This paper introduces a new folding amplifier in a folding and interpolating 10-b ADC. The amplifier consists of current mirrors and differential stages. Only one current source is exploited in cascaded differential pairs, which reduces the power consumption significantly. In the folding circuit, the interpolation is implemented with a current division technique. An experiment of the amplifier in 10-b folding signal has been integrated in a single-poly four-metal 0.35 µm CMOS process. The simulation in 10-b folding ADC shows that power consumption is 225 mW at the sampling speed of 250 Msample/s and the power supply of 3.3 V. The preliminary experiment indicates the current steering folder and digital bits operate as expected.

Frequent Itemsets(FI) mining is a popular and important first step in analyzing datasets across a broad range of applications. There are two main problems with the traditional approach for finding frequent itemsets. Firstly, it may often derive an undesirably huge set of frequent itemsets and association rules. Secondly, it is vulnerable to noise. There are two approaches which have been proposed to address these problems individually. The first problem is addressed by the approach Frequent Closed Itemsets(FCI), FCI removes all the redundant information from the result and makes sure there is no information loss. The second problem is addressed by the approach Approximate Frequent Itemsets(AFI), AFI could identify and fix the noises in the datasets. Each of these two concepts has its own limitations, however, the authors find that if FCI and AFI are put together, they could help each other to overcome the limitations and amplify the advantages. The new integrated approach is termed Noise-tolerant Frequent Closed Itemset(NFCI). The results of the experiments demonstrate the advantages of the new approach: (1) It is noise tolerant. (2) The number of itemsets generated would be dramatically reduced with almost no information loss except for the noise and the infrequent patterns. (3) Hence, it is both time and space efficient. (4) No redundant information is in the result.

In this letter, we propose a speech reinforcement technique based on soft decision under both the far-end and near-end noise environments. We amplify the estimated clean speech signal at the far-end based on the estimated ambient noise spectrum at the near-end, as opposed to reinforcing the noisy far-end speech signal, so that it can be heard more intelligibly in far-end noisy environments. To obtain an effective reinforcement technique, we adopt the soft decision scheme incorporating a speech absence probability (SAP) in the frequency dependent signal-to-noise ratio (SNR) recovery method where the clean speech spectrum is estimated and the reinforcement gain is inherently derived and modified within the unified framework. Performance of the proposed method is evaluated by a subjective testing under various noisy environments. This is an improvement over previous approaches.

The rapid growth of the Internet provides people with tremendous opportunities for data collection, knowledge discovery and cooperative computation. However, it also brings the problem of sensitive information leakage. Both individuals and enterprises may suffer from the massive data collection and the information retrieval by distrusted parties. In this paper, we propose a privacy-preserving protocol for the distributed kernel density estimation-based clustering. Our scheme applies random data perturbation (RDP) technique and the verifiable secret sharing to solve the security problem of distributed kernel density estimation in [4] which assumed a mediate party to help in the computation.

In this paper, a novel eight-way Ka-band spatial power combining structure based on SIW/HMSIW is presented and studied. The power-combining structure is realized by transitions between HMSIW and parallel multiport planar microstrip lines. The power combiner is designed and fabricated in 33.5-35 GHz. The measured results show a good agreement with simulation and a combining efficiency of 72% is achieved at 34.3 GHz.

In this paper, we present a forecasting method for the view of Mt. Fuji as an application of Earth observation data (EOD) obtained by satellites. We defined the Mt. Fuji viewing index (FVI) that characterises how well the mountain looks on a given day, based on photo data produced by a fixed-point observation. A long-term predictor of FVI, ranging from 0 to 30 days, was constructed through support vector machine regression on climate and earth observation data. It was found that the aerosol mass concentration (AMC) improves prediction performance, and such performance is particularly significant in the long-term range.

