One notable difference between 3D test flow and 2D test flow mainly lies in the mid-bond test, in which the stacking yield can be further enhanced through optimized bonding arrangement. In contrast to the existing sequential stacking, this paper proposes a novel rearranged stacking scheme which estimates the probability and cost of failed bonding in each stacking step and optimizes the mid-bond order to screen out the failed component as early as possible. The effect of the rearranged stacking has been extensively analyzed using the yield model and cost model of 3D-SICs considering different process parameters such as die yield, stacking size, failure rate and redundancy degree of TSVs. Experimental results demonstrate that the proposed rearranged stacking method is only a half of the sequential stacking in terms of Failed area ratio (FAR).

How to find desired resources efficiently and accurately is one fundamental challenge of any unstructured P2P networks, which is mainly involved some difficulties in the P2P overlay topology, data representation in peers and routing mechanism. In this paper, we address the issue of resilient routing in unstructured P2P networks. An efficient algorithm called Query routing tree (QRT) based on maximum mutual information is proposed to improve the performance of resource searching, which has tightly associated the resource contents of peers with the logical links in P2P network that makes the query messages forwarded more effectively in similar peers and can hit more target resources faster. Additionally, we present an optimized routing scheme with the query conditions taken into account, to obtain the optimal routing tree with the minimal information gain from the candidate tree set so as to adapt to different query types more flexibly. The simulation results show the proposed QRT can reduce the search cost more effectively and maintain higher targets hit rate than existing typical algorithms such as Flooding, k-RW and APS. Finally, our optimized scheme is also proved to conduct high searching performance with nicer self-adaptability and scalability in unstructured P2P networks.

Against the defect of the real time system fault tolerant model that can only tolerate one fault, the Local optimum checkpoint (LOC) algorithm was proposed. Then according to the schedulability analysis, the worst case response time formula under the fault tolerant priority mixed strategy based on the local optimum checkpoint was deduced. Finally the Fault tolerant priority configuration search algorithm under Mixed strategy based on LOC (FTPCS-MS-LOC) was proposed. The FTPCS-MS-LOC can toleratemultiple transient faults. The algorithm can effectively reduce the search space compared to the enumeration method. The simulation shows that the FTPCS-MSLOC can significantly improve the system fault resilience than the fault tolerant priority inheritance, promotion and demotion strategy, and also the mixed strategy based on the checkpoint interval in the condition of single fault occurred.

We propose a novel face image representation – Local gabor dominant direction pattern (LGDDP) for face recognition. The face image is convolved with the Gabor filters, resulting in multiple response images of different orientations and scales. The response images’ each pixel is encoded by the LGDDP descriptor from the pixel’s dominant neighboring one or two pixels. The image formed by the LGDDP descriptor is partitioned into multiple regions and the histogram is extracted from each region. All the histograms are concatenated into the spatial histogram. The nearest neighbor classifier and the weighted intersection histogram similarity measure are used for face image classification. The advantage of the proposed LGDDP method lies in the high recognition rate performance and the low computational complexity. Extensive experiments are performed on FERET face image database and the experimental results verify the LGDDP’s effectiveness by comparing LGDDP with other well-known published face recognition methods.

This paper proposes an approach to sample data stream based on differential geometry. Our aim is to take advantage of information of discarded data and support stream to generate different number of transactions during different periods. To this end, we establish a novel data stream model represented by a surface, within which time is quantified and probability, value and time, viewed as one united body, could be calculated simultaneously. We project data stream onto a surface of the model and replace points which have the shortest geodesic distance with their mid-point. To the best of our knowledge, this is the first work on introducing differential geometry as a sampling trick. Experimental results show that our approach is effective.

