In this paper, we investigate a combination scheme of subcarrier intensity-modulation (SIM) with spatial modulation (SM) for optical wireless communication. Using computer simulation, the performances of the proposed SIM/SM scheme are investigated and compared with those of the conventional SIM scheme in the additive white gaussian noise (AWGN) as well as in outdoor environment with turbulence induced fading characteristics. Numerical results show that the proposed SIM/SM scheme can outperform the conventional SIM in an environment with different spectral efficiencies. When the spectral efficiency is varied from 2bits/s/Hz to 4bits/s/Hz, an Eb/N0 gain of 2dB to 5dB is achieved, when the bit error rate of 10-5 is maintained. It shows that the employment of SM may further improve the power efficiency of SIM, when the number of subcarriers increases according to the spectral efficiency. When the spectral efficiency is 4bits/s/Hz, the SIM/SM scheme for 0.5 of log-irradiance variance in the log-normal turbulence channel shows the same performance as SIM with variance of 0.3. This means that the SIM/SM can be an alternative choice in even worse environments.

Toward the actualization of an automatic navigation system for fetoscopic tracheal occlusion (FETO) surgery, this paper proposes a 3D ultrasound (US) calibration-based approach that can locate the fetal facial surface, oral cavity, and airways by a registration between a 3D fetal model and 3D US images. The proposed approach consists of an offline process and online process. The offline process first reconstructs the 3D fetal model with the anatomies of the oral cavity and airways. Then, a point-based 3D US calibration system based on real-time 3D US images, an electromagnetic (EM) tracking device, and a novel cones' phantom, computes the matrix that transforms the 3D US image space into the world coordinate system. In the online process, by scanning the mother's body with a 3D US probe, 3D US images containing the fetus are obtained. The fetal facial surface extracted from the 3D US images is registered to the 3D fetal model using an ICP-based (iterative closest point) algorithm and the calibration matrices, so that the fetal facial surface as well as the oral cavity and airways are located. The results indicate that the 3D US calibration system achieves an FRE (fiducial registration error) of 1.49±0.44mm and a TRE (target registration error) of 1.81±0.56mm by using 24 fiducial points from two US volumes. A mean TRE of 1.55±0.46 mm is also achieved for measuring location accuracy of the 3D fetal facial surface extracted from 3D US images by 14 target markers, and mean location errors of 2.51±0.47 mm and 3.04±0.59 mm are achieved for indirectly measuring location accuracy of the pharynx and the entrance of the trachea, respectively, which satisfy the requirement of the FETO surgery.

This letter presents a novel phase synchronization algorithm for a MIMO radar system in order to overcome the limitation of the existing algorithms relying on channel reciprocity, or line-of-sight, assumption between radar elements. The proposed algorithm is capable of synchronizing local oscillator phases among radar elements even if line-of-sight communication links are not available. Furthermore, the proposed algorithm exhibits robust MSE performance in the presence of frequency estimation error. The performance of the proposed algorithm was analyzed theoretically and verified by simulations.

We proposes a new adaptive spectral masking method of algebraic vector quantization (AVQ) for non-sparse signals in the modified discreet cosine transform (MDCT) domain. This paper also proposes switching the adaptive spectral masking on and off depending on whether or not the target signal is non-sparse. The switching decision is based on the results of MDCT-domain sparseness analysis. When the target signal is categorized as non-sparse, the masking level of the target MDCT coefficients is adaptively controlled using spectral envelope information. The performance of the proposed method, as a part of ITU-T G.711.1 Annex D, is evaluated in comparison with conventional AVQ. Subjective listening test results showed that the proposed method improves sound quality by more than 0.1 points on a five-point scale on average for speech, music, and mixed content, which indicates significant improvement.

Numerous studies have been focusing on the improvement of bag of features (BOF), histogram of oriented gradient (HOG) and scale invariant feature transform (SIFT). However, few works have attempted to learn the connection between them even though the latter two are widely used as local feature descriptor for the former one. Motivated by the resemblance between BOF and HOG/SIFT in the descriptor construction, we improve the performance of HOG/SIFT by a) interpreting HOG/SIFT as a variant of BOF in descriptor construction, and then b) introducing recently proposed approaches of BOF such as locality preservation, data-driven vocabulary, and spatial information preservation into the descriptor construction of HOG/SIFT, which yields the BOF-driven HOG/SIFT. Experimental results show that the BOF-driven HOG/SIFT outperform the original ones in pedestrian detection (for HOG), scene matching and image classification (for SIFT). Our proposed BOF-driven HOG/SIFT can be easily applied as replacements of the original HOG/SIFT in current systems since they are generalized versions of the original ones.

