Curve extension is a useful function in shape modeling for cyberworlds, while a Ball B-spline Curve (BBSC) has its advantages in representing freeform tubular objects. In this paper, an extension algorithm for the BBSC with G2-continuity is investigated. We apply the extending method of B-Spline curves to the skeleton of the BBSC through generalizing a minimal strain energy method from 2D to 3D. And the initial value of the G2-continuity parameter for the skeleton is selected by minimizing the approximate energy function which is a problem with O(1) time complexity. The corresponding radius function of the control ball points is determined through applying the G2-continuity conditions for the skeleton to the scalar function. In order to ensure the radii of the control ball points are positive, we make a decision about the range of the G2-continuity parameter for the radius and then determine it by minimizing the strain energy in the affected area. Some experiments comparing our method with other methods are given. And at the same time, we present the advantage of our method in modeling flexibility from the aspects of the skeleton and radius. The results illustrate our method for extending the BBSC is effective.

This paper proposes the assignment of resource blocks (RBs) to reduce the peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) in a multi-user OFDM system. This system ranks the users according to the channel state information (CSI) for RB assignment. In our proposed technique, an RB is assigned to either the first- or second-ranked mobile station (MS) to minimize the PAPR of the OFDM signal. While this process reduces the PAPR, the throughput is also reduced because of the user diversity gain loss. A PAPR-throughput tradeoff is then established. Theoretical analyses and computer simulations confirm that when the number of MSs becomes large, the PAPR-throughput tradeoff is eased because of the minimal effect of the diversity gain loss. Therefore, significant PAPR reduction is achieved with only a slight degradation in the throughput.

Ultra-wide band (UWB) radar has a great advantage for range resolution, and is suitable for 3-dimensional (3-D) imaging sensor, such as for rescue robots or surveillance systems, where an accurate 3-dimensional measurement, impervious to optical environments, is indispensable. However, in indoor sensing situations, an available aperture size is severely limited by obstacles such as collapsed furniture or rubles. Thus, an estimated region of target image often becomes too small to identify whether it is a human body or other object. To address this issue, we previously proposed the image expansion method based on the ellipse extrapolation, where the fitting space is converted from real space to data space defined by range points to enhance the extrapolation accuracy. Although this method achieves an accurate image expansion for some cases, by exploiting the feature of the efficient imaging method as range points migration (RPM), there are still many cases, where it cannot maintain sufficient extrapolation accuracy because it only employs the single scattered component for imaging. For more accurate extrapolation, this paper extends the above image expansion method by exploiting double-scattered signals between the target and the wall in an indoor environment. The results from numerical simulation validate that the proposed method significantly expands the extrapolated region for multiple elliptical objects, compared with that obtained using only single scattered signal.

In this paper, a 1.8-V 10-bit 100,MS/s successive approximation register (SAR) analog-to-digital converter (ADC) simulated in a TSMC 0.18-$mu$m CMOS process is presented. By applying ten comparators followed by an asynchronous trigger logic, the proposed SAR ADC achieves high speed operation. Compared to the conventional SAR ADC, there is no significant delay in the digital feedback logic in this design. With the sampling rate limited only by the ten delays of the capacitor DAC settling and comparators quantization, the proposed SAR ADC achieves a peak SNDR of 61.2,dB at 100,MS/s and 80,MS/s, consuming 3.2,mW and 3.1,mW respectively.

A scalable low voltage signaling (SLVS) transmitter, with asymmetric impedance calibration, is proposed for mobile applications which require low power consumption. The voltage swing of the proposed SLVS transmitter is scalable from 40,mV to 440,mV. The proposed asymmetric impedance calibration asymmetrically controls the pull-up and pull-down drivers for the SLVS transmitter with an impedance of 50,$Omega$. This makes it possible to remove the additional regulator used to calibrate the impedance of an output driver by controlling the swing level of a pre-driver. It also maintains the common mode voltage at the center voltage level of the transmitted signal. The proposed SVLS transmitter is implemented using a 0.18-$mu $m 1-poly 6-metal CMOS process with a 1.2-V supply. The active area and power consumption of the transmitter are $250 imes 123 mu$ m$^{2}$ and 2.9,mW/Gb/s, respectively.

In our previous work, we proposed to combine ConceptNet and WordNet for Word Sense Disambiguation (WSD). The ConceptNet was automatically disambiguated through Normalized Google Distance (NGD) similarity. In this letter, we present several techniques to enhance the performance of the ConceptNet disambiguation and use this enriched semantic knowledge in WSD task. We propose to enrich both the WordNet semantic knowledge and NGD to disambiguate the concepts in ConceptNet. Furthermore, we apply the enriched semantic knowledge to improve the performance of WSD. From a number of experiments, the proposed method has been obtained enhanced results.