Support vector machine (SVM) is an effective tool in deal with small sample, nonlinear and high dimension classification problems. In this paper, an improved pre-treatment binary-tree SVM is proposed to solve fault diagnosis. Furthermore an ensemble method is presented to establish ensemble SVM. Here the improved SVM is used as weak learning machine. The new ensemble SVM can improve the performance of single binary-tree SVM. At the end, the new algorithm is applied to fault diagnosis of blast furnace faults and the Tennessee Eastman process (TEP). The experiments results show that the improved binary-tree SVM algorithm has an excellent performance on diagnosis speed and accuracy.

The trends of exponential growing core counts incur new requirements on operating systems. The contemporary monolithic OSs protect shared kernel data by locking in multicore environment. However, the lock contention of OS functions may lead to overall performance degradation. This paper adopts microkernel architecture for scalability concerns, since it has flexibilities for the management of computing resources and explicit data layout to avoid locking. We present a scalable Memory management service (MMS) based on microkernel OS. The physical memory is distributed into servers to remove the lock contention over page pools. Then we discuss the new problems, including load balance and “distributed memory fragmentation”. MMS is divided into one master and multiple slaves. The master is a coordinator for adjusting loads and routing requests with a global memory view, while the slaves are responsible for the management of distributed page zones and virtual memory areas. The experimental results show that MMS achieves better scalability than Linux on a 32-core machine.

Resource location strategy has become very popular for mass data distribution in complex and dynamic network. Searching for the objects is a fundamental problem to unstructured Peer to peer (P2P) networks. In this paper, we propose a resource location strategy for user requirements, which employs the collaborative exchange of information between peers to construct interest communities. This strategy can gather similar peers and disseminate useful information among them. Furthermore, we design an algorithm in unstructured P2P systems named Appropriate degree gossip search algorithm (ADGSA). Using this search algorithm, the performance including search success ratio, recall rate and search response time has been improved dramatically. An efficient resource location system with a fast organization of users cluster based on their requirements can be provided, preventing the creation of unliked communities. The simulation results show that our strategy has better search efficiency, short response time and high recall ratio.

Multi-label learning deals with each instance which may be associated with a set of class labels simultaneously. We propose a novel multi-label classification approach named MFSM (Multi-task joint feature selection for multi-label classification). In MFSM, we compute the asymmetric label correlation matrix in the label space. The multi-label learning problem can be formulated as a joint optimization problem including two regularization terms, one aims to consider the label correlations and the other is used to select the similar sparse features shared among multiple different classification tasks (each for one label). Our model can be reformulated into an equivalent smooth convex optimization problem which can be solved by the Nesterov’s method. The experiments on sixteen benchmark multi-label data sets demonstrate that our method outperforms the state-of-the-art multi-label learning algorithms.

Wide Single instruction multiple data (SIMD) architectures are very important in the computeintensive applications. The SIMD execution model is inefficient when it suffers from the divergent control flow. The divergent execution paths across loop iterations take place sequentially on SIMD, which defeats part of the efficiency advantage of SIMD execution. This paper proposes a mechanism to compact the divergent branch threads to mitigate the impact of branch thread divergence on SIMD architectures. It relaxes the SIMD execution model by allowing the distinct instruction flows to be scheduled independently, instead of one single instruction flow. It increases flexibility and mitigates the synchronization cost of co-issuing instructions from different divergent branch threads by giving the Vector processing elements (VPEs) the ability to direct their own control flow. The proposed divergent branch threads compaction mechanism improves performance by 2.56x over traditional SIMD architecture for a wide variety of general purpose parallel applications while the area overhead only increases 6.48%.

V² controlled buck converter with ramp compensation is studied, its two border voltages are derived and one-dimensional discrete-time model is established, upon which the dynamical effect of V² control technique on stability control and mode shift of V² controlled buck converter with ramp compensation is studied by using the bifurcation diagram, Lyapunov exponent spectrum, and parameter space map. The research results verified by simulation and experimental measurements indicate that by utilizing ramp compensation, V² controlled buck converter can not only shift its operation mode from discontinuous conduction mode to continuous conduction mode, but also effectively stabilize its operation state from unstable subharmonic oscillation to stable periodic state.

