Memory accesses are a major cause of energy consumption for embedded systems. This paper presents the implementation of a fully software technique which places stack and static data into a scratch-pad memory (SPM) in order to reduce the energy consumed by the processor while accessing them. Since an SPM is usually too small to include all these data, some of them must be left into the external main memory (MM). Therefore, further energy reduction is achieved by moving some stack data between both memories at run time. The technique employs integer linear programming in order to find at compile time the optimal placement of static data and management of the stack and implements it by inserting stack operations inside the code. Experimental results show that with an SPM of only 1 KB, our technique is able to exploit it for reducing the energy consumption related to the static and stack data accesses by more than 90% for several applications and on an average by 57% compared to the case where these data are fully placed into the main memory.

In this study, we propose a one dimensional (1D) based successive generalized sidelobe canceller (GSC) structure for the implementation of 2D adaptive beamformers using a uniform rectangular antenna array (URA). The proposed approach takes advantage of the URA feature that the 2D spatial signature of the receive signal can be decomposed into an outer product of two 1D spatial signatures. The 1D spatial signatures lie in the column and the row spaces of the receive signal matrix, respectively. It follows that the interferers can be successively eliminated by two rounds of 1D-based GSC structure. As compared to the conventional 2D-GSC structure, computer simulations show that in addition to having significantly low computational complexity, the proposed adaptive approach possesses higher convergence rate.

To reduce the error of channel estimation caused by noise, a novel noise suppression method based on the degree of confidence is proposed in this paper. The false alarm and false dismissal probabilities, corresponding to noise being taken as part of channel impulse response (CIR) and part of the CIR being mis-detected as noise, respectively, are also investigated. A false alarm reduction method is therefore presented to reduce the false alarms in the estimated CIR while the mis-detection ratio still remains low. Simulation results show the effectiveness of the proposed method.

Histogram-based image features such as HoG, SIFT and histogram of visual words are generally represented as high-dimensional, non-negative vectors. We propose a supervised method of reducing the dimensionality of histogram-based features by using non-negative matrix factorization (NMF). We define a cost function for supervised NMF that consists of two terms. The first term is the generalized divergence term between an input matrix and a product of factorized matrices. The second term is the penalty term that reflects prior knowledge on a training set by assigning predefined constants to cannot-links and must-links in pairs of training data. A multiplicative update rule for minimizing the newly-defined cost function is also proposed. We tested our method on a task of scene classification using histograms of visual words. The experimental results revealed that each of the low-dimensional basis vectors obtained from the proposed method only appeared in a single specific category in most cases. This interesting characteristic not only makes it easy to interpret the meaning of each basis but also improves the power of classification.

A novel algorithm is presented for near-field source localization with a symmetric uniform linear array (ULA) consisting of an even number of sensors. Based on element reordering of a symmetric ULA, the steering vector is factorised with respect to the range-independent bearing parameters and range-relevant 2-D location parameters, which allows the range-independent bearing estimation with rank-reduction idea. With the estimated bearing, the range estimation for each source is then obtained by defining the 1-D MUSIC spectrum. Simulation results are presented to validate the performance of the proposed algorithm.

We propose a novel method of supervised feature projection called class-distance-based discriminant analysis (CDDA), which is suitable for automatic age estimation (AAE) from facial images. Most methods of supervised feature projection, e.g., Fisher discriminant analysis (FDA) and local Fisher discriminant analysis (LFDA), focus on determining whether two samples belong to the same class (i.e., the same age in AAE) or not. Even if an estimated age is not consistent with the correct age in AAE systems, i.e., the AAE system induces error, smaller errors are better. To treat such characteristics in AAE, CDDA determines between-class separability according to the class distance (i.e., difference in ages); two samples with similar ages are imposed to be close and those with spaced ages are imposed to be far apart. Furthermore, we propose an extension of CDDA called local CDDA (LCDDA), which aims at handling multimodality in samples. Experimental results revealed that CDDA and LCDDA could extract more discriminative features than FDA and LFDA.

