Abstract The present study investigated ice accretion thickness under non‐incoming flow icing conditions on the ground using an infrared thermography system that converts infrared radiation temperature. Two back propagation (BP) neural network models were developed to measure ice thickness. Both theoretical model and polynomials were employed to fit the icing surface temperature elevation sequence to extract the pixel‐level temperature attenuation characteristics, which were served as the input to the BP neural networks. The prediction method of ice thickness by the BP neural network was analysed from three perspectives of sensitivity, dimension, and precision. In addition, K‐nearest neighbour (KNN) and support vector regression (SVR) algorithms were compared with BP neural network. In terms of prediction effect, the BP neural network performed the best. The verification of the BP neural network based on the characteristics of the theoretical model proved that the method can effectively predict the thickness of ice accretion, and its prediction error does not exceed 10%.

Despite significant advantages, wider usage of field-programmable gate arrays (FPGAs) has been limited by lengthy compilation and a lack of portability. Virtual-architecture overlays have partially addressed these problems, but previous work focuses mainly on heavily pipelined applications with minimal control requirements. We expand previous work by enabling more flexible control via overlay architectures for finite-state machines. Although not appropriate for control-intensive circuits, the presented architectures reduced compilation times of control changes in a convolution case study from 7 hours to less than 1 second, with no performance overhead and an area overhead of 0.2%.

FPGA productivity suffers from lengthy compilation times and limited portability. To address these issues, previous work introduced virtual architecture overlays that enable application portability across FPGAs and place-androute that is orders-of-magnitude faster than device vendor tools. However, those previous approaches have limited applicability due to a focus on pipelines with few control requirements. In this paper, we expand control capabilities of previous work by introducing virtual control architectures for finite state machines. The presented architectures reduce lookup-table requirements by multiple orders-of-magnitude and enable larger finite state machines used in common benchmarks.