Correctness of Java programs is important because they are executed in distributed computing environments. The object initialization scheme in the Java programming language is complicated, and this complexity may lead to undesirable semantic bugs. Various tools have been developed for detecting program patterns that might cause errors during program execution. However, current tools cannot identify code patterns in which an uninitialized field is accessed when an object is initialized. We refer to such erroneous patterns as uninitialized field references. In this paper, we propose a static pattern detection algorithm for identifying uninitialized field references. We design a sound analysis for this problem and implement an analyzer using the Soot framework. In addition, we apply our algorithm to some real Java applications. From the experiments, we identify 12 suspicious field references in the applications, and among those we find two suspected errors by manual inspection.

Due to the continuous growth of wireless communication technology and mobile equipment, the history management of moving object is important in a wide range of location-based applications. To process queries for history data, trajectories, we generally use trajectory-preserving index schemes based on the trajectory preservation property. This property means that a leaf node only contains segments belonging to a particular trajectory, regardless of the spatiotemporal locality of segments. The sacrifice of spatiotemporal locality, however, causes the index to increase the dead space of MBBs of non-leaf nodes and the overlap between the MBBs of nodes. Therefore, an index scheme for trajectories shows good performance with trajectory-based queries, but not with coordinate-based queries, such as range queries. We propose new index schemes that improve the performance of range queries without reducing performance with trajectory based queries.

In this paper, we present a general object tracking method based on a newly proposed pixel-wise clustering algorithm. To track an object in a cluttered environment is a challenging issue because a target object may be in concave shape or have apertures (e.g. a hand or a comb). In those cases, it is difficult to separate the target from the background completely by simply modifying the shape of the search area. Our algorithm solves the problem by 1) describing the target object by a set of pixels; 2) using a K-means based algorithm to detect all target pixels. To realize stable and reliable detection of target pixels, we firstly use a 5D feature vector to describe both the color ("Y, U, V") and the position ("x, y") of each pixel uniformly. This enables the simultaneous adaptation to both the color and geometric features during tracking. Secondly, we use a variable ellipse model to describe the shape of the search area and to model the surrounding background. This guarantees the stable object tracking under various geometric transformations. The robust tracking is realized by classifying the pixels within the search area into "target" and "background" groups with a K-means clustering based algorithm that uses the "positive" and "negative" samples. We also propose a method that can detect the tracking failure and recover from it during tracking by making use of both the "positive" and "negative" samples. This feature makes our method become a more reliable tracking algorithm because it can discover the target once again when the target has become lost. Through the extensive experiments under various environments and conditions, the effectiveness and efficiency of the proposed algorithm is confirmed.

In the age of pervasive computing, ubiquitous collaboration has become an every-day life paradigm. Without an ideal computing infrastructure, issues with ubiquitous collaboration, such as network unreliability, platform heterogeneity, and client's resource constraints, are inevitable. The traditional replication scheme copes with network unreliability by replicating all the objects of a shared application together at once. This is, however, suitable for neither cooperative applications nor mobile computing devices. These problems can be naturally addressed by using a fine-grained replication scheme that enables a portion of the application objects to be replicated. This paper presents an object-oriented middleware that is capable of dynamically and transparently replicating remotely shared Java applications in a partially and on-demand incremental manner. It is also able to maintain various consistency semantics and enables the coexistence of fine-grained replications and conventional remote method invocations. Empirical results indicate several practical benefits of the middleware.

We present the use of a Modified Generalized Hough Transform (MGHT) and deformable contours for image data retrieval where a given contour, gray-scale, or color template image can be detected in the target image, irrespective of its position, size, rotation, and smooth deformation transformations. Potential template positions are found in the target image using our novel modified Generalized Hough Transform method that takes measurements from the template features by extending a line from each edge contour point in its gradient direction to the other end of the object. The gradient difference is used to create a relationship with the orientation and length of this line segment. Potential matching positions in the target image are then searched by also extending a line from each target edge point to another end along the normal, then looking up the measurements data from the template image. Positions with high votes become candidate positions. Each candidate position is used to find a match by allowing the template to undergo a contour transformation. The deformed template contour is matched with the target by measuring the similarity in contour tangent direction and the smoothness of the matching vector. The deformation parameters are then updated via a Bayesian algorithm to find the best match. To avoid getting stuck in a local minimum solution, a novel coarse-and-fine model for contour matching is included. Results are presented for real images of several kinds including bin picking and fingerprint identification.

Multilayered network interaction among various networks such as IP/MPLS packet networks and optical fiber networks are now achieved using generalized multiprotocol label switching (GMPLS) technology. One unique feature of GMPLS networks is that GMPLS packet-layer label switching paths (LSPs), such as IP/MPLS LSPs, sometimes tunnel through GMPLS lower layer LSPs such as optical fiber/lambda LSPs. One problem that occurs in this situation is protecting an important primary packet LSP by using a protection LSP that is physically separated from the primary LSP. The packet router has difficulty recognizing lower layer LSPs that are totally disjointed from the primary LSP. This is because, in a GMPLS's packet layer, a source router only differentiates one lower layer LSP from another, and does not check the disjointedness of segments through which the lower layer path passes. Sometimes, different lower LSPs pass through the same optical fiber, and a malfunction of one optical fiber sometimes causes many lower layer LSPs to malfunction at the same time. To solve this problem, a shared risk link group (SRLG) is introduced. Network links that belong to the same SRLG share a common physical resource. We apply this SRLG to the proposed hierarchically distributed path computation elements (HDPCEs) and achieve effective disjointed SRLG protection for important primary GMPLS packet paths.

We propose a Fault-Tolerant Object Group framework that provides group management and fault-tolerance services for consistency maintenance and state transparency as well. Through a virtual home-network simulation, we validate that the FTOG framework supports both of the reliability and the stability of the distributed home-network systems.

Object caching is a common feature in the scalable distributed object systems. Fine-grained replication optimizes the performance and resource utilization in object caching by enabling a remote object-oriented application to be partially and incrementally on-demand replicated in units of cluster. Despite these benefits, the lack of common and simple implementation framework makes the fine-grained replication scheme not extensively used. This paper proposes the novel frameworks for dynamic, transparent, partial and automatically incremental replication of distributed Java objects based on three techniques that are lazy-object creation, proxy and hook. One framework enables the fine-grained replication of server-side stateful in-memory application, and the other framework enables the fine-grained replication of server-side stateless in-memory application, client-side program, or standalone application. The experimental evaluation demonstrates that the efficiency in terms of response time of both frameworks are relatively practical to the extent of a local method invocation.

In computer simulation of a large number of moving objects (MOs), how to enlarge Δt (the interval between the simulation time steps) without introducing causality errors is one of the primary keys to enhancing performance. Causality errors can be avoided by using the same Δt among related MOs when they are in the scene of detection (SoD). But in a large-scale MO simulation, MOs interact with one another in a complicated manner requiring a large calculation cost to predict the beginning time of SoD. In this paper we propose an event-aware dynamic time step synchronization method (DTSS) for distributed MO simulation, which increases Δt without introducing causality errors and speeds up the simulation. DTSS can be implemented with little calculation cost because: (1) DTSS does not calculate the beginning time of SoD exactly, but calculates the time for possible entry into SoD with a simple mechanisim, and (2) MