MCAM - The AR Photography Composition Education Prototype for Mobile Device Based on Augmented Reality Interactive

The current digital age and era where technological advancements and innovation have revolutionized various sectors, photos and videos are the most convenient and direct medium for people to record and share their personal lives today. In this digital era and internet age, mobile phone cameras have replaced cameras as the most widely used photography tools. While documenting personal life with photos, people gradually pay more and more attention to the artistry and aesthetics of photos. Just recording that life can no longer satisfy them, how to present more of their own life is a new pursuit of videos and pictures as it can be seen from the webpage. Many phone applications and specifically camera apps have a designed filter function that helps people to optimize their photos. These camera apps have gradually filled people\u27s mobile phones. Selecting a filter and then taking a photo-shoot has become a standard process for daily photography. If the image is not satisfactory after the shooting completed, the photos can be re-optimized by light adjustment and liquefaction deformation in the camera apps as it can be noted from the webpage on page 16. Unfortunately, the power of those filter functions cannot change the reality of the release of mediocre images because of the lack of composition skills. Compared to filters and other photo re-optimized functions, photography composition is the skill that people need to learn and master in the earlier stage. However, most people find it difficult to determine. In this project there will be a demonstration of how AR interactive instruction is rationally helping people in learning photography compositions; give users an enjoyable experience to improve the better skill of photography. This project will present in an interactive interface, and a prototype shows the use of this application

Exploring Views on Data Centre Power Consumption and Server Virtualization

The primary purpose of this Thesis is to explore views on Green IT/Computing and how it relates to Server Virtualization, in particular for Data Centre IT environments. Our secondary purpose is to explore other important aspects of Server Virtualization, in the same context. The primary research question was to determine if Data Centre (DC) power consumption reduction is related to, or perceived as, a success factor for implementing and deploying server virtualization for consolidation purposes, and if not, what other decision areas affect Server Virtualization and power consumption reduction, respectively. The conclusions from our research are that there is a difference of opinion regarding how to factor power consumption reduction from server equipment, both from promoters and deployers. However, it was a common view that power consumption reduction was usually achieved, but not necessarily considered, and thus not evaluated, as a success factor, nor that actual power consumption was measured or monitored after server virtualization deployment. We found that other factors seemed more important, such as lower cost through higher physical machine utilization, simplified high availability and disaster recovery capabilities

Improved collaborative optimization in multidisciplinary design optimization problems

This paper is about a new approach for concurrent design based on collaborative optimization, a distributed optimization method for multidisciplinary designs. The key idea of the proposed method is to consider the global objective in each subspace optimization problem with an additional interaction channel for coupling variables, while maintaining an easy coordination of design variables for system level problem. The improved collaborative optimisation is applied to two academic test cases to demonstrate its feasibility and validity

MILI: Multi-person inference from a low-resolution image

Existing multi-person reconstruction methods require the human bodies in the input image to occupy a considerable portion of the picture. However, low-resolution human objects are ubiquitous due to trade-off between the field of view and target distance given a limited camera resolution. In this paper, we propose an end-to-end multi-task framework for multi-person inference from a low-resolution image (MILI). To perceive more information from a low-resolution image, we use pair-wise images at high resolution and low resolution for training, and design a restoration network with a simple loss for better feature extraction from the low-resolution image. To address the occlusion problem in multi-person scenes, we propose an occlusion-aware mask prediction network to estimate the mask of each person during 3D mesh regression. Experimental results on both small-scale scenes and large-scale scenes demonstrate that our method outperforms the state-of-the-art methods both quantitatively and qualitatively. The code is available at http://cic.tju.edu.cn/faculty/likun/projects/MILI

Chyle leakage in port incision after video-assisted thoracoscopic surgery: case report

A 26-year-old Asian male was found to have chyle leakage from the port incision after video-assisted thoracoscopic surgery (VATS) for excision of pulmonary bullae. The diagnosis was confirmed by oral intake of Sudan black and by lymphoscintigraphy. The leakage resolved after 5 days of restricted oral intake and total parenteral nutrition. No leakage recurred after return of oral intake. Possible explanations for the port incision chyle leakage are obstruction of the thoracic duct, which induced retrograde drainage of the lymphoid fluid, or an aberrant collateral branch of the thoracic duct in the chest wall

Synchronous intercept strategies for a robotic defense-intrusion game with two defenders

We study the defense-intrusion game, in which a single attacker robot tries to reach a stationary target that is protected by two defender robots. We focus on the "synchronous intercept problem", where both robots have to reach the attacker robot synchronously to intercept it. Assume that the attacker robot has the control policy which is based on attraction to the target and repulsion from the defenders, two kinds of synchronous intercept strategies are proposed for the defense-intrusion game, introduced here as Attacker-oriented and Neutral-position-oriented. Theoretical analysis and simulation results show that: (1) the two strategies are able to generate different synchronous intercept patterns: contact intercept pattern and stable non-contact intercept pattern, respectively. (2) The contact intercept pattern allows the defender robots to intercept the attacker robot in finite time, while the stable non-contact intercept pattern generates a periodic attractor that prevents the attack robot from reaching the target for infinite time. There is potential to apply the insights obtained into defense-intrusion in real systems, including aircraft escort and the defense of military targets or territorial boundaries

Group chase and escape with prey's anti-attack behavior

Group chase and escape is widely observed in nature, where the predators approach the prey and the prey try to escape. An interesting phenomenon occurs when a prey group is under attack, whereby some individuals perform anti-attack behavior that places themselves at a greater risks of being caught. It remains unclear why certain prey would risk their survival and what conditions and internal mechanisms trigger this anti-attack response. Using a set of local interaction rules among prey and predators, we proposed a continuous-space and discrete-time model that incorporates energy level, variable speed and handling time by considering different aggregation preferences of prey. We found that anti-attack behavior contributes to enhance the survivability of the prey group and the effect is more efficient in the presence of aggregation preference. The survivability can be improved if the fleeing prey have no aggregation preference while the anti-attack prey use a general aggregation preference

Geometry-guided dense perspective network for speech-driven facial animation

Realistic speech-driven 3D facial animation is a challenging problem due to the complex relationship between speech and face. In this paper, we propose a deep architecture, called Geometry-guided Dense Perspective Network (GDPnet), to achieve speaker-independent realistic 3D facial animation. The encoder is designed with dense connections to strengthen feature propagation and encourage the re-use of audio features, and the decoder is integrated with an attention mechanism to adaptively recalibrate point-wise feature responses by explicitly modeling interdependencies between different neuron units. We also introduce a non-linear face reconstruction representation as a guidance of latent space to obtain more accurate deformation, which helps solve the geometry-related deformation and is good for generalization across subjects. Huber and HSIC (Hilbert-Schmidt Independence Criterion) constraints are adopted to promote the robustness of our model and to better exploit the non-linear and high-order correlations. Experimental results on the public dataset and real scanned dataset validate the superiority of our proposed GDPnet compared with state-of-the-art model. We will make the code available for research purposes