The optimization of polarimetric contrast enhancement (OPCE) is a widely used method for maximizing the received power ratio of a desired target versus an undesired target (clutter). In this letter, a new model of the OPCE is proposed based on the Fisher criterion. By introducing the well known two-class problem of linear discriminant analysis (LDA), the proposed model is to enlarge the normalized distance of mean value between the target and the clutter. In addition, a cross-iterative numerical method is proposed for solving the optimization with a quadratic constraint. Experimental results with the polarimetric SAR (POLSAR) data demonstrate the effectiveness of the proposed method.

In this paper, we present a discriminative word-character hybrid model for joint Chinese word segmentation and POS tagging. Our word-character hybrid model offers high performance since it can handle both known and unknown words. We describe our strategies that yield good balance for learning the characteristics of known and unknown words and propose an error-driven policy that delivers such balance by acquiring examples of unknown words from particular errors in a training corpus. We describe an efficient framework for training our model based on the Margin Infused Relaxed Algorithm (MIRA), evaluate our approach on the Penn Chinese Treebank, and show that it achieves superior performance compared to the state-of-the-art approaches reported in the literature.

This paper presents an intuitive interaction technique for data exchange between multiple co-located devices. In the proposed system, CrossOverlayDesktop, desktop graphics of the devices are graphically overlaid with each other (i.e., alpha-blended). Users can exchange file data by the usual drag-and-drop manipulation through an overlaid area. The overlaid area is determined by the physical six degrees of freedom (6-DOF) correlation of the devices and thus changes according to users' direct movements of the devices. Because familiar operations such as drag-and-drop can be applied to file exchange between multiple devices, seamless, consistent, and thus intuitive multi-user collaboration is realized. Furthermore, dynamic overlay of desktop graphics allows users to intuitively establish communication, identify connected devices, and perform access control. For access control of the data, users can protect their own data by simply dragging them out of the overlaid area, because only the overlaid area becomes a public space. Several proof-of-concept experiments and evaluations were conducted. Results show the effectiveness of the proposed interaction technique.

This paper reviews applications of fuzzy logic to telecommunications and proposes a novel fuzzy combining scheme for cooperative spectrum sensing in cognitive radio systems. A summary of previous applications of fuzzy logic to telecommunications is given outlining also potential applications of fuzzy logic in future cognitive radio systems. In complex and dynamic operational environments, future cognitive radio systems will need sophisticated decision making and environment awareness techniques that are capable of handling multidimensional, conflicting and usually non-predictable decision making problems where optimal solutions can not be necessarily found. The results indicate that fuzzy logic can be used in cooperative spectrum sensing to provide additional flexibility to existing combining methods.

A voltage-controlled oscillator (VCO) tolerant to process variations at lower supply voltage was proposed. The circuit consists of an on-chip threshold-voltage-monitoring circuit, a current-source circuit, a body- biasing control circuit, and the delay cells of the VCO. Because variations in low-voltage VCO frequency are mainly determined by that of the current in delay cells, a current-compensation technique was adopted by using an on-chip threshold-voltage-monitoring circuit and body-biasing circuit techniques. Monte Carlo SPICE simulations demonstrated that variations in the oscillation frequency by using the proposed techniques were able to be suppressed about 65% at a 1-V supply voltage, compared to frequencies with and without the techniques.

In the present paper, the performance of cooperative relaying networks with adaptive relaying scheme selection is analyzed. Cooperative relaying is a new technique to achieve spatial diversity gain by using neighboring stations. However, when multiple stations transmit simultaneously, the number of interference signals increases. Therefore, the introduction of cooperative relaying in radio communication systems does not always increase the network capacity due to the co-channel interference. Therefore, in order to achieve high spectral efficiency, it is necessary to select cooperative relaying or non-cooperative relaying adaptively. Assuming both centralized and decentralized adaptive controls, the spectrum efficiency is evaluated. The performance under decentralized control is evaluated using a game-theoretic approach. Simulation results show that the introduction of cooperative relaying with centralized control always increases the spectral efficiency. On the other hand, Simulation results also show that, when each source selects a relaying scheme independently and selfishly to maximize its own spectral efficiency, the introduction of the cooperative relaying may reduce the spectral efficiency due to the increase in the number of interference signals.

We propose a video ontology system to overcome semantic gap in video retrieval. The proposed video ontology is aimed at bridging of the gap between the semantic nature of user queries and raw video contents. Also, results of semantic retrieval shows not only the concept of topic keyword but also a sub-concept of the topic keyword using semantic query extension. Through this process, recall is likely to provide high accuracy results in our method. The experiments compared with keyframe-based indexing have demonstrated that this proposed scene-based indexing presents better results in several kinds of videos.

The purpose of the paper is to show that boundary implication results hold for complex-valued uncertain linear time-delay systems. The results are derived by the Lambert W function and yield tractable robust stability criteria for simultaneously triangularizable linear time-delay systems. The setting is similar to a recently reported extreme-point result, but the assumed uncertainty sets can be much more free in shape.

