The role of an optical low-pass filter (OLPF) in a digital still camera is to remove the high spatial frequencies that cause aliasing, thereby enhancing the image quality. However, this also causes some loss of detail. Yet, when an image is captured without the OLPF, moiré generally appears in the high spatial frequency region of the image. Accordingly, this paper presents a moiré reduction method that allows omission of the OLPF. Since most digital still cameras use a CCD or a CMOS with a Bayer pattern, moiré patterns and color artifacts are simultaneously induced by aliasing at high spatial frequencies. Therefore, in this study, moiré reduction is performed in both the luminance channel to remove the moiré patterns and the color channel to reduce color smearing. To detect the moiré patterns, the spatial frequency response (SFR) of the camera is first analyzed. The moiré regions are identified using patterns related to the SFR of the camera and then analyzed in the frequency domain. The moiré patterns are reduced by removing their frequency components, represented by the inflection point between the high-frequency and DC components in the moiré region. To reduce the color smearing, color changing regions are detected using the color variation ratios for the RGB channels and then corrected by multiplying with the average surrounding colors. Experiments confirm that the proposed method is able to reduce the moiré in both the luminance and color channels, while also preserving the detail.

Non-volatile memory has many advantages such as high density and low leakage power but it consumes larger writing energy than SRAM. It is quite necessary to reduce writing energy in non-volatile memory design. In this paper, we propose write-reduction codes based on error correcting codes and reduce writing energy in non-volatile memory by decreasing the number of writing bits. When a data is written into a memory cell, we do not write it directly but encode it into a codeword. In our write-reduction codes, every data corresponds to an information vector in an error-correcting code and an information vector corresponds not to a single codeword but a set of write-reduction codewords. Given a writing data and current memory bits, we can deterministically select a particular write-reduction codeword corresponding to the data to be written, where the maximum number of flipped bits are theoretically minimized. Then the number of writing bits into memory cells will also be minimized. Experimental results demonstrate that we have achieved writing-bits reduction by an average of 51% and energy reduction by an average of 33% compared to non-encoded memory.

In the case of a disaster such as an earthquake or a tsunami, the city, town, and village administration usually issues an evacuation advisory and other information through the Outdoor Public Address Speakers for the disaster reduction broadcasting system covering its area of jurisdiction. However, in areas those have previous experience of a disaster, people frequently voice the lack of audibility of the disaster reduction broadcast. In this research, we conducted a questionnaire survey on the residents in the central area of Ishinomaki City, Miyagi Prefecture, who are the victims of the Great East Japan Earthquake Disaster, on the audible quality of outdoor public address (PA) speakers of the disaster reduction broadcasting system so as to understand the current state of such broadcasts and to propose ideal methods of sending and receiving information at the time of a future disaster.

As semiconductor manufacturing processing scaling down, leakage current of CMOS circuits is becoming a dominant contributor to power dissipation. This paper provides an efficient leakage current reduction (LCR) technique for low-power and low-frequency circuit designs in terms of design rules and layout parameters related to layout dependent effects. We address the LCR technique both for analog and digital circuits, and present a design case when applying the LCR techniqe to a successive-approximation-register (SAR) analog-to-digital converter (ADC), which typically employs analog and digital transistors. In the post-layout simulation results by HSPICE, an SAR-ADC with the LCR technique achieves 38.6-nW as the total power consumption. Comparing with the design without the LCR technique, we attain about 30% total energy reduction.

Modern processors use Branch Target Buffer (BTB)[1] to relax control dependence. Unfortunately, the energy consumption of the BTB is high. In order to effectively fetch instructions, it is necessary to perform a branch prediction at the fetch stage, regardless of whether the fetched instruction is a branch or a nonbranch. Therefore, the number of accesses to the BTB is large, and the energy consumption of the BTB is high. However, accesses from nonbranches to the BTB waste energy. In this paper, we focus on accesses from nonbranches to the BTB, which we call useless accesses from a viewpoint of power. For reducing energy consumption without performance loss, we present a method that reduces useless accesses by using information that indicates whether a fetched instruction is a branch or not. To realize the above approach, we propose a branch bit called B-Bit. A B-Bit is associated with an instruction and indicates whether it is a branch or not. A B-Bit is available at the beginning of the fetch stage. If a B-Bit is “1” signifying a branch, the BTB is accessed. If a B-Bit is “0” signifying a nonbranch, the BTB is not accessed. The experimental results show that the total energy consumption can be reduced by 54.3% without performance loss.

In this letter, an enhanced detection scheme using threshold and lattice-reduction algorithm is proposed. The first step of the proposed detection scheme finds another basis channel matrix H' which has good properties from the channel matrix H by using lattice-reduction algorithm. And QRD-M detection scheme using threshold algorithm is executed in the next step. Simulation results show that the proposed method has better performance than the conventional QRD-M detection scheme at high SNR. Also, it reduces candidate symbols because of the threshold algorithm.

