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Character recognizers have generally focused on printed text on paper with an emphasis on generality rather than speed. This makes the proposed algorithms not applicable in the context of the very high-speed industrial validation of expiration codes printed on the metal surface of a can. The extreme demands of speed and the adverse effects of lighting and movement, among other things, make it necessary to develop an original and specific strategy. The strategy presented in this paper first selects which of the segmented shapes of a printed can are the best candidates for comparison with expected characters. This is followed by a technique based on the comparison of templates (templates matching), which we call “morphologies”, and are represented as bitmaps to take advantage of the hardware capabilities of general-purpose processors. The use of templates has the advantage of avoiding the construction of a feature vector. In an acquisition test in a real industrial plant, we have been able to successfully treat 438 cans in 44 s, with only one validation error in one character, achieving a compromise between speed and quality that is sufficient for industrial validation in the conditions cited.

Indoor positioning systems (IPS) are used to locate people or objects in environments where the global positioning system (GPS) fails. The commitment to make bluetooth low energy (BLE) technology the leader in IPS and their applications is clear: Since 2009, the Bluetooth Special Interest Group (SIG) has released several improved versions. BLE offers many advantages for IPS, e.g., their emitters or beacons are easily deployable, have low power consumption, give a high positioning accuracy and can provide advanced services to users. Fingerprinting is a popular indoor positioning algorithm that is based on the received signal strength (RSS); however, its main drawbacks are that data collection is a time-consuming and labor-intensive process and its main challenge is that positioning accuracy is affected by various factors. The purpose of this work was to develop a semi-automatic data collection support system in a BLE fingerprinting-based IPS to: (1) Streamline and shorten the data collection process, (2) carry out impact studies by protocol and channel on the static positioning accuracy related to configuration parameters of the beacons, such as transmission power (Tx) and the advertising interval (A), and their number and geometric distribution. With two types of systems-on-chip (SoCs) integrated in Bluetooth 5 beacons and in two different environments, our results showed that on average in the three BLE advertising channels, the configuration of the highest Tx (+4 dBm) in the beacons produced the best accuracy results. However, the lowest Tx (−20 dBm) did not worsen them excessively (only 11.8%). In addition, in both scenarios, when lowering the density of beacons by around 42.7%−50%, the error increase was only around 8%−9.2%.