This paper presents a design of a monolithic transformer using bond wires. The proposed transformer structure has several advantages such as high power handling and high efficiency. It shows that the measured insertion loss at the 1.9 GHz range is -1.54 dB (70%), which is higher than the spiral transformer of the same size. Also, it shows a phase error of less than 1 degree.

In this paper, we propose a technique for estimating the degree or intensity of emotional expressions and speaking styles appearing in speech. The key idea is based on a style control technique for speech synthesis using a multiple regression hidden semi-Markov model (MRHSMM), and the proposed technique can be viewed as the inverse of the style control. In the proposed technique, the acoustic features of spectrum, power, fundamental frequency, and duration are simultaneously modeled using the MRHSMM. We derive an algorithm for estimating explanatory variables of the MRHSMM, each of which represents the degree or intensity of emotional expressions and speaking styles appearing in acoustic features of speech, based on a maximum likelihood criterion. We show experimental results to demonstrate the ability of the proposed technique using two types of speech data, simulated emotional speech and spontaneous speech with different speaking styles. It is found that the estimated values have correlation with human perception.

Analytical solutions for the frequency-dependent transmission line model parameters of a stranded coaxial cable, which are not trivial due to the complex geometry, are presented and discussed in this paper. A frequency-dependent effective conductor radius of a stranded wire coaxial cable is proposed to estimate the internal impedance using the Bessel function solutions of a solid wire coaxial cable. The performance of the proposed model is verified by electromagnetic field solver simulation and by experimental measurement. The results show that the proposed model successfully calculates the broadband frequency-dependent RLGC model parameters and characteristic impedance of a stranded wire coaxial cable with high accuracy.

We present a new variant of Attribute based encryption (ABE) called Dual-Policy ABE. Basically, it is a conjunctively combined scheme between Key-Policy and Ciphertext-Policy ABE, the only two previous types of ABE. Dual-Policy ABE allows simultaneously two access control mechanisms over encrypted data: one involves policies over objective attributes ascribed to data and the other involves policies over subjective attributes ascribed to user credentials. The previous two types of ABE can only allow either functionality above one at a time.

An anonymous password-authenticated key exchange (anonymous PAKE) protocol is designed to provide both password-only authentication and user anonymity against a semi-honest server, who follows the protocol honestly. Very recently, Yang and Zhang have proposed a new anonymous PAKE (NAPAKE) protocol that is claimed efficient compared to the previous constructions. In this paper, we propose a very-efficient anonymous PAKE (called, VEAP) protocol that provides the most efficiency among their kinds in terms of computation and communication costs. The VEAP protocol guarantees semantic security of session keys in the random oracle model under the chosen target CDH problem, and unconditional user anonymity against a semi-honest server. If the pre-computation is allowed, both the user and the server are required to compute only one modular exponentiation, respectively. Surprisingly, this is the same computation cost of the well-known Diffie-Hellman protocol that does not provide authentication at all. In addition, we extend the VEAP protocol in two ways: the first is designed to reduce the communication costs of the VEAP protocol and the second shows that stripping off anonymity parts from the VEAP protocol results in a new PAKE protocol.

A digital 3 Gbps 0.2 V differential transmitter is proposed using a voltage-mode pseudo-LVDS output driver. The delay mismatch between two pre-drivers is digitally calibrated by a modified digital DLL with the duty cycle correction. The height and width of eye opening are improved by 103% and 46%, respectively. The power consumption is 11.4 mW at 1.2 V with 0.18 µm process.

This paper presents algorithms for searching text regions with specifying annotated information in tag-annotated text by using Region Algebra. The original algebra and its efficient algorithms are extended to handle both nested regions and crossed regions. The extensions are necessary for text search by using rich linguistic annotations. We first assign a depth number to every nested tag region to order these regions and write efficient algorithms using the depth number for the containment operations which can treat nested tag regions. Next, we introduce variables for attribute values of tags into the algebra to treat annotations in which attributes indicate another tag regions, and propose an efficient method of treating re-entrancy by incrementally determining values for variables. Our algorithms have been implemented in a text search engine for MEDLINE, which is a large textbase of abstracts in medical science. Experiments in tag-annotated MEDLINE abstracts demonstrate the effectiveness of specifying annotations and the efficiency of our algorithms. The system is made publicly accessible at http://www-tsujii.is.s.u-tokyo.ac.jp/medie/.

This paper proposes a method for handling out-of-vocabulary (OOV) words that cannot be translated using conventional phrase-based statistical machine translation (SMT) systems. For a given OOV word, lexical approximation techniques are utilized to identify spelling and inflectional word variants that occur in the training data. All OOV words in the source sentence are then replaced with appropriate word variants found in the training corpus, thus reducing the number of OOV words in the input. Moreover, in order to increase the coverage of such word translations, the SMT translation model is extended by adding new phrase translations for all source language words that do not have a single-word entry in the original phrase-table but only appear in the context of larger phrases. The effectiveness of the proposed methods is investigated for the translation of Hindi to English, Chinese, and Japanese.