A novel object-based framework is proposed for HSI compression, where targets of interest are extracted and separately coded. With objects removed, the holes are filled with the background average to form a new but more homogenous background for better compression. An improved sparse representation with adaptive spatial support is proposed for target detection. By applying the proposed framework to 2D/3D DCT approaches, reconstructed images from conventional and proposed approaches are compared. Six criteria in three groups are employed for quantitative evaluations to measure the degree of data reduction, the distortion of reconstructed image quality and accuracy in target detection, respectively. Comprehensive experiments on two datasets are used for performance evaluation. It is found that the proposed approaches yield much improved results.

We describe a technique for interactively rendering diffuse scenes with near-field area lighting by introducing a new light type called VAL (Virtual area light). The VALs are generated via the subdivision of the primary planar luminaire, and near-field area lighting effects can be simulated directly from the geometric and radiometric information of these VALs. To ensure a high fidelity of soft shadows, we propose the multi-PCF (Percentage closer filter) algorithm which evaluates visibility through several PCF values of each VAL. Experimental results reveal that our approach can render visually convincing near-field area lighting effects as well as soft shadows without any precomputation. Our strategy also enables high-quality direct illumination results in fully dynamic scenes under both time-varying and spatially-varying area lighting conditions in the order of milliseconds.

In this part of the paper, based on the fundamentals derived in Part I, we develop and implement two new algorithms. The first one, based on the proposed correlation operation for Linear canonical transform (LCT), is designed to detect and estimate linear Frequency modulated (FM) signals. The second one, based on the proposed joint canonical distribution, aims at reducing the cross-terms which are caused by the quadratic nature of Wigner distribution (WD). Several simulation results are given to illustrate both the strengths and weaknesses of the two algorithms.

A quick convex hull building algorithm using grid and binary tree is proposed for the minimum convex buidling of planar point set. Grids are used to assess and eliminate those interior points wihtout any contribution to convex hull building and points are sought in the boundary grid only so as to enhance the efficiency of algorithm. The minimum convex bull is built by taking such advantages of binary tree as quick, convenient and applicable for various point sets with different distributions, so as to resolve the description problem of concave point. The time complexity of the algorithm is low because of grid pretreatment. As the results of comparative expriment of random point and actual picture show, the proposed algorithm can obtain the best profile of 2D planar picture with minimum time, which is applicable for describing the shape of irregular convex-concave objects.

A novel watermarking scheme for quantum image is proposed based on Quantum cosine transform (QCT). The cosine coefficients are extracted by executing QCT on quantum image. A dynamic vector for controlling embedding strength instead of a fixed parameter for embedding process in other scheme is utilized to process quantum watermark. The adder operation and the inverse QCT are implemented which offset the QCT and guarantee the embedding process having smaller impact on the quantum carrier image. Simulation results and analysis show that the proposed dynamic watermarking scheme has better visual quality under a higher embedding capacity.

This paper presents a joint detection and tracking filter for a single extended target in the presence of clutter measurements and missed detections. The filter is obtained by adapting the Poisson extended target model into the Bernoulli filter proposed by Mahler. The resulted filter is an optimal extended target joint detection and tracking filter. Predictor and corrector are derived follows the random set filtering framework. A particle filter implementation is presented, in which simplification methods are used to make it easy to be realized. Simulation results show that the proposed filter is effective at detection and tracking of extended target in dense clutter backgrounds.

Based on revisiting the RYY⁺ Identitybased (ID-Based) key agreement protocol, we find it’s vulnerable to Intermediate results leakage (IRL) and Keycompromise impersonation (KCI) attack. A novel protocol called RYY⁺⁺ is proposed to make up for its deficiencies. Our protocol follows the Full dual exponential challenge response (FDCR-1) scheme to ensure the signature change every time, so the master public key of Private key generator (PKG) joined in signature generation can guarantee two parties trust each other. The RYY⁺⁺ protocol is also proven to be secure in the Strengthened extended Canetti– Krawczyk (SeCK) model which provides better support for adversary’s query and has an advantage over most existing protocols on security and efficiency.