When Zero-Forcing (ZF) is adopted as a detector, decreasing the condition number of the channel matrix increases the BER performance. In this paper, we propose a new detection algorithm which reduces the condition number of channel matrix down to nearly 2 on average. Since the least singular value of the channel matrix is a major factor determining the condition number, we, first, project the received signal into a space spanned by singular vectors that are orthogonal to the one corresponding to the least singular value. Then, LR decomposition is performed to reduce further the condition number of the projected channel matrix. Computer simulations show that the performance of the proposed algorithm is comparable to that of the ML detector for both correlated and uncorrelated channels. And also the proposed algorithm provides an at least 2dB improvement compared to the conventional LR-based Ordered Successive Interference Cancellation (LR-OSIC) detector with a Bit Error Rate (BER) of 10-3 and a comparable computation load.

This paper proposes a method of improving the performance of blind reverberation time (RT) estimation in noisy environments. RT estimation is conducted using a maximum likelihood (ML) method based on the autocorrelation function of the linear predictive residual signal. To reduce the effect of environmental noise, a noise reduction technique is applied to the noisy speech signal. In addition, a frequency coefficient selection is performed to eliminate signal components with low signal-to-noise ratio (SNR). Experimental results confirm that the proposed method improves the accuracy of RT measures, particularly when the speech signal is corrupted by a colored noise with a narrow bandwidth.

Dysarthric speech results from damage to the central nervous system involving the articulator, which can mainly be characterized by poor articulation due to irregular sub-glottal pressure, loudness bursts, phoneme elongation, and unexpected pauses during utterances. Since dysarthric speakers have physical disabilities due to the impairment of their nervous system, they cannot easily control electronic devices. For this reason, automatic speech recognition (ASR) can be a convenient interface for dysarthric speakers to control electronic devices. However, the performance of dysarthric ASR severely degrades when there is background noise. Thus, in this paper, we propose a noise reduction method that improves the performance of dysarthric ASR. The proposed method selectively applies either a Wiener filtering algorithm or a Kalman filtering algorithm according to the result of voiced or unvoiced classification. Then, the performance of the proposed method is compared to a conventional Wiener filtering method in terms of ASR accuracy.

This paper proposes a background noise estimation method using an outer product expansion with non-linear filters for ELF (extremely low frequency) electromagnetic (EM) waves. We proposed a novel source separation technique that uses a tensor product expansion. This signal separation technique means that the background noise, which is observed in almost all input signals, can be estimated using a tensor product expansion (TPE) where the absolute error (AE) is used as the error function, which is thus known as TPE-AE. TPE-AE has two problems: the first is that the results of TPE-AE are strongly affected by Gaussian random noise, and the second is that the estimated signal varies widely because of the random search. To solve these problems, an outer product expansion based on a modified trimmed mean (MTM) is proposed in this paper. The results show that this novel technique separates the background noise from the signal more accurately than conventional methods.

This paper proposes a noise reduction method for impact noise with damped oscillation caused by clinking a glass, hitting a bottle, and so on. The proposed method is based on the zero phase (ZP) signal defined as the IDFT of the spectral amplitude. When the target noise can be modeled as the sum of the impact part and the damped oscillation part, the proposed method can reduce them individually. First, the proposed method estimates the damped oscillation spectra and subtracts them from the observed spectra. Then, the impact part is reduced by replacing several samples of the ZP observed signal. Simulation results show that the proposed method improved 10dB of SNR of real impact noise.

The goal of dimension reduction is to represent high-dimensional data in a lower-dimensional subspace, while intrinsic properties of the original data are kept as much as possible. An important challenge in unsupervised dimension reduction is the choice of tuning parameters, because no supervised information is available and thus parameter selection tends to be subjective and heuristic. In this paper, we propose an information-theoretic approach to unsupervised dimension reduction that allows objective tuning parameter selection. We employ quadratic mutual information (QMI) as our information measure, which is known to be less sensitive to outliers than ordinary mutual information, and QMI is estimated analytically by a least-squares method in a computationally efficient way. Then, we provide an eigenvector-based efficient implementation for performing unsupervised dimension reduction based on the QMI estimator. The usefulness of the proposed method is demonstrated through experiments.

The IEEE 802.11 distributed coordinated function (DCF) adopts carrier sense multiple access with collision avoidance (CSMA/CA) as its medium access control (MAC) protocol. In a wireless local area network (WLAN) stations (STAs) congested situation, the performance of the WLAN system is significantly degraded due to a collision between the STAs. In this paper, we propose a simple method that decreases the number of frame collisions. After a successful transmission, the proposed method refrains from transmission during certain time which is defined as post-inter-frame space (Post-IFS). This mechanism improves the system performance including the throughput characteristics and access delay by reducing the number of competing STAs. The length of the Post-IFS is a key factor in improving the system performance for the proposed method. If the access point (AP) can estimate the optimal value of the Post-IFS, collision-free operation similar to that in centralized control is performed. Even if the optimal Post-IFS is not estimated, the number of competing STAs and the collision probability are decreased. Computer simulations verify that the proposed method achieves 40% higher system throughput compared to the conventional CSMA/CA for a network with 50 STAs.

