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Sökning: WFRF:(Martinez Rau Luciano)

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1.
  • Martinez Rau, Luciano, et al. (författare)
  • Real-Time Acoustic Monitoring of Foraging Behavior of Grazing Cattle Using Low-Power Embedded Devices
  • 2023
  • Ingår i: 2023 IEEE Sensors Applications Symposium (SAS). - : IEEE conference proceedings. - 9798350323078
  • Konferensbidrag (refereegranskat)abstract
    • Precision livestock farming allows farmers to optimize herd management while significantly reducing labor needs. Individualized monitoring of cattle feeding behavior offers valuable data to assess animal performance and provides valuable insights into animal welfare. Current acoustic foraging activity recognizers achieve high recognition rates operating on computers. However, their implementations on portable embedded systems (for use on farms) need further investigation. This work presents two embedded deployments of a state-of-the-art foraging activity recognizer on a low-power ARM Cortex-M0+ microcontroller. The parameters of the algorithm were optimized to reduce power consumption. The embedded algorithm processes masticatory sounds in real-time and uses machine-learning techniques to identify grazing, rumination and other activities. The overall classification performance of the two embedded deployments achieves an 84% and 89% balanced accuracy with a mean power consumption of 1.8 mW and 12.7 mW, respectively. These results will allow this deployment to be integrated into a self-powered acoustic sensor with wireless communication to operate autonomously on cattle. 
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2.
  • Martinez Rau, Luciano S., et al. (författare)
  • Daylong acoustic recordings of grazing and rumination activities in dairy cows
  • 2023
  • Ingår i: Scientific Data. - : Springer Nature. - 2052-4463. ; 10:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Monitoring livestock feeding behavior may help assess animal welfare and nutritional status, and to optimize pasture management. The need for continuous and sustained monitoring requires the use of automatic techniques based on the acquisition and analysis of sensor data. This work describes an open dataset of acoustic recordings of the foraging behavior of dairy cows. The dataset includes 708 h of daily records obtained using unobtrusive and non-invasive instrumentation mounted on five lactating multiparous Holstein cows continuously monitored for six non-consecutive days in pasture and barn. Labeled recordings precisely delimiting grazing and rumination bouts are provided for a total of 392 h and for over 6,200 ingestive and rumination jaw movements. Companion information on the audio recording quality and expert-generated labels is also provided to facilitate data interpretation and analysis. This comprehensive dataset is a useful resource for studies aimed at exploring new tools and solutions for precision livestock farming. 
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3.
  • Muthumala, Uditha, et al. (författare)
  • Comparison of Tiny Machine Learning Techniques for Embedded Acoustic Emission Analysis
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • This paper compares machine learning approaches with different input data formats for the classification of acoustic emission (AE) signals. AE signals are a promising monitoring technique in many structural health monitoring applications. Machine learning has been demonstrated as an effective data analysis method, classifying different AE signals according to the damage mechanism they represent. These classifications can be performed based on the entire AE waveform or specific features that have been extracted from it. However, it is currently unknown which of these approaches is preferred. With the goal of model deployment on resource-constrained embedded Internet of Things (IoT) systems, this work evaluates and compares both approaches in terms of classification accuracy, memory requirement, processing time, and energy consumption. To accomplish this, features are extracted and carefully selected, neural network models are designed and optimized for each input data scenario, and the models are deployed on a low-power IoT node. The comparative analysis reveals that all models can achieve high classification accuracies of over 99\%, but that embedded feature extraction is computationally expensive. Consequently, models utilizing the raw AE signal as input have the fastest processing speed and thus the lowest energy consumption, which comes at the cost of a larger memory requirement.
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