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.

Estimation of distribution algorithms (EDAs), since they were introduced, have been successfully used to solve discrete optimization problems and hence proven to be an effective methodology for discrete optimization. To enhance the applicability of EDAs, researchers started to integrate EDAs with discretization methods such that the EDAs designed for discrete variables can be made capable of solving continuous optimization problems. In order to further our understandings of the collaboration between EDAs and discretization methods, in this paper, we propose a quality measure of discretization methods for EDAs. We then utilize the proposed quality measure to analyze three discretization methods: fixed-width histogram (FWH), fixed-height histogram (FHH), and greedy random split (GRS). Analytical measurements are obtained for FHH and FWH, and sampling measurements are conducted for FHH, FWH, and GRS. Furthermore, we integrate Bayesian optimization algorithm (BOA), a representative EDA, with the three discretization methods to conduct experiments and to observe the performance difference. A good agreement is reached between the discretization quality measurements and the numerical optimization results. The empirical results show that the proposed quality measure can be considered as an indicator of the suitability for a discretization method to work with EDAs.

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.

A new algorithm is proposed for finding all solutions of piecewise-linear resistive circuits using separable programming. In this algorithm, the problem of finding all solutions is formulated as a separable programming problem, and it is solved by the modified simplex method using the restricted-basis entry rule. Since the modified simplex method finds one solution per application, the proposed algorithm can find all solutions efficiently. Numerical examples are given to confirm the effectiveness of the proposed algorithm.

In this letter, the orthogonal projection (OP) estimation of the direction of arrival (DOA) and direction of departure (DOD) of multiple targets for bistatic multiple-input multiple-output radars is addressed. First, a two-dimensional direction finding estimator based on OP technique with automatic pairing is developed. Second, this letter also presents a modified reduced-dimension estimator by utilizing the characteristic of Kronecker product, which only performs two one-dimensional angle estimates. Furthermore, the DOA and DOD pairing is given automatically. Finally, simulation results are presented to verify the efficiency of the proposed estimators.

This letter presents an automatic 2D-to-3D conversion method using a structure from motion (SfM) process for multi-object scenes. The foreground and background regions may have different depth values in an image. First, we detect the foreground objects and the background by using a depth histogram. Then, the proposed method creates the virtual image by projecting each region with its computed projective matrix. Experimental results compared to previous research show that the proposed method provides realistic stereoscopic images.

Emerging networks characterized by growing speed and data insensitivity demand faster and scalable error handling. Prevalent decoders are based on dedicated hardware, offering considerable processing speed, but limited flexibility, programmability and scalability. This paper proposes an efficient approach to accelerate the extended-Hamming code decoder using a graphics processing unit (GPU), chosen for its low cost and extremely high-throughput parallel-computing capability. This paper compares the performance of the GPU-based approach with the equivalent sequential approaches that are performed on a central processing unit (CPU) and Texas Instruments TMS320C6742 digital signal processor (DSP) with varying packet sizes and error tolerances. Experimental results demonstrate that the proposed GPU-based approach outperforms the sequential approaches in terms of execution time and energy consumption.

A service-oriented architecture builds the entire system using a combination of independent software components. Such an architecture can be applied to a wide variety of computer systems. The problem of synthesizing service implementation models from choreography representing the overall specifications of service interaction is known as the choreography realization problem. In automatic synthesis, software models should be simple enough to be easily understood by software engineers. In this paper, we discuss a semi-formal method for synthesizing hierarchical state machine models for the choreography realization problem. The proposed method is evaluated using metrics for intelligibility.

In this paper, the diversity multiplexing tradeoff (DMT) analysis of the non-coherent block-fading multiple antenna channel which uses a training-based channel estimation scheme at asymptotically high signal-to-noise ratios (SNRs) is extended to finite SNRs. This extension is performed for a single input multiple output (SIMO) maximal ratio combining (MRC) scheme. This analysis at finite SNRs is more useful because in practice, the training schemes operate at finite SNRs and their impact on DMT is more relevant at such SNRs. We show the non-applicability of the asymptotically high SNR relation, given by Zheng, to finite SNRs. We also show the equivalence of two existing training-based channel estimation schemes for any SIMO system, and using one of these, we compute the achievable finite-SNR DMT of the non-coherent SIMO-MRC scheme for two modes of the training scheme. We analyze the achievable finite-SNR DMT for different durations of training, modes of the training scheme, and SNRs. We show that the impact of the mode of the training scheme on finite-SNR DMT decreases as SNR increases. We also show that at asymptotically high SNRs, the achievable DMT in both modes of the SIMO-MRC scheme is equal to that of the non-coherent SIMO channel, as derived by Zheng.