Locally linear embedding (LLE) is a well-known method for nonlinear dimensionality reduction. The mathematical proof and experimental results presented in this paper show that the neighborhood sizes in LLE must be smaller than the dimensions of input data spaces, otherwise LLE would degenerate from a nonlinear method for dimensionality reduction into a linear method for dimensionality reduction. Furthermore, when the neighborhood sizes are larger than the dimensions of input data spaces, the solutions to LLE are not unique. In these cases, the addition of some regularization method is often proposed. The experimental results presented in this paper show that the regularization method is not robust. Too large or too small regularization parameters cannot unwrap S-curve. Although a moderate regularization parameters can unwrap S-curve, the relative distance in the input data will be distorted in unwrapping. Therefore, in order to make LLE play fully its advantage in nonlinear dimensionality reduction and avoid multiple solutions happening, the best way is to make sure that the neighborhood sizes are smaller than the dimensions of input data spaces.

In this study, we propose a method to reduce the mutual coupling between two J-shaped folded monopole antennas (JFMAs), which cover the IEEE 802.11 b/g (2400-2484 MHz) band. First, the change in mutual coupling with the spacing between the two antenna elements is investigated by considering two feeding models, and the effects of changes in the coupling on the antenna efficiency are studied. Subsequently, we try the method to reduce mutual coupling, the method involves the use of a bridge line that links the two antennas. The mutual coupling can be significantly reduced and the total antenna efficiency can be improved by linking two shorting strips with the bridge line. In a past study, we had found that in the case of L-shaped folded monopole antennas (LFMAs), the mutual coupling and antenna efficiency vary with the linking location on the bridge line. Moreover, we compare the characteristics of the LFMA and JFMA and show that the JFMA is effective when miniaturized.

Due to the reuse factor reduction, the attendant increase in co-channel interference (CCI) becomes the limiting factor in the performance of the orthogonal frequency division multiplexing (OFDM) based cellular systems. In the previous work, we proposed the least mean square-blind joint maximum likelihood sequence estimation (LMS-BJMLSE) algorithm, which is effective for CCI cancellation in OFDM systems with only one receive antenna. However, LMS-BJMLSE requires a long training sequence (TS) for channel estimation, which reduces the transmission efficiency. In this paper, we propose a subcarrier identification and interpolation algorithm, in which the subcarriers are divided into groups based on the coherence bandwidth, and the slowest converging subcarrier in each group is identified by exploiting the correlation between the mean-square error (MSE) produced by LMS and the mean-square deviation (MSD) of the desired channel estimate. The identified poor channel estimate is replaced by the interpolation result using the adjacent subcarriers' channel estimates. Simulation results demonstrate that the proposed algorithm can reduce the required training sequence dramatically for both the cases of single interference and dual interference. We also generalize LMS-BJMLSE from single antenna to receiver diversity, which is shown to provide a huge improvement.

In this article, we present a new image-stabilization technology for still images based on blind deconvolution and introduce it to a consumer digital still camera. This technology consists of three features: (1)double-exposure-based PSF detection, (2)efficient image deblurring filter, and (3)edge-based ringing reduction. Without deteriorating the deblurring performance, the new technology allows us to reduce processing time and ringing artifacts, both of which are common problems in image deconvolution.

Probabilistic model checking is an emerging technology for analyzing systems which exhibit stochastic behaviors. The verification of a larger system using probabilistic model checking faces the same state explosion problem as ordinary model checking. Probabilistic symmetry reduction is a technique to tackle this problem. In this paper, we study probabilistic symmetry reduction for a system with a ring buffer which can describe various applications. A key of probabilistic symmetry reduction is identifying symmetry of states with respect to the structure of the target system. We introduce two functions; Shiftδ and Reverse to clarify such symmetry. Using these functions, we also present pseudo code to construct a quotient model. Then, we show two practical case studies; the one-dimensional Ising model and the Automatic Identification System (AIS). Behaviors of them were verified, but suffered from the state explosion problem. Through the case studies, we show that probabilistic symmetry reduction takes advantage of reducing the size of state space.

