The unification problem for term rewriting systems (TRSs) is the problem of deciding, for a TRS R and two terms s and t, whether s and t are unifiable modulo R. We have shown that the problem is decidable for confluent simple TRSs. Here, a simple TRS means one where the right-hand side of every rewrite rule is a ground term or a variable. In this paper, we extend this result and show that the unification problem for confluent semi-constructor TRSs is decidable. Here, a semi-constructor TRS means one where all defined symbols appearing in the right-hand side of each rewrite rule occur only in its ground subterms.

Performance analysis of a dual-hop semi-blind amplify-and-forward (AF) relay system in mixed Nakagami-m and Rician fading channels, is proposed. We derived the closed-form expression for the cumulative distribution function (CDF) of the equivalent end-to-end signal to noise ratio (SNR), based on which the exact outage probability and symbol error probability (SEP) are investigated. The theoretical analysis is validated by Monte Carlo simulation results.

We address the problem of perceived age estimation from face images, and propose a new semi-supervised approach involving two novel aspects. The first novelty is an efficient active learning strategy for reducing the cost of labeling face samples. Given a large number of unlabeled face samples, we reveal the cluster structure of the data and propose to label cluster-representative samples for covering as many clusters as possible. This simple sampling strategy allows us to boost the performance of a manifold-based semi-supervised learning method only with a relatively small number of labeled samples. The second contribution is to take the heterogeneous characteristics of human age perception into account. It is rare to misjudge the age of a 5-year-old child as 15 years old, but the age of a 35-year-old person is often misjudged as 45 years old. Thus, magnitude of the error is different depending on subjects' age. We carried out a large-scale questionnaire survey for quantifying human age perception characteristics, and propose to utilize the quantified characteristics in the framework of weighted regression. Consequently, our proposed method is expressed in the form of weighted least-squares with a manifold regularizer, which is scalable to massive datasets. Through real-world age estimation experiments, we demonstrate the usefulness of the proposed method.

Multi-source broadcasting is one of the information dissemination problems on interconnection networks such that some (but not all) units disseminate distinct information to all other units. In this paper, we discuss multi-source broadcasting on the Kautz digraph which is one of the models of interconnection networks. We decompose the Kautz digraph K(d,n) into isomorphic cycle-rooted trees whose root-cycle has length 2, then we present an algorithm for multi-source broadcasting using these cycle-rooted trees. This algorithm is able to treat d(d+1) messages simultaneously and takes the same order for required times as lower bound.

In this report, Focused Ion Beam (FIB) -- SEM technique was applied to observe the tin plated fretting contacts. Spatial distributions of tin, tin oxide and so on have been confirmed quantitatively in two plating thickness of 1 and 5 µm.

Digital signal processing requires digital filters with variable frequency characteristics. A variable digital filter (VDF) is a filter whose frequency characteristics can be easily and instantaneously changed. In this paper, we present a design method for variable linear-phase finite impulse response (FIR) filters with multiple variable factors and a reduction method for the number of polynomial coefficients. The obtained filter has a high piecewise attenuation in the stopband. The stopband edge and the position and magnitude of the high piecewise stopband attenuation can be varied by changing some parameters. Variable parameters are normalized in this paper. An optimization methodology known as semidefinite programming (SDP) is used to design the filter. In addition, we present that the proposed VDF can be implemented using the Farrow structure, which suitable for real time signal processing. The usefulness of the proposed filter is demonstrated through examples.

Passive optical network topology has been widely adopted in access networks due to its low-cost and yet flexible network structure. To further promote the passive optical networks, the cost reduction of optical modules is critical. Relatively expensive combination of a conventional index-coupled distributed feedback laser diode (IC-DFB-LD) and an optical isolator is commonly used for passive optical networks with transmission distance more than 30 km. Although gain-coupled DFB-LDs (GC-DFB-LD) have been widely investigated in the hope of eliminating the isolator in optical modules, their limited output power keeps them from practical use in passive optical networks. In this paper, we describe the development of 1.31 µm and 1.49 µm GC-DFB-LDs with high output power and optical feed back tolerance for isolator-free optical modules in access networks. The relative intensity noise (RIN) degradation was well suppressed below -120 dB/Hz at -8 dB optical feedback in the temperatures range from 0 to 85 from both 1.31 µm and 1.49 µm GC-DFB-LDs. Optical feedback tolerance of 1.31 µm and 1.49 µm GC-DFB-LDs were improved by more than 6 dB and 4 dB as compared with conventional IC-DFB-LDs. Dispersion power penalty after over 30 km transmission at 1.25 Gbps were achieved less than 0.3 dB and 0.7 dB under -15 dB optical feedback conditions. The proposed 1.31 µm GC-DFB-LD prototypes experimentally demonstrated 14 mW output power with over 5,000-hour operation at 85. Our devices are found to fully complying IEEE 802.3ah standard and seem to be promising for the low-cost optical modules in long-reach access network applications. The details of the device structure as well as transmission experiments are also reported.

