Body biasing can be used to control the leakage power and performance by changing the threshold voltage of transistors after fabrication. Especially, a new process called Silicon-On-Thin Box (SOTB) CMOS can control their balance widely. When it is applied to a Coarse Grained Reconfigurable Array (CGRA), the leakage power can be much reduced by precise bias control with small domain size including a small number of PEs. On the other hand, the area overhead for separating power domain and delivering a lot of wires for body bias voltage supply increases. This paper explores the grain of domain size of an energy efficient CGRA called CMA (Cool Mega Array). By using Genetic Algorithm based body bias assignment method, the leakage reduction of various grain size was evaluated. As a result, a domain with 2x1 PEs achieved about 40% power reduction with a 6% area overhead. It has appeared that a combination of three body bias voltages; zero bias, weak reverse bias and strong reverse bias can achieve the optimal leakage reduction and area overhead balance in most cases.

Multivariate Public Key Cryptography (MPKC) is one of the main candidates for secure communication in a post-quantum era. Recently, Yasuda and Sakurai proposed at ICICS 2015 a new multivariate encryption scheme called SRP, which offers efficient decryption, a small blow up factor between plaintext and ciphertext and resists all known attacks against multivariate schemes. However, similar to other MPKC schemes, the key sizes of SRP are quite large. In this paper we propose a technique to reduce the key size of the SRP scheme, which enables us to reduce the size of the public key by up to 54%. Furthermore, we can use the additional structure in the public key polynomials to speed up the encryption process of the scheme by up to 50%. We show by experiments that our modifications do not weaken the security of the scheme.

High power dissipation can occur by high launch-induced switching activity when the response to a test pattern is captured by flip-flops (FFs) in at-speed scan testing, resulting in excessive IR drop. IR drop may cause significant capture-induced yield loss in the deep submicron era. It is known that test modification methods using X-identification and X-filling are effective to reduce power dissipation in the capture cycle. Conventional low power dissipation oriented X-filling methods consecutively select FFs and assign values to decrease the number of transitions on the FFs. In this paper, we propose a novel low power dissipation oriented X-filling method using SAT Solvers that conducts simultaneous X-filling for some FFs. We also proposed a selection order of FFs based on a correlation coefficient between transitions of FFs and power dissipation. Experimental results show that the proposed method was effective for ISCAS'89 and ITC'99 benchmark circuits compared with justification-probability-based fill.

An efficient reciprocity and passivity preserving balanced truncation for RLC networks is presented in this paper. Reciprocity and passivity are fundamental principles of linear passive networks. Hence, reduction with preservation of reciprocity and passivity is necessary to simulate behavior of the circuits including the RLC networks accurately and stably. Moreover, the proposed method is more efficient than the previous balanced truncation methods, because sparsity patterns of the coefficient matrices for the circuit equations of the RLC networks are fully available. In the illustrative examples, we will show that the proposed method is compatible with PRIMA, which is known as a general reduction method of RLC networks, in efficiency and used memory, and is more accurate at high frequencies than PRIMA.

In this letter, a switching detection scheme based on a channel condition number for the MIMO-OFDM system is proposed. The switching algorithm operates by selecting one of three detection schemes of QRD-M, LR-aided MMSE-DFE, and LR-aided MMSE. The switching detection uses the threshold based on the switching algorithm according to the channel condition number. From the simulation results, the proposed detection scheme shows error detection performance and computational complexity in accordance with the threshold for switching detection.

The Helmholtz-Kohlraush effect is a visual characteristic that humans perceive color having higher saturation as brighter. In the proposed method, the pixel value is reduced by increasing the saturation while maintaining the hue and value of HSV color space, resulting in power saving of OLED displays since the power consumption of OLED displays directly depends on the pixel value. Although the luminance decreases, brightness of image is maintained by the Helmholtz-Kohlraush effect. In order to suppress excessive increase of saturation, the increase factor of saturation is reduced with an increase in brightness. As maximum increase factor of saturation, kMAX, increases, more power is reduced but unpleasant color change takes place. From the subjective evaluation experiment with the 23 test images consisting of skin, natural and non-natural images, it is found that kMAX is less than 2.0 to suppress the unpleasant color change. When kMAX is 2.0, the power saving is 8.0%. The effectiveness of the proposed technique is confirmed by using a smart phone having 4.5 inches diagonal RGB AMOLED display.

