Ivan Lee

Wireless Multimedia Sensor Networks

The emergence of low-cost and mature technologies in wireless communication, visual sensor devices, and digital signal processing facilitate of wireless multimedia sensor networks (WMSN). Like sensor networks which respond to sensory information such as temperature and humidity, WMSN interconnects autonomous devices for capturing and processing video and audio sensory information. This survey highlights the following topics (1) a summary of applications and challenges of WMSN; (2) an overview of advanced coding techniques for WMSN, including video and audio source coding, and distributed coding techniques; (3) a survey of WMSN communication protocols, including routing techniques and physical layer standards; and (4) a summary of Quality-of-Service (QoS) and security aspects of WMSN.

Distributed Throughput Maximization in P2P VoD Applications

In peer-to-peer (P2P) video-on-demand (VoD) systems, a scalable source coding is a promising solution to provide heterogeneous peers with different video quality. In this paper, we present a systematic study on the throughput maximization problem in P2P VoD applications. We apply network coding to scalable P2P systems to eliminate the delivery redundancy. Since each peer receives distinct packets, a peer with a higher throughput can reconstruct the video at a higher quality. We maximize the throughput in the existing buffer-forwarding P2P VoD systems using a fully distributed algorithm. We demonstrate in the simulations that the proposed distributed algorithm achieves a higher throughput compared to the proportional allocation scheme or the equal allocation scheme. The existing buffer-forwarding architecture has a limitation in total upload capacity. Therefore we propose a hybrid-forwarding P2P VoD architecture to improve the throughput by combining the buffer-forwarding approach with the storage-forwarding approach. The throughput maximization problem in the hybrid-forwarding architecture is also solved using a fully distributed algorithm. We demonstrate that the proposed hybrid-forwarding architecture greatly improves the throughput compared to the existing buffer-forwarding architecture. In addition, by adjusting the priority weight at each peer, we can implement the differentiated throughput among different users within a video session in the buffer-forwarding architecture, and the differentiated throughput among different video sessions in the hybrid-forwarding architecture.

Applying spatial augmented reality to facilitate in-situ support for automotive spot welding inspection

In automotive manufacturing, the quality of spot welding on car bodies needs to be inspected frequently. Operators often only check different subsets of spots on different car bodies with a predetermined sequence. Currently, spot welding inspections rely on a printed drawing of the testing body, with the inspection points marked on this drawing. Operators have to locate the matching spot on the drawing and the body manually to perform the inspection. The manual inspection process suffers from inefficiencies and potential mistakes. This paper describes a system that projects visual data onto arbitrary surfaces for providing just-in-time information to a user in-situ within a physical work-cell. Spatial Augmented Reality (SAR) is the key technology utilized in our system. SAR facilitates presentation of projected digital Augmented Reality (AR) information on surfaces of car bodies. Four types of digital AR information are projected onto the surfaces of car body parts in structured work environments: 1) Location of spot welds; 2) Inspection methods; 3) Operation Description Sheet (ODS) information; 4) Visualization of weld locating methods. Various visualization methods are used to indicate the position of spot welds and the method used for spot welding inspection. Dynamical visualizations are used to assist operators to locate spot welds more easily. The SAR approach does not require additional special models in finding spot welds, but only needs knowledge of location of spot welds on the part. Our system allows operators becoming more effective and efficient to in performing proper inspections, by providing them the required information at the required time without the need to refer to paper-based manuals or computer terminals.

Bibliographic Analysis of Nature Based on Twitter and Facebook Altmetrics Data

This paper presents a bibliographic analysis of Nature articles based on altmetrics. We assess the concern degree of social users on the Nature articles through the coverage analysis of Twitter and Facebook by publication year and discipline. The social media impact of a Nature article is examined by evaluating the mention rates on Twitter and on Facebook. Moreover, the correlation between tweets and citations is analyzed by publication year, discipline and Twitter user type to explore factors affecting the correlation. The results show that Twitter users have a higher concern degree on Nature articles than Facebook users, and Nature articles have higher and faster-growing impact on Twitter than on Facebook. The results also show that tweets and citations are somewhat related, and they mostly measure different types of impact. In addition, the correlation between tweets and citations highly depends on publication year, discipline and Twitter user type.

Video Analysis of Human Gait and Posture to Determine Neurological Disorders

This paper investigates the application of digital image processing techniques to the detection of neurological disorder. Visual information extracted from the postures and movements of a human gait cycle can be used by an experienced neurologist to determine the mental health of the person. However, the current visual assessment of diagnosing neurological disorder is based very much on subjective observation, and hence the accuracy of diagnosis heavily relies on experience. Other diagnostic techniques employed involve the use of imaging systems which can only be operated under highly constructed environment. A prototype has been developed in this work that is able to capture the subjects gait on video in a relatively simple setup, and from which to process the selected frames of the gait in a computer. Based on the static visual features such as swing distances and joint angles of human limbs, the system identifies patients with Parkinsonism from the test subjects. To our knowledge, it is the first time swing distances are utilized and identified as an effective means for characterizing human gait. The experimental results have shown a promising potential in medical application to assist the clinicians in diagnosing Parkinsonism.

