C-C successive approximation register analog-to-digital converter (C-C SAR-ADC) is space-saving architecture compared to SAR-ADC with binary weighted capacitive digital-to-analog converter (CDAC). However, the accuracy of C-C SAR-ADC is degraded due to parasitic capacitance of floating nodes. This paper proposes an algorithm calibrating the non-linearity by γ-estimation to accurately estimate radix greater than 2 required to realize C-C SAR-ADC. Behavioral analyses show that the radix γ-estimation error become within 1.5, 0.4 and 0.1% in case of 8-, 10- and 12-bit resolution ADC, respectively. SPICE simulations show that the γ-estimation satisfies the requirement of 10-bit resolution C-C SAR-ADC. The C-C SAR-ADC using γ-estimation achieves 9.72bit of ENOB, 0.8/-0.5LSB and 0.5/-0.4LSB of DNL/INL.

To realize the hardware object which facilitates the application development in the reconfigurable computing system, a hardware module (HwModule) is proposed and implemented. To access the circuit in the HwModule from the standard PC without detailed knowledge of the hardware, an object manager (ObjectManager) is also implemented. With the help of the ObjectManager, the programmers can use the hardware objects like the usual software objects. The HwModule is applied to the image matching, and the easiness of the application development for the HwModule is confirmed.

A processing system with multiple field programmable gate array (FPGA) cards is described. Each FPGA card can interconnect using six I/O (up, down, left, right, front, and back) terminals. The communication network among FPGAs is scalable according to user design. When the system operates multi-dimensional applications, transmission efficiency among FPGA improved through user-adjusted dimensionality and network topologies for different applications. We provide a fast and flexible circuit configuration method for FPGAs of a multi-dimensional FPGA array. To demonstrate the effectiveness of the proposed method, we assess performance and power consumption of a circuit that calculated 3D Poisson equations using the finite difference method.

A hybrid integrated optical module composed of a silica-based planar lightwave circuit (PLC), a laser diode with an integrated monitor-photodiode, and a pin-photodiode is fabricated for use in high-performance, compact and cost-effective fiber optic subscriber systems. Its applicability to a wavelength-division-multiplex (WDM) system with a 1.3-µm bi-directional signal and a 1.5-µm one-way signal is demonstrated. The PLC was fabricated by a combination of flame hydrolysis deposition (FHD) and reactive ion etching (RIE), and it simultaneously achieved 1.3-µm/1.5-µm multi/demultiplexing and 1.3-µm Y-branching functions. The optical module exhibited insertion losses of 4.1dB at 1.31µm (including a Y-branch circuit loss of 3dB) and 0.5dB at 1.53µm. An optical output power of more than -4dBm was obtained from the optical module and the crosstalk was sufficiently low at less than -20dB between wavelengths of 1.3µm and 1.5µm. Temperature cycle tests on the optical module showed reliable and stable operation with an optical power fluctuation of less than 0.3dB for 500 cycles.

Spectral transform methods have been widely studied for classification and analysis of logic functions. Spectral methods have also been used for logic synthesis, and by use of BDDs, practical-sized synthesis problems have been solved. Wavelet theory has recently attracted the attention of researchers in the signal processing field. The Haar function is used in both spectral methods and in signal processing to obtain spectral coefficients of logic functions of signals. In this paper spectral transform-based analysis of neural nets verifying signal processing and discrete function is presented. A neural net element is defined as a discrete function with multi-valued input signals and multi-valued or binary outputs. The multi-valued variable is realized as a variable (V, W) formed by a pair of a binary value and a multi-value pulse width. The multi-valued encoding is used with the multi-valued Haar function to give meanings to the wavelet coefficients from the view of Boolean algebra. A design example shows that these conceptually different concepts are closely related.

This paper addresses the task of assigning labels of fine-grained named entity (NE) types to Wikipedia articles. Information of NE types are useful when extracting knowledge of NEs from natural language text. It is common to apply an approach based on supervised machine learning to named entity classification. However, in a setting of classifying into fine-grained types, one big challenge is how to alleviate the data sparseness problem since one may obtain far fewer instances for each fine-grained types. To address this problem, we propose two methods. First, we introduce a multi-task learning framework, in which NE type classifiers are all jointly trained with a neural network. The neural network has a hidden layer, where we expect that effective combinations of input features are learned across different NE types. Second, we propose to extend the input feature set by exploiting the hyperlink structure of Wikipedia. While most of previous studies are focusing on engineering features from the articles' contents, we observe that the information of the contexts the article is mentioned can also be a useful clue for NE type classification. Concretely, we propose to learn article vectors (i.e. entity embeddings) from Wikipedia's hyperlink structure using a Skip-gram model. Then we incorporate the learned article vectors into the input feature set for NE type classification. To conduct large-scale practical experiments, we created a new dataset containing over 22,000 manually labeled articles. With the dataset, we empirically show that both of our ideas gained their own statistically significant improvement separately in classification accuracy. Moreover, we show that our proposed methods are particularly effective in labeling infrequent NE types. We've made the learned article vectors publicly available. The labeled dataset is available if one contacts the authors.

The array-enhanced coherence resonance (AECR) in the diffusively coupled active rotators is investigated and its analysis with the nonlinear Fokker-Planck equation is presented. By considering the nonlinear Fokker-Planck equation of the rotators, it is found that the time-periodic solution exists in some parameter range. By solving the equation of a rotator and the Fokker-Planck equation simultaneously, the behavior of a rotator in the system with infinite number of rotators is considered, and it is found that AECR also takes place in this infinite system. Thus it is concluded that AECR is caused by the time-periodic solution of the probability density induced by noise.

Small-size directional coupler type optical multi/demultiplexers using high refractive-index-difference composite-glass waveguides have been investigated. A novel design to minimize wavelength response sensitivity to waveguide width deviation in the coupler part was proposed. Composite glass waveguides with 5% were fabricated using Corning 7059 glass for the core and SiO2 for the cladding. 1.3/1.55-µm optical multi/demultiplexers with short coupler length of 300 µm, low insertion loss of 5 dB and high crosstalk attenuation of more than 25 dB were realized.