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.

A new stock index trend analysis approach is proposed in this paper, which is based on a newly developed signal decomposition approach - adaptive Fourier decomposition (AFD). AFD can effectively extract the signal's primary trend, which specifically suits the Dow Theory based technique analysis. The proposed approach integrates two different kinds of forecasting approaches, including the Dow theory the RBF neural network. Effectiveness of the proposed approach is assessed through comparison with the direct RBF neural network approach. The result is proved to be promising.

A smoothing method for minimum phone error linear regression (MPELR) is proposed in this paper. We show that the objective function for minimum phone error (MPE) can be combined with a prior mean distribution. When the prior mean distribution is based on maximum likelihood (ML) estimates, the proposed method is the same as the previous smoothing technique for MPELR. Instead of ML estimates, maximum a posteriori (MAP) parameter estimate is used to define the mode of prior mean distribution to improve the performance of MPELR. Experiments on a large vocabulary speech recognition task show that the proposed method can obtain 8.4% relative reduction in word error rate when the amount of data is limited, while retaining the same asymptotic performance as conventional MPELR. When compared with discriminative maximum a posteriori linear regression (DMAPLR), the proposed method shows improvement except for the case of limited adaptation data for supervised adaptation.

This paper proposes a so called quasi-linear support vector machine (SVM), which is an SVM with a composite quasi-linear kernel. In the quasi-linear SVM model, the nonlinear separation hyperplane is approximated by multiple local linear models with interpolation. Instead of building multiple local SVM models separately, the quasi-linear SVM realizes the multi local linear model approach in the kernel level. That is, it is built exactly in the same way as a single SVM model, by composing a quasi-linear kernel. A guided partitioning method is proposed to obtain the local partitions for the composition of quasi-linear kernel function. Experiment results on artificial data and benchmark datasets show that the proposed method is effective and improves classification performances.

ID-based authenticated key exchange (ID-AKE) is a cryptographic tool to establish a common session key between parties with authentication based on their IDs. If IDs contain some hierarchical structure such as an e-mail address, hierarchical ID-AKE (HID-AKE) is especially suitable because of scalability. However, most of existing HID-AKE schemes do not satisfy advanced security properties such as forward secrecy, and the only known strongly secure HID-AKE scheme is inefficient. In this paper, we propose a new HID-AKE scheme which achieves both strong security and efficiency. We prove that our scheme is eCK-secure (which ensures maximal-exposure-resilience including forward secrecy) without random oracles, while existing schemes is proved in the random oracle model. Moreover, the number of messages and pairing operations are independent of the hierarchy depth; that is, really scalable and practical for a large-system.

One of the most important problems in web services application is the integration of different existing services into a new composite service. Existing work has the following disadvantages: (i) developers are often required to provide a composite service model first and perform formal verifications to check whether the model is correct. This makes the synthesis process of composite services semi-automatic, complex and inefficient; (ii) there is no assurance that composite services synthesized by using the fully-automatic approaches are correct; (iii) some approaches only handle simple composition problems where existing services are atomic. To address these problems, we propose a correct assurance approach for automatically synthesizing composite services based on finite state machine model. The syntax and semantics of the requirement model specifying composition requirements is also proposed. Given a set of abstract BPEL descriptions of existing services, and a composition requirement, our approach automatically generate the BPEL implementation of the composite service. Compared with existing approaches, the composite service generated by utilizing our proposed approach is guaranteed to be correct and does not require any formal verification. The correctness of our approach is proved. Moreover, the case analysis indicates that our approach is feasible and effective.

This letter presents a new entropy measure for electroencephalograms (EEGs), which reflects the underlying dynamics of EEG over multiple time scales. The motivation behind this study is that neurological signals such as EEG possess distinct dynamics over different spectral modes. To deal with the nonlinear and nonstationary nature of EEG, the recently developed empirical mode decomposition (EMD) is incorporated, allowing an EEG to be decomposed into its inherent spectral components, referred to as intrinsic mode functions (IMFs). By calculating Shannon entropy of IMFs in a time-dependent manner and summing them over adaptive multiple scales, the result is an adaptive subscale entropy measure of EEG. Simulation and experimental results show that the proposed entropy properly reveals the dynamical changes over multiple scales.

