A Model-Based Approach to Evaluation of the Efficacy of FEC Coding in Combating Network Packet Losses

We propose a model-based analytic approach for evaluating the overall efficacy of FEC coding combined with interleaving in combating packet losses in IP networks. In particular, by modeling the network path in terms of a single bottleneck node, described as a G/M/1/K queue, we develop a recursive procedure for the exact evaluation of the packet-loss statistics for general arrival processes, based on the framework originally introduced by Cidon et al., 1993. To include the effects of interleaving, we incorporate a discrete-time Markov chain (DTMC) into our analytic framework. We study both single-session and multiple-session scenarios, and provide a simple algorithm for the more complicated multiple-session scenario. We show that the unified approach provides an integrated framework for exploring the tradeoffs between the key coding parameters; specifically, interleaving depths, channel coding rates and block lengths. The approach facilitates the selection of optimal coding strategies for different multimedia applications with various user quality-of-service (QoS) requirements and system constraints. We also provide an information-theoretic bound on the performance achievable with FEC coding in IP networks.     

Fusing semantic aspects for image annotation and retrieval

In this paper, we present an approach based on probabilistic latent semantic analysis (PLSA) to achieve the task of automatic image annotation and retrieval. In order to model training data precisely, each image is represented as a bag of visual words. Then a probabilistic framework is designed to capture semantic aspects from visual and textual modalities, respectively. Furthermore, an adaptive asymmetric learning algorithm is proposed to fuse these aspects. For each image document, the aspect distributions of different modalities are fused by multiplying different weights, which are determined by the visual representations of images. Consequently, the probabilistic framework can predict semantic annotation precisely for unseen images because it associates visual and textual modalities properly. We compare our approach with several state-of-the-art approaches on a standard Corel dataset. The experimental results show that our approach performs more effectively and accurately.     

A framework for automated cell tracking in phase contrast microscopic videos based on normal velocities

This paper introduces a novel framework for the automated tracking of cells, with a particular focus on the challenging situation of phase contrast microscopic videos. Our framework is based on a topology preserving variational segmentation approach applied to normal velocity components obtained from optical flow computations, which appears to yield robust tracking and automated extraction of cell trajectories. In order to obtain improved trackings of local shape features we discuss an additional correction step based on active contours and the image Laplacian which we optimize for an example class of transformed renal epithelial (MDCK-F) cells. We also test the framework for human melanoma cells and murine neutrophil granulocytes that were seeded on different types of extracellular matrices. The results are validated with manual tracking results.     

Specification and analysis of power-managed systems

Dynamic power management encompasses several techniques for reducing energy dissipation in electronic systems by selective slowdown or shutdown of components. We present a theoretical framework for explaining and classifying different approaches to power management. Within this framework, we model power-manageable components, workloads, and controllers as discrete-event systems (DESs). The structure of these DESs is specified in terms of physical states (representing operation modes) and events (triggering state transitions), while system behavior is specified in terms of next-event and next-state functions. In particular, nondeterministic next-event and next-state functions are modeled by conditional probability distributions, according to generalized semi-Markov processes (GSMPs). The modeling framework provides a general denotational model for system specification and a rigorous execution semantics that enables event-driven simulation. We introduce a modeling framework, built on top of MathWork's Simulink, supporting the specification and execution of our model. In particular, we present templates for the Simulink simulator to execute GSMP models, and we describe how to use such templates for specifying, analyzing, and optimizing dynamic power-managed systems. Finally, we demonstrate the expressive power and versatility of the proposed approach by using the modeling framework and the simulator for the analysis of representative real-life case studies, including the Intel Xscale processor architecture, a multitasking real-time system, and a sensor network.     

A Generic Framework for Mobility Modeling and Performance Analysis in Next-Generation Heterogeneous Wireless Networks

Integration of different wireless radio cellular technologies is emerging as an effective approach to accommodate the increasing demand of next-generation multimedia-based applications. In such systems user roaming among different technologies, commonly known as vertical handoff, will significantly affect different aspects of network design and planning due to the characteristically wide-ranging diversity in access technologies and supported applications. Hence, the development of new mobility models that accurately depict vertical mobility is crucial for studying different design problems in these heterogeneous systems. This article presents a generic framework for mobility modeling and performance analysis of integrated heterogeneous networks using phase-type distributions. This framework realizes all modeling requirements in next-generation user mobility including accuracy, analytical tractability, and accommodating the correlation between different residence times within different access technologies. Additionally, we present general guidelines to evaluate application performance based on the new mobility models introduced in this article. We show the accuracy of our modeling approach through simulation and analysis given different applications.     

