing their computation onto the units of the IoT system. We formalize such a methodology as an optimization problem where the latency between the data-gathering phase and the subsequent decision-making one is minimized. The methodology supports multiple IoT sources of data as well as multiple CNNs in execution on the same IoT system, making it a general-purpose distributed computing platform for CNN-based applications demanding autonomy, low decision-latency, and high Quality-of-Service.