Improving estrus detection accuracy could improve sow conception rates,leading to higher production efficiency. Current observation-based estrusdetection practices are labor intensive and less accurate. Around estrus, bodytemperature and activity change. Therefore in this study a telemetric monitoringsystem for body temperature and activity was tested. Firstly Templant2 sensors(TeleMetronics) were validated under lab conditions for temperatures from 35°Cto 45°C, using a water basin with a Julabo heater and a P600 thermometer.Activity measurements were validated with the sensors attached to a stick,simulating sow movements. Secondly, sensors were attached externally to 4gilts and 4 sows for 30 minutes, testing functionality. Thirdly, activity of sowswas recorded manually for 3 days around estrus. Results showed that under labconditions temperature results of sensors, heater and thermometer were highlycorrelated (linear regression, R2=0,96; slope 1,1). Simulated activitiescorresponded consistently with peaks in sensor values. Activity was measuredreliably with the sensor attached externally to the sows. On the farm, sowsshowed more activity (manual observations, P<0.05 for standing up, lying down,sitting down and walking) the day before insemination. We conclude thatmonitoring activity and body temperature is a promising tool for estrousdetection in sows.
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This work provides a feasibility study on estimating the 3-D locations of several thousand miniaturized free-floating sensor platforms. The localization is performed on basis of sparse ultrasound range measurements between sensor platforms and without the use of beacons. We show that this task can be viewed as a specific type of pose graph optimization. The main challenge is robustly estimating an initial pose graph, that models the locations of sensor platforms. For this, we introduce a novel graph growing strategy that uses random sample consensus in alternation with non-linear refinement. The theoretical properties of our sensor cloud localization method are analyzed and its robustness is investigated using simulations. These simulations are based on inlier-outlier measurement models and focus on the application of subterranean 3-D mapping of liquid environments, such as pipe infrastructures and oil wells.
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A low-cost sensornode is introduced to monitor the 5G EMF exposure in the Netherlands for the four FR1 frequency bands. The sensornode is validated with in-lab measurements both with CW signals as for QAM signals and perform for both cases and for all frequency bands an error less than 1 dB for a dynamic range of 40 dB. This sensor is a follow up of the earlier version of our previously developed sensor and have substantial improvements in terms of linearity, error, and stability.
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