Multi-source and Multi-target Node Selection in Energy-efficient Fog Computing Model

In the fog computing model to realize the IoT, each fog node supports application processes to calculate output data on input data received from a fog node and sends the output data to another fog node. In our previous studies, types of the TBFC (Tree-Based Fog Computing) models are proposed to reduce the electric energy consumption and execution time of fog nodes and servers and to be tolerant of node faults. In the TBFC models, the tree structure of fog nodes is not changed even if some fog node is overloaded and underloaded. In this paper, we consider the DNFC (Dynamic Network-based Fog Computing) model. Here, there is one or more than one possible target fog node for each fog node and also one or more than one possible source node for each target node. A pair of a source node and target node which exchange data have to be selected. In this paper, we propose an MSMT (Multi-Source and Multi-Target node selection) protocol among multiple source and target nodes. Here, a pair of a source node and a target node are selected so that the total energy consumption of the nodes can be reduced. In the evaluation, we show the total energy consumption and total execution time by target nodes can be more reduced in the MSMT protocol

Low melting point alkali metal borohydride mixtures for hydrogen storage

With relatively high gravimetric and volumetric hydrogen capacities and low hydrogen operating pressures, borohydrides are being investigated for their potential use as solid-state hydrogen storage media. This work focuses on investigating the hydrogen sorption mechanisms for $LiBH_4$-based low-melting-point borohydride mixtures (e.g. $0.62LiBH_4$-$0.38NaBH_4$, $0.75LiBH_4$-$0.25KBH_4$), and their destabilized systems using selected additives. Solid solutions and bimetallic borohydride are found in the as-prepared $0.62LiBH_4$-$0.38NaBH_4$ and $0.75LiBH_4$-$0.25KBH$ mixtures, respectively. Under Ar, the $0.62LiBH_4$-$0.38NaBH_4$ mixture releases 10.8 wt.% of hydrogen at 650 °C; whilst the $0.75LiBH_4$-$0.25KBH_4$ mixture releases 8.9 wt.% of hydrogen at 700 °C. Their dehydrogenation peak temperatures are strongly affected by Na+ or K+ and therefore higher than $LiBH_4$. These mixtures have poor cycling stabilities. Additives, such as micron-sized $SiO_2$ and nano-sized Ni, cannot affect their melting points; but they cause lower dehydrogenation temperatures, decrease the hydrogen evolution, and facilitate the formation of metal dodecaborates. Besides, the addition of nano-sized Ni cannot significantly improve the cycling stability; however, it leads to partial reversible $LiBH_4$. Therefore, a further compositional optimization with respect to the rehydrogenation conditions, in parallel with the use of nano-confinement of the mixture via an infiltration approach, is needed before practical use of a low-melting-point alkali metal borohydride mixture

Real-Time Non-Invasive Imaging and Detection of Spreading Depolarizations through EEG: An Ultra-Light Explainable Deep Learning Approach

A core aim of neurocritical care is to prevent secondary brain injury. Spreading depolarizations (SDs) have been identified as an important independent cause of secondary brain injury. SDs are usually detected using invasive electrocorticography recorded at high sampling frequency. Recent pilot studies suggest a possible utility of scalp electrodes generated electroencephalogram (EEG) for non-invasive SD detection. However, noise and attenuation of EEG signals makes this detection task extremely challenging. Previous methods focus on detecting temporal power change of EEG over a fixed high-density map of scalp electrodes, which is not always clinically feasible. Having a specialized spectrogram as an input to the automatic SD detection model, this study is the first to transform SD identification problem from a detection task on a 1-D time-series wave to a task on a sequential 2-D rendered imaging. This study presented a novel ultra-light-weight multi-modal deep-learning network to fuse EEG spectrogram imaging and temporal power vectors to enhance SD identification accuracy over each single electrode, allowing flexible EEG map and paving the way for SD detection on ultra-low-density EEG with variable electrode positioning. Our proposed model has an ultra-fast processing speed (<0.3 sec). Compared to the conventional methods (2 hours), this is a huge advancement towards early SD detection and to facilitate instant brain injury prognosis. Seeing SDs with a new dimension - frequency on spectrograms, we demonstrated that such additional dimension could improve SD detection accuracy, providing preliminary evidence to support the hypothesis that SDs may show implicit features over the frequency profile

High-Resolution Reference Image Assisted Volumetric Super-Resolution of Cardiac Diffusion Weighted Imaging

Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) is the only in vivo method to non-invasively examine the microstructure of the human heart. Current research in DT-CMR aims to improve the understanding of how the cardiac microstructure relates to the macroscopic function of the healthy heart as well as how microstructural dysfunction contributes to disease. To get the final DT-CMR metrics, we need to acquire diffusion weighted images of at least 6 directions. However, due to DWI's low signal-to-noise ratio, the standard voxel size is quite big on the scale for microstructures. In this study, we explored the potential of deep-learning-based methods in improving the image quality volumetrically (x4 in all dimensions). This study proposed a novel framework to enable volumetric super-resolution, with an additional model input of high-resolution b0 DWI. We demonstrated that the additional input could offer higher super-resolved image quality. Going beyond, the model is also able to super-resolve DWIs of unseen b-values, proving the model framework's generalizability for cardiac DWI superresolution. In conclusion, we would then recommend giving the model a high-resolution reference image as an additional input to the low-resolution image for training and inference to guide all super-resolution frameworks for parametric imaging where a reference image is available.Comment: Accepted by SPIE Medical Imaging 202

Swin transformer for fast MRI

Magnetic resonance imaging (MRI) is an important non-invasive clinical tool that can produce high-resolution and reproducible images. However, a long scanning time is required for high-quality MR images, which leads to exhaustion and discomfort of patients, inducing more artefacts due to voluntary movements of the patients and involuntary physiological movements. To accelerate the scanning process, methods by k-space undersampling and deep learning based reconstruction have been popularised. This work introduced SwinMR, a novel Swin transformer based method for fast MRI reconstruction. The whole network consisted of an input module (IM), a feature extraction module (FEM) and an output module (OM). The IM and OM were 2D convolutional layers and the FEM was composed of a cascaded of residual Swin transformer blocks (RSTBs) and 2D convolutional layers. The RSTB consisted of a series of Swin transformer layers (STLs). The shifted windows multi-head self-attention (W-MSA/SW-MSA) of STL was performed in shifted windows rather than the multi-head self-attention (MSA) of the original transformer in the whole image space. A novel multi-channel loss was proposed by using the sensitivity maps, which was proved to reserve more textures and details. We performed a series of comparative studies and ablation studies in the Calgary-Campinas public brain MR dataset and conducted a downstream segmentation experiment in the Multi-modal Brain Tumour Segmentation Challenge 2017 dataset. The results demonstrate our SwinMR achieved high-quality reconstruction compared with other benchmark methods, and it shows great robustness with different undersampling masks, under noise interruption and on different datasets. The code is publicly available at https://github.com/ayanglab/SwinMR.This work was supported in part by the UK Research and Inno- vation Future Leaders Fellowship [MR/V023799/1], in part by the Medical Research Council [MC/PC/21013], in part by the European Research Council Innovative Medicines Initiative [DRAGON, H2020-JTI-IMI2 101005122], in part by the AI for Health Imaging Award [CHAIMELEON, H2020-SC1-FA-DTS-2019-1 952172], in part by the British Heart Foundation [Project Number: TG/18/5/34111, PG/16/78/32402], in part by the NVIDIA Academic Hardware Grant Program, in part by the Project of Shenzhen International Cooper- ation Foundation [GJHZ20180926165402083], in part by the Bas- que Government through the ELKARTEK funding program [KK- 2020/00049], and in part by the consolidated research group MATHMODE [IT1294-19

Efficient Post-processing of Diffusion Tensor Cardiac Magnetic Imaging Using Texture-conserving Deformable Registration

Diffusion tensor based cardiac magnetic resonance (DT-CMR) is a method capable of providing non-invasive measurements of myocardial microstructure. Image registration is essential to correct image shifts due to intra and inter breath-hold motion. Registration is challenging in DT-CMR due to the low signal-to-noise and various contrasts induced by the diffusion encoding in the myocardial and surrounding organs. Traditional deformable registration destroys the texture information while rigid registration inefficiently discards frames with local deformation. In this study, we explored the possibility of deep learning-based deformable registration on DT- CMR. Based on the noise suppression using low-rank features and diffusion encoding suppression using variational auto encoder-decoder, a B-spline based registration network extracted the displacement fields and maintained the texture features of DT-CMR. In this way, our method improved the efficiency of frame utilization, manual cropping, and computational speed.Comment: 4 pages, 4 figures, conferenc