We consider the problem of learning a classifier using only positive and unlabeled samples. In this setting, it is known that a classifier can be successfully learned if the class prior is available. However, in practice, the class prior is unknown and thus must be estimated from data. In this paper, we propose a new method to estimate the class prior by partially matching the class-conditional density of the positive class to the input density. By performing this partial matching in terms of the Pearson divergence, which we estimate directly without density estimation via lower-bound maximization, we can obtain an analytical estimator of the class prior. We further show that an existing class prior estimation method can also be interpreted as performing partial matching under the Pearson divergence, but in an indirect manner. The superiority of our direct class prior estimation method is illustrated on several benchmark datasets.

Developers often face difficulties while using APIs. API usage patterns can aid them in using APIs efficiently, which are extracted from source code stored in software repositories. Previous approaches have mined repositories to extract API usage patterns by simply applying data mining techniques to the collection of method invocations of API objects. In these approaches, respective functional roles of invoked methods within API objects are ignored. The functional role represents what type of purpose each method actually achieves, and a method has a specific predefined order of invocation in accordance with its role. Therefore, the simple application of conventional mining techniques fails to produce API usage patterns that are helpful for code completion. This paper proposes an improved approach that extracts API usage patterns at a higher abstraction level rather than directly mining the actual method invocations. It embraces a multilevel sequential mining technique and uses categorization of method invocations based on their functional roles. We have implemented a mining tool and an extended Eclipse's code completion facility with extracted API usage patterns. Evaluation results of this tool show that our approach improves existing code completion.

We have succeeded in highly selective growth and positioning of Si- and SiGe-quantum-dots (QDs) on SiO2 patterns by controlling the reactive area, whose surface is terminated with OH bonds for Si nucleation in low-pressure chemical vapor deposition (LPCVD). The selective growth of QDs on thermally grown SiO2 line-patterns was demonstrated in LPCVD of SiH4 and GeH4 just after Si nucleation by controlling the early stages of Si2H6-LPCVD, which indicates effectively enhanced initial nucleation on OH-terminated SiO2 surface and suppression of the nucleation and growth of dots on as-grown SiO2 surface during Si2H6-LPCVD prior to SiH4-LPCVD.

In this paper, we present a high frame rate (HFR) vision system that can automatically control its exposure time by executing brightness histogram-based image processing in real time at a high frame rate. Our aim is to obtain high-quality HFR images for robust image processing of high-speed phenomena even under dynamically changing illumination, such as lamps flickering at 100 Hz, corresponding to an AC power supply at 50 / 60 Hz. Our vision system can simultaneously calculate a 256-bin brightness histogram for an 8-bit gray image of 512×512 pixels at 2000 fps by implementing a brightness histogram calculation circuit module as parallel hardware logic on an FPGA-based high-speed vision platform. Based on the HFR brightness histogram calculation, our method realizes automatic exposure (AE) control of 512×512 images at 2000 fps using our proposed AE algorithm. The proposed AE algorithm can maximize the number of pixels in the effective range of the brightness histogram, thus excluding much darker and brighter pixels, to improve the dynamic range of the captured image without over- and under-exposure. The effectiveness of our HFR system with AE control is evaluated according to experimental results for several scenes with illumination flickering at 100 Hz, which is too fast for the human eye to see.

This letter investigates the relationship between antenna position and data communication performance in a magnetic resonance wireless power transfer (MRWPT) system. In MRWPT information such as the types of equipments, the required amount of electrical power, or the timing of power transfer should be exchanged. It is assumed here that power transfer coils in the MRWPT system are employed as antennas for data communication. The frequency characteristics of the antennas change due to coil displacements. The power transfer coils are modeled as a band pass filter (BPF) and the frequency characteristics of the filter are presented in this letter. The characteristics of the filter are derived through circuit simulation and resulting data communication performance is evaluated. Numerical results obtained through computer simulation show that the bit error late (BER) performance can be improved by controlling the center frequency of the communication link.

In this paper, we propose a message passing decoding algorithm which lowers decoding error rates in the error floor regions for non-binary low-density parity-check (LDPC) codes transmitted over the binary erasure channel (BEC) and the memoryless binary-input output-symmetric (MBIOS) channels. In the case for the BEC, this decoding algorithm is a combination with belief propagation (BP) decoding and maximum a posteriori (MAP) decoding on zigzag cycles, which cause decoding errors in the error floor region. We show that MAP decoding on the zigzag cycles is realized by means of a message passing algorithm. Moreover, we extend this decoding algorithm to the MBIOS channels. Simulation results demonstrate that the decoding error rates in the error floor regions by the proposed decoding algorithm are lower than those by the BP decoder.

