This letter proposes a new hybrid EM wave absorber with the crossed-wedge shape, which can be applied to 3 m semi anechoic chambers. In this study, we designed a new hybrid EM wave absorber with the crossed-wedge shape, which consisted of the inorganic and organic thin corrugated dielectric lossy sheet containing organic conductive fibers. Then the 3 m semi anechoic chamber is constructed in the size of 9.0 m6.0 m5.7 m (LWH) using these absorbers, and also the normalized site attenuation (NSA) is measured according to ANSI C63.4 in the frequency range of 30 MHz to 1 GHz. As a result, the measured NSA is obtained within 3 dB of the theoretical one.

The single input rule modules connected fuzzy inference method (SIRMs method) by Yubazaki et al. can decrease the number of fuzzy rules drastically in comparison with the conventional fuzzy inference methods. Moreover, Seki et al. have proposed a functional-type SIRMs method which generalizes the consequent part of the SIRMs method to function. However, these SIRMs methods can not be applied to XOR (Exclusive OR). In this paper, we propose a "kernel-type SIRMs method" which uses the kernel trick to the SIRMs method, and show that this method can treat XOR. Further, a learning algorithm of the proposed SIRMs method is derived by using the steepest descent method, and compared with the one of conventional SIRMs method and kernel perceptron by applying to identification of nonlinear functions, medical diagnostic system and discriminant analysis of Iris data.

Boundary-based corner detection has been widely applied in spline curve fitting, automated optical inspection, image segmentation, object recognition, etc. In order to obtain good results, users usually need to adjust the length of region of support to resist zigzags due to quantization and random noise on digital boundaries. To automatically determine the length of region of support for corner detection, Teh-Chin and Guru-Dinesh presented adaptive approaches based on some local properties of boundary points. However, these local-property based approaches are sensitive to noise. In this paper, we propose a new approach to find the optimum length of region of support for corner detection based on a statistic discriminant criterion. Since our approach is based on the global perspective of all boundary points, rather than the local properties of some points, the experiments show that the determined length of region of support increases as the noise intensity strengthens. In addition, the detected corners based on the optimum length of region of support are consistent with human experts' judgment, even for noisy boundaries.

Location Based Services (LBS) are expected to become one of the major drivers of ubiquitous services due to recent inception of GPS-enabled mobile devices, the development of Web2.0 paradigm, and emergence of 3G broadband networks. Having this vision in mind, Community Context-attribute-oriented Collaborative Information Environment (CCCIE) based Autonomous Decentralized Community System (ADCS) is proposed to enable provision of services to specific users in specific place at specific time considering various context-attributes. This paper presents autonomous community construction technology that share service discovered by one member among others in flexible way to improve timeliness and reduce network cost. In order to meet crucial goal of real-time and context-aware community construction (provision of service/ service information to users with common interests), and defining flexible service area in highly dynamic operating environment of ADCS, proposed progressive ripple based service discovery technique introduces novel idea of snail's pace and steady advancing search followed by swift boundary confining mechanism; while service area construction shares the discovered service among members in defined area to further improve timeliness and reduce network cost. Analysis and empirical results verify the effectiveness of the proposed technique.

This paper proposes a parallelized DVC framework that treats each bitplane independently to reduce the decoding time. Unfortunately, simple parallelization generates inaccurate bit probabilities because additional side information is not available for the decoding of subsequent bitplanes, which degrades encoding efficiency. Our solution is an effective estimation method that can calculate the bit probability as accurately as possible by index assignment without recourse to side information. Moreover, we improve the coding performance of Rate-Adaptive LDPC (RA-LDPC), which is used in the parallelized DVC framework. This proposal selects a fitting sparse matrix for each bitplane according to the syndrome rate estimation results at the encoder side. Simulations show that our parallelization method reduces the decoding time by up to 35[%] and achieves a bit rate reduction of about 10[%].

A method was developed for deriving the control input for a multi-dimensional discrete-time nonlinear system so that a performance index is approximately minimized. First, a moment vector equation (MVE) is derived; it is a multi-dimensional linear equation that approximates a nonlinear system in the whole domain of the system state and control input. Next, the performance index is approximated by using a quadratic form with respect to the moment vector. On the basis of the MVE and the quadratic form, an approximate optimal controller is derived by solving the linear quadratic optimal control problem. A bilinear optimal control problem and a mountain-car problem were solved using this method, and the solutions were nearly optimal.

For a cyclic code, the BCH Bound and the Hartmann-Tzeng bound are two of well-known lower bounds for its minimum distance. New bounds are proposed by N. Boston in 2001, that depend on defining set of cyclic code. In this paper, we consider the between the Boston bound and these two bounds for non-binary cyclic codes from numerical examples.

