In this paper, we introduce a path embedding problem inspired by the well-known hydrophobic-polar (HP) model of protein folding. A graph is said bicolored if each vertex is assigned a label in the set {red, blue}. For a given bicolored path P and a given bicolored graph G, our problem asks whether we can embed P into G in such a way as to match the colors of the vertices. In our model, G represents a protein's “blueprint,” and P is an amino acid sequence that has to be folded to form (part of) G. We first show that the bicolored path embedding problem is NP-complete even if G is a rectangular grid (a typical scenario in protein folding models) and P and G have the same number of vertices. By contrast, we prove that the problem becomes tractable if the height of the rectangular grid G is constant, even if the length of P is independent of G. Our proof is constructive: we give a polynomial-time algorithm that computes an embedding (or reports that no embedding exists), which implies that the problem is in XP when parameterized according to the height of G. Additionally, we show that the problem of embedding P into a rectangular grid G in such a way as to maximize the number of red-red contacts is NP-hard. (This problem is directly inspired by the HP model of protein folding; it was previously known to be NP-hard if G is not given, and P can be embedded in any way on a grid.) Finally, we show that, given a bicolored graph G, the problem of constructing a path P that embeds in G maximizing red-red contacts is Poly-APX-hard.

This paper proposes a Petri net based mathematical programming approach to combinatorial optimization, in which we generate integer linear programming problems from Petri net models instead of the direct mathematical formulation. We treat two types of combinatorial optimization problems, ordinary problems and time-dependent problems. Firstly, we present autonomous Petri net modeling for ordinary optimization problems, where we obtain fundamental constraints derived from Petri net properties and additional problem-specific ones. Secondly, we propose a colored timed Petri net modeling approach to time-dependent problems, where we generate variables and constraints for time management and for resolving conflicts. Our Petri net approach can drastically reduce the difficulty of the mathematical formulation in a sense that (1) the Petri net modeling does not require deep knowledge of mathematical programming and technique of integer linear model formulations, (2) our automatic formulation allows us to generate large size of integer linear programming problems, and (3) the Petri net modeling approach is flexible for input parameter changes of the original problem.

This paper proposes a scheme for automatic generation of mixed-integer programming problems for scheduling with multiple resources based on colored timed Petri nets. Our method reads Petri net data modeled by users, extracts the precedence and conflict relations among transitions, information on the available resources, and finally generates a mixed integer linear programming for exactly solving the target scheduling problem. The mathematical programing problems generated by our tool can be easily inputted to well-known optimizers. The results of this research can extend the usability of optimizers since our tool requires just simple rules of Petri nets but not deep mathematical knowledge.

The aim of this research is realizing a high resolution and a fast color switching of electronic papers. In this report, we realized basis of electric papers comprised on magnetic Janus particles was established. Colored and magnetic Janus particles were successfully prepared, and magnetic Janus particles were introduced into honeycomb matrices. Introduced magnetic Janus particles quickly respond to an external magnetic field.

The recently proposed distributed adaptive direct position determination (D-ADPD) algorithm provides an efficient way to locating a radio emitter using a sensor network. However, this algorithm may be suboptimal in the situation of colored emitted signals. We propose an enhanced distributed adaptive direct position determination (EDA-DPD) algorithm. Simulations validate that the proposed EDA-DPD outperforms the D-ADPD in colored emitted signals scenarios and has the similar performance with the D-ADPD in white emitted signal scenarios.

This paper proposes the MIMO MC-CDMA channel estimation method for the various mobile environments. The distinctive feature of the proposed method is possible to robustly estimate with respect to the mobile velocity using the Kalman filter with the colored driving source. Effectiveness of the proposed method are shown by computer simulations.

Wyner has shown in his seminal paper on (discrete memoryless) wiretap channels that if the channel between the sender and an eavesdropper is a degraded version of the channel between the sender and the legitimate receiver, then the sender can reliably and securely transmit a message to the receiver, while the eavesdropper obtains absolutely no information about the message. Later, Leung-Yan-Cheong and Hellman extended Wyner's result to the case where the noise is white Gaussian. In this paper we extend the white Gaussian wiretap channel to the colored Gaussian case and show the finite block length secrecy capacity of colored Gaussian wiretap channels. We also show the asymptotic secrecy capacity of a specific colored Gaussian wiretap channel for which optimal power allocation can be found by a water-filling procedure.