An efficient beamforming algorithm for large-scale phased arrays with lossy digital phase shifters is presented. This problem, which arises in microwave power transmission from solar power satellites, is to maximize the array gain in a desired direction with the gain loss of the phase shifters taken into account. In this paper the problem is first formulated as a discrete optimization problem, which is then decomposed into element-wise subproblems by the real rotation theorem. Based on this approach, a polynomial-time algorithm to solve the problem numerically is constructed and its effectiveness is verified by numerical simulations.

The availability of more and more Web services provides great varieties for users to design service processes. However, there are situations that services or service processes cannot meet users' requirements in functional QoS dimensions (e.g., translation quality in a machine translation service). In those cases, composing Web services and human tasks is expected to be a possible alternative solution. However, analysis of such practical efforts were rarely reported in previous researches, most of which focus on the technology of embedding human tasks in software environments. Therefore, this study aims at analyzing the effects of composing Web services and human activities using a case study in the domain of language service with large scale experiments. From the experiments and analysis, we find out that (1) service implementation variety can be greatly increased by composing Web services and human activities for satisfying users' QoS requirements; (2) functional QoS of a Web service can be significantly improved by inducing human activities with limited cost and execution time provided certain quality of human activities; and (3) multiple QoS attributes of a composite service are affected in different ways with different quality of human activities.

This letter deals with angular position classification using the synthesized active sonar returns from targets. For the synthesis of active sonar returns, we synthesized active sonar returns based on ray tracing algorithm for 3D highlight models. Then, a fractional Fourier transform (FrFT) was applied to the sonar returns to extract the angular position information depending on the target aspect by utilizing separation capability of the time-delayed combination of linear frequency modulated (LFM) signals in the FrFT domain. With the FrFT-based features, three different target angular positions were classified using neural networks.

This paper proposes and investigates a multiplexing and error control scheme for Body Area Network (BAN). In February 2012, an international standard of WBAN, IEEE802.15.6, was published and it supports error control schemes. This standard also defines seven different QoS modes however, how to utilize them is not clearly specified. In this paper, an optimization method of the QoS is proposed. In order to utilize the QoS parameters, a multiplexing scheme is introduced. Then, the Hybrid ARQ in IEEE 802.15.6 is modified to employ decomposable codes and Weldon's ARQ protocol for more associations with channel conditions and required QoS. The proposed scheme has higher flexibility for optimizing the QoS parameters according to the required QoS.

Even with the recent development of new types of social networking services such as microblogs, Bulletin Board Systems (BBS) remains popular for local communities and vertical discussions. These BBS sites have high volume of traffic everyday with user discussions on a variety of topics. Therefore it is difficult for BBS visitors to find the posts that they are interested in from the large amount of discussion threads. We attempt to explore several main characteristics of BBS, including organizational flexibility of BBS texts, high data volume and aging characteristic of BBS topics. Based on these characteristics, we propose a novel method of Online Topic Detection (OTD) on BBS, which mainly includes a representative post selection procedure based on Markov chain model and an efficient topic clustering algorithm with candidate topic set generation based on Aging Theory. Experimental results show that our method improves the performance of OTD in BBS environment in both detection accuracy and time efficiency. In addition, analysis on the aging characteristic of discussion topics shows that the generation and aging of topics on BBS is very fast, so it is wise to introduce candidate topic set generation strategy based on Aging Theory into the topic clustering algorithm.

In this letter, advanced QRD-M detection using iterative scheme is proposed. This scheme has a higher diversity degree than conventional QRD-M detection. According to the simulation results, the performance of proposed QRD-M detection is 0.5dB to 5.5dB better than the performance of conventional QRD-M detection and average iteration time is approximately 1 in the value of M = 1, 2, 3. Therefore, the proposed QRD-M detection has better performance than conventional QRD-M detection, particularly in a high SNR environment and low modulation order.

Multivariate Public Key Cryptosystems (MPKC) are candidates for post-quantum cryptography. Rainbow is a digital signature scheme in MPKC, whose signature generation and verification are relatively efficient. However, the security of MPKC depends on the difficulty in solving a system of multivariate polynomials, and the key length of MPKC becomes substantially large compared with that of RSA cryptosystems for the same level of security. The size of the secret and public keys in MPKC has been reduced in previous research. The NC-Rainbow is a signature scheme in MPKC, which was proposed in order to reduce the size of secret key of Rainbow. So far, several attacks against NC-Rainbow have been proposed. In this paper, we summarize attacks against NC-Rainbow, containing attacks against the original Rainbow, and analyze the total security of NC-Rainbow. Based on the cryptanalysis, we estimate the security parameter of NC-Rainbow at the several security level.