Detailed high capacity vector maps must be compressed effectively for transmission or storage in Web GIS (geographic information system) and mobile GIS applications. In this paper, we present a polyline compression method that consists of polyline feature-based hybrid simplification and second derivative-based data compression. The polyline hybrid simplification function detects the feature points from a polyline using DP, SF, and TF algorithms, and divides the polyline into sectors using these feature points. It then simplifies the sectors using an algorithm to determine the minimum area difference among the DP, SF, and TF results. The polyline data compression method segments the second derivatives of the simplified polylines into integer and fractional parts. The integer parts are compressed using the minimum bounding box of the layer to determine the broad position of the object. The fractional parts are compressed using hierarchical precision levels. Experimental results verify that our method has higher simplification and compression efficiency than conventional methods and produces good quality compressed maps.

Smart city services are implemented using various data collected from houses and infrastructure within a city. As the volume and variety of the smart city data becomes huge, individual services have suffered from expensive computation effort and large processing time. In order to reduce the effort and time, this paper proposes a concept of Materialized View as a Service (MVaaS). Using the MVaaS, every application can easily and dynamically construct its own materialized view, in which the raw data is converted and stored in a convenient format with appropriate granularity. Thus, once the view is constructed, the application can quickly access necessary data. In this paper, we design a framework of MVaaS specifically for large-scale house log, managed in a smart-city data platform. In the framework, each application first specifies how the raw data should be filtered, grouped and aggregated. For a given data specification, MVaaS dynamically constructs a MapReduce batch program that converts the raw data into a desired view. The batch is then executed on Hadoop, and the resultant view is stored in HBase. We present case studies using house log in a real home network system. We also conduct an experimental evaluation to compare the response time between cases with and without MVaaS.

Scheduling imposes a trade-off between sum capacity and fairness among users. In some situations, fairness needs to be given the first priority. Therefore, a scheduling algorithm which can flexibly control sum capacity and fairness is desirable. In this paper, assuming the single-carrier frequency division multiple access (SC-FDMA), we propose three scheduling algorithms: modified max-map, proportional fairness (PF)-map, and max-min. The available subcarriers are grouped into a number of subcarrier-blocks each having the same number of subcarriers. The scheduling is done on a subcarrier-block by subcarrier-block basis to take advantage of the channel frequency-selectivity. The same number of non-contiguous subcarrier-blocks is assigned to selected users. The trade-off between sum capacity and fairness is controlled by changing the number of simultaneously scheduling users per time-slot. Capacity, fairness, and peak-to-average power ratio (PAPR) when using the proposed scheduling algorithms are examined by computer simulation.

The recently suggested regular-type triangular quadrature amplitude modulation (TQAM) provides considerable power gain over square quadrature amplitude modulation (SQAM) at the expense of a slight increase in detection complexity. However, the power gain of the TQAM is limited due to the constraint that signal points should be regularly located at the vertexes of contiguous equilateral triangles. In this paper, we investigate two irregular (optimum and suboptimum) TQAMs where signal points are irregularly distributed while preserving the equilateral triangular lattice, and calculate achievable power gains of the proposed constellations. We also address optimum and suboptimum bit stream mapping methods and suggest a simple and optimum detection method for the constellations to be meaningful in practical implementation, and present analytical and simulation results. The proposed constellations can provide the asymptotic power gains of 0.825dB and 0.245dB over SQAM and regular TQAM, respectively.

In this study, we address the problem of salient region detection. Recently, saliency detection with contrast based approaches has shown to give promising results. However, different individual features exhibit different performance. In this paper, we show that the combination of color uniqueness and color spatial distribution is an effective way to detect saliency. A Color Adaptive Thresholding Watershed Fusion Segmentation (CAT-WFS) method is first given to retain boundary information and delete unnecessary details. Based on the segmentation, color uniqueness and color spatial distribution are defined separately. The color uniqueness denotes the color rareness of salient object, while the color spatial distribution represents the color attribute of the background. Aiming at highlighting the salient object and downplaying the background, we combine the two characters to generate the final saliency map. Experimental results demonstrate that the proposed algorithm outperforms existing salient object detection methods.

