In this paper, online sparse Volterra system identification is proposed. For that purpose, the conventional adaptive projection-based algorithm with weighted l1 balls (APWL1) is revisited for nonlinear system identification, whereby the linear-in-parameters nature of Volterra systems is utilized. Compared with sparsity-aware recursive least squares (RLS) based algorithms, requiring higher computational complexity and showing faster convergence and lower steady-state error due to their long memory in time-invariant cases, the proposed approach yields better tracking capability in time-varying cases due to short-term data dependence in updating the weight. Also, when N is the number of sparse Volterra kernels and q is the number of input vectors involved to update the weight, the proposed algorithm requires O(qN) multiplication complexity and O(Nlog 2N) sorting-operation complexity. Furthermore, sparsity-aware least mean-squares and affine projection based algorithms are also tested.

The reliability of a connector depends on the contact force generated by the spring in the terminal of a connector. The springs are commonly formed by stamping from a strip of spring material. Therefore, the prediction of the force — displacement relation by the finite element (FE) method is very important for the design of terminals. For simulation, an accurate model of stress-strain (s-s) responses of the materials is required. When the materials are deformed in the forward and then the reverse directions, almost all spring materials show different s-s responses between the two directions, due to the Bauschinger effect. This phenomenon makes simulation difficult because the s-s response depends on the prior deformation of the material. Hence, the measurement of the s-s response is the elementary process, by cyclic tension and compression testing in which materials deform elastically and plastically. Then, the s-s responses should be described accurately by mathematical models for FE simulation. In this paper, the authors compare the experimental s-s responses of copper-based materials with conventional mathematical models and the Yoshida-Uemori model, which is a constitutive model having high capability of describing the elastic and plastic behavior of cyclic deformation. The calculated s-s responses only by Yoshida-Uemori model were in very good agreement with the corresponding experimental results. Therefore, the use of this model for FE simulation would be recommended for a more accurate prediction of force-displacement relation of the spring.

This paper proposes an enhancement to a previously reported adaptive peak-to-average power ratio (PAPR) reduction method based on clipping and filtering (CF) for eigenmode multiple-input multiple-output (MIMO) — orthogonal frequency division multiplexing (OFDM) signals. We enhance the method to accommodate the case with adaptive modulation and channel coding (AMC). Since the PAPR reduction process degrades the signal-to-interference and noise power ratio (SINR), the AMC should take into account this degradation before PAPR reduction to select accurately the modulation scheme and coding rate (MCS) for each spatial stream. We use the lookup table-based prediction of SINR after PAPR reduction, in which the interference caused by the PAPR reduction is obtained as a function of the stream index, frequency block index, clipping threshold for PAPR reduction, and input backoff (IBO) of the power amplifier. Simulation results show that the proposed PAPR reduction method increases the average throughput compared to the conventional CF method for a given adjacent channel leakage power ratio (ACLR) when we assume practical AMC.

Based on sample-pair refinement and local optimization, this paper proposes a high-accuracy and quick matting algorithm. First, in order to gather foreground/background samples effectively, we shoot rays in hybrid (gradient and uniform) directions. This strategy utilizes the prior knowledge to adjust the directions for effective searching. Second, we refine sample-pairs of pixels by taking into account neighbors'. Both high confidence sample-pairs and usable foreground/background components are utilized and thus more accurate and smoother matting results are achieved. Third, to reduce the computational cost of sample-pair selection in coarse matting, this paper proposes an adaptive sample clustering approach. Most redundant samples are eliminated adaptively, where the computational cost decreases significantly. Finally, we convert fine matting into a de-noising problem, which is optimized by minimizing the observation and state errors iteratively and locally. This leads to less space and time complexity compared with global optimization. Experiments demonstrate that we outperform other state-of-the-art methods in local matting both on accuracy and efficiency.

