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A hybrid technique is proposed to enhance the localization performance of a time difference of arrival (TDOA) deployed in non-line-of-sight (NLOS) suburban scenario. The idea was to use Machine Learning framework on the dataset, produced by the ray tracing simulation, and the Channel Impulse Response estimation from the real signal received by each sensor. Conventional localization techniques mitigate errors trying to avoid NLOS measurements in processing emitter position, while the proposed method uses the multipath fingerprint information produced by ray tracing (RT) simulation together with calibration emitters to refine a Machine Learning engine, which gives an extra layer of information to improve the emitter position estimation. The ray-tracing fingerprints perform the target localization embedding all the reflection and diffraction in the propagation scenario. A validation campaign was performed and showed the feasibility of the proposed method, provided that the buildings can be appropriately included in the scenario description.

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The purpose of this study is to evaluate the performance of new and reground cemented carbide twist drills considering the variation in the form of regrinding applied to a drill after it reaches its wear limit. The following variables were taken into account: coating material (uncoated, TiAlN and AlCrN) and recoating routine after regrinding (no recoating, recoating over existing coating and recoating after removal of the existing coating). The performance evaluation parameters were tool life, thrust force and torque. The results indicated that the performance of reground drills was generally inferior than that of new drills. Among the coated drills, only TiAlN-coated drills that were stripped (removal of the existing coating), reground and recoated (application of a new TiAlN coating) reached performance values approaching those of new drills. In most of the tests, the uncoated drills produced higher thrust forces and torque, underwent higher wear and had shorter tool lives.