Single-shot compressed ultrafast photography: a review

Compressed ultrafast photography (CUP) is a burgeoning single-shot computational imaging technique that provides an imaging speed as high as 10 trillion frames per second and a sequence depth of up to a few hundred frames. This technique synergizes compressed sensing and the streak camera technique to capture nonrepeatable ultrafast transient events with a single shot. With recent unprecedented technical developments and extensions of this methodology, it has been widely used in ultrafast optical imaging and metrology, ultrafast electron diffraction and microscopy, and information security protection. We review the basic principles of CUP, its recent advances in data acquisition and image reconstruction, its fusions with other modalities, and its unique applications in multiple research fields

Single-shot compressed ultrafast photography: a review

Compressed ultrafast photography (CUP) is a burgeoning single-shot computational imaging technique that provides an imaging speed as high as 10 trillion frames per second and a sequence depth of up to a few hundred frames. This technique synergizes compressed sensing and the streak camera technique to capture nonrepeatable ultrafast transient events with a single shot. With recent unprecedented technical developments and extensions of this methodology, it has been widely used in ultrafast optical imaging and metrology, ultrafast electron diffraction and microscopy, and information security protection. We review the basic principles of CUP, its recent advances in data acquisition and image reconstruction, its fusions with other modalities, and its unique applications in multiple research fields

STFNets: Learning Sensing Signals from the Time-Frequency Perspective with Short-Time Fourier Neural Networks

Recent advances in deep learning motivate the use of deep neural networks in Internet-of-Things (IoT) applications. These networks are modelled after signal processing in the human brain, thereby leading to significant advantages at perceptual tasks such as vision and speech recognition. IoT applications, however, often measure physical phenomena, where the underlying physics (such as inertia, wireless signal propagation, or the natural frequency of oscillation) are fundamentally a function of signal frequencies, offering better features in the frequency domain. This observation leads to a fundamental question: For IoT applications, can one develop a new brand of neural network structures that synthesize features inspired not only by the biology of human perception but also by the fundamental nature of physics? Hence, in this paper, instead of using conventional building blocks (e.g., convolutional and recurrent layers), we propose a new foundational neural network building block, the Short-Time Fourier Neural Network (STFNet). It integrates a widely-used time-frequency analysis method, the Short-Time Fourier Transform, into data processing to learn features directly in the frequency domain, where the physics of underlying phenomena leave better foot-prints. STFNets bring additional flexibility to time-frequency analysis by offering novel nonlinear learnable operations that are spectral-compatible. Moreover, STFNets show that transforming signals to a domain that is more connected to the underlying physics greatly simplifies the learning process. We demonstrate the effectiveness of STFNets with extensive experiments. STFNets significantly outperform the state-of-the-art deep learning models in all experiments. A STFNet, therefore, demonstrates superior capability as the fundamental building block of deep neural networks for IoT applications for various sensor inputs

Desgin of On-line Monitoring Device for MOA (Metal Oxide Arrestor) Based on FPGA and C8051F

Monitoring of metal oxide surge arresters (MOA) due to aging, moisture and other components cause increased resistive current. Through a lot of practices, it has been proved that in the early days, MOA insulation damage and current increase is not obvious. The accurate working conditions of the MOA are also not obvious but it can reflect the aging or moisture of MOA. When the resistive current of the fundamental component increases, there is no increment in the harmonic components that is the general performance of a serious or moisture contamination. In the same way when the resistive current of harmonic components increases, the fundamental component is not increased and it is the general performance of aging. Therefore, this paper designed an experiment-based FPGA and C8051F-line monitoring device. This device uses resistive current as a detection target. The main monitoring parameters are the fundamental and peak value of resistive current, third harmonic content of the leakage current, phase angle difference and power consumption. Through laboratory tests, the device can be used with a network arrester line monitoring, maintenance, reduce the economic losses caused by power outages and improve the distribution network reliability