For mobile video codecs, the huge energy dissipation for external memory traffic is a critical challenge under the battery power constraint. Lossy embedded compression (EC), as a solution to this challenge, is considered in this paper. While previous studies in lossy EC mostly focused on algorithm optimization to reduce distortion, this work, to the best of our knowledge, is the first one that addresses the distortion control. Firstly, from both theoretical analysis and experiments for distortion optimization, a conclusion is drawn that, at the frame level, allocating memory traffic evenly is a reliable approximation to the optimal solution to minimize quality loss. Then, to reduce the complexity of decoding twice, the distortion between two sequences is estimated by a linear function of that calculated within one sequence. Finally, on the basis of even allocation, the distortion control is proposed to determine the amount of memory traffic according to a given distortion limitation. With the adaptive target setting and estimating function updating in each group of pictures (GOP), the scene change in video stream is supported without adding a detector or retraining process. From experimental results, the proposed distortion control is able to accurately fix the quality loss to the target. Compared to the baseline of negative feedback on non-referred B frames, it achieves about twice memory traffic reduction.

This paper presents an interactive locomotion controller using motion capture data and an inverted pendulum model (IPM). The motion data of a character is decomposed into those of upper and lower bodies, which are then dimension-reduced via what we call hierarchical Gaussian process dynamical model (H-GPDM). The locomotion controller receives the desired walking direction from the user. It is integrated into the IPM to determine the pose of the center of mass and the stance-foot position of the character. They are input to the H-GPDM, which interpolates the low-dimensional data to synthesise a redirected motion sequence on an uneven surface. The locomotion controller allows the upper and lower bodies to be independently controlled and helps us generate natural locomotion. It can be used in various real-time applications such as games.

A single-dimensional interface which enables users to obtain diverse localizations of audio sources is proposed. In many conventional interfaces for arranging audio sources, there are multiple arrangement parameters, some of which allow users to control positions of audio sources. However, it is difficult for users who are unfamiliar with these systems to optimize the arrangement parameters since the number of possible settings is huge. We propose a simple, single-dimensional interface for adjusting arrangement parameters, allowing users to sample several diverse audio source arrangements and easily find their preferred auditory localizations. To select subsets of arrangement parameters from all of the possible choices, auditory-localization space vectors (ASVs) are defined to represent the auditory localization of each arrangement parameter. By selecting subsets of ASVs which are approximately orthogonal, we can choose arrangement parameters which will produce diverse auditory localizations. Experimental evaluations were conducted using music composed of three audio sources. Subjective evaluations confirmed that novice users can obtain diverse localizations using the proposed interface.

The breadth-first tree searching (BFTS) detection algorithm such as the QR decomposition with M algorithm (QRD-M) which is the generally K-best detection algorithm is suboptimal, but has high complexity. In this letter, the K-best BFTS detection algorithm having reduced complexity is proposed. The proposed detection algorithm calculates the channel condition to decide the thresholds for regulating complexity and performance and from the simulation results, it has good error performance with very low complexity.

Multichannel speech enhancement systems (MSES') have been widely utilized for diverse types of speech interface applications. A state-of-the-art MSES primarily utilizes multichannel minima-controlled recursive averaging for noise estimations and a parameterized multichannel Wiener filter for noise reduction. Many MSES' are implemented in the frequency domain, but they are computationally burdensome due to the numerous complex matrix operations involved. In this paper, a novel MSES intended to reduce the computational complexity with improved performance is proposed. The proposed system is implemented in the mel-filterbank domain using a frequency-averaging technique. Through a performance evaluation, it is verified that the proposed mel-filterbank MSES achieves improvements in the perceptual speech quality with a reduced level of computation compared to a conventional MSES.

