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Automated detection of motion artifacts in MRI is feasible with good accuracy in the head and abdomen. The proposed method provides quantification and localization of artifacts as well as a visualization of the learned content. It may be extended to other anatomic areas and used for quality assurance of MR images.

This paper presents a new supervised approach to extract the power trace of individual loads from single channel aggregate power signals in non-intrusive load monitoring (NILM) systems. Recent approaches to this source separation problem are based on factorial hidden markov models (FHMM). Drawbacks are the needed knowledge of HMM models for all loads, what is infeasible for large buildings, and the large combinatorial complexity. Our approach trains HMM with two emission probabilities, one for the single load to be extracted and the other for the aggregate power signal. A Gaussian distribution is used to model observations of the single load whereas observations of the aggregate signal are modeled with a Deep Neural Network (DNN). By doing so, a single load can be extracted from the aggregate power signal without knowledge of the remaining loads. The performance of the algorithm is evaluated on the Reference Energy Disaggregation (REDD) dataset.Index Terms-Non-intrusive load monitoring (NILM), supervised power disaggregation, Hidden Markov Model (HMM), Deep Neural Networks (DNN)

Magnetic resonance (MR) imaging offers a wide variety of imaging techniques. A large amount of data is created per examination which needs to be checked for sufficient quality in order to derive a meaningful diagnosis. This is a manual process and therefore time- and cost-intensive. Any imaging artifacts originating from scanner hardware, signal processing or induced by the patient may reduce the image quality and complicate the diagnosis or any image post-processing. Therefore, the assessment or the ensurance of sufficient image quality in an automated manner is of high interest. Usually no reference image is available or difficult to define. Therefore, classical reference-based approaches are not applicable. Model observers mimicking the human observers (HO) can assist in this task. Thus, we propose a new machine-learning-based reference-free MR image quality assessment framework which is trained on HO-derived labels to assess MR image quality immediately after each acquisition. We include the concept of active learning and present an efficient blinded reading platform to reduce the effort in the HO labeling procedure. Derived image features and the applied classifiers (support-vector-machine, deep neural network) are investigated for a cohort of 250 patients. The MR image quality assessment framework can achieve a high test accuracy of 93.7% for estimating quality classes on a 5-point Likert-scale. The proposed MR image quality assessment framework is able to provide an accurate and efficient quality estimation which can be used as a prospective quality assurance including automatic acquisition adaptation or guided MR scanner operation, and/or as a retrospective quality assessment including support of diagnostic decisions or quality control in cohort studies.

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