Two types of similarities between words have been studied in the natural language processing community: synonymy and relational similarity. A high degree of similarity exist between synonymous words. On the other hand, a high degree of relational similarity exists between analogous word pairs. We present and empirically test a hypothesis that links these two types of similarities. Specifically, we propose a method to measure the degree of synonymy between two words using relational similarity between word pairs as a proxy. Given two words, first, we represent the semantic relations that hold between those words using lexical patterns. We use a sequential pattern clustering algorithm to identify different lexical patterns that represent the same semantic relation. Second, we compute the degree of synonymy between two words using an inter-cluster covariance matrix. We compare the proposed method for measuring the degree of synonymy against previously proposed methods on the Miller-Charles dataset and the WordSimilarity-353 dataset. Our proposed method outperforms all existing Web-based similarity measures, achieving a statistically significant Pearson correlation coefficient of 0.867 on the Miller-Charles dataset.

A new clock gating circuit suitable for shift register is presented. The proposed clock gating circuit that consists of basic NOR gates is low power and small area. The power consumption of a 16-bit shift register implemented with the proposed clock gating circuit is about 66% lower than that found when using the conventional design.

We investigate in detail the scattering properties and heating characteristics in various commercially available optical fibers and fiber cables when a bubble train forms in the middle of the fiber as a result of the fiber fuse phenomenon that occurs when a high power signal is launched into the fiber. We found theoretically and experimentally that almost all the optical light is scattered at the top of the bubble train. The scattered light heats UV coated fiber, nylon jacketed silica fiber, fire-retardant jacketed fiber (PVC or FRPE jacketed fiber) and fire-retardant fiber cable (PVC or FRPE fiber cable), to around 100, over 200 and over 600, respectively, and finally the fiber burns and is destroyed at a launched optical power of 3 W. Furthermore, it is confirmed that the combustion does not spread when we use fire retardant jacketed fibers.

Ultra-wide band (UWB) pulse radar with high range resolution and dielectric permeability is promising as an internal imaging technique for non-destructive testing or breast cancer detection. Various imaging algorithms for buried objects within a dielectric medium have been proposed, such as aperture synthesis, the time reversal approach and the space-time beamforming algorithm. However, these algorithms mostly require a priori knowledge of the dielectric medium boundary in image focusing, and often suffer from inadequate accuracy to identify the detailed structure of buried targets, such as an edge or specular surface owing to employing the waveform focusing scheme. To overcome these difficulties, this paper proposes an accurate and non-parametric (i.e. using an arbitrary shape without target modeling) imaging algorithm for targets buried in a homogeneous dielectric medium by advancing the RPM (Range Points Migration) algorithm to internal imaging issues, which has been demonstrated to provide an accurate image even for complex-shaped objects in free-space measurement. Numerical simulations, including those for two-dimensional (2-D) and three-dimensional (3-D) cases, verify that the proposed algorithm enhances the imaging accuracy by less than 1/10 of the wavelength and significantly reduces the computational cost by specifying boundary extraction compared with the conventional SAR-based algorithm.

To reduce the cost of fault management in all-optical networks, it is a promising approach to detect the degradation of optical signal quality solely at the terminal points of all-optical monitoring paths. The all-optical monitoring paths must be routed so that all single-link failures can be localized using route information of monitoring paths where signal quality degradation is detected. However, route computation for the all-optical monitoring paths that satisfy the above condition is time consuming. This paper proposes a procedure for deriving the lower bounds of the required number of monitoring paths to localize all single-link failures, and proposes an efficient monitoring path computation method based on the derived lower bounds. The proposed method repeats the route computation for the monitoring paths until feasible routes can be found, while the assumed number of monitoring paths increases, starting from the lower bounds. With the proposed method, the minimum number of monitoring paths with the overall shortest routes can be obtained quickly by solving several small-scale integer linear programming problems when the possible terminal nodes of monitoring paths are arbitrarily given. Thus, the proposed method can minimize the required number of monitors for detecting the degradation of signal quality and the total overhead traffic volume transferred through the monitoring paths.

Novel thermopiles based on modulation doped AlGaAs/InGaAs and AlGaN/GaN heterostructures are proposed and developed for the first time, for uncooled infrared FPA (Focal Plane Array) image sensor application. The high responsivity with the high speed response time are designed to 4,900 V/W with 110 µs for AlGaAs/InGaAs, and to 460 V/W with 9 µs for AlGaN/GaN thermopiles, respectively. Based on integrated HEMT-MEMS technology, the AlGaAs/InGaAs 3232 matrix FPAs are fabricated to demonstrate its enhanced performances by black body measurement. The technology presented here demonstrates the potential of this approach for low-cost uncooled infrared FPA image sensor application.

