Since multiview video coding (MVC) based on H.264/AVC uses a prediction scheme exploiting inter-view correlation among multiview video, MVC encoder compresses multiple views more efficiently than simulcast H.264/AVC encoder. However, in case that the number of views to be encoded increases in MVC, the total encoding time will be greatly increased. To reduce computational complexity in MVC, a fast mode decision using both Macroblock-based region segmentation information and global disparity vector among views is proposed to reduce the encoding time. The proposed method achieves on the average 1.5 2.9 reduction of the total encoding time with the PSNR (Peak Signal-to-Noise Ratio) degradation of about 0.05 dB.

In this paper, a highly parallel deblocking filter architecture for H.264/AVC is proposed to process one macroblock in 48 clock cycles and give real-time support to QFHD@60 fps sequences at less than 100 MHz. 4 edge filters organized in 2 groups for simultaneously processing vertical and horizontal edges are applied in this architecture to enhance its throughput. While parallelism increases, pipeline hazards arise owing to the latency of edge filters and data dependency of deblocking algorithm. To solve this problem, a zig-zag processing schedule is proposed to eliminate the pipeline bubbles. Data path of the architecture is then derived according to the processing schedule and optimized through data flow merging, so as to minimize the cost of logic and internal buffer. Meanwhile, the architecture's data input rate is designed to be identical to its throughput, while the transmission order of input data can also match the zig-zag processing schedule. Therefore no intercommunication buffer is required between the deblocking filter and its previous component for speed matching or data reordering. As a result, only one 2464 two-port SRAM as internal buffer is required in this design. When synthesized with SMIC 130 nm process, the architecture costs a gate count of 30.2 k, which is competitive considering its high performance.

This paper presents a novel method using multi-objective optimization algorithm to automatically find the best solution from a topology library of analog circuits. Firstly this method abstracts the Pareto-front of each topology in the library by SPICE simulation. Then, the Pareto-front of the topology library is abstracted from the individual Pareto-fronts of topologies in the library followed by the theorem we proved. The best solution which is defined as the nearest point to specification on the Pareto-front of the topology library is then calculated by the equations derived from collinearity theorem. After the local searching using Nelder-Mead method maps the calculated best solution backs to design variable space, the non-dominated best solution is obtained. Comparing to the traditional optimization methods using single-objective optimization algorithms, this work can efficiently find the best non-dominated solution from multiple topologies for different specifications without additional time-consuming optimizing iterations. The experiments demonstrate that this method is feasible and practical in actual analog designs especially for uncertain or variant multi-dimensional specifications.

In this paper, we propose a bandwidth-scalable stereo audio coding method based on a layered structure. The proposed stereo coding method encodes super-wideband (SWB) stereo signals and is able to decode either wideband (WB) stereo signals or SWB stereo signals, depending on the network congestion. The performance of the proposed stereo coding method is then compared with that of a conventional stereo coding method that separately decodes WB or SWB stereo signals, in terms of subjective quality, algorithmic delay, and computational complexity. Experimental results show that when stereo audio signals sampled at a rate of 32 kHz are compressed to 64 kbit/s, the proposed method provides significantly better audio quality with a 64-sample shorter algorithmic delay, and comparable computational complexity.

In this paper, we study the performance of dual hop relaying in which the best relay selected by partial relay selection will help the source-destination link to overcome the channel impairment. Specifically, closed-form expressions for outage probability, symbol error probability and achievable diversity gain are derived using the statistical characteristic of the signal-to-noise ratio. Numerical investigation shows that the system achieves diversity of two regardless of relay number and also confirms the correctness of the analytical results. Furthermore, the performance loss due to partial relay selection is investigated.

