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.

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.

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.

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.

Resource sharing is to ensure required resources available for their demanders. However, due to the lack of proper sharing model, the current sharing rate of the scientific and technological resources is low, impeding technological innovation and value chain development. Here we propose a novel method to share scientific and technological resources by storing resources as nodes and correlations as links to form a complex network. We present a few-shot relational learning model to solve the cold-start and long-tail problems that are induced by newly added resources. Experimentally, using NELL-One and Wiki-One datasets, our one-shot results outperform the baseline framework - metaR by 40.2% and 4.1% on MRR in Pre-Train setting. We also show two practical applications, a resource graph and a resource map, to demonstrate how the complex network helps resource sharing.

The tens of billions of devices expected to be connected to the Internet will include so many sensors that the demand for sensor-based services is rising. The task of effectively utilizing the enormous numbers of sensors deployed is daunting. The need for automatic sensor identification has expanded the need for research on sensor similarity searches. The Internet of Things (IoT) features massive non-textual dynamic data, which is raising the critical challenge of efficiently and effectively searching for and selecting the sensors most related to a need. Unfortunately, single-attribute similarity searches are highly inaccurate when searching among similar attribute values. In this paper, we propose a group-fitting correlation calculation algorithm (GFC) that can identify the most similar clusters of sensors. The GFC method considers multiple attributes (e.g., humidity, temperature) to calculate sensor similarity; thus, it performs more accurate searches than do existing solutions.

We present a novel model, named Category Constraint-Latent Dirichlet Allocation (CC-LDA), to learn and recognize natural scene category. Previous work had to resort to additional classifier after obtaining image topic representation. Our model puts the category information in topic inference, so every category is represented in a different topics simplex and topic size, which is consistent with human cognitive habit. The significant feature in our model is that it can do discrimination without combined additional classifier, during the same time of getting topic representation. We investigate the classification performance with variable scene category tasks. The experiments have demonstrated that our learning model can get better performance with less training data.

The Lee complete ρ weight enumerator and the exact complete ρ weight enumerator over Mn×s(Fl+vFl+v2Fl)(v3=v) are defined, and the MacWilliams identities with respect to RT metric for these two weight enumerators of linear codes over Mn×s(Fl+vFl+v2Fl) are obtained, respectively. Finally, we give two examples to illustrate the obtained results.

This letter presents an image rectification scheme that can be used by any image watermarking algorithms to provide robustness against rotation, scaling and translation (RST) transformations.

This letter introduces a prime-factor Galois field Fourier transform (PF-GFFT) architecture to frequency domain decoding (FDD) of cyclic codes. Firstly, a fast FDD scheme is designed which converts the original single longer Fourier transform to a multi-dimensional smaller transform. Furthermore, a ladder-shift architecture for PF-GFFT is explored to solve the rearrangement problem of input and output data. In this regard, PF-GFFT is considered as a lower order spectral calculation scheme, which has sufficient preponderance in reducing the computational complexity. Simulation results show that PF-GFFT compares favorably with the current general GFFT, simplified-GFFT (S-GFFT), and circular shifts-GFFT (CS-GFFT) algorithms in time-consuming cost, and is nearly an order of magnitude or smaller than them. The superiority is a benefit to improving the decoding speed and has potential application value in decoding cyclic codes with longer code lengths.

Localized content-based image retrieval (LCBIR) has emerged as a hot topic more recently because in the scenario of CBIR, the user is interested in a portion of the image and the rest of the image is irrelevant. In this paper, we propose a novel region-level relevance feedback method to solve the LCBIR problem. Firstly, the visual attention model is employed to measure the regional saliency of each image in the feedback image set provided by the user. Secondly, the regions in the image set are constructed to form an affinity matrix and a novel propagation energy function is defined which takes both low-level visual features and regional significance into consideration. After the iteration, regions in the positive images with high confident scores are selected as the candidate query set to conduct the next-round retrieval task until the retrieval results are satisfactory. Experimental results conducted on the SIVAL dataset demonstrate the effectiveness of the proposed approach.

Face verification in the presence of age progression is an important problem that has not been widely addressed. Despite appearance changes for same person due to aging, they are more similar compared to facial images from different individuals. Hence, we design common and adapted vocabularies, where common vocabulary describes contents of general population and adapted vocabulary represents specific characteristics of one of image facial pairs. And the other image is characterized with a concatenation histogram of common and adapted visual words counts, termed as “age-invariant distinctive representation”. The representation describes whether the image content is best modeled by the common vocabulary or the corresponding adapted vocabulary, which is further used to accomplish the face verification. The proposed approach is tested on the FGnet dataset and a collection of real-world facial images from identification card. The experimental results demonstrate the effectiveness of the proposed method for verification of identity at a modest computational cost.

