The weakness of implementation for LDPC encoder is that conventional binary Matrix Vector Multiplier has many clock cycles which lead to limited throughput. In this letter in order to construct efficient architecture, we target on IEEE 802.16e LDPC encoders. Over the standard H matrices with Circulant Permutation Matrices, we propose semi-parallel architecture by using cyclic right shift registers and exclusive-OR instead of complex Matrix Vector Multipliers. Proposed efficient encoder for IEEE 802.16e LDPC satisfies compact size and high throughput.

This paper presents a motion planning method for a bimanual robot for executing assembly tasks. The method employs an adaptive modeling which can automatically generate an assembly model and modify the model during actual assembly. Bimanual robotic assembly is modeled at the task-level using contact states of workpieces and their transitions. The lower-level velocity commands of the workpieces are automatically derived by solving optimization problem formulated with assembly constraints, position of the workpieces, and kinematics of manipulators. Motion requirements of the workpieces are transformed to motion commands of the bimanual robot. The proposed approach is evaluated with experiments on peg-in-hole assembly with an L-shaped peg.

In UMTS (universal mobile telecommunications system) networks upgraded with HSPA (high speed packet access) technology, the high access bandwidth and advanced mobile devices make it applicable to share large files among mobile users by peer-to-peer applications. To receive files quickly is essential for mobile users in file sharing applications, mainly because they are subject to unstable signal strength and battery failures. While many researches present peer-to-peer file sharing architectures in mobile environments, few works focus on decreasing the time spent in disseminating files among users. In this paper, we present an efficient peer-to-peer file sharing design for HSPA networks called AFAM -- Adaptive efficient File shAring for uMts networks. AFAM can decrease the dissemination time by efficiently utilizing the upload-bandwidth of mobile nodes. It uses an adaptive rearrangement of a node's concurrent uploads, which causes the count of the node's concurrent uploads to lower while ensuring that the node's upload-bandwidth can be efficiently utilized. AFAM also uses URF -- Upload Rarest First policy for the block selection and receiver selection, which achieves real rarest-first for the spread of blocks and effectively avoids the "last-block" problem in file sharing applications. Our simulations show that, AFAM achieves much less dissemination time than other protocols including BulletPrime and a direct implementation of BitTorrent for mobile environments.

In a hidden Markov model (HMM), state duration probabilities decrease exponentially with time, which fails to adequately represent the temporal structure of speech. One of the solutions to this problem is integrating state duration probability distributions explicitly into the HMM. This form is known as a hidden semi-Markov model (HSMM). However, though a number of attempts to use HSMMs in speech recognition systems have been proposed, they are not consistent because various approximations were used in both training and decoding. By avoiding these approximations using a generalized forward-backward algorithm, a context-dependent duration modeling technique and weighted finite-state transducers (WFSTs), we construct a fully consistent HSMM-based speech recognition system. In a speaker-dependent continuous speech recognition experiment, our system achieved about 9.1% relative error reduction over the corresponding HMM-based system.

A novel CMOS LC quadrature oscillator (QO) which adopts complementary-coupling circuitry has been proposed. The performance improvement in I/Q phase error and phase noise of the proposed QO, is explained in comparison with conventional QOs. The proposed QO is implemented in 0.18 µm CMOS technology along with conventional QOs. The measurement result of the proposed QO shows -133.5 dBc/Hz of phase noise at 1 MHz offset and 0.6 I/Q phase difference, while oscillating at 1.77 GHz. The proposed QO shows more than 6.5 dB phase noise improvement compared to that of the conventional QOs over the offset frequency range of 10 K-1 MHz, while dissipating 4 mA from 1.4 V supply.