Towards Reliable Learning for High Stakes Applications

In this paper, we focus on delivering reliable learning results for high stakes applications such as self-driving, financial investment and clinical diagnosis, where the accuracy of predictions is considered as a more crucial requirement than giving predictions for all query samples. We adopt the learning with reject option framework where the learning model only predict those samples which they convince to give the correct answer. However, for most prevailing deep learning predictors, the confidence estimated by the model themselves are far from reflecting the real generalization performance. To model the reliability of prediction concisely, we propose an exploratory solution called GALVE (Generative Adversarial Learning with Variance Expansion) which adopts generative adversarial learning to implicitly measure the region where the model achieve good generalization performance. By applying GALVE to measure the reliability of predictions, we achieved an error rate less than half of which straightforwardly measured by confidence in CIFAR10 and SVHN computer vision tasks

Defocus Blur Detection and Estimation from Imaging Sensors

Sparse representation has been proven to be a very effective technique for various image restoration applications. In this paper, an improved sparse representation based method is proposed to detect and estimate defocus blur of imaging sensors. Considering the fact that the patterns usually vary remarkably across different images or different patches in a single image, it is unstable and time-consuming for sparse representation over an over-complete dictionary. We propose an adaptive domain selection scheme to prelearn a set of compact dictionaries and adaptively select the optimal dictionary to each image patch. Then, with nonlocal structure similarity, the proposed method learns nonzero-mean coefficients’ distributions that are much more closer to the real ones. More accurate sparse coefficients can be obtained and further improve the performance of results. Experimental results validate that the proposed method outperforms existing defocus blur estimation approaches, both qualitatively and quantitatively

Four decades of China’s agricultural extension reform and its impact on agents’ time allocation

The Chinese Government has initiated a series of agricultural reforms since the 1970s to encourage agents to provide more services to farmers. In 2006, a new round of agricultural reforms was extended nationwide; however, the effectiveness of these reforms has not been examined. Based on a comparison of survey data sets before and after the reforms, we found that overall they significantly increased the time agents spend on agricultural extension services, although their effectiveness differs among three major components of the reforms. While the financial assurance reform had little impact on agents’ time allocation, the administrative reform actually reduced the time allocation to agricultural extension. However, we found strong evidence that the ‘three rights’ management reform (comprising the rights of personnel, financial and asset management) successfully increased agents’ time allocation to agricultural extension services. We also found that institutional incentives and the Government’s investment did not increase the time agents spent on agricultural extension. The lack of incentives is a problem that needs to be addressed in future reforms. We found that professional agents spent more time providing extension services than their non-professional counterparts. We suggest that local Governments should avoid recruiting nonprofessional agents into agricultural extension stations

A three-stage decision-making process for cost-effective passive solutions in office buildings in the hot summer and cold winter zone in China

China, with the largest energy consumption system in the world, faces numerous challenges in achieving the government’s commitment to reach a carbon-peak and carbon–neutral target. As the most common public building type in terms of floor area, office buildings have great potential for energy saving and emissions reduction. To meet this target, building designers target passive solutions that can meet the thermal comfort needs of occupants and also reduce energy consumption. This study aims to develop a decision-making method to select optimal solutions from among tens of thousands of design options considering the factors of energy consumption, comfort, and cost. We developed a novel optimization decision approach with the above-mentioned three objectives. The model consists of three stages: 1) the establishment of the reference building model, 2) sensitivity analysis to identify the main influencing variables and 3) the establishment of the optimization and decision-making model by applying NSGA-II and TOPSIS methods. By applying this three-stage decision-making model, this paper first proposes cost-effective passive design solutions for office buildings throughout the Hot Summer and Cold Winter climate zone. Finally, an office building in Shanghai was chosen as a case study to demonstrate the practical implementation of the proposed solutions through a post-occupancy evaluation with a two-year energy auditing and thermal comfort survey. It is evident that the proposed solutions provide support for the new low energy building design guide for office buildings along with necessary revisions to the existing standards for the Hot Summer and Cold Winter climate zone in China