Efficient extraction of interconnect parasitic parameters has become very important for present deep submicron designs. In this paper, the improved boundary element method (BEM) is presented for 3-D interconnect resistance extraction. The BEM is accelerated by the recently proposed quasi-multiple medium (QMM) technology, which quasi-cuts the calculated region to enlarge the sparsity of the overall coefficient matrix to solve. An un-average quasi-cutting scheme for QMM, advanced nonuniform element partition and technique of employing the linear element for some special surfaces are proposed. These improvements considerably condense the computational resource of the QMM-based BEM without loss of accuracy. Experiments on actual layout cases show that the presented method is several hundred to several thousand times faster than the well-known commercial software Raphael, while preserving the high accuracy.

We investigate an online problem of a robot exploring the outer boundary of an unknown simple polygon P. The robot starts from a specified vertex s and walks an exploration tour outside P. It has to see all points of the polygon's outer boundary and to return to the start. We provide lower and upper bounds on the ratio of the distance traveled by the robot in comparison to the length of the shortest path. We consider P in two scenarios: convex polygon and concave polygon. For the first scenario, we prove a lower bound of 5 and propose a 23.78-competitive strategy. For the second scenario, we prove a lower bound of 5.03 and propose a 26.5-competitive strategy.

This paper presents an efficient yet powerful codebook model, named classified codebook model, to categorize natural scene category. The current codebook model typically resorts to large codebook to obtain higher performance for scene categorization, which severely limits the practical applicability of the model. Our model formulates the codebook model with the theory of vector quantization, and thus uses the famous technique of classified vector quantization for scene-category modeling. The significant feature in our model is that it is beneficial for scene categorization, especially at small codebook size, while saving much computation complexity for quantization. We evaluate the proposed model on a well-known challenging scene dataset: 15 Natural Scenes. The experiments have demonstrated that our model can decrease the computation time for codebook generation. What is more, our model can get better performance for scene categorization, and the gain of performance becomes more pronounced at small codebook size.

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.

Multi-hypothesis prediction technique, which exploits inter-frame correlation efficiently, is widely used in block-based distributed compressive video sensing. To solve the problem of inaccurate prediction in multi-hypothesis prediction technique at a low sampling rate and enhance the reconstruction quality of non-key frames, we present a resample-based hybrid multi-hypothesis scheme for block-based distributed compressive video sensing. The innovations in this paper include: (1) multi-hypothesis reconstruction based on measurements reorganization (MR-MH) which integrates side information into the original measurements; (2) hybrid multi-hypothesis (H-MH) reconstruction which mixes multiple multi-hypothesis reconstructions adaptively by resampling each reconstruction. Experimental results show that the proposed scheme outperforms the state-of-the-art technique at the same low sampling rate.

Scheduling is a key problem in high level synthesis, as the scheduling results affect most of the important design metrics. In this paper, we propose a novel scheduling method to simultaneously optimize the leakage power of functional units with dual-Vth techniques and the number of registers under given timing and resource constraints. The mobility overlaps between operations are removed to eliminate data dependencies, and a simulated-annealing-based method is introduced to explore the mobility overlap removal solution space. Given the overlap-free mobilities, the resource usage and register usage in each control step can be accurately estimated. Meanwhile, operations are scheduled so as to optimize the leakage power of functional units with minimal number of registers. Then, a set of operations is iteratively selected, reassigned as low-Vth, and rescheduled until the resource constraints are all satisfied. Experimental results show the efficiency of the proposed algorithm.

Subspace representation model is an important subset of visual tracking algorithms. Compared with models performed on the original data space, subspace representation model can effectively reduce the computational complexity, and filter out high dimensional noises. However, for some complicated situations, e.g., dramatic illumination changing, large area of occlusion and abrupt object drifting, traditional subspace representation models may fail to handle the visual tracking task. In this paper, we propose a novel subspace representation algorithm for robust visual tracking by using low-rank representation with graph constraints (LRGC). Low-rank representation has been well-known for its superiority of handling corrupted samples, and graph constraint is flexible to characterize sample relationship. In this paper, we aim to exploit benefits from both low-rank representation and graph constraint, and deploy it to handle challenging visual tracking problems. Specifically, we first propose a novel graph structure to characterize the relationship of target object in different observation states. Then we learn a subspace by jointly optimizing low-rank representation and graph embedding in a unified framework. Finally, the learned subspace is embedded into a Bayesian inference framework by using the dynamical model and the observation model. Experiments on several video benchmarks demonstrate that our algorithm performs better than traditional ones, especially in dynamically changing and drifting situations.