This paper proposes a coarse-fine Time-to-Digital Converter (TDC), based on a Ring-Tapped Delay Line (RTDL). The TDC achieves the picosecond's level timing resolution and microsecond's level dynamic range at low cost. The TDC is composed of two coarse time measurement blocks, a time residue generator, and a fine time measurement block. In the coarse blocks, RTDL is constructed by redesigning the conventional Tapped Delay Line (TDL) in a ring structure. A 12-bit counter is employed in one of the two coarse blocks to count the cycle times of the signal traveling in the RTDL. In this way, the input range is increased up to 20.3µs without use of an external reference clock. Besides, the setup time of soft-edged D-flip-flops (SDFFs) adopted in RTDL is set to zero. The adjustable time residue generator picks up the time residue of the coarse block and propagates the residue to the fine block. In the fine block, we use a Vernier Ring Oscillator (VRO) with MOS capacitors to achieve a scalable timing resolution of 11.8ps (1 LSB). Experimental results show that the measured characteristic curve has high-level linearity; the measured DNL and INL are within ± 0.6 LSB and ± 1.5 LSB, respectively. When stimulated by constant interval input, the standard deviation of the system is below 0.35 LSB. The dead time of the proposed TDC is less than 650ps. When operating at 5 MSPS at 3.3V power supply, the power consumption of the chip is 21.5mW. Owing to the use of RTDL and VRO structures, the chip core area is only 0.35mm × 0.28mm in a 0.35µm CMOS process.

In this letter, a distributed TDMA-based data gathering scheme for wireless sensor networks, called DTDGS, is proposed in order to avoid transmission collisions, achieve high levels of power conservation and improve network lifetime. Our study is based on corona-based network division and a distributed TDMA-based scheduling mechanism. Different from a centralized algorithm, DTDGS does not need a centralized gateway to assign the transmission time slots and compute the route for each node. In DTDGS, each node selects its transmission slots and next-hop forwarding node according to the information gathered from neighbor nodes. It aims at avoiding transmission collisions and balancing energy consumption among nodes in the same corona. Compared with previous data gathering schemes, DTDGS is highly scalable and energy efficient. Simulation results show high the energy efficiency of DTDGS.

This paper presents a new adaptive peak-to-average power ratio (PAPR) reduction method based on clipping and filtering (CF) for precoded orthogonal frequency division multiplexing (OFDM)-multiple-input multiple-output (MIMO) transmission. While the conventional CF method adds roughly the same interference power to each of the transmission streams, the proposed method suppresses the addition of interference power to the streams with good channel conditions. Since the sum capacity is dominated by the capacity of the streams under good channel conditions and the interference caused by the PAPR reduction process severely degrades the achievable capacity for these streams, the proposed method significantly improves the achievable sum capacity compared to the conventional CF method for a given PAPR. Simulation results show the capacity gain by using the proposed method compared to the conventional method.

This paper introduces a radio frequency identification (RFID) tag for urination detection. The proposed tag is embedded into paper diapers in order to detect the patient's urination immediately. For this tag, we designed an RFID tag antenna at 950MHz, which matches the impedance of the associated integrated circuit (IC) chip. In addition, we calculate the antenna characteristics and measure the reflection coefficient (S11) and radiation pattern of the antenna. The results show that this system can be used to detect urination.

In this paper, we propose a hybrid fuzzy geometric active contour method, which embeds the spatial fuzzy clustering into the evolution of geometric active contour. In every iteration, the evolving curve works as a spatial constraint on the fuzzy clustering, and the clustering result is utilized to construct the fuzzy region force. On one hand, the fuzzy region force provides a powerful capability to avoid the leakages at weak boundaries and enhances the robustness to various noises. On the other hand, the local information obtained from the gradient feature map contributes to locating the object boundaries accurately and improves the performance on the images with heterogeneous foreground or background. Experimental results on synthetic and real images have shown that our model can precisely extract object boundaries and perform better than the existing representative hybrid active contour approaches.

