Abstract:Halophiles are extremophilic microorganisms able to grow in high-salt and high-pH environments. Synthetic biology studies on halophilic bacteria lead to the development of “Next Generation Industrial Biotechnology” (NGIB). Engineered Halomonas spp. is succeeded in the production of various polyhydroxyalkanoates (PHA) and their co-productions with other high value-added chemicals, under continuous and nonsterile fermentation processes using seawater to replace fresh water. NGIB based on halophilic bacteria provides advantages including energy-saving, water-saving, time-saving, less process complexity and low-cost. It will help to become “Carbon Neutral” for our society. This article analyzes the characteristics of the next generation of industrial biotechnology based on halophilic bacteria and their synthetic biology, providing a new perspective and ideas for synthetic biology and the next generation of industrial biotechnology.

Abstract:The traditional binocular endoscope can only provide clear images, and can not carry out threedimensional measurement/reconstruction. In this paper, a binocular endoscope system based on the stereo vision principle is proposed to realize three-dimensional measurement/reconstruction, and a speckle threedimensional reconstruction system based on a binocular endoscope is developed. To improve the calibration accuracy and 3D reconstruction quality of the system, a high-precision binocular endoscope calibration parameters optimization method and a stereo correction algorithm based on twice rotation of the optical axis are proposed in this paper. The measurement system consists of a structured light illumination system and a binocular endoscope. Firstly, the image with random speckle is characterized by projection through the structured light illumination system, and then the epipolar correction and stereo matching are carried out after stereo imaging by the binocular endoscope. Finally, the three-dimensional information of the object is reconstructed according to the principle of triangulation. The experimental results show that compared with the traditional method, the measurement accuracy of standard bodies such as cone and ball is significantly improved.

Abstract:The fluctuation of the stock market greatly depends on investors’ sentiment-based factors. Sentiment analysis of investors’ reviews on financial exchange web platforms such as Guba stock forum (guba.com.cn), can help stockholders to understand the stock market more effectively. However, due to the unavailability of high-quality labeled datasets and deficient features of stock comments extracted by a single model, the accuracy of the existing sentiment methods still requires further improvements. This paper proposes a method that utilizes the FinBERT-CNN-based sentiment model for Guba comments. The semantic features of Guba comments are extracted by using the FinBERT pre-training model. Meanwhile, a convolution neural network (CNN) is applied to learn the local features of Guba comments. It enables the proposed method to learn features more precisely and improve the emotion classification’s accuracy significantly. Experiments show that the proposed method outperforms the existing models. Furthermore, the correlation analysis on Guba comments and stock market data demonstrates a relationship between the investors’ emotions and stock market volatility.

Abstract:The indoor scene generation task is an important research topic in recent years. It can not only provide a natural annotated indoor scene dataset for computer vision tasks to help better understand the scene, but also can be applied to many real scenes such as robot navigation. The diversity of indoor scene layouts makes scene generation a very challenging task. This paper reviews the recent research progress in the field of indoor scene generation, summarizes and classifies the generation algorithms in terms of scene input, scene generation method, scene representation, scene generation order, and scene context relationship. The three categories of the generation algorithms including sample-free generation method based on object relationship, sample-free generation method based on human activities, and sample-based object relationship based on object relationship are analyzed with advantages and disadvantages. In addition, this article also summarizes the limitations of the existing algorithms and points out the direction that can be explored in the field of indoor scene generation in the future.

Abstract:The information industry is developing rapidly and the mainstream digital media has produced an evolution from text to pictures to videos. How to quickly and effectively extract the key points of interest of characters in videos has become a hot topic in the field of Internet entertainment and big data analysis. However, existing methods for acquiring character information usually have significant limitations in obtaining information directly from the video interface. To address this problem, this paper proposes a novel “coarse to fine” intelligent face search framework based on feature hybrid clustering and key point detection. The real-time search of face data under big data is subdivided. First, the face detection algorithm based on multi-scale depth feature hybrid clustering uses the Softmax function to achieve data classification, and then uses the central loss function to form clustering centers that are modified by the regression of centroids to achieve coarse screening of faces. Then, based on the face key point detection algorithm, 68 individual face key feature points are extracted to generate standardized features that are easy to calculate and process to realize the fine search of faces under big data. This enables real-time and highly robust intelligent face search from Internet video data. Notably, this paper also constructs two film and television face datasets to provide big data analysis for subsequent related Internet industry and entertainment multimedia. System’s overall experimental results prove that this paper has a certain improvement in recognition accuracy and efficiency compared with existing mainstream face detection methods, including a 31.2% improvement in recognition efficiency and 3 times improvement in discrimination of false-positive samples, and the overall operation efficiency meets the standard and has certain practical value.

Abstract:There are certain limitations in the information display dimension of traditional interactive devices. Augmented reality technology can expand the interactive space and information dimension, but it is slightly insufficient in the overall information interaction and decision-making. This paper combined the traditional mobile interactive device iPad and the augmented reality device HoloLens: the overall characteristic information is displayed on the iPad, and the details are explored in HoloLens. Based on this idea, the paper designed and implemented a new interactive environment. A brand-new interactive environment, designed to achieve multi-level, multiplayer, multi-terminal spatial information display and interaction methods, takes the sensor log data of the venue as an example to demonstrate the advantages of the system in analyzing and processing problems.

Abstract:With the development of computer multimedia, visual analytic system goes beyond the traditional screen-mouse-keyboard. Digital sandbox projection system has attracted more and more attention in recent years. In the context of smart campus, this work proposes an interactive campus digital sandbox, that supports multi-user cooperation and multi-item interaction by constructing a visual analytic pipeline of sensor input, intelligent computing terminal, visual output driven by deep learning. Taking the heatmap simulation of campus activities as an application scenario, the feasibility of the system is verified.

Abstract:In continuous mask projection 3D printing, due to the limited resin filling distance in a limited time, continuous forming is suitable for hollow structure or sheet structure. For the solid structures it is easy to fail in printing due to the insufficient resin filling in the process of printing. In addition, continuous printing based on ploydimethylsiloxane (PDMS) film soaked in lubricating fluid has limited printable height due to the loss of lubricating fluid in the printing process. To solve the above problems, a model guided 3D printing scheme combining layer-wise and continuous printing is proposed. The printing plane is formed by a certain thickness of fluorinated oil (also known as lubricating fluid) with a density greater than the resin and immiscible with the resin, which greatly reduces the adhesion between the printed object and the forming plane. Furthermore, experiments are designed to estimate the Maximum Filling Distance (MFD) and the Optimal Lifting Height (OLH) under the liquid-liquid interface. Based on MFD, combined with the Minimum-Maximum Distance (DisMax-Min) of model slices, the continuous printing and layer-wise printing are effectively connected. Based on OLH, with model analysis, the optimal lifting height of the platform could be estimated for layer-wise printing. The experimental results demonstrate that the proposed scheme can print the model height without restriction by lubrication fluid , and it can print the models of both solid and hollow structures. Compared with the traditional layer-wise printing scheme, the proposed method is more efficient.