At Eurocrypt’06, Lu et al. presented the first Sequential aggregate signature (SAS) provably secure without random oracles. The drawback of their scheme is that users need long public keys and the security model makes the Knowledge of secure key (KOSK) assumption. We present the first SAS scheme, which the user needs short public keys and the security is proven without random oracles, in the plain public key model. We also present the first Multisignature (MS) scheme in the plain public key model, which the security is proven without random oracles.

We propose improved differential and linear active S-boxes search techniques for Feistel type ciphers. We give a uniform representation of Feistel type structures which can benefit the analysis of differential propagation. By analyzing the properties of Feistel type environment, we present some important observations of differential propagation and propose a notion of equivalent state set which can narrow down the search space noticeably. We present a practical algorithm to improve the search of active S-boxes for Feistel type ciphers. It is basically a Viterbi search operating on equivalent state set and also adopts the pruning mechanism. Our experimental results show that the improved algorithm have advantages in respect to memory and time complexities, and it can be applied efficiently to Feistel cipher with large blocks. The search program can be implemented in normal PC, which will be more practical and useful for the designers and cryptanalysts.

In this paper, we propose a variational approach for the affine point set matching problems under the Bayesian probabilistic framework. A directed acyclic graph is provided for the representation of the joint probability over affine transformation, random variables and the point sets. Based on the directed graph, a variational iterative algorithm is derived to approximate the posteriors of the random variables and the anisotropic Gaussian mixtures are used for the estimation of the spurious outliers instead of the frequently-used uniform distribution. Experimental results demonstrate that our method achieves good performance in terms of both robustness and accuracy and is comparable to other state-of-the-art point registration algorithms especially in the case of complicated outliers.

With the fast growing size and dimensionality of scientific datasets, inspection and rendering data features has become an important topic when traversing through it. We propose opacity transfer function with trapezoid shape having an overlapping region to extract different layers when using Compute unified device architecture (CUDA)-based volume ray-casting. We define different tools such as probe for 3D inspection and virtual lenses for 2D inspection of inner layers in Region-ofinterest (ROI). We verify the effectiveness which allowing inspection of structures both interior layers in ROI and exterior semi-transparent ones at interactive frame rates.

A novel model for segmentation of water/ land in Synthetic aperture radar (SAR) images is presented. The proposed model calculates the global matrix of oriented gradient of histograms on wavelet subbands based on the multi-scale property, shift invariance and directional selectivity of Complex wavelet transform (CWT), then acquires edge information by thresholding the matrix. It improves the traditional Chan-vese (CV) model by adding the constraint of extracted edge information. A new water/ land segmentation algorithm is then proposed based on the new model. The performance of proposed method is compared with some other existing algorithms and the experimental results confirm its effectiveness.

The properties of error linear complexity of binary sequences with period of power of two are studied in this paper. Using the Games-Chan algorithm as main tool, accurate formulas of the minimum value k for which the k-error linear complexity is strictly less than the first and second critical error linear complexity are provided respectively.

In the emerging area of sensor-based systems, a significant challenge is to develop reliable, energy efficient methods to extract useful information from the distributed sensory data. Existing top-k query algorithms of sensor networks are only applicable to the sensor networks with single Microenvironment (ME). Besides, these approaches rely on using raw sensor readings, which handling these raw sensory data requires large amount of data transmission and is memory-consuming. In this paper we highlight the multiple MEs in sensor networks and propose a novel event-driven approximate top-k query framework based on fuzzy method. Firstly, membership function of fuzzy method is introduced to describe the global potential event confidence. Subsequently, non-uniform membership degree subranges based linguistic variables instead of numeric sensor readings are used for in-network top-k event fusion. Also two distributed event-driven approximate top-k query algorithms are devised. Theoretical analysis as well as extensive simulations based on synthetic and real data sets show that our framework reduces the data transmission significantly and the query results are more interpretable with quality guarantees.