In this paper, we present a novel analysis approach for large on-chip power grid circuit analysis. The new approach, called ETBR for extended truncated balanced realization, is based on model order reduction techniques to reduce the circuit matrices before the simulation. Different from the (improved) extended Krylov subspace methods EKS/IEKS, ETBR performs fast truncated balanced realization on response Gramian to reduce the original system. ETBR also avoids the adverse explicit moment representation of the input signals. Instead, it uses spectrum representation in frequency domain for input signals by fast Fourier transformation. The proposed method is very amenable for threading-based parallel computing, as the response Gramian is computed in a Monte-Carlo-like sampling style and each sampling can be computed in parallel. This contrasts with all the Krylov subspace based methods like the EKS method, where moments have to be computed in a sequential order. ETBR is also more flexible for different types of input sources and can better capture the high frequency contents than EKS, and this leads to more accurate results especially for fast changing input signals. Experimental results on a number of large networks (up to one million nodes) show that, given the same order of the reduced model, ETBR is indeed more accurate than the EKS method especially for input sources rich in high-frequency components. If parallel computing is explored, ETBR can be an order of magnitude faster than the EKS/IEKS method.

Recently, two-level cache, L1 cache and L2 cache, is commonly used in a processor. Particularly in an embedded system whereby a single application or a class of applications is repeatedly executed on a processor, its cache configuration can be customized such that an optimal one is achieved. An optimal two-level cache configuration can be obtained which minimizes overall memory access time or memory energy consumption by varying the three cache parameters: the number of sets, a line size, and an associativity, for L1 cache and L2 cache. In this paper, we first extend the L1 cache simulation algorithm so that we can explore two-level cache configuration. Second, we propose two-level cache design space exploration algorithms: CRCB-T1 and CRCB-T2, each of which is based on applying Cache Inclusion Property to two-level cache configuration. Each of the proposed algorithms realizes exact cache simulation but decreases the number of cache hit/miss judgments by a factor of several thousands. Experimental results show that, by using our approach, the number of cache hit/miss judgments required to optimize a cache configurations is reduced to 1/50-1/5500 compared to the exhaustive approach. As a result, our proposed approach totally runs an average of 1398.25 times faster compared to the exhaustive approach. Our proposed cache simulation approach achieves the world fastest two-level cache design space exploration.

In this paper, we propose a Modified nested sparse grid based Adaptive Stochastic Collocation Method (MASCM) for block-based Statistical Static Timing Analysis (SSTA). The proposed MASCM employs an improved adaptive strategy derived from the existing Adaptive Stochastic Collocation Method (ASCM) to approximate the key operator MAX during timing analysis. In contrast to ASCM which uses non-nested sparse grid and tensor product quadratures to approximate the MAX operator for weakly and strongly nonlinear conditions respectively, MASCM proposes a modified nested sparse grid quadrature to approximate the MAX operator for both weakly and strongly nonlinear conditions. In the modified nested sparse grid quadrature, we firstly construct the second order quadrature points based on extended Gauss-Hermite quadrature and nested sparse grid technique, and then discard those quadrature points that do not contribute significantly to the computation accuracy to enhance the efficiency of the MAX approximation. Compared with the non-nested sparse grid quadrature, the proposed modified nested sparse grid quadrature not only employs much fewer collocation points, but also offers much higher accuracy. Compared with the tensor product quadrature, the modified nested sparse grid quadrature greatly reduced the computational cost, while still maintains sufficient accuracy for the MAX operator approximation. As a result, the proposed MASCM provides comparable accuracy while remarkably reduces the computational cost compared with ASCM. The numerical results show that with comparable accuracy MASCM has 50% reduction in run time compared with ASCM.

In this paper, a new heuristic algorithm is proposed to optimize the power domain clustering in controlling-value-based (CV-based) power gating technology. In this algorithm, both the switching activity of sleep signals (p) and the overall numbers of sleep gates (gate count, N) are considered, and the sum of the product of p and N is optimized. The algorithm effectively exerts the total power reduction obtained from the CV-based power gating. Even when the maximum depth is kept to be the same, the proposed algorithm can still achieve power reduction approximately 10% more than that of the prior algorithms. Furthermore, detailed comparison between the proposed heuristic algorithm and other possible heuristic algorithms are also presented. HSPICE simulation results show that over 26% of total power reduction can be obtained by using the new heuristic algorithm. In addition, the effect of dynamic power reduction through the CV-based power gating method and the delay overhead caused by the switching of sleep transistors are also shown in this paper.

We investigated an analogue modulation of magneto-optic spatial light modulator (MOSLM). For enhancement of the modulation from the voltage-driving MOSLM, magnetostriction and saturation magnetization of magnetic garnet films and piezoelectric constant of PZT films were investigated. The performance was expected to be improved by using Bismuth, Dysprosium and Aluminum substituted Yttrium Iron garnet, which effective magnetic field showed 20 times higher than Yttrium Iron garnet.