This paper presents a reordering model using a source-side parse-tree for phrase-based statistical machine translation. The proposed model is an extension of IST-ITG (imposing source tree on inversion transduction grammar) constraints. In the proposed method, the target-side word order is obtained by rotating nodes of the source-side parse-tree. We modeled the node rotation, monotone or swap, using word alignments based on a training parallel corpus and source-side parse-trees. The model efficiently suppresses erroneous target word orderings, especially global orderings. Furthermore, the proposed method conducts a probabilistic evaluation of target word reorderings. In English-to-Japanese and English-to-Chinese translation experiments, the proposed method resulted in a 0.49-point improvement (29.31 to 29.80) and a 0.33-point improvement (18.60 to 18.93) in word BLEU-4 compared with IST-ITG constraints, respectively. This indicates the validity of the proposed reordering model.

Alumina supported cobalt-ferrum catalysts were prepared using wet impregnation method by applying 3 different conditions, namely hotplate (A), sonication (B) and soaking (C). The alumina supported cobalt-ferrum catalysts were applied in the synthesis of multi-walled carbon nanotubes (MWNTs) using catalytic chemical vapour deposition (CCVD) technique. The morphology and particle size of the cobalt-ferrum catalysts and the MWNTs yield were examined by field emission-scanning electron microscopy (FE-SEM) while the surface elemental composition of the samples was obtained by energy dispersive X-ray analysis (EDX). The morphology of catalysts A, B and C were found to be different, the particle sizes were ranged from 20-40 nm. The diameters of the MWNTs yield from samples A, B and C were found to be related to the catalyst particle size, thus the smaller the catalyst particle, the thinner the MWNTs obtained. The MWNTs with smaller diameter were obtained with higher purity and quality becuase the nanotube surface are free from amorphous carbon. Therefore, different catalyst preparation methods resulted in different sizes of the catalyst particle in order to synthesize MWNTs with desired diameter.

Due to the advancement of software radio and RF technology, cognitive radio(CR) has become an enabling technology to realize dynamic spectrum access through its spectrum sensing and reconfiguration capability. Robust and reliable spectrum sensing is a key factor to discover spectrum opportunity. Single cognitive radios often fail to provide such reliable information because of their inherent sensitivity limitation. Primary signals that are subject to detection by cognitive radios may become weak due to several factors such as fading and shadowing. One approach to overcome this problem is to perform spectrum sensing by using multiple CRs or multiple spectrum sensors. This approach is known as distributed sensing because sensing is carried out through cooperation of spatially distributed sensors. In distributed sensing, sensors should perform spectrum sensing and forward the result to a destination where data fusion is carried out. Depending on the channel conditions between sensors (sensor-to-sensor channel) and between the sensor and the radio (user-channel), we explore different spectrum sensing algorithms where sensors provide the sensing information either cooperatively or independently. Moreover we investigate sensing schemes based on soft information combining (SC), hard information combining (HC). Finally we propose a two-stage detection scheme that uses both SC and HC. The newly proposed detection scheme is shown to provide improved performance compared to sensing based on either HC or SC alone. Computer simulation results are provided to illustrate the performances of the different sensing algorithms.

The public safety spectrum is generally under-utilized due to the unique traffic characteristics of bursty and mission critical. This letter considers the application of dynamic spectrum access (DSA) to the combined spectrum of public safety (PS) and commercial (CMR) users in a common shared network that can provide both PS and CMR services. Our scenario includes the 700 MHz Public/Private Partnership which was recently issued by the Federal Communications Commission. We first propose an efficient DSA mechanism to coordinate the combined spectrum, and then establish a call admission control that reflects the proposed DSA in a wideband code division multiple access based network. The essentials of our proposed DSA are opportunistic access to the public safety spectrum and priority access to the commercial spectrum. Simulation results show that these schemes are well harmonized in various network environments.

Partial forwarding is a design method to place forwarding paths on a part of processor pipeline. Hardware cost of processor can be reduced without performance loss by partial forwarding. However, compiler with the instruction scheduler which considers partial forwarding structure of the target processor is required since conventional scheduling algorithm cannot make the most of partial forwarding structure. In this paper, we propose a heuristic instruction scheduling method for processors with partial forwarding structure. The proposed algorithm uses available distance to schedule instructions which are suitable for the target partial forwarding processor. Experimental results show that the proposed method generates near-optimal solutions in practical time and some of the optimized codes for partial forwarding processor run in the shortest time among the target processors. It also shows that the proposed method is superior to hazard detection unit.