This paper deals with the modeling, analysis, and measurement of a Small scale fading (SSF) mobile radio channel. The physics of SSF are first reviewed to reveal the generating mechanisms of channel selectivity. A stochastic channel model is then derived as a function of time, antenna array displacement and frequency, which falls in the category of tapped delay line model. Specifically, the taps can be represented as a combination of a possible line of sight or dominant reflected path and a Gaussian distributed component comprised of unresolvable scattered paths. After that, general analytical solutions are provided for the 3D temporal-spectral-spatial correlations of SSF via the exploitation of channel statistical properties. We show that this function can be expressed as the product of three low order temporal, spectral and spatial correlations individually under appropriate assumptions on the associated wireless propagation environment. This will definitely facilitate the derivations of the closed form expression regarding the correlation function of SSF. From engineering perspective, this analysis can be utilized to develop network correlation maps for example. Finally, out field measurement results verify the validity of our theoretical analysis.

In Wireless sensor networks (WSNs), missed measurements may be caused by the sensor malfunction and interruption of communication between sensor nodes. The feasibility of exact recovery of WSNs data with missed measurements is analyzed in the framework of compressed sensing. A new incomplete measurement model was developed and the data reconstruction algorithm was proposed. The required number of the missing measurements and the sparsity condition of network data are found for exact compressed sensing ofWSNs data. Theoretical derivation shows that aWSNs data of length N with no more than M/(log(N/M)+1) nonzero coefficients can be exactly recovered with M Gaussian measurements, provided that fraction of the missed measurements is less than a quarter of the Restricted isometry property (RIP) constant squared. Simulation results validate the theoretical results.

In price-based cognitive radio networks, the Primary user (PU) can allow the Secondary users (SUs) to access by pricing if their interference power is under the Interference power constraint (IPC). The interaction between the PU and the SUs is modeled as a Stackelberg game with the consideration of the Quality of service (QoS) of the SUs. The revenue maximization problem of PU is expressed as an equivalent convex optimization problem if the minimum Signal-to-interference and noise ratio (SINR) constraints for the SUs are greater than or equal to 0dB. An optimal pricing algorithm is proposed based on this equivalent convex optimization problem. Simulation results show that the proposed pricing algorithm outperforms the non-uniform pricing algorithm in terms of the revenue of the PU, the sum rate of SUs and the number of admitted SUs.

It is important to analyze the efficiency of resource allocation with game theory in congested networks in which the users are selfish. The results are often obtained from a one-shot game, while in reality, the transmission is frequent and occurs more than once. We develop a repeated inter-session network coding game that is based on a novel Average cost share (ACS) pricing mechanism. The users choose repeated transmission rates and transmission modes between network coding and routing to maximize their own payoffs. The Price of anarchy (PoA) is adopted to analyze the efficiency of the resource allocation. Through considering different strategies for the multiusers at the next stage, we find that network coding can improve the efficiency of resource allocation in the congested networks. We discuss trigger strategies that keep players from routing at new stages.

In this paper, a wide-band miniaturized Conformal conical Archimedean spiral (CCARS) antenna is proposed for Ultra wideband (UWB) communications. The CCARS antenna utilizes the vertical space instead of horizontal space significantly saving the antenna installation space. We investigate the transformation from Planar Archimedean spiral (PARS) antenna to the CCARS antenna and the antenna miniaturization technique. Due to the using of flexible dielectric substrate, the PARS antenna can be bent freely, which assures the transformation from the PARS antenna to the CCARS antenna. To realize antenna miniaturization, zigzag shape arms are applied to the PARS antenna. Moreover, the CCARS antenna has good unidirectional radiation property that avoids the using of traditional absorber-loaded back cavity. The measured results of antenna prototype indicate that the Voltage standing wave ratio (VSWR) is below 2 over the frequency range from 1.3GHz to 9GHz. The antenna gain is higher than 3.4dBi and the Front to back (F/B) ratio is better than 20dB from 2GHz to 7GHz. In addition, good radiation patterns and broad 3dB beam width are also available for the CCARS antenna.

