Scientists from Tokyo University of Science (TUS) achieved a significant milestone in artificial intelligence Artificial Synaps with driveThis imitates the extraordinary ability of the human eye to recognize color with exceptional precision. This innovation can transform a machine vision into a wide range of real applications-from improving autonomous vehicles after improving advanced medical diagnostics.
The study introduces a neuromorphic device capable of distinguishing colors in the entire visible spectrum with a resolution of 10 nanometers, the level of discrimination approaching strictly from the resolution of human vision. What really distinguishes this breakthrough is its inseparable energy independence: Synaps generates its own electricity through integrated solar cells sensitive to dyes. This ability to power independently eliminates the need for bulky external power supplies, a critical limitation, which historically hindered the widespread implementation of machine vision systems in compact devices based on edges, such as drones, smartphones and wearing devices.
The research team, led by Professor Takashi Ikuno, designed their device, integrating two separate types of solar cells sensitive to dyes, each of which was designed to respond differently to specific wavelengths of light. This innovative double cell configuration not only ensures the necessary power of synapse, but also enables the performance of complex logical operators-which usually require many conventional electronic components-as part of one, high compact device.
Dr Ikuno emphasizes the deep potential of this optoelectronic new generation device for developing low -power AI systems, which require both discrimination against high -resolution colors and efficient logic processing.
To demonstrate their real life, the syndrome tested the synapse as part of the physical reservoir. The system successfully recognized 18 different combinations of movements and colors (red, green and blue) with an impressive accuracy of 82%. Most importantly, this was achieved using only one device, significant improvement compared to conventional systems that would require many photodiodes for similar tasks.
This technology aims to improve computer vision in many sectors. In the automotive industry, it can improve the recognition of lights, road signs and pedestrians in autonomous vehicles, while consuming minimal power. In the case of consumer electronics, it promises the development of smarter and more energy -saving headphone sets, extended/virtual reality (AR/VR), wearing devices and mobile devices, dramatically improving the life of the battery without prejudice to advanced visual recognition.
In health care, in which performance and precise detection is the most important, this technology is a special promise. Self -proclaimed visual sensors can be smoothly integrated with compact diagnostic tools, making it easier to monitor life parameters such as oxygen saturation or skin conditions, without a constant need to charge the battery.
This progress is strictly in line with QDAT's work. Our team develops a wide spectrum of computer solutions adapted to real needs. Qudata specialization extends to various applications, including precise healthcare.
One of our outstanding cartridges lies in the field of medical imaging and radiology. Here, our team uses advanced visual analysis based on AI to support early detection of breast cancer. I trained a model of identification of subtle patterns and anomalies in complex medical scans, such as mammograms, Qudata technology gives specialists to doctors detecting cancer in its earliest stages, when treatment is most effective and patients' results are significantly improved. The QDATA solution goes beyond simple detection, often helping in classification and analysis, thus increasing the diagnostic accuracy and performance in radiology departments.
Thanks to devices that act autonomously and process complex visual data with the law of human performance, advanced diagnostics can become more accessible and reliable for a larger global population, fundamentally transforming healthcare provision.