We propose an enhanced method for translating Topic Maps to RDF/RDF Schema, to realize the Semantic Web. A critical issue for the Semantic Web is to efficiently and precisely describe Web information resources, i.e., Web metadata. Two representative standards, Topic Maps and RDF have been used for Web metadata. RDF-based standardization and implementation of the Semantic Web have been actively performed. Since the Semantic Web must accept and understand all Web information resources that are represented with the other methods, Topic Maps-to-RDF translation has become an issue. Even though many Topic Maps to RDF translation methods have been devised, they still have several problems (e.g. semantic loss, complex expression, etc.). Our translation method provides an improved solution to these problems. This method shows lower semantic loss than the previous methods due to extract both explicit semantics and implicit semantics. Compared to the previous methods, our method reduces the encoding complexity of resulting RDF. In addition, in terms of reversibility, the proposed method regenerates all Topic Maps constructs in an original source when is reverse translated.

In order to timely response to a user query at run-time, next generation Semantic Web applications demand a robust mechanism to dynamically select one or more existing ontologies available on the Web and combine them automatically if needed. Although existing ontology retrieval systems return a lengthy list of resultant ontologies, they cannot identify which ones can completely meet the query requirements nor determine a minimum set of resultant ontologies that can jointly satisfy the requirements if no single ontology is available to satisfy them. Therefore, this paper presents an ontology retrieval system, namely combiSQORE, which can return single or combinative ontologies that completely satisfy a submitted query when the available ontology database is adequate to answer such query. In addition, the proposed system ranks the returned results based on their semantic similarities to the given query and their modification (integration) costs. The experimental results show that combiSQORE system yields practical combinative ontologies and useful rankings.

Since participants at both end of the communication channel must share common pictogram interpretation to communicate, the pictogram selection task must consider both participants' pictogram interpretations. Pictogram interpretation, however, can be ambiguous. To assist the selection of pictograms more likely to be interpreted as intended, we propose a categorical semantic relevance measure which calculates how relevant a pictogram is to a given interpretation in terms of a given category. The proposed measure defines similarity measurement and probability of interpretation words using pictogram interpretations and frequencies gathered from a web survey. Moreover, the proposed measure is applied to categorized pictogram interpretations to enhance pictogram retrieval performance. Five pictogram categories used for categorizing pictogram interpretations are defined based on the five first-level classifications defined in the Concept Dictionary of the EDR Electronic Dictionary. Retrieval performances among not-categorized interpretations, categorized interpretations, and categorized and weighted interpretations using semantic relevance measure were compared, and the categorized semantic relevance approaches showed more stable performances than the not-categorized approach.

Luby et al. derived evolution of degree distributions in residual graphs for irregular LDPC code ensembles. Evolution of degree distributions in residual graphs is important characteristic which is used for finite-length analysis of the expected block and bit error probability over the binary erasure channel. In this paper, we derive detailed evolution of degree distributions in residual graphs for irregular LDPC code ensembles with joint degree distributions.

A projection onto convex sets (POCS)-based annotation method for semantic image retrieval is presented in this paper. Utilizing database images previously annotated by keywords, the proposed method estimates unknown semantic features of a query image from its known visual features based on a POCS algorithm, which includes two novel approaches. First, the proposed method semantically assigns database images some clusters and introduces a nonlinear eigenspace of visual and semantic features in each cluster into the constraint of the POCS algorithm. This approach accurately provides semantic features for each cluster by using its visual features in the least squares sense. Furthermore, the proposed method monitors the error converged by the POCS algorithm in order to select the optimal cluster including the query image. By introducing the above two approaches into the POCS algorithm, the unknown semantic features of the query image are successfully estimated from its known visual features. Consequently, similar images can be easily retrieved from the database based on the obtained semantic features. Experimental results verify the effectiveness of the proposed method for semantic image retrieval.

We propose a new bidirectional gigabit mm-wave wavelength division multiplexed-radio over fiber link which shares the same wavelength. As the downlink, the central station transmits a 30 GHz single sideband wireless signal which is modulated 1.25 Gbps and also transmits a remote 32 GHz local oscillator for down-conversion of a uplink wireless signal by using a mach-zehnder modulator and a fiber bragg grating. As the uplink, the base station transmits a down-converted 1.25 Gbps wireless signal by using a reflective semiconductor optical amplifier. We achieve a BER < 10-9 in the downlink at -14.05 dBm and uplink at -12.5 dBm after 20 km transmission.

