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All the time integrated modular avionics (IMA) is a hot research topic in fields of aircraft and spacecraft. IMA mainly focuses on the deterministic partition protection in avionics resource sharing, which is the core technology to allow mixed-criticality applications developed by multi-vendors to run on the same system. Firstly, the avionic development before 2010 year is reviewed briefly, mainly focused on the key technologies in IMA. Furthermore, heterogeneous computing and virtualization are surveyed respectively, because they grew most rapidly during 2010 and 2020. The content surveyed cover the background and reasons of the fast growth, the critical technologies and their development trends. Finally, some technology and management recommendations are proposed to promote avionics development in the next decade.

With the increased demand on economy and efficiency of measurement technology, Non-Intrusive Load Monitoring (NILM) has received more and more attention as a cost-effective way to monitor electricity and provide feedback to users. Deep neural networks has been shown a great potential in the field of load disaggregation. In this paper, firstly, a new convolutional model based on residual blocks is proposed to avoid the degradation problem which traditional networks more or less suffer from when network layers are increased in order to learn more complex features. Secondly, we propose dilated convolution to curtail the excessive quantity of model parameters and obtain bigger receptive field, and multi-scale structure to learn mixed data features in a more targeted way. Thirdly, we give details about generating training and test set under certain rules. Finally, the algorithm is tested on real-house public dataset, UK-DALE, with three existing neural networks. The results are compared and analysed, the proposed model shows improvements on F1 score, MAE as well as model complexity across different appliances., 10 pages, 10 figures

State-of-the-art Graphics Processing Unit (GPU) has superior performances on float-pointing calculation and memory bandwidth, and therefore has great potential in many computationally intensive power system applications, one of which is batch-Electromechanical transient time simulation (ETTS) of power system. The computation speeds of the traditional method is slow. When dealing with batch simulation in multiple scenarios, the power consumption of multi-machine cluster system is large and the acceleration effect is limited by the number of processors. This paper proposes a superior GPU-Accelerated algorithm for batch-ETTS based on the implicit integration alternating solution method (IIASM), which extracts data-level fine-grained parallelism and increases the efficiency of memory access by combed design in solving batch dynamic element injection current and batch sparse linear systems (SLS). By offloading the tremendous computational burden to GPU, the algorithm of this paper can limit the time in one alternate iteration of single ETTS within 0.5 ms for one system with over 10,000 nodes.

Microarray image gridding is one important step of microarray image analysis to determine 2D image coordinates of all array spots in the hybridized gene chip image. Accuracy of image gridding will affect the reliability of gene-chip data extraction and even the final analysis results of gene-chip assays. To promote microarray image gridding accuracy and computation efficiency, we presented a microarray image gridding method based on image projection transformation and power spectral analysis. Firstly we transformed 2D microarray image into vertical and horizontal 1D projection sequences, secondly utilized signal processing methods of low pass filtering, zero mean, FFT, and power spectral estimation by periodogram method to get spots array row and column span information, and finally realized the microarray image gridding according to the local maxima and span information of spots array. The gridding experiments showed that this method had met the requirements of computing accuracy and efficiency of microarray image gridding.