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Artificial Intelligence

‘Signing’ the Future: AI-Powered ASL Recognition System Revolutionizes Deaf Communication

American Sign Language (ASL) recognition systems often struggle with accuracy due to similar gestures, poor image quality and inconsistent lighting. To address this, researchers developed a system that translates gestures into text with 98.2% accuracy, operating in real time under varying conditions. Using a standard webcam and advanced tracking, it offers a scalable solution for real-world use, with MediaPipe tracking 21 keypoints on each hand and YOLOv11 classifying ASL letters precisely.

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The world is on the cusp of a revolution in communication for the deaf and hard-of-hearing community. Traditional sign language interpreters are often scarce, expensive, and dependent on human availability. In an increasingly digital world, the demand for smart, assistive technologies that offer real-time, accurate, and accessible communication solutions is growing.

Enter American Sign Language (ASL), one of the most widely used sign languages worldwide. Comprising distinct hand gestures representing letters, words, and phrases, ASL recognition systems have long struggled with real-time performance, accuracy, and robustness across diverse environments.

Researchers from Florida Atlantic University’s College of Engineering and Computer Science have developed an innovative AI-powered ASL interpretation system that tackles these challenges head-on. Combining the object detection power of YOLOv11 with MediaPipe’s precise hand tracking, the system accurately recognizes ASL alphabet letters in real-time.

Using advanced deep learning and key hand point tracking, it translates ASL gestures into text, enabling users to interactively spell names, locations, and more with remarkable accuracy. The built-in webcam serves as a contact-free sensor, capturing live visual data that is converted into digital frames for gesture analysis.

The system’s effectiveness has been confirmed through results published in the journal Sensors, achieving a 98.2% accuracy (mean Average Precision, mAP@0.5) with minimal latency. This finding highlights the system’s ability to deliver high precision in real-time, making it an ideal solution for applications that require fast and reliable performance.

The ASL Alphabet Hand Gesture Dataset includes a wide variety of hand gestures captured under different conditions to help models generalize better. These conditions cover diverse lighting environments (bright, dim, and shadowed), backgrounds (both outdoor and indoor scenes), and various hand angles and orientations.

Each image is carefully annotated with 21 keypoints, highlighting essential hand structures such as fingertips, knuckles, and the wrist. These annotations provide a skeletal map of the hand, allowing models to distinguish between similar gestures with exceptional accuracy.

This project demonstrates how cutting-edge AI can be applied to serve humanity. By fusing deep learning with hand landmark detection, the team created a system that not only achieves high accuracy but also remains accessible and practical for everyday use.

The significance of this research lies in its potential to transform communication for the deaf community by providing an AI-driven tool that translates ASL gestures into text, enabling smoother interactions across education, workplaces, healthcare, and social settings.

Artificial Intelligence

Unlocking Digital Carpentry for Everyone

Many products in the modern world are in some way fabricated using computer numerical control (CNC) machines, which use computers to automate machine operations in manufacturing. While simple in concept, the ways to instruct these machines is in reality often complex. A team of researchers has devised a system to demonstrate how to mitigate some of this complexity.

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The world of digital carpentry has long been dominated by complex computer numerical control (CNC) machines, which use computers to automate manufacturing processes. However, a team of researchers from the University of Tokyo has developed a revolutionary system called Draw2Cut that makes it possible for anyone to create intricate designs and objects without prior knowledge of CNC machines or their typical workflows.

Draw2Cut allows users to draw desired designs directly onto material to be cut or milled using standard marker pens. The colors used in these drawings instruct the system on how to mill and cut the design into wood, making it a highly accessible mode of manufacture. This novel approach has been inspired by the way carpenters mark wood for cutting, making it possible for people without extensive experience to create complex designs.

The key to Draw2Cut lies in its unique drawing language, where colors and symbols are assigned specific meanings to produce unambiguous machine instructions. Purple lines mark the general shape of a path to mill, while red and green marks and lines provide instructions to cut straight down into the material or produce gradients. This intuitive workflow makes it possible for users to create complex designs without prior knowledge of CNC machines.

While Draw2Cut is not yet capable of producing items of professional quality, its main aim is to open up this mode of manufacture to more people, making it a valuable tool for hobbyists and professionals alike. The system has been tested with wood, but can also work on other materials such as metal, depending on the capabilities of the CNC machine.

The developers of Draw2Cut have made their source code open-source, allowing developers with different needs to customize it accordingly. This means that users can tailor the color language and stroke patterns to suit their specific requirements, making it an even more versatile tool for digital fabrication.