In this paper, we propose a bandwidth-scalable stereo audio coding method based on a layered structure. The proposed stereo coding method encodes super-wideband (SWB) stereo signals and is able to decode either wideband (WB) stereo signals or SWB stereo signals, depending on the network congestion. The performance of the proposed stereo coding method is then compared with that of a conventional stereo coding method that separately decodes WB or SWB stereo signals, in terms of subjective quality, algorithmic delay, and computational complexity. Experimental results show that when stereo audio signals sampled at a rate of 32 kHz are compressed to 64 kbit/s, the proposed method provides significantly better audio quality with a 64-sample shorter algorithmic delay, and comparable computational complexity.

This paper presents an extended Relief-F algorithm for nominal attribute estimation, for application to small-document classification. Relief algorithms are general and successful instance-based feature-filtering algorithms for data classification and regression. Many improved Relief algorithms have been introduced as solutions to problems of redundancy and irrelevant noisy features and to the limitations of the algorithms for multiclass datasets. However, these algorithms have only rarely been applied to text classification, because the numerous features in multiclass datasets lead to great time complexity. Therefore, in considering their application to text feature filtering and classification, we presented an extended Relief-F algorithm for numerical attribute estimation (E-Relief-F) in 2007. However, we found limitations and some problems with it. Therefore, in this paper, we introduce additional problems with Relief algorithms for text feature filtering, including the negative influence of computation similarities and weights caused by a small number of features in an instance, the absence of nearest hits and misses for some instances, and great time complexity. We then suggest a new extended Relief-F algorithm for nominal attribute estimation (E-Relief-Fd) to solve these problems, and we apply it to small text-document classification. We used the algorithm in experiments to estimate feature quality for various datasets, its application to classification, and its performance in comparison with existing Relief algorithms. The experimental results show that the new E-Relief-Fd algorithm offers better performance than previous Relief algorithms, including E-Relief-F.

The key problem of object-based attention is the definition of objects, while contour grouping methods aim at detecting the complete boundaries of objects in images. In this paper, we develop a new contour grouping method which shows several characteristics. First, it is guided by the global saliency information. By detecting multiple boundaries in a hierarchical way, we actually construct an object-based attention model. Second, it is optimized by the grouping cost, which is decided both by Gestalt cues of directed tangents and by region saliency. Third, it gives a new definition of Gestalt cues for tangents which includes image information as well as tangent information. In this way, we can improve the robustness of our model against noise. Experiment results are shown in this paper, with a comparison against other grouping model and space-based attention model.

Factorization of Hadamard matrices can provide fast algorithm and facilitate efficient hardware realization. In this letter, constructions of factorizable multilevel Hadamard matrices, which can be considered as special case of unitary matrices, are inverstigated. In particular, a class of ternary Hadamard matrices, together with its application, is presented.

As device feature size decreases, interconnection delay becomes the dominating factor of circuit total delay. Distributed-register architectures can reduce the influence of interconnection delay. They may, however, increase circuit area because they require many local registers. Moreover original distributed-register architectures do not consider control signal delay, which may be the bottleneck in a circuit. In this paper, we propose a high-level synthesis method targeting generalized distributed-register architecture in which we introduce shared/local registers and global/local controllers. Our method is based on iterative improvement of scheduling/binding and floorplanning. First, we prepare shared-register groups with global controllers, each of which corresponds to a single functional unit. As iterations proceed, we use local registers and local controllers for functional units on a critical path. Shared-register groups physically located close to each other are merged into a single group. Accordingly, global controllers are merged. Finally, our method obtains a generalized distributed-register architecture where its scheduling/binding as well as floorplanning are simultaneously optimized. Experimental results show that the area is decreased by 4.7% while maintaining the performance of the circuit equal with that using original distributed-register architectures.

Adjustment of a certain parameter in the course of performing a trajectory task such as drawing or gesturing is a common manipulation in pen-based interaction. Since pen tip information is confined to x-y coordinate data, such concurrent parameter adjustment is not easily accomplished in devices using only a pen tip. This paper comparatively investigates the performance of inherent pen input modalities (Pressure, Tilt, Azimuth, and Rolling) and Key Pressing with the non-preferred hand used for precision parameter manipulation during pen sliding actions. We elaborate our experimental design framework here and conduct experimentation to evaluate the effect of the five techniques. Results show that Pressure enabled the fastest performance along with the lowest error rate, while Azimuth exhibited the worst performance. Tilt showed slightly faster performance and achieved a lower error rate than Rolling. However, Rolling achieved the most significant learning effect on Selection Time and was favored over Tilt in subjective evaluations. Our experimental results afford a general understanding of the performance of inherent pen input modalities in the course of a trajectory task in HCI (human computer interaction).

