Ling Guan
Ryerson University
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Publication
Featured researches published by Ling Guan.
IEEE Transactions on Smart Grid | 2012
Yifeng He; Bala Venkatesh; Ling Guan
The vehicle electrification will have a significant impact on the power grid due to the increase in electricity consumption. It is important to perform intelligent scheduling for charging and discharging of electric vehicles (EVs). However, there are two major challenges in the scheduling problem. First, it is challenging to find the globally optimal scheduling solution which can minimize the total cost. Second, it is difficult to find a distributed scheduling scheme which can handle a large population and the random arrivals of the EVs. In this paper, we propose a globally optimal scheduling scheme and a locally optimal scheduling scheme for EV charging and discharging. We first formulate a global scheduling optimization problem, in which the charging powers are optimized to minimize the total cost of all EVs which perform charging and discharging during the day. The globally optimal solution provides the globally minimal total cost. However, the globally optimal scheduling scheme is impractical since it requires the information on the future base loads and the arrival times and the charging periods of the EVs that will arrive in the future time of the day. To develop a practical scheduling scheme, we then formulate a local scheduling optimization problem, which aims to minimize the total cost of the EVs in the current ongoing EV set in the local group. The locally optimal scheduling scheme is not only scalable to a large EV population but also resilient to the dynamic EV arrivals. Through simulations, we demonstrate that the locally optimal scheduling scheme can achieve a close performance compared to the globally optimal scheduling scheme.
multimedia signal processing | 2011
Xiaoming Nan; Yifeng He; Ling Guan
Multimedia cloud, as a specific cloud paradigm, addresses how cloud can effectively process multimedia services and provide QoS provisioning for multimedia applications. There are two major challenges in multimedia cloud. The first challenge is the service response time in multimedia cloud, and the second challenge is the cost of cloud resources. In this paper, we optimize resource allocation for multimedia cloud based on queuing model. Specifically, we optimize the resource allocation in both single-class service case and multiple-class service case. In each case, we formulate and solve the response time minimization problem and resource cost minimization problem, respectively. Simulation results demonstrate that the proposed optimal allocation scheme can optimally utilize the cloud resources to achieve a minimal mean response time or a minimal resource cost.
IEEE Transactions on Circuits and Systems for Video Technology | 2004
Richard D. Green; Ling Guan
Research into tracking and recognizing human movement has so far been mostly limited to gait or frontal posing. Part I of this paper presents a continuous human movement recognition (CHMR) framework which forms a basis for the general biometric analysis of continuous human motion as demonstrated through tracking and recognition of hundreds of skills from gait to twisting saltos. Part II of this paper presents CHMR applications to the biometric authentication of gait, anthropometric data, human activities, and movement disorders. In Part I of this paper, a novel three-dimensional color clone-body-model is dynamically sized and texture mapped to each person for more robust tracking of both edges and textured regions. Tracking is further stabilized by estimating the joint angles for the next frame using a forward smoothing particle filter with the search space optimized by utilizing feedback from the CHMR system. A new paradigm defines an alphabet of dynemes, units of full-body movement skills, to enable recognition of diverse skills. Using multiple hidden Markov models, the CHMR system attempts to infer the human movement skill that could have produced the observed sequence of dynemes. The novel clone-body-model and dyneme paradigm presented in this paper enable the CHMR system to track and recognize hundreds of full-body movement skills, thus laying the basis for effective biometric authentication associated with full-body motion and body proportions.
IEEE Transactions on Neural Networks | 2002
Paisarn Muneesawang; Ling Guan
In this paper, an unsupervised learning network is explored to incorporate a self-learning capability into image retrieval systems. Our proposal is a new attempt to automate recursive content-based image retrieval. The adoption of a self-organizing tree map (SOTM) is introduced, to minimize the user participation in an effort to automate interactive retrieval. The automatic learning mode has been applied to optimize the relevance feedback (RF) method and the single radial basis function-based RF method. In addition, a semiautomatic version is proposed to support retrieval with different user subjectivities. Image similarity is evaluated by a nonlinear model, which performs discrimination based on local analysis. Experimental results show robust and accurate performance by the proposed method, as compared with conventional noninteractive content-based image retrieval (CBIR) systems and user controlled interactive systems, when applied to image retrieval in compressed and uncompressed image databases.
IEEE Transactions on Circuits and Systems for Video Technology | 2009
Yifeng He; Intae Lee; Ling Guan
Network lifetime maximization is a critical issue in wireless sensor networks since each sensor has a limited energy supply. In contrast with conventional sensor networks, video sensor nodes compress the video before transmission. The encoding process demands a high power consumption, and thus raises a great challenge to the maintenance of a long network lifetime. In this paper, we examine a strategy for maximizing the network lifetime in wireless visual sensor networks by jointly optimizing the source rates, the encoding powers, and the routing scheme. Fully distributed algorithms are developed using the Lagrangian duality to solve the lifetime maximization problem. We also examine the relationship between the collected video quality and the maximal network lifetime. Through extensive numerical simulations, we demonstrate that the proposed algorithm can achieve a much longer network lifetime compared to the scheme optimized for the conventional wireless sensor networks.
