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.

Smart Bandages have long been envisioned as a “lab on skin” that could monitor and treat chronic wounds in patients. Caltech Professor of Medical Engineering Wei Gao and his colleagues are now one step closer to achieving this goal. After successfully demonstrating the efficacy of their smart bandage, iCares, in animal models, they have now cleared another hurdle by showing its ability to continually sample fluid from human patients with chronic wounds.

The improved version of the smart bandage, which integrates three microfluidic components, is designed to clear excess moisture from wounds while providing real-time data about biomarkers present. The innovative microfluidics system ensures that only fresh samples are analyzed, allowing for accurate measurements of biomarkers such as nitric oxide and hydrogen peroxide.

Gao’s team has demonstrated the potential of their smart bandage to detect signs of inflammation and infection in patients up to three days before symptoms appear. Furthermore, they have developed a machine-learning algorithm that can accurately classify wounds and predict healing time with a level of accuracy comparable to expert clinicians.

The iCares system consists of a flexible, biocompatible polymer strip that can be 3D printed at low cost. It integrates nanoengineered biomarker sensor arrays for single-use applications and reuses signal processing and wireless data transmission through a user interface like a smartphone. The triad of microfluidic modules includes a membrane that draws wound fluid from the surface, a bioinspired component that shuttles it to the sensor array where analysis takes place, and a micropillar module that carries the sampled fluid away from the bandage.

The implications of this innovation are vast, with potential applications extending beyond chronic wound care. By integrating real-time monitoring and treatment capabilities into wearable devices, we may soon see significant improvements in patient outcomes and quality of life.

The use of artificial intelligence (AI) has become ubiquitous in various fields, from data analysis to image recognition. Its performance has surpassed that of humans in many areas, but its energy consumption is vast and will increase exponentially in the upcoming years. To address this issue, scientists are researching physical systems that could support or partially replace electronic systems for certain tasks.

Artificial neural networks (ANNs) inspired by the brain are one such option. They consist of nodes linked in a complex structure, most commonly implemented using digital connections. However, recent experience with training large language models has made it clear that their energy consumption is substantial. Therefore, scientists are exploring alternative physical systems, including optics and photonics.

Optics and photonics have several advantages over conventional electronic systems, such as high bandwidths and information encoding in high-dimensional symbols. They also allow parallel processing and connection to established systems like the optical fiber-based world-wide internet. When scaling up, photonics holds the promise of lower energy consumption for complex problems.

Researchers at the Stiller Research Group at the Max Planck Institute for the Science of Light (MPL) and Leibniz University Hannover (LUH), in collaboration with Dirk Englund at MIT, have now experimentally demonstrated an all-optically controlled activation function based on traveling sound waves. This development is essential for photonic computing, a physical analog computing alternative that promises to be able to realize energy-efficient AI in the long term.

The nonlinear activation function is crucial for deep learning models to learn complex tasks. In optical neural networks, these parts should ideally be implemented in the photonic domain as well. For the weighted sum – a matrix operator – a plethora of photonic approaches already exist. However, few approaches have been demonstrated experimentally for the nonlinear activation function.

The scientists’ approach uses sound waves as a mediator to introduce nonlinearity into photonic computing systems. The optical information does not need to leave the optical domain and is directly processed in optical fibers or photonic waveguides. Via the effect of stimulated Brillouin scattering, the optical input information undergoes a nonlinear change depending on the level of optical intensity.

“Our photonic activation function can be tuned in a versatile way: we show the implementation of a sigmoid, ReLU, and quadratic function,” says one of the lead authors, Grigorii Slinkov. “An interesting advantage comes from a strict phase-matching rule in stimulated Brillouin scattering: different optical frequencies – for parallel computing – can be addressed individually, which may enhance the computational performance of the neural network.”

Including a photonic activation function in an optical neural network preserves the bandwidth of the optical data, avoids electro-optic conversion, and maintains the coherence of the signal. The versatile control of the nonlinear activation function with the help of sound waves allows the implementation of the scheme in existing optical fiber systems as well as photonic chips.

The long-term prospect of creating more energy-efficient optical neural networks depends on whether it is possible to scale up physical computing systems, a process potentially facilitated by a photonic activation function. As Birgit Stiller, head of the research group “Quantum Optoacoustics,” notes, “The long-term prospect of creating more energy efficient optical neural networks depends on whether we are able to scale up the physical computing systems, a process potentially facilitated by a photonic activation function.”