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Deep neural networks have been widely used for medical image analysis. However, the lack of access to a large-scale annotated dataset poses a great challenge, especially in the case of rare diseases or new domains for the research society. Transfer of pre-trained features from the relatively large dataset is a considerable solution. In this paper, we have explored supervised segmentation using domain adaptation for optic nerve and orbital tumour, when only small sampled CT images are given. Even the lung image database consortium image collection (LIDC-IDRI) is a cross-domain to orbital CT, but the proposed domain adaptation method improved the performance of attention U-Net for the segmentation in public optic nerve dataset and our clinical orbital tumour dataset by 3.7% and 13.7% in the Dice score, respectively. The code and dataset are available at https://github.com/cmcbigdata. [ABSTRACT FROM AUTHOR]

In this paper, we present a method of recognizing the barcodes of individual strips using a single camera in a quantitative fluorescence analysis system that can process multiple lateralflow strips simultaneously. The proposed method eliminates the need for multiple scanners or a scanning mechanism. The presence or absence of strips in each slot was determined, and the position of the start bit was detected by template matching to find the barcode area. After the start bit was located, the rest of the bit area was found using the barcode dimensions and the estimated image spatial resolution obtained while calibrating the instrument. The value of each bit was determined using the average along the central line with 60% of the width of the bit area. Sixteen cartridge images were obtained under various illuminations to statistically evaluate the thresholds for the investigation of various parameters, such as the existence of the strip, the successful detection of the start bit position, and each bit value. The threshold values were determined using the two-class mixed Gaussian model to minimize errors. Experimental results show that the error rates for all parameters were negligible. [ABSTRACT FROM AUTHOR]

This study was conducted to develop an artificial neural network (ANN)-based prediction model that can calculate the amount of cooling energy during the setback period of accommodation buildings. By comparing the amount of energy needed for diverse setback temperatures, the most energy-efficient optimal setback temperature could be found and applied in the thermal control logic. Three major processes that used the numerical simulation method were conducted for the development and optimization of an ANN model and for the testing of its prediction performance, respectively. First, the structure and learning method of the initial ANN model was determined to predict the amount of cooling energy consumption during the setback period. Then, the initial structure and learning methods of the ANN model were optimized using parametrical analysis to compare its prediction accuracy levels. Finally, the performance tests of the optimized model proved its prediction accuracy with the lower coefficient of variation of the root mean square errors (CVRMSEs) of the simulated results and the predicted results under generally accepted levels. In conclusion, the proposed ANN model proved its potential to be applied to the thermal control logic for setting up the most energy-efficient setback temperature. [ABSTRACT FROM AUTHOR]

Situational awareness in a disaster is critical to effective response. Disaster responders require timely delivery of high volumes of accurate data to make correct decisions. To meet these needs, we present DistressNet, an ad hoc wireless architecture that supports disaster response with distributed collaborative sensing, topology-aware routing using a multi-channel protocol, and accurate resource localization. Sensing suites use collaborative and distributed mechanisms to optimize data collection and minimize total energy use. Message delivery is aided by novel topology management, while congestion is minimized through the use of mediated multichannel radio protocols. Estimation techniques improve localization accuracy in difficult environments. [ABSTRACT FROM AUTHOR]