Compressive sensing (CS) exploits the sparsity or compressibility of signals to recover themselves from a small set of nonadaptive, linear measurements. The number of measurements is much smaller than Nyquist-rate, thus signal recovery is achieved at relatively expense. Thus, many signal processing problems which do not require exact signal recovery have attracted considerable attention recently. In this paper, we establish a framework for parameter estimation of a signal corrupted by additive colored Gaussian noise (ACGN) based on compressive measurements. We also derive the Cramer-Rao lower bound (CRB) for the frequency estimation problems in compressive domain and prove some useful properties of the CRB under different compressive measurements. Finally, we show that the theoretical conclusions are along with experimental results.

We study the synchronization of dynamical systems induced by common additional external colored noise. In particular, we consider the special case that the external input noise is generated by a linear second-order differential equation forced by Gaussian white noise. So the frequency spectrum of this noise is not constant. In the case that noise-free dynamics is chaotic, we find examples where the synchronization is enhanced when the peak of the input noise is close to the peak of the noise-free dynamics in frequency space. In the case that noise-free dynamics is non-chaotic, we do not observe this phenomenon.

We propose a noise suppression algorithm with the Kalman filter theory. The algorithm aims to achieve robust noise suppression for the additive white and colored disturbance from the canonical state space models with (i) a state equation composed of the speech signal and (ii) an observation equation composed of the speech signal and additive noise. The remarkable features of the proposed algorithm are (1) applied to adaptive white and colored noises where the additive colored noise uses babble noise, (2) realization of high performance noise suppression without sacrificing high quality of the speech signal despite simple noise suppression using only the Kalman filter algorithm, while many conventional methods based on the Kalman filter theory usually perform the noise suppression using the parameter estimation algorithm of AR (auto-regressive) system and the Kalman filter algorithm. We show the effectiveness of the proposed method, which utilizes the Kalman filter theory for the proposed canonical state space model with the colored driving source, using numerical results and subjective evaluation results.

We have proposed in [5] a practical blind channel identification algorithm for the white observation noise. In this paper, we examine the effectiveness of the algorithm given in [5] for the colored observation noise. The proposed algorithm utilizes Gram-Schmidt orthogonalization procedure and estimates (1) the channel order, (2) the noise variance and then (3) the channel impulse response with less computational complexity compared to the conventional algorithms using eigenvalue decomposition. It can be shown through numerical examples that the algorithm proposed in [5] is quite effective in the colored noise case.

In databases based on a multi-aspects object data model whcih enables multiple aspects of a real-world entity to be represented and to be acquired/lost dynamically, Object Migration (OM) updating membership relationships between an object and classes occurs, as the properties of the object evolve in its lifetime. We have proposed an OM behavior modeling framework using Colored Petri Nets (CPN) to analyze OM behavior. Based on the proposed framework, this paper presents a technique for constructing OM behavior models from OM constraint descriptions and class schemas as its input. The presented technique makes it easy to construct consistent and complete OM behavior models, since OM constraints are described in a simple, modular, and declarative form.

The present paper deals with the blind deconvolution of a Multiple-Input Multiple-Output Finite Impulse Response (MIMO-FIR) system. To deal with the blind deconvolution problem using the second-order statistics (SOS) of the outputs, Hua and Tugnait considered it under the conditions that a) the FIR system is irreducible and b) the input signals are spatially uncorrelated and have distinct power spectra. In the present paper, the problem is considered under a weaker condition than the condition a). Namely, we assume that c) the FIR system is equalizable by means of the SOS of the outputs. Under b) and c), we show that the system can be blindly identified up to a permutation, a scaling, and a delay using the SOS of the outputs. Moreover, based on this identifiability, we show a novel necessary and sufficiently condition for solving the blind deconvolution problem, and then, based on the condition, we propose a new algorithm for finding an equalizer using the SOS of the outputs, while Hua and Tugnait have not proposed any algorithm for solving the blind deconvolution under the conditions a) and b).

An approach to the enhancement of speech signals corrupted by additive colored noise is proposed and the system architecture to implement the proposed idea in real-time communication is introduced in this paper. A combination of a bandpass FIR filtering technique with wiener filtering is used to improve the SNR for speech signals. The average SNR improvement (between input and output SNR) is 22.48 dB. The additive noises are the sound from a turbo prop aircraft. The system, which shows excellent performance, is designed based on a 16 bits fixed point DSP (ADSP-2181) from Analog Devices. Experiment results demonstrate that the FIR filter leads to a significant gain in SNR, thus visibly improvement for the quality and the intelligibility of the speech.

