Error reduction method for singularity point detection using Shack–Hartmann wavefront sensor

AbstractA new framework is proposed for realizing high-spatial-resolution detection of singularity points in optical vortex beams using a Shack–Hartmann wavefront sensor (SHWS). The method uses a Shack–Hartmann wavefront sensor (SHWS) to record a Hartmanngram. A map of evaluation values related to phase slope is then calculated from the Hartmanngram. We first determined the singularity's position precisely by calculating the centroid of the circulation of 3×3 crosspoints. After that, we analyzed the error distribution of it, and proposed hybrid centroiding framework for reducing its error. Optical experiments were carried out to verify the method. Good linearity was showed in detecting positions of the singularity points, and it was indicated that the accuracy of detection the position of OV was improved. The average root mean square (RMS) error over various measurements was better than correlation matching method, which we proposed before. The method not only shows higher accuracy, but also consumes much less time than our former work

Tile-Based Two-Dimensional Phase Unwrapping for Digital Holography Using a Modular Framework

A variety of physical and biomedical imaging techniques, such as digital holography, interferometric synthetic aperture radar (InSAR), or magnetic resonance imaging (MRI) enable measurement of the phase of a physical quantity additionally to its amplitude. However, the phase can commonly only be measured modulo 2π, as a so called wrapped phase map. Phase unwrapping is the process of obtaining the underlying physical phase map from the wrapped phase. Tile-based phase unwrapping algorithms operate by first tessellating the phase map, then unwrapping individual tiles, and finally merging them to a continuous phase map. They can be implemented computationally efficiently and are robust to noise. However, they are prone to failure in the presence of phase residues or erroneous unwraps of single tiles. We tried to overcome these shortcomings by creating novel tile unwrapping and merging algorithms as well as creating a framework that allows to combine them in modular fashion. To increase the robustness of the tile unwrapping step, we implemented a model-based algorithm that makes efficient use of linear algebra to unwrap individual tiles. Furthermore, we adapted an established pixel-based unwrapping algorithm to create a quality guided tile merger. These original algorithms as well as previously existing ones were implemented in a modular phase unwrapping C++ framework. By examining different combinations of unwrapping and merging algorithms we compared our method to existing approaches. We could show that the appropriate choice of unwrapping and merging algorithms can significantly improve the unwrapped result in the presence of phase residues and noise. Beyond that, our modular framework allows for efficient design and test of new tile-based phase unwrapping algorithms. The software developed in this study is freely available

SaliencyGAN: Deep Learning Semisupervised Salient Object Detection in the Fog of IoT

In modern Internet of Things (IoT), visual analysis and predictions are often performed by deep learning models. Salient object detection (SOD) is a fundamental preprocessing for these applications. Executing SOD on the fog devices is a challenging task due to the diversity of data and fog devices. To adopt convolutional neural networks (CNN) on fog-cloud infrastructures for SOD-based applications, we introduce a semisupervised adversarial learning method in this article. The proposed model, named as SaliencyGAN, is empowered by a novel concatenated generative adversarial network (GAN) framework with partially shared parameters. The backbone CNN can be chosen flexibly based on the specific devices and applications. In the meanwhile, our method uses both the labeled and unlabeled data from different problem domains for training. Using multiple popular benchmark datasets, we compared state-of-the-art baseline methods to our SaliencyGAN obtained with 10-100% labeled training data. SaliencyGAN gained performance comparable to the supervised baselines when the percentage of labeled data reached 30%, and outperformed the weakly supervised and unsupervised baselines. Furthermore, our ablation study shows that SaliencyGAN were more robust to the common “mode missing” (or “mode collapse”) issue compared to the selected popular GAN models. The visualized ablation results have proved that SaliencyGAN learned a better estimation of data distributions. To the best of our knowledge, this is the first IoT-oriented semisupervised SOD method

Industrial Cyber-Physical Systems-based Cloud IoT Edge for Federated Heterogeneous Distillation.

Deep convoloutional networks have achieved remarkable performance in a wide range of vision-based tasks in modern internet of things (IoT). Due to privacy issue and transmission cost, mannually annotated data for training the deep learning models are usually stored in different sites with fog and edge devices of various computing capacity. It has been proved that knowledge distillation technique can effectively compress well trained neural networks into light-weight models suitable to particular devices. However, different fog and edge devices may perform different sub-tasks, and simplely performing model compression on powerful cloud servers failed to make use of the private data sotred at different sites. To overcome these obstacles, we propose an novel knowledge distillation method for object recognition in real-world IoT sencarios. Our method enables flexible bidirectional online training of heterogeneous models distributed datasets with a new ``brain storming'' mechanism and optimizable temperature parameters. In our comparison experiments, this heterogeneous brain storming method were compared to multiple state-of-the-art single-model compression methods, as well as the newest heterogeneous and homogeneous multi-teacher knowledge distillation methods. Our methods outperformed the state of the arts in both conventional and heterogeneous tasks. Further analysis of the ablation expxeriment results shows that introducing the trainable temperature parameters into the conventional knowledge distillation loss can effectively ease the learning process of student networks in different methods. To the best of our knowledge, this is the IoT-oriented method that allows asynchronous bidirectional heterogeneous knowledge distillation in deep networks