With the advent of high speed links, online flow measurement for, e.g., flow round trip time (RTT), has become difficult due to the enormous demands placed on computational resources. Most existing measurement methods are designed to count the numbers of flows or sizes of flows, but we address the flow RTT measurement, which is an important QoS metric for network management and cannot be measured with existing measurement methods. We first adapt a standard Bloom Filter (BF) for the flow RTT distribution estimation. However, due to the existence of multipath routing and Syn flooding attacks, the standard BF does not perform well. We further design the double-deletion bloom filter (DDBF) scheme, which alleviates potential hash collisions of the standard BF by explicitly deleting used records and implicitly deleting out-of-date records. Because of these double deletion operations, the DDBF accurately estimates the RTT distribution of TCP flows with limited memory space, even with the appearance of multipath routing and Syn flooding attacks. Theoretical analysis indicates that the DDBF scheme achieves a higher accuracy with a constant and smaller amount of memory compared with the standard BF. In addition, we validate our scheme using real traces and demonstrate significant memory-savings without degrading accuracy.

In dense femtocell networks (DFNs), one of the main issues is interference management since interference between femtocell access points (FAPs) reduces the system performance significantly. Further, FAPs serve different numbers of femtocell user equipments (FUEs), i.e., some FAPs have more than one FUE while others have one or no FUEs. Therefore, for DFNs, an intelligent channel assignment scheme is necessary considering both the number of FUEs connected to the same FAPs and interference mitigation to improve system performance. This paper proposes a two-stage dynamic channel assignment (TS-DCA) scheme for downlink DFNs based on orthogonal frequency division multiple access/frequency division duplex (OFDMA/FDD). In stage 1, using graph coloring algorithm, a femtocell gateway (FGW) first groups FUEs based on an interference graph that considers different numbers of FUEs per FAP. Then, in stage 2, the FGW dynamically assigns subchannels to FUE clusters according to the order of maximum capacity of FAP clusters. In addition, FAPs adaptively assign remaining subchannels in FUE clusters to their FUEs in other FUE clusters. Through simulations, we first find optimum parameters of the FUE clustering to maximize the system capacity and then evaluate system performance in terms of the mean FUE capacity, unsatisfied FUE probability, and mean FAP transmission energy consumption according to the different numbers of FUEs and FAPs with a given FUE traffic load.

To enhance the throughput of wireless local area networks (WLANs) by efficiently utilizing the radio resource, a distributed coordination function-based (DCF-based) orthogonal frequency division multiple access (OFDMA) WLAN system has been proposed. In the system, since each OFDMA sub-channel is assigned to the associated station with the highest channel gain, the transmission rate of DATA frames can be enhanced thanks to multi-user diversity. However, the optimum allocation of OFDMA sub-channels requires the estimation of channel state information (CSI) of all associated stations, and this incurs excessive signaling overhead. As the number of associated stations increases, the signaling overhead severely degrades the throughput of DCF-based OFDMA WLAN. To reduce the signaling overhead while obtaining a sufficient diversity gain, this paper proposes a channel access scheme that performs multiple DCF-based channel access. The key idea of the proposed scheme is to introduce additional DCF-based prioritized access along with the traditional DCF-based random access. In the additional DCF-based prioritized access, by dynamically adjusting contention window size according to the CSI of each station, only the stations with better channel state inform their CSI to the access point (AP), and the signaling overhead can be reduced while maintaining high multi-user diversity gain. Numerical results confirm that the proposed channel access scheme enhances the throughput of OFDMA WLAN.

The applicability of at-speed scan-based logic built-in self-test (BIST) is being severely challenged by excessive capture power that may cause erroneous test responses even for good circuits. Different from conventional low-power BIST, this paper is the first to explicitly focus on achieving capture power safety with a novel and practical scheme, called capture-power-safe logic BIST (CPS-LBIST). The basic idea is to identify all possibly-erroneous test responses caused by excessive capture power and use the well-known approach of masking (bit-masking, slice-masking,vector-masking) to block them from reaching the multiple-input signature register(MISR). Experiments with large benchmark circuits and a large industrial circuit demonstrate that CPS-LBIST can achieve capture power safety with negligible impact on test quality and circuit overhead.

The Hough voting framework is a popular approach to parts based pedestrian detection. It works by allowing image features to vote for the positions and scales of pedestrians within a test image. Each vote is cast independently from other votes, which allows for strong occlusion robustness. However this approach can produce false pedestrian detections by accumulating votes inconsistent with each other, especially in cluttered scenes such as typical street scenes. This work aims to reduce the sensibility to clutter in the Hough voting framework. Our idea is to use object segmentation and object pose parameters to enforce votes' consistency both at training and testing time. Specifically, we use segmentation and pose parameters to guide the learning of a pedestrian model able to cast mutually consistent votes. At test time, each candidate detection's support votes are looked upon from a segmentation and pose viewpoints to measure their level of agreement. We show that this measure provides an efficient way to discriminate between true and false detections. We tested our method on four challenging pedestrian datasets. Our method shows clear improvements over the original Hough based detectors and performs on par with recent enhanced Hough based detectors. In addition, our method can perform segmentation and pose estimation as byproducts of the detection process.