This paper describes a method for the efficient computation of the total autocorrelation for large multiple-output Boolean functions over a Shared Binary Decision Diagram (SBDD). The existing methods for computing the total autocorrelation over decision diagrams are restricted to single output functions and in the case of multiple-output functions require repeating the procedure k times where k is the number of outputs. The proposed method permits to perform the computation in a single traversal of SBDD. In that order, compared to standard BDD packages, we modified the way of traversing sub-diagrams in SBDD and introduced an additional memory function kept in the hash table for storing results of the computation of the autocorrelation between two subdiagrams in the SBDD. Due to that, the total amount of computations is reduced which makes the method feasible in practical applications. Experimental results over standard benchmarks confirm the efficiency of the method.

Computational fluid dynamics (CFD) is an important tool for designing aircraft components. FaSTAR (Fast Aerodynamics Routines) is one of the most recent CFD packages and has various subroutines. However, its irregular and complicated data structure makes it difficult to execute FaSTAR on parallel machines due to memory access problem. The use of a reconfigurable platform based on field programmable gate arrays (FPGAs) is a promising approach to accelerating memory-bottlenecked applications like FaSTAR. However, even with hardware execution, a large number of pipeline stalls can occur due to read-after-write (RAW) data hazards. Moreover, it is difficult to predict when such stalls will occur because of the unstructured mesh used in FaSTAR. To eliminate this problem, we developed an out-of-order mechanism for permuting the data order so as to prevent RAW hazards. It uses an execution monitor and a wait buffer. The former identifies the state of the computation units, and the latter temporarily stores data to be processed in the computation units. This out-of-order mechanism can be applied to various types of computations with data dependency by changing the number of execution monitors and wait buffers in accordance with the equations used in the target computation. An out-of-order system can be reconfigured by automatic changing of the parameters. Application of the proposed mechanism to five subroutines in FaSTAR showed that its use reduces the number of stalls to less than 1% compared to without the mechanism. In-order execution was speeded up 2.6-fold and software execution was speeded up 2.9-fold using an Intel Core 2 Duo processor with a reasonable amount of overhead.

We propose a notion of attribute-based identification (ABID) in two flavors: prover-policy ABID (PP-ABID) and verifier-policy ABID (VP-ABID). In a PP-ABID scheme, a prover has an authorized access policy written as a boolean formula over attributes, while each verifier maintains a set of attributes. The prover is accepted when his access policy fits the verifier's set of attributes. In a VP-ABID scheme, a verifier maintains an access policy written as a boolean formula over attributes, while each prover has a set of authorized attributes. The prover is accepted when his set of attributes satisfies the verifier's access policy. Our design principle is first to construct key-policy and ciphertext-policy attribute-based key encapsulation mechanisms (KP-ABKEM and CP-ABKEM). Second, we convert KP-ABKEM and CP-ABKEM into challenge-and-response PP-ABID and VP-ABID, respectively, by encapsulation-and-decapsulation. There, we show that KP-ABKEM and CP-ABKEM only have to be secure against chosen-ciphertext attacks on one-wayness (OW-CCA secure) for the obtained PP-ABID and VP-ABID to be secure against concurrent man-in-the-middle attacks (cMiM secure). According to the design principle, we construct concrete KP-ABKEM and CP-ABKEM with the OW-CCA security by enhancing the KP-ABKEM of Ostrovsky, Sahai and Waters and CP-ABKEM of Waters, respectively. Finally, we obtain concrete PP-ABID and VP-ABID schemes that are proved to be selectively secure in the standard model against cMiM attacks.

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.

