In the past few years, discriminant analysis and manifold learning have been widely used in feature extraction. Recently, the sparse representation technique has advanced the development of pattern recognition. In this paper, we combine both discriminant analysis and manifold learning with sparse representation technique and propose a novel feature extraction approach named sparsity preserving embedding with manifold learning and discriminant analysis. It seeks an embedded space, where not only the sparse reconstructive relations among original samples are preserved, but also the manifold and discriminant information of both original sample set and the corresponding reconstructed sample set is maintained. Experimental results on the public AR and FERET face databases show that our approach outperforms relevant methods in recognition performance.

Beamforming with sparse constraint has shown significant performance improvement. In this letter, a least squares constant modulus blind adaptive beamforming with sparse constraint is proposed. Simulation results indicate that the proposed approach exhibits better performance than the well-known least squares constant modulus algorithm (LSCMA).

In this paper, we evaluate the downlink performance of Transparent mode (T-mode) and Non-Transparent mode (NT-mode) in a two-hop cellular system based on IEEE 802.16j. In particular, we evaluate the performance in terms of the system capacity, optimal resource allocation, and outage probability using Monte Carlo simulation with various system parameters such as different Frequency Reuse Factors (FRFs) and the distance between Base Station (BS) and Relay Station (RS). To analyze the Signal to Interference and Noise Ratio (SINR) of the access and relay links, an SINR model is introduced for cellular multihop systems considering intra- and inter-cell interferences. Then, we present a method of optimal resource allocation for the Access Zone (AZ) and Relay Zone (RZ) to maximize the system capacity. Consequently, the simulation results provide an insight into choosing the appropriate RS position and optimal resource allocation. Through numerical examples, it is found that the FRFs of two and three are good choices to achieve the highest capacity with low outage in T- and NT-modes, respectively.

This paper presents novel hybrid push-pull protocols for peer-to-peer video streaming. Our approaches intend to reap the best of push- and pull-based schemes by adaptively switching back and forth between the two modes according to video chunk distributions. The efficacy of the proposed protocols is validated through an evaluation study that demonstrates substantial performance gains.

A new direction of arrival (DOA) estimation method is introduced with arbitrary array geometry when uncorrelated and coherent signals coexist. The DOAs of uncorrelated signals are first estimated via subspace-based high resolution DOA estimation technique. Then a matrix that only contains the information of coherent signals can be formulated by eliminating the contribution of uncorrelated signals. Finally a subspace block sparse reconstruction approach is taken for DOA estimations of the coherent signals.

This paper addresses the problem of ordering the color SIFT descriptors in the independent component analysis for image classification. Component ordering is of great importance for image classification, since it is the foundation of feature selection. To select distinctive and compact independent components (IC) of the color SIFT descriptors, we propose two ordering approaches based on local variation, named as the localization-based IC ordering and the sparseness-based IC ordering. We evaluate the performance of proposed methods, the conventional IC selection method (global variation based components selection) and original color SIFT descriptors on object and scene databases, and obtain the following two main results. First, the proposed methods are able to obtain acceptable classification results in comparison with original color SIFT descriptors. Second, the highest classification rate can be obtained by using the global selection method in the scene database, while the local ordering methods give the best performance for the object database.

In this paper, a novel algorithm, Cross Low-dimension Pursuit, based on a new structured sparse matrix, Permuted Block Diagonal (PBD) matrix, is proposed in order to recover sparse signals from incomplete linear measurements. The main idea of the proposed method is using the PBD matrix to convert a high-dimension sparse recovery problem into two (or more) groups of highly low-dimension problems and crossly recover the entries of the original signal from them in an iterative way. By sampling a sufficiently sparse signal with a PBD matrix, the proposed algorithm can recover it efficiently. It has the following advantages over conventional algorithms: (1) low complexity, i.e., the algorithm has linear complexity, which is much lower than that of existing algorithms including greedy algorithms such as Orthogonal Matching Pursuit and (2) high recovery ability, i.e., the proposed algorithm can recover much less sparse signals than even 1-norm minimization algorithms. Moreover, we demonstrate both theoretically and empirically that the proposed algorithm can reliably recover a sparse signal from highly incomplete measurements.

