Leading Undergraduate Students to Big Data Generation

People are facing a flood of data today. Data are being collected at unprecedented scale in many areas, such as networking, image processing, virtualization, scientific computation, and algorithms. The huge data nowadays are called Big Data. Big data is an all encompassing term for any collection of data sets so large and complex that it becomes difficult to process them using traditional data processing applications. In this article, the authors present a unique way which uses network simulator and tools of image processing to train students abilities to learn, analyze, manipulate, and apply Big Data. Thus they develop students handson abilities on Big Data and their critical thinking abilities. The authors used novel image based rendering algorithm with user intervention to generate realistic 3D virtual world. The learning outcomes are significant

Improving Routing Efficiency through Intermediate Target Based Geographic Routing

The greedy strategy of geographical routing may cause the local minimum problem when there is a hole in the routing area. It depends on other strategies such as perimeter routing to find a detour path, which can be long and result in inefficiency of the routing protocol. In this paper, we propose a new approach called Intermediate Target based Geographic Routing (ITGR) to solve the long detour path problem. The basic idea is to use previous experience to determine the destination areas that are shaded by the holes. The novelty of the approach is that a single forwarding path can be used to determine a shaded area that may cover many destination nodes. We design an efficient method for the source to find out whether a destination node belongs to a shaded area. The source then selects an intermediate node as the tentative target and greedily forwards packets to it, which in turn forwards the packet to the final destination by greedy routing. ITGR can combine multiple shaded areas to improve the efficiency of representation and routing. We perform simulations and demonstrate that ITGR significantly reduces the routing path length, compared with existing geographic routing protocols

Document Clustering Based On Max-Correntropy Non-Negative Matrix Factorization

Nonnegative matrix factorization (NMF) has been successfully applied to many areas for classification and clustering. Commonly-used NMF algorithms mainly target on minimizing the $l_2$ distance or Kullback-Leibler (KL) divergence, which may not be suitable for nonlinear case. In this paper, we propose a new decomposition method by maximizing the correntropy between the original and the product of two low-rank matrices for document clustering. This method also allows us to learn the new basis vectors of the semantic feature space from the data. To our knowledge, we haven't seen any work has been done by maximizing correntropy in NMF to cluster high dimensional document data. Our experiment results show the supremacy of our proposed method over other variants of NMF algorithm on Reuters21578 and TDT2 databasets.Comment: International Conference of Machine Learning and Cybernetics (ICMLC) 201

RESOURCE ALLOCATION AND EFFICIENT ROUTING IN WIRELESS NETWORKS

In wireless networks, devices (nodes) are connected by wireless links. An important issue is to set up high quality (high bandwidth) and efficient routing paths when one node wants to send packets to other nodes. Resource allocation is the foundation to guarantee high quality connections. In addition, it is critical to handle void areas in order to set up detour-free paths. Moreover, fast message broadcasting is essential in mobile wireless networks. Thus, my research includes dynamic channel allocation in wireless mesh networks, geographic routing in Ad Hoc networks, and message broadcasting in vehicular networks. The quality of connections in a wireless mesh network can be improved by equip- ping mesh nodes with multi-radios capable of tuning to non-overlapping channels. The essential problem is how to allocate channels to these multi-radio nodes. We develop a new bipartite-graph based channel allocation algorithm, which can improve bandwidth utilization and lower the possibility of starvation. Geographic routing in Ad Hoc networks is scalable and normally loop-free. However, traditional routing protocols often result in long detour paths when holes exist. We propose a routing protocol-Intermediate Target based Geographic Routing (ITGR) to solve this problem. The novelty is that a single forwarding path can be used to reduce the lengths of many future routing paths. We also develop a protocol called Hole Detection and Adaptive Geographic Routing, which identifies the holes efficiently by comparing the length of a routing path with the Euclidean distance between a pair of nodes. We then set up the shortest path based on it. Vehicles play an important role in our daily life. During inter-vehicle communication, it is essential that emergency information can be broadcast to surrounding vehicles quickly. We devise an approach that can find the best re-broadcasting node and propagate the message as fast as possible

Facile synthesis and enhanced visible light photocatalytic activity of N and Zr co-doped TiO2 nanostructures from nanotubular titanic acid precursors

Zr/N co-doped TiO2 nanostructures were successfully synthesized using nanotubular titanic acid (NTA) as precursors by a facile wet chemical route and subsequent calcination. These Zr/N-doped TiO2 nanostructures made by NTA precursors show significantly enhanced visible light absorption and much higher photocatalytic performance than the Zr/N-doped P25 TiO2 nanoparticles. Impacts of Zr/N co-doping on the morphologies, optical properties, and photocatalytic activities of the NTA precursor-based TiO2 were thoroughly investigated. The origin of the enhanced visible light photocatalytic activity is discussed in detail.Comment: 8 pages, 7 figure

Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation

As the demand for enabling high-level autonomous driving has increased in recent years and visual perception is one of the critical features to enable fully autonomous driving, in this paper, we introduce an efficient approach for simultaneous object detection, depth estimation and pixel-level semantic segmentation using a shared convolutional architecture. The proposed network model, which we named Driving Scene Perception Network (DSPNet), uses multi-level feature maps and multi-task learning to improve the accuracy and efficiency of object detection, depth estimation and image segmentation tasks from a single input image. Hence, the resulting network model uses less than 850 MiB of GPU memory and achieves 14.0 fps on NVIDIA GeForce GTX 1080 with a 1024x512 input image, and both precision and efficiency have been improved over combination of single tasks.Comment: 9 pages, 7 figures, WACV'1

Enhancement of Visible-Light-Induced Photocurrent and Photocatalytic Activity of V and N Codoped TiO2 Nanotube Array Films

Highly ordered TiO2 nanotube arrays (TNAs) codoped with V and N were synthesized by electrochemical anodization in association with hydrothermal treatment. The samples were characterized by field emission scanning electron microscopy, X-ray diffraction and X-ray photoelectron spectroscopy. The photocurrent and photocatalytic activity of codoped TiO2 nanotube arrays were investigated under visible light irradiation. Moreover, the production of hydroxyl radicals on the surface of visible light-irradiated samples is detected by a photoluminescence technique using terephthalic acid (TA) as a probe molecule. It was found that the V+N co-doped TiO2 nanotube arrays showed remarkably enhanced photocurrent and photocatalytic activity than undoped sample due to the V and N codoping.Comment: 15 Pages, 6 figure

An Immersive Telepresence System using RGB-D Sensors and Head Mounted Display

We present a tele-immersive system that enables people to interact with each other in a virtual world using body gestures in addition to verbal communication. Beyond the obvious applications, including general online conversations and gaming, we hypothesize that our proposed system would be particularly beneficial to education by offering rich visual contents and interactivity. One distinct feature is the integration of egocentric pose recognition that allows participants to use their gestures to demonstrate and manipulate virtual objects simultaneously. This functionality enables the instructor to ef- fectively and efficiently explain and illustrate complex concepts or sophisticated problems in an intuitive manner. The highly interactive and flexible environment can capture and sustain more student attention than the traditional classroom setting and, thus, delivers a compelling experience to the students. Our main focus here is to investigate possible solutions for the system design and implementation and devise strategies for fast, efficient computation suitable for visual data processing and network transmission. We describe the technique and experiments in details and provide quantitative performance results, demonstrating our system can be run comfortably and reliably for different application scenarios. Our preliminary results are promising and demonstrate the potential for more compelling directions in cyberlearning.Comment: IEEE International Symposium on Multimedia 201