To reduce the inaccuracy caused by inappropriate time window, we propose two probabilistic fault localization schemes based on the idea of "extending time window." The global window extension algorithm (GWE) uses a window extension strategy for all candidate faults, while the on-demand window extension algorithm (OWE) uses the extended window only for a small set of faults when necessary. Both algorithms can increase the metric values of actual faults and thus improve the accuracy of fault localization. Simulation results show that both schemes perform better than existing algorithms. Furthermore, OWE performs better than GWE at the cost of a bit more computing time.

This paper discusses a dual-stage detection scheme composed of coarse detection stage and refined detection stage for the continuous detection operation of Ultra-Wideband (UWB) detect and avoid (DAA). The threshold factor for the probability of indefinite detection is first proposed and defined to combine the two stages. The proposed scheme focuses on the integration of two different detection schemes with different complexities in order to reduce total computational complexity. A Single-carrier Frequency Division Multiple Access (SC-FDMA) uplink system operating in a Time Division Duplex (TDD) mode is utilized to evaluate the proposed detection scheme. Simulation results indicate that the proposed scheme can make a tradeoff between the detection performance and the computational complexity by setting the probability of indefinite detection.

This paper proposes a novel service configuration approach that can realize dynamic critical Quality of Service (QoS) adaptation to ever-changing and resource-limited ubiquitous computing environments. In the approach, service configuration is reduced to a Fuzzy Control System (FCS) which aims to achieve critical QoS variations on minimal level with less power cost. Two configuration strategies, service chain reconfiguration and QoS parameters adjustment, along with a configuration algorithm, are implemented to handle different types of QoS variations. A self-optimizing algorithm is designed to enhance the adaptation of the FCS. Simulation results validate the proposed approach.

In the Cloud computing era, users could have their data outsourced to cloud service provider (CSP) to enjoy on-demand high quality service. On the behalf of the user, a third party auditor (TPA) which could verify the real data possession on CSP is critically important. The central challenge is to build efficient and provably secure data verification scheme while ensuring that no users' privacy is leaked to any unauthorized party, including TPA. In this paper, we propose the first identity-based public verification scheme, based on the identity-based aggregate signature (IBAS). In particular, by minimizing information that verification messages carry and TPA obtains or stores, we could simplify key management and greatly reduce the overheads of communication and computation. Unlike the existing works based on certificates, in our scheme, only a private key generator (PKG) has a traditional public key while the user just keeps its identity without binding with certificate. Meanwhile, we utilize privacy-preserving technology to keep users' private data off TPA. We also extend our scheme with the support of batch verification task to enable TPA to perform public audits among different users simultaneously. Our scheme is provably secure in the random oracle model under the hardness of computational Diffie-Hellman assumption over pairing-friendly groups and Discrete Logarithm assumption.

Due to network users' different time-preference, network traffic load usually significantly differs at different time. In traffic peak time, network congestion may happen, which make the quality of service for network users deteriorate. There are essentially two ways to improve the quality of services in this case: (1) Network service providers (NSPs) over-provision network capacity by investment; (2) NSPs use time-dependent pricing (TDP) to reduce the traffic at traffic peak time. However, over-provisioning network capacity can be costly. Therefore, some researchers have proposed TDP to control congestion as well as improve the revenue of NSP. But to the best of our knowledge, all of the literature related time-dependent pricing scheme only consider the monopoly NSP case. In this paper, a duopoly NSP case is studied. The NSPs try to maximize their overall revenue by setting time-dependent price, while users choose NSP by considering their own preference, congestion status in the networks and the price set by the NSPs. Analytical and experimental results show that the TDP benefits the NSPs, but the revenue improvement is limited due to the competition effect.