Linear discriminant analysis (LDA) is one of the well-known schemes for feature extraction and dimensionality reduction of labeled data. Recently, two-dimensional LDA (2DLDA) for matrices such as images has been reformulated into symmetric 2DLDA (S2DLDA), which is solved by an iterative algorithm. In this paper, we propose a non-iterative S2DLDA and experimentally show that the proposed method achieves comparable classification accuracy with the conventional S2DLDA, while the proposed method is computationally more efficient than the conventional S2DLDA.

This paper presents an improved Gini-Index algorithm to correct feature-selection bias in text classification. Gini-Index has been used as a split measure for choosing the most appropriate splitting attribute in decision tree. Recently, an improved Gini-Index algorithm for feature selection, designed for text categorization and based on Gini-Index theory, was introduced, and it has proved to be better than the other methods. However, we found that the Gini-Index still shows a feature selection bias in text classification, specifically for unbalanced datasets having a huge number of features. The feature selection bias of the Gini-Index in feature selection is shown in three ways: 1) the Gini values of low-frequency features are low (on purity measure) overall, irrespective of the distribution of features among classes, 2) for high-frequency features, the Gini values are always relatively high and 3) for specific features belonging to large classes, the Gini values are relatively lower than those belonging to small classes. Therefore, to correct that bias and improve feature selection in text classification using Gini-Index, we propose an improved Gini-Index (I-GI) algorithm with three reformulated Gini-Index expressions. In the present study, we used global dimensionality reduction (DR) and local DR to measure the goodness of features in feature selections. In experimental results for the I-GI algorithm, we obtained unbiased feature values and eliminated many irrelevant general features while retaining many specific features. Furthermore, we could improve the overall classification performances when we used the local DR method. The total averages of the classification performance were increased by 19.4 %, 15.9 %, 3.3 %, 2.8 % and 2.9 % (kNN) in Micro-F1, 14 %, 9.8 %, 9.2 %, 3.5 % and 4.3 % (SVM) in Micro-F1, 20 %, 16.9 %, 2.8 %, 3.6 % and 3.1 % (kNN) in Macro-F1, 16.3 %, 14 %, 7.1 %, 4.4 %, 6.3 % (SVM) in Macro-F1, compared with tf*idf, χ2, Information Gain, Odds Ratio and the existing Gini-Index methods according to each classifier.

Effect of an addition of a cooled step-up transformer to a flux locked loop (FLL) circuit was studied to reduce indirect rf interference to HTS-dc-SQUID. First, we demonstrated that a noise level of an HTS-dc-SQUID system using the FLL circuit with single room-temperature transformer could be easily degraded by radiation of rf electromagnetic wave to cables in the FLL circuit. It is thought that the rf radiation induced rf current in the circuit, and was transmitted to the SQUID to modulate the bias current, resulting in the increase of the noise level. To avoid the degradation due to such indirect rf interference, the cooled set-up transformer was added to the FLL circuit since it was expected that the additional transformer would work as a "step-down" transformer against the induced rf current. It was shown that the noise level of a HTS-SQUID system (SQUITEM system) operated in an electromagnetically unshielded environment could be improved to the same level as that measured in a magnetically shielded room by the additional cooled transformer and appropriate impedance matching.

In situ observation of the adsorption process and the states of cytochrome c on glass/solution interfaces, and the functionality of the reduction reaction of adsorbed cytochorome c were performed by using slab optical waveguide (SOWG) spectroscopy. The peak position of the absorption band of cytochorome c adsorbed on a bare glass surface was almost the same as that of that in solution. The cytochorome c adsorbed on glass/solution interface was reduced by sodium dithionite solution. The adsorbed cytochorome c was still maintained its functionality after immobilization.

