We describe a technique for the registration of three dimensional (3D) knee femur surface points from MR image data sets; it is a technique that can track local cartilage thickness changes over time. In the first coarse registration step, we use the direction vectors of the volume given by the cloud of points of the MR image to correct for different knee joint positions and orientations in the MR scanner. In the second fine registration step, we propose a global search algorithm that simultaneously determines the optimal transformation parameters and point correspondences through searching a six dimensional space of Euclidean motion vectors (translation and rotation). The present algorithm is grounded on a mathematical theory - Lipschitz optimization. Compared with the other three registration approaches (ICP, EM-ICP, and genetic algorithms), the proposed method achieved the highest registration accuracy on both animal and clinical data.

Hypercube Qn is a well-known graph structure having three different kinds of equivalent definitions that are: 1. binary n bit sequences with the adjacency condition, 2. Q1=K2, Qn=Qn-1 K2, where means the Cartesian product, 3. the Cayley graph on Z2n with the generator set {100, 0100, , 001}. We give a necessary and sufficient condition for a set of binary sequences to be a generator set for the hypercube. Then, we give relations between some generator sets and relational products. These results show the wide variety of representability of hypercubes which would be used for many applications.

This letter studies a pulse-coupled system constructed by delayed cross-switching between two bifurcating neurons. The system can exhibit an interesting bifurcation: the delay-coupling can change chaotic behavior of single neurons into stable periodic behavior. Using the 1D phase map, it is clarified that the phenomenon is caused by the tangent bifurcation for the delay parameter. Presenting a simple test circuit, the phenomenon can be confirmed experimentally.

We report on the enhancement of system detection efficiency in a superconducting nanowire single-photon detector (SNSPD) by applying the optical cavity structure. The nanowire was made using 4-nm-thick NbN thin films and covered with an SiO cavity and Au mirror designed for 1300-1600 nm wavelengths. The device is mounted into fiber-coupled packages, and installed in a practical multichannel system based on GM cryocoolers. System detection efficiency depends on the absorptance of cavity structure, and reached 28% and 40% at 1550 nm and 1310 nm wavelengths, respectively. These values were considerably higher than an SNSPD without optical cavity.

This paper presents a novel dynamic subchannel allocation scheme that can improve the cell capacity by coordinating the intercell interference (ICI) in a cellular orthogonal frequency division multiple access (OFDMA) system. The proposed scheme mitigates the ICI by adopting the virtual cell concept and improves the frequency reuse factor through subchannel reuse among different virtual cells. In particular, each virtual cell is assigned a primary and a secondary subchannel group, and each sector base station (BSs) allocates the subchannel resulting in the least ICI in probability out of the candidate subchannels to the mobile stations, dynamically searching from its primary group and then secondary group. In addition, an optional use of pico-cell overlay at the intersection of the virtual cells is also proposed to enhance the fairness of the proposed scheme with the BS-MS distance. Through computer simulation, it is shown that the proposed scheme has the advantages of improved cell capacity and fairness compared to the conventional schemes.

A new type of modulation called continuous-phase parallel-combinatory high-compaction multicarrier modulation (CPPC/HC-MCM) is proposed. CPPC/HC-MCM employs the technique of continuous-phase modulation (CPM) and avoids the formation of amplitude gaps between two successive signals to enhance the spectral efficiency of conventional PC/HC-MCM. Results of simulations show that CPPC/HC-MCM is spectrally efficient and achieves a smaller bit error rate than conventional (unmodulated) PC/HC-MCM at a common spectral efficiency even if the peak-to-average power ratio is considered.

In this paper, we establish our child safety system model related to the addressing contradictory issue of wireless sensor networks caused by the mutual authentication and privacy protection of an end-user. Based on the system model, we propose the novel location-aware and privacy-preserving approach for providing child safety over wireless sensor networks. Although we illustrate our protocol over the sensor networks, the proposed protocol can be operated by various wireless networks (e.g., WiFi and UWB) which can support RSSI (Received Signal Strength Indication). Compared to a few previous works, the proposed approach can show the potential of enhancing accuracy with location information, preserve the privacy of an end-user, and provide the capability of controlling the child safety service to an end-user.

One kind of functional noncoding RNAs, microRNAs (miRNAs), form a class of endogenous RNAs that can have important regulatory roles in animals and plants by targeting transcripts for cleavage or translation repression. Researches on both experimental and computational approaches have shown that miRNAs indeed involve in the human cancer development and progression. However, the miRNAs that contribute more information to the distinction between the normal and tumor samples (tissues) are still undetermined. Recently, the high-throughput microarray technology was used as a powerful technique to measure the expression level of miRNAs in cells. Analyzing this expression data can allow us to determine the functional roles of miRNAs in the living cells. In this paper, we present a computational method to (1) predicting the tumor tissues using high-throughput miRNA expression profiles; (2) finding the informative miRNAs that show strong distinction of expression level in tumor tissues. To this end, we perform a support vector machine (SVM) based method to deeply examine one recent miRNA expression dataset. The experimental results show that SVM-based method outperforms other supervised learning methods such as decision trees, Bayesian networks, and backpropagation neural networks. Furthermore, by using the miRNA-target information and Gene Ontology annotations, we showed that the informative miRNAs have strong evidences related to some types of human cancer including breast, lung, and colon cancer.