In this paper, an improved lattice reduction (LR)-aided soft-output multiple-input multiple-output (MIMO) detector is proposed. Conventional LR-aided soft-output MIMO detectors involve the empty set problem (ESP), in which an entry with a particular bit in the candidate list might not exist. To overcome the performance degradation resulting from this ESP, a post-processing algorithm that modifies the candidate list is proposed. The proposed algorithm efficiently resolves the ESP by utilizing the near-orthogonality of the lattice-reduced system model so that the bit error rate (BER) performance is enhanced. In addition, as the complexity of the candidate list generation is reduced with the aid of the post-processing algorithm, the overall complexity is also reduced. Simulation results and the complexity comparisons demonstrate that our proposed method lowers the required Eb/No by 4-5 dB at the BER of 10-5 and the complexity by 13%-55%, compared to the conventional method.

Multi-label classification is an appealing and challenging supervised learning problem, where multiple labels, rather than a single label, are associated with an unseen test instance. To remove possible noises in labels and features of high-dimensionality, multi-label dimension reduction has attracted more and more attentions in recent years. The existing methods usually suffer from several problems, such as ignoring label outliers and label correlations. In addition, most of them emphasize on conducting dimension reduction in an unsupervised or supervised way, therefore, unable to utilize the label information or a large amount of unlabeled data to improve the performance. In order to cope with these problems, we propose a novel method termed Robust sEmi-supervised multi-lAbel DimEnsion Reduction, shortly READER. From the viewpoint of empirical risk minimization, READER selects most discriminative features for all the labels in a semi-supervised way. Specifically, the ℓ2,1-norm induced loss function and regularization term make READER robust to the outliers in the data points. READER finds a feature subspace so as to keep originally neighbor instances close and embeds labels into a low-dimensional latent space nonlinearly. To optimize the objective function, an efficient algorithm is developed with convergence property. Extensive empirical studies on real-world datasets demonstrate the superior performance of the proposed method.

Identity-based encryption (IBE) is an advanced form of public key encryption and one of the most important cryptographic primitives. Of the many constructions of IBE schemes, the one proposed by Boneh and Boyen (in Eurocrypt 2004) is quite important from both practical and theoretical points of view. The scheme was standardized as IEEE P1363.3 and is the basis for many subsequent constructions. In this paper, we investigate its multi-challenge security, which means that an adversary is allowed to query challenge ciphertexts multiple times rather than only once. Since single-challenge security implies multi-challenge security, and since Boneh and Boyen provided a security proof for the scheme in the single-challenge setting, the scheme is also secure in the multi-challenge setting. However, this reduction results in a large security loss. Instead, we give tight security reduction for the scheme in the multi-challenge setting. Our reduction is tight even if the number of challenge queries is not fixed in advance (that is, the queries are unbounded). Unfortunately, we are only able to prove the security in a selective setting and rely on a non-standard parameterized assumption. Nevertheless, we believe that our new security proof is of interest and provides new insight into the security of the Boneh-Boyen IBE scheme.

Structured signatures are digital signatures where relationship between signers is guaranteed in addition to the validity of individually generated data for each signer, and have been expected for the digital right management. Nevertheless, we mention that there is no scheme with a tight security reduction, to the best of our knowledge. Loosely speaking, it means that the security is downgraded against an adversary who obtains a large amount of signatures. Since contents are widely utilized in general, achieving a tighter reduction is desirable. Based on this background, we propose the first structured signature scheme with a tight security reduction in the conventional public key cryptography and the one with a rigorous reduction proof in the ID-based cryptography via our new proof method. Moreover, the security of our schemes can be proven under the CDH assumption which is the most standard. Our schemes are also based on bilinear maps whose implementation can be provided via well-known cryptographic libraries.

A novel dimensionality reduction method, Fisher Discriminant Locality Preserving Canonical Correlation Analysis (FDLP-CCA), for visualizing Web images is presented in this paper. FDLP-CCA can integrate two modalities and discriminate target items in terms of their semantics by considering unique characteristics of the two modalities. In this paper, we focus on Web images with text uploaded on Social Networking Services for these two modalities. Specifically, text features have high discriminate power in terms of semantics. On the other hand, visual features of images give their perceptual relationships. In order to consider both of the above unique characteristics of these two modalities, FDLP-CCA estimates the correlation between the text and visual features with consideration of the cluster structure based on the text features and the local structures based on the visual features. Thus, FDLP-CCA can integrate the different modalities and provide separated manifolds to organize enhanced compactness within each natural cluster.