In this paper, we show how information causality leads to Tsirelson bounds in a much easier way. Furthermore, a series of new Tsirelson bounds are then derived. We then define the communication protocols based on Random access code (RAC) and No-signaling box (NS-box) and then derive the objects for the communication complexity of symmetric quantum channels with i.i.d.(independent identically distribution) and uniform input marginal probabilities, consequently we obtain the multi-level Bell-type inequality. Deep ramifications concerning non-local quantum computation are also found and discussed.

Coordinated multi-point (CoMP) transmission has been regarded as a fascinating technology for 4G wireless communication systems. In this paper, the CoMP dynamic clustering problem is studied, a Friends algorithm (F-A) is proposed based on the procedure of making friends in human society, and then a dynamic clustering scheme is proposed based on F-A. The mutual interference among cells of the network is considerate in this algorithm, which can be obtained by CSI feedback. Compared to Traversal search (T-S) algorithm, the proposed algorithm has lower computational complexity, and the performance is almost equal. Simulation results show the proposed scheme is effective for dynamic clustering.

The capacity of wireless mesh networks can be greatly enhanced by equipping each mesh router with multiple radios and by exploiting multiple channels to reduce network interference. It has been proved that an efficient channel assignment scheme is critical to achieve the optimal network throughput. This paper presents a centralized channel assignment scheme for multi-radio multichannel wireless mesh networks. The scheme includes a spanner-based topology control operation and a linkranking channel assignment algorithm. In the topology control procedure, several links that cause intense interference are removed from the network without damage to the basic network connectivity. The channel assignment procedure improves the network throughput via ranking links based on the principle of load balance. The simulation results demonstrate that the proposed scheme outperforms the compared centralized channel assignment approach in terms of network throughput. The experimental results also indicate that the effectiveness of the proposed scheme is even evident in the case of heavy input traffic load where the potential interference in the network is severe.

Space-time adaptive processing (STAP) technique can mitigate airborne radar clutter and detect moving targets effectively. Based on adaptive radar theory, the Degrees-of-freedom (DoF) of a STAP processor should be larger than that of the received clutter. The local-DoFs of clutter for Reduced-dimensional STAP (RDSTAP) methods with sparse linear array are studied. The proposed formulas show that the local-DoFs of clutter can be estimated as the product of the equivalent clutter bandwidth with the equivalent sampling interval.

A novel parameter estimation algorithm based on Compressed sensing (CS) for the Direct sequences spread spectrum (DSSS) signals in high dynamic environments is proposed. In this algorithm, Fractional Fourier transform (FrFT) is first employed to estimate Doppler frequency rate, followed by the quadric phase term compensation. The compensation results are divided into several segments with equal length and coherent integration is carried out within each segment respectively. A convex optimization algorithm is applied to estimate the velocity and initial range of the target simultaneously based on the sparsity of target in the code phase domain. The proposed algorithm is capable of overcoming the limitation of Doppler frequency ambiguity and obtaining the accurate parameter estimates without correcting the code phase drift. Simulation results are presented to demonstrate the validity of the proposed algorithm.

Using the Inter-satellite links (ISLs) to enhance the GNSS’s positioning accuracy and integrity is attracting much more attentions. In order to ensure the autonomous positioning accuracy and stability based on ISLs, a satellite and ground joint positioning mode is presented in this paper, which is constrained by quality grade of position error. Spatial error ellipsoid is applied to calculate parameters and achieve Signal-in-space (SIS) integrity monitoring. To check the accuracy of the mode and performance of monitoring, mixed constellation of Beidou system is simulated to support the analysis and investigate the impact of the high-low ISLs. The orbit determination results verify that the introduced integritymonitoring method can satisfy the integrity risk of 10⁻⁵/h and achieve the SIS integrity monitoring.