We study nn random symmetric matrices whose entries above the diagonal are iid random variables each of which takes 1 with probability p and 0 with probability 1-p, for a given density parameter p=α/n for sufficiently large α. For a given such matrix A, we consider a matrix A ' that is obtained by removing some rows and corresponding columns with too many value 1 entries. Then for this A', we show that the largest eigenvalue is asymptotically close to α+1 and its eigenvector is almost parallel to all one vector (1,...,1).

In this paper, we show the recent progress of photonic network technologies for the new generation network (NWGN). The NWGN is based on new design concepts that look beyond the next generation network (NGN) and the Internet. The NWGN will maintain the sustainability of our prosperous civilization and help resolve various social issues and problems by the use of information and communication technologies. In order to realize the NWGN, many novel technologies in the physical layer are required, in addition to technologies in the network control layer. Examples of cutting-edge physical layer technologies required to realize the NWGN include a terabit/s/port or greater ultra-wideband optical packet switching system, a modulation-format-free optical packet switching (OPS) node, a hybrid optoelectronic packet switching node, a packet-based reconfigurable optical add/drop multiplexer (ROADM) system, an optical packet and circuit integrated node system, and optical buffering technologies.

A novel method for single image super resolution without any training samples is presented in the paper. By sparse representation, the method attempts to recover at each pixel its best possible resolution increase based on the self similarity of the image patches across different scale and rotation transforms. The experiments indicate that the proposed method can produce robust and competitive results.

Oxide material can make transparent devices with transparent electrodes. We developed a transparent oscillator and rectifier circuits with oxide TFTs. The source/drain and gate electrodes were made by indium thin oxide (ITO), and active layer made by transparent material of IGZO (Indium Gallium Zinc Oxide) on a glass substrate. The RC oscillator was composed of bootstrapped inverters, and 813 kHz oscillation frequency was accomplished at VDD = 15 V. For DC voltage generation from RF, transparent rectifier was fabricated and evaluated. This DC voltage from rectifier powered to the oscillator which operated successfully to create RF. For data transmission, RF transmission was evaluated with RF from the transparent oscillator. An antenna was connected to the oscillator and RF transmission to a receiving antenna was verified. Through this transmission antenna, RF was transmitted to a receiving antenna successfully. For transparent system of RFID, transparent antenna was developed and verified sending and receiving of data.

In this letter, we propose a low-complexity sparse channel estimation method for orthogonal frequency division multiplexing (OFDM) systems. The proposed method uses a discrete Fourier transform (DFT)-based technique for channel estimation and a novel sorted noise space discrimination technique to estimate the channel length and tap positions. Simulation results demonstrate that the reduction in signal space improves the channel estimation performance.

Micro-gap electrostatic discharge (ESD) events due to a human with charge voltages below 1000 V cause serious malfunctions in high-tech information devices. For clarifying such a mechanism, it is indispensable to grasp the spark process of such micro-gap ESDs. For this purpose, two types of spark-resistance laws proposed by Rompe-Weizel and Toepler have often been used, which were derived from the hypotheses that spark conductivity be proportional to the internal energies and charges injected into a spark channel, respectively. However, their validity has not well been verified. To examine which spark-resistance formula could be applied for micro-gap ESDs, with a 12-GHz digital oscilloscope, we previously measured the discharge currents through the hand-held metal piece from a charged human with respect to charged voltages of 200 V and 2000 V, and thereby derived the conductance of a spark gap to reveal that both of their hypotheses are roughly valid in the initial stage of sparks. In this study, to further verify the above spark hypotheses, we derived the discharge voltages in closed forms across a spark gap based on the above spark-resistance formulae, and investigated which spark-resistance formula could be applied for micro-gap ESDs in comparison of spark gaps estimated from the measured discharge currents. As a result, we found that Rompe-Weizel's formula could well explain spark properties for micro-gap ESDs than Toepler's one regardless of charge voltages and approach speeds.

