Point Pattern Matching (PPM) is an essential problem in many image analysis and computer vision tasks. This paper presents a two-stage algorithm for PPM problem using ellipse fitting and dual Hilbert scans. In the first matching stage, transformation parameters are coarsely estimated by using four node points of ellipses which are fitted by Weighted Least Square Fitting (WLSF). Then, Hilbert scans are used in two aspects of the second matching stage: it is applied to the similarity measure and it is also used for search space reduction. The similarity measure named Hilbert Scanning Distance (HSD) can be computed fast by converting the 2-D coordinates of 2-D points into 1-D space information using Hilbert scan. On the other hand, the N-D search space can be converted to a 1-D search space sequence by N-D Hilbert Scan and an efficient search strategy is proposed on the 1-D search space sequence. In the experiments, we use both simulated point set data and real fingerprint images to evaluate the performance of our algorithm, and our algorithm gives satisfying results both in accuracy and efficiency.

The ηT pairing for supersingular elliptic curves over GF(3m) has been paid attention because of its computational efficiency. Since most computation parts of the ηT pairing are GF(3m) multiplications, it is important to improve the speed of the multiplication when implementing the ηT pairing. In this paper we investigate software implementation of GF(3m) multiplication and propose using irreducible trinomials xm+axk+b over GF(3) such that k is a multiple of w, where w is the bit length of the word of targeted CPU. We call the trinomials "reduction optimal trinomials (ROTs)." ROTs actually exist for several m's and for typical values of w = 16 and 32. We list them for extension degrees m = 97, 167, 193, 239, 317, and 487. These m's are derived from security considerations. Using ROTs, we are able to implement efficient modulo operations (reductions) for GF(3m) multiplication compared with cases in which other types of irreducible trinomials are used (e.g., trinomials with a minimum k for each m). The reason for this is that for cases using ROTs, the number of shift operations on multiple precision data is reduced to less than half compared with cases using other trinomials. Our implementation results show that programs of reduction specialized for ROTs are 20-30% faster on 32-bit CPU and approximately 40% faster on 16-bit CPU compared with programs using irreducible trinomials with general k.

This paper describes the matrix order reduction method by the nodal analysis formulation and the application of relaxation-based simulation technique to interconnect and plane networks. First, the characteristics of the power/ground plane networks are considered. Next, the formulation of the plane network by nodal analysis (NA) method is suggested. Furthermore, application and estimation results of the relaxation-based numerical analyses are shown. Finally, it is confirmed that the relaxation-based methods improved by the suggested formulation are much more efficient than the conventional direct-based methods.

The passive and sparse reduced-order modeling of a RLC network is presented, where eigenvalues and eigenvectors of the original network are used, and thus the obtained macromodel is more accurate than that provided by the Krylov subspace methods or TBR procedures for a class of circuits. Furthermore, the proposed method is applied to low pass filtering of a reduced-order model produced by these methods without breaking the passivity condition. Therefore, the proposed eigenspace method is not only a reduced-order macromodeling method, but also is embedded in other methods enhancing their performances.

For wideband MIMO-OFDM systems, scheduling and link adaptation are key techniques to improve the throughput performance. However, in systems without reciprocity between the uplink and the downlink channels, these techniques require a high feedback overhead of the channel quality indication (CQI) information. In this paper, we propose a novel CQI feedback reduction method, which is based on the conventional compression techniques exploiting the discrete cosine transformation (DCT). The basic idea is to adaptively permute the CQI sequences of different MIMO streams according to one of the possible patterns before the DCT compression so that the amount of feedback bits is minimized. The possible patterns used are carefully designed according to our analysis of the two types of correlations (the inter-stream correlation and the inter-subband correlation) that exist in MIMO-OFDM transmission, as well as their impact on the compression efficiency. Simulation results verify that the proposed method can effectively reduce the CQI feedback overhead under varying channel conditions.

Orthogonal frequency-division multiplexing (OFDM) is an attractive transmission technique for high-bit-rate communication systems. One major drawback of OFDM is the high peak-to-average power ratio (PAPR) of the transmitted signal. This study introduces a low-complexity selected mapping (SLM) OFDM scheme based on discrete Fourier transform (DFT) constellation-shaping. The DFT-based constellation-shaping algorithm applied with conventional SLM scheme usually requires a bank of DFT-shaping matrices to generate low-correlation constellation sequences and a bank of inverse fast Fourier transforms (IFFTs) to generate a set of candidate transmission signals, and this process usually results in high computational complexity. Therefore, a sparse matrix algorithm with low-complexity is proposed to replace the IFFT blocks and the DFT-shaping blocks in the proposed DFT constellation-shaping SLM scheme. By using the proposed sparse matrix, the candidate transmission signal with the lowest PAPR can be achieved with lower complexity than that of the conventional SLM scheme. The complexity analysis of the proposed algorithm shows great an improvement in the reduction of the number of multiplications. Moreover, this new low-complexity technique offers a PAPR that is significantly lower than that of the conventional SLM without any loss in terms of energy and spectral efficiency.