An inversion method of bridge height over water by polarimetric synthetic aperture radar (SAR) is developed. A geometric ray description to illustrate scattering mechanism of a bridge over water surface is identified by polarimetric image analysis. Using the mapping and projecting algorithm, a polarimetric SAR image of a bridge model is first simulated and shows that scattering from a bridge over water can be identified by three strip lines corresponding to single-, double-, and triple-order scattering, respectively. A set of polarimetric parameters based on the de-orientation theory is applied to analysis of three types scattering, and the thinning-clustering algorithm and Hough transform are then employed to locate the image positions of these strip lines. These lines are used to invert the bridge height. Fully polarimetric image data of airborne Pi-SAR at X-band are applied to inversion of the height and width of the Naruto Bridge in Japan. Based on the same principle, this approach is also applicable to spaceborne ALOSPALSAR single-polarization data of the Eastern Ocean Bridge in China. The results show good feasibility to realize the bridge height inversion.

We report the growth of carbon nanofibers (CNFs) from carbon particles by chemical vapor deposition (CVD) with ultrasonic neblizer using ethanol as carbon source. Dense CNFs having diameters of several tens of nanometers have been successfully synthesized by the CVD without using any metal catalysts. The carbon particles formed from decompostion of fullerene were found to be suitable for the synthesis of CNFs. Details of the optimum conditions for producing CNFs and the expected growth mechanism are also described.

This paper describes an incremental parser based on an adjoining operation. By using the operation, we can avoid the problem of infinite local ambiguity. This paper further proposes a restricted version of the adjoining operation, which preserves lexical dependencies of partial parse trees. Our experimental results showed that the restriction enhances the accuracy of the incremental parsing.

In this paper, we propose a Modified nested sparse grid based Adaptive Stochastic Collocation Method (MASCM) for block-based Statistical Static Timing Analysis (SSTA). The proposed MASCM employs an improved adaptive strategy derived from the existing Adaptive Stochastic Collocation Method (ASCM) to approximate the key operator MAX during timing analysis. In contrast to ASCM which uses non-nested sparse grid and tensor product quadratures to approximate the MAX operator for weakly and strongly nonlinear conditions respectively, MASCM proposes a modified nested sparse grid quadrature to approximate the MAX operator for both weakly and strongly nonlinear conditions. In the modified nested sparse grid quadrature, we firstly construct the second order quadrature points based on extended Gauss-Hermite quadrature and nested sparse grid technique, and then discard those quadrature points that do not contribute significantly to the computation accuracy to enhance the efficiency of the MAX approximation. Compared with the non-nested sparse grid quadrature, the proposed modified nested sparse grid quadrature not only employs much fewer collocation points, but also offers much higher accuracy. Compared with the tensor product quadrature, the modified nested sparse grid quadrature greatly reduced the computational cost, while still maintains sufficient accuracy for the MAX operator approximation. As a result, the proposed MASCM provides comparable accuracy while remarkably reduces the computational cost compared with ASCM. The numerical results show that with comparable accuracy MASCM has 50% reduction in run time compared with ASCM.

In this paper, the issue of carrier frequency offset (CFO) compensation in interleaved orthogonal frequency division multiple access (OFDMA) uplink system is investigated. To mitigate the effect of multiple access interference (MAI) caused by CFOs of different users, a new parallel interference cancellation (PIC) compensation algorithm is proposed. This scheme uses minimum mean square error (MMSE) criterion to obtain the estimation of interference users, then circular convolutions are employed to restore MAI and compensate CFO. To tackle the complexity problem of circular convolutions, an efficient MAI restoration and cancellation method is developed. Simulations illustrate the good performance and low computational complexity of the proposed algorithm.

In this paper, we propose a new modeling method to express discrete-time hybrid systems with parameter uncertainty as a mixed logical dynamical (MLD) model. In analysis and control of hybrid systems, there are problem formulations in which convex polyhedra are computed, but for high-dimensional systems, it is difficult to solve these problems within a practical computation time. The key idea of this paper is to use an interval method, which is one of the classical methods in verified numerical computation, and to regard an interval as an over-approximation of a convex polyhedron. By using the obtained MLD model, analysis and synthesis of robust control systems are formulated.

