In this letter, a new approximation of log-likelihood ratio (LLR) for soft input channel decoding is proposed. Conventional simplified LLR using log-sum approximation can degrade the performance of bit interleaved coded modulation (BICM) systems employing hybrid automatic repeat request (HARQ) at low SNR. The proposed LLR performs as well as the exact LLR, and at the same time, requires only a small number of elementary operations.

In this paper, we propose a novel method based on the second-order conditional maximum a posteriori (CMAP) to improve the performance of the global soft decision in speech enhancement. The conventional global soft decision scheme is found through investigation to have a disadvantage in that the global speech absence probability (GSAP) in that scheme is adjusted by a fixed parameter, which could be a restrictive assumption in the consecutive occurrences of speech frames. To address this problem, we devise a method to incorporate the second-order CMAP in determining the GSAP, which is clearly different from the previous approach in that not only current observation but also the speech activity decisions of the previous two frames are exploited. Performances of the proposed method are evaluated by a number of tests in various environments and show better results than previous work.

Recently, Hu et al. have suggested a fully secure hierarchical identity-based encryption (HIBE) scheme that achieves constant size ciphertext and tight security reduction. Their construction was based on Gentry's IBE scheme that supports their security proof. In this paper, we show that their security proof is incorrect. We point out the difference between Gentry's proof and that of Hu et al., and we show that the security of Hu et al.'s HIBE scheme cannot be reduced to their claimed complexity assumption.

In this paper, we propose an inconsistency resolution method based on a new concept, insecure backtracking role mapping. By analyzing the role graph, we prove that the root cause of security inconsistency in distributed interoperation is the existence of insecure backtracking role mapping. We propose a novel and efficient algorithm to detect the inconsistency via finding all of the insecure backtracking role mappings. Our detection algorithm will not only report the existence of inconsistency, but also generate the inconsistency information for the resolution. We reduce the inconsistency resolution problem to the known Minimum-Cut problem, and based on the results generated by our detection algorithm we propose an inconsistency resolution algorithm which could guarantee the security of distributed interoperation. We demonstrate the effectiveness of our approach through simulated tests and a case study.

A generalized Gray map for codes over the ring Fq[u]/ is introduced, where q=pm is a prime power. It is shown that the generalized Gray image of a linear length-N (1-ut)-cyclic code over Fq[u]/ is a distance-invariant linear length-qtN quasi-cyclic code of index qt/p over Fq. It turns out that if (N,p)=1 then every linear code over Fq that is the generalized Gray image of a length-N cyclic code over Fq[u]/, is also equivalent to a linear length-qtN quasi-cyclic code of index qt/p over Fq. The relationship between linear length-pN cyclic codes with (N,p)=1 over Fp and linear length-N cyclic codes over Fp+uFp is explicitly determined.

In recent years, digital maps have been used in a variety of scenarios, including car navigation systems and map information services over the Internet. These digital maps are formed by multiple layers of maps of different scales; the map data most suitable for the specific situation are used. Currently, the production of map data of different scales is done by hand due to constraints related to processing time and accuracy. We conducted research concerning technologies for automatic generation of simplified map data from detailed map data. In the present paper, the authors propose the following: (1) a method to transform data related to streets, rivers, etc. containing widths into line data, (2) a method to eliminate the component points of the data, and (3) a method to eliminate data that lie below a certain threshold. In addition, in order to evaluate the proposed method, a user survey was conducted; in this survey we compared maps generated using the proposed method with the commercially available maps. From the viewpoint of the amount of data reduction and processing time, and on the basis of the results of the survey, we confirmed the effectiveness of the automatic generation of simplified maps using the proposed methods.

Digital fingerprinting is used to trace back illegal users, where unique ID known as digital fingerprints is embedded into a content before distribution. On the generation of such fingerprints, one of the important properties is collusion-resistance. Binary codes for fingerprinting with a code length of theoretically minimum order were proposed by Tardos, and the related works mainly focused on the reduction of the code length were presented. In this paper, we present a concrete and systematic construction of the Tardos's fingerprinting code using a chaotic map. Using a statistical model for correlation scores, the actual number of true-positive and false-positive detection is measured. The collusion-resistance of the generated fingerprinting codes is evaluated by a computer simulation.

This paper presents a novel approach for robot localization using landmark maps. With recent progress in SLAM researches, it has become crucial for a robot to obtain and use large-size maps that are incrementally built by other mapper robots. Our localization approach successfully works with such incremental and large-size maps. In literature, RANSAC map-matching has been a promising approach for large-size maps. We extend the RANSAC map-matching so as to deal with incremental maps. We combine the incremental RANSAC with an incremental LSH database and develop a hybrid of the position-based and the appearance-based approaches. A series of experiments using radish dataset show promising results.