Various types of optical connector with a precise alignment mechanism and long-term reliability have been researched, developed and improved during about 30 years since practical optical communication systems were first introduced in Japan in 1981. The main issues related to optical fiber connector development changed from performance improvement to miniaturization, cost reduction and ease of field assembly when optical communication systems expanded from optical trunk networks to optical access networks. Various different key technologies for optical connectors have been developed to meet these requirements, and a large number of optical connectors are currently being used for the flexible and efficient construction, maintenance and operation of optical access networks. This paper describes the structure, features, and basic technologies of the optical connectors employed in optical access networks in Japan and their standardization and future prospects.

We exploit a structural knowledge representation scheme called System Entity Structure (SES) methodology to represent and manage wafer failure patterns which can make a significant influence to FABs in the semiconductor industry. It is important for the engineers to simulate various system verification processes by using predefined system entities (e.g., decomposition, taxonomy, and coupling relationships of a system) contained in the SES. For better computational performance, given a certain failure pattern, a Pruned SES (PES) can be extracted by selecting the only relevant system entities from the SES. Therefore, the SES-based simulation system allows the engineers to efficiently evaluate and monitor semiconductor data by i) analyzing failures to find out the corresponding causes and ii) managing historical data related to such failures.

TR-UWB (Transmitted Reference-Ultra Wide Band) systems have low system complexity since they transmit data with the corresponding reference signals and demodulate the data through correlation using these received signals. However, the BER (Bit Error Rate) performance in the conventional TR-UWB systems is sensitive to the SNR (Signal-to-Noise Ratio) of the reference templates used in the correlator. We propose an improved recursive transceiver structure that effectively minimizes the BER for TR-UWB systems by increasing the SNR of reference templates.

In this paper, a fully integrated 5 GHz voltage controlled oscillator (VCO) is presented. The VCO is designed with 0.18 µm silicon complementary metal oxide semiconductor (Si-CMOS) process. To achieve low phase noise, a novel varactors pair circuit is proposed to cancel effects of capacitance fluctuation that makes harmonic currents which increase phase noise of VCO. The VCO with the proposed varactor circuit has tuning range from 5.1 GHz to 6.1 GHz (relative value of 17.9%) and phase noise of lower than -110.8 dBc/Hz at 1 MHz offset over the full tuning range. Figure-of-merit-with-tuning-range (FOMT) of the proposed VCO is -182 dBc/Hz.

Chinese new words and their part-of-speech (POS) are particularly problematic in Chinese natural language processing. With the fast development of internet and information technology, it is impossible to get a complete system dictionary for Chinese natural language processing, as new words out of the basic system dictionary are always being created. A latent semi-CRF model, which combines the strengths of LDCRF (Latent-Dynamic Conditional Random Field) and semi-CRF, is proposed to detect the new words together with their POS synchronously regardless of the types of the new words from the Chinese text without being pre-segmented. Unlike the original semi-CRF, the LDCRF is applied to generate the candidate entities for training and testing the latent semi-CRF, which accelerates the training speed and decreases the computation cost. The complexity of the latent semi-CRF could be further adjusted by tuning the number of hidden variables in LDCRF and the number of the candidate entities from the Nbest outputs of the LDCRF. A new-words-generating framework is proposed for model training and testing, under which the definitions and distributions of the new words conform to the ones existing in real text. Specific features called "Global Fragment Information" for new word detection and POS tagging are adopted in the model training and testing. The experimental results show that the proposed method is capable of detecting even low frequency new words together with their POS tags. The proposed model is found to be performing competitively with the state-of-the-art models presented.

Bag-of-Visual-Words representation has recently become popular for scene classification. However, learning the visual words in an unsupervised manner suffers from the problem when faced these patches with similar appearances corresponding to distinct semantic concepts. This paper proposes a novel supervised learning framework, which aims at taking full advantage of label information to address the problem. Specifically, the Gaussian Mixture Modeling (GMM) is firstly applied to obtain "semantic interpretation" of patches using scene labels. Each scene induces a probability density on the low-level visual features space, and patches are represented as vectors of posterior scene semantic concepts probabilities. And then the Information Bottleneck (IB) algorithm is introduce to cluster the patches into "visual words" via a supervised manner, from the perspective of semantic interpretations. Such operation can maximize the semantic information of the visual words. Once obtained the visual words, the appearing frequency of the corresponding visual words in a given image forms a histogram, which can be subsequently used in the scene categorization task via the Support Vector Machine (SVM) classifier. Experiments on a challenging dataset show that the proposed visual words better perform scene classification task than most existing methods.