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.

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.

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.

This paper introduces an ontology-based method for checking requirements specification. Requirements ontology is a knowledge structure that contains functional requirements (FR), attributes of FR and relations among FR. Requirements specification is compared with functional nodes in the requirements ontology, then rules are used to find errors in requirements. On the basis of the results, requirements team can ask questions to customers and correctly and efficiently revise requirements. To support this method, an ontology-based checking tool for verification of requirements has been developed. Finally, the requirements checking method is evaluated through an experiment.

In this paper we report on experiences gained and lessons learned by the use of the Timed OTS/CafeOBJ method in the formal verification of TESLA source authentication protocol. These experiences can be a useful guide for the users of the OTS/CafeOBJ, especially when dealing with such complex systems and protocols.

There have been many researches about construction and application of ontology in reality, notably the usage of ontology to support requirements engineering. The effect of ontology-based requirements engineering depends on quality of ontology. With the increasing size of ontology, it is difficult to verify the correctness of information stored in ontology. This paper will propose a method of using rules for verification the correctness of requirements ontology. We provide a rule description language to specify properties that requirements ontology should satisfy. Then, by checking whether the rules are consistent with requirements ontology, we verify the correctness of the ontology. We have developed a verification tool to support the method and evaluated the tool through experiments.

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.

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.

This study proposes a feature-level fusion method that uses finger veins (FVs) and finger dorsal texture (FDT) for personal authentication based on orientation selection (OS). The orientation codes obtained by the filters correspond to different parts of an image (foreground or background) and thus different orientations offer different levels of discrimination performance. We have conducted an orientation component analysis on both FVs and FDT. Based on the analysis, an OS scheme is devised which combines the discriminative orientation features of both modalities. Our experiments demonstrate the effectiveness of the proposed method.

In this paper, a novel White-RGB (WRGB) color filter array-based imaging system for cell phone is presented to reduce noise and reproduce color in low illumination. The core process is based on adaptive diagonal color separation to recover color components from a white signal using diagonal reference blocks and location-based color ratio estimation in the luminance space. The experiments, which are compared with the RGB and state-of-the-art WRGB approaches, show that our imaging system performs well for various spatial frequency images and color restoration in low-light environments.

Software engineering education at universities plays an increasingly important role as software quality is becoming essential in realizing a safe and dependable society. This paper proposes a practical state transition model (Practical-STM) based on the Organizational Expectancy Model for the improvement of software process education based on the Personal Software Process (PSP) from a motivation point of view. The Practical-STM treats an individual trainee of the PSP course as a state machine, and formalizes a motivation process of a trainee using a set of states represented by factors regarding motivation and a set of operations carried out by course instructors. The state transition function of this model represents the features or characteristics of a trainee in terms of motivation. The model allows a formal description of the states of a trainee in terms of motivation and the educational actions of the instructors in the PSP course. The instructors are able to decide effective and efficient actions to take toward the trainees objectively by presuming a state and a state transition function of the trainees formally. Typical patterns of state transitions from an initial state to a final state, which is called a scenario, are useful for inferring possible transitions of a trainee and taking proactive operations from a motivation point of view. Therefore, the model is useful not only for improving the educational effect of the PSP course, but also for the standardization of the course management and the quality management of the instructors.

We propose a novel motion segmentation method based on a Clausius Normalized Field (CNF), a probabilistic model for treating time-varying imagery, which estimates entropy variations by observing the entropy definitions of Clausius and Boltzmann. As pixels of an image are viewed as a state of lattice-like molecules in a thermodynamic system, estimating entropy variations of pixels is the same as estimating their degrees of disorder. A greater increase in entropy means that a pixel has a higher chance of belonging to moving objects rather than to the background, because of its higher disorder. In addition to these homologous operations, a CNF naturally takes into consideration both spatial and temporal information to avoid local maxima, which substantially improves the accuracy of motion segmentation. Our motion segmentation system using CNF clearly separates moving objects from their backgrounds. It also effectively eliminates noise to a level achieved when refined post-processing steps are applied to the results of general motion segmentations. It requires less computational power than other random fields and generates automatically normalized outputs without additional post-processes.

A novel real-time solar photovoltaic (SPV) emulator system, based on a systolic array network (SAN), is presented. This architecture follows the piecewise polynomial approximation and parallel computing techniques, and shows its capability to generate high-accuracy I-V, P-V curves, instead of traditional DSP and lookup table-based SPV systems.