Gas leaks and arc discharges present significant risks in industrial environments, requiring robust detection systems to ensure safety and operational efficiency. Inspired by human protocols that combine visual identification with acoustic verification, this study proposes a deep learning-based robotic system for autonomously detecting and classifying gas leaks and arc discharges in manufacturing settings. The system is designed to execute all experimental tasks entirely onboard the robot. Utilizing a 112-channel acoustic camera operating at a 96 kHz sampling rate to capture ultrasonic frequencies, the system processes real-world datasets recorded in diverse industrial scenarios. These datasets include multiple gas leak configurations (e.g., pinhole, open end) and partial discharge types (Corona, Surface, Floating) under varying environmental noise conditions. Proposed system integrates visual detection and a beamforming-enhanced acoustic analysis pipeline. Signals are transformed using STFT and refined through Gamma Correction, enabling robust feature extraction. An Inception-inspired CNN further classifies hazards, achieving 99% gas leak detection accuracy. The system not only detects individual hazard sources but also enhances classification reliability by fusing multi-modal data from both vision and acoustic sensors. When tested in reverberation and noise-augmented environments, the system outperformed conventional models by up to 44%p, with experimental tasks meticulously designed to ensure fairness and reproducibility. Additionally, the system is optimized for real-time deployment, maintaining an inference time of 2.1 seconds on a mobile robotic platform. By emulating human-like inspection protocols and integrating vision with acoustic modalities, this study presents an effective solution for industrial automation, significantly improving safety and operational reliability.

Apple's AirPods could soon verify a user's identity based on the inside of their ear, which could stop them from being used by thieves. The tech giant has filed a patent for an in-ear biometric device that uses ultrasonic signals reflected against the walls of a user's ear canal. Such technology would prevent lost AirPods from being used by anyone other than the owner if they get misplaced or stolen. Currently, AirPods and other headphones pose a security risk because anyone can wear them to give Siri commands or even access private information. The product could'determine whether [the] users is an authorised user', although the patent doesn't specifically mention AirPods Biometrics are any metrics related to human features.

As technology advances, things don't always become bigger and better, objects also become smaller. In fact, nanotechnology is one of the fastest-growing technological fields, worth over 1 trillion USD, and it's forecast to grow by approximately 17% over the next half-decade. Nanobots are a major part of the nanotechnology field, but what are they exactly and how do they operate? Let's take a closer look at nanobots to understand how this transformative technology works and what it's used for. The field of nanotechnology is concerned with the research and development of technology approximately one to 100 nanometres in scale.

Smart speakers, like Amazon's Alexa and Apple's Siri, have come under fire over the past few years for'listening' to its owner's conversations. Now, a team of scientists believe they have developed the ultimate weapon to block the devices' spying abilities - a wearable that jams the microphone. Dubbed the'bracelet of silence', the chunky bracelet is fitted with 23 speakers around it that emit ultrasonic signals that drown out any speech of the wearer. While these ultrasonic signals are undetectable to human ears, they leak into the audible spectrum after being captured by the microphones, producing a jamming signal inside the microphone circuit disrupts voice recordings. Scientists developed the ultimate weapon to block the devices' spying abilities - a wearable that jams the microphone.

A new breed of robot mimics bats by using sound alone to find its way around. The four-wheeled Robat, developed by Israeli researchers, uses an echo-based sonar system to navigate and map its surroundings. A speaker sends out ultrasonic signals which bounce back off of objects and are picked up by two ultra-sensitive microphones. Experts behind the autonomous technology say it could be used during rescue operations in areas that are too dangerous for humans to reach. The Tel Aviv University team showed that Robat can pick its way through an obstacle course using sonar alone.

An attacker just needs to play ultrasonic sounds through a built-in speaker of a target computer or by using a speaker in its proximity. The principle is simple, the technique leverages specially crafted acoustic signals to cause significant vibrations in the HDDs components that could cause severe damage. Modern HDDs use shock sensors to prevent the head crash, but the team of researchers has demonstrated that sonic and ultrasonic sounds could cause false positives in the shock sensor, causing a drive to park the head in a wrong position. "We created and modeled a new feedback controller that could be deployed as a firmware update to attenuate the intentional acoustic interference. Our sensor fusion method prevents unnecessary head parking by detecting ultrasonic triggering of the shock sensor" reads the paper published by the experts.