A major challenge for speech emotion recognition is that when the training and deployment conditions do not use the same speech corpus, the recognition rates will obviously drop. Transfer learning, which has successfully addressed the cross-domain classification or recognition problem, is presented for cross-corpus speech emotion recognition. First, by using the maximum mean discrepancy embedding (MMDE) optimization and dimension reduction algorithms, two close low-dimensional feature spaces are obtained for source and target speech corpora, respectively. Then, a classifier function is trained using the learned low-dimensional features in the labeled source corpus, and directly applied to the unlabeled target corpus for emotion label recognition. Experimental results demonstrate that the transfer learning method can significantly outperform the traditional automatic recognition technique for cross-corpus speech emotion recognition.

We consider some attacks on multi-prime RSA (MPRSA) with a modulus N = p1p2 . . . pr (r ≥ 3). It is believed that the small private exponent attack on the MPRSA is less effective than that on RSA (see Hinek et al.'s work at SAC 2003), which means smaller private exponents can be used in the MPRSA to speed up the decryption process. Our work shows that even if a private exponent is significantly beyond Hinek et al.'s bound, it still may be insecure if the prime difference Δ (Δ = pr - p1 = Nγ, supposing p1 < p2 < … < pr) is small, i.e. 0 < γ < 1/r. Specifically, by taking full advantage of prime properties, our small private exponent attack reveals that the MPRSA is insecure when $delta<1-sqrt{1+2gamma-3/r}$ (if $gammagerac{3}{2r}-rac{1+delta}{4}$) or $deltale rac{3}{r}-rac{1}{4}-2gamma$ (if $gamma < rac{3}{2r}-rac{1+delta}{4}$), where δ is the exponential of the private exponent d with base N, i.e., d = Nδ. In addition, we present a Fermat-like factoring attack which factors N efficiently when Δ < N1/r2. These proposed attacks surpass previous works (e.g. Bahig et al.'s at ICICS 2012), and are proved effective in practice.

High Efficiency Video Coding (HEVC) is the latest video coding standard that is supported by JCT-VC. In this letter, an encoding algorithm for early termination of Coding Unit (CU) and Prediction Unit (PU) based on the texture direction is proposed for the HEVC intra prediction. Experimental results show that the proposed algorithm provides an average 40% total encoding time reduction with the negligible loss of rate-distortion.

Although the QR decomposition M algorithm (QRD-M) detection reduces the complexity and achieves near-optimal detection performance, its complexity is still very high. In the proposed scheme, the received symbols through bad channel conditions are arranged in reverse order due to the performance of a system depending on the detection capability of the first layer. Simulation results show that the proposed scheme provides almost the same performance as the QRD-M. Moreover, the complexity is about 33.6% of the QRD-M for a bit error rate (BER) with 4×4 multi input multi output (MIMO) system.

Blind source separation is a technique that can separate sound sources without such information as source location, the number of sources, and the utterance content. Multi-channel source separation using many microphones separates signals with high accuracy, even if there are many sources. However, these methods have extremely high computational complexity, which must be reduced. In this paper, we propose a computational complexity reduction method for blind source separation based on frequency domain independent component analysis (FDICA) and examine temporal data that are effective for source separation. A frame with many sound sources is effective for FDICA source separation. We assume that a frame with a low kurtosis has many sound sources and preferentially select such frames. In our proposed method, we used the log power spectrum and the kurtosis of the magnitude distribution of the observed data as selection criteria and conducted source separation experiments using speech signals from twelve speakers. We evaluated the separation performances by the signal-to-interference ratio (SIR) improvement score. From our results, the SIR improvement score was 24.3dB when all the frames were used, and 23.3dB when the 300 frames selected by our criteria were used. These results clarified that our proposed selection criteria based on kurtosis and magnitude is effective. Furthermore, we significantly reduced the computational complexity because it is proportional to the number of selected frames.

This paper is concerned with a method to reduce the computation time of the Discrete Ray Tracing Method (DRTM) which was proposed to numerically analyze electromagnetic fields above Random Rough Surfaces (RRSs). The essence of DRTM is firstly to search rays between source and receiver and secondly to compute electric fields based on the traced rays. In the DRTM, the method discretizes not only RRSs but also ray tracing procedure. In order to reduce computation time for ray searching, the authors propose to modify the conventional algorithm discretizing RRSs with equal intervals to a new one which discretizes them with unequal intervals according to their profiles. The authors also use an approximation of Fresnel function which enables us to reduce field computation time. The authors discuss the reduction rate for computation time of the DRTM from the numerical view points of ray searching and field computation. Finally, this paper shows how much computation time is reduced by the new method.

This paper studies the complexity of computing discrete logarithms over algebraic tori. We show that the order certified version of the discrete logarithm problem over general finite fields (OCDL, in symbols) reduces to the discrete logarithm problem over algebraic tori (TDL, in symbols) with respect to the polynomial-time Turing reducibility. This reduction means that if the prime factorization can be computed in polynomial time, then TDL is equivalent to the discrete logarithm problem over general finite fields with respect to the Turing reducibility.