This paper proposes novel algorithms for computing double-size modular multiplications with few modulus-dependent precomputations. Low-end devices such as smartcards are usually equipped with hardware Montgomery multipliers. However, due to progresses of mathematical attacks, security institutions such as NIST have steadily demanded longer bit-lengths for public-key cryptography, making the multipliers quickly obsolete. In an attempt to extend the lifespan of such multipliers, double-size techniques compute modular multiplications with twice the bit-length of the multipliers. Techniques are known for extending the bit-length of classical Euclidean multipliers, of Montgomery multipliers and the combination thereof, namely bipartite multipliers. However, unlike classical and bipartite multiplications, Montgomery multiplications involve modulus-dependent precomputations, which amount to a large part of an RSA encryption or signature verification. The proposed double-size technique simulates double-size multiplications based on single-size Montgomery multipliers, and yet precomputations are essentially free: in an 2048-bit RSA encryption or signature verification with public exponent e=216+1, the proposal with a 1024-bit Montgomery multiplier is at least 1.5 times faster than previous double-size Montgomery multiplications.

In this paper, we propose a new feature selection algorithm for multi-class classification. The proposed algorithm is based on Gaussian mixture models (GMMs) of the features, and it uses the distance between the two least separable classes as a metric for feature selection. The proposed system was tested with a support vector machine (SVM) for multi-class classification of music. Results show that the proposed feature selection scheme is superior to conventional schemes.

In this letter the complexity of factoring an α-LSBS modulus is analyzed. This gives an improvement on the lower bound of the previous results.

In this paper, a method for adaptive reconstruction of missing textures based on kernel canonical correlation analysis (CCA) with a new clustering scheme is presented. The proposed method estimates the correlation between two areas, which respectively correspond to a missing area and its neighboring area, from known parts within the target image and realizes reconstruction of the missing texture. In order to obtain this correlation, the kernel CCA is applied to each cluster containing the same kind of textures, and the optimal result is selected for the target missing area. Specifically, a new approach monitoring errors caused in the above kernel CCA-based reconstruction process enables selection of the optimal result. This approach provides a solution to the problem in traditional methods of not being able to perform adaptive reconstruction of the target textures due to missing intensities. Consequently, all of the missing textures are successfully estimated by the optimal cluster's correlation, which provides accurate reconstruction of the same kinds of textures. In addition, the proposed method can obtain the correlation more accurately than our previous works, and more successful reconstruction performance can be expected. Experimental results show impressive improvement of the proposed reconstruction technique over previously reported reconstruction techniques.

In this paper, an ER (Error-Reduction) algorithm-based method for removal of adherent water drops from images obtained by a rear view camera mounted on a vehicle in rainy conditions is proposed. Since Fourier-domain and object-domain constraints are needed for any ER algorithm-based method, the proposed method introduces the following two novel constraints for the removal of adherent water drops. The first one is the Fourier-domain constraint that utilizes the Fourier transform magnitude of the previous frame in the obtained images as that of the target frame. Noting that images obtained by the rear view camera have the unique characteristics of objects moving like ripples because the rear view camera is generally composed of a fish-eye lens for a wide view angle, the proposed method assumes that the Fourier transform magnitudes of the target frame and the previous frame are the same in the polar coordinate system. The second constraint is the object-domain constraint that utilizes intensities in an area of the target frame to which water drops have adhered. Specifically, the proposed method models a deterioration process of intensities that are corrupted by the water drop adhering to the rear view camera lens. By utilizing these novel constraints, the proposed ER algorithm can remove adherent water drops from images obtained by the rear view camera. Experimental results that verify the performance of the proposed method are represented.

This paper studies switched dynamical systems based on a simplified model of two-paralleled dc-dc buck converters in current mode control. In the system, we present novel four switching rules depending on both state variables and periodic clock. The system has piecewise constant vector field and piecewise linear solutions: they are well suited for precise analysis. We then clarify parameter conditions that guarantee generation of stable 2-phase synchronization and hyperchaos for each switching rule. Especially, it is clarified that stable synchronization is always possible by proper use of the switching rules and adjustment of clock period. Presenting a simple test circuit, typical phenomena are confirmed experimentally.

The encoding/decoding process of JPEG2000 requires much more computation power than that of conventional JPEG mainly due to the complexity of the entropy encoding/decoding. Thus usually multiple entropy codec hardware modules are implemented in parallel to process the entropy encoding/decoding. This module, however, requests many small-size memories to store intermediate data, and when multiple modules are implemented on a chip, employment of the large number of SRAMs increases difficulty of whole chip layout. In this paper, an efficient memory organization framework for the entropy encoding/decoding module is proposed, in which not only existing memory organizations but also our proposed novel memory organization methods are attempted to expand the design space to be explored. As a result, the efficient memory organization for a target process technology can be explored.