In this paper, we propose a two-microphone noise reduction method to deal with non-stationary interfering noises in multiple-noise-source environments in which the traditional two-microphone algorithms cannot function well. In the proposed algorithm, multiple interfering noise sources are regarded as one virtually integrated noise source in each subband, and the spectrum of the integrated noise is then estimated using its virtual direction of arrival. To do this, we suggest a direction finder for the integrated noise using only two microphones that performs well even in speech active periods. The noise spectrum estimate is further improved by integrating a single-channel noise estimation approach and then subtracted from that of the noisy signal, finally enhancing the desired target signal. The performance of the proposed algorithm is evaluated and compared with the traditional algorithms in various conditions. Experimental results demonstrate that the proposed algorithm outperforms the traditional algorithms in various conditions in terms of objective and subjective speech quality measures.

Multi-user MIMO (Multiple Input Multiple Output) systems, in which multiple Mobile Stations (MSs) equipped with multiple antennas simultaneously communicate with a Base Station (BS) equipped with multiple antennas, at the same frequency, are attracting attention because of their potential for improved transmission performance in wireless communications. In the uplink of Space Division Multiplexing based multi-user MIMO (multi-user MIMO/SDM) systems that do not require full Channel State Information (CSI) at the transmitters, selecting active MS antennas, which corresponds to scheduling transmit antennas, is an effective technique. The Full search Selection Algorithm based on exhaustive search (FSA) has been studied as an optimal active MS antenna selection algorithm for multi-user MIMO systems. Unfortunately, FSA suffers from extreme computational complexity given large numbers of MSs. To solve this problem, this paper introduces the Gram-Schmidt orthogonalization based Selection Algorithm (GSSA) to uplink multi-user MIMO/SDM systems. GSSA is a suboptimal active MS antenna selection algorithm that offers lower computational complexity than the optimal algorithm. This paper evaluates the transmission performance improvement of GSSA in uplink multi-user MIMO/SDM systems under realistic propagation conditions such as spatially correlated BS antennas and clarifies the effectiveness of GSSA.

In this letter, we propose two computationally efficient precoding algorithms that achieve near-ML performance for multiuser MIMO downlink. The proposed algorithms perform tree expansion after lattice reduction. The first full expansion is tried by selecting the first level node with a minimum metric, constituting a reference metric. To find an optimal sequence, they iteratively visit each node and terminate the expansion by comparing node metrics with the calculated reference metric. By doing this, they significantly reduce the number of undesirable node visit. Monte-Carlo simulations show that both proposed algorithms yield near-ML performance with considerable reduction in complexity compared with that of the conventional schemes such as sphere encoding.

A motion refinement algorithm is proposed to enhance motion compensated noise reduction (MCNR) efficiency. Instead of the vector with minimum distortion, the vector with minimum distance from motion vectors of neighboring blocks is selected as the best motion vector among vectors which have distortion values within the range set by noise level. This motion refinement finds more accurate motion vectors in the noisy sequences. The MCNR with the proposed algorithm maintains the details of an image sequence very well without blurring and joggling. And it achieves 10% bit-usage reduction or 0.5 dB objective quality enhancement in subsequent video coding.

In this paper, we propose a simple peak power reduction (PPR) method based on adaptive inversion of parity-check block of codeword in BCH-coded OFDM system. In the proposed method, the entire parity-check block of the codeword is adaptively inversed by multiplying weighting factors (WFs) so as to minimize PAPR of the OFDM signal, symbol-by-symbol. At the receiver, these WFs are estimated based on the property of BCH decoding. When the primitive BCH code with single error correction such as (31,26) code is used, to estimate the WFs, the proposed method employs a significant bit protection method which assigns a significant bit to the best subcarrier selected among all possible subcarriers. With computer simulation, when (31,26), (31,21) and (32,21) BCH codes are employed, PAPR of the OFDM signal at the CCDF (Complementary Cumulative Distribution Function) of 10-4 is reduced by about 1.9, 2.5 and 2.5 dB by applying the PPR method, while achieving the BER performance comparable to the case with the perfect WF estimation in exponentially decaying 12-path Rayleigh fading condition.

We present a method for the objective quality evaluation of noise-reduced speech in wideband speech communication services, which utilize speech with a wider bandwidth (e.g., 7 kHz) than the usual telephone bandwidth. Experiments indicate that the amount of residual noise and the distortion of speech and noise, which are quality factors, influence the perceived quality degradation of noise-reduced speech. From the results, we observe the principal relationships between these quality factors and perceived speech quality. On the basis of these relationships, we propose a method that quantifies each quality factor in noise-reduced speech by analyzing signals that can be measured and assesses the overall perceived quality of noise-reduced speech using values of these quality factors. To verify the validity of the method, we perform a subjective listening test and compare subjective quality of noise-reduced speech with its estimation. In the test, we use various types of background noise and noise-reduction algorithms. The verification results indicate that the correlation between subjective quality and its objective estimation is sufficiently high regardless of the type of background noise and noise-reduction algorithm.

