Recent idea visualization programs still lack automatic idea summarization capabilities. This paper presents a knowledge-based method for automatically providing a short piece of English text about a topic to each idea group in idea charts. This automatic topic identification makes used Yet Another General Ontology (YAGO) and Wordnet as its knowledge bases. We propose a novel topic selection method and we compared its performance with three existing methods using two experimental datasets constructed using two idea visualization programs, i.e., the KJ Method (Kawakita Jiro Method) and mind-mapping programs. Our proposed topic identification method outperformed the baseline method in terms of both performance and consistency.

Histogram modification based image enhancement algorithms have been extensively used in 2-D image applications. In this letter, we apply a histogram modification framework to stereoscopic image enhancement. The proposed algorithm estimates the histogram of a stereo image pair without explicitly computing the pixel-wise disparity. Then, the histogram in the occluded regions is estimated and used to determine the target histogram of the stereo image. Experimental results demonstrate the effectiveness of the proposed algorithm.

Three methods of presenting a three-dimensional (3-D) image – a real object, a protruding stereoscopic display, and the depth-fused 3-D (DFD) display – have different tendencies for the change in perceived depth produced when the visual acuity of the dominant eye is decreased by an occlusion foil. These different tendencies are estimated from the slope and correlation coefficient of the plot of perceived depth difference versus stimuli depth difference. This estimation was derived using the same experimental system setup composed of two displays and a half mirror for all three 3-D display methods. The perceived depth difference was measured for four subjects by calipers using two fingers. The slope and correlation coefficient had almost the same tendencies as follows. The real object had the smallest decrease among the three 3-D display methods when the dominant eye's visual acuity was decreased and the protruding stereoscopic display had the largest decrease. The DFD display method had an intermediate decrease between those of the real object and protruding stereoscopic display. When the dominant eye's visual acuity was high enough, the differences among the three 3-D display methods were small. When its visual acuity was decreased, the differences increased among the three 3-D display methods and became statistically significant.

Reverse-recovery modeling for p-i-n diodes in the high current-density conditions are discussed. With the dynamic carrier-distribution-based modeling approach, the reverse recovery behaviors are explained in the high current-density conditions, where the nonquasi-static (NQS) behavior of carriers in the drift region is considered. In addition, a specific feature under the high current-density condition is discussed. The proposed model is implemented into a commercial circuit simulator in the Verilog-A language and its reverse recovery modeling ability is verified with a two-dimensional (2D) device simulator, in comparison to the conventional lumped-charge modeling technique.

Nowadays, there are a great deal of e-documents being accessed on the Internet. It would be helpful if those documents and significant extract contents could be automatically analyzed. Similarity analysis and topic extraction are widely used as document relation analysis techniques. Most of the methods being proposed need some processes such as stemming, stop words removal, and etc. In those methods, natural language processing (NLP) technology is necessary and hence they are dependent on the language feature and the dataset. In this study, we propose novel document relation analysis and topic extraction methods based on text compression. Our proposed approaches do not require NLP, and can also automatically evaluate documents. We challenge our proposal with model documents, URCS and Reuters-21578 dataset, for relation analysis and topic extraction. The effectiveness of the proposed methods is shown by the simulations.

This paper presents a Microscopic Local Binary Pattern (MLBP) for texture classification. The conventional LBP methods which rely on the uniform patterns discard some texture information by merging the nonuniform patterns. MLBP preserves the information by classifying the nonuniform patterns using the structure similarity at microscopic level. First, the nonuniform patterns are classified into three groups using the macroscopic information. Second, the three groups are individually divided into several subgroups based on the microscopic structure information. The experiments show that MLBP achieves a better result compared with the other LBP related methods.

In this paper, an improved B-picture coding algorithm based on the symmetric bi-directional motion estimation (ME) is proposed. In addition to the block match error between blocks in the forward and backward reference frames, the proposed method exploits the previously-reconstructed template regions in the current and reference frames for bi-directional ME. The side match error between the predicted target block and its template is also employed in order to alleviate block discontinuities. To efficiently perform ME, an initial motion vector (MV) is adaptively derived by exploiting temporal correlations. Experimental results show that the number of generated bits is reduced by up to 9.31% when the proposed algorithm is employed as a new macroblock (MB) coding mode for the H.264/AVC standard.