IEEE Transactions on Multimedia | 2001
Min Wu; Robert A. Joyce; Hau-San Wong; Ling Guan; Sun-Yuan Kung
The reliable and efficient transmission of high-quality variable bit rate (VBR) video through the Internet generally requires network resources be allocated in a dynamic fashion. This includes the determination of when to renegotiate for network resources, as well as how much to request at a given time. The accuracy of any resource request method depends critically on its prediction of future traffic patterns. Such a prediction can be performed using the content and traffic information of short video segments. This paper presents a systematic approach to select the best features for prediction, indicating that while content is important in predicting the bandwidth of a video hit stream, the use of both content and available short-term bandwidth statistics can yield significant improvements. A new framework for traffic prediction is proposed in this paper; experimental results show a smaller mean-square resource prediction error and higher overall link utilization.
Archive | 2000
Ling Guan; Sun-Yuan Kung; Jan Larsen
From the Publisher: Multimedia stands as one of the most challenging and excitingaspects of the information era. Although there are books available that deal with various facets of multimedia, the field has urgently needed a comprehensive look at recent developments in the systems, processing, and applications of image and video data in a multimedia environment. Multimedia Image and Video Processing fills that need. Beginning with existing standards and their impact on multimedia image and video processing, experts from around the world address a broad spectrum of topics in a tutorial style. Their authoritative contributions cover the pros and cons of current and new architectures, conventional and intelligent image processing techniques, new developments in the compression and coding of video and images, and content-based image and video retrieval. The books final chapters examine new results in multimedia applications, including transcoding for multipoint video conferencing, distance education, video-on-demand and telemedicine. The extremely rapid growth of this field means that books even just a few years old may offer information that is already obsolete. Multimedia Image and Video Processing offers not only state-of-the-art research and developments, but does so in a way that provides a solid introduction to each topic and builds a basis for future study, research, and development.
IEEE Transactions on Multimedia | 2012
Yongjin Wang; Ling Guan; Anastasios N. Venetsanopoulos
In this paper, we investigate kernel based methods for multimodal information analysis and fusion. We introduce a novel approach, kernel cross-modal factor analysis, which identifies the optimal transformations that are capable of representing the coupled patterns between two different subsets of features by minimizing the Frobenius norm in the transformed domain. The kernel trick is utilized for modeling the nonlinear relationship between two multidimensional variables. We examine and compare with kernel canonical correlation analysis which finds projection directions that maximize the correlation between two modalities, and kernel matrix fusion which integrates the kernel matrices of respective modalities through algebraic operations. The performance of the introduced method is evaluated on an audiovisual based bimodal emotion recognition problem. We first perform feature extraction from the audio and visual channels respectively. The presented approaches are then utilized to analyze the cross-modal relationship between audio and visual features. A hidden Markov model is subsequently applied for characterizing the statistical dependence across successive time segments, and identifying the inherent temporal structure of the features in the transformed domain. The effectiveness of the proposed solution is demonstrated through extensive experimentation.
IEEE Transactions on Mobile Computing | 2011
Yifeng He; Wenwu Zhu; Ling Guan
Pervasive health monitoring is an eHealth service, which plays an important role in prevention and early detection of diseases. There are two major challenges in pervasive health monitoring systems with Body Sensor Networks (BSNs). The first challenge is the sustainable power supply for BSNs. The second challenge is Quality of Service (QoS) guarantee for the delivery of data streams. In this paper, we optimize the resource allocations to provide a sustainable and high-quality service in health monitoring systems. Specifically, we formulate and solve two resource optimization problems, respectively. In the first optimization problem, steady-rate optimization problem, we optimize the source rate at each sensor to minimize the rate fluctuation with respect to the average sustainable rate, subject to the requirement of uninterrupted service. The first optimization problem is solved by a proposed analytical solution. The second optimization problem is formulated based on the optimal source rates of the sensors obtained in the steady-rate optimization problem. In the second optimization problem, we jointly optimize the transmission power and the transmission rate at each aggregator to provide QoS guarantee to data delivery. The second optimization problem is converted into a convex optimization problem, which is then solved efficiently. In the simulations, we demonstrate that the proposed optimized scheme enables the pervasive health monitoring system to provide a sustainable service with guaranteed low delay and low Packet Loss Rate (PLR) to subscribers.
multimedia signal processing | 2004
Yongjin Wang; Ling Guan
This paper presents our recent work on recognizing human emotion from the speech signal. The proposed recognition system was tested over a language, speaker, and context independent emotional speech database. Prosodic, Mel-frequency cepstral coefficient (MFCC), and formant frequency features are extracted from the speech utterances. We perform feature selection by using the stepwise method based on Mahalanobis distance. The selected features are used to classify the speeches into their corresponding emotional classes. Different classification algorithms including maximum likelihood classifier (MLC), Gaussian mixture model (GMM), neural network (NN), K-nearest neighbors (K-NN), and Fishers linear discriminant analysis (FLDA) are compared in this study. The recognition results show that FLDA gives the best recognition accuracy by using the selected features.