In this paper, we propose a new approximate algorithm for the model predictive control (MPC) problem with a time-varying reference of hybrid systems. The proposed algorithm consists of an offline computation and an online computation. In the offline computation, candidates of mode sequences are derived. In the online computation, after the mode sequence is uniquely decided among candidates, the finite-time optimal control problem, i.e., the quadratic programming problem, is solved. So by applying the proposed algorithm, the computational amount of the online computation is decreased. First, the MPC problem with a time-varying reference is formulated. Next, the proposed algorithm is explained, and the accuracy of the obtained approximate solution is discussed. Finally, the effectiveness of the proposed method is shown by a numerical example.

In this letter we give a lower bound on the worst-case time complexity of Dijkstra's three-state mutual exclusion algorithm by specifying a concrete behavior of the algorithm. We also show that our result is more accurate than the known best bound.

A direct short proof of Horiguchi's formula for error values in alternant codes is provided. Horiguchi's formula employs only output polynomials of Berlekamp-Massey algorithm, which has less computational complexity than extended Euclidean algorithm for decoding alternant codes. As an application of our proof, we provide an explicit formula for the generator and parity check matrices of alternant codes and their singly- and doubly-extended codes.

In this letter, we propose a two-bit representation method for turbo decoder extrinsic information based on bit error count minimization and parameter reset. We show that the performance of the proposed system approaches that of the full precision decoder within 0.17 dB and 0.48 dB at 1 % packet error rate for packet lengths of 500 and 10,000 information bits. The idea of parameter reset we introduce can be used not only in turbo decoder but also in many other iterative algorithms.

A group signature scheme introduced by Chaum and Van Heyst allows a group member to sign messages anonymously on behalf of the group. However, in the case of a dispute, the identity of a signer of a group signature can be revealed only by a privileged entity, called a group manager. The group signature scheme has mainly been studied from the viewpoint of computational security setting so far. The main contribution of this paper is to study group signature schemes in unconditional security. More specifically, we newly introduce strong security notions of unconditionally secure group signatures (USGS for short) based on the idea of those of computationally secure group signatures proposed by Bellare, Micciancio and Warinschi. We also provide a generic method to construct USGS that is provably secure in our security definition. More precisely, we construct USGS by combining an encryption scheme with a signature, and show that the constructed scheme is unconditionally secure if the encryption and the signature used in the construction are unconditionally secure. Finally, we provide an instantiation of the one-time secure group signature scheme based on the generic construction.

Electrical contacts are the most important parts of electrical circuits, and many reliability problems of the circuits are related to contact failure. The contact resistance is one of the important factors for assessing connector reliability, and thus the prediction of contact resistance is essential to designing electrical terminals. In this study, embossments, each 1 mm to 3 mm in radius, were brought into contact with flat planes to simulate the point of contact on a terminal, and the contact resistance was measured using a four-probe method under a load up to 40 N. Copper alloy samples, each plated with tin or silver and having an embossment of 1 mm to 3 mm in radius, were used and the visually clear indentations resulting from the embossment to plane contact were measured to determine their areas. Since the contact resistance is dependent on the contact area, an FEM analysis must be carried out to determine the contact areas correctly. In this paper, an elasto-plastic FEM analysis was performed taking the plating layers into account, and a method was established to make precise determination of the contact areas for different shapes of contacts and loads. The resultant contact areas were used to calculate the contact resistance, which showed a good agreement with experimental results. It was established that the load-resistance curves can be predicted on the basis of the shapes of the contacts as well as plating.

In this letter, we propose a novel frequency-domain equalizer (FDE) for single-carrier systems characterized by severe inter-symbol interference (ISI) channels; it consists of a linear FDE and an iterative block noise-predictor (IBNP). Unlike the FDE with time-domain noise predictor (FDE-NP), the proposed scheme allows the feedback equalizer being an uncausal filter, and performs the noise prediction in an iterative manner. For this reason, FDE-IBNP can remove both precursor and postcursor ISI, and alleviate the impact of error-propagation. Besides, our scheme has lower computational complexity than the present iterative block equalizers.