Using Bloom filters is one of the most popular and efficient lookup methods in P2P networks. A Bloom filter is a representation of data item indices, which achieves small memory requirement by allowing one-sided errors (false positive). In the lookup scheme besed on the Bloom filter, each peer disseminates a Bloom filter representing indices of the data items it owns in advance. Using the information of disseminated Bloom filters as a clue, each query can find a short path to its destination. In this paper, we propose an efficient extension of the Bloom filter, called a Deterministic Decay Bloom Filter (DDBF) and an index dissemination method based on it. While the index dissemination based on a standard Bloom filter suffers performance degradation by containing information of too many data items when its dissemination radius is large, the DDBF can circumvent such degradation by limiting information according to the distance between the filter holder and the items holders, i.e., a DDBF contains less information for faraway items and more information for nearby items. Interestingly, the construction of DDBFs requires no extra cost above that of standard filters. We also show by simulation that our method can achieve better lookup performance than existing ones.

The heterogeneous integration of GaAs HEMTs on a polyimide-covered AlN ceramic substrate was demonstrated using a fluidic self-assembly (FSA) technique. We used thin device blocks for the FSA, which have various advantages. In particular, they can reduce the drain-source capacitance Cds of the assembled HEMTs if the substrate has a low dielectric constant. This is a novel kind of semiconductor-on-insulator (SOI) technology. The dc and RF properties of the GaAs HEMTs on the polyimide/AlN substrate were studied and the reduction of Cds was confirmed. This technique was successfully applied to the SPDT switch, where a low Cds is essential for good isolation.

All-optical wavelength conversion and modulation format conversion will be needed in the next generation high-speed optical communication networks. We have proposed and successfully demonstrated the error free operation of all-optical modulation format conversion from NRZ-OOK to RZ-BPSK using SOA based MZI wavelength converter. In this paper, we experimentally investigate the wavelength conversion characteristics of the proposed NRZ-OOK/RZ-BPSK modulation format converter. The results show that error free modulation format conversion is possible over the entire C band.

We propose HHWeb, an extension to LogicWeb with hereditary Harrop formulas. HHWeb extends the LogicWeb of Loke and Davison by allowing goals of the form ( x1... xn D) G (or equivalently x1... xn(D G)) where D is a web page and G is a goal. This goal is intended to be solved by instantiating x1,...,xn in D by new names and then solving the resulting goal. The existential quantifications at the head of web pages are particularly flexible in controlling the visibility of names. For example, they can provide scope to functions and constants as well as to predicates. In addition, they have such simple semantics that implementation becomes more efficient. Finally, they provide a client-side interface which is useful for customizing web pages.

In this study, we propose a complete architecture based on digital watermarking techniques to solve the issue of copyright protection and authentication for digital contents. We apply visible and semi-fragile watermarks as dual watermarks where visible watermarking is used to establish the copyright protection and semi-fragile watermarking authenticates and verifies the integrity of the watermarked image. In order to get the best tradeoff between the embedding energy of watermark and the perceptual translucence for visible watermark, the composite coefficients using global and local characteristics of the host and watermark images in the discrete wavelet transform (DWT) domain is considered with Human Vision System (HVS) models. To achieve the optimum noise reduction of the visibility thresholds for HVS in DWT domain, the contrast-sensitive function (CSF) and noise visible function (NVF) of perceptual model is applied which characterizes the global and local image properties and identifies texture and edge regions to determine the optimal watermark locations and strength at the watermark embedding stage. In addition, the perceptual weights according to the basis function amplitudes of DWT coefficients is fine tuned for the best quality of perceptual translucence in the design of the proposed watermarking algorithm. Furthermore, the semi-fragile watermark can detect and localize malicious attack effectively yet tolerate mild modifications such as JPEG compression and channel additive white Gaussian noise (AWGN). From the experimental results, our proposed technique not only improves the PSNR values and visual quality than other algorithms but also preserves the visibility of the watermark visible under various signal processing and advanced image recovery attacks.

