Ingestive Behaviour Activities Based on Bioacoustic Signals in Grazing Cattle

This work focuses on the development of an autonomous, electronic platform based on bioacoustic techniques to monitor animal sounds related to ingestive activities and sub-activities in beef cattle. The authors have developed an electronic platform that leverage the usage of applications to predict health and welfare conditions based on the bioacoustic signals. The platform architecture is comprised of electronic subsystems covering the signal acquiring and filtering; sampling, processing, data storage system; and the coupling of the electronic and computing. The platform is coupled to the animal's halter so that the electronic system is in a case on the animal's forehead. Experiments were performed on grazing cattle (Nelore breed) in three field tests. The system was fully able to acquire, process, and store the animal's bioacoustic sounds. The authors used an audio file processing software to identify ingestive activities such as grazing, rumination, and idle periods. The results, based on the signal waveform, achieved high accuracy when compared to the visual monitoring method.

BatchDeconvolution: a Fiji plugin for increasing deconvolution workflow

Deconvolution microscopy is a very useful, software-based technique allowing to deblur microscopy images and increase both lateral and axial resolutions. It can be used along with many of fluorescence microscopy imaging techniques. By increasing axial resolution, it also enables three-dimensional imaging using a basic wide-field fluorescence microscope. Unfortunately, commercially available deconvolution software is expensive, while freely available programs have limited capabilities of a batch file processing. In this work we present BatchDeconvolution, a Fiji plugin that bridges two programs that we used subsequently in an image deconvolution pipeline: PSF Generator and DeconvolutionLab2, both from Biomedical Imaging Group, EPFL. Our software provides a simple way to perform a batch processing of multiple microscopy files with minimal working time required from the user.

Distributed File Sharing and Retrieval Model for Cloud Virtual Environment

Cloud-based storage services are multiplying and are being adapted mostly for data storage. At the same time, many potential problems pertaining to data storage and security are being addressed. This research provides the architecture for splitting user data and the solution in retrieving different data chunks, stored on different cloud storages. By doing this, not only the load on a single server is reduced but security and storage are efficiently used. Data would be stored and retrieved in slices, hence the chances of data forgery are diminishing. File processing will be faster as split parts would be fetched from different clouds, enabling parallel processing.