A semi-coherent multiple-input multiple-output (MIMO) scheme, which only requires one of its receivers to operate coherently while others can be non-coherent, is proposed. In this scheme, the information symbols at the non-coherent receivers are estimated with the aid of a coherent receiver through a covariance estimator, thus allowing signals to be decoded coherently in the final stage. Results show that the proposed system is able to provide performance on par with that of the more complicated fully coherent MIMO systems.

In this Letter, we explore semi-definite relaxation (SDR) program for finding the real roots of a real polynomial. By utilizing the square of the polynomial, the problem is approximated using the convex optimization framework and a real root is estimated from the corresponding minimum point. When there is only one real root, the proposed SDR method will give the exact solution. In case of multiple real roots, the resultant solution can be employed as an accurate initial guess for the iterative approach to get one of the real roots. Through factorization using the obtained root, the reminding real roots can then be solved in a sequential manner.

In and we have presented a simulation-based algorithm for optimizing the average reward in a parameterized continuous-time, finite-state semi-Markov Decision Process (SMDP). We approximated the gradient of the average reward. Then, a simulation-based algorithm was proposed to estimate the approximate gradient of the average reward (called GSMDP), using only a single sample path of the underlying Markov chain. GSMDP was proved to converge with probability 1. In this paper, we give bounds on the approximation and estimation errors for GSMDP algorithm. The approximation error of that approximation is the size of the difference between the true gradient and the approximate gradient. The estimation error, the size of the difference between the output of the algorithm and its asymptotic output, arises because the algorithm sees only a finite data sequence.

In this paper, we propose a technique for estimating the degree or intensity of emotional expressions and speaking styles appearing in speech. The key idea is based on a style control technique for speech synthesis using a multiple regression hidden semi-Markov model (MRHSMM), and the proposed technique can be viewed as the inverse of the style control. In the proposed technique, the acoustic features of spectrum, power, fundamental frequency, and duration are simultaneously modeled using the MRHSMM. We derive an algorithm for estimating explanatory variables of the MRHSMM, each of which represents the degree or intensity of emotional expressions and speaking styles appearing in acoustic features of speech, based on a maximum likelihood criterion. We show experimental results to demonstrate the ability of the proposed technique using two types of speech data, simulated emotional speech and spontaneous speech with different speaking styles. It is found that the estimated values have correlation with human perception.

This paper studies scattering and diffraction of a TE plane wave from a periodic surface with semi-infinite extent. By use of a combination of the Wiener-Hopf technique and a perturbation method, a concrete representation of the wavefield is explicitly obtained in terms of a sum of two types of Fourier integrals. It is then found that effects of surface roughness mainly appear on the illuminated side, but weakly on the shadow side. Moreover, ripples on the angular distribution of the first-order scattering in the shadow side are newly found as interference between a cylindrical wave radiated from the edge and an inhomogeneous plane wave supported by the periodic surface.

In this paper, we suggest a new probabilistic model of semantic role labeling, which uses the frameset of the predicate as explicit linguistic knowledge for providing global information on the predicate-argument structure that local classifier is unable to catch. The proposed model consists of three sub-models: role sequence generation model, frameset generation model, and matching model. The role sequence generation model generates the semantic role sequence candidates of a given predicate by using the local classification approach, which is a widely used approach in previous research. The frameset generation model estimates the probability of each frameset that the predicate can take. The matching model is designed to measure the degree of the matching between the generated role sequence and the frameset by using several features. These features are developed to represent the predicate-argument structure information described in the frameset. In the experiments, our model shows that the use of knowledge about the predicate-argument structure is effective for selecting a more appropriate semantic role sequence.

We propose a video ontology system to overcome semantic gap in video retrieval. The proposed video ontology is aimed at bridging of the gap between the semantic nature of user queries and raw video contents. Also, results of semantic retrieval shows not only the concept of topic keyword but also a sub-concept of the topic keyword using semantic query extension. Through this process, recall is likely to provide high accuracy results in our method. The experiments compared with keyframe-based indexing have demonstrated that this proposed scene-based indexing presents better results in several kinds of videos.