Non-contiguous orthogonal frequency-division multiplexing (OFDM) is a promising technique for cognitive radio systems. The secondary users transmit on the selected subcarriers to avoid the frequencies being used by the primary users. However, the out-of-band power (OBP) of the OFDM-modulated tones induces interference to the primary users. Another major drawback of OFDM-based system is their high peak-to-average power ratio (PAPR). In this paper, algorithms are proposed to jointly reduce the OBP and the PAPR for non-contiguous OFDM based on the method of alternating projections onto convex sets. Several OFDM subcarriers are selected to accommodate the adjusting weights for OBP and PAPR reduction. The frequency-domain OFDM symbol is projected onto two convex sets that are defined according to the OBP requirements and the PAPR limits. Each projection iteration solves a convex optimization problem. The projection onto the set constrained by the OBP requirement can be calculated using an iterative algorithm which has low computational complexity. Simulation results show good performance of joint reduction of the OBP and the PAPR. The proposed algorithms converge quickly in a few iterations.

Shrinking feature sizes and higher levels of integration in semiconductor device manufacturing technologies are increasingly causing the gap between defect levels estimated in the design stage and reported ones for fabricated devices. In this paper, we propose a unified weighted fault coverage approach that includes both bridge and open faults, considering the critical area as the incident rate of each fault. We then propose a test pattern reordering scheme that incorporates our weighted fault coverage with an aim to reduce test costs. Here we apply a greedy algorithm to reorder test patterns generated by the bridge and stuck-at automatic test pattern generator (ATPG), evaluating the relationship between the number of patterns and the weighted fault coverage. Experimental results show that by applying this reordering scheme, the number of test patterns was reduced, on average, by approximately 50%. Our results also indicate that relaxing coverage constraints can drastically reduce test pattern set sizes to a level comparable to traditional 100% coverage stuck-at pattern sets, while targeting the majority of bridge faults and keeping the defect level to no more than 10 defective parts per milion (DPPM) with a 99% manufacturing yield.

This paper first reviews the state-of-the-art noise reduction methods and points out their vulnerability in noise reduction performance and speech quality, especially under the low signal-noise ratios (SNR) environments. Then this paper presents an improved perceptual multiband spectral subtraction (MBSS) noise reduction algorithm (NRA) and a novel robust voice activity detection (VAD) based on the amended sub-band SNR. The proposed SNR-based VAD can considerably increase the accuracy of discrimination between noise and speech frame. The simulation results show that the proposed NRA has better segmental SNR (segSNR) and perceptual evaluation of speech quality (PESQ) performance than other noise reduction algorithms especially under low SNR environments. In addition, a fully operational digital hearing aid chip is designed and fabricated in the 0.13 µm CMOS process based on the proposed NRA. The final chip implementation shows that the whole chip dissipates 1.3 mA at the 1.2 V operation. The acoustic test result shows that the maximum output sound pressure level (OSPL) is 114.6 dB SPL, the equivalent input noise is 5.9 dB SPL, and the total harmonic distortion is 2.5%. So the proposed digital hearing aid chip is a promising candidate for high performance hearing-aid systems.

As a hot topic of speech signal processing, speech emotion recognition methods have been developed rapidly in recent years. Some satisfactory results have been achieved. However, it should be noted that most of these methods are trained and evaluated on the same corpus. In reality, the training data and testing data are often collected from different corpora, and the feature distributions of different datasets often follow different distributions. These discrepancies will greatly affect the recognition performance. To tackle this problem, a novel corpus-invariant discriminant feature representation algorithm, called transfer discriminant analysis (TDA), is presented for speech emotion recognition. The basic idea of TDA is to integrate the kernel LDA algorithm and the similarity measurement of distributions into one objective function. Experimental results under the cross-corpus conditions show that our proposed method can significantly improve the recognition rates.