The development of 3D High Efficiency Video Coding (3D-HEVC) has resulted in a growing interest in the compression of depth-maps. To achieve better intra prediction performance, the Depth Modeling Mode (DMM) technique is employed as an intra prediction technique for depth-maps. However, the complexity and computation load have dramatically increased with the application of DMM. Therefore, in view of the limited colors in depth-maps, this paper presents a novel fast intra coding scheme based on Base Colors and Index Map (BCIM) to reduce the complexity of DMM effectively. Furthermore, the index map is remapped, and the Base Colors are coded by predictive coding in BCIM to improve compression efficiency. Compared with the intra prediction coding in DMM, the experimental results illustrate that the proposed scheme provides a decrease of approximately 51.2% in the intra prediction time. Meanwhile, the BD-rate increase is only 0.83% for the virtual intermediate views generated by Depth-Image-Based Rendering.

Since substitution boxes (S-boxes) are the only nonlinear portion of most block ciphers, the design of cryptographically strong and low-complexity S-boxes is of great importance in cryptosystems. In this paper, a new kind of S-boxes obtained by iterating a discretized piecewise linear map is proposed. The S-box has an implementation efficiency both in software and hardware. Moreover, the results of performance test show that the proposed S-box has good cryptographic properties.

In the process of production design, engineers usually find it is difficult to seek and reuse others' empirical knowledge which is in the forms of lesson-learned documents. This study proposed a novel approach, which uses a semantic-based topic knowledge map system (STKMS) to support timely and precisely lesson-learned documents finding and reusing. The architecture of STKMS is designed, which has five major functional modules: lesson-learned documents pre-processing, topic extraction, topic relation computation, topic weights computation, and topic knowledge map generation modules. Then STKMS implementation is briefly introduced. We have conducted two sets of experiments to evaluate quality of knowledge map and the performance of utilizing STKMS in outfitting design of a ship-building company. The first experiment shows that knowledge maps generated by STKMS are accepted by domain experts from the evaluation since precision and recall are high. The second experiment shows that STKMS-based group outperforms browse-based group in both learning score and satisfaction level, which are two measurements of performance of utilizing STKMS. The promising results confirm the feasibility of STKMS in helping engineers to find needed lesson-learned documents and reuse related knowledge easily and precisely.

Most of the recent classification methods require tuning of the hyper-parameters, such as the kernel function parameter and the regularization parameter. Cross-validation or the leave-one-out method is often used for the tuning, however their computational costs are much higher than that of obtaining a classifier. Quadratically constrained maximum a posteriori (QCMAP) classifiers, which are based on the Bayes classification rule, do not have the regularization parameter, and exhibit higher classification accuracy than support vector machine (SVM). In this paper, we propose a multiple kernel learning (MKL) for QCMAP to tune the kernel parameter automatically and improve the classification performance. By introducing MKL, QCMAP has no parameter to be tuned. Experiments show that the proposed classifier has comparable or higher classification performance than conventional MKL classifiers.

To achieve high quality output speech synthesis systems, data-driven grapheme-to-phoneme (G2P) conversion is usually used to generate the phonetic transcription of out-of-vocabulary (OOV) words. To improve the performance of G2P conversion, this paper deals with the problem of conflicting phonemes, where an input grapheme can, in the same context, produce many possible output phonemes at the same time. To this end, we propose a two-stage neural network-based approach that converts the input text to phoneme sequences in the first stage and then predicts each output phoneme in the second stage using the phonemic information obtained. The first-stage neural network is fundamentally implemented as a many-to-many mapping model for automatic conversion of word to phoneme sequences, while the second stage uses a combination of the obtained phoneme sequences to predict the output phoneme corresponding to each input grapheme in a given word. We evaluate the performance of this approach using the American English words-based pronunciation dictionary known as the auto-aligned CMUDict corpus[1]. In terms of phoneme and word accuracy of the OOV words, on comparison with several proposed baseline approaches, the evaluation results show that our proposed approach improves on the previous one-stage neural network-based approach for G2P conversion. The results of comparison with another existing approach indicate that it provides higher phoneme accuracy but lower word accuracy on a general dataset, and slightly higher phoneme and word accuracy on a selection of words consisting of more than one phoneme conflicts.