The researches on predicting and removing of lithographic hot-spots have been prevalent in recent semiconductor industries, and known to be one of the most difficult challenges to achieve high quality detection coverage. To provide physical design implementation with designer's favors on fixing hot-spots, in this paper, we present a noble and accurate hot-spot detection method, so-called "leveling and scoring" algorithm based on weighted combination of image quality parameters (i.e., normalized image log-slope (NILS), mask error enhancement factor (MEEF), and depth of focus (DOF)) from lithography simulation. In our algorithm, firstly, hot-spot scoring function considering severity level is calibrated with process window qualification, and then least-square regression method is used to calibrate weighting coefficients for each image quality parameter. In this way, after we obtain the scoring function with wafer results, our method can be applied to future designs of using the same process. Using this calibrated scoring function, we can successfully generate fixing guidance and rule to detect hot-spot area by locating edge bias value which leads to a hot-spot-free score level. Finally, we integrate the hot-spot fixing guidance information into layout editor to facilitate the user-favorable design environment. Applying our method to memory devices of 60 nm node and below, we could successfully attain sufficient process window margin to yield high mass production.

Leakage current is an important qualitative metric of LSI (Large Scale Integrated circuit). In this paper, we focus on reduction of leakage current variation under the process variation. Firstly, we derive a set of quadratic equations to evaluate delay and leakage current under the process variation. Using these equations, we discuss the cases of varying leakage current without degrading delay distribution and propose a procedure to reduce the leakage current variations. From the experiments, we show the proposed method effectively reduces the leakage current variation up to 50% at 90 percentile point of the distribution compared with the conventional design approach.

Shorts and opens are two major kind of defects that are most likely to occur in Very Large Scale Integrated Circuits. In modern Integrated Circuit devices these defects must be considered not only at gate-level but also at transistor level. In this paper, we propose a method for generating test vectors that targets both transistor shorts (tr-shorts) and transistor opens (tr-opens). Since two consecutive test vectors need to be applied in order to detect tr-opens, we assume launch on capture (LOC) test application mechanism. This makes it possible to detect delay type defects. Further, the proposed method employs existing stuck-at test generation tools thus requiring no change in the design and development flow and development of no new tools is needed. Experimental results for benchmark circuits demonstrate the effectiveness of the proposed method by providing 100% fault efficiency while the test set size is still moderate.

A scan chain is one of the most important testing techniques, but it can be used as side-channel attacks against a cryptography LSI. We focus on scan-based attacks, in which scan chains are targeted for side-channel attacks. The conventional scan-based attacks only consider the scan chain composed of only the registers in a cryptography circuit. However, a cryptography LSI usually uses many circuits such as memories, micro processors and other circuits. This means that the conventional attacks cannot be applied to the practical scan chain composed of various types of registers. In this paper, a scan-based attack which enables to decipher the secret key in an AES cryptography LSI composed of an AES circuit and other circuits is proposed. By focusing on bit pattern of the specific register and monitoring its change, our scan-based attack eliminates the influence of registers included in other circuits than AES. Our attack does not depend on scan chain architecture, and it can decipher practical AES cryptography LSIs.

Previous researches on ad-hoc networks did not consider the dynamic rate adaptation for wireless multicast. Instead, they statically use the lowest data rate for multicast transmission. The MAC protocol proposed in this paper utilizes the OFDMA mechanism, so that all members can report their rate preference at one time. As a result, the best rate for each member is dynamically selected.

Mining correlated patterns in large transaction databases is one of the essential tasks in data mining since a huge number of patterns are usually mined, but it is hard to find patterns with the correlation. The needed data analysis should be made according to the requirements of the particular real application. In previous mining approaches, patterns with the weak affinity are found even with a high minimum support. In this paper, we suggest weighted support affinity pattern mining in which a new measure, weighted support confidence (ws-confidence) is developed to identify correlated patterns with the weighted support affinity. To efficiently prune the weak affinity patterns, we prove that the ws-confidence measure satisfies the anti-monotone and cross weighted support properties which can be applied to eliminate patterns with dissimilar weighted support levels. Based on the two properties, we develop a weighted support affinity pattern mining algorithm (WSP). The weighted support affinity patterns can be useful to answer the comparative analysis queries such as finding itemsets containing items which give similar total selling expense levels with an acceptable error range α% and detecting item lists with similar levels of total profits. In addition, our performance study shows that WSP is efficient and scalable for mining weighted support affinity patterns.