Many studies of on-line comprehension of semantic violations have shown that the human sentence processor rapidly constructs a higher-order semantic interpretation of the sentence. What remains unclear, however, is the amount of time required to detect semantic anomalies while concatenating two words to form a phrase with very rapid stimuli presentation. We aimed to examine the time course of semantic integration in concatenating two words in phrase structure building, using magnetoencephalography (MEG). In the MEG experiment, subjects decided whether two words (a classifier and its corresponding noun), presented each for 66 ms, form a semantically correct noun phrase. Half of the stimuli were matched pairs of classifiers and nouns. The other half were mismatched pairs of classifiers and nouns. In the analysis of MEG data, there were three primary peaks found at approximately 25 ms (M1), 170 ms (M2) and 250 ms (M3) after the presentation of the target words. As a result, only the M3 latencies were significantly affected by the stimulus conditions. Thus, the present results indicate that the semantic integration in concatenating two words starts from approximately 250 ms.

We investigate the problem of joint transmitter and receiver power allocation with the minimax mean square error (MSE) criterion for uplink transmissions in a multi-carrier code division multiple access (MC-CDMA) system. The objective of power allocation is to minimize the maximum MSE among all users each of which has limited transmit power. This problem is a nonlinear optimization problem. Using the Lagrange multiplier method, we derive the Karush-Kuhn-Tucker (KKT) conditions which are necessary for a power allocation to be optimal. Numerical results indicate that, compared to the minimum total MSE criterion, the minimax MSE criterion yields a higher total MSE but provides a fairer treatment across the users. The advantages of the minimax MSE criterion are more evident when we consider the bit error rate (BER) estimates. Numerical results show that the minimax MSE criterion yields a lower maximum BER and a lower average BER. We also observe that, with the minimax MSE criterion, some users do not transmit at full power. For comparison, with the minimum total MSE criterion, all users transmit at full power. In addition, we investigate robust joint transmitter and receiver power allocation where the channel state information (CSI) is not perfect. The CSI error is assumed to be unknown but bounded by a deterministic value. This problem is formulated as a semidefinite programming (SDP) problem with bilinear matrix inequality (BMI) constraints. Numerical results show that, with imperfect CSI, the minimax MSE criterion also outperforms the minimum total MSE criterion in terms of the maximum and average BERs.

Effective classification and analysis of semantic contents are very important for the content-based indexing and retrieval of video database. Our research attempts to classify movie clips into three groups of commonly elicited emotions, namely excitement, joy and sadness, based on a set of abstract-level semantic features extracted from the film sequence. In particular, these features consist of six visual and audio measures grounded on the artistic film theories. A unique sieving-structured neural network is proposed to be the classifying model due to its robustness. The performance of the proposed model is tested with 101 movie clips excerpted from 24 award-winning and well-known Hollywood feature films. The experimental result of 97.8% correct classification rate, measured against the collected human-judges, indicates the great potential of using abstract-level semantic features as an engineered tool for the application of video-content retrieval/indexing.

In this paper, we propose new external context features for the semantic classification of bio-entities. In the previous approaches, the words located on the left or the right context of bio-entities are frequently used as the external context features. However, in our prior experiments, the external contexts in a flat representation did not improve the performance. In this study, we incorporate predicate-argument features into training the ME-based classifier. Through parsing and argument identification, we recognize biomedical verbs that have argument relations with the constituents including a bio-entity, and then use the predicate-argument structures as the external context features. The extraction of predicate-argument features can be done by performing two identification tasks: the biomedically salient word identification which determines whether a word is a biomedically salient word or not, and the target verb identification which identifies biomedical verbs that have argument relations with the constituents including a bio-entity. Experiments show that the performance of semantic classification in the bio domain can be improved by utilizing such predicate-argument features.