The goal of dimension reduction is to represent high-dimensional data in a lower-dimensional subspace, while intrinsic properties of the original data are kept as much as possible. An important challenge in unsupervised dimension reduction is the choice of tuning parameters, because no supervised information is available and thus parameter selection tends to be subjective and heuristic. In this paper, we propose an information-theoretic approach to unsupervised dimension reduction that allows objective tuning parameter selection. We employ quadratic mutual information (QMI) as our information measure, which is known to be less sensitive to outliers than ordinary mutual information, and QMI is estimated analytically by a least-squares method in a computationally efficient way. Then, we provide an eigenvector-based efficient implementation for performing unsupervised dimension reduction based on the QMI estimator. The usefulness of the proposed method is demonstrated through experiments.

This paper deals with the problem of reconstructing a high-resolution digital image from a single low-resolution digital image and proposes a new intra-frame super-resolution algorithm based on the mixed lp/l1 norm minimization. Introducing some assumptions, this paper formulates the super-resolution problem as a mixed l0/l1 norm minimization and relaxes the l0 norm term to the lp norm to avoid ill-posedness. A heuristic iterative algorithm is proposed based on the iterative reweighted least squares (IRLS). Numerical examples show that the proposed algorithm achieves super-resolution efficiently.

Visual quality evaluation is crucially important for various video and image processing systems. Traditionally, subjective image quality assessment (IQA) given by the judgments of people can be perfectly consistent with human visual system (HVS). However, subjective IQA metrics are cumbersome and easily affected by experimental environment. These problems further limits its applications of evaluating massive pictures. Therefore, objective IQA metrics are desired which can be incorporated into machines and automatically evaluate image quality. Effective objective IQA methods should predict accurate quality in accord with the subjective evaluation. Motivated by observations that HVS is highly adapted to extract irregularity information of textures in a scene, we introduce multifractal formalism into an image quality assessment scheme in this paper. Based on multifractal analysis, statistical complexity features of nature images are extracted robustly. Then a novel framework for image quality assessment is further proposed by quantifying the discrepancies between multifractal spectrums of images. A total of 982 images are used to validate the proposed algorithm, including five type of distortions: JPEG2000 compression, JPEG compression, white noise, Gaussian blur, and Fast Fading. Experimental results demonstrate that the proposed metric is highly effective for evaluating perceived image quality and it outperforms many state-of-the-art methods.

We present a way to correct light distortion of views looking into an aquarium. When we see fish in an aquarium, they appear closer and distorted due to light distortion. In order to correct the distortion, light rays travelling in the aquarium directly towards an observer should hit him/her after emerging from the aquarium. In this manuscript, those light rays are captured by a perspective camera at specific positions, not the observer position. And then it is shown that the taken images are successfully merged as a single one that is not affected by light distortion.

In this letter, we propose an improved single image haze removal algorithm using image segmentation. It can effectively resolve two common problems of conventional algorithms which are based on dark channel prior: halo artifact and wrong estimation of atmospheric light. The process flow of our algorithm is as follows. First, the input hazy image is over-segmented. Then, the segmentation results are used for improving the conventional dark channel computation which uses fixed local patches. Also, the segmentation results are used for accurately estimating the atmospheric light. Finally, from the improved dark channel and atmospheric light, an accurate transmission map is computed allowing us to recover a high quality haze-free image.

When ultra-high-frequency (UHF) method is applied in partial discharge (PD) detection for GIS, the propagation process and rules of electromagnetic (EM) wave need to be understood clearly for conducting diagnosis and assessment about the real insulation status. The preceding researches are mainly concerning about the radial component of the UHF signal, but the propagation of the signal components in axial and radial directions and that perpendicular to the radial direction of the GIS tank are rarely considered. So in this paper, for a 252,kV GIS with T-shaped structure (TS), the propagation and attenuation of PD-induced EM wave in different circumferential angles and directions are investigated profoundly in time and frequency domain based on Finite Difference Time Domain (FDTD) method. The attenuation rules of the peak to peak value (Vpp) and cumulative energy are concluded. By comparing the results of straight branch and T branch, the influence of T-shaped structure over the propagation of different signal components are summarized. Moreover, the new circumferential and axial location methods proposed in the previous work are verified to be still applicable. This paper discusses the propagation mechanism of UHF signal in T-shaped tank, which provides some referential significance towards the utilization of UHF technique and better implementation of PD detection.

