FastDu is a file system service that tracks file system changes by intercepting file system calls to maintain directory summaries, which play important roles in both storage administration and improvement of user experiences for some applications. In most circumstances, directory summaries are independently harvested by applications via traversing the file system hierarchy and calling stat () on every file in each directory. For large file systems, this brute-force traverse-based approach can take many hours to complete, even if only a small percentage of the files have changed. This paper describes FastDu, which uses a pre-built database to store harvested directory summaries, and tracks the file system changes by intercepting file system calls, so that new harvesting is restricted to the small subset of directories that contain modified files. Tests using FastDu show that this approach reduces the time needed to get a directory summary by one or two orders of magnitude with almost negligible penalty to application-aware file system performance.

This paper presents a mixed H₂/H_∞ control using fuzzy singularly perturbed model (FSPM) with multiple perturbation parameters. Since FSPM with multiple perturbation parameters is an extension of models with a single perturbation parameter, the theoretical results are applicable to a larger class of systems described by multiple time scale nonlinear models, such as flying aircraft and flexible space robots. The parameter-independent solution of the mixed H₂/H_∞ controller was obtained in the form of linear matrix inequalities (LMIs). The application of this approach to gust load alleviation of a flying vehicle verifies its effectiveness and flexibility.

In order to improve micro-CT's capability of accurate quantification of linear attenuation coefficient μ, a dual energy method was developed to correct beam hardening artifacts caused by the polychromatic spectra of X-ray tubes. In this method, two sets of scans, taken at different energy levels, were combined to create a synthetic monochromatic image. A physical polychromatic model of μ in dual energy imaging was developed with an iterative method to solve the model for a few selected pixels. To find a high-speed and effective computing approach, the physics model was approximated by a polynomial function of the measured intensities. The method was tested on a PMMA-aluminum phantom and CaCl₂ admixtures. The results show that streak and cupping artifacts are completely eliminated and that the measurement of the reconstructed attenuation coefficient μ is observed to be over 95% accurate.

A reverse detection based QoS routing algorithm was developed based on the dynamic topology of low earth orbit (LEO) satellite constellation networks. This distributed algorithm has very low computational complexity, so it is suitable for on-board processing. This algorithm improves the autonomous operating capability and throughput of satellite networks. Simulations show that the algorithm has better performance than the static sequent snap shots algorithm with acceptable low overhead signaling.

Extended target detection performance can be enhanced by using phase-modulated waveform designs in band-limited radar systems. Unlike waveforms designed for the total energy constraint, phase-modulated waveforms can fully exploit the transmit power in the pulse duration, which is more suitable for practical radar systems. An alternating iterative algorithm was developed to optimize the phase-modulated baseband waveform by maximizing the signal-to-noise ratio (SNR) at the receiver filter output. The output SNR increases continuously with the number of iterations and the algorithm is guaranteed to converge. Simulations validate the effectiveness of this approach. The waveforms designed by this method outperform other commonly used waveforms for extended target detection.

Time-division multiplexed switching (TDMS)-based multiple-input multiple-output (MIMO) channel sounders are widely used for wireless channel measurements due to their effective costs. However, measurement noise such as phase noise in the local oscillators as well as additive white Gaussian noise (AWGN) can result in significant errors in channel capacity estimates. This study analyzes the impact of phase noise and AWGN on channel capacity in TDMS-based MIMO measurements, with a channel capacity estimator presented that reduces the impact of noise on both the spatial multiplexing gain and on the power gain. Simulations demonstrate that the estimator consistently obtains the true capacity for various MIMO channel scenarios, even if only a limited number of observations are available.

Relevance feedback plays a key role in multiple feature-based image retrieval applications. This paper describes an online metric learning approach for a set of ranking functions. In the feedback round, the most relevant and most nonrelevant images related to the target image are selected to construct a relative comparison triplet. The weighting parameters of the multiple ranking functions are updated by minimizing a quadratic objective function constrained by the triplet. The approach unifies the learning algorithm for the most commonly used ranking functions. Thus, multiple features with their own ranking function can easily be employed in the ranking module without feature reconstruction. The method is computationally inexpensive and appropriate for large-scale e-commerce image retrieval applications. Customized ranking functions are well supported. Practically, simplified ranking functions yield better results when the number of query rounds is relatively small. Experiments with an image dataset from a real e-commerce platform show the superiority of the proposed approach.

Cooperative energy spectrum sensing has been widely applied in cognitive radio (CR) networks. In this paper, two cooperative sensing algorithms based on the received signals' correlation matrix were proposed. The first proposed algorithm made use of both diagonal elements and non-diagonal elements in the cooperative scheme. In the second algorithm, when the sensing station can obtain the information of the channel gains between the primary user and the sensing nodes, the weighted linear model can be adopted to improve the sensing performance. This paper analyzed the effectiveness of these two proposed cooperative algorithms and demonstrated that they can considerably improve the sensing performance compared with the traditional linear cooperative sensing algorithms. Simulation results showed that the sensing performance can be significantly enhanced by using the proposed algorithms, especially when the number of cooperative nodes is large.