Network Lifetime Maximization in Wireless Visual Sensor Networks using a Distributed Algorithm

Network lifetime maximization is a critical issue in wireless sensor networks since each sensor has a limited energy supply. Different from conventional sensors, video sensors compress the captured video before transmission. The encoding processing demands high power consumption, thus raises challenges to maintain a long network lifetime. In this paper, we formulate the network lifetime maximization problem in wireless visual sensor networks, and propose a fully distributed algorithm to solve this problem. The proposed algorithm maximizes the network lifetime by jointly optimizing the encoding powers, the source rates, and the link rates.

A Lifetime-Enhanced Data Collecting Scheme for the Internet of Things

A backpressure-based data collecting scheme has been applied in the Internet of Things, which can control the network congestion effectively and increase the network throughput. However, there is an obvious shortcoming in the traditional backpressure data collecting scheme for the network service chain. It attempts to search all possible paths between source node and destination node in the networks, causing an unnecessary long path for data collection, which results in large end-toend delay and redundant energy consumption. To address this shortcoming of backpressure data collecting scheme in the Internet of Things, this article proposes an energy-aware and distance-aware data collecting scheme to enhance the lifetime of backpressure-based data collecting schemes. We propose an energy- and distance-based model that combines the factors of queue backlog, hop counts, and residual energy for making routing decisions. It not only reduces the unnecessary energy consumption, but also balances the residual energy. The experiment results show that the proposed scheme can reduce unnecessary energy consumption and end-to-end delay compared to the traditional and LIFO-based schemes. Meanwhile, it balances the energy of nodes and extends the lifetime of an Internet of Things.

Scientific Article Recommendation: Exploiting Common Author Relations and Historical Preferences

Scientific article recommender systems are playing an increasingly important role for researchers in retrieving scientific articles of interest in the coming era of big scholarly data. Most existing studies have designed unified methods for all target researchers and hence the same algorithms are run to generate recommendations for all researchers no matter which situations they are in. However, different researchers may have their own features and there might be corresponding methods for them resulting in better recommendations. In this paper, we propose a novel recommendation method which incorporates information on common author relations between articles (i.e., two articles with the same author(s)). The rationale underlying our method is that researchers often search articles published by the same author(s). Since not all researchers have such author-based search patterns, we present two features, which are defined based on information about pairwise articles with common author relations and frequently appeared authors, to determine target researchers for recommendation. Extensive experiments we performed on a real-world dataset demonstrate that the defined features are effective to determine relevant target researchers and the proposed method generates more accurate recommendations for relevant researchers when compared to a Baseline method.

Optimized Video Multicasting Over Wireless Ad Hoc Networks Using Distributed Algorithm

Recently there has been a compelling need to support real-time video multicast from a single source to multiple receivers in wireless ad hoc networks. The existing work uses tree-based schemes to perform video multicast. The optimization of those schemes typically requires a centralized computation, which is not suitable for wireless ad hoc networks. In this paper, we propose an optimized video multicast scheme over wireless ad hoc networks. First, we apply a prioritized coding scheme to enable the heterogeneous receivers to reconstruct the video at different quality levels. Then we formulate the video multicasting problem using the network model, the packet loss model, and the video distortion model. To solve the optimization problem, we propose a distributed algorithm to jointly optimize the source rate, the routing scheme, and the power allocation using hierarchical dual decompositions. The distributed nature of the proposed algorithm makes it very appropriate for wireless ad hoc networks. Through extensive simulations, we demonstrate that the proposed video multicast scheme can achieve much higher video quality compared to the uniform-power scheme or the tree-based routing schemes.

Identifying Anomalous Citations for Objective Evaluation of Scholarly Article Impact.

Evaluating the impact of a scholarly article is of great significance and has attracted great attentions. Although citation-based evaluation approaches have been widely used, these approaches face limitations e.g. in identifying anomalous citations patterns. This negligence would inevitably cause unfairness and inaccuracy to the article impact evaluation. In this study, in order to discover the anomalous citations and ensure the fairness and accuracy of research outcome evaluation, we investigate the citation relationships between articles using the following factors: collaboration times, the time span of collaboration, citing times and the time span of citing to weaken the relationship of Conflict of Interest (COI) in the citation network. Meanwhile, we study a special kind of COI, namely suspected COI relationship. Based on the COI relationship, we further bring forward the COIRank algorithm, an innovative scheme for accurately assessing the impact of an article. Our method distinguishes the citation strength, and utilizes PageRank and HITS algorithms to rank scholarly articles comprehensively. The experiments are conducted on the American Physical Society (APS) dataset. We find that about 80.88% articles contain contributed citations by co-authors in 26,366 articles and 75.55% articles among these articles are cited by the authors belonging to the same affiliation, indicating COI and suspected COI should not be ignored for evaluating impact of scientific papers objectively. Moreover, our experimental results demonstrate COIRank algorithm significantly outperforms the state-of-art solutions. The validity of our approach is verified by using the probability of Recommendation Intensity.