We propose a speaker adaptation method based on the probabilistic principal component analysis (PPCA) of acoustic models. We define a training matrix which is represented in a two-way array and decompose the training models by PPCA to construct bases. In the two-way array representation, each training model is represented as a matrix and the columns of each training matrix are treated as training vectors. We formulate the adaptation equation in the maximum a posteriori (MAP) framework using the bases and the prior.

The increasing demand of indoor location based service (LBS) has promoted the development of localization techniques. As an important alternative, fingerprinting localization technique can achieve higher localization accuracy than traditional trilateration and triangulation algorithms. However, it is computational expensive to construct the fingerprint database in the offline phase, which limits its applications. In this paper, we propose an efficient indoor positioning system that uses a new empirical propagation model, called regional propagation model (RPM), which is based on the cluster based propagation model theory. The system first collects the sparse fingerprints at some certain reference points (RPs) in the whole testing scenario. Then affinity propagation clustering algorithm operates on the sparse fingerprints to automatically divide the whole scenario into several clusters or sub-regions. The parameters of RPM are obtained in the next step and are further used to recover the entire fingerprint database. Finally, the location estimation is obtained through the weighted k-nearest neighbor algorithm (WkNN) in the online localization phase. We also theoretically analyze the localization accuracy of the proposed algorithm. The numerical results demonstrate that the proposed propagation model can predict the received signal strength (RSS) values more accurately than other models. Furthermore, experiments also show that the proposed positioning system achieves higher localization accuracy than other existing systems while cutting workload of fingerprint calibration by more than 50% in the offline phase.

Dubé and Beaudoin proposed a lossless data compression called compression via substring enumeration (CSE) in 2010. We evaluate an upper bound of the number of bits used by the CSE technique to encode any binary string from an unknown member of a known class of k-th order Markov processes. We compare the worst case maximum redundancy obtained by the CSE technique for any binary string with the least possible value of the worst case maximum redundancy obtained by the best fixed-to-variable length code that satisfies the Kraft inequality.

We propose a variant of OMP algorithm named BROMP for sparse solution. In our algorithm, the update rule of MP algorithm is employed to reduce the number of least square calculations and the refining strategy is introduced to further improve its performance. Simulations show that the proposed algorithm performs better than the OMP algorithm with significantly lower complexity.

A challenge faced by the video game industry is to develop believable and more intelligent Non-Playable Characters (NPCs). To tackle this problem a low-cost and simple approach has been proposed in this research, which is the development of a gossip virtual social network for NPCs. The network allows simple individual NPCs to communicate their knowledge amongst themselves. The communication within this social network is governed by social-psychological rules. These rules are categorized into four types: Contact, whether the NPC are within a contactable range of each other; Observation, whether the NPCs actually want to talk to each other based on their personal traits; Status, the current representation of the NPCs; and Relationships which determines the long term ties of the NPCs. Evaluations of the proposed gossip virtual social network was conducted, both through statistical analysis and a survey of real users. Highly satisfactory results have been achieved.

The next generation of information technology demands both high capacity and mobility for applications such as high speed wireless access capable of supporting broadband services. The transport of wireless and wireline signals is converging into a common telecommunication infrastructure. In this paper, we will present the Marie Curie Framework Program 7 project “Wireless and wireline service convergence in next generation optical access networks” (WISCON), which focuses on the conception and study of novel architectures for wavelength-division-multiplexing (WDM) optical multi-modulation format radio-over-fiber (RoF) systems; this is a promising solution to implement broadband seamless wireless -wireline access networks. This project successfully concluded in autumn 2013, and is being follow up by another Marie Curie project entitled “flexible edge nodes for dynamic optical interconnection of access and core networks” (FENDOI), which will be also briefly described.

There have been many matching pursuit algorithms (MPAs) which handle the sparse signal recovery problem, called compressed sensing (CS). In the MPAs, the correlation step makes a dominant computational complexity. In this paper, we propose a new fast correlation method for the MPA when we use partial Fourier sensing matrices and partial Hadamard sensing matrices which are widely used as the sensing matrix in CS. The proposed correlation method can be applied to almost all MPAs without causing any degradation of their recovery performance. Also, the proposed correlation method can reduce the computational complexity of the MPAs well even though there are restrictions depending on a used MPA and parameters.