In recent years, the adoption of Software as a Service (SaaS) cloud services has surpassed that of Infrastructure as a Service (IaaS) cloud service and is now the focus of attention in cloud computing. The cloud market is becoming highly competitive owing to the increasing number of cloud service providers (CSPs), who are likely to exhibit different cloud capacities, i.e., the cloud market is heterogeneous. Moreover, as different users generally exhibit different Quality of Service (QoS) preferences, it is challenging to set prices for cloud services of good QoS. In this study, we investigate the price competition in the heterogeneous cloud market where two SaaS providers, denoted by CSP1 and CSP2, lease virtual machine (VM) instances from IaaS providers to offer cloud-based application services to users. We assume that CSP1 only has M/M/1 queue of VM instances owing to its limited cloud resources, whereas CSP2 has M/M/∞ queue of VM instances reflecting its adequate resources. We consider two price competition scenarios in which two CSPs engage in two games: one is a noncooperative strategic game (NSG) where the two CSPs set prices simultaneously and the other is a Stackelberg game (SG) where CSP2 sets the price first as the leader and is followed by CSP1, who sets the price in response to CSP2. Each user decides which cloud services to purchase (if purchases are to be made) based on the prices and QoS. The NSG scenario corresponds to the practical cloud market, where two CSPs with different cloud capacities begin to offer cloud services simultaneously; meanwhile, the SG scenario covers the instance where a more recent CSP plans to enter a cloud market whose incumbent CSP has larger cloud resources. Equilibrium is achieved in each of the scenarios. Numerical results are presented to verify our theoretical analysis.

A deeply-coupled system can feed the INS information into a GNSS receiver, and the signal tracking precision can be improved under dynamic conditions by reducing tracking loop bandwidth without losing tracking reliability. In contrast to the vector-based deep integration, the scalar-based GNSS/INS deep integration is a relatively simple and practical architecture, in which all individual DLL and PLL are still exist. Since the implementation of a deeply-couple system needs to modify the firmware of a commercial hardware GNSS receiver, very few studies are reported on deep integration based on hardware platform, especially from academic institutions. This implementation-complexity issue has impeded the development of the deeply-coupled GNSS receivers. This paper introduces a scalar-based MEMS IMU/GNSS deeply-coupled system based on an integrated embedded hardware platform for real-time implementation. The design of the deeply-coupled technologies is described including the system architecture, the model of the inertial-aided tracking loop, and the relevant tracking errors analysis. The implementation issues, which include platform structure, real-time optimization, and generation of aiding information, are discussed as well. The performance of the inertial aided tracking loop and the final navigation solution of the developed deeply-coupled system are tested through the dynamic road test scenarios created by a hardware GNSS/INS simulator with GPS L1 C/A signals and low-level MEMS IMU analog signals outputs. The dynamic tests show that the inertial-aided PLL enables a much narrow tracking loop bandwidth (e.g. 3Hz) under dynamic scenarios; while the non-aided loop would lose lock with such narrow loop bandwidth once maneuvering commences. The dynamic zero-baseline tests show that the Doppler observation errors can be reduced by more than 50% with inertial aided tracking loop. The corresponding navigation results also show that the deep integration improved the velocity precision significantly.

Continuous and explosive growth of the Internet has shown that current TCP mechanisms can obstruct efficient use of high-speed, long-delay networks. To address this problem we propose an enhanced transport-layer protocol called gHSTCP, based on HighSpeed TCP proposed by Sally Floyd. It uses two modes in the congestion avoidance phase based on the changing trend of RTT. Simulation results show gHSTCP can significantly improve performance in mixed environments, in terms of throughput and fairness against the traditional TCP Reno flows. However, the performance improvement is limited due to the nature of TailDrop router, and the RED/ARED routers can not alleviate the problem completely. Therefore, we present a modified version of Adaptive RED, called gARED, directed at the problem of simultaneous packet drops by multiple flows in high speed networks. gARED can eliminate weaknesses found in Adaptive RED by monitoring the trend in variation of the average queue length of the router buffer. Our approach, combining gARED and gHSTCP, is quite effective and fair to competing traffic than Adaptive RED with HighSpeed TCP.

In this letter, a two channel frequency domain speech enhancement algorithm is proposed. The algorithm is designed to achieve better overall performance with relatively small array size. An improved version of adaptive null-forming is used, in which noise cancelation is implemented in auditory subbands. And an OM-LSA based postfiltering stage further purifies the output. The algorithm also features interaction between the array processing and the postfilter to make the filter adaptation more robust. This approach achieves considerable improvement on signal-to-noise ratio (SNR) and subjective quality of the desired speech. Experiments confirm the effectiveness of the proposed system.

