Applications of IOT :- Smart Homes Smart City Self-driven Cars IoT Retail Shops Farming Wearables Smart Grids Industrial Internet Telehealth Smart Supply-chain Management Traffic management Water and Waste management

This article discusses oneAPI Innovator of the Month, Abhishek Nandy, his accomplishments, and more about the oneAPI Innovator Program.

What is OpenVINO? OpenVINO is a cross-platform deep learning toolkit developed by Intel. The name stands for “Open Visual Inference and Neural Network Optimization.” OpenVINO focuses on optimizing neural network inference with a write-once, deploy-anywhere approach for Intel hardware platforms. The toolkit is free for use under Apache License version 2.0 and has two versions: OpenVINO toolkit, which is supported by the open-source community and the Intel Distribution of OpenVINO toolkit, which i

Creation of definition of the sets of directives and instructions that describe the source files, build directives and the targets of the onevpl example projects (executable, library or both).

In this study, face recognition is addressed using Machine Learning models by identification with hyperparameters and models in binary format saved into database. Face recognition performed through face classification is applied over FaceNet and a bandwidth-limited neural network. The bandwidth-limited neural network accepts clustered faces through Face clustering which improves the accuracy. A Canny edge detector applied on face classification models improves the accuracy further.

This is a tweet that points to a github repository aimed at teaching the basics of DPC++ to beginners.

The healthcare industry has gone digital in a big way in recent years. The impact of digital technologies like IoT devices and monitors is changing the way doctors and hospitals administer care for their patients, and it’s a positive trend that is helping to simplify healthcare, lower costs, and improve access to critical medical information.

In a regression problem, a time series representation of the target variable with the corresponding feature (time-dependent variable) will lead to regime detection using Gaussian Mixture Model, provided sudden peaks or troughs can be detected. In a rolling statistic these peaks/troughs get smoothed away such that we will be able to split the time-series into statistical distributions occurring at various time steps. Such statistics change when rolling statistics are taken on the time series data

Back pain is more common in aged people but it typically occurs between the ages of 30 and 50. It keeps getting worse.

Integrated GPUs, such as Intel HD graphics, share the memory with the main CPU, offering a common view of the main memory. This means that, potentially, we could use buffer pointers allocated on the host side on the GPU, and, therefore, save the data transfer time (e.g., a copy from host memory to device memory, which can be expensive in many cases). However, does share memory really impact performance if we measure end-to-end applications on GPUs? In this post, we try to answer this question.