We present IND-ID-CPA secure identity-based encryption (IBE) schemes with tight reductions to the bilinear Diffie-Hellman (BDH) problem. Since the methods for obtaining IND-ID-CCA secure schemes from IND-ID-CPA secure schemes with tight reductions are already known, we can consequently obtain IND-ID-CCA secure schemes with tight reductions to the BDH problem. Our constructions are based on IBE schemes with tight reductions to the list bilinear Diffie-Hellman (LBDH) problem, and the schemes are converted to those with tight reductions to the BDH problem. Interestingly, it can be shown that there exists a black box construction, in which the former IBE schemes are given as black boxes. Our constructions are very simple and reasonably efficient.

Variable Block Size Motion Estimation (VBSME) costs a lot of computation during video coding. Search range reduction algorithm is widely used to reduce computational cost of motion estimation. Current VBSME designs are not suitable for this algorithm. This paper proposes a reconfigurable design of VBSME which can be efficiently used with search range reduction algorithm. While using proposed design, nm reference MBs form an MB array which can be processed in parallel. n and m can be configured according to the new search range shape calculated by algorithm. In this way, the parallelism of proposed design is very flexible and can be adapted to any search range shape. The hardware resource is also fully used while performing VBSME. There are two primary reconfigurable modules in this design: PEGA (PE Group Array) and SAD comparator. By using TSMC 0.18 µm standard cell library, the implementation results show that the hardware cost of design which uses 16 PEGs (PE Groups) is about 179 K Gates, the clock frequency is 167 MHz.

Feature transformation in automatic text classification (ATC) can lead to better classification performance. Furthermore dimensionality reduction is important in ATC. Hence, feature transformation and dimensionality reduction are performed to obtain lower computational costs with improved classification performance. However, feature transformation and dimension reduction techniques have been conventionally considered in isolation. In such cases classification performance can be lower than when integrated. Therefore, we propose an integrated feature analysis approach which improves the classification performance at lower dimensionality. Moreover, we propose a multiple feature integration technique which also improves classification effectiveness.

In this paper, we propose a scheme for test data reduction which uses broadcaster along with bit-flipping circuit. The proposed scheme can reduce test data without degrading the fault coverage of ATPG, and without requiring or modifying the arrangement of CUT. We theoretically analyze the test data size by the proposed scheme. The numerical examples obtained by the analysis and experimental results show that our scheme can effectively reduce test data if the care-bit rate is not so much low according to the number of scan chains. We also discuss the hybrid scheme of random-pattern-based flipping and single-input-based flipping.

This letter mainly focuses on improving current noise reduction methods to solve the critical speech distortion problems with robust noise reduction in noisy speech signals for speech enhancement over IP networks. For robust noise reduction with packet loss recovery, we propose a novel optimized Wiener filtering technique that uses the estimated SNR (Signal-to-Noise Ratio) with packet loss recovery method which is applied as post-filtering over IP-networks. Simulation results demonstrate that the proposed scheme provides better reduction and recovery rates with considering packet loss and SNR environment than other methods.

The accuracy of automatic speech recognition in a car is significantly degraded in a very low SNR (Signal to Noise Ratio) situation such as "Fan high" or "Window open". In such cases, speech signals are often buried in broadband noise. Although several existing noise reduction algorithms are known to improve the accuracy, other approaches that can work with them are still required for further improvement. One of the candidates is enhancement of the harmonic structures in human voices. However, most conventional approaches are based on comb filtering, and it is difficult to use them in practical situations, because their assumptions for F0 detection and for voiced/unvoiced detection are not accurate enough in realistic noisy environments. In this paper, we propose a new approach that does not rely on such detection. An observed power spectrum is directly converted into a filter for speech enhancement, by retaining only the local peaks considered to be harmonic structures in the human voice. In our experiments, this approach reduced the word error rate by 17% in realistic automobile environments. Also, it showed further improvement when used with existing noise reduction methods.

The combination of deliberate clipping and an adaptive symbol selection scheme (ASSS) can be used to reduce the peak to average power ratio (PAPR) for Orthogonal Frequency Division Multiplexing (OFDM) signals. The probability density function (pdf) of a sample's amplitude of an adaptively selected OFDM signal without over-sampling has been considered to be approximately equal to the Rayleigh pdf. In this letter, we derive the exact pdf showing the relationship between the probability distribution of the sample's amplitude and the number of candidate OFDM symbols for ASSS. The use of the newly derived pdf can measure the effect of deliberate clipping on the adaptively selected OFDM signal more accurately.

Based on an analysis of the error patterns in lattice-reduction (LR) precoding in a multiple-antenna broadcast channel, this paper proposes a simple precoding technique that can reduce the quantization error. The proposed scheme establishes a lattice list to provide more candidates for transmission power reduction based on the analysis of the patterns of the error in the LR precoding method [9]. Simulation results show that the proposed scheme matches the BER performance of more complex precedents (such as the vector perturbation using sphere encoding) with significant saving in complexity.