This paper presents an active quality of service (QoS) aware service composition protocol for mobile ad hoc networks (MANETs), with the goal of conserving resources subject to QoS requirements. A problem of QoS based service composition in MANETs is transformed into a problem of the service path discovery. We extend Dynamic Source Routing protocol to discover and compose elementary services across the network. Some message processing measures are taken to effectively reduce control overhead. Simulation results demonstrate the effectiveness of the proposed protocol.

With the rapid increase in demand for mobile data, mobile network operators are trying to expand wireless network capacity by deploying wireless local area network (LAN) hotspots on to which they can offload their mobile traffic. However, these network-centric methods usually do not fulfill the interests of mobile users (MUs). Taking into consideration many issues such as different applications' deadlines, monetary cost and energy consumption, how the MU decides whether to offload their traffic to a complementary wireless LAN is an important issue. Previous studies assume the MU's mobility pattern is known in advance, which is not always true. In this paper, we study the MU's policy to minimize his monetary cost and energy consumption without known MU mobility pattern. We propose to use a kind of reinforcement learning technique called deep Q-network (DQN) for MU to learn the optimal offloading policy from past experiences. In the proposed DQN based offloading algorithm, MU's mobility pattern is no longer needed. Furthermore, MU's state of remaining data is directly fed into the convolution neural network in DQN without discretization. Therefore, not only does the discretization error present in previous work disappear, but also it makes the proposed algorithm has the ability to generalize the past experiences, which is especially effective when the number of states is large. Extensive simulations are conducted to validate our proposed offloading algorithms.

This paper presents a high power efficient pulse VCO with tail-filter for the chip-scale atomic clock (CSAC) application. The stringent power and clock stability specifications of next-generation CSAC demand a VCO with ultra-low power consumption and low phase noise. The proposed VCO architecture aims for the high power efficiency, while further reducing the phase noise using tail filtering technique. The VCO has been implemented in a standard 45nm SOI technology for validation. At an oscillation frequency of 5.0GHz, the proposed VCO achieves a phase noise of -120dBc/Hz at 1MHz offset, while consuming 1.35mW. This translates into an FoM of -191dBc/Hz.

Tracking-by-detection methods consider tracking task as a continuous detection problem applied over video frames. Modern tracking-by-detection trackers have online learning ability; the update stage is essential because it determines how to modify the classifier inherent in a tracker. However, most trackers search for the target within a fixed region centered at the previous object position; thus, they lack spatiotemporal consistency. This becomes a problem when the tracker detects an incorrect object during short-term occlusion. In addition, the scale of the bounding box that contains the target object is usually assumed not to change. This assumption is unrealistic for long-term tracking, where the scale of the target varies as the distance between the target and the camera changes. The accumulation of errors resulting from these shortcomings results in the drift problem, i.e. drifting away from the target object. To resolve this problem, we present a drift-free, online learning-based tracking-by-detection method using a single static camera. We improve the latent structured support vector machine (SVM) tracker by designing a more robust tracker update step by incorporating two Kalman filter modules: the first is used to predict an adaptive search region in consideration of the object motion; the second is used to adjust the scale of the bounding box by accounting for the background model. We propose a hierarchical search strategy that combines Bhattacharyya coefficient similarity analysis and Kalman predictors. This strategy facilitates overcoming occlusion and increases tracking efficiency. We evaluate this work using publicly available videos thoroughly. Experimental results show that the proposed method outperforms the state-of-the-art trackers.

The unconditional stable finite-difference time-domain (US-FDTD) method based on Laguerre polynomial expansion and Galerkin temporal testing is used to model thin-film bulk acoustic wave resonators (TFBAR). Numerical results show the efficiency of the US-FDTD algorithm.

Sequences that attain the smallest possible absolute sidelobes (SPASs) of periodic autocorrelation function (PACF) play fairly important roles in synchronization of communication systems, Large scale integrated circuit testing, and so on. This letter presents an approach to construct 16-QAM sequences of even periods, based on the known quaternary sequences. A relationship between the PACFs of 16-QAM and quaternary sequences is established, by which when quaternary sequences that attain the SPASs of PACF are employed, the proposed 16-QAM sequences have good PACF.