In this paper, we propose a novel method of measuring the perceived picture quality of H.264 coded video based on parametric analysis of the coded bitstream. The parametric analysis means that the proposed method utilizes only bitstream parameters to evaluate video quality, while it does not have any access to the baseband signal (pixel level information) of the decoded video. The proposed method extracts quantiser-scale, macro block type and transform coefficients from each macroblock. These parameters are used to calculate spatiotemporal image features to reflect the perception of coding artifacts which have a strong relation to the subjective quality. A computer simulation shows that the proposed method can estimate the subjective quality at a correlation coefficient of 0.923 whereas the PSNR metric, which is referred to as a benchmark, correlates the subjective quality at a correlation coefficient of 0.793.

Our research is focused on examining a stereoscopic quality assessment model for stereoscopic images with disparate quality in left and right images for glasses-free stereo vision. In this paper, we examine the objective assessment model of 3-D images, considering the difference in image quality between each view-point generated by the disparity-compensated coding. A overall stereoscopic image quality can be estimated by using only predicted values of left and right 2-D image qualities based on the MPEG-7 descriptor information without using any disparity information. As a result, the stereoscopic still image quality is assessed with high prediction accuracy with correlation coefficient=0.98 and average error=0.17.

In this paper, we propose an optimized virtual re-convergence system especially to reduce the visual fatigue caused by binocular stereoscopy. Our unique idea to reduce visual fatigue is to utilize the virtual re-convergence based on the optimized disparity-map that contains more depth information in the negative disparity area than in the positive area. Therefore, our system facilitates a unique search-range scheme, especially for negative disparity exploration. In addition, we used a dedicated method, using a so-called Global-Shift Value (GSV), which are the total shift values of each image in stereoscopy to converge a main object that can mostly affect visual fatigue. The experimental result, which is a subjective assessment by participants, shows that the proposed method makes stereoscopy significantly comfortable and attractive to view than existing methods.

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.

This paper describes the application of two attractive categories of topic modeling techniques to the problem of spoken document retrieval (SDR), viz. document topic model (DTM) and word topic model (WTM). Apart from using the conventional unsupervised training strategy, we explore a supervised training strategy for estimating these topic models, imagining a scenario that user query logs along with click-through information of relevant documents can be utilized to build an SDR system. This attempt has the potential to associate relevant documents with queries even if they do not share any of the query words, thereby improving on retrieval quality over the baseline system. Likewise, we also study a novel use of pseudo-supervised training to associate relevant documents with queries through a pseudo-feedback procedure. Moreover, in order to lessen SDR performance degradation caused by imperfect speech recognition, we investigate leveraging different levels of index features for topic modeling, including words, syllable-level units, and their combination. We provide a series of experiments conducted on the TDT (TDT-2 and TDT-3) Chinese SDR collections. The empirical results show that the methods deduced from our proposed modeling framework are very effective when compared with a few existing retrieval approaches.

In Digital Library (DL) applications, digital book clustering is an important and urgent research task. However, it is difficult to conduct effectively because of the great length of digital books. To do the correct clustering for digital books, a novel method based on probabilistic topic model is proposed. Firstly, we build a topic model named LDAC. The main goal of LDAC topic modeling is to effectively extract topics from digital books. Subsequently, Gibbs sampling is applied for parameter inference. Once the model parameters are learned, each book is assigned to the cluster which maximizes the posterior probability. Experimental results demonstrate that our approach based on LDAC is able to achieve significant improvement as compared to the related methods.

In this paper, a sepic-type single-stage electronic ballast (STSSEB) is proposed, which is derived from the combination of a sepic converter and a half-bridge inverter. The ballast can not only step down input voltage directly but achieve high power factor, reduce voltage stress, improve efficiency and lower cost. Since component stress is reduced significantly, the presented ballast can be applied to high voltage mains. Derivation of the STSSEB is first presented. Then, analysis, design and practical consideration for the STSSEB are discussed. A 347 Vac 60 W prototype has been simulated and implemented. Simulations and experimental results have verified the feasibility of the proposed STSSEB.

We present a review of several types of microwave antennas made of metamaterials, including the resonant electrically small antennas, metamaterial-substrate patch antennas, metamaterial flat-lens antennas, and Luneburg lens antennas. In particular, we propose a new type of conformal antennas using anisotropic zero-index metamaterials, which have high gains and low sidelobes. Numerical simulations and experimental results show that metamaterials have unique properties to design new antennas with high performance.

