We report on the fabrication and analysis of basic digital circuits containing InAs nanowire transistors on a host substrate. The nanowires were assembled at predefined positions by means of electric field-assisted self-assembly within each run generating numerous circuits simultaneously. Inverter circuits composed of two separated nanowire transistors forming a driver and an active load have been fabricated. The inverter circuits exhibit a gain (>1) in the MHz regime and a time constant of about 0.9 ns. A sample & hold core element is fabricated based on an InAs nanowire transistor connected to a hold capacitor, both on a Silicon and an InP isolating substrate, respectively. The low leakage read-out of the hold capacitor is done by InP-based metal-insulator heterojunction FET grown on the same substrate prior to nanowire FET fabrication. Experimental operation of the circuit is demonstrated at 100 MHz sampling frequency. The presented approach enables III/V high-speed, low-voltage logic circuits on a wide variety of host substrates which may be up scaled to high volume circuits.

The objective of pool-based incremental active learning is to choose a sample to label from a pool of unlabeled samples in an incremental manner so that the generalization error is minimized. In this scenario, the generalization error often hits a minimum in the middle of the incremental active learning procedure and then it starts to increase. In this paper, we address the problem of early labeling stopping in probabilistic classification for minimizing the generalization error and the labeling cost. Among several possible strategies, we propose to stop labeling when the empirical class-posterior approximation error is maximized. Experiments on benchmark datasets demonstrate the usefulness of the proposed strategy.

Reduction of the intensity noise in semiconductor lasers is an important subject for the higher performance of an application. Simultaneous usage of the superposition of high frequency current and the electric negative feedback loop was proposed to suppress the noise for the higher power operation of semiconductor lasers. Effective noise reduction of more than 25 dB with 80 mW operation was experimentally demonstrated.

The conventional semi-orthogonal user pairing algorithm in uplink virtual MIMO systems can be used to improve the total system throughput but it usually fails to maintain good throughput performance for users experiencing relatively poor channel conditions. A novel user paring algorithm is presented in this paper to solve this fairness issue. Based on our analysis of the MMSE receiver, a new criterion called “inverse selection” is proposed for use in conjunction with the semi-orthogonal user selection. Simulation results show that the proposed algorithm can significantly improve the throughput of users with poor channel condition at only a small reduction of the overall throughput.

In a large software system development project, many documents are prepared and updated frequently. In such a situation, support is needed for looking through these documents easily to identify inconsistencies and to maintain traceability. In this research, we focus on the requirements documents such as use cases and consider how to create models from the use case descriptions in unformatted text. In the model construction, we propose a few semantic constraints based on the features of the use cases and use them for a predicate argument structure analysis to assign semantic labels to actors and actions. With this approach, we show that we can assign semantic labels without enhancing any existing general lexical resources such as case frame dictionaries and design a less language-dependent model construction architecture. By using the constructed model, we consider a system for quality analysis of the use cases and automated test case generation to keep the traceability between document sets. We evaluated the reuse of the existing use cases and generated test case steps automatically with the proposed prototype system from real-world use cases in the development of a system using a packaged application. Based on the evaluation, we show how to construct models with high precision from English and Japanese use case data. Also, we could generate good test cases for about 90% of the real use cases through the manual improvement of the descriptions based on the feedback from the quality analysis system.

A dual-core spot size converter (DC-SSC) is integrated with a lateral grating assisted lateral co-directional coupler (LGLC) tunable laser by using no additional complicated fabrication processes. The excess loss due to the DC-SSC is only 0.5 dB, and narrow full width half maximums (FWHMs) of vertical and horizontal far-field patterns (FFPs) produced by the laser are about 25° and 20°. This integration causes no degradations of the performance of the LGLC laser; in other words, it maintains good lasing characteristics, namely, wide tuning range of over 68 nm and SMSR of over 35 dB in the C-band under a 50 semi-cooled condition.

In this paper, we consider the problem of waveform optimization for multi-input multi-output (MIMO) radar in the presence of signal-dependent noise. A novel diagonal loading (DL) based method is proposed to optimize the waveform covariance matrix (WCM) for minimizing the Cramer-Rao bound (CRB) which improves the performance of parameter estimation. The resulting nonlinear optimization problem is solved by resorting to a convex relaxation that belongs to the semidefinite programming (SDP) class. An optimal solution to the initial problem is then constructed through a suitable approximation to an optimal solution of the relaxed one (in a least squares (LS) sense). Numerical results show that the performance of parameter estimation can be improved considerably by the proposed method compared to uncorrelated waveforms.

