Approaches, challenges, and recent advances in automated bee counting devices – a review

For nearly 100 years, electronic bee counters have been developed using various technologies to track the foraging activity of mostly honey bee colonies. These counters should enable remote monitoring of the hives without disturbing natural flight behavior while generating precise scientific data. Today, however, there are not many counters on the market, that are able to fulfill this task. One main challenge is the lack of standardized methods to validate a counter’s precision, but validation is crucial to categorize and judge the data produced by the counter, especially for scientific purposes. Another challenge is the interpretation of flight data to measure the effects of environmental or anthropogenic sources. Nevertheless, recent developments in the field are promising. This review describes the historic development of automated bee flight measurement and critically compares validation methods to encourage their improvement. Lastly, to increase the comparability of future analyses with bee counters, current advances in data interpretation are also presented.

Accurate Ultrasound Indoor Localization Using Spring-Relaxation Technique

This paper reports on the development of an ultrasonic sensing-based active localization system. The system employs an ultrasonic array to transmit chirp signals and time-of-flight measurement for ranging. The position of the receiver is estimated iteratively using the spring-relaxation technique. A median and 90-percentile error of 12.4 and 21.7 mm, respectively, were obtained for measurements at 625 positions within a 1.2 × 1.2 m area testbed. The spring-relaxation technique outperforms the widely adopted linear least square-based lateration technique while using the same ranging data. The performance of the system is benchmarked against that of visible light positioning using the same platform setup. The reported results show the ultrasonic system to be more accurate when compared with the visible light positioning system, which achieved median and 90-percentile errors of 33.7 and 58.6 mm, respectively.

Machine Learning Based Preprocessing to Ensure Validity of Cross-Correlated Ultrasound Signals for Time-of-Flight Measurements

High precision ultrasonic time-of-flight measurement is a well known part of non-destructive evaluation used in many scientific and industrial applications, for example stress evaluation or defect detection. Although ultrasonic time-of-flight measurements are widely used there are some limitations where high noise and distorted ultrasonic signals are conflicting with the demand for high precision measurements. Cross-correlation based time-of-flight measurement is one strategy to increase reliability but also exhibits some ambiguous correlation states yielding to wrong time-of-flight results. To improve the reliability of these measurements a new machine learning based approach is presented based on experimental data collected on tightened bolts. Due to the complex structure of the bolts the ultrasonic signal is influenced by boundary conditions of the geometry which lead to high number of the ambiguous cross-correlation results in practice. In this particular application, bolts are in practice evaluated discontinuously and without knowledge of the time-of-flight in the unloaded condition which prevents the use of all other available comparative preprocessing techniques to detect time-of-flight shifts. Three different preprocessing strategies were investigated based on variations in the bolting configurations to ensure a machine learning based model capable of predicting the state of the cross-correlation function for different bolting parameters. With this approach, we achieve up to 100% classification accuracy for both longitudinal and transversal ultrasonic signals under laboratory conditions. In the future the method should be extended to become more robust and be applicable in real-time for industrial applications.