In this paper, an indoor repeater antenna with high isolation for WCDMA application is proposed. The designed repeater has very small separation of 20 mm between the donor and server antennas. The antenna has two resonance frequencies to cover the WCDMA band from 1.92 GHz to 2.17 GHz. The fabricated antenna has VSWR below 1.5, gain over 8 dBi, and isolation between server and donor antennas less than -80 dB in the WCDMA band.

The hidden terminal problem leads to frequent collisions and decreases the throughput of ad hoc networks dramatically. Low network spatial reuse also results in fewer parallel transmissions, which further leads to reduced network throughput. Eliminating the hidden terminals and improving the spatial reuse are two important approaches to improving network throughput. In this paper, spatial distribution of the hidden terminals is analyzed in consideration of accumulated interference and environmental noise. As the distribution of hidden terminals is affected by many factors such as transmitter-receiver distance, SINR requirement and nodes density, it is inefficient to use fixed busy tone transmission power. To eliminate the hidden terminals and improve network spatial reuse, an enhancement to DBTMA named EDBTMA is proposed. This is achieved by using an adaptive busy tone power control scheme. Receivers adjust the transmission power of busy tone according to received signal power and accumulated interference adaptively so that all hidden terminals (and only hidden terminals) are covered by the busy tone. Simulation results show that EDBTMA protocol can solve the hidden terminal problem and improve network spatial reuse better than DBTMA and achieves 65% additional network throughput compared to DBTMA.

We propose a method of assigning polarity to causal information extracted from Japanese financial articles concerning business performance of companies. Our method assigns polarity (positive or negative) to causal information in accordance with business performance, e.g. "zidousya no uriage ga koutyou: (Sales of cars are good)" (The polarity positive is assigned in this example). We may use causal expressions assigned polarity by our method, e.g., to analyze content of articles concerning business performance circumstantially. First, our method classifies articles concerning business performance into positive articles and negative articles. Using them, our method assigns polarity (positive or negative) to causal information extracted from the set of articles concerning business performance. Although our method needs training dataset for classifying articles concerning business performance into positive and negative ones, our method does not need a training dataset for assigning polarity to causal information. Hence, even if causal information not appearing in the training dataset for classifying articles concerning business performance into positive and negative ones exist, our method is able to assign it polarity by using statistical information of this classified sets of articles. We evaluated our method and confirmed that it attained 74.4% precision and 50.4% recall of assigning polarity positive, and 76.8% precision and 61.5% recall of assigning polarity negative, respectively.

Despite the growing research effort in formal verification, industrial verification often relies on the constrained random simulation methodology, which is supported by constraint solvers as the stimulus generator integrated within simulator, especially for the large design with complex constraints nowadays. These stimulus generators need to be fast and well-distributed to maintain simulation performance. In this paper, we propose a dynamic method to guide stimulus generation by SAT solvers. An adjusting strategy named Tabu Search with Memory (TSwM) is integrated in the stimulus generator for the search and prune processes along with the constraint solver. Experimental results show that the method proposed in this paper could generate well-distributed stimuli with good performance.

Recent technology mappers for LUT based FPGAs employ cut enumeration. Although many cuts are often needed to find a good network, enumerating all the cuts with large size consumes a lot of run-time. Existing algorithms employ the bottom-up merging which calculates Cartesian products of the fanins' cuts for each node. The number of cuts is much smaller than the size of the Cartesian products in most cases. Thus, the existing algorithms are inefficient. Furthermore, the number of cuts exponentially increases with the size of cuts, that makes the run-time much longer. Several algorithms to enumerate not all the cuts but partial cuts have been presented, but they tend to disturb the quality of networks. This paper presents two algorithms to enumerate cuts; an exhaustive enumeration and a partial enumeration. Both of them are efficient because they do not employ the bottom-up merging. The partial enumeration reduces the number of enumerated cuts with a guarantee that a depth-minimum network can be constructed. The experimental results show that the exhaustive enumeration runs about 5 and 13 times faster than the existing bottom-up algorithm for K=8, 9 respectively, while keeping the same results. On the other hand, the partial enumeration runs about 9 and 29 times faster than the existing algorithm for K = 8, 9, respectively. The average area of networks derived by the sets of cuts enumerated by the partial enumeration is only 4% larger than that derived with using all the cuts, and the depth is the same.

Information theoretic security is an important security notion in cryptography as it provides a true lower bound for attack complexities. However, in practice attacks often have a higher cost than the information theoretic bound. In this paper we study the relationship between information theoretic attack costs and real costs. We show that in the information theoretic model, many well-known and commonly used hash functions such as MD5 and SHA-256 fail to be preimage resistant.

This letter proposes a new adaptive filtering method that uses the last L desired signal samples as an extra input vector, besides the existing input data, to reduce mean square error. We have improved the convergence rate by adopting the squared norm of the past error samples, in addition to the modified cost function. The modified variable error-data normalized step-size least mean square algorithm provides fast convergence, ensuring a small final misadjustment. Simulation results indicate its superior mean square error performance, while its convergence rate equals that of existing methods. In addition, the proposed algorithm shows superior tracking capability when the system is subjected to an abrupt disturbance.