In [1], Bellare, Boldyreva, and Micali addressed the security of public-key encryptions (PKEs) in a multi-user setting (called the BBM model in this paper). They showed that although the indistinguishability in the BBM model is induced from that in the conventional model, its reduction is far from tight in general, and this brings a serious key length problem. In this paper, we discuss PKE schemes in which the IND-CCA security in the BBM model can be obtained tightly from the IND-CCA security. We call such PKE schemes IND-CCA secure in the BBM model with invariant security reductions (briefly, SR-invariant IND-CCABBM secure). These schemes never suffer from the underlying key length problem in the BBM model. We present three instances of an SR-invariant IND-CCABBM secure PKE scheme: the first is based on the Fujisaki-Okamoto PKE scheme [7], the second is based on the Bellare-Rogaway PKE scheme [3], and the last is based on the Cramer-Shoup PKE scheme [5].

In this article, a simple structure of the Wilkinson power divider which can suppress the nth harmonics of the Wilkinson power divider is proposed. By replacing the quarter wavelength transmission lines of the conventional Wilkinson power divider with the equivalent P-type transmission lines, a compact power divider which can suppress the nth harmonic is achieved. Design equations of proposed P-type line are achieved by ABCD matrices. To verify the design approach, the proposed power divider is designed, simulated (by ADS, CST Studio, and Sonnet simulators), and fabricated at 1 GHz to suppress the fifth harmonic. The proposed structure is 46% of the conventional Wilkinson power divider, while maintaining the characteristics of the conventional Wilkinson power divider at the fundamental frequency. The insertion losses at the fifth harmonic are larger than 35 dB. Furthermore, the second to seventh harmonic are suppressed by least 10 dB. Here is an excellent agreement between simulated results and measured results.

Channel-factorization aided detector (CFAD) is one of the important low-complexity detectors used in multiple input, multiple output (MIMO) receivers. Through channel factorization, this method transforms the original MIMO system into an equivalent system with a better-conditioned channel where detection is performed with a low-complexity detector; the estimate is then transferred back to the original system to obtain the final decision. Traditionally, the channel factorization is done with the lattice reduction algorithms such as the Lenstra-Lenstra-Lovasz (LLL) and Seysen's algorithms with no consideration of the low-complexity detector used. In this paper, we propose a different approach: the channel factorization is designed specifically for the minimum mean-square-error (MMSE) detector that is a popular low-complexity detector in CFADs. Two new types of factorization algorithms are proposed. Type-I is LLL based, where the well-known DLLL-extended algorithm, the LLL algorithm working on the dual matrix of the extended channel matrix, is a member of this type but with a higher complexity. DLLL-extended is the best-performed factorization algorithm found in the literature, Type-II is greedy-search based where its members are differentiated with different algorithm's parameters. Type-II algorithms can provide around 0.5-1.0 dB gain over Type-I algorithms and have a fixed computational complexity which is advantageous in hardware implementation.

Clock gating is the insertion of control signal for registers to switch off unnecessary clock signals selectively without violating the functional correctness of the original design so as to reduce the dynamic power consumption. Commercial EDA tools usually have a mechanism to generate clock gating logic based on the structural method where the control signals specified by designers are used, and the effectiveness of the clock gating depends on the specified control signals. In the research, we focus on the automatic clock gating logic generation and propose a method based on the candidate extraction and control signal selection. We formalize the control signal selection using linear formulae and devise an optimization method based on BDD. The method is effective for circuits with a lot of shared candidates by different registers. The method is applied to counter circuits to check the co-relation with power simulation results and a set of benchmark circuits. 19.1-71.9% power reduction has been found on counter circuitsafter layout and 2.3-18.0% cost reduction on benchmark circuits.

A new type of digital filter for removing impulsive noise in color images is proposed using interactive evolutionary computing. This filter is realized as a rule-based system containing switching median filters. This filter detects impulsive noise in color images with rules and applies switching median filters only at the noisy pixel. Interactive evolutionary computing (IEC) is adopted to optimize the filter parameters, considering the subjective assessment by human vision. In order to detect impulsive noise precisely, complicated rules with multiple parameters are required. Here, the relationship between color components and the degree of peculiarity of the pixel value are utilized in the rules. Usually, optimization of such a complicated rule-based system is difficult, but IEC enables such optimization easily. Moreover, human taste and subjective sense are highly considered in the filter performance. Computer simulations are shown for noisy images to verify its high performance.