A statistical modeling approach to location estimation

Some location estimation methods, such as the GPS satellite navigation system, require nonstandard features either in the mobile terminal or the network. Solutions based on generic technologies not intended for location estimation purposes, such as the cell-ID method in GSM/GPRS cellular networks, are usually problematic due to their inadequate location estimation accuracy. In order to enable accurate location estimation when only inaccurate measurements are available, we present an approach to location estimation that is different from the prevailing geometric one. We call our approach the statistical modeling approach. As an example application of the proposed statistical modeling framework, we present a location estimation method based on a statistical signal power model. We also present encouraging empirical results from simulated experiments supported by real-world field tests.     

Sink-to-sensors congestion control

The problem of congestion in sensor networks is significantly different from that of conventional ad-hoc networks and has not been studied to any great extent thus far. In this paper, we focus on providing congestion control from the sink to the sensors in a sensor field. We identify the different reasons for congestion from the sink to the sensors and show the uniqueness of the problem in sensor network environments. We propose a generic framework that addresses congestion from the sink to the sensors in a sensor network. Through ns2 based simulations, we evaluate the proposed approach and compare its performance with three baseline approaches.     

Extended kalman filtering for the modeling and analysis of ICG pharmacokinetics in cancerous tumors using NIR optical methods

Compartmental modeling of indocyanine green (ICG) pharmacokinetics, as measured by near infrared (NIR) techniques, has the potential to provide diagnostic information for tumor differentiation. In this paper, we present three different compartmental models to model the pharmacokinetics of ICG in cancerous tumors. We introduce a systematic and robust approach to model and analyze ICG pharmacokinetics based on the extended Kalman filtering (EKF) framework. The proposed EKF framework effectively models multiple-compartment and multiple-measurement systems in the presence of measurement noise and uncertainties in model dynamics. It provides simultaneous estimation of pharmacokinetic parameters and ICG concentrations in each compartment. Moreover, the recursive nature of the Kalman filter estimator potentially allows real-time monitoring of time varying pharmacokinetic rates and concentration changes in different compartments. Additionally, we introduce an information theoretic criteria for the best compartmental model order selection, and residual analysis for the statistical validation of the estimates. We tested our approach using the ICG concentration data acquired from four Fischer rats carrying adenocarcinoma tumor cells. Our study indicates that, in addition to the pharmacokinetic rates, the EKF model may provide parameters that may be useful for tumor differentiation.     

Multimodal hand gesture recognition combining temporal and pose information based on CNN descriptors and histogram of cumulative magnitudes

In this paper, we present a new approach for dynamic hand gesture recognition. Our goal is to integrate spatiotemporal features extracted from multimodal data captured by the Kinect sensor. In case the skeleton data is not provided, we apply a novel skeleton estimation method to compute temporal features. Furthermore, we introduce an effective method to extract a fixed number of keyframes to reduce the processing time. To extract pose features from RGB-D data, we take advantage of two different approaches: (1) Convolutional Neural Networks and (2) Histogram of Cumulative Magnitudes. We test different integration methods to fuse the extracted spatiotemporal features to boost recognition performance in a linear SVM classifier. Extensive experiments prove the effectiveness and feasibility of the proposed framework for hand gesture recognition.     

Automatic detection and recognition of signs from natural scenes

In this paper, we present an approach to automatic detection and recognition of signs from natural scenes, and its application to a sign translation task. The proposed approach embeds multiresolution and multiscale edge detection, adaptive searching, color analysis, and affine rectification in a hierarchical framework for sign detection, with different emphases at each phase to handle the text in different sizes, orientations, color distributions and backgrounds. We use affine rectification to recover deformation of the text regions caused by an inappropriate camera view angle. The procedure can significantly improve text detection rate and optical character recognition (OCR) accuracy. Instead of using binary information for OCR, we extract features from an intensity image directly. We propose a local intensity normalization method to effectively handle lighting variations, followed by a Gabor transform to obtain local features, and finally a linear discriminant analysis (LDA) method for feature selection. We have applied the approach in developing a Chinese sign translation system, which can automatically detect and recognize Chinese signs as input from a camera, and translate the recognized text into English.     

