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High-resolution esophageal manometry is used for the study of esophageal motility disorders, with the help of catheters with up to 36 sensors. Color pressure topography plots are generated and analyzed and using the Chicago algorithm a final diagnosis is established. One of the main parameters in this algorithm is integrated relaxation pressure (IRP). The procedure is time consuming. Our aim was to firstly develop a machine learning based solution to detect probe positioning failure and to create a classifier to automatically determine whether the IRP is in the normal range or higher than the cut-off, based solely on the raw images. The first step was the preprocessing of the images, by finding the region of interest—the exact moment of swallowing. Afterwards, the images were resized and rescaled, so they could be used as input for deep learning models. We used the InceptionV3 deep learning model to classify the images as correct or failure in catheter positioning and to determine the exact class of the IRP. The accuracy of the trained convolutional neural networks was above 90% for both problems. This work is just the first step in fully automating the Chicago Classification, reducing human intervention.

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Data processing is a challenging problem in space applications. The limited bandwidth available for communication between satellites and the ground and the increasing resolution of scientific instruments make it virtually impossible to transfer all the data recorded on board. Although various mitigation strategies were developed, large amounts of on-board data are still lost. This paper presents a Field Programmable Gate Array (FPGA)-based architecture which is able to perform on-board nonlinear analysis of data and compute probability distribution functions of fluctuations. We propose two implementations for our solution, which can be used for space applications and also other computational contexts. On a spacecraft, the logic resources of the FPGA will typically be shared by several designs running various digital signal processing algorithms. That is why each algorithm should be designed in variants, optimized for different criteria, so that the entire group of algorithms makes an efficient usage of the FPGA resources. The proposed solution focuses on two major optimization criteria, area and speed, such that the FPGA resources are efficiently used. Also, the power consumption is at least two orders of magnitude less in comparison with classical software implementations. The solution was tested with both synthetic and real data and shows excellent results paving the way towards an application that can be ported on a space-grade FPGA.

Time-series are ordered sequences of discrete-time data. Due to their temporal dimension, anomaly detection techniques used in time-series have to take into consideration time correlations and other time-related particularities. Generally, in order to evaluate the quality of an anomaly detection technique, the confusion matrix and its derived metrics such as precision and recall are used. These metrics, however, do not take this temporal dimension into consideration. In this paper, we propose three metrics that can be used to evaluate the quality of a classification, while accounting for the temporal dimension found in time-series data.

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