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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. [ABSTRACT FROM AUTHOR]

We consider the point-to-point adaptive transmission of scalable video coding streams over a fading channel in energy harvesting (EH) aided communication systems, where the transmitter is equipped with an EH device and a rechargeable battery. The aim of this paper is to enable the transmitter having the ability of adaptively adjusting the number of video layers to be transmitted according to the available energy. Since the depletion of the energy in the battery acts as a trigger for the interruption of the video transmission, we define the energy starvation probability for equivalently characterizing a quality-of-experience (QoE) metric. We formulate the problem of EH-aided video transmission as a constrained optimization problem, maximizing the number of video layers for transmission while keeping the energy starvation probability below a threshold. Classic large deviation theory is invoked for estimating the energy starvation probability from online measurements. Consequently, our dynamic layer switch algorithm operates with no prior statistical knowledge of both the EH process and of the channel quality. Furthermore, the technique of perturbation analysis that constructs a perturbed sample path from an original sample path is invoked for improving the performance of the proposed method. Our experimental results verify that the algorithms 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. [ABSTRACT FROM AUTHOR]

Machine-to-machine (M2M) communications have many applications, such as in smart grid, health monitoring/alerting, and intelligent transportation. Due to unique characteristics that are different from the traditional human-to-human communications, numerous design challenges have to be tackled in M2M. A critical issue is that of dealing with the massive access of machine-type communication (MTC) devices while guaranteeing the desired quality of service (QoS). In this paper, we group MTC devices into clusters based on their QoS requirement. By utilizing the time-controlled M2M feature, we formulate the problem of massive access with QoS guarantee as a queuing problem. We derive an overflow probability estimation model by applying a large deviation principle and then propose an online-measurement-based adaptive massive access management for deciding the amount of resources allocated for each cluster. The proposed method bases the decision on the observed traffic workload with no prior knowledge of its statistics. We conduct the simulations in multicluster scenarios with distinct maximum tolerable delay and corresponding acceptable probability in a long-term evolution system. The simulation results show the adaptability of the proposed massive access management regime to the variations in the traffic workload; thus, it is capable of accommodating dynamic propagation conditions and unknown traffic characteristics, particularly in burst-by-burst communications. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

Wireless Multimedia service faces the difficulty of QoS decrease caused by the cache underflow, due to both the unreliable and time-varying nature of the wireless channel. In order to reduce the probability of the cache outage, a statistical model is established to estimate the underflow time of the cache. Based on the estimation of the underflow time, an adaptive playout algorithm with dualthreshold to adjust the frame rate is presented. This approach adjusts the threshold dynamically according to the channel conditions and the status of the cache, and thus regulates the frame rate timely. Moreover, the algorithm possesses scene-aware characteristics, since it adjusts the playout rate according to the motion intensity of the playout scenes to reduce the underflow probability of the cache and provide better QoS. Simulation results show that as compared to other adaptive media playout algorithms, the proposed algorithm efficiently reduces the probability of the cache underflow, especially in low rate channel and timevarying rate environment, and provides better visual quality1. [ABSTRACT FROM AUTHOR]