AI-enabled Internet of Autonomous Things (IoAT) devices including autonomous machines, vehicles, robots, and drones can think for themselves using capabilities like machine learning, computer vision, and self-governing navigation – thanks to hardware innovations. Let’s see how power-packed robotics-specific Qualcomm® QRB5165, eInfochips Edge Labs, and Aikri portfolio can be game-changers for developing these Autonomous Things (AuT) devices for a wide array of applications across industries – from manufacturing and logistics to retail and healthcare.

Digital transformation is changing the healthcare sector rapidly – from early diagnosis of critical disease

Surgery and AI Throughout the years, we have seen many examples where surgeons seem skeptical

WHO estimates that out of three cancers diagnosed, one is skin cancer. Further, it is

The fundamental function of a hearing aid is to provide optimal and comfortable speech quality

Effective water management, conservation, and equitable access to water are key to sustainable development across the globe. Governments and regulatory authorities worldwide have defined action plans for achieving UN’s collective sustainable development goal 6 (SDG6) – ensuring water and sanitation for all. It covers systems for smart metering, equipment monitoring for distribution, purification, heating, and cooling. Digital technologies like IoT, AI/ML, Cloud, and mobility are seen as key enablers in designing and monitoring the systems for achieving this goal.

Given the recent trends in machine learning and deep learning, we have tried to give a high-level overview of how digital signal processing, machine learning, and deep learning algorithms can go hand-in-hand to categorize or draw inferences from audio signals. Audio-specific neural network models can also be built using signal processing, machine learning, and deep learning (neural networks) algorithms. In this blog will see how to build Intelligent Audio Systems, Audio Feature Extraction using Machine Learning.

Data scientists train the AI/ML models for computer vision and Natural language processing (NLP) problems

The future of medical imaging: beyond CT scans The traditional medical imaging systems primarily include