“…In the measurement process (and withing the raw data) more entities were collected, including BLE RSS, accelerometer data, or the GNSS signals. During the site survey, almost 9 000 individual valid fingerprint samples were collected with the mobile application GetSensorData [1] version 2.1. and later labelled with the corresponding coordinates. Fig.…”
Section: Experimental Design Materials and Methodsmentioning
confidence: 99%
“…The samples were obtained by manually measuring both grids with each device using continuous data logging with the GetSensorData application [1] , stopping at every point for at least 10 s before making the measurement tag. The measured device was held in natural pose in front of the surveyor, as if normally using the device.…”
Section: Experimental Design Materials and Methodsmentioning
confidence: 99%
“… Device labels: “DATASET\Device_labels.csv” is a text file which refers to the table “device_labels” linking the device indexes with the devices themselves according to Table 2 . Raw data files: “RAW\AAA\AAA_LogFiles_GetSensorData\logfile_2022_MM_DD_HH_mm_SS.txt” correspond to the raw measurements acquired from the GetSensorData [1] application and contain logs of the measured entities, along with a header specifying the application version and the device. The “AAA” corresponds to the device name, “MM” specifies the month, “DD” the day, “HH” the hour, “mm” the minute, and “SS” the second of the beginning of the measurement.…”
“…In the measurement process (and withing the raw data) more entities were collected, including BLE RSS, accelerometer data, or the GNSS signals. During the site survey, almost 9 000 individual valid fingerprint samples were collected with the mobile application GetSensorData [1] version 2.1. and later labelled with the corresponding coordinates. Fig.…”
Section: Experimental Design Materials and Methodsmentioning
confidence: 99%
“…The samples were obtained by manually measuring both grids with each device using continuous data logging with the GetSensorData application [1] , stopping at every point for at least 10 s before making the measurement tag. The measured device was held in natural pose in front of the surveyor, as if normally using the device.…”
Section: Experimental Design Materials and Methodsmentioning
confidence: 99%
“… Device labels: “DATASET\Device_labels.csv” is a text file which refers to the table “device_labels” linking the device indexes with the devices themselves according to Table 2 . Raw data files: “RAW\AAA\AAA_LogFiles_GetSensorData\logfile_2022_MM_DD_HH_mm_SS.txt” correspond to the raw measurements acquired from the GetSensorData [1] application and contain logs of the measured entities, along with a header specifying the application version and the device. The “AAA” corresponds to the device name, “MM” specifies the month, “DD” the day, “HH” the hour, “mm” the minute, and “SS” the second of the beginning of the measurement.…”
“…All data for Track 3 has been collected with the Android app "GetSensorData" [17], [18], which records and stores all data coming from sensors available in the smartphone into a single text file, i.e., into a logfile. As usual, the data set is split into three independent subsets, namely training, validation and evaluation using machine learning terminology.…”
Indoor positioning is a thriving research area which is slowly gaining market momentum. Its applications are mostly customised, ad hoc installations; ubiquitous applications analogous to GNSS for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the Indoor Positioning and Indoor Navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing Tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site Tracks. In contrast with on-site Tracks where competitors bring their systems on sitewhich were impossible to organise during 2021 and 2022 -in off-site Tracks competitors download pre-recorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, Tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem.
“…With an intuitive and responsive interface, students can study independently, review challenging topics, and assess their comprehension through various activities and tests [15], [21]. Moreover, Android-based courseware ensures wider accessibility as it can be accessed from a variety of commonly used Android devices [10], [22], [23]. These advantages make Android-based courseware an appealing and effective solution for enhancing electrical circuit learning for students.…”
The evaluation and analysis of the practicality of Android-based courseware in enhancing vocational students’ understanding of electrical circuits (EC) is the primary focus of this research. A quantitative survey-based research approach was employed, utilizing the Practicality Assessment Instrument to evaluate the practicality level of the Android-based courseware among students. The collected data will undergo statistical analysis using descriptive analysis techniques. The practicality assessment results for each aspect will be calculated as a percentage and grouped into various categories. The findings reveal a high practicality level across different aspects, namely 90.19% for Availability and Accessibility, 89.88% for Performance and Responsiveness, 86.96% for Content Compatibility and Completeness, and 90.06% for Functionality and Resource Utilization. These outcomes demonstrate that the Android-based courseware serves as a highly practical learning medium for enhancing the understanding of electrical circuits. The integration of Android-based technology in the educational environment has proven to be effective and beneficial. These findings offer valuable insights for educators, instructional designers, and stakeholders to enhance modern learning environments. Future research can further investigate the impact of Android-based courseware on learning outcomes and explore additional practical dimensions to comprehensively evaluate its effectiveness.
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