Reducing the time complexity of character matching is critical to the development of efficient Japanese Optical Character Recognition (OCR) systems. To shorten the processing time, recognition is usually split into separate pre-classification and precise recognition stages. For high overall recognition performance, the pre-classification stage must both have very high classification accuracy and return only a small number of putative character categories for further processing. Furthermore, for any practical system, the speed of the pre-classification stage is also critical. The associative matching (AM) method has often been used for fast pre-classification because of its use of a hash table and reliance on just logical bit operations to select categories, both of which make it highly efficient. However, a certain level of redundancy exists in the hash table because it is constructed using only the minimum and maximum values of the data on each axis and therefore does not take account of the distribution of the data. We propose a novel method based on the AM method that satisfies the performance criteria described above but in a fraction of the time by modifying the hash table to reduce the range of each category of training characters. Furthermore, we show that our approach outperforms pre-classification by VQ clustering, ANN, LSH and AM in terms of classification accuracy, reducing the number of candidate categories and total processing time across an evaluation test set comprising 116,528 Japanese character images.

This study extends conventional fingerprint recognition from a supervised to an unsupervised framework. Instead of enrolling fingerprints from known persons to identify unknown fingerprints, our aim is to partition a collection of unknown fingerprints into clusters, so that each cluster consists of fingerprints from the same finger and the number of generated clusters equals the number of distinct fingers involved in the collection. Such an unsupervised framework is helpful to handle the situation where a collection of captured fingerprints are not from the enrolled people. The task of fingerprint clustering is formulated as a problem of minimizing the clustering errors characterized by the Rand index. We estimate the Rand index by computing the similarities between fingerprints and then apply a genetic algorithm to minimize the Rand index. Experiments conducted using the FVC2002 database show that the proposed fingerprint clustering method outperforms an intuitive method based on hierarchical agglomerative clustering. The experiments also show that the number of clusters determined by our system is close to the true number of distinct fingers involved in the collection.

We propose a non-photorealistic rendering method for generating moire-picture-like color images from color photographs. The proposed method is performed in two steps. First, images with a staircasing effect are generated by a bilateral filter. Second, moire patterns are generated with an improved bilateral filter called an anti-bilateral filter. The characteristic of the anti-bilateral filter is to emphasize gradual boundaries.

This paper presents a fine-grain bit-serial reconfigurable VLSI architecture using multiple-valued switch blocks and binary logic modules. Multiple-valued signaling is utilized to implement a compact switch block. A binary-controlled current-steering technique is introduced, utilizing a programmable three-level differential-pair circuit to implement a high-performance low-power arbitrary two-variable binary function, and increase the noise margins in comparison with the quaternary-controlled differential-pair circuit. A current-source sharing technique between a series-gating differential-pair circuit and a current-mode D-latch is proposed to reduce the current source count and improve the speed. It is demonstrated that the power consumption and the delay of the proposed multiple-valued cell based on the binary-controlled current-steering technique and the current-source-sharing technique are reduced to 63% and 72%, respectively, in comparison with those of a previous multiple-valued cell.

Clustering methods are divided into hierarchical clustering, partitioning clustering, and more. K-Means is a method of partitioning clustering. We improve the performance of a K-Means, selecting the initial centers of a cluster through a calculation rather than using random selecting. This method maximizes the distance among the initial centers of clusters. Subsequently, the centers are distributed evenly and the results are more accurate than for initial cluster centers selected at random. This is time-consuming, but it can reduce the total clustering time by minimizing allocation and recalculation. Compared with the standard algorithm, F-Measure is more accurate by 5.1%.

