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In order to reduce energy consumption of homes, it is important to make transparent which devices consume how much energy. However, power consumption is often only monitored aggregated at the house energy meter. Disaggregating this power consumption into the contributions of individual devices can be achieved using Machine Learning. Our work aims at making state of the art disaggregation algorithms accessibe for users of the open source home automation platform Home Assistant.
Purpose
To design and evaluate a modular transceiver coil array with 32 independent channels for cardiac MRI at 7.0T.
Methods
The modular coil array comprises eight independent building blocks, each containing four transceiver loop elements. Numerical simulations were used for B1+ field homogenization and radiofrequency (RF) safety validation. RF characteristics were examined in a phantom study. The array's suitability for accelerated high spatial resolution two-dimensional (2D) FLASH CINE imaging of the heart was examined in a volunteer study.
Results
Transmission field adjustments and RF characteristics were found to be suitable for the volunteer study. The signal-to-noise intrinsic to 7.0T together with the coil performance afforded a spatial resolution of 1.1 × 1.1 × 2.5 mm3 for 2D CINE FLASH MRI, which is by a factor of 6 superior to standardized CINE protocols used in clinical practice at 1.5T. The 32-channel transceiver array supports one-dimensional acceleration factors of up to R = 4 without impairing image quality significantly.
Conclusion
The modular 32-channel transceiver cardiac array supports accelerated and high spatial resolution cardiac MRI. The array is compatible with multichannel transmission and provides a technological basis for future clinical assessment of parallel transmission techniques at 7.0T.
Cardiac MR (CMR) at ultrahigh (≥7.0 T) fields is regarded as one of the most challenging MRI applications. At 7.0 T image quality is not always exclusively defined by signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Detrimental effects bear the potential to spoil the signal-to-noise (SNR) and contrast-to-noise (CNR) benefits of cardiac MR (CMR) at 7.0 T. B₁⁺-inhomogeneities and signal voids represent the main challenges. Various pioneering coil concepts have been proposed to tackle these issues, enabling cardiac MRI at 7.0 T. This includes a trend towards an ever larger number of transmit and receive channels. This approach affords multi-dimensional B₁⁺ modulations to improve B₁⁺ shimming performance and to enhance RF efficiency. Also, parallel imaging benefits from a high number of receive channels enabling two-dimensional acceleration. Realizing the limitations of existing coil designs tailored for UHF CMR and recognizing the opportunities of a many element TX/RX channel architecture this work proposes a modular, two dimensional 32-channel transmit and receive array using loop elements and examines its efficacy for enhanced B¹+ homogeneity and improved parallel imaging performance.
Today, the assembly of laser systems requires a large share of manual operations due to its complexity regarding the optimal alignment of optics. Although the feasibility of automated alignment of laser optics has been shown in research labs, the development effort for the automation of assembly does not meet economic requirements – especially for low-volume laser production. This paper presents a model-based and sensor-integrated assembly execution approach for flexible assembly cells consisting of a macro-positioner covering a large workspace and a compact micromanipulator with camera attached to the positioner. In order to make full use of available models from computer-aided design (CAD) and optical simulation, sensor systems at different levels of accuracy are used for matching perceived information with model data. This approach is named "chain of refined perception", and it allows for automated planning of complex assembly tasks along all major phases of assembly such as collision-free path planning, part feeding, and active and passive alignment. The focus of the paper is put on the in-process image-based metrology and information extraction used for identifying and calibrating local coordinate systems as well as the exploitation of that information for a part feeding process for micro-optics. Results will be presented regarding the processes of automated calibration of the robot camera as well as the local coordinate systems of part feeding area and robot base.