We propose a compact magnetic tunnel junction (MTJ) model for circuit simulation by de-facto standard SPICE in this paper. It is implemented by Verilog-A language which makes it easy to simulate MTJs with other standard devices. Based on the switching probability, we smoothly connect the adiabatic precessional model and the thermal activation model by using an interpolation technique based on the cubic spline method. We can predict the switching time after a current is applied. Meanwhile, we use appropriate physical models to describe other MTJ characteristics. Simulation results validate that the model is consistent with experimental data and effective for MTJ/CMOS hybrid circuit simulation.

In this paper, an adaptive updating probabilistic model is proposed to track an object in real-world environment that includes motion blur, illumination changes, pose variations, and occlusions. This model adaptively updates tracker with the searching and updating process. The searching process focuses on how to learn appropriate tracker and updating process aims to correct it as a robust and efficient tracker in unconstrained real-world environments. Specifically, according to various changes in an object's appearance and recent probability matrix (TPM), tracker probability is achieved in Expectation-Maximization (EM) manner. When the tracking in each frame is completed, the estimated object's state is obtained and then fed into update current TPM and tracker probability via running EM in a similar manner. The highest tracker probability denotes the object location in every frame. The experimental result demonstrates that our method tracks targets accurately and robustly in the real-world tracking environments.

Appearance changes conform to certain rules for a same person,while for different individuals the changes are uncontrolled. Hence, this paper studies the age progression rules to tackle face verification task. The age progression rules are discovered in the difference space of facial image pairs. For this, we first represent an image pair as a matrix whose elements are the difference of a set of visual words. Thereafter, the age progression rules are trained using Support Vector Machine (SVM) based on this matrix representation. Finally, we use these rules to accomplish the face verification tasks. The proposed approach is tested on the FGnet dataset and a collection of real-world images from identification card. The experimental results demonstrate the effectiveness of the proposed method for verification of identity.

This paper proposes the Extended Bag-of-Visterms (EBOV) to represent semantic scenes. In previous methods, most representations are bag-of-visterms (BOV), where visterms referred to the quantized local texture information. Our new representation is built by introducing global texture information to extend standard bag-of-visterms. In particular we apply the adaptive weight to fuse the local and global information together in order to provide a better visterm representation. Given these representations, scene classification can be performed by pLSA (probabilistic Latent Semantic Analysis) model. The experiment results show that the appropriate use of global information improves the performance of scene classification, as compared with BOV representation that only takes the local information into account.

This paper presents a technique for reducing spurious output of balanced modulators used in transmitters and arbitrary waveform generators. Two-step upconversion is a convenient way to produce a desired single-sideband (SSB) radio-frequency (RF) signal--baseband quadrature I and Q signals (which are analog outputs of direct digital frequency synthesizers) are upconverted by mixers and local oscillators (LOs)--but mismatches between the DACs in I and Q paths cause spurious output. We propose a method of dynamically matching the I and Q paths by multiplexing two DACs between I and Q paths in a pseudo-random manner. MATLAB simulation shows that multiplexing the two DACs spreads the spurious output, caused by mismatches between the two DACs, in the frequency domain, and reduces the peak level of spurious signals.

In this paper, a program generator for communication protocol software will be presented. Our program generator takes an extended finite state machine as a domain model and generates a group of C++ classes needed for an implementation. For each state of the FSM, a C++ class is generated, where the interface events are implemented as member functions of the corresponding state object. Protocol data units (PDUs) are embedded as Message objects and specified in the same way as packet filter and is interpreted to generate necessary PDU definition statements and PDU manipulation statements. Also, protocol objects from different layers can be linked together by using an organization model, where a protocol entity is invoked by its upper layer entity or lower layer entity by member function calls.

Salient Region Extraction provides an alternative methodology to image description in many applications such as adaptive content delivery and image retrieval. In this paper, we propose a robust approach to extracting the salient region based on bottom-up visual attention. The main contributions are twofold: 1) Instead of the feature parallel integration, the proposed saliencies are derived by serial processing between texture and color features. Hence, the proposed approach intrinsically provides an alternative methodology to model attention with low implementation complexity. 2) A constructive approach is proposed for rendering an image by a non-linear intensity mapping, which can efficiently eliminate high contrast noise regions in the image. And then the salient map can be robustly generated for a variety of nature images. Experiments show that the proposed algorithm is effective and can characterize the human perception well.

This paper discusses the issues involved in the design of a complete information retrieval system for DataSpace based on user relevance probabilistic schemes. First, Information Hidden Model (IHM) is constructed taking into account the users' perception of similarity between documents. The system accumulates feedback from the users and employs it to construct user oriented clusters. IHM allows integrating uncertainty over multiple, interdependent classifications and collectively determines the most likely global assignment. Second, Three different learning strategies are proposed, namely query-related UHH, UHB and UHS (User Hidden Habit, User Hidden Background, and User Hidden keyword Semantics) to closely represent the user mind. Finally, the probability ranking principle shows that optimum retrieval quality can be achieved under certain assumptions. An optimization algorithm to improve the effectiveness of the probabilistic process is developed. We first predict the data sources where the query results could be found. Therefor, compared with existing approaches, our precision of retrieval is better and do not depend on the size and the DataSpace heterogeneity.