Maximum margin clustering (MMC) is a newly proposed clustering method that extends the large-margin computation of support vector machine (SVM) to unsupervised learning. Traditionally, MMC is formulated as a nonconvex integer programming problem which makes it difficult to solve. Several methods rely on reformulating and relaxing the nonconvex optimization problem as semidefinite programming (SDP) or second-order cone program (SOCP), which are computationally expensive and have difficulty handling large-scale data sets. In linear cases, by making use of the constrained concave-convex procedure (CCCP) and cutting plane algorithm, several MMC methods take linear time to converge to a local optimum, but in nonlinear cases, time complexity is still high. Since extreme learning machine (ELM) has achieved similar generalization performance at much faster learning speed than traditional SVM and LS-SVM, we propose an extreme maximum margin clustering (EMMC) algorithm based on ELM. It can perform well in nonlinear cases. Moreover, the kernel parameters of EMMC need not be tuned by means of random feature mappings. Experimental results on several real-world data sets show that EMMC performs better than traditional MMC methods, especially in handling large-scale data sets.

In this paper, we propose a new online authoring tool for e-Learning system to meet the social demands for internationalized higher education. The tool includes two functions – an authoring function for creating video-based content by the instructor, and a viewing function for self-learning by students. In the authoring function, an instructor creates key markings onto the raw video stream to produce virtual video clips related to each slide. With key markings, some parts of the raw video stream can be easily skipped. The virtual video clips form an aggregated video stream that is used to synchronize with the slide presentation to create learning content. The synchronized content can be previewed immediately at the client computer prior to saving at the server. The aggregated video becomes the baseline for the viewing function. Based on aggregated video stream methodology, content editing requires only the changing of key markings without editing the raw video file. Furthermore, video and pointer synchronization is also proposed for enhancing the students' learning efficiency. In viewing function, video quality control and an adaptive video buffering method are implemented to support usage in various network environments. The total system is optimized to support cross-platform and cloud computing to break the limitation of various usages. The proposed method can provide simple authoring processes with clear user interface design for instructors, and help students utilize learning contents effectively and efficiently. In the user acceptance evaluation, most respondents agree with the usefulness, ease-of-use, and user satisfaction of the proposed system. The overall results show that the proposed authoring and viewing tools have higher user acceptance as a tool for e-Learning.

In most phrase-based statistical machine translation (SMT) systems, the translation model relies on word alignment, which serves as a constraint for the subsequent building of a phrase table. Word alignment is usually inferred by GIZA++, which implements all the IBM models and HMM model in the framework of Expectation Maximum (EM). In this paper, we present a fully Bayesian inference for word alignment. Different from the EM approach, the Bayesian inference makes use of all possible parameter values rather than estimating a single parameter value, from which we expect a more robust inference. After inferring the word alignment, current SMT systems usually train the phrase table from Viterbi word alignment, which is prone to learn incorrect phrases due to the word alignment mistakes. To overcome this drawback, a new phrase extraction method is proposed based on multiple Gibbs samples from Bayesian inference for word alignment. Empirical results show promising improvements over baselines in alignment quality as well as the translation performance.

In this paper, we proposed that an area- and speed-effective fixed-point pipelined divider be used for reducing the bit-width of a division unit to fit a mobile rendering processor. To decide the bit-width of a division unit, error analysis has been carried out in various ways. As a result, when the original bit-width was 31-bit, the proposed method reduced the bit-width to 24-bit and reduced the area by 42% with a maximum error of 0.00001%.

In this letter, we present a fast image/video super resolution framework using edge and nonlocal constraint. The proposed method has three steps. First, we improve the initial estimation using content-adaptive bilateral filtering to strengthen edge. Second, the high resolution image is estimated by using classical back projection method. Third, we use joint content-adaptive nonlocal means filtering to get the final result, and self-similarity structures are obtained by the low resolution image. Furthermore, content-adaptive filtering and fast self-similarity search strategy can effectively reduce computation complexity. The experimental results show the proposed method has good performance with low complexity and can be used for real-time environment.

I propose an acoustic model adaptation method using bases constructed through the sparse principal component analysis (SPCA) of acoustic models trained in a clean environment. I perform experiments on adaptation to a new speaker and noise. The SPCA-based method outperforms the PCA-based method in the presence of babble noise.