MapReduce is commonly used as a parallel massive data processing model. When deploying it as a service over the open systems, the computational integrity of the participants is becoming an important issue due to the untrustworthy workers. Current duplication-based solutions can effectively solve non-collusive attacks, yet most of them require a centralized worker to re-compute additional sampled tasks to defend collusive attacks, which makes the worker a bottleneck. In this paper, we try to explore a trusted worker scheduling framework, named VAWS, to detect collusive attackers and assure the integrity of data processing without extra re-computation. Based on the historical results of verification, we construct an Integrity Attestation Graph (IAG) in VAWS to identify malicious mappers and remove them from the framework. To further improve the efficiency of identification, a verification-couple selection method with the IAG guidance is introduced to detect the potential accomplices of the confirmed malicious worker. We have proven the effectiveness of our proposed method on the improvement of system performance in theoretical analysis. Intensive experiments show the accuracy of VAWS is over 97% and the overhead of computation is closed to the ideal value of 2 with the increasing of the number of map tasks in our scheme.

This letter presents new methods for transforming perfect ternary sequences into perfect 8-QAM+ sequences. Firstly, based on perfect ternary sequences with even period, two mappings which can map two ternary variables to an 8-QAM+ symbol are employed for constructing new perfect 8-QAM+ sequences. In this case, the proposed construction is a generalization of the existing one. Then based on perfect ternary sequence with odd period, perfect 8-QAM sequences are generated. Compared with perfect 8-QAM+ sequences, the resultant sequences have no energy loss.

In this paper, we focus on the multi-way relaying channel where K users wish to exchange information with each other within two phases. Precoding at each user and the relay is carefully constructed to ensure that the signals from the same user pair are grouped together and cross-pair interference can be cancelled. Reliable detection is challenging at the relay since the observation constellation is no longer one of the regular ones, due to the fact that a relay observation is the superposition of the messages from one of the $rac{1}{2}K(K-1)$ user pairs. When the trellis coded modulation is used at each node, a simple constellation mapping function and a reduced-states decoding scheme can be applied at the relay, which result in much lower complexity. Furthermore, a modified version of the decoding method is also developed which is called the re-encoding-avoidance scheme at the relay. Monte-Carlo simulation results are provided to demonstrate the performance of the proposed scheme.

This paper presents an implementation of a digital chaotic signal generator based on compound one-dimensional sinusoidal maps. The proposed chaotic map not only offers high chaoticity measured from a positive lyapunov exponent but also provides diverse bifurcation structures with robust chaos over most regions of parameter spaces. Implementation on FPGA realizes small number of components and offers a highly random chaotic sequence with no autocorrelation. The proposed chaotic signal generator offers a potential alternative in random test pattern generation or in secured data communication applications.

AC conversion has a huge variety of applications and so there are many ongoing research topics as in every type of power electronic conversion. New semiconductors allow the increase of the switching frequency fact that brings a whole new prospective improvement in converter's operation. Many other possible nonlinear operation regimes, including period doubling and chaotic oscillations, appear besides the conventional steady state operation. In this work, a nonlinear discrete-time model of an AC/AC buck type converter is proposed. A discrete time iterative map is derived to highlight the sensitive switching dynamics. The model is able to observe fast scale phenomena and short transient effects. It offers more information compared to other methods such as the averaging ones. According to Electro-Magnetic Compatibility (EMC) regulations, low wide-band noise is more acceptable than the high narrow-band, therefore the goal of this work is to spread the harmonic noise into a wide frequency spectrum which has lower amplitudes compared to the conventional comb-like spectrum with distinctive amplitudes at switching frequency multiples. Through the numerical and experimental consideration the converter can operate in a chaotic motion and the advantages of the performance improvement are also discussed.

This paper presents a novel acoustic modeling technique of large vocabulary automatic speech recognition for under-resourced languages by leveraging well-trained acoustic models of other languages (called source languages). The idea is to use source language acoustic model to score the acoustic features of the target language, and then map these scores to the posteriors of the target phones using a classifier. The target phone posteriors are then used for decoding in the usual way of hybrid acoustic modeling. The motivation of such a strategy is that human languages usually share similar phone sets and hence it may be easier to predict the target phone posteriors from the scores generated by source language acoustic models than to train from scratch an under-resourced language acoustic model. The proposed method is evaluated using on the Aurora-4 task with less than 1 hour of training data. Two types of source language acoustic models are considered, i.e. hybrid HMM/MLP and conventional HMM/GMM models. In addition, we also use triphone tied states in the mapping. Our experimental results show that by leveraging well trained Malay and Hungarian acoustic models, we achieved 9.0% word error rate (WER) given 55 minutes of English training data. This is close to the WER of 7.9% obtained by using the full 15 hours of training data and much better than the WER of 14.4% obtained by conventional acoustic modeling techniques with the same 55 minutes of training data.

