A Novel Method of Near-Miss Event Detection with Software Defined RADAR in Improving Railyard Safety

Abstract

Railyards are one of the most challenging and complex workplace environments in any industry. Railyard workers are constantly surrounded by dangerous moving objects, in a noisy environment where distractions can easily result in accidents or casualties. Throughout the years, yards have been contributing 20–30% of the total accidents that happen in railroads. Monitoring the railyard workspace to keep personnel safe from falls, slips, being struck by large object, etc. and preventing fatal accidents can be particularly challenging due to the sheer number of factors involved, such as the need to protect a large geographical space, the inherent dynamicity of the situation workers find themselves in, the presence of heavy rolling stock, blind spots, uneven surfaces and a plethora of trip hazards, just to name a few. Since workers spend the majority of time outdoors, weather conditions also play an important role, i.e., snow, fog, rain, etc. Conventional sensor deployments in yards thus fail to consistently monitor this workspace. In this paper, the authors have identified these challenges and addressed them with a novel detection method using a multi-sensor approach. They have also proposed novel algorithms to detect, classify and remotely monitor Employees-on-Duty (EoDs) without hindering real-time decision-making of the EoD. In the proposed solution, the authors have used a fast spherical-to-rectilinear transform algorithm on fish-eye images to monitor a wide area and to address blindspots in visual monitoring, and employed Software-Defined RADAR (SDRADAR) to address the low-visibility problem. The sensors manage to monitor the workspace for 100 m with blind detection and classification. These algorithms have successfully maintained real-time processing delay of ≤0.1 s between consecutive frames for both SDRADAR and visual processing.

Automatic Modulation Classification Using Hybrid Data Augmentation and Lightweight Neural Network

“…Adapting the modulation used for changing channel conditions is a crucial way to maximize the throughput of a communication link and maintain continuous link reliability in a mobile environment [1][2][3][4]. By changing the modulation method, the transmitted signal can adjust to the changing channel environment, minimizing the difference between channel capacity and throughput without sacrificing robustness [5]. Automatic modulation classification (AMC) [6][7][8][9][10][11] is a crucial technology for non-cooperative communications, serving as an intermediate step in signal detection and demodulation in communications.…”