Computationally efficient mutual entity authentication in wireless sensor networks

Mutual entity authentication plays an important role in securing wireless sensor networks. In this paper, we propose a computationally efficient authentication framework, based on a well-studied problem—learning parity with noise (LPN). This kind of LPN-based authentication approaches only involve simplest bit-operations, which makes them suitable for resource-restrained sensor nodes. The proposed HB-hybrid framework introduces a new noise mode to prevent a general man-in-the-middle attack against previous LPN-based protocols, presents a gentle approach to securely combine several HB-like one-way authentication protocols and extends to mutual authentication which effectively thwarts the reflection attack. It spawns three specific protocols with different trade-offs between communication overload and memory cost. We extensively analyze their performance and security properties. Moreover, some optimization mechanisms on communication costs are given and the application scenarios for different wireless sensor networks are discussed at length.     

Optimisation of combined collaborative recommender systems

A new approach to collaborative user modelling is presented in this paper. We have developed a framework that can be used for easy testing of different concepts. We have also introduced three different areas where collaborative modelling can be further improved. For the first phase of testing, we have created a hybrid system based on three different collaborative recommender techniques. Since this system implements multiple collaboration techniques, we decided to call this approach Combined Collaborative Recommender. Although each prediction technique can produce adequate results, we have proved that the combination of these techniques into a unified system provides a much more stable system. It should also be pointed out that these analyses were done using a very large dataset (more than 2 million ratings) providing reliable results. Results of these optimisations are presented along with pointers for further development.     

Tandem Queue Models with Applications to QoS Routing in Multihop Wireless Networks

We consider the problem of quality of service (QoS) routing in multi-hop wireless networks where data are transmitted from a source node to a destination node via multiple hops. The routing component of a QoS-routing algorithm essentially involves the link and path metric calculation which depends on many factors such as the physical and link layer designs of the underlying wireless network, transmission errors due to channel fading and interference, etc. The task of link metric calculation basically requires us to solve a tandem queueing problem which is the focus of this paper. We present a unified tandem queue framework which is applicable for many different physical layer designs. We present both exact and approximated decomposition approaches. Using the queueing framework, we can derive different performance measures, namely, end-to-end loss rate, end-to-end average delay, and end-to-end delay distribution. The proposed decomposition approach is validated and some interesting insights into the system performance are highlighted. We then present how to use the decomposition queueing approach to calculate the link metric and incorporate this into the route discovery process of the QoS routing algorithm. The extension of the queueing and QoS routing framework to wireless networks with class-based queueing for QoS differentiation is also presented.     

ARCH-Based Traffic Forecasting and Dynamic Bandwidth Provisioning for Periodically Measured Nonstationary Traffic

Network providers are often interested in providing dynamically provisioned bandwidth to customers based on periodically measured nonstationary traffic while meeting service level agreements (SLAs). In this paper, we propose a dynamic bandwidth provisioning framework for such a situation. In order to have a good sense of nonstationary periodically measured traffic data, measurements were first collected over a period of three weeks excluding the weekends in three different months from an Internet access link. To characterize the traffic data rate dynamics of these data sets, we develop a seasonal autoregressive conditional heteroskedasticity (ARCH) based model with the innovation process (disturbances) generalized to the class of heavy-tailed distributions. We observed a strong empirical evidence for the proposed model. Based on the ARCH-model, we present a probability-hop forecasting algorithm, an augmented forecast mechanism using the confidence-bounds of the mean forecast value from the conditional forecast distribution. For bandwidth estimation, we present different bandwidth provisioning schemes that allocate or deallocate the bandwidth based on the traffic forecast generated by our forecasting algorithm. These provisioning schemes are developed to allow trade off between the underprovisioning and the utilization, while addressing the overhead cost of updating bandwidth. Based on extensive studies with three different data sets, we have found that our approach provides a robust dynamic bandwidth provisioning framework for real-world periodically measured nonstationary traffic.     