There are various existing methods translating timed Petri nets to timed automata. However, there is a trade-off between the amount of description and the size of state space. The amount of description and the size of state space affect the feasibility of modeling and analysis like model checking. In this paper, we propose a new translation method from timed Petri nets to timed automata. Our method translates from a timed Petri net to an automaton with the following features: (i) The number of location is 1; (ii) Each edge represents the firing of transition; (iii) Each state implemented as clocks and variables represents a state of the timed Petri net one-to-one correspondingly. Through these features, the amount of description is linear order and the size of state space is the same order as that of the Petri net. We applied our method to three Petri net models of signaling pathways and compared our method with existing methods from the view points of the amount of description and the size of state space. And the comparison results show that our method keeps a good balance between the amount of description and the size of state space. These results also show that our method is effective when checking properties of timed Petri nets.

This letter presents the architecture of multi-gigabit parallel demodulator suitable for demodulating high order QAM modulated signal and easy to implement on FPGA platform. The parallel architecture is based on frequency domain implementation of matched filter and timing phase correction. Parallel FIFO based delete-keep algorithm is proposed for timing synchronization, while a kind of reduced constellation phase-frequency detector based parallel decision feedback PLL is designed for carrier synchronization. A fully pipelined parallel adaptive blind equalization algorithm is also proposed. Their parallel implementation structures suitable for FPGA platform are investigated. Besides, in the demonstration of 2 Gbps demodulator for 16QAM modulation, the architecture is implemented and validated on a Xilinx V6 FPGA platform with performance loss less than 2 dB.

In this letter, a post-detection signal to noise ratio (SNR) is considered for transmit antenna selection, when a sorted QR decomposition (SQRD) algorithm is used for signal detection in spatial multiplexing (SM) ultra-wideband (UWB) multiple input multiple output systems. The post-detection SNR expression is obtained using a QR factorization algorithm based on a sorted Gram-Schmidt process. The employed antenna selection criterion is to utilize the largest minimum post-detection SNR value. It is shown via simulations that the antenna selection significantly enhances the BER performance of the SQRD-based SM UWB systems on a log-normal multipath fading channel.

In this letter, an improved residual symbol timing offset (STO) estimation scheme is suggested in an orthogonal frequency division multiplexing (OFDM) based digital radio mondiale plus (DRM+) system with cyclic delay diversity (CDD). The robust residual STO estimator is derived by properly selecting the amount of cyclic delay and a pilot pattern in the presence of frequency selectivity. Via computer simulation, it is shown that the proposed STO estimation scheme is robust to the frequency selectivity of the channel, with a performance better than the conventional scheme.

When computing resources are consolidated in a few huge data centers, a massive amount of data is transferred to each data center over a wide area network (WAN). This results in increased power consumption in the WAN. A distributed computing network (DCN), such as a content delivery network, can reduce the traffic from/to the data center, thereby decreasing the power consumed in the WAN. In this paper, we focus on the energy-saving aspect of the DCN and evaluate its effectiveness, especially considering traffic locality, i.e., the amount of traffic related to the geographical vicinity. We first formulate the problem of optimizing the DCN power consumption and describe the DCN in detail. Then, numerical evaluations show that, when there is strong traffic locality and the router has ideal energy proportionality, the system's power consumption is reduced to about 50% of the power consumed in the case where a DCN is not used; moreover, this advantage becomes even larger (up to about 30%) when the data center is located farthest from the center of the network topology.

The design, fabrication and characterization of GaN based varactor diodes are presented. MOCVD was used for layer growth and the DC characteristic of 4 µm diameter diodes showed a turn-on voltage of 0.5 V, a breakdown voltage of 21 V and a modulation ratio of 1.63. High frequency characterization allowed obtaining the diode equivalent circuit and observed the bias dependence of the series resistance. The diode cutoff frequency was 900 GHz. A large-signal model was developed for the diode and the device power performance was evaluated. A power of 7.2 dBm with an efficiency of 16.6% was predicted for 47 GHz to 94 GHz doubling.