A 125MHz 64-phase delay-locked loop (DLL) is implemented for time recovery in a digital wire-line system. The architecture of the proposed DLL comprises a coarse-locking circuit added to a conventional DLL circuit, which consists of a delay line including a bias circuit, phase detector, charge pump, and loop filter. The proposed coarse-locking circuit reduces the locking time of the DLL and prevents harmonic locking, regardless of the duty cycle of the clock. In order to verify the performance of the proposed coarse-locking circuit, a 64-phase DLL with an operating frequency range of 40 to 200MHz is fabricated using a 0.18-µm 1-poly 6-metal CMOS process with a 1.8V supply. The measured rms and peak-to-peak jitter of the output clock are 3.07ps and 21.1ps, respectively. The DNL and INL of the 64-phase output clock are measured to be -0.338/+0.164 LSB and -0.464/+0.171 LSB, respectively, at an operating frequency of 125MHz. The area and power consumption of the implemented DLL are 0.3mm2 and 12.7mW, respectively.

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.

This paper firstly presents a processor design with Derivative ASIP approach. The architecture of processor is designed by making use of a well-known embedded processor's instruction-set as a base architecture. To improve its performance, the architecture is enhanced with more hardware resources such as registers, interfaces and instruction extensions which might achieve target specifications. Secondly, a new approach for retargeting compiler by means of assembly converter tool is proposed. Our retargeting approach is practical because it is performed by the assembly converter tool with a simple configuration file and independent from a base compiler. With our proposed approach, both architecture flexibility and a good quality of assembly code can be obtained at once. Compared to other compilers, experiments show that our approach capable of generating code as high efficiency as its base compiler and the developed ASIP results in better performance than its base processor.

This paper designs tag antennas to satisfy three key goals: mounting on very small objects, extending the reading range with planar structures, and maintaining stable performance on various materials. First, the size of the tag is reduced up to 17% compared to the half-wavelength dipole without a large reduction in bandwidth and efficiency by introducing an inductively coupled feed structure. Second, the reading range is increased to 1.68 times that of the reference dipole tags while maintaining the planar structure using circular polarization characteristics. Finally, a stable reading range is achieved with a deviation in the reading range of only 30% of that of commercial tags on various objects by employing the capacitively-loaded and T-matching network.

Flexural waves on a thin elastic plate are governed by the fourth-order differential equation, which is attractive not only from a harmonic analysis viewpoint but also useful for an efficient numerical method in the elastdynamics. In this paper, we proposed two novel ideas: (1) use of the tensor bases to describe flexural waves on inhomogeneous elastic plates, (2) weak form discretization to derive the second-order difference equation from the fourth-order differential equation. The discretization method proposed in this study is of preliminary consideration about the recursive transfer method (RTM) to analyse the scattering problem of flexural waves. More importantly, the proposed discretization method can be applied to any system which can be formulated by the weak form theory. The accuracy of the difference equation derived by the proposed discretization method is confirmed by comparing the analytical and numerical solutions of waveguide modes. As a typical problem to confirm the validity of the resultant governing equation, the influence of the spatially modulated elastic constant in waveguide modes is discussed.

We analyze the connectivity of simulation ad hoc networks, which use random mobility models. Based on the theorem of minimum degree, the study of connectivity probability is converted into an analysis of the probability of minimum node degree. Detailed numerical analyses are performed for three mobility models: random waypoint model, random direction model, and random walk model. For each model, the connectivity probability is calculated and its relations with the communication range r and the node number n are illustrated. Results of the analyses show that with the same network settings, the connectivity performance decreases in the following order: random walk model, random direction model, and random waypoint model. This is because of the non-uniform node distribution in the last two models. Our work can be used by researchers to choose, modify, or apply a reasonable mobility model for network simulations.

In vehicular ad hoc networks, the efficient and reliable dissemination of emergency messages in a highly mobile environment under dense or sparse network is a significant challenge. This paper proposes a new vehicular broadcast protocol, called ACK-CAST, that can operate effectively in both dense and sparse network scenarios. ACK-CAST relies on acknowledgment messages from one-hop neighbors to select the next rebroadcasting vehicle. Simulation results show that ACK-CAST outperforms the SERVUS protocol in terms of the end-to-end delay, message delivery ratio and network overhead.