In this paper, a joint optimal design is investigated for orthogonal frequency division multiplexing (OFDM) systems to reduce peak interference-to-carrier ratio (PICR), out-of-band power (OBP) emissions, and peak-to-average power ratio (PAPR). Two approaches, namely, the phase rotation approach and the constellation extension approach, are proposed to convert this joint design problem into a second order cone programming (SOCP) problem, whose global optimal solution has been shown to exist and can be obtained efficiently. Simulation results are presented to demonstrate efficacy of the proposed algorithms in joint PICR, OBP, and PAPR reduction.

The non-reference method is widely useful for picture quality estimation on the decoder side. In other work, we discussed pure non-reference estimation using only the decoded picture, and we proposed quantitative estimation methods for mosquito noise and block artifacts. In this paper, we discuss the estimation method as it applies to the degradation of the temporal domain. In the proposed method, motion compensated inter-picture differences and motion vector activity are the basic parameters of temporal degradation. To obtain these parameters, accurate but unstable motion estimation is used with a 1/16 reduction of processing power. Similar values of the parameters in the pictures can be seen in the stable original picture, but temporal degradation caused by the coding increases them. For intra-coded pictures, the values increase significantly. However, for inter-coded pictures, the values are the same or decrease. Therefore, by taking the ratio of the peak frame and other frames, the absolute value of the temporal degradation can be estimated. In this case, the peak frame may be intra-coded. Finally, we evaluate the proposed method using coded pictures with different quantization.

This paper presents a visual knowledge structure reasoning method using Intelligent Topic Map which extends the conventional Topic Map in structure and enhances its reasoning functions. Visual knowledge structure reasoning method integrates two types of knowledge reasoning: the knowledge logical relation reasoning and the knowledge structure reasoning. The knowledge logical relation reasoning implements knowledge consistency checking and the implicit associations reasoning between knowledge points. We propose a Knowledge Unit Circle Search strategy for the knowledge structure reasoning. It implements the semantic implication extension, the semantic relevant extension and the semantic class belonging confirmation. Moreover, the knowledge structure reasoning results are visualized using ITM Toolkit. A prototype system of visual knowledge structure reasoning has been implemented and applied to the massive knowledge organization, management and service for education.

We present a new paging algorithm for wireless networks with ultra-short-range radio access links (picocells). The ubiquitous office (u-office) network is a good example of such a network, and we present some u-office example applications. In addition, we show that conventional paging algorithms are not feasible in such networks. Therefore, we derived a new paging algorithm from the measurement results of an experimental sensor network with short-range wireless links deployed in our office. We equipped persons with sensors and deployed sensor readers at selected places in our office. The sensors transmit messages to the sensor readers at regular intervals. If no sensor reader is in range, the message is lost. Our main observation is that, if a picocell shows an attraction property to a certain person, the residence time of an attached mobile terminal is not gamma distributed (as described in the literature) and the probability of long-lasting residences increases. Thus, if the residence time is larger than a certain threshold, the probability of a long-lasting residence time increases if a sensor reader location has an attraction property to a person. Based on this observation, our proposed paging algorithm registers the location of the mobile terminal only when the residence time in the cell is longer than a predetermined constant. By appropriately setting this constant, we can significantly reduce the registration message frequency while ensuring that the probability of the network successfully connecting to a mobile terminal remains high.

In speech recognition, confidence annotation adopts a single confidence feature or a combination of different features for classification. These confidence features are always extracted from decoding information. However, it is proved that about 30% of knowledge of human speech understanding is mainly derived from high-level information. Thus, how to extract a high-level confidence feature statistically independent of decoding information is worth researching in speech recognition. In this paper, a novel confidence feature extraction algorithm based on latent topic similarity is proposed. Each word topic distribution and context topic distribution in one recognition result is firstly obtained using the latent Dirichlet allocation (LDA) topic model, and then, the proposed word confidence feature is extracted by determining the similarities between these two topic distributions. The experiments show that the proposed feature increases the number of information sources of confidence features with a good information complementary effect and can effectively improve the performance of confidence annotation combined with confidence features from decoding information.