Browse Topic: Flight recorders
With the advancement of automotive industries, the need for wireless connectivity between vehicle and smartphone is increasing. To meet the demand for wireless connectivity, Bluetooth plays a vital role. Testing Bluetooth systems is challenging and complex when development cycles of the system involve multiple partners. The system under test must fulfil consumers expectation of Bluetooth functionality paired with their personal devices. Despite many advances and existence of a few reliable systems, hardware limitation, and lack of standardization in Bluetooth test system are some of the prolonged issues. Throughout the course, various capabilities and existing traditional Bluetooth testing system practice were researched, which majorly at a system level (Black box). The gap of such testing is the escape of defect which involves the interoperability of multiple profiles like AVRCP, HFP, and A2DP. This paper focuses on a reliable testing approach which is based on packet level testing
Machine learning is used for the research and development of ITS services and the rider assistance for on-road motorcycle racing. Meanwhile, rider assistance systems for off-road motorcycles have yet to be developed, partly due to the complexity of the measurement conditions, as described in the previous paper. This research aims to create a reliable AI which is capable of classifying typical jump behaviors in off-road riding by machine learning to create a rider assistance system for off-road motorcycles. Motorcycle manufacturers and certain research institutes use motion sensors to collect data, but the data is obtained from a limited number of vehicles and riders. The creation of a rider assistance system requires a large amount of validation data. Furthermore, it is desirable to achieve the target with data that can be measured in mass-produced vehicles, which will make it possible to collect data even from general users. In addition, recent machine learning models are black boxes
This AS covers ULD utilized in finding flight data recorders, cockpit voice recorders or aircraft. Such ULDs are installed adjacent to the recorders in a manner that they are unlikely to become separated during crash conditions
This standard covers three (3) basic types of flight recorders as defined below: All requirements specified in Sections 3, 4, 5, 6 and 7 of this standard shall be applicable to all recorder types unless otherwise noted
This AS covers Underwater Locating Devices (ULD) to assist in finding flight recorders, cockpit recorders or aircraft or both. Such ULDs are to be installed adjacent to the recorders in a manner that they are unlikely to become separated during crash conditions
This paper describes a forward looking on-board vehicle detection and driver alert system that provides a distance indication and alert tone to the driver. The system, called VORAD (Vehicular On-board Radar), is the first of its kind to be fielded. The system can be programmed to function in different operating modes, allowing customization to the user's requirements. Some possible operating modes include simply providing an alert to the driver, providing following distance indication measured in seconds based on the vehicles' speeds, or providing following distance measured in feet. The VORAD System also has optional features to enhance its usefulness, including a blind spot alert system and a built-in event recorder. The blind spot system provides additional information to the driver regarding the presence of vehicles in his blind spot for use in making lane changes. The event recorder can act as a “flight recorder” for accident reconstruction, safety training and fleet management
This standard covers three (3) basic types of flight recorders as defined below: All requirements specified in sections 3, 4, 5, 6 & 7 of this standard shall be applicable to all recorder types unless otherwise noted
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