For the direct measurement of very fast transient current (VFTC) due to switch operation in gas insulated switchgear (GIS), usually it will interfere the original operation or change the structure of switch. In this paper a method for calculation of transient current caused by the disconnect operation in GIS by the inverse operation of the electromagnetic (EM) near field is presented. A GIS is modeled by the finite integration technique (FIT), and all the media between the excitation source and the observation position are considered as a black box whose input is VFTC and output is EM field. A coefficient matrix is established to reflect the connection between the input and output in frequency domain, and the VFTC in frequency domain will be the result of multiplying the inverse matrix by the measurement result minus the EM field caused by transient grounding potential rise (TGPR) or transient enclosure voltage (TEV) in the observation position. Finally the time domain form of VFTC can be obtained by the interpolation and IFFT. Comparison between the result and simulation shows the validation of this method.

Increased inductance values and contact resistance in connector contact surfaces due to degradation of connector contact performance have been reported. In particular, inductance increases while degradation remains minimal. We focus on slight loosening in which increased inductance values are observed without increased resistance values, and investigate the effect of loose connectors on transmission line coupling noise under such circumstances. We find a proportional relation between coupled noise current and frequency. Moreover, we find a proportional relation between the increased inductance value, which depends on the change in connector contact distribution, and the coupled noise current in the transmission line.

We propose a new algorithm to suppress both stationary background noise and nonstationary directional interference noise in a speech enhancement system that employs the generalized sidelobe canceller. Our approach builds on advances in generalized sidelobe canceller design involving the transfer function ratio. Our system is composed of three stages. The first stage estimates the transfer function ratio on the acoustic path, from the nonstationary directional interference noise source to the microphones, and the powers of the stationary background noise components. Secondly, the estimated powers of the stationary background noise components are used to execute spectral subtraction with respect to input signals. Finally, the estimated transfer function ratio is used for speech enhancement on the primary channel, and an adaptive filter reduces the residual correlated noise components of the signal. These algorithmic improvements give consistently better performance than the transfer function generalized sidelobe canceller when input signal-to-noise ratio is 10 dB or lower.

Registration consistency (RC) stands out as a widely-used automatic measure from existing image registration evaluation measures. However the original RC neglects the influence brought by the image intensity variation, leading to several problems. This letter proposes a rectified registration consistency, which takes both image intensity variation and geometrical transformation into consideration. Therefore the geometrical transformation is evaluated more by decreasing the influence of intensity variation. Experiments on real image pairs demonstrated the superiority of the proposed measure over the original RC.

This letter describe target classification from the synthesized active sonar returns from targets. A fractional Fourier transform is applied to the sonar returns to extract shape variation in the fractional Fourier domain depending on the highlight points and aspects of the target. With the proposed features, four different targets are classified using two neural network classifiers.

A new theorem is proposed on BIBO (Bounded Input Bounded Output) stability of a general feedback amplifier circuit formulated by BIBO operators. The proposed theorem holds for both linear and nonlinear BIBO operators. The meaning of this theorem is clarified by applying it to continuous-time linear cases.

Despite the increasing use of mobile computing, exploiting its full potential is difficult due to its inherent characteristics such as error-prone transmission channels, diverse node capabilities, frequent disconnections and mobility. Mobile Cloud Computing (MCC) is a paradigm that is aimed at overcoming previous problems through integrating mobile devices with cloud computing. Mobile devices, in the traditional client-server architecture of MCC, offload their tasks to the cloud to utilize the computation and storage resources of data centers. However, along with the development of hardware and software technologies in mobile devices, researchers have begun to take into consideration local resource sharing among mobile devices themselves. This is defined as the cooperation based architecture of MCC. Analogous to the conventional terminology, the resource platforms that are comprised of surrounding surrogate mobile devices are called local resource clouds. Some researchers have recently verified the feasibility and benefits of this strategy. However, existing work has neglected an important issue with this approach, i.e., how to construct local resource clouds in dynamic mobile wireless networks. This paper presents the concept and design of a local resource cloud that is both energy and time efficient. Along with theoretical models and formal definitions of problems, an efficient heuristic algorithm with low computational complexity is also presented. The results from simulations demonstrate the effectiveness of the proposed models and method.

Activity recognition has recently been playing an important role in several research domains, especially within the healthcare system. It is important for physicians to know what their patients do in daily life. Nevertheless, existing research work has failed to adequately identify human activity because of the variety of human lifestyles. To address this shortcoming, we propose the high performance activity recognition framework by introducing a new user context and activity location in the activity log (AL2). In this paper, the user's context is comprised by context-aware infrastructure and human posture. We propose a context sensor network to collect information from the surrounding home environment. We also propose a range-based algorithm to classify human posture for combination with the traditional user's context. For recognition process, ontology-based activity recognition (OBAR) is developed. The ontology concept is the main approach that uses to define the semantic information and model human activity in OBAR. We also introduce a new activity log ontology, called AL2 for investigating activities that occur at the user's location at that time. Through experimental studies, the results reveal that the proposed context-aware activity recognition engine architecture can achieve an average accuracy of 96.60%.