In this paper, a Stochastic Collocation Algorithm combined with Sparse Grid technique (SSCA) is proposed to deal with the periodic steady-state analysis for nonlinear systems with process variations. Compared to the existing approaches, SSCA has several considerable merits. Firstly, compared with the moment-matching parameterized model order reduction (PMOR) which equally treats the circuit response on process variables and frequency parameter by Taylor approximation, SSCA employs Homogeneous Chaos to capture the impact of process variations with exponential convergence rate and adopts Fourier series or Wavelet Bases to model the steady-state behavior in time domain. Secondly, contrary to Stochastic Galerkin Algorithm (SGA), which is efficient for stochastic linear system analysis, the complexity of SSCA is much smaller than that of SGA for nonlinear case. Thirdly, different from Efficient Collocation Method, the heuristic approach which may result in "Rank deficient problem" and "Runge phenomenon," Sparse Grid technique is developed to select the collocation points needed in SSCA in order to reduce the complexity while guaranteing the approximation accuracy. Furthermore, though SSCA is proposed for the stochastic nonlinear steady-state analysis, it can be applied to any other kind of nonlinear system simulation with process variations, such as transient analysis, etc.

We present a graph model and an ILP model for TAM design for transparency-based SoC testing. The proposed method is an extension of a previous work proposed by Chakrabarty with respect to the following three points: (1) constraint relaxation by considering test data flow for each core separately, (2) optimization of the cost for transparency as well as the cost for additional interconnect area simultaneously and (3) consideration of additional bypass paths. Therefore, the proposed ILP model can represent various problems including the same problem as the previous work and produce better results. Experimental results show the effectiveness and flexibility of the proposed method compared to the previous work.

The proportionate normalized least mean square algorithm (PNLMS) greatly improves the convergence of the sparse impulse response. It exploits the shape of the impulse response to decide the proportionate step gain for each coefficient. This is not always suitable. Actually, the proportionate step gain should be determined according to the difference between the current estimate of the coefficient and its optimal value. Based on this idea, an approach is proposed to determine the proportionate step gain. The proposed approach can improve the convergence of proportionate adaptive algorithms after a fast initial period. It even behaves well for the non-sparse impulse response. Simulations verify the effectiveness of the proposed approach.

A virtualized optical network (VON) is proposed as a key to implementing increased agility and flexibility into the future Internet and applications by providing any-to-any connectivity with the appropriate optical bandwidth at the appropriate time. The VON is enabled by introducing optical transparentization and optical fine granular grooming based on optical orthogonal frequency division multiplexing.

This paper presents generating stable and sparse reluctance/inductance matrix from the inductance matrix which is extracted under insufficient discretization. To generate the sparse reluctance matrix with guaranteed stability, the original matrix has to be (strictly) diagonally dominant M matrix. Hence, the repeated inductance extractions with a smaller grid size are necessary in order to obtain the well-defined matrix. Alternatively, this paper provides some ideas for generating the sparse reluctance matrix, even if the extracted reluctance matrix is not diagonally dominant M matrix. These ease the extraction tasks greatly. Furthermore, the sparse inductance matrix is also generated by using double inverse methods. Since reluctance components are not still supported in SPICE-like simulators, generating the sparse inductance matrix is more useful than the sparse reluctance one.

Recently, proportionate adaptive algorithms have been proposed to speed up convergence in the identification of sparse impulse response. Although they can improve convergence for sparse impulse responses, the steady-state misalignment is limited by the constant step-size parameter. In this article, based on the principle of least perturbation, we first present a derivation of normalized version of proportionate algorithms. Then by taking the disturbance signal into account, we propose a variable step-size proportionate NLMS algorithm to combine the benefits of both variable step-size algorithms and proportionate algorithms. The proposed approach can achieve fast convergence with a large step size when the identification error is large, and then considerably decrease the steady-state misalignment with a small step size after the adaptive filter reaches a certain degree of convergence. Simulation results verify the effectiveness of the proposed approach.

In this letter, we propose a novel sparse channel estimation method for orthogonal frequency division multiplexing (OFDM) systems. The proposed method uses a discrete Fourier transform (DFT)-based technique for channel estimation and a sorted generalized Akaike information criterion (GAIC) to estimate the channel length and tap positions. Simulation results demonstrate that an improved channel estimation performance is obtained due to the reduction of signal space.