Search Results (3)

Gun violence in U.S. schools not only causes loss of life and physical injury but also leaves enduring psychological trauma, damages property, and results in significant economic losses. One way to reduce this loss is to detect the gun early, notify the police as soon as possible, and implement lockdown procedures immediately. In this project, a novel gun detector Internet of Things (IoT) system is developed that automatically detects the presence of a gun either from images or from gunshot sounds, and sends notifications with exact location information to the first responder’s smartphones using the Internet within a second. The device also sends wireless commands using Message Queuing Telemetry Transport (MQTT) protocol to close the smart door locks in classrooms and announce to act using public address (PA) system automatically. The proposed system will remove the burden of manually calling the police and implementing the lockdown procedure during such traumatic situations. Police will arrive sooner, and thus it will help to stop the shooter early, the injured people can be taken to the hospital quickly, and more lives can be saved. Two custom deep learning AI models are used: (a) to detect guns from image data having an accuracy of 94.6%, and (b) the gunshot sounds from audio data having an accuracy of 99%. No single gun detector device is available in the literature that can detect guns from both image and audio data, implement lockdown and make PA announcement automatically. A prototype of the proposed gunshot detector IoT system, and a smartphone app is developed, and tested with gun replicas and blank guns in real-time. Full article

In the processes of deep hole drilling and boring, tool deflection and chatter are prevalent problems that significantly affect the quality and efficiency of deep hole part machining. This paper designs a Helical-Type Vibration-Damping and Deflection Correction Device for BTA (boring and trepanning association) deep hole drilling based on the principles of fluid dynamic pressure lubrication and squeeze film damping. By leveraging the flow field characteristics of cutting oil during machining, the device achieves vibration-damping, deflection correction, and enhanced support for the tool system throughout the drilling operation. Through theoretical analysis, this research examines the oil film pressure distribution and stability of the Designed Vibration-Damping and Deviation Correction Device. It also explores the influence patterns of factors such as cutting parameters, device structure, minimum film thickness, film thickness ratio, and length-to-diameter ratio on its vibration-damping, deviation correction, and stability performance. Taking a $\varphi 29.35$ deep hole as the research object, an experimental platform was designed and constructed to measure and verify the device’s vibration-damping and deviation correction effects under different operating conditions. Deep hole drilling tests were carried out on 10 conventional gun steel specimens ($\varphi 29.35$ × 3000 $\mathrm{m}\mathrm{m}$). The results indicate that, when the minimum oil film gap of the Vibration-Damping and Deflection Correction Device is 0.08$\mathrm{m}\mathrm{m}$, the axis deviation range is 0.27~0.45$\mathrm{m}\mathrm{m}$, with a surface roughness of 0.589 to 0.677$\mathsf{\mu }\mathrm{m}$. Compared to similar conditions without the device, these represent reductions of 55~73% and 47.07~53.95%, respectively. It allows for a reduction of over 10% in blank material allowance and an increase of 5–15% in tool feed rates. Full article

Although the U.S. makes up only 5% of the global population, it accounts for approximately 31% of public mass shootings. Gun violence and mass shootings not only result in loss of life and injury but also inflict lasting psychological trauma, cause property damage, and lead to significant economic losses. We recently developed and published an embedded system prototype for detecting gunshots in an indoor environment. The proposed device can be attached to the walls or ceilings of schools, offices, clubs, places of worship, etc., similar to smoke detectors or night lights, and they can notify the first responders as soon as a gunshot is fired. The proposed system will help to stop the shooter early and the injured people can be taken to the hospital quickly, thus more lives can be saved. In this project, a new custom dataset of blank gunshot sounds is recorded, and a deep learning model using both time and frequency domain features is trained to classify gunshot and non-gunshot sounds with 99% accuracy. The previously developed system suffered from several security and privacy vulnerabilities. In this research, those vulnerabilities are addressed by implementing secure Message Queuing Telemetry Transport (MQTT) communication protocols for IoT systems, better authentication methods, Wi-Fi provisioning without Bluetooth, and over-the-air (OTA) firmware update features. The prototype is implemented in a Raspberry Pi Zero 2W embedded system platform and successfully tested with blank gunshots and possible false alarms. Full article