Implementing video applications on emerging multi-core processors is a promising technique for personal, real-time multi-media applications. However, when porting the legacy parallel video encoders developed for clusters to shared-memory multi-cores, the existing parallel algorithms result in workload imbalances on different cores and communication inefficiencies. This paper describes a strip-wise parallel scheme to balance workloads and a hybrid communication mechanism to reduce communication overhead. The implementation of the H.264 parallel encoder on an eight CPU Intel Xeon system achieves 5x to 6x speed-up over a single thread encoder and achieves a 29% performance improvement over the commonly used master-slave schemes on clusters. The paper also gives further analysis on scalability, parallel efficiency, workload balance, and communication overhead as the number of cores varies.

Decreasing the power supply voltage in dynamic voltage frequency scaling to save power consumption may introduce extra delays in CMOS circuits, which may cause errors. This paper presents the probabilistic delay fault model (PDFM), which describes the probability of an error occurring as a function of the power supply voltage and the clock period in synchronous CMOS circuits. In a wide range of applications (graphic, video, digital filtering, etc.), errors occurring with low probability and not remaining for a long time are acceptable. For combinational circuits which have long critical paths with low probability of excitation, a performance increase is achieved with a certain rate of errors determined by the PDFM compared with the traditional design which considers the worst case. The PDFM applied to array multipliers and ripple carry adders shows the agreement of the predicted probabilities with simulated delay histograms to support the practicality of using the PDFM to select power supply voltage and clock period in dynamic voltage frequency scaling circuits with tolerable error rates.

A ball grid array (BGA) package based on Si interposer with through silicon via (TSV) was designed. Thermal behaviors of the designed BGA with Si interposer has been analyzed and compared to a conventional BGA with BT substrate in the approach of finite element modeling (FEM). The Si interposer with TSV was then fabricated and the designed BGA package was demonstrated. The designed BGA package includes a 100 μm thick Si interposer, which has redistribution copper traces on both sides. Through vias with 25 to 40 μm diameter were fabricated on the Si interposer using deep reactive ion etching (DRIE), plasma enhanced chemical vapor deposition (PECVD), copper electroplating and chemical mechanical polishing (CMP), etc. TSV in the designed interposer is used as electrical interconnections and cooling channels. 5 mm by 5 mm and 10 mm by 10 mm thermal chips were assembled on the Si interposer.

Support vector machines (SVMs) have widespread use in various classification problems. Although SVMs are often used as an off-the-shelf tool, there are still some important issues which require improvement, such as feature rescaling. Standardization is the most commonly used feature rescaling method. However, standardization does not always improve classification accuracy. This paper describes two feature rescaling methods: multiple kernel learning-based rescaling (MKL-SVM) and kernel-target alignment-based rescaling (KTA-SVM). MKL-SVM makes use of the framework of multiple kernel learning (MKL) and KTA-SVM is built upon the concept of kernel alignment, which measures the similarity between kernels. The proposed methods were compared with three other methods: an SVM method without rescaling, an SVM method with standardization, and SCADSVM. Test results demonstrate that different rescaling methods apply to different situations and that the proposed methods outperform the others in general.

Modern datacenter and enterprise networks require application identification to enable granular traffic control that either improves data transfer rates or ensures network security. Providing application visibility as a core network function is challenging due to its performance requirements, including high throughput, low memory usage, and high identification accuracy. This paper presents a payload-based application identification method using a signature matching engine utilizing characteristics of the application identification. The solution uses two-stage matching and pre-classification to simultaneously improve the throughput and reduce the memory. Compared to a state-of-the-art common regular expression engine, this matching engine achieves 38% memory use reduction and triples the throughput. In addition, the solution is orthogonal to most existing optimization techniques for regular expression matching, which means it can be leveraged to further increase the performance of other matching algorithms.

Packet classification is crucial to the implementation of advanced network services that require the capability to distinguish traffic in different flows, such as access control in firewalls and protocol analysis in intrusion detection systems. This paper proposes a novel packet classification algorithm optimized for multi-core network processors. The proposed algorithm, AggreCuts, has an explicit worst-case search time with modest memory usage. The data structure of AggreCuts is flexible and well-adapted to different types of multi-core platforms. The algorithm on both Intel IXP2850 32-bit and Cavium OCTEON3860 64-bit multi-core platforms was implemented to evaluate the performance of AggreCuts. The experimental results show that AggreCuts outperforms the best-known existing algorithm in terms of memory usage and classification speed.

The performance of distributed computing systems is partially dependent on configuration parameters recorded in configuration files. Evolutionary strategies, with their ability to have a global view of the structural information, have been shown to effectively improve performance. However, most of these methods consume too much measurement time. This paper introduces an ordinal optimization based strategy combined with a back propagation neural network for autotuning of the configuration parameters. The strategy was first proposed in the automation community for complex manufacturing system optimization and is customized here for improving distributed system performance. The method is compared with the covariance matrix algorithm. Tests using a real distributed system with three-tier servers show that the strategy reduces the testing time by 40% on average at a reasonable performance cost.