This paper introduces a novel approach of feature description by integrating the intensity order and textures in different support regions into a compact vector. We first propose the Intensity Order Local Binary Pattern (IO-LBP) operator, which simultaneously encodes the gradient and texture information in the local neighborhood of a pixel. We divide each region of interest into segments according to the order of pixel intensities, build one histogram of IO-LBP patterns for each segment, and then concatenate all histograms to obtain a feature descriptor. Furthermore, multi support regions are adopted to enhance the distinctiveness. The proposed descriptor effectively describes a region at both local and global levels, and thus high performance is expected. Experimental results on the Oxford benchmark and images of cast shadows show that our approach is invariant to common photometric and geometric transformations, such as illumination change and image rotation, and robust to complex lighting effects caused by shadows. It achieves a comparable accuracy to that of state-of-art methods while performs considerably faster.

A social network is a useful model for identifying hidden structures and meaningful knowledge among social atoms, which have complicated interactions. In recent years, most studies have focused on the real data of the social space such as emails, tweets, and human communities. In this paper, we construct a social network from literary fiction by mapping characters to vertices and their relationship strengths to edges. The main contribution of this paper is that our model can be exploited to reveal the deep structures of fiction novels by using graph theoretic concepts, without the involvement of any manual work. Experimental evaluation showed that our model successfully classified fictional characters in terms of their importance to the plot of a novel.

Fault localization is a necessary process of locating faults in buggy programs. This paper proposes a novel approach using dynamic slicing and association analysis to improve the effectiveness of fault localization. Our approach utilizes dynamic slicing to generate a reduced candidate set to narrow the range of faults, and introduces association analysis to mine the relationship between the statements in the execution traces and the test results. In addition, we develop a prototype tool DSFL to implement our approach. Furthermore, we perform a set of empirical studies with 12 Java programs to evaluate the effectiveness of the proposed approach. The experimental results show that our approach is more effective than the compared approaches.

In this paper, we propose a superpixel based depth map generation scheme for the application to monoscopic to stereoscopic video conversion. The proposed algorithm employs four main processes to generate depth maps for all frames in the video sequences. First, the depth maps of the key frames in the input sequence are generated by superpixel merging and some user interactions. Second, the frames in the input sequences are over-segmented by Simple Linear Iterative Clustering (SLIC) or depth aided SLIC method depending on whether or not they have the depth maps. Third, each superpixel in current frame is used to match the corresponding superpixel in its previous frame. Finally, depth map is propagated with a joint bilateral filter based on the estimated matching vector of each superpixel. We show an improved performance of the proposed algorithm through experimental results.

Wireless patient monitoring is an active research area with the goal of ubiquitous health care services. This study presents a novel means of exploiting the distributed source coding (DSC) in low-complexity compression of ECG signals. We first convert the ECG data compression to an equivalent channel coding problem and exploit a linear channel code for the DSC construction. Performance is further enhanced by the use of a correlation channel that more precisely characterizes the statistical dependencies of ECG signals. Also proposed is a modified BCJR algorithm which performs symbol decoding of binary convolutional codes to better exploit the source's a priori information. Finally, a complete setup system for online ambulatory ECG monitoring via mobile cellular networks is presented. Experiments on the MIT-BIH arrhythmia database and real-time acquired ECG signals demonstrate that the proposed system outperforms other schemes in terms of encoder complexity and coding efficiency.

We consider a unified approach to the tracking analysis of adaptive filters with error and matrix data nonlinearities. Using energy-conservation arguments, we not only derive earlier results in a unified manner, but we also obtain new performance results for more general adaptive algorithms without requiring the restriction of the regression data to a particular distribution. Numerical simulations support the theoretical results.

Scheduling is a key problem in high level synthesis, as the scheduling results affect most of the important design metrics. In this paper, we propose a novel scheduling method to simultaneously optimize the leakage power of functional units with dual-Vth techniques and the number of registers under given timing and resource constraints. The mobility overlaps between operations are removed to eliminate data dependencies, and a simulated-annealing-based method is introduced to explore the mobility overlap removal solution space. Given the overlap-free mobilities, the resource usage and register usage in each control step can be accurately estimated. Meanwhile, operations are scheduled so as to optimize the leakage power of functional units with minimal number of registers. Then, a set of operations is iteratively selected, reassigned as low-Vth, and rescheduled until the resource constraints are all satisfied. Experimental results show the efficiency of the proposed algorithm.