PCWorld demonstrates using Google's Gemini AI to diagnose Plex Media Server issues through a systematic four-phase checkup process.

'Super' virus spreading uncontrollably... as New York sees most flu cases ever and experts warn'we don't know when it will stop' Behind-the-scenes snaps from Kimberly Guilfoyle's magazine shoot look VERY different than the published photos Trump's HHS halts child care funding to ALL states after viral video sparks Somali daycare scandal in Minnesota She wore the ultimate revenge dress after their brutal break up. But Nashville's hottest couple is'trying again'... and her friends are terrified MARK HALPERIN blows apart the Minnesota scandal... and reveals who will pay the ultimate price Marla Maples' chilling Epstein warning as she feared his growing sway over Trump Megyn Kelly names'meanest' celebrities... and hints NBC's twinkly-eyed TV grandpa is not as nice as he seems Democrat mayor blasted for sneaking through reparations plan'in dark of night' that could see city's black residents handed $5m each Leonardo DiCaprio flaunts weight loss for much-younger girlfriend... but chooses very middle-aged accessory I tried the $12 'miracle' hangover cure... but I made a critical mistake that left me full of regret Snitch reveals all the gossip from inside the New England Patriots after Stefon Diggs allegedly'strangled' his female chef St Barts regulars complain paradise island is'tacky' and overrun with vulgar yachts blocking ocean views as A-Listers and billionaires flock there for annual New Year's Eve celebrations My husband set me a kinky New Year's resolution... DEAR JANE, I'm disgusted. But I'm afraid I can't say no'Work maintained my sense of self': How one woman's cancer journey inspired her company to give back New York City mayor-elect Zohran Mamdani is set to ring in his inauguration with a public block party open to residents on January 1. But alongside the celebration, the Democratic socialist has also released a lengthy list of items barred from the event, some expected, others raising eyebrows. While weapons, explosives, and illegal substances are banned, the list also prohibits strollers, Flipper Zero devices and Raspberry Pis, two pieces of consumer technology that are legal and widely used.

When you purchase through links in our articles, we may earn a small commission. Picochess is a chess program for the Raspberry Pi that you can use to carry out analyses, train openings, and master games. The Picochess chess program already has a long and storied history behind it--something you should be aware of if you're looking to download and use it to play chess with on Raspberry Pi. After years of development, version 1.0 was released in 2019, but only offered minor improvements compared to 0.9N. This was followed by version 2.01 at the beginning of 2020 and 3.0 towards the end of the year.

This study presents a federated learning (FL) framework for privacy-preserving electrocardiogram (ECG) classification in Internet of Things (IoT) healthcare environments. By transforming 1D ECG signals into 2D Gramian Angular Field (GAF) images, the proposed approach enables efficient feature extraction through Convolutional Neural Networks (CNNs) while ensuring that sensitive medical data remain local to each device. This work is among the first to experimentally validate GAF-based federated ECG classification across heterogeneous IoT devices, quantifying both performance and communication efficiency. To evaluate feasibility in realistic IoT settings, we deployed the framework across a server, a laptop, and a resource-constrained Raspberry Pi 4, reflecting edge-cloud integration in IoT ecosystems. Experimental results demonstrate that the FL-GAF model achieves a high classification accuracy of 95.18% in a multi-client setup, significantly outperforming a single-client baseline in both accuracy and training time. Despite the added computational complexity of GAF transformations, the framework maintains efficient resource utilization and communication overhead. These findings highlight the potential of lightweight, privacy-preserving AI for IoT-based healthcare monitoring, supporting scalable and secure edge deployments in smart health systems.