In this paper we present a construction method for quaternary sequences from a binary sequence of even period, which preserves the period and autocorrelation of the given binary sequence. By applying the method to the binary sequences with three-valued autocorrelation, we construct new quaternary sequences with three-valued autocorrelation, which are balanced or almost balanced. In particular, we construct new balanced quaternary sequences whose autocorrelations are three-valued and have out-of-phase magnitude 2, when their periods are N=pm-1 and N≡ 2 (mod 4) for any odd prime p and any odd integer m. Their out-of-phase autocorrelation magnitude is the known optimal value for N≠ 2,4,8, and 16.

In order to reduce the iterative decoding delay of convolutional turbo codes, this paper presents a concurrent decoding algorithm for the hardware implementation of turbo convolutional decoders. Different than a general turbo code, the hardware turbo decoder based on the proposed algorithm can update the priori information of message for each component code in a bit-by-bit manner as soon as it is generated by the other component code. The two component codes in a turbo code can thus be decoded concurrently, by using a single MAP decoder, subsequently reducing the decoding latency by approximately half while maintaining the bit error rate performance and a comparable hardware complexity, as a general turbo decoder.

Recent technology mappers for LUT based FPGAs employ cut enumeration. Although many cuts are often needed to find a good network, enumerating all the cuts with large size consumes a lot of run-time. Existing algorithms employ the bottom-up merging which calculates Cartesian products of the fanins' cuts for each node. The number of cuts is much smaller than the size of the Cartesian products in most cases. Thus, the existing algorithms are inefficient. Furthermore, the number of cuts exponentially increases with the size of cuts, that makes the run-time much longer. Several algorithms to enumerate not all the cuts but partial cuts have been presented, but they tend to disturb the quality of networks. This paper presents two algorithms to enumerate cuts; an exhaustive enumeration and a partial enumeration. Both of them are efficient because they do not employ the bottom-up merging. The partial enumeration reduces the number of enumerated cuts with a guarantee that a depth-minimum network can be constructed. The experimental results show that the exhaustive enumeration runs about 5 and 13 times faster than the existing bottom-up algorithm for K=8, 9 respectively, while keeping the same results. On the other hand, the partial enumeration runs about 9 and 29 times faster than the existing algorithm for K = 8, 9, respectively. The average area of networks derived by the sets of cuts enumerated by the partial enumeration is only 4% larger than that derived with using all the cuts, and the depth is the same.

The key problem of object-based attention is the definition of objects, while contour grouping methods aim at detecting the complete boundaries of objects in images. In this paper, we develop a new contour grouping method which shows several characteristics. First, it is guided by the global saliency information. By detecting multiple boundaries in a hierarchical way, we actually construct an object-based attention model. Second, it is optimized by the grouping cost, which is decided both by Gestalt cues of directed tangents and by region saliency. Third, it gives a new definition of Gestalt cues for tangents which includes image information as well as tangent information. In this way, we can improve the robustness of our model against noise. Experiment results are shown in this paper, with a comparison against other grouping model and space-based attention model.

In this paper, we present a Double-Anchoring Based Tone Mapping (DABTM) algorithm for displaying high dynamic range (HDR) images. First, two anchoring values are obtained using the double-anchoring theory. Second, we use the two values to formulate the compressing operator, which can achieve the aim of tone mapping directly. A new method based on accelerated K-means for the decomposition of HDR images into groups (frameworks) is proposed. Most importantly, a group of piecewise-overlap linear functions is put forward to define the belongingness of pixels to their locating frameworks. Experiments show that our algorithm is capable of achieving dynamic range compression, while preserving fine details and avoiding common artifacts such as gradient reversals, halos, or loss of local contrast.

A 4+12+16 amplitude phase shift keying (APSK) modulation outperforms other 32-APSK modulations such as rectangular or cross 32-quadrature amplitude modulations (QAMs) which have a high peak to average power ratio that causes non-negligible AM/AM and AM/PM distortions when the signal is amplified by a high-power amplifier (HPA). This modulation scheme has therefore been recommended as a standard in the digital video broadcasting-satellite2 (DVB-S2) system. In this letter, we present a new bits-to-symbol mapping with a better bit error rate (BER) for a 4+12+16 APSK signal in a nonlinear satellite channel.