Robust crater recognition is a research focus on deep space exploration mission, and sparse representation methods can achieve desirable robustness and accuracy. Due to destruction and noise incurred by complex topography and varied illumination in planetary images, a robust crater recognition approach is proposed based on dictionary learning with a low-rank error correction model in a sparse representation framework. In this approach, all the training images are learned as a compact and discriminative dictionary. A low-rank error correction term is introduced into the dictionary learning to deal with gross error and corruption. Experimental results on crater images show that the proposed method achieves competitive performance in both recognition accuracy and efficiency.

A hybrid Retinex-based image enhancement algorithm is proposed to improve the quality of images captured by unmanned aerial vehicles (UAVs) in this paper. Hyperparameters of the employed multi-scale Retinex with chromaticity preservation (MSRCP) model are automatically tuned via a two-phase evolutionary computing algorithm. In the two-phase optimization algorithm, the Rao-2 algorithm is applied to performing the global search and a solution is obtained by maximizing the objective function. Next, the Nelder-Mead simplex method is used to improve the solution via local search. Real UAV-taken images of bad quality are collected to verify the performance of the proposed algorithm. Meanwhile, four famous image enhancement algorithms, Multi-Scale Retinex, Multi-Scale Retinex with Color Restoration, Automated Multi-Scale Retinex, and MSRCP are utilized as benchmarking methods. Meanwhile, two commonly used evolutionary computing algorithms, particle swarm optimization and flower pollination algorithm, are considered to verify the efficiency of the proposed method in tuning parameters of the MSRCP model. Experimental results demonstrate that the proposed method achieves the best performance compared with benchmarks and thus the proposed method is applicable for real UAV-based applications.

Android occupies a very large market share in the field of mobile devices, and quantities of applications are created everyday allowing users to easily use them. However, privacy leaks on Android terminals may result in serious losses to businesses and individuals. Current permission model cannot effectively prevent privacy data leakage. In this paper, we find a way to protect privacy data on Android terminals from the perspective of privacy information propagation by porting the concept of contextual integrity to the realm of privacy protection. We propose a computational model of contextual integrity suiting for Android platform and design a privacy protection system based on the model. The system consists of an online phase and offline phase; the main function of online phase is to computing the value of distribution norm and making privacy decisions, while the main function of offline phase is to create a classification model that can calculate the value of the appropriateness norm. Based on the 6 million permission requests records along with 2.3 million runtime contextual records collected by dynamic analysis, we build the system and verify its feasibility. Experiment shows that the accuracy of offline classifier reaches up to 0.94. The experiment of the overall system feasibility illustrates that 70% location data requests, 84% phone data requests and 46% storage requests etc., violate the contextual integrity.

Traditional action recognition approaches use pre-defined rigid areas to process the space-time information, e.g. spatial pyramids, cuboids. However, most action categories happen in an unconstrained manner, that is, the same action in different videos can happen at different places. Thus we need a better video representation to deal with the space-time variations. In this paper, we introduce the idea of mining spatial temporal saliency. To better handle the uniqueness of each video, we use a space-time over-segmentation approach, e.g. supervoxel. We choose three different saliency measures that take not only the appearance cues, but also the motion cues into consideration. Furthermore, we design a category-specific mining process to find the discriminative power in each action category. Experiments on action recognition datasets such as UCF11 and HMDB51 show that the proposed spatial temporal saliency video representation can match or surpass some of the state-of-the-art alternatives in the task of action recognition.

Software diversity can be utilized in cyberspace security to defend against the zero-day attacks. Existing researches have proved the effectiveness of diversity in bringing security benefits, but few of them touch the problem that whether there is a positive correlation between the security and the diversity. In addition, there is little guidance on how to construct an effective diversified system. For that, this paper develops two diversity metrics based on system attribute matrix, proposes a diversity measurement and verifies the effectiveness of the measurement. Through several simulations on the diversified systems which use voting strategy, the relationship between diversity and security is analyzed. The results show that there is an overall positive correlation between security and diversity. Though some cases are against the correlation, further analysis is made to explain the phenomenon. In addition, the effect of voting strategy is also discussed through simulations. The results show that the voting strategy have a dominant impact on the security, which implies that security benefits can be obtained only with proper strategies. According to the conclusions, some guidance is provided in constructing a more diversified as well as securer system.

The differential fault analysis of SOSEMNAUK was presented in Africacrypt in 2011. In this paper, we improve previous work with algebraic techniques which can result in a considerable reduction not only in the number of fault injections but also in time complexity. First, we propose an enhanced method to determine the fault position with a success rate up to 99% based on the single-word fault model. Then, instead of following the design of SOSEMANUK at word levels, we view SOSEMANUK at bit levels during the fault analysis and calculate most components of SOSEMANUK as bit-oriented. We show how to build algebraic equations for SOSEMANUK and how to represent the injected faults in bit-level. Finally, an SAT solver is exploited to solve the combined equations to recover the secret inner state. The results of simulations on a PC show that the full 384 bits initial inner state of SOSEMANUK can be recovered with only 15 fault injections in 3.97h.

