Holo-UNet: hologram-to-hologram neural network restoration for high fidelity low light quantitative phase imaging of live cells

Intensity shot noise in digital holograms distorts the quality of the phase images after phase retrieval, limiting the usefulness of quantitative phase microscopy (QPM) systems in long term live cell imaging. In this paper, we devise a hologram-to-hologram neural network, Holo-UNet, that restores high quality digital holograms under high shot noise conditions (sub-mW/cm2 intensities) at high acquisition rates (sub-milliseconds). In comparison to current phase recovery methods, Holo-UNet denoises the recorded hologram, and so prevents shot noise from propagating through the phase retrieval step that in turn adversely affects phase and intensity images. Holo-UNet was tested on 2 independent QPM systems without any adjustment to the hardware setting. In both cases, Holo-UNet outperformed existing phase recovery and block-matching techniques by ∼ 1.8 folds in phase fidelity as measured by SSIM. Holo-UNet is immediately applicable to a wide range of other high-speed interferometric phase imaging techniques. The network paves the way towards the expansion of high-speed low light QPM biological imaging with minimal dependence on hardware constraints.Australian Research Council (DE160100843, DP190100039, DP200100364

Exploring 3D Printing Strategies for Designers to Reach Circularity

Additive Manufacturing has been identified as a disruptive emerging technology with great potential for sustainability and implementing the circular economy. However, new generations of designers have used it as a mere tool for the three-dimensional representation of a solution conceived and designed for other supply chains. This not only creates experiential and perceptual problems in relation to AM but actually represents a misuse of material resources, which are utilized in an uninformed manner. With this in mind, the paper aims to chart possible directions and strategies to foster an informed use of AM within the Circular Design design and production process. After an introductory framing of the current issues and peculiarities of AM, we present the five strategies identified to enhance the potential of 3D printing within the framework of the ecological transition. These strategies are the starting point for defining a roadmap to understand better and consciously use AM to design circular and sustainable solutions

Algorithms And System For Segmentation And Structure Analysis In Soccer Video

In this paper, we present a novel system and effective algorithms for soccer video segmentation. The output, about whether the ball is in play, reveals high-level structure of the content. The first step is to classify each sample frame into 3 kinds of view using a unique domain-specific feature, grass-area-ratio. Here the grass value and classification rules are learned and automatically adjusted to each new clip. Then heuristic rules are used in processing the view label sequence, and obtain play/break status of the game. The results provide good basis for detailed content analysis in next step. We also show that low- level features and mid-level view classes can be combined to extract more information about the game, via the example of detecting grass orientation in the field. The results are evaluated under different metrics intended for different applications; the best result in segmentation is 86.5%

Social multimedia computing

This article summarizes the corresponding full-day tutorial at ACM Multimedia 2014. This tutorial reviews recent progresses in social multimedia computing from two perspectives: social-sensed multimedia computing (3 hours) and user-centric social multimedia computing (3 hours)

Structure Analysis of Soccer Video with Domain Knowledge and Hidden Markov Models

In this paper, we present statistical techniques for parsing the structure of produced soccer programs. The problem is important for applications such as personalized video streaming and browsing systems, in which video are segmented into di#erent states and important states are selected based on user preferences. While prior work focuses on the detection of special events such as goals or corner kicks, this paper is concerned with generic structural elements of the game. We define two mutually exclusive states of the game, play and break based on the rules of soccer. Automatic detection of such generic states represents an original, challenging issue due to high appearance diversities and temporal dynamics of such states in di#erent videos. We select a salient feature set from the compressed domain, dominant color ratio and motion intensity, based on the special syntax and content characteristics of soccer videos. We then model the stochastic structures of each state of the game with a set of hidden Markov models. Finally, higher-level transitions are taken into account and dynamic programming techniques are used to obtain the maximum likelihood segmentation of the video sequence. The system achieves a promising classification accuracy of 83.5%, with light-weight computation on feature extraction and model inference, as well as a satisfactory accuracy in boundary timing

Structure analysis of soccer video with hidden Markov models

In this paper, we present statistical techniques for parsing the structure of produced soccer programs. The problem is important for applicaitons such as personalized video streaming and browsing systems, in which vides are segmented into different states and important states are selected based on user preferences. While prior work focuses on the detection of special events such as goals or corner kicks, this paper is concerned with generic structural elements of the game. We define two mutually exclusive states of the fame, play and break based on the rules of soccer. Automatic detection of such generic states represents an original challenging issue due to high appearance diversities and temporal dynamics of such states in different videos. We select a salient feature set from the compressed domain, dominant color ratio and motion intensity, based on the special syntax and content characteristics of soccer videos. We then model the stochastic structures of each state of the game with a set of hidden Markov models. Finally, higher-level transitions are taken into account and dynamic programming techniques are used to obtain the maximum likelihood segmentation of the video sequence. The system achieves a promising classification accuracy of 83.5%, with light-weight computation on feature extraction and model inference, aas well as a satisfactory accuracy in boundary timing

Systematic Review and Meta-Analysis of Randomized Controlled Trials of Xingnaojing Treatment for Stroke

Objective. Xingnaojing injection (XNJ) is a well-known traditional Chinese patent medicine (TCPM) for stroke. The aim of this study is to assess the efficacy of XNJ for stroke including ischemic stroke, intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH). Methods. An extensive search was performed within using eight databases up to November 2013. Randomized controlled trials (RCTs) on XNJ for treatment of stroke were collected. Study selection, data extraction, quality assessment, and meta-analysis were conducted according to the Cochrane standards, and RevMan5.0 was used for meta-analysis. Results. This review included 13 RCTs and a total of 1,514 subjects. The overall methodological quality was poor. The meta-analysis showed that XNJ combined with conventional treatment was more effective for total efficacy, neurological deficit improvement, and reduction of TNF-α levels compared with those of conventional treatment alone. Three trials reported adverse events, of these one trial reported mild impairment of kidney and liver function, whereas the other two studies failed to report specific adverse events. Conclusion. Despite the limitations of this review, we suggest that XNJ in combination with conventional medicines might be beneficial for the treatment of stroke. Currently there are various methodological problems in the studies. Therefore, high-quality, large-scale RCTs are urgently needed