In this paper, a novel framework for extracting visual feature-based keyword relationships from an image database is proposed. From the characteristic that a set of relevant keywords tends to have common visual features, the keyword relationships in a target image database are extracted by using the following two steps. First, the relationship between each keyword and its corresponding visual features is modeled by using a classifier. This step enables detection of visual features related to each keyword. In the second step, the keyword relationships are extracted from the obtained results. Specifically, in order to measure the relevance between two keywords, the proposed method removes visual features related to one keyword from training images and monitors the performance of the classifier obtained for the other keyword. This measurement is the biggest difference from other conventional methods that focus on only keyword co-occurrences or visual similarities. Results of experiments conducted using an image database showed the effectiveness of the proposed method.

Recent advance in digital storage technology has enabled us to archive a large volume of video data. Thanks to this trend, we have archived more than 1,800 hours of video data from a daily Japanese news show in the last ten years. When considering the effective use of such a large news video archive, we assumed that analysis of its chronological and semantic structure becomes important. We also consider that providing the users with the development of news topics is more important to help their understanding of current affairs, rather than providing a list of relevant news stories as in most of the current news video retrieval systems. Therefore, in this paper, we propose a structuring method for a news video archive, together with an interface that visualizes the structure, so that users could track the development of news topics according to their interest, efficiently. The proposed news video structure, namely the “topic thread structure”, is obtained as a result of an analysis of the chronological and semantic relation between news stories. Meanwhile, the proposed interface, namely “mediaWalker II”, allows users to track the development of news topics along the topic thread structure, and at the same time watch the video footage corresponding to each news story. Analyses on the topic thread structures obtained by applying the proposed method to actual news video footages revealed interesting and comprehensible relations between news topics in the real world. At the same time, analyses on their size quantified the efficiency of tracking a user's topic-of-interest based on the proposed topic thread structure. We consider this as a first step towards facilitating video authoring by users based on existing contents in a large-scale news video archive.

We study the properties of information dissemination over location-aware gossiping networks leveraging location-based real-time communication applications. Gossiping is a promising method for quickly disseminating messages in a large-scale system, but in its application to information dissemination for location-aware applications, it is important to consider the network topology and patterns of spatial dissemination over the network in order to achieve effective delivery of messages to potentially interested users. To this end, we propose a continuous-space network model extended from Kleinberg's small-world model applicable to actual location-based applications. Analytical and simulation-based study shows that the proposed network achieves high dissemination efficiency resulting from geographically neutral dissemination patterns as well as selective dissemination to proximate users. We have designed a highly scalable location management method capable of promptly updating the network topology in response to node movement and have implemented a distributed simulator to perform dynamic target pursuit experiments as one example of applications that are the most sensitive to message forwarding delay. The experimental results show that the proposed network surpasses other types of networks in pursuit efficiency and achieves the desirable dissemination patterns.

Users recently find their interests by checking the contents published or mentioned by their immediate neighbors in social networking services. We propose semantics-based link navigation; links guide the active user to potential neighbors who may provide new interests. Our method first creates a graph that has users as nodes and shared interests as links. Then it divides the graph by link pruning to extract practical numbers, that the active user can navigate, of interest-sharing groups, i.e. communities of interests (COIs). It then attaches a different semantic tag to the link to each representative user, which best reflects the interests of COIs that they are included in, and to the link to each immediate neighbor of the active user. It finally calculates link attractiveness by analyzing the semantic tags on links. The active user can select the link to access by checking the semantic tags and link attractiveness. User interests extracted from large scale actual blog-entries are used to confirm the efficiency of our proposal. Results show that navigation based on link attractiveness and representative users allows the user to find new interests much more accurately than is otherwise possible.

The development of ontology at the instance level requires the extraction of the terms defining the instances from various data sources. These instances then are linked to the concepts of the ontology, and relationships are created between these instances for the next step. However, before establishing links among data, ontology engineers must classify terms or instances from a web document into an ontology concept. The tool for help ontology engineer in this task is called ontology population. The present research is not suitable for ontology development applications, such as long time processing or analyzing large or noisy data sets. OntoPop system introduces a methodology to solve these problems, which comprises two parts. First, we select meaningful features from syntactic relations, which can produce more significant features than any other method. Second, we differentiate feature meaning and reduce noise based on latent semantic analysis. Experimental evaluation demonstrates that the OntoPop works well, significantly out-performing the accuracy of 49.64%, a learning accuracy of 76.93%, and executes time of 5.46 second/instance.

The general method for estimating phrase translation probabilities consists of sequential processes: word alignment, phrase pair extraction, and phrase translation probability calculation. However, during this sequential process, errors may propagate from the word alignment step through the translation probability calculation step. In this paper, we propose a new method for estimating phrase translation probabilities that reduce the effects of error propagation. By considering the semantic recoverability of phrase retranslation, our method identifies incorrect phrase pairs that have propagated from alignment errors. Furthermore, we define retranslation similarity which represents the semantic recoverability of phrase retranslation, and use this when computing translation probabilities. Experimental results show that the proposed phrase translation estimation method effectively prevents a PBSMT system from selecting incorrect phrase pairs, and consistently improves the translation quality in various language pairs.

