Counting on-road vehicles in the highway is fundamental for intelligent transportation management. This paper presents the first highway vehicle counting method in compressed domain, aiming at achieving comparable estimation performance with the pixel-domain methods. Counting in compressed domain is rather challenging due to limited information about vehicles and large variance in vehicle numbers. To address this problem, we develop new low-level features to mitigate the challenge from insufficient information in compressed videos. The new proposed features can be easily extracted from the coding-related metadata. Then, we propose a hierarchical classification-based regression (HCR) model to estimate the number of vehicles from the compressed-domain low-level features for individual frame. HCR hierarchically divides the traffic scenes into different cases according to the density of vehicles such that the large variance of traffic scenes can be effectively captured. Beside the spatial regression in each frame, we propose a locally temporal regression model to further refine the counting results, which exploits the continuous variation characteristics of the traffic flow. We extensively evaluate the proposed method on real highway surveillance videos. The experimental results consistently show that the proposed method is very competitive compared with the pixel-domain methods, which can reach similar performance with much lower computational cost.

Energy-efficient streaming of real-time scalable video over wireless fading channels with imperfect channel state information is considered in this paper. A traffic rate adaptation (TRA) scheme is presented to minimize the transmission energy consumption while satisfying the quality of experience (QoE) requirement by dynamically selecting the delivered encoding layers of streaming media depending on the channel condition. A partially observable Markov decision process model is introduced to formulate the energy-efficient QoE-guaranteed TRA problem. Based on the Lagrange method of multipliers, an online policy iteration algorithm is proposed to solve the constrained optimization problem. This algorithm performs policy evaluation and improvement without intensive computations and prior knowledge of channel statistics. The adaptability to unknown environments and high computation efficiency make the proposed scheme attractive for implementation in real-life mobile systems. Simulation results demonstrate the effectiveness of the proposed method.

This paper considers adaptive transmission of Scalable Video Coding (SVC) stream over a fading channel in Energy Harvesting (EH) aided communication systems. The aim is to enable the transmitter having the ability of adaptively adjusting the number of video layers to be transmitted based on the available energy. Since the depletion of the energy in the battery impacts the interruption of the video transmission, the energy starvation probability is defined for equivalently characterizing a Quality-of-Experience (QoE) metric. We formulate the problem of EH-aided video transmission as maximizing the number of video layers for transmission while keeping the energy starvation probability below a threshold. Classic large deviation theory is applied to estimate the energy starvation probability from online measurements. The simulation results verify that the algorithm proposed have the adaptation capability to accommodate both the energy-dynamics and the channel-dynamics for improving the video quality subject to an unobjectionable level of transmission interruption rate.

In an IaaS cloud, virtual machines (VMs), also called instances, may be classified as reserved instances and on-demand instances. The reserved instances having long-term commitments and one-time payment are appropriate for the steady or predictable workloads, while for short-term, spiky or unpredictable workloads, the on-demand instances having flexible hourly payment and no long-term commitments may be more suitable for reducing the cost. In this paper, we consider the economical provisioning of reserved and/or on-demand instances for meeting time-varying computing workload of compute-intensive applications. In order to achieve this, we conceive a strategy for determining the amount of the purchased instances dynamically in order to minimize the total computing cost while keeping quality-of-service (QoS). By mapping QoS as the overload probability, we propose a dynamic instance provisioning strategy based on the large deviation principle, which is capable of calculating the minimum number of instances for the upcoming demands subject to the overload probability below a desired threshold. In addition, a reserved instance provisioning strategy for further reducing the total cost is also proposed by applying the autoregressive (AR) model to calculate the number of reserved instances for the average computation requirements. Finally, the simulations are performed based on real workload traces to show the attainable performance of the proposed instance provisioning strategy for the computing service in an IaaS cloud.

This paper considers the problem of opportunistically accessing a wide range of frequency band in which multiple subbands may be occupied. A major obstacle to utilizing such wideband spectrum is that performing Nyquist sampling on the wideband signal is either infeasible or too expensive. We propose an adaptive energy-constrained sensing scheme based on sub-Nyquist sampling and stochastic control theory. In contrast to the existing sub-Nyquist approaches, we select the subband that has high probability to be idle based on the sub-Nyquist samples and spectrum prediction, without reconstructing the wideband signal. The sensing process is formulated as a constrained partially observable Markov decision process to exploit the statistical characteristics of the wideband signal, and a simulation-based gradient algorithm is proposed to compute the optimal adaptive sensing policy. The algorithm is shown to converge to the optimal solution with probability one. Simulation results show that with low computational complexity, the adaptive sensing policy performs well even in the crowded spectrum with low SNR.