This paper introduces a novel design method of an asynchronous pipeline based on dual-rail dynamic logic. The overhead of handshake control logic is greatly reduced by constructing a reliable critical datapath, which offers the pipeline high throughput as well as low power consumption. Synchronizing Logic Gates (SLGs), which have no data dependency problem, are used in the design to construct the reliable critical datapath. The design targets latch-free and extremely fine-grain or gate-level pipeline, where the depth of every pipeline stage is only one dual-rail dynamic logic. HSPICE simulation results, in a 65 nm design technology, indicate that the proposed design increases the throughput by 120% and decreases the power consumption by 54% compared with PS0, a classic dual-rail asynchronous pipeline implementation style, in 4-bit wide FIFOs. Moreover, this method is applied to design an array style multiplier. It shows that the proposed design reduces power by 37.9% compared to classic synchronous design when the workloads are 55%. A chip has been fabricated with a 44 multiplier function, which works well at 2.16G data-set/s (Post-layout simulation).

This paper proposes a new generative model which can deal with rotational data variations by extending Separable Lattice 2-D HMMs (SL2D-HMMs). In image recognition, geometrical variations such as size, location and rotation degrade the performance. Therefore, the appropriate normalization processes for such variations are required. SL2D-HMMs can perform an elastic matching in both horizontal and vertical directions; this makes it possible to model invariance to size and location. To deal with rotational variations, we introduce additional HMM states which represent the shifts of the state alignments among the observation lines in a particular direction. Face recognition experiments show that the proposed method improves the performance significantly for rotational variation data.

This paper proposes a new first-scan method for two-scan labeling algorithms. In the first scan, our proposed method first scans every fourth image line, and processes the scan line and its two neighbor lines. Then, it processes the remaining lines from top to bottom one by one. Our method decreases the average number of times that must be checked to process a foreground pixel will; thus, the efficiency of labeling can be improved.

A complete analysis of GaN-based structures with very promising characteristics for future optical waveguide devices, such as modulators, is presented. First the material growth was optimized for low dislocation density and surface roughness. Optical measurements demonstrate excellent waveguide properties in terms of index and temperature dependence while planar propagation losses are below 1 dB/cm. Bias was applied on both sides of the epitaxially grown films to evaluate the refractive index dependence on reverse voltage and a variation of 2.10-3 was found for 30 V. These results support the possibility of using structures of this type for the fabrication of modulator devices such as Mach-Zehnder interferometers.

Our research is focused on examining the video quality assessment model based on the MPEG-7 descriptor. Video quality is estimated by using several features based on the predicted frame quality such as average value, worst value, best value, standard deviation, and the predicted frame rate obtained from descriptor information. As a result, assessment of video quality can be conducted with a high prediction accuracy with correlation coefficient=0.94, standard deviation of error=0.24, maximum error=0.68 and outlier ratio=0.23.

In this paper, we propose Hidden Conditional Neural Fields (HCNF) for continuous phoneme speech recognition, which are a combination of Hidden Conditional Random Fields (HCRF) and a Multi-Layer Perceptron (MLP), and inherit their merits, namely, the discriminative property for sequences from HCRF and the ability to extract non-linear features from an MLP. HCNF can incorporate many types of features from which non-linear features can be extracted, and is trained by sequential criteria. We first present the formulation of HCNF and then examine three methods to further improve automatic speech recognition using HCNF, which is an objective function that explicitly considers training errors, provides a hierarchical tandem-style feature and includes a deep non-linear feature extractor for the observation function. We show that HCNF can be trained realistically without any initial model and outperforms HCRF and the triphone hidden Markov model trained by the minimum phone error (MPE) manner using experimental results for continuous English phoneme recognition on the TIMIT core test set and Japanese phoneme recognition on the IPA 100 test set.

The performance of English automatic speech recognition systems decreases when recognizing spontaneous speech mainly due to multiple pronunciation variants in the utterances. Previous approaches address this problem by modeling the alteration of the pronunciation on a phoneme to phoneme level. However, the phonetic transformation effects induced by the pronunciation of the whole sentence have not yet been considered. In this article, the sequence-based pronunciation variation is modeled using a noisy channel approach where the spontaneous phoneme sequence is considered as a “noisy” string and the goal is to recover the “clean” string of the word sequence. Hereby, the whole word sequence and its effect on the alternation of the phonemes will be taken into consideration. Moreover, the system not only learns the phoneme transformation but also the mapping from the phoneme to the word directly. In this study, first the phonemes will be recognized with the present recognition system and afterwards the pronunciation variation model based on the noisy channel approach will map from the phoneme to the word level. Two well-known natural language processing approaches are adopted and derived from the noisy channel model theory: Joint-sequence models and statistical machine translation. Both of them are applied and various experiments are conducted using microphone and telephone of spontaneous speech.

In this comment, an inequality of algebraic immunity of the sum of two Boolean functions is pointed out to be generally incorrect. Then we present some results on how to impose conditions such that the inequality is true. Finally, complete proofs of two existing results are given.