We propose a novel re-ranking method for content-based medical image retrieval based on the idea of pseudo-relevance feedback (PRF). Since the highest ranked images in original retrieval results are not always relevant, a naive PRF based re-ranking approach is not capable of producing a satisfactory result. We employ a two-step approach to address this issue. In step 1, a Pearson's correlation coefficient based similarity update method is used to re-rank the high ranked images. In step 2, after estimating a relevance probability for each of the highest ranked images, a fuzzy SVM ensemble based approach is adopted to re-rank the images. The experiments demonstrate that the proposed method outperforms two other re-ranking methods.

Large capacity tags are becoming available to meet the demands of industry, but the UHF RFID protocol is unable to reliably and efficiently read large data sets from tags. First of all, large data sets are not well protected. The tag merely relies on 16-bit CRC for ensuring the validity of up to 4,096-bit user-specific data in EPCglobal C1G2 protocol. Furthermore, the reliability will be even worse if large capacity tags are implemented using semi-active technology which is likely to prevail among sensor-integrated RFID tags. Since semi-active tags greatly alleviate the performance limitation imposed by the turn-on power of the tag chip, backscattering signal of semi-active tags could be a serious challenge for most readers because it is much weaker than signals emitted by passive tags due to longer reading distance. In this paper, Interim CRC is presented to enhance transmission reliability and efficiency when the tag is backscattering a large data set. By taking advantage of Interim CRC, the large data set can be divided into several blocks, and 16-bit checksum is calculated over each block. The tag backscatters all blocks at the first time and only retransmits certain blocks if CRC error occurs in those blocks. The result of simulation shows that the reading error rate can be confined to a preset threshold and the accumulative total of transmitted data are greatly reduced if optimal block size and transmission times are complied with. The simulation also conclusively proves that semi-active tags derive even longer reading range from Interim CRC. In addition, Interim CRC is totally compliant with the EPCglobal C1G2 protocol. It fully makes use of CRC-16 encoder and does not involve any other data encoding schematics and hardware modifications.

To characterize and predict the dynamics of the nonlinear polarization rotation in SOAs, an experimental method based on the frequency response technique and a model based on the density matrix and effective index formalisms are presented. Both determine the angular displacement, at the Poincare Sphere, that produces the evolution of the polarization of the output signal.

We fabricated solution-processed organic complementary inverters based on α,ω-bis(2-hexyldecyl)sexithiophene (BHD6T) for p-channel and C60-fused N-methylpyrrolidine-meta-dodecyl phenyl (C60MC12) for n-channel. The BHD6T and C60MC12 thin-film transistors showed high field-effect mobilities of 0.035 and 0.057 cm2/Vs, respectively. The complementary inverter with a supply voltage of 50 V exhibited inverting voltages of 26.8 V for forward and 27.0 V for backward sweeps and a high gain of 76.

Thin film glucose biosensors were fabricated with organic/inorganic hybrid films based on glucose oxidase (GOx) and Prussian Blue nano-clusters. The biosensors composed of hybrid films were characterized by the low operating potential and the advantage to interference-free detection. In this research, we employed two kinds of thin films for GOx immobilization: Langmuir-Blodgett (LB) and self-assembled monolayer (SAM). The LB film immobilizes GOx in its inside through the electrostatic force, while the SAM immobilizes GOx with the covalent bond. The sensors with LB film produced a relatively high current signal, while the non-linear behavior and a low stability were recognized. On the other hand, the sensors with SAM presented a good linear relationship and a very stable performance.

Thin-film transistors based on Liquid-crystalline phenylterthiophenes, 3-TTPPh-5 and 3-TTPPhF4-6 are fabricated with a spin-coating method. The devices exhibit p-type operation with the mobility on the order of 10-2 cm2V-1s-1. The field-effect mobilities of the transistors using 3-TTPPh-5 and 3-TTPPhF4-6 are almost independent of the temperature above room temperature. In particular, the temperature range in which the mobility is constant is between 230 and 350 K for 3-TTPPh-5.

We have succeeded in developing high-performance p-type of organic semiconductors with phenylethynyl groups, which have high filed-effect mobilities (>3 cm2V-1s-1) by improving molecular planarity. A single crystal of the organic semiconductors has a herringbone structure. It plays an important role for carrier transport. In addition, we found that they had lower contact resistances to Au electrodes as well. Then, we used the materials for the carrier injection layer deposited onto another organic semiconductor we developed recently, which achieved a high field-effect mobility, and a low threshold voltage (Vth).