In this paper, we propose a scalable connection-based time division multiple access architecture for wireless NoC. In this architecture, only one-hop transmission is needed when a packet is transmitted from one wired subnet to another wired subnet, which improves the communication performance and cuts down the energy consumption. Furthermore, by carefully designing the central arbiter, the bandwidth of the wireless channel can be fully used. Simulation results show that compared with the traditional WCube wireless NoC architecture, the proposed architecture can greatly improve the network throughput, and cut down the transmission latency and energy consumption with a reasonable area overhead.

This paper designs the closed-form precoding matrices for non-regenerative MIMO relay system with the direct link. A multiple power constrained non-convex optimization problem is formulated by using the minimum-mean-squared error (MMSE) criterion. We decompose the original problem into two sub-problems. The relay transceiver Wiener filter structure is first rigorously derived, then the source transmit and destination receive matrices are jointly designed by solving an equivalent dual problem. Through our proposed joint iterative algorithm, the closed-form solutions can be finally obtained. The effectiveness of our proposed scheme is validated by simulations with comparison to some of the existing schemes.

In cloud computing, multiple users coexist in one datacenter infrastructure and the network is always shared using VMs. Network bandwidth allocation is necessary for security and performance guarantees in the datacenter. InfiniBand (IB) is more widely applied in the construction of datacenter cluster and attracts more interest from the academic field. In this paper, we propose an IB dynamic bandwidth allocation mechanism IBShare to achieve different Weight-proportional and Min-guarantee requirements of allocation entities. The differentiated IB Congestion Control (CC) configuration is proven to offer the proportional throughput characteristic at the flow level. IBShare leverages distributed congestion detection, global congestion computation and configuration to dynamically provide predictable bandwidth division. The real IB experiment results showed IBShare can promptly adapt to the congestion variation and achieve the above two allocation demands through CC reconfiguration. IBShare improved the network utilization than reservation and its computation/configuration overhead was low.

Aggregate signature (AS) schemes enable anyone to compress signatures under different keys into one. In sequential aggregate signature (SAS) schemes, the aggregate signature is computed incrementally by the sighers. Several trapdoor-permutation-based SAS have been proposed. In this paper, we give a constructions of SAS based on the first SAS scheme with lazy verification proposed by Brogle et al. in ASIACRYPT 2012. In Brogle et al.'s scheme, the size of the aggregate signature is linear of the number of the signers. In our scheme, the aggregate signature has constant length which satisfies the original ideal of compressing the size of signatures.

How to restore virtual network against substrate network failure (e.g. link cut) is one of the key challenges of network virtualization. The traditional virtual network recovery (VNR) methods are mostly based on the idea of centralized control. However, if multiple virtual networks fail at the same time, their recovery processes are usually queued according to a specific priority, which may increase the average waiting time of users. In this letter, we study distributed virtual network recovery (DVNR) method to improve the virtual network recovery efficiency. We establish exclusive virtual machine (VM) for each virtual network and process recovery requests of multiple virtual networks in parallel. Simulation results show that the proposed DVNR method can obtain recovery success rate closely to centralized VNR method while yield ~70% less average recovery time.

Irreversible thermal paints or temperature sensitive paints are a kind of special temperature sensor which can indicate the temperature grad by judging the color change and is widely used for off-line temperature measurement during aero engine test. Unfortunately, the hot gases flow within the engine during measuring always make the paint color degraded, which means a serious saturation reduction and contrast loss of the paint colors. This phenomenon makes it more difficult to interpret the thermal paint test results. Present contrast enhancement algorithms can significantly increase the image contrast but can't protect the hue feature of the paint images effectively, which always cause color shift. In this paper, we propose a color restoration method for thermal paint image. This method utilizes the atmospheric scattering model to restore the lost contrast and saturation information, so that the hue can be protected and the temperature can be precisely interpreted based on the image.

A wide-range multiphase delay-locked loop (DLL) using mixed-mode voltage-controlled delay lines (VCDLs) is presented. An edge-triggered duty cycle corrector is introduced to generate output clocks with 50% duty cycle. This DLL using an analog 3-states phase-frequency detector (PFD) and the proposed digital PFD can achieve low jitter operation over a wide frequency range without harmonic locking problems. It has been fabricated in a standard 0.25-µm CMOS technology and occupies a core area of 1 mm2 including the on-chip regulator and loop filter. For reference clocks from 20 MHz to 550 MHz, all the measured rms and peak-to-peak jitters are below 10 ps and 78 ps, respectively.