Abstract--Wi-Fi Channel State Information (CSI) has been extensively studied for sensing activities. However, its practical application in user authentication still needs to be explored. This study presents a novel approach to biometric authentication using Wi-Fi Channel State Information (CSI) data for palm recognition. The research delves into utilizing a Raspberry Pi encased in a custom-built box with antenna power reduced to 1dBm, which was used to capture CSI data from the right hands of 20 participants (10 men and 10 women). The dataset was normalized using MinMax scaling to ensure uniformity and accuracy. By focusing on biophysical aspects such as hand size, shape, angular spread between fingers, and finger phalanx lengths, among other characteristics, the study explores how these features affect electromagnetic signals, which are then reflected in Wi-Fi CSI, allowing for precise user identification. Five classification algorithms were evaluated, with the Random Forest classifier achieving an average F1-Score of 99.82% using 10-fold cross-validation. Amplitude and Phase data were used, with each capture session recording approximately 1000 packets per second in five 5-second intervals for each User . This high accuracy highlights the potential of Wi-Fi CSI in developing robust and reliable user authentication systems based on palm biometric data. Over the years, security systems based on recognition have evolved significantly to authenticate users and limit access, mainly to protect sensitive environments and data. However, the rise in malicious cyber threats has questioned the reliability of traditional authentication methods such as passwords, biometrics, and facial recognition.

We explored whether SSL performance systematically varied as a function of reverberance using acoustic simulations.

Convolutional Neural Networks (CNNs) have demonstrated exceptional performance in audio tagging tasks. However, deploying these models on resource-constrained devices like the Raspberry Pi poses challenges related to computational efficiency and thermal management. In this paper, a comprehensive evaluation of multiple convolutional neural network (CNN) architectures for audio tagging on the Raspberry Pi is conducted, encompassing all 1D and 2D models from the Pretrained Audio Neural Networks (PANNs) framework, a ConvNeXt-based model adapted for audio classification, as well as MobileNetV3 architectures. In addition, two PANNs-derived networks, CNN9 and CNN13, recently proposed, are also evaluated. To enhance deployment efficiency and portability across diverse hardware platforms, all models are converted to the Open Neural Network Exchange (ONNX) format. Unlike previous works that focus on a single model, our analysis encompasses a broader range of architectures and involves continuous 24-hour inference sessions to assess performance stability. Our experiments reveal that, with appropriate model selection and optimization, it is possible to maintain consistent inference latency and manage thermal behavior effectively over extended periods. These findings provide valuable insights for deploying audio tagging models in real-world edge computing scenarios.

Preterm birth remains a leading cause of neonatal mortality, disproportionately affecting low-resource settings with limited access to advanced neonatal intensive care units (NICUs).Continuous monitoring of infant behavior, such as sleep/awake states and crying episodes, is critical but relies on manual observation or invasive sensors, which are prone to error, impractical, and can cause skin damage. This paper presents a novel, noninvasive, and automated vision-based framework to address this gap. We introduce an embedded monitoring system that utilizes a quantized MobileNet model deployed on a Raspberry Pi for real-time behavioral state detection. When trained and evaluated on public neonatal image datasets, our system achieves state-of-the-art accuracy (91.8% for sleep detection and 97.7% for crying/normal classification) while maintaining computational efficiency suitable for edge deployment. Through comparative benchmarking, we provide a critical analysis of the trade-offs between model size, inference latency, and diagnostic accuracy. Our findings demonstrate that while larger architectures (e.g., ResNet152, VGG19) offer marginal gains in accuracy, their computational cost is prohibitive for real-time edge use. The proposed framework integrates three key innovations: model quantization for memory-efficient inference (68% reduction in size), Raspberry Pi-optimized vision pipelines, and secure IoT communication for clinical alerts. This work conclusively shows that lightweight, optimized models such as the MobileNet offer the most viable foundation for scalable, low-cost, and clinically actionable NICU monitoring systems, paving the way for improved preterm care in resource-constrained environments.

We build a mobile surveillance robot that streams video in real time and responds to speech so a user can monitor and steer it from a phone or browser. The system uses two Raspberry Pi 4 units: a front unit on a differential drive base with camera, mic, and speaker, and a central unit that serves the live feed and runs perception. Video is sent with FFmpeg. Objects in the scene are detected using YOLOv3 to support navigation and event awareness. For voice interaction, we use Python libraries for speech recognition, multilingual translation, and text-to-speech, so the robot can take spoken commands and read back responses in the requested language. A Kinect RGB-D sensor provides visual input and obstacle cues. In indoor tests the robot detects common objects at interactive frame rates on CPU, recognises commands reliably, and translates them to actions without manual control. The design relies on off-the-shelf hardware and open software, making it easy to reproduce. We discuss limits and practical extensions, including sensor fusion with ultrasonic range data, GPU acceleration, and adding face and text recognition.