This paper presents a framework of supporting system for a drug formulation. We designed ontology to represent the related knowledge for reusable and sharing purposes. The designed ontology is applied with operation rules to suggest an appropriate generic drug production based on information of original drug. The system also provides a validation module to preliminarily approve a pharmaceutical equivalence of the suggested result. Preliminary testing with four random samples shows potential to reformulate a generic product by returning a satisfactory and acceptable of the system suggestions for all samples.

Practitioners of the Jiro Kawakita (KJ) method, a method for organizing ideas, typically use paper labels and four-colored ball-point pens to record their ideas during the creative thinking process. A similar approach is used in group KJ method sessions; however, the effectiveness of capturing and sharing the diagrams and information is limited because of the large amount of paper required. Considering the merits of the conventional paper-pen approach and the demand for quick sharing of diagrams after a session, we designed and implemented a system to digitize group KJ sessions--not just the diagrams but also the details of the creative process. We used digital pens during the session to capture the position and orientation of labels as well as their content. We confirmed the efficiency of our system by applying it to several GKJ sessions.

This paper introduces a future and past search engine, ChronoSeeker, which can help users to develop long-term strategies for their organizations. To provide on-demand searches, we tackled two technical issues: (1) organizing efficient event searches and (2) filtering out noises from search results. Our system employed query expansion with typical expressions related to event information such as year expressions, temporal modifiers, and context terms for efficient event searches. We utilized a machine-learning technique of filtering noise to classify candidates into information or non-event information, using heuristic features and lexical patterns derived from a text-mining approach. Our experiment revealed that filtering achieved an 85% F-measure, and that query expansion could collect dozens more events than those without expansion.

In this paper, we propose a novel stakeholder mining mechanism for analyzing bias in news articles by comparing descriptions of stakeholders. Our mechanism is based on the presumption that interests often induce bias of news agencies. As we use the term, a "stakeholder" is a participant in an event described in a news article who should have some relationships with other participants in the article. Our approach attempts to elucidate bias of articles from three aspects: stakeholders, interests of stakeholders, and the descriptive polarity of each stakeholder. Mining of stakeholders and their interests is achieved by analysis of sentence structure and the use of RelationshipWordNet, a lexical resource that we developed. For analyzing polarities of stakeholder descriptions, we propose an opinion mining method based on the lexical resource SentiWordNet. As a result of analysis, we construct a relations graph of stakeholders to group stakeholders sharing mutual interests and to represent the interests of stakeholders. We also describe an application system we developed for news comparison based on the mining mechanism. This paper presents some experimental results to validate the proposed methods.

This paper proposes an efficient combination of state likelihood recycling and batch state likelihood calculation for accelerating acoustic likelihood calculation in an HMM-based speech recognizer. Recycling and batch calculation are each based on different technical approaches, i.e. the former is a purely algorithmic technique while the latter fully exploits computer architecture. To accelerate the recognition process further by combining them efficiently, we introduce conditional fast processing and acoustic backing-off. Conditional fast processing is based on two criteria. The first potential activity criterion is used to control not only the recycling of state likelihoods at the current frame but also the precalculation of state likelihoods for several succeeding frames. The second reliability criterion and acoustic backing-off are used to control the choice of recycled or batch calculated state likelihoods when they are contradictory in the combination and to prevent word accuracies from degrading. Large vocabulary spontaneous speech recognition experiments using four different CPU machines under two environmental conditions showed that, compared with the baseline recognizer, recycling and batch calculation, our combined acceleration technique further reduced both of the acoustic likelihood calculation time and the total recognition time. We also performed detailed analyses to reveal each technique's acceleration and environmental dependency mechanisms by classifying types of state likelihoods and counting each of them. The analysis results comfirmed the effectiveness of the combined acceleration technique.