Quantification of the hip cartilages is clinically important. In this study, we propose an automatic technique for segmentation and visualization of the acetabular and femoral head cartilages based on clinically obtained multi-slice T1-weighted MR data and a hybrid approach. We follow a knowledge based approach by employing several features such as the anatomical shapes of the hip femoral and acetabular cartilages and corresponding image intensities. We estimate the center of the femoral head by a Hough transform and then automatically select the volume of interest. We then automatically segment the hip bones by a self-adaptive vector quantization technique. Next, we localize the articular central line by a modified canny edge detector based on the first and second derivative filters along the radial lines originated from the femoral head center and anatomical constraint. We then roughly segment the acetabular and femoral head cartilages using derivative images obtained in the previous step and a top-hat filter. Final masks of the acetabular and femoral head cartilages are automatically performed by employing the rough results, the estimated articular center line and the anatomical knowledge. Next, we generate a thickness map for each cartilage in the radial direction based on a Euclidian distance. Three dimensional pelvic bones, acetabular and femoral cartilages and corresponding thicknesses are overlaid and visualized. The techniques have been implemented in C++ and MATLAB environment. We have evaluated and clarified the usefulness of the proposed techniques in the presence of 40 clinical hips multi-slice MR images.

Let us introduce n ( ≥ 2) mappings fi (i=1,,n ≡ 0) defined on reflexive real Banach spaces Xi-1 and let fi:Xi-1 → Yi be completely continuous on bounded convex closed subsets Xi-1(0) ⊂ Xi-1. Moreover, let us introduce n set-valued mappings Fi : Xi-1 Yi → Fc(Xi) (the family of all non-empty compact subsets of Xi), (i=1,,n ≡ 0). Here, we have a fixed point theorem in weak topology on the successively recurrent system of set-valued mapping equations:xi ∈ Fi(xi-1, fi(xi-1)), (i=1,,n ≡ 0). This theorem can be applied immediately to analysis of the availability of system of circular networks of channels undergone by uncertain fluctuations and to evaluation of the tolerability of behaviors of those systems.

In the modern day, MPEG-4 image compression technique have been commonly applied in various indoor wireless communication systems. The efficient system design mostly relies on the joint source channel coding algorithms, which aim to reduce the complexity of channel coding process, while maintaining the quality of the receiving images. In this paper, we design the MAP source-controlled channel decoders with both random and semirandom interleavers for Rician slow flat block-fading channels. The MAP-Viterbi decoder employs the residual redundancy from zerotree symbol sequences of MPEG-4 HFS packets. The interleaving processes are done after the overall channel coding process to combat the block-fading effects. The computer simulations summarize the system performance in terms of average WER and PSNR (dB). With the interleavers, the significant improvement in PSNR of about 15-17 dB is obtained for both ML and MAP decoding. Also in many cases, we obtain more improvement of about 0.2-0.4 dB for using MAP decoding with the interleavers.

Self-Organizing Map (SOM) is a powerful tool for the exploratory of clustering methods. Clustering is the most important task in unsupervised learning and clustering validity is a major issue in cluster analysis. In this paper, a new clustering validity index is proposed to generate the clustering result of a two-level SOM. This is performed by using the separation rate of inter-cluster, the relative density of inter-cluster, and the cohesion rate of intra-cluster. The clustering validity index is proposed to find the optimal numbers of clusters and determine which two neighboring clusters can be merged in a hierarchical clustering of a two-level SOM. Experiments show that, the proposed algorithm is able to cluster data more accurately than the classical clustering algorithms which is based on a two-level SOM and is better able to find an optimal number of clusters by maximizing the clustering validity index.

A quaternary sequence is constructed by Gray mapping of a binary sequence with even period and its shift. The autocorrelation of the new quaternary sequence is the same as that of the binary sequence employed. Quaternary sequences with the maximum autocorrelation 2 can be obtained by the construction for period N≡ 2 ( mod 4).

This paper presents an efficient algorithm for large-scale multi-system learning task. The proposed architecture, referred to as the 'RBF×SOM', is based on the SOM2, that is, a'SOM of SOMs'. As is the case in the modular network SOM (mnSOM) with multilayer perceptron modules (MLP-mnSOM), the aim of the RBF×SOM is to organize a continuous map of nonlinear functions representing multi-class input-output relations of the given datasets. By adopting the algorithm for the SOM2, the RBF×SOM generates a map much faster than the original mnSOM, and without the local minima problem. In addition, the RBF×SOM can be applied to more difficult cases, that were not easily dealt with by the MLP-mnSOM. Thus, the RBF×SOM can deal with cases in which the probability density of the inputs is dependent on the classes. This tends to happen more often as the input dimension increases. The RBF×SOM therefore, overcomes many of the problems inherent in the MLP-mnSOM, and this is crucial for application to large scale tasks. Simulation results with artificial datasets and a meteorological dataset confirm the performance of the RBF×SOM.