This paper presents a highway vehicle counting method in compressed domain, aiming at achieving acceptable estimation performance approaching the pixel-domain methods. Such a task essentially is challenging because the available information (e.g. motion vector) to describe vehicles in videos is quite limited and inaccurate, and the vehicle count in realistic traffic scenes always varies greatly. To tackle this issue, we first develop a batch of low-level features, which can be extracted from the encoding metadata of videos, to mitigate the informational insufficiency of compressed videos. Then we propose a Hierarchical Classification based Regression (HCR) model to estimate the vehicle count from features. HCR hierarchically divides the traffic scenes into different cases according to vehicle density, such that the broad-variation characteristics of traffic scenes can be better approximated. Finally, we evaluated the proposed method on the real highway surveillance videos. The results show that our method is very competitive to the pixel-domain methods, which can reach similar performance along with its lower complexity.

In future mobile computing systems, cognitive radio (CR) emerges as a promising solution for alleviating spectrum shortage and satisfying the high bandwidth demand of multimedia streaming, while it presents tough challenges in provisioning user experience-perceived quality of service (QoS) and conserving transmission energy. In this paper, an adaptive rate control (ARC) scheme with the aid of the receive buffer is presented for energy-efficient transmission of streaming media over CR with QoS guarantee. The QoS metric, either display smoothness or transmission delay, is quantified by the state of the receive buffer. Cross-layer information is utilized to form a closed-loop feedback optimal control. A novel analytical model called event-driven discrete-time Markov control process is introduced to formulate the QoS-guaranteed energy-efficient ARC problem. Based on potential theory, a policy iteration algorithm that combines potentials estimation and stochastic approximation is proposed for finding the optimal policy online. By exploiting the system dynamics, this algorithm does not depend on any prior knowledge of channel availability or fading statistics, and it can converge to the global optimum with a low computational cost and reasonable speed. Simulation results demonstrate the effectiveness of the proposed method.

In this paper, we consider the adaptive transmission problem of scalable video in an energy harvesting communication system. The stochastic nature of the harvested energy puts a new challenge on the video transmission. Against this challenge, we formulate the adaptive scalable video transmission problem as maximizing the time average quality of the transmitted video subject to the energy constraint for reducing the playback interruption and the video quality smoothness constraint. In order to solve this problem, the Lyapunov optimization method is applied to derive an online dynamic layer transmission algorithm (DLTA). The simulation results show that the proposed DLTA can achieve better performance in terms of the received video quality and the convergence rate than a conventional reinforcement learning algorithm like the Q-learning method. It is also illustrated that the energy and smoothness constraints are beneficial for controlling the behavior of DLTA.

Generalised eigendecomposition problem for a symmetric matrix pencil is reinterpreted as an unconstrained minimisation problem with a weighted non‐linear criterion. The analytical results show that the proposed criterion has a unique global minimum which corresponds to the principal generalised eigenvectors, thus guaranteeing the global convergence via iterative methods to search the minimum. A gradient‐based adaptive algorithm and a fixed point iteration‐based adaptive algorithm are derived for the generalised eigendecomposition, which both work in parallel and avoid the error propagation effect of sequential‐type algorithms. By applying the stochastic approximation theory, the global convergence of the proposed adaptive algorithm is proved. The performance of the proposed method is evaluated by simulations in terms of convergence rate, estimation accuracy as well as tracking capability.

We consider adaptively extracting multiple principal generalized eigenvectors, which can be widely applied in modern signal processing. By using the deflation technique, the problem is reformulated into an unconstrained minimization problem. An adaptive sequential algorithm based on the Newton method is proposed to solve this problem. To improve its real-time performance, a parallel version of this algorithm is provided on the basis of certain approximation. Furthermore, a two-layer neural network is constructed to execute the adaptive algorithm. The asymptotic convergence of this algorithm is rigorously proved by stochastic approximation theory. The simulation results demonstrate the effectiveness of the proposed algorithms.

A receiver-driven adaptive layer switching algorithm is proposed for adapting the video bitrate to match the achievable network throughput. It relies on a QoS-constrained equivalent bandwidth estimator employed at the receiver, which is used for triggering the adjustment of video layers at the video source. Simulations are conducted to illustrate its efficiency by showing that it is capable of accommodating different channel qualities without their prior knowledge.