Scalable Video Coding (SVC) is an extension of H.264/AVC, aiming to provide the ability to adapt to heterogeneous networks or requirements. It offers great flexibility for bitstream adaptation in multi-point applications such as videoconferencing. However, transcoding between SVC and AVC is necessary due to the existence of legacy AVC-based systems. The straightforward re-encoding method requires great computational cost, and delay-sensitive applications like videoconferencing require much faster transcoding scheme. This paper proposes a 3-stage fast SVC-to-AVC transcoder with medium-grain quality scalability (MGS) for videoconferencing applications. Hierarchical-P structured SVC bitstream is transcoded into IPPP structured AVC bitstream with multiple reference frames. In the first stage, mode decision is accelerated by proposed SVC-to-AVC mode mapping scheme. In the second stage, INTER motion estimation is accelerated by an optimized motion vector (MV) conjunction method to predict the MV with a reduced search range. In the last stage, hadamard-based all zero block (AZB) detection is utilized for early termination. Simulation results show that proposed transcoder achieves very similar coding efficiency to the optimal result, but with averagely 89.6% computational time saving.

Computation integrity is difficult to verify when mass data processing is outsourced. Current integrity protection mechanisms and policies verify results generated by participating nodes within a computing environment of service providers (SP), which cannot prevent the subjective cheating of SPs. This paper provides an analysis and modeling of computation integrity for mass data processing services. A third-party sampling-result verification method, named TS-TRV, is proposed to prevent lazy cheating by SPs. TS-TRV is a general solution of verification on the intermediate results of common MapReduce jobs, and it utilizes the powerful computing capability of SPs to support verification computing, thus lessening the computing and transmission burdens of the verifier. Theoretical analysis indicates that TS-TRV is effective on detecting the incorrect results with no false positivity and almost no false negativity, while ensuring the authenticity of sampling. Intensive experiments show that the cheating detection rate of TS-TRV achieves over 99% with only a few samples needed, the computation overhead is mainly on the SP, while the network transmission overhead of TS-TRV is only O(log N).

With the increasing demand for geographic information and position information, the geographic information system (GIS) has come to be widely used in city planning, utilities management, natural resource environments, land surveying, etc. While most GIS maps use vector data to represent geographic information more easily and in greater detail, a GIS vector map can be easily copied, edited, and illegally distributed, like most digital data. This paper presents an invisible, blind, secure, and robust watermarking method that provides copyright protection of GIS vector digital maps by means of arc length distribution. In our method, we calculate the arc lengths of all the polylines/polygons in a map and cluster these arc lengths into a number of groups. We then embed a watermark bit by changing the arc length distribution of a suitable group. For greater security and robustness, we use a pseudo-random number sequence for processing the watermark and embed the watermark multiple times in all maps. Experimental results verify that our method has good invisibility, security, and robustness against various geometric attacks and that the original map is not needed in the watermark extraction process.

In this paper, we propose a new buffer map notification scheme for Peer-to-Peer Video-on-Demand systems (P2P VoDs) which support VCR operations such as fast-forward, fast-backward, and seek. To enhance the fluidity of such VCR operations, we need to refine the size of each piece as small as possible. However, such a refinement significantly degrades the performance of buffer map notification schemes with respect to the overhead, piece availability and the efficiency of resource utilizations. The basic idea behind our proposed scheme is to use a piece-based buffer map with a segment-based buffer map in a complementary manner. The result of simulations indicates that the proposed scheme certainly increases the accuracy of the information on the piece availability in the neighborhood with a sufficiently low cost, which reduces the intermittent waiting time of each peer by more than 40% even under a situation in which 50% of peers conduct the fast-forward operation over a range of 30% of the entire video.