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The document discusses virtual reality and augmented reality technologies including HTC Vive, Kinect, and Vive Tracker. It provides links to videos demonstrating their use for VR/AR applications and games at conferences like DojoCon2017. The technologies allow adding interactivity and virtual elements to physical spaces.

http://nantokaworks.com/unity-vive-tracker-ika-game/

This document discusses several topics related to artificial intelligence including self-supervised learning, deep learning, and neural networks. It explains how models are now being trained on massive datasets without human labels using techniques like contrastive learning and how this could help AI systems become more robust. The document also briefly touches on applications of AI like computer vision, natural language processing and drug discovery.

Socket.ioとBabylonJSで作ったIoT的ななにか

kamiyam .

This document discusses using various technologies for an Internet of Things project including Socket.io for real-time communication, BabylonJS for 3D rendering, and Node.js with Express.js for the backend server. It also mentions using Arduino and the Johnny-five library to control LEDs and interfaces between the physical devices and web application. Formats like glTF are discussed for 3D asset loading and exporting between tools like Cinema4D, Unity, and three.js.

Managing multi-package repositories

kamiyam .

Lerna is a tool that helps manage JavaScript projects with multiple packages in a monorepo. It is useful for projects like Babel, React, and Angular that have many related packages developed within a single repository. Lerna allows installing dependencies and publishing updates across all the packages at once rather than separately per package. It offers different modes for dependency management across packages like fixed or independent modes.

TypeScript + Express

kamiyam .

kyoto.js13

kamiyam .

Reflux is a library for implementing unidirectional data flow architectures for React applications. It allows for creating actions that trigger stores to update, and hooking stores to React components so their state propagates as props. Examples demonstrate creating actions and stores, and connecting a store to a component to display its state. Reflux supports both manual and automatic listening to stores from components.

HomeKitとNode.jsを使ってSiriでコントロールするなにか

kamiyam .

This document discusses Homebridge, an open source Node.js server that allows third party accessories like smart home devices to integrate with Apple's HomeKit. It provides instructions on installing Homebridge and examples of plugins that enable support for devices like IR kits and garage door openers. Key information discussed includes using Homebridge to emulate the HomeKit API, installing and configuring required Node modules, and examples of plugins that enable support for specific device types.

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Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to precisely delineate tumor boundaries from magnetic resonance imaging (MRI) scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The model is rigorously trained and evaluated, exhibiting remarkable performance metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical image analysis and enhance healthcare outcomes. This research paves the way for future exploration and optimization of advanced CNN models in medical imaging, emphasizing addressing false positives and resource efficiency.

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IJECEIAES

Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.

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