The feature coding-pooling framework is shown to perform well in image classification tasks, because it can generate discriminative and robust image representations. The unavoidable information loss incurred by feature quantization in the coding process and the undesired dependence of pooling on the image spatial layout, however, may severely limit the classification. In this paper, we propose a linear distance coding (LDC) method to capture the discriminative information lost in traditional coding methods while simultaneously alleviating the dependence of pooling on the image spatial layout. The core of the LDC lies in transforming local features of an image into more discriminative distance vectors, where the robust image-to-class distance is employed. These distance vectors are further encoded into sparse codes to capture the salient features of the image. The LDC is theoretically and experimentally shown to be complementary to the traditional coding methods, and thus their combination can achieve higher classification accuracy. We demonstrate the effectiveness of LDC on six data sets, two of each of three types (specific object, scene, and general object), i.e., Flower 102 and PFID 61, Scene 15 and Indoor 67, Caltech 101 and Caltech 256. The results show that our method generally outperforms the traditional coding methods, and achieves or is comparable to the state-of-the-art performance on these data sets.

In this paper, we propose an object-aware holistic superpixel selection (HPS) method to automatically select the discriminative superpixels of an image for image classification purpose. Through only considering the selected superpixels, the interference of cluttered background on the object can be alleviated effectively and thus the classification performance is significantly enhanced. In particular, for an image, HPS first selects the discriminative superpixels for the characteristics of certain class, which can together match the object template of this class well. In addition, these superpixels compose a class-specific matching region. Through performing such superpixel selection for several most probable classes, respectively, HPS generates multiple class-specific matching regions for a single image. Then, HPS merges these matching regions into an integral object region through exploiting their pixel-level intersection information. Finally, such object region instead of the original image is used for image classification. An appealing advantage of HPS is the ability to alleviate the interference of cluttered background yet not require the object to be segmented out accurately. We evaluate the proposed HPS on four challenging image classification benchmark datasets: Oxford-IIIT PET 37, Caltech-UCSD Birds 200, Caltech 101, and PASCAL VOC 2011. The experimental results consistently show that the proposed HPS can remarkably improve the classification performance.

A class of discrete-time Markovian jumping systems with mode-dependent delays and actuators saturation is investigated in this paper. Sufficient conditions for local stochastic stability is presented, and an estimate of domain of attraction in mean square (DOAMS for short) is provided for the systems. Controller design problems for both mode-dependent and mode-independent cases are considered. A globally convergent algorithm including convex optimization is given to construct corresponding effective controllers. Finally, a numerical example is presented to show the effectiveness of the results.

This paper considers the problem of dynamic cluster reconfiguration for computation intensive services. In order to provide a quality-of-service in terms of overload probability, we formulate the problem of energy consumption as a constrained optimization problem, i.e., minimizing the number of active servers to reduce the energy consumption while keeping the overload probability below a desired threshold. An overload probability estimation model is derived by applying large deviation principle, and an online measurement based algorithm is developed to decide the number of servers to power on/off, which makes decision based on current workload without any prior knowledge of the workload statistics. Moreover, the proposed dynamic cluster reconfiguration algorithm iteratively adjusts the number of the active servers, instead of directly determining the number of active servers that is hard to guarantee optimality for the nonstationary workloads. Since the distribution of the workloads among the servers has an impact on potential active servers to turn off, a server scheduling strategy is proposed to collaborate with the proposed decision algorithm to achieve better energy conservation. In order to provide an integrated solution, we present an event model-based implementation to demonstrate the practical application of the proposed approach. Finally, we evaluate the performance of the scheme by using real workloads. The experimental results show the adaptability of the proposed approach to the variations in the workload and robustness of quality-of-service for nonstationary workloads.

An analytical model for streaming multiple heterogeneous multimedia services over home networks is established,in which the quality of service (QoS) provisioning problems are investigated. The goal is to simultaneously support as many service streams as possible, and to optimally guarantee all users' QoS within residential environments. A novel admission control algorithm, in conjunction with a quality adaptation scheme is proposed, which is capable of effectively guaranteeing and improving the service quality. Upon examination of the simulation results, we conclude that the proposed model and schemes provide valuable guidelines for the planning of future home networks and multimedia systems.

Readahead is an important technique to deal with the huge gap between disk drives and applications. It has become a standard in modern operating systems and advanced storage systems. However, it is difficult to develop the common kernel readahead and to achieve full testing coverage of all cases. In this paper, we formulate the kernel read handling, caching and readahead behavior as an absorbing Markov decision process, and present performance evaluations to compare or verify various readaheads. We also introduce algorithms to find optimal prefetching policies for specific read pattern and present the convergence analysis. By exploiting sample-path-based methods, it becomes much easier to evaluate and optimize prefetching policies, which can provide valuable informations to help the design and improvement of practical readaheads. For illustration and verification, we present two examples and some experiments on the readaheads in Linux kernel. The results show that the model-based evaluations agree with the practice and the improved prefetching policy significantly outperforms the original one in Linux kernel for the specified workloads.

