A Network Scheduling Strategy of Android-Based Virtual Reality System

Mobile platform develops rapidly in recent years, with its performance and capacity increasing. A large number of virtual reality applications such as 3D simulation and visualization have appeared on mobile platform. However, due to restrictions, such as the lack of computing resources and the limited network bandwidth, the virtual reality systems on mobile platform could not have comparable performance as their peers on desktop platform. Taking resource restrictions into consideration, trade-offs have been made regarding the display of virtual reality systems. We propose a unified network scheduling strategy based on asynchronous multi-thread, database cache, prioritization and elimination of overdue requests. This strategy has been tested on the prototype system of virtual reality system on Android. The results demonstrate higher network resource utilization and better user experience.

Real-Time Rendering of Large-Scale Ocean Environments

Simulation and rendering of large-scale natural environments, especially the ocean, has always been one of the hot issues in computer graphics, which can provide realism for various applications such as computer game, movie and military usage. Simulation of ocean environment is often lack of realism for real-time application due to its complexity of dynamic waves. In this paper, a method based on FFT Wave model is proposed to solve this problem, which can also simulate the ocean optic property with atmosphere scattering. Furthermore, our method has a lot of advantages including global ocean dataset support, real-time, dynamic reflection of ocean, the foam on the wave, smooth transition from deep ocean to seacoast, etc. The experimental results demonstrate the realism and effectiveness of our approach.

Real-time Visualization of Urban with Gigantic Amount of Detailed Buildings

Complex urban scenery is generally composed of gigantic amount of detailed buildings, efficient representation and rendering are essential for its visualization. We present an accelerating method for urban visualization. Our approach can optimize the organization of models in accordance with the quadtree based terrain, which makes the parallelization easier. Through minimizing the draw call within one rendering process, our approach can reduce the time cost of each frame and improve the framerate greatly. Hereby, our system can handle large-scale detailed models with high diversity and also can afford the ability to adjust the rendering strategy automatically according to the state of the hardware.

Intelligent Development Environment and Software Knowledge Graph

Software intelligent development has become one of the most important research trends in software engineering. In this paper, we put forward two key concepts -- intelligent development environment （IntelliDE） and software knowledge graph -- for the first time. IntelliDE is an ecosystem in which software big data are aggregated, mined and analyzed to provide intelligent assistance in the life cycle of software development. We present its architecture and discuss its key research issues and challenges. Software knowledge graph is a software knowledge representation and management framework, which plays an important role in IntelliDE. We study its concept and introduce some concrete details and examples to show how it could be constructed and leveraged.

Real-time and generic queue time estimation based on mobile crowdsensing

People often have to queue for a busy service in many places around a city, and knowing the queue time can be helpful for making better activity plans to avoid long queues. Traditional solutions to the queue time monitoring are based on pre-deployed infrastructures, such as cameras and infrared sensors, which are costly and fail to deliver the queue time information to scattered citizens. This paper presents CrowdQTE, a mobile crowdsensing system, which utilizes the sensor-enhanced mobile devices and crowd hu- man intelligence to monitor and provide real-time queue time information for various queuing scenarios. When people are waiting in a line, we utilize the accelerometer sensor data and ambient contexts to automatically detect the queueing behav- ior and calculate the queue time. When people are not waiting in a line, it estimates the queue time based on the information reported manually by participants. We evaluate the perfor- mance of the system with a two-week and 12-person deploy- ment using commercially-available smartphones. The results demonstrate that CrowdQTE is effective in estimating queu- ing status.

Towards application-level elasticity on shared cluster： an actor-based approach

In current cluster computing, several distributed frameworks are designed to support elasticity for business services adapting to environment fluctuation. However, most existing works support elasticity mainly at the resource level, leaving application level elasticity support problem to domain-specific frameworks and applications. This paper proposes an actor-based general approach to support application-level elasticity for multiple cluster computing frameworks. The actor model offers scalability and decouples language-level concurrency from the runtime environment. By extending actors, a new middle layer called Unisupervisor is designed to ＂sit＂ between the resource management layer and application framework layer. Actors in Unisupervisor can automatically distribute and execute tasks over clusters and dynamically scale in/out. Based on Unisupervisor, high-level profiles （MasterSlave, MapReduce, Streaming, Graph, and Pipeline） for diverse cluster computing requirements can be supported. The entire approach is implemented in a prototype system called UniAS. In the evaluation, both benchmarks and real applications are tested and analyzed in a small scale cluster. Results show that UniAS is expressive and efficiently elastic.