Upgrade your space with a smart toilet, new tools, a robotic lawn mower, and several dozen more clever devices and technologies that make home a better place. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. We may earn revenue from the products available on this page and participate in affiliate programs. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy . The home doesn't announce its upgrades the way a phone does. Progress shows up as a battery that recharges in about ten minutes, a wall paint that's EPA-registered to kill germs, and a shower that reuses the water it just heated. For our second annual Home of the Future Awards, we evaluated dozens of new products and selected those that use new or clever technology to do something that actually matters. They improve daily life, solve a real problem, make a home more accessible, or waste less of everything. You don't need fire starting skills.

Penn State engineers developed a paint-on electronic tattoo using conductive ink that monitors ECG heart rhythm, EMG muscle signals and EEG brain activity via Bluetooth.

Panasonic's newest microwave ditches steam and measures point-to-point temperatures. The results are mostly terrific. No guessing needed for microwave times. Heat is not as even near the oven's perimeter. Food must be visible to the sensor to heat appropriately.

The colorful and customizable wearables can monitor heart rate, brain activity, and more. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Using a conductive, face-paint-like ink, researchers can now paint electrodes to monitor a wearer's heart, muscle or brain activity in style. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

With these high-tech automatic litter boxes, gone are the days of scooping and smells. I can confidently say that having an automatic litter box has been the biggest game changer for me when it comes to cat ownership. I can't recommend them enough. They can be pricey, but never having to scoop poop again is priceless. We at WIRED--well, our WIRED cats--tested a dozen of the highest-rated and most technologically advanced automatic litter boxes on the market for at least a week, often more (using standard clay clumping litter unless otherwise indicated), to see if they were really worth the money--or the hype. Our new favorite automatic litter box is the Purobot Max Pro 2 because of its compact design and camera for monitoring. The Litter-Robot 4 is a close second, mostly because of its LitterHopper attachment that easily refills litter for you. Plus, we still love the more basic Petkit PuraMax 2 (and think it's a great option for most people). Check out our related guides, like the Best Automatic Feeders, Best Cat Water Fountains, Best Pet Cameras, and Best Cat Toys . We've got you covered; see our Best Dog Accessories . The Purobot Max Pro 2 is the newest model from Petkit.

A growing number of techniques leverage the spatial structures that underlie many real-world datasets. Despite these advances, the complementary task of estimating spatial structures and understanding their role within these techniques has often been overlooked. In neurophysiological data analysis specifically, numerous methods exist to estimate brain connectivity, but most are not explicitly model-based, dynamic, multivariate, or directed. To address these limitations, we previously introduced noise-driven heat modelling on graphs for neurophysiological connectivity estimation. In this study, we extend this framework by relaxing earlier noise assumptions and adding regularisation to improve robustness. We also develop a simulation procedure to characterise and evaluate our technique in a controlled setting. Finally, we demonstrate that the technique is able to capture meaningful spatial structure across two experiments, each using two real-world datasets. The explicit model formulation of our connectivity estimator has the potential to improve the interpretability of graph-based techniques across a wide range of applications. The code implementing our method is available at https://github.com/sgoerttler/Heat_Connectivity.

When you purchase through links in our articles, we may earn a small commission. Then why not go for the latest flagship mouse with 50,000 DPI! Add to that an 8,000 Hz polling rate, third-generation optical switches, and a whole host of buzzwords that sound like they've come straight out of a NASA lab, a World Cup, or an influencer's lifestyle. The most important question isn't even being asked amidst all this consumer voodoo: who actually needs this? Fifteen or twenty years ago, expensive gaming mice really were a noticeable upgrade. Back then, many standard devices still had imprecise sensors, suffered from "angle snapping" (unintended line straightening), or simply refused to work at all during fast movements - a sure-fire virtual death sentence for ambitious gamers.

Long-separated research has been conducted on two highly correlated tracks: traffic and incidents. Traffic track witnesses complicating deep learning models, e.g., to push the prediction a few percent more accurate, and the incident track only studies the incidents alone, e.g., to infer the incident risk. We, for the first time, spatiotemporally aligned the two tracks in a large-scale region (16,972 traffic nodes) from year 2022 to 2024: our TraffiDent dataset includes traffic, i.e., time-series indexes on traffic flow, lane occupancy, and average vehicle speed, and incident, whose records are spatiotemporally aligned with traffic data, with seven different incident classes. Additionally, each node includes detailed physical and policylevel meta-attributes of lanes. Previous datasets typically contain only traffic or incident data in isolation, limiting research to general forecasting tasks.

MEMS gyroscopes play a critical role in inertial navigation and motion control applications but typically suffer from a fundamental trade-off between measurement range and noise performance. Existing hardware-based solutions aimed at mitigating this issue introduce additional complexity, cost, and scalability challenges. Deep-learning methods primarily focus on noise reduction and typically require precisely aligned ground-truth signals, making them difficult to deploy in practical scenarios and leaving the fundamental trade-off unresolved. To address these challenges, we introduce Mixture of Experts for MEMSGyroscopes (MoEGyro), a novel self-supervised framework specifically designed for simultaneous over-range signal reconstruction and noise suppression. MoE-Gyro employs two experts: an Over-Range Reconstruction Expert (ORE), featuring a Gaussian-Decay Attention mechanism for reconstructing saturated segments; and a Denoise Expert (DE), utilizing dual-branch complementary masking combined with FFT-guided augmentation for robust noise reduction. A lightweight gating module dynamically routes input segments to the appropriate expert.

Palpation, the use of touch in medical examination, is almost exclusively performed by humans. We investigate a proof of concept for an artificial palpation method based on self-supervised learning. Our key idea is that an encoder-decoder framework can learn a representation from a sequence of tactile measurements that contains all the relevant information about the palpated object. We conjecture that such a representation can be used for downstream tasks such as tactile imaging and change detection. With enough training data, it should capture intricate patterns in the tactile measurements that go beyond a simple map of forces - the current state of the art. To validate our approach, we both develop a simulation environment and collect a real-world dataset of soft objects and corresponding ground truth images obtained by magnetic resonance imaging (MRI). We collect palpation sequences using a robot equipped with a tactile sensor, and train a model that predicts sensory readings at different positions on the object. We investigate the representation learned in this process, and demonstrate its use in imaging and change detection.