Technology Engineering Man solves ceiling fans' most annoying problem His 3D-printed device finally shows a ceiling fans' speed. Breakthroughs, discoveries, and DIY tips sent six days a week. Anyone who's used an overhead ceiling fan knows it can be a pain to work. Yanking its chain gets the motor running, but there's no easy visual indication of what speed setting the fan is on. The blades can also take a frustratingly long time to reach their full speed.

Legacy floor plans, often preserved only as scanned documents, remain essential resources for architecture, urban planning, and facility management in the construction industry. However, the lack of machine-readable floor plans render large-scale interpretation both time-consuming and error-prone. Automated symbol spotting offers a scalable solution by enabling the identification of service key symbols directly from floor plans, supporting workflows such as cost estimation, infrastructure maintenance, and regulatory compliance. This work introduces a labelled Digitised Electrical Layout Plans (DELP) dataset comprising 45 scanned electrical layout plans annotated with 2,450 instances across 34 distinct service key classes. A systematic evaluation framework is proposed using pretrained object detection models for DELP dataset. Among the models benchmarked, YOLOv8 achieves the highest performance with a mean Average Precision (mAP) of 82.5\%. Using YOLOv8, we develop SkeySpot, a lightweight, open-source toolkit for real-time detection, classification, and quantification of electrical symbols. SkeySpot produces structured, standardised outputs that can be scaled up for interoperable building information workflows, ultimately enabling compatibility across downstream applications and regulatory platforms. By lowering dependency on proprietary CAD systems and reducing manual annotation effort, this approach makes the digitisation of electrical layouts more accessible to small and medium-sized enterprises (SMEs) in the construction industry, while supporting broader goals of standardisation, interoperability, and sustainability in the built environment.

I explain Artificial Intelligence terms and news to non-experts. BlobGAN allows for unreal manipulation of images, made super easily controlling simple blobs. All these small blobs represent an object, and you can move them around or make them bigger, smaller, or even remove them, and it will have the same effect on the object it represents in the image. As the authors shared in their results, you can even create novel images by duplicating blobs, creating unseen images in the dataset like a room with two ceiling fans! Correct me if I'm wrong, but I believe it is one of, if not the first, paper to make the modification of images as simple as moving blobs around and allowing for edits that were unseen in the training dataset.

Retailers are always on the lookout for ways to make themselves more competitive -- trends in advertising, social media channels to leverage, new takes on email and online marketing. Particularly for smaller businesses, the focus is often on initiatives that could result in fast ROI, and this tends to mean efforts explicitly aimed at increasing sales volume. For larger chains, it can mean a focus on expanding into new markets or opening new locations. The problem is that these efforts take time and attention away from the simplest way retailers could increase both their short-term success and their long-term ability to compete: implementing AI-powered automation. This is especially important in the current retail climate, where customers are shopping online more than in person.

This project is an example demonstrating how to use Python to train two different machine learning models to detect anomalies in an electric motor. The first model relies on the classic machine learning technique of Mahalanobis distance. The second model is an autoencoder neural network created with TensorFlow and Keras. Data was captured using an ESP32 and MSA301 3-axis accelerometer taped to a ceiling fan. Each sample is about 200 samples of all 3 axes captured over the course of 1 second.

Smart ceiling fans--models with wireless remote controls and digital-assistant compatibility--are a relatively new thing. But you can still control a legacy fan if you replace your in-wall switch with something like Lutron's Caséta Fan Control. It's not an inexpensive option, especially if you don't already have a Caséta bridge connected to your router, but it is a very good one. The Caséta bridge isn't required if you have no intention of integrating this fan controller into your broader smart home system, but most TechHive readers will. Lutron's Clear Connect Wireless technology is at the heart of the Caséta product line, and it's a robust competitor to the broader Z-Wave and ZigBee ecosystems.

The second-generation Switchmate--Switchmate Bright--is even better than the original. The new model is about one-third narrower, it has a built-in motion sensor for hands-free operation (you can also operate it manually, of course), you can program a timer that will automatically turn the light (or ceiling fan, or whatever the switch controls) on and off, and it can be integrated into Wink-based smart homes. Like the much bulkier original, the Switchmate Bright doesn't require any wiring changes or even tools to install it. You just mount it over your existing switch and a pair of strong magnets hold the device fast to the screws holding the cover plate. If you have a multi-gang box, the narrower design enables multiple Switchmates to operate side by side (the app can control several Switchmates, with each assigned to a room and having a unique name).

If you make a purchase by clicking one of our links, we may earn a small share of the revenue. Our picks and opinions are independent from any business incentives. When Home Alone debuted more than 25 years ago, poor Kevin McCallister, left behind by his family, was forced to use regular household objects like glue, paint cans, wire and blow torches to protect his house (and himself) from would-be robbers. Kevin made do with his supplies, successfully booby trapping the house, but we can't help thinking his job would have been a lot easier if he had today's smart home technology on his side. Here's how we imagine Kevin would booby trap his house in 2017 with the help of the latest home tech.