The optimal design of an extrapolated impulse response (EIR) filter (in the mini-max sense) is a non-linear programming problem. In this paper, the optimal design of the EIR filter by the semi-infinite programming (SIP) is investigated and an iterative technique for optimally designing the EIR filter is proposed. The simulation experiment validates the effectiveness of the SIP technique and the proposed iterative technique in the optimal design of the EIR filter.

This paper explores the problem of semantic enrichment of mathematical expressions. We formulate this task as the translation of mathematical expressions from presentation markup to content markup. We use MathML, an application of XML, to describe both the structure and content of mathematical notations. We apply a method based on statistical machine translation to extract translation rules automatically. This approach contrasts with previous research, which tends to rely on manually encoded rules. We also introduce segmentation rules used to segment mathematical expressions. Combining segmentation rules and translation rules strengthens the translation system and archives significant improvements over a prior rule-based system.

The Semantic Web uses RDF/RDFS, which can enable a machine to understand web data without human interference. But most web data is not available in RDF/RDFS documents because most web data is still stored in databases. It is much more favorable to use stored data in a database to build the Semantic Web. This paper proposes an enhanced relational RDF/RDFS interoperable data model (ER2iDM) and a transformation procedure from relational data model (RDM) to RDF/RDFS based on ER2iDM. The ER2iDM is a data model that plays the role of an inter-mediator between RDM and RDF/RDFS during a transformation procedure. The data and schema information in the database are migrated to the ER2iDM according to the proposed translation procedures without incurring loss of meaning of the entities, relationships, and data. The RDF/RDFS generation tool makes a RDF/RDFS XML document automatically from the ER2iDM. The proposed ER2iDM and transformation procedure provides detailed guidelines for transformation from RDM to RDF/RDFS unlike existing studies; therefore, we can more efficiently build up the Semantic Web using database stored data.

We have succeeded in demonstrating high-performance four-channel 25 Gb/s integrated receiver for 100 Gb/s Ethernet with a built-in spatial Demux optics and an integrated PD array. All components which configure to the Demux optics adhered to a prism. Because of the shaping accuracy for prism, the insertion loss was able to suppress to 0.8 dB with small size. The connection point of the package for high speed electrical signals was improved to decrease the transmission loss. The small size of 12 mm 17 mm 7 mm compact package with a side-wall electrical connector has been achieved, which is compatible with the assembly in CFP2 form-factor. We observed the sensitivity at average power of -12.1 dBm and the power penalty of sensitivity due to the crosstalk of less than 0.1 dB.

We have designed and fabricated a compact 4-array integrated SOA module using a novel parallel optical coupling scheme and polarization-insensitive built-in array isolators. We achieved ultra-high On/Off extinction ratio of more than 60 dB and low cross talk of better than -60 dB as well as high-isolation of over 47 dB in wide wavelength ranges. We also developed a wavelength-insensitive parallel optical coupling scheme and an efficient thermal dissipating structure for a 4-array SOA module. We applied these technologies into 4-array SOA module fabrication and demonstrated a uniform optical coupling with the loss variance of 1 dB over the 140-nm wavelength ranges. We also demonstrated simultaneous operation of 300 mA 4 channels with low thermal degradation of the module gain less than 1 dB.

There has been an explosion in wireless devices and mobile data traffic, and cellular network alone is unable to support such fast growing demand on data transmission. Therefore, it is reasonable to add another network to the cellular network to augment the capacity. In fact, the dilemma of cellular network is mainly caused by that the same content is repeatedly transmitted in the network, since many people are interested in the same content. A broadcast network, however, could mitigate this problem and save wireless bandwidth by delivering popular content to multiple clients simultaneously. This paper presents a content dissemination system that combines broadcast and cellular networks. Using the model of Markov Decision Process (MDP), we propose an online optimal scheme to maximize the expected number of clients receiving their interested content, which takes clients' interests and queuing length at broadcast and cellular base stations into full consideration. Simulations demonstrate that the proposed scheme effectively decreases item drop rate at base stations and enhances the average number of clients who receive their interested content.

Reliable broadcasting in vehicular ad-hoc networks is challenging due to their unique characteristics including intermittent connectivity and various vehicular scenarios. Applications and services in intelligent transportation systems need an efficient, fast and reliable broadcasting protocol especially for safety and emergency applications. In our previous work, we have proposed DECA, a new reliable broadcasting protocol which suits such characteristics. To address the issue of various vehicular scenarios, our protocol performs beaconing to gather local density information of 1-hop neighbors and uses such information to adapt its broadcasting decision dynamically. Specifically, before broadcasting a message, a node selects a neighbor with the highest density and adds the selected neighbor's identifier to the message. Upon receiving of a broadcast message, each node checks whether or not it is the selected neighbor. If so, it is responsible for rebroadcasting the message immediately. This mechanism can raise the data dissemination speed of our protocol so that it is as fast as simple flooding. To address the issue of intermittent connectivity, identifiers of broadcast messages are added into beacons. This helps nodes to check if there are any broadcast messages that have not yet been received. In this paper, we propose DECA with a new beaconing algorithm and a new waiting timeout calculation, so-called DECA-bewa. Our new protocol can reduce redundant retransmissions and overall overhead in high density areas. The protocol performance is evaluated on the network simulator (NS-2). Simulation results show that DECA-bewa outperforms existing protocols in terms of reliability, overhead and speed of data dissemination.

This paper presents an ensemble learning algorithm for liver tumour segmentation from a CT volume in the form of U-Boost and extends the loss functions to improve performance. Five segmentation algorithms trained by the ensemble learning algorithm with different loss functions are compared in terms of error rate and Jaccard Index between the extracted regions and true ones.

We extend the Nonparametric Discriminant Analysis (NDA) algorithm to a semi-supervised dimensionality reduction technique, called Semi-supervised Nonparametric Discriminant Analysis (SNDA). SNDA preserves the inherent advantages of NDA, that is, relaxing the Gaussian assumption required for the traditional LDA-based methods. SNDA takes advantage of both the discriminating power provided by the NDA method and the locality-preserving power provided by the manifold learning. Specifically, the labeled data points are used to maximize the separability between different classes and both the labeled and unlabeled data points are used to build a graph incorporating neighborhood information of the data set. Experiments on synthetic as well as real datasets demonstrate the effectiveness of the proposed approach.

The authors propose a method to make a movement plan for relocation of the railway cars in preparation for the final assembly. It obtains solution through three steps. The first step is to extract the order constraints between the movements of the railway cars based on their locations before and after relocation. The second step is to introduce the movement which puts a railway car into another location temporarily, in order to avoid a deadlock in the movements. And the final step is to obtain the movement order for carrying out the relocation in the shortest time in accordance with the calculated order constraints by using the genetic algorithm (GA). The order constraints are resolved in advance and therefore the movement order can easily be decided by GA. As the result, the developed system takes time shorter than an expert for planning the relocation.

In OFDM systems, residual inter-block interference can be suppressed by a time-domain equalizer that blindly shortens the effective length of a channel impulse response. To further improve the performance of blind equalizers, we propose a channel shortening method that attempts to maximize the minimum FFT output power over data subcarriers. Simulation results indicate that the max-min strategy has performance improvement over a conventional channel shortening method.

The Linking Open Data (LOD) cloud is a collection of linked Resource Description Framework (RDF) data with over 31 billion RDF triples. Accessing linked data is a challenging task because each data set in the LOD cloud has a specific ontology schema, and familiarity with the ontology schema used is required in order to query various linked data sets. However, manually checking each data set is time-consuming, especially when many data sets from various domains are used. This difficulty can be overcome without user interaction by using an automatic method that integrates different ontology schema. In this paper, we propose a Mid-Ontology learning approach that can automatically construct a simple ontology, linking related ontology predicates (class or property) in different data sets. Our Mid-Ontology learning approach consists of three main phases: data collection, predicate grouping, and Mid-Ontology construction. Experiments show that our Mid-Ontology learning approach successfully integrates diverse ontology schema with a high quality, and effectively retrieves related information with the constructed Mid-Ontology.

A semi-analytical method for a planar periodic array formed by a finite number of magnetized ferrite circular cylinders is presented using a model of layered cylindrical structures. The method uses the T-matrix approach and the extraction of the reflection and transmission matrices based on the cylindrical harmonic mode expansion. Based on the proposed method, plane wave scattering by the finite number of magnetized ferrite circular cylinders is numerically studied from the viewpoint of realization the electronic switching and electronic scanning effects by varying the applied magnetic field.

Network survivability is defined as the ability of a network keeping connected under failures and/or attacks. In this paper, we propose two stochastic models; binomial model and negative binomial model, to quantify the network survivability and compare them with the existing Poisson model. We give mathematical formulae of approximate network survivability for respective models and use them to carry out the sensitivity analysis on model parameters. Throughout numerical examples it is shown that the network survivability can change drastically when the number of network nodes is relatively small under a severe attack mode which is called the Black hole attack.