In this study, we introduce shift-invariant sparse image representations using tree-structured dictionaries. Sparse coding is a generative signal model that approximates signals by the linear combinations of atoms in a dictionary. Since a sparsity penalty is introduced during signal approximation and dictionary learning, the dictionary represents the primal structures of the signals. Under the shift-invariance constraint, the dictionary comprises translated structuring elements (SEs). The computational cost and number of atoms in the dictionary increase along with the increasing number of SEs. In this paper, we propose an algorithm for shift-invariant sparse image representation, in which SEs are learnt with a tree-structured approach. By using a tree-structured dictionary, we can reduce the computational cost of the image decomposition to the logarithmic order of the number of SEs. We also present the results of our experiments on the SE learning and the use of our algorithm in image recovery applications.

Recently, many researchers tackle accurate object recognition algorithms and many algorithms are proposed. However, these algorithms have some problems caused by variety of real environments such as a direction change of the object or its shading change. The new tracking algorithm, Cascade Particle Filter, is proposed to fill such demands in real environments by constructing the object model while tracking the objects. We have been investigating to implement accurate object recognition on embedded systems in real-time. In order to apply the Cascade Particle Filter to embedded applications such as surveillance, automotives, and robotics, a hardware accelerator is indispensable because of limitations in power consumption. In this paper we propose a hardware implementation of the Discrete AdaBoost algorithm that is the most computationally intensive part of the Cascade Particle Filter. To implement the proposed hardware, we use PICO Express, a high level synthesis tool provided by Synfora, for rapid prototyping. Implementation result shows that the synthesized hardware has 1,132,038 transistors and the die area is 2,195 µm 1,985 µm under a 0.180 µm library. The simulation result shows that total processing time is about 8.2 milliseconds at 65 MHz operation frequency.

Incremental Redundancy Hybrid ARQ (IR-HARQ) based on rate-compatible punctured low-density parity-check (LDPC) codes can achieve high throughput over a wide range of SNRs. One drawback of such IR-HARQ schemes is high computational complexity of decoding for early transmission at high rates. In order to overcome this problem, a HARQ scheme based on rate-compatible LDPC codes by shortening and extending is presented in this paper. In the HARQ scheme, a high-rate mother code is transmitted at first, and parity-bits of a shortened code are transmitted for early retransmission requests. With a low-complexity decoder of the high-rate mother code, this shortened-code approach would result in low computational complexity of decoding, but it causes smaller length and larger number of shortened codes to be decoded as retransmission repeats. To prevent the resultant degradation of performance and complexity, extending is efficiently applied to the shortened codes after predetermined retransmission-times. A multi-edge type code-design is employed to construct irregular LDPC codes that meet the requirement of the HARQ scheme. Simulation results show that the HARQ scheme can achieve lower computational complexity of decoding than a conventional IR-HARQ scheme with good throughput over a wide range of SNRs.

We have fabricated the transparent bottom gate and top gate TFTs using new oxide material of Al-Zn-Sn-O (AZTO) as an active layer. The AZTO active layer was deposited by RF magnetron sputtering at room temperature. Our novel TFT showed good TFT performance without post-annealing. The field effect mobility and the sub-threshold swing were improved by the post-annealing, and the mobility increased with SnO2 content. The AZTO TFT (about 4 mol% AlOx, 66 mol% ZnO, and 30 mol% SnO2) exhibited a mobility of 10.3 cm2/Vs, a turn-on voltage of 0.4 V, a sub-threshold swing of 0.6 V/dec, and an on/off ratio of 109. Though the bottom gate AZTO TFT showed good electrical performance, the bias stability was relatively poor. The bias stability was significantly improved in the top gate AZTO TFT. We have successfully fabricated the transparent AMOLED panel using the back-plane composed with top gate AZTO TFT array.