We propose a design optimization flow for a high-speed and low-power operational transconductance amplifier (OTA) using a gm/ID lookup table design methodology in scaled CMOS. This methodology advantages from using gm/ID as a primary design parameter to consider all operation regions including strong, moderate, and weak inversion regions, and enables the lowest power design. SPICE-based lookup table approach is employed to optimize the operation region specified by the gm/ID with sufficient accuracy for short-channel transistors. The optimized design flow features 1) a proposal of the worst-case design scenario for specification and gm/ID lookup table generations from worst-case SPICE simulations, 2) an optimization procedure accomplished by the combination of analytical and simulation-based approaches in order to eliminate tweaking of circuit parameters, and 3) an additional use of gm/ID subplots to take second-order effects into account. A gain-boosted folded-cascode OTA for a switched capacitor circuit is adopted as a target topology to explore the effectiveness of the proposed design methodology for a circuit with complex topology. Analytical expressions of the gain-boosted folded-cascode OTA in terms of DC gain, frequency response and output noise are presented, and detailed optimization of gm/IDs as well as circuit parameters are illustrated. The optimization flow is verified for the application to a residue amplifier in a 10-bit 125 MS/s pipeline A/D converter implemented in a 0.18 µm CMOS technology. The optimized circuit satisfies the required specification for all corner simulations without additional tweaking of circuit parameters. We finally explore the possibility of applying this design methodology as a technology migration tool, and illustrate the failure analysis by comparing the differences in the gm/ID characteristics.

The actual blurring function or point spread function (PSF) in an image, in most cases, is similar to a parametric or ideal model. Recently proposed blind deconvolution methods employ this idea for learning during the estimation of PSF. Its dependence on the estimated values may result in ineffective learning when the model is erroneously selected. To overcome this problem, we propose to exploit the image maxima in order to extract a reference point spread function (RPSF). This is only dependent on the degraded image and has a structure that closely resembles a parametric motion blur assuming a known blur support size. Its usage will result in a more stable learning and estimation process since it does not change with respect to iteration or any estimated value. We define a cost function in the vector-matrix form which accounts for the blurring function contour as well as learning towards the RPSF. The effectiveness of using RPSF and the proposed cost function under various motion directions and support sizes will be demonstrated by the experimental results.

A critical problem with wireless LAN-based positioning systems is the degradation in position estimation accuracy due to complex variation in the strength of the received signal, which originates in the nature of the underlying wireless channel. Handling such variation effectively is essential for the creation of practical wireless LAN-based positioning systems. In this paper, we propose a collaborative mechanism for correcting position estimation errors. The main objective is to assist users in correcting estimation errors manually by providing access to a shared body of accumulated information on corrections made by many other users. In particular, the mechanism is designed to enable any group of users to collaboratively build upon this body of information. Finally, we evaluate the effectiveness of the proposed mechanism through experiments. The results confirm that the proposed mechanism can provide effective estimation error correction in a realistic environment.

This paper presents a low complexity partially folded architecture of transposed FIR filter and cubic B-spline interpolator for ATSC terrestrial broadcasting systems. By using the multiplexer, the proposed FIR filter and interpolator can provide high clock frequency and low hardware complexity. A binary representation method was used for designing the high order FIR filter. Also, in order to compensate the truncation error of FIR filter outputs, a fixed-point range detection method was used. The proposed partially folded architecture was designed and implemented with 90-nm CMOS technology that had a supply voltage of 1.1 V. The implementation results show that the proposed architectures have 12% and 16% less hardware complexity than the other kinds of architecture. Also, both the filter and the interpolator operate at a clock frequency of 200 MHz and 385 MHz, respectively.

A new state estimation algorithm is presented for a class of LTI systems that have an input disturbance in polynomial form and a sinusoidal sensor disturbance in the measurement output. Adaptation rules are developed for identifying the unknown magnitude, phase and frequency of the sensor disturbance from the system output measurement. For the application of the identification result to the state estimation problem, the sinusoidal signal with arbitrary initial phase has been considered in this paper. In order to test the performance of the proposed algorithm, comparative computer simulations have been carried out with a robust state observer. Simulation results show the effectiveness of the proposed method.

Improving quality of healthcare for people with chronic conditions requires informed and knowledgeable healthcare providers and patients. Decision support and clinical information system are two of the main components to support improving chronic care. In this paper, we describe an ontology-based information and knowledge management framework that is important for chronic disease care management. Ontology-based knowledge acquisition and modeling based on knowledge engineering approach provides an effective mechanism in capturing expert opinion in form of clinical practice guidelines. The framework focuses on building of healthcare ontology and clinical reminder system that link clinical guideline knowledge with patient registries to support evidenced-based healthcare. We describe implementation and approaches in integrating clinical reminder services to existing healthcare provider environment by focusing on augmenting decision making and improving quality of patient care services.

This study shows a fast simulation method for turbo codes over an additive white class A noise (AWAN) channel. The reduction of the estimation time is achieved by applying importance sampling (IS) which is one of the variance reduction simulation methods. In order to adapt the AWAN channel, we propose a design method of a simulation probability density function (PDF) utilized in IS. The proposed simulation PDF is related to the Bhattacharyya bound to evaluate wider area of the signal space than the conventional method. Since the mean translation method, which is a conventional design method of the simulation PDF used in IS, is optimized for an additive white Gaussian noise channel, the evaluation time of the error performance of turbo codes over the AWAN channel can not be reduced. To evaluate BER of 10-8, the simulation time of the proposed method can be reduced to 1/104 under the condition of the same accuracy of the traditional Monte Carlo simulation method.