In this paper, we propose Local Curvelet Binary Patterns (LCBP) and Learned Local Curvelet Patterns (LLCP) for presenting the local features of facial images. The proposed methods are based on Curvelet transform which can overcome the weakness of traditional Gabor wavelets in higher dimensions, and better capture the curve singularities and hyperplane singularities of facial images. LCBP can be regarded as a combination of Curvelet features and LBP operator while LLCP designs several learned codebooks from patch sets, which are constructed by sampling patches from Curvelet filtered facial images. Each facial image can be encoded into multiple pattern maps and block-based histograms of these patterns are concatenated into an histogram sequence to be used as a face descriptor. During the face representation phase, one input patch is encoded by one pattern in LCBP while multi-patterns in LLCP. Finally, an effective classifier called Weighted Histogram Spatially constrained Earth Mover's Distance (WHSEMD) which utilizes the discriminative powers of different facial parts, the different patterns and the spatial information of face is proposed. Performance assessment in face recognition and gender estimation under different challenges shows that the proposed approaches are superior than traditional ones.

This paper describes a new method for semi-supervised discriminative language modeling, which is designed to improve the robustness of a discriminative language model (LM) obtained from manually transcribed (labeled) data. The discriminative LM is implemented as a log-linear model, which employs a set of linguistic features derived from word or phoneme sequences. The proposed semi-supervised discriminative modeling is formulated as a multi-objective optimization programming problem (MOP), which consists of two objective functions defined on both labeled lattices and automatic speech recognition (ASR) lattices as unlabeled data. The objectives are coherently designed based on the expected risks that reflect information about word errors for the training data. The model is trained in a discriminative manner and acquired as a solution to the MOP problem. In transcribing Japanese broadcast programs, the proposed method reduced relatively a word error rate by 6.3% compared with that achieved by a conventional trigram LM.

Microblogs are a rising social network with distinguishing features such as simplicity and convenience and has already attracted a large number of users and triggered massive information explosion concerning individuals' own statuses and opinions. While sentiment analysis of the messages in microblogs is of great value, most of present studies are on English microblogs and few are on Chinese microblogs. Compared to English, Chinese has its unique expression style, such as no spaces or other word delimiters. Furthermore, Chinese short text also has its own properties. Thus we are inspired to explore effective features for sentiment classification of Chinese short text. In this paper, we propose to study user-related sentiment classification of Chinese microblogs in terms of the statistical and semantic characteristics, and deisgn the corresponding features: ratio of positive words and negative words (PNR), position feature (POS), collocation of verbs (COL), auxiliary words (AU). Then we employ an SVM-based method to classify the sentiment. Experiments show that the features we design is effective in recognizing the sentiment of messages in microblogs.

Fast and accurate estimation of missing relations, e.g., similarity, distance and kernel, among objects is now one of the most important techniques required by major data mining tasks, because the missing information of the relations is needed in many applications such as economics, psychology, and social network communities. Though some approaches have been proposed in the last several years, the practical balance between their required computation amount and obtained accuracy are insufficient for some class of the relation estimation. The objective of this paper is to formalize a problem to quickly and efficiently estimate missing relations among objects from the other known relations among the objects and to propose techniques called “PSD Estimation” and “Row Reduction” for the estimation problem. This technique uses a characteristic of the relations named “Positive Semi-Definiteness (PSD)” and a special assumption for known relations in a matrix. The superior performance of our approach in both efficiency and accuracy is demonstrated through an evaluation based on artificial and real-world data sets.

Microwave photoconductivity decay (µ-PCD) method was applied to evaluate the effects of chemical composition and Ar+ plasma induced damage on the bulk and the surface states in amorphous In-Ga-Zn-O (a-IGZO) films. It was found that the peak reflectivity signal in the photoconductivity response increased with decreasing the Ga content, and had a strong correlation with the a-IGZO transistor performances. In addition, the peak reflectivity signals obtained after various Ar+ plasma treatment duration were well correlated with the transistor characteristics. With Ar+ plasma treatment, the peak reflectivity signal decreased in accordance with degradation of transistor characteristics. The µ-PCD method was found to be a very useful tool not only to evaluate the bulk and the surface states, but also to predict the performance of a-IGZO transistors subjected to various plasma processes in the production.

3 dB bandwidth enhancement of single mode semiconductor lasers is confirmed numerically and experimentally when they are operated by intensity modulated signal light injection. 3 dB bandwidth is enlarged to 2.5 times of resonant frequency. The numerical analysis of rate equations predicts that the bandwidth enhancement is accomplished by the modal gain control of semiconductor lasers with injected intensity modulated signal light through non-linear gain coefficient term.