Overall, Draw2Cut represents a significant breakthrough in the field of digital carpentry, making it possible for anyone to create complex designs and objects without extensive experience or knowledge of CNC machines. Its potential impact on the world of manufacturing is vast, and its intuitive workflow and unique drawing language make it an invaluable tool for hobbyists and professionals alike.

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Artificial Intelligence

Unlocking Speed and Efficiency: Scientists Uncover Hidden Mechanisms in Next-Generation AI Memory Device

As artificial intelligence (AI) continues to advance, researchers have identified a breakthrough that could make AI technologies faster and more efficient.

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Researchers at Pohang University of Science and Technology (POSTECH) have made a groundbreaking discovery that could revolutionize the field of artificial intelligence (AI). By uncovering the hidden operating mechanisms of Electrochemical Random-Access Memory (ECRAM), a promising next-generation technology for AI, scientists may soon be able to create faster, more efficient AI systems that consume less energy.

As data processing demands continue to skyrocket with advancements in AI, current computing systems separate data storage from data processing, leading to significant time and energy consumption due to data transfers between these units. To address this issue, researchers developed the concept of “In-Memory Computing,” which enables calculations directly within memory, eliminating data movement and achieving faster operations.

ECRAM is a critical technology for implementing this concept. ECRAM devices store and process information using ionic movements, allowing for continuous analog-type data storage. However, understanding their complex structure and high-resistive oxide materials has remained challenging, significantly hindering commercialization.

To overcome this hurdle, the research team developed a multi-terminal structured ECRAM device using tungsten oxide and applied the “Parallel Dipole Line Hall System.” This innovative setup enabled observation of internal electron dynamics from ultra-low temperatures (-223°C) to room temperature (300K). For the first time, they observed that oxygen vacancies inside the ECRAM create shallow donor states (~0.1 eV), effectively forming ‘shortcuts’ through which electrons move freely.

This mechanism remains stable even at extremely low temperatures, demonstrating the robustness and durability of the ECRAM device. According to Prof. Seyoung Kim from POSTECH, “This research is significant as it experimentally clarified the switching mechanism of ECRAM across various temperatures.” Commercializing this technology could lead to faster AI performance and extended battery life in devices such as smartphones, tablets, and laptops.

This work was supported by K-CHIPS, a Korea Collaborative & High-tech Initiative for Prospective Semiconductor Research funded by the Ministry of Trade, Industry & Energy of Korea (MOTIE).

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Artificial Intelligence

Engineering a Robot that Can Leap Like a Nematode

Inspired by the movements of a tiny parasitic worm, engineers have created a 5-inch soft robot that can jump as high as a basketball hoop. Their device, a silicone rod with a carbon-fiber spine, can leap 10 feet high even though it doesn’t have legs. The researchers made it after watching high-speed video of nematodes pinching themselves into odd shapes to fling themselves forward and backward.

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The tiny parasitic worm, nematode, has long been a subject of fascination for scientists. These creatures can jump as high as 20 times their body length, which is an incredible feat considering they don’t have legs. Inspired by this remarkable ability, researchers at Georgia Tech have created a soft robot that can leap 10 feet high without any legs.

The robot’s design is based on the unique way nematodes move. They can bend their bodies into different shapes to propel themselves forward and backward. By watching high-speed videos of these creatures, the researchers were able to develop simulations of their jumping behavior. This led them to create soft robots that could replicate the leaping worms’ movement.

The key to the robot’s success lies in its ability to store energy when it kinks its body. This stored energy is then rapidly released to propel the robot forward or backward. The researchers found that by reinforcing the robot with carbon fibers, they could accelerate the jumps and make them more efficient.

This breakthrough has significant implications for robotics and engineering. With the ability to create simple elastic systems made of carbon fiber or other materials, engineers can design robots that can hop across various terrain. This technology could be used in search and rescue missions where robots need to traverse unpredictable terrain and obstacles.

Lead researcher Sunny Kumar said, “We’re not aware of any other organism at this tiny scale that can efficiently leap in both directions at the same height.” The researchers are continuing to study the unique way nematodes use their bodies to move and build robots to mimic them. This research has the potential to lead to innovative solutions for robotics and engineering.

Associate Professor Saad Bhamla’s lab collaborated on this project with researchers from the University of California, Berkeley, and the University of California, Riverside. The study was published in Science Robotics on April 23.

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