Manifold learning for face recognition under changing illumination

Appearance-based methods have been proven to be useful for face recognition tasks. The main problem with appearance-based methods originates from the multimodality of face images. It is known that images of different people in the original data space are more closely located to each other than those of the same person under different imaging conditions. In this paper, we propose a novel approach based on the nonlinear manifold embedding to define a linear subspace for illumination variations. This embedding based framework utilizes an optimization scheme to calculate the bases of the subspace. Since the optimization problem does not rely on the physical properties of the factor, the framework can also be used for other types of factors such as pose and expression. We obtained some promising recognition results under changing illumination conditions. Our error rates are comparable with state of art methods.     

An integrated routing and rate adaptation framework for multi-rate multi-hop wireless networks

In this paper, we propose a new integrated framework for joint routing and rate adaptation in multi-rate multi-hop wireless networks. Unlike many previous efforts, our framework considers several factors that affect end-to-end performance. Among these factors, the framework takes into account the effect of the relative positions of the links on a path when choosing the rates of operation and the importance of avoiding congested areas. The key element of our framework is a new comprehensive path metric that we call ETM (for expected transmission cost in multi-rate wireless networks). We analytically derive the ETM metric. We show that the ETM metric can be used to determine the best end-to-end path with a greedy routing approach. We also show that the metric can be used to dynamically select the best transmission rate for each link on the path via a dynamic programming approach. We implement the ETM-framework on an indoor wireless mesh network and compare its performance with that of frameworks based on the popular ETT and the recently proposed ETOP metrics. Our experiments demonstrate that the ETM-framework can yield throughput improvements of up to 253 and 368 % as compared with the ETT and ETOP frameworks.     

Engineering the embedded software radio

Software-defined radios allow flexible implementation of different basebands in digital signal processors. For communications transceivers this introduces an engineering problem: how to manage the different protocol stacks that may be required to work above the baseband software. To solve this, we propose a consistent software architectural framework for the dynamic implementation of these different protocols within an embedded environment. We discuss its requirements and present one possible approach based on STREAMS     

Tracking in Wireless Sensor Networks Using Particle Filtering: Physical Layer Considerations

In this paper, a new framework for target tracking in a wireless sensor network using particle filters is proposed. Under this framework, the imperfect nature of the wireless communication channels between sensors and the fusion center along with some physical layer design parameters of the network are incorporated in the tracking algorithm based on particle filters. We call this approach ldquochannel-aware particle filtering.rdquo Channel-aware particle filtering schemes are derived for different wireless channel models and receiver architectures. Furthermore, we derive the posterior Cramer-Rao lower bounds (PCRLBs) for our proposed channel-aware particle filters. Simulation results are presented to demonstrate that the tracking performance of the channel-aware particle filters can reach their theoretical performance bounds even with relatively small number of sensors and they have superior performance compared to channel-unaware particle filters.     

On max-min fair congestion control for multicast ABR service in ATM

In the ATM Forum activities, considerable efforts have focused on the congestion control of point-to-point available bit rate (ABR) service. We present a novel approach that extends existing point-to-point (unicast) congestion control protocols to a point-to-multipoint (multicast) environment. In particular, we establish a unified framework to derive a multicast congestion control protocol for an ABR service from a given rate-based unicast protocol. We generalize a known necessary and sufficient condition on the max-min fairness of unicast rate allocation for a multicast service. Using this condition, we show that the resulting multicast protocol derived using our framework preserves the fairness characteristics of the underlying unicast protocol. The practical significance of our approach is illustrated by extending a standard congestion control mechanism for an ABR service to a multicast environment. The performance of the resulting multicast protocol is examined using benchmark network configurations suggested by the traffic management subworking group at the ATM Forum, and simulation results are presented to substantiate our claims     

Graph-Based Semi-Supervised Learning and Spectral Kernel Design

In this paper, we consider a framework for semi-supervised learning using spectral decomposition-based unsupervised kernel design. We relate this approach to previously proposed semi-supervised learning methods on graphs. We examine various theoretical properties of such methods. In particular, we present learning bounds and derive optimal kernel representation by minimizing the bound. Based on the theoretical analysis, we are able to demonstrate why spectral kernel design based methods can improve the predictive performance. Empirical examples are included to illustrate the main consequences of our analysis.