Adaptation of software components to the requirements is one of the key concerns in Component Based Software Development (CBSD). In this paper, we propose a formal approach to compose component based systems which are adaptable to the requirements. We focus on the functional aspects of software components and requirements, which are expressed in S-sorted functions. Those S-sorted functions are transformed into Colored Petri Nets (CPN) models in order to evaluate connectivity between the components, and to evaluate adaptability of composed systems to the requirements. The connectivity is measured based on colors or data types in CPN, while the adaptability is measured based on functional equivalency. We introduce simple glue codes to connect the components each other. The paper focuses on business applications, however the proposed approach can be applied to any other domains as far as the functional adaptability is concerned.

It is one of the difficulties in enterprise modeling that we must deal with many fragmented pieces of knowledge provided by various domain-experts, which are usually based on mutually different viewpoints of them. This paper presents a formal approach to integrate those pieces into enterprise-wide model units using Rough Set Theory (RST). We focus on business processes in order to recognize and identify the constituents or units of enterprise models, which would compose the model expressing the various aspects of the enterprise. We defined five model unit types of "resource," "organization," "task," "function," and "behavior. " The first three types represent the static aspect of the enterprise, whereas the last two types represent the dynamic aspect of it. Those units are initially elicited from each domain-expert as his/her own individual model units, then they are integrated into enterprise-wide units using RST. Our approach is methodology-free, and any methodologies can include it in their early stage to identify what composes the enterprise.

Efficient content-based retrieval of complex images is a challenging task since the detected object may appear in various scale, rotation and orientation with a wide variety of background colors and forms. In this paper, we propose a novel representation of objects with multiple colors, the spatial neighborhood-adjacency graph(SNAG), which can serve as a basis for detecting object by color contents from the candidate image. The SNAG consists of a set of main-vertices and two sets of edges. Each main-vertex represents a single color region of multi-colored object, and edges are divided into two classes: Neighborhood edges representing neighborhood relationship between two main-vertices with similar color, and adjacency edges representing adjacency relationship between a main-vertex and another vertex with different color. By investigating whether SNAG of object image is an isomorphic subgraph of SNAG of a candidate image, we can determine whether the similar object exists in the candidate image. In addition, we have also applied the proposed approach to a range of different object detection problems involving complex background, and effectiveness has been proved.

Software Composition is one of the major concerns in component based software development (CBSD). In this paper, we present a formal approach to construct software systems from requirements models using available components. We focus on the knowledge resides in the requirements and the components in order to deal with those heterogeneous concepts. This approach consists of three steps. The first step is selecting adaptable components to the requirements model. The requirements and the components are transformed into the form of Σ algebra, and the component adaptability is evaluated by Σ homomorphism. Rough Set Theory (RST) is used to make carriers of two Σ algebras common, which are derived from the requirements and the components. The second step is identifying the control structure of the requirements. Decision tables are used for representing the knowledge on the requirements, and RST is used to optimize the control structure. The third step is to implement the control structure as glue codes which would perform the components appropriately. This approach mainly focuses on enterprise back-office applications in this paper, however, it can be easily applied to other domains, since it assumes the requirements to be expressed in Colored Petri Nets (CPN), and CPN can express various problem domains other than enterprise back-office applications.

Adaptability evaluation of software systems is one of the key concerns in both software engineering and requirements engineering. In this paper, we present a formal and systematic approach to evaluate adaptability of software systems to requirements in enterprise business applications. Our approach consists of three major parts, that is, the common modeling method for both business realms and software realms, functional adaptability evaluation between the models with Σ algebra and behavioral adaptability evaluation with process algebra. By our approach, one can rigorously and uniquely determine whether a software system is adaptable to the requirements, either totally or partially. A sample application from an order processing is illustrated to show how this approach is effective in solving the adaptability evolution problem.

In this paper, an effective method of system modeling and dynamic scheduling to improve operation and control for the Back-End process of semiconductor manufacturing is developed by using Colored Timed Petri-Nets (CTPNs). The simulator of a CTPNs model was utilized to generate a new heuristic scheduling method with genetic algorithm(GA) which enables us to obtain the optimal values of the weighted delay time and standard deviation of lead time.