Adaptive media playout (AMP) control is proposed in order to compensate for the bit-rate fluctuation of networks, which may result in playout interruptions in video streaming application. Most AMP algorithms found in the literature trigger playout-rate adjustments based on the buffer fullness or its variation. However, the challenge of the threshold based methods is to select the appropriate threshold for triggering a playout-rate adjustment owing to the unknown fluctuation of the channel quality and the video bitrate. We conceive an adaptive media playout regime based on underflow probability estimation, which requires no significant statistical knowledge of the previous tele-traffic load. To achieve this, we present an underflow probability estimation model based on large deviation theory relying on the buffer fullness and on its variation. We will then directly use the underflow probability to trigger the actions of playout control, instead of using indirect methods based on a buffer fullness threshold or buffer fullness variation threshold. Experiments based on MPEG-4 Variable Bit-Rate encoded video and VBR channels associated with Adaptive Modulation and Coding are conducted in order to investigate the achievable performance of the proposed algorithm. Our simulation results demonstrate an improved performance in comparison to other recent AMP algorithms.

In this brief, we consider extracting generalized eigenvectors in parallel for the generalized eigendecomposition problem. The problem is formulated as an optimization problem of minimizing an unconstrained quartic cost function based on the weighted rule. It is shown that the proposed weighted cost function has a unique global minimum, which corresponds to the principal generalized eigenvectors. In order to estimate the principal generalized eigenvector matrix efficiently, we simplify the quartic cost function as a quadric one by making an appropriate approximation, and then derive a fast algorithm for extracting the principal generalized eigenvector in parallel. We also show the application of the proposed algorithm in blind source separation. Numerical simulations are performed, and the results demonstrate the performance of the proposed algorithm.

As a trade-off technique, bandwidth smoothing can reduce the client buffer requirements and simultaneously keep transmission scheme as smooth as possible. In this paper, bandwidth smoothing is formulated into a binary classification problem of the underflow and overflow points. We propose a novel method to solve that problem based on support vector machine (SVM). Our method is proven to be able to achieve the minimum buffer requirements of constant rate transmission and transport. Furthermore, it directly computes the transmission rate without exhaustively searching buffer size and startup delay. Besides this method, this paper provides a systematic video segmentation algorithm, which can intelligently partition the playback curve into some unequal segments to naturally track the trends of playback curve. The smoothing results with the playback curve ofy=xndemonstrate that this video systematic segmentation requires smaller than half of the buffer of the equal segmentation algorithm. Finally, we construct a generalized piecewise constant rate transmission and transport algorithm with SVM and the systematic video segmentation method. The experiments of some real MPEG4 and H.264 video data confirmed the efficiency of our proposed algorithm.

A class of resource location service for distributed VoD system, which combines one-hop k-random walk and global centralized indexing service, is studied. First, in order to minimizing the cost of communication and guaranteeing the response time performance, a Markov model is proposed to describe the queue phenomenon, admission control and the process of location. In this model, control is related with not only states but also events, which introduce more information as the control basis. Then, an optimization algorithm that combines policy gradient estimation and stochastic approximation is proposed. This algorithm can deal with constraints and depend on no system parameter. Finally, an illustrative simulation is performed to demonstrate the effectiveness of model and algorithm.

The sensitivity-based optimization of Markov systems has become an increasingly important area. From the perspective of performance sensitivity analysis, policy-iteration algorithms and gradient estimation methods can be directly obtained for Markov decision processes (MDPs). In this correspondence, the sensitivity-based optimization is extended to average reward partially observable MDPs (POMDPs). We derive the performance-difference and performance-derivative formulas of POMDPs. On the basis of the performance-derivative formula, we present a new method to estimate the performance gradients. From the performance-difference formula, we obtain a sufficient optimality condition without the discounted reward formulation. We also propose a policy-iteration algorithm to obtain a nearly optimal finite-state-controller policy.

In this letter, we study the problem of adaptive retrieval of multiple sinusoids in white noise. The frequency estimation problem can be reformulated as an unconstrained optimization problem. From the proposed unconstrained cost function, a numerically robust and low complexity modified Newton algorithm is derived for tracking frequencies. A rigorous convergence analysis of the proposed algorithm by adopting Ljung's ordinary differential equation approach is presented. Simulation results show that the proposed adaptive frequency estimation algorithm has fast convergence and excellent tracking capability in nonstationary environment.