This paper proposes a novel reversible image authentication method that does not memorize the parameters for extracting embedded authentication data from an image. The proposed method once distorts an image to hide data for authentication into the image, it recovers the original image from the distorted image unless tamper is applied to the image, i.e., reversible. By comparing extracted data and data generated from the restored image, this method detects image tampering and further localizes tampered regions by the unit of block. The proposed method extracts hidden data without memorization of parameters used in its algorithm. This feature makes the proposed method practical. Whereas any method memorizing parameters faces severe problems with storage and management of parameters, according to the increase in the number of memorized parameters that is caused by serving accurate tamper localization and/or by applying itself to a huge number of image collection, e.g., video sequences. Simulation results show the effectiveness of the proposed method.

This paper presents a novel approach for robust impulsive synchronization of uncertain complex dynamical networks, each node of which possesses chaotic dynamics with different parameters perturbation and external disturbances as well as unknown but bounded network coupling effects. A new sufficient condition is proposed that guarantees the global robust synchronizing of such a network. Finally, the effectiveness of the proposed approach is evaluated by performing simulations on two illustrative examples.

Orthogonal frequency division multiplexing (OFDM) is a critical technology in 3G evolution systems, which can effectively avoid intra-cell interference, but may bring with serious inter-cell interference. Inter-cell interference cancellation is one of effective schemes taken in mitigating inter-cell interference, but for many existing schemes in inter-cell interference cancellation, various generalized spatial diversities are taken, which always bring with extra interference and blind spots, or even need to acquire extra information on source and channel. In this paper, a novel inter-cell interference mitigation method is proposed for 3G evolution systems. This method is based on independent component analysis in blind source separation, and the input signal to interference plus noise ratio (SINR) is set as objective function. By generalized eigenvalue decomposition and algorithm iterations, maximum signal noise ratio (SNR) can be obtained in output. On the other hand, this method can be worked with no precise knowledge of source signal and channel information. Performance evaluation shows that such method can mitigate inter-cell interference in a semi-blind state, and effectively improve output SNR with the condition that lower input SINR, higher input SNR and longer lengths of the processing frame.

This paper analyzes transient behaviors of the polarization-mode-dispersion (PMD) vector for the Foschini and Poole's birefringence vector model. We find an asymptotic solution of the corresponding Fokker-Planck equation representing the solution as a superposition of angular components characterized by the Legendre polynomials. The distribution tail for the PMD vector magnitude evolves slowly to the Maxwellian owing to the residual couplings between adjacent angular components. Of particular interest, the distribution tail for the PMD vector magnitude lies well below the Maxwellian fit during the transient.

This paper proposes an integrated approach to simultaneous detection and localization of multiple object categories using both generative and discriminative models. Our approach consists of first generating a set of hypotheses for each object category using a generative model (pLSA) with a bag of visual words representing each object. Based on the variation of objects within a category, the pLSA model automatically fits to an optimal number of topics. Then, the discriminative part verifies each hypothesis using a multi-class SVM classifier with merging features that combines spatial shape and appearance of an object. In the post-processing stage, environmental context information along with the probabilistic output of the SVM classifier is used to improve the overall performance of the system. Our integrated approach with merging features and context information allows reliable detection and localization of various object categories in the same image. The performance of the proposed framework is evaluated on the various standards (MIT-CSAIL, UIUC, TUD etc.) and the authors' own datasets. In experiments we achieved superior results to some state of the art methods over a number of standard datasets. An extensive experimental evaluation on up to ten diverse object categories over thousands of images demonstrates that our system works for detecting and localizing multiple objects within an image in the presence of cluttered background, substantial occlusion, and significant scale changes.

Applications usually have their own phases in heap memory usage. The traditional garbage collector fails to match various application phases because the same heuristic on the object behavior is used throughout the entire execution. This paper introduces a phase-adaptive garbage collector which reorganizes the heap layout and adjusts the invocation time of the garbage collection according to the phases. The proposed collector identifies phases by detecting the application methods strongly related to the phase boundaries. The experimental results show that the proposed phase-adaptive collector successfully recognizes application phases and improves the garbage collection time by as much as 41%.

Localization is an important problem for Wireless Sensor Networks (WSN). The localization method can be categorized as range-free or range-based schemes. Since sensor nodes are usually cheap and small, the range-based schemes that require range measurement unit are unsuitable in WSN. The DV-hop algorithm is one of the range-free localization algorithms in which average hop-distance and hop counts are used for range estimation. But it requires heavy communication cost if the number of nodes increases in the network. Therefore, we propose a simple algorithm to reduce the communication cost and its performance is verified via computer simulations.

In this letter we show that the dynamic optimal PCID allocation problem in LTE systems is NP-complete. Subsequently we provide a near-optimal solution using SON which models the problem using new merge operations and explores the search space using a suitable randomized algorithmic approach. Two feasible options for dynamic auto-configuration of the system are also discussed. Simulation results point out that the approach provides near-optimal auto-configuration of PCIDs in computationally feasible time.

When we design a robust vector quantizer (VQ) for noisy channels, an appropriate index assignment function should be contrived to minimize the channel-error effect. For relatively high rates, the complexity for finding an optimal index assignment function is too high to be implemented. To overcome such a problem, we use a structurally constrained VQ, which is called the sample-adaptive product quantizer (SAPQ) [12], for low complexities of quantization and index assignment. The product quantizer (PQ) and its variation SAPQ [13], which are based on the scalar quantizer (SQ) and thus belong to a class of the binary lattice VQ [16], have inherent error resilience even though the conventional affine index assignment functions, such as the natural binary code, are employed. The error resilience of SAPQ is observed in a weak sense through worst-case bounds. Using SAPQ for noisy channels is useful especially for high rates, e.g., > 1 bit/sample, and it is numerically shown that the channel-limit performance of SAPQ is comparable to that of the best codebook permutation of binary switching algorithm (BSA) [23]. Further, the PQ or SAPQ codebook with an affine index assignment function is used for the initial guess of the conventional clustering algorithm, and it is shown that the performance of the best BSA can be easily achieved.

Multiuser -- Multiple Input Multiple Output (MU-MIMO) techniques were proposed to increase spectrum efficiency; a key assumption was that the Mobile Terminals (MTs) were simple with only a few antennas. This paper focuses on the Block Diagonalization algorithm (BD) based on the equal power allocation strategy as a practical MU-MIMO technique. When there are many MTs inside the service area of the access point (AP), the AP must determine, at each time slot, the subset of the MTs to be spatially multiplexed. Since the transmission performance depends on the subsets of MTs, the user selection method needs to use the Channel State Information (CSI) obtained in the physical layer to maximize the Achievable Transmission Rate (ATR). In this paper, we clarify the relationship between ATR with SU-MIMO and that with MU-MIMO in a high eigenvalue channel. Based on the derived relationship, we propose a new measure for user selection. The new measure, the eigenvalue decay factor, represents the degradation of the eigenvalues in null space compared to those in SU-MIMO; it is obtained from the signal space vectors of the MTs. A user selection method based on the proposed measure identifies the combination of MTs that yields the highest ATR; our approach also reduces the computational load of user selection. We evaluate the effectiveness of user selection with the new measure using numerical formulations and computer simulations.

This Letter proposes a new kind of features for color image retrieval based on Distance-weighted Boundary Predictive Vector Quantization (DWBPVQ) Index Histograms. For each color image in the database, 6 histograms (2 for each color component) are calculated from the six corresponding DWBPVQ index sequences. The retrieval simulation results show that, compared with the traditional Spatial-domain Color-Histogram-based (SCH) features and the DCTVQ index histogram-based (DCTVQIH) features, the proposed DWBPVQIH features can greatly improve the recall and precision performance.

This paper proposes a robust adaptive fuzzy PID control scheme augmented with a supervisory controller for unknown systems. In this scheme, a generalized fuzzy model is used to describe a class of unknown systems. The control strategy allows each part of the control law, i.e., a supervisory controller, a compensator, and an adaptive fuzzy PID controller, to be designed incrementally according to different guidelines. The supervisory controller in the outer loop aims at enhancing system robustness in the face of extra disturbances, variation in system parameters, and parameter drift in the adaptation law. Furthermore, an H∞ control design method using the fuzzy Lyapunov function is presented for the design of the initial control gains that guarantees transient performance at the start of closed-loop control, which is generally overlooked in many adaptive control systems. This design of the initial control gains is a compound search strategy called conditional linear matrix inequality (CLMI) approach with IROA (Improved random optimal algorithm), it leads to less complex designs than a standard LMI method by fuzzy Lyapunov function. Numerical studies of the tracking control of an uncertain inverted pendulum system demonstrate the effectiveness of the control strategy. From results of this simulation, the generalized fuzzy model reduces the rule number of T-S fuzzy model indeed.

This paper describes a novel algorithm for approximate nearest neighbor searching. For solving this problem especially in high dimensional spaces, one of the best-known algorithm is Locality-Sensitive Hashing (LSH). This paper presents a variant of the LSH algorithm that outperforms previously proposed methods when the dataset consists of vectors normalized to unit length, which is often the case in pattern recognition. The LSH scheme is based on a family of hash functions that preserves the locality of points. This paper points out that for our special case problem we can design efficient hash functions that map a point on the hypersphere into the closest vertex of the randomly rotated regular polytope. The computational analysis confirmed that the proposed method could improve the exponent ρ, the main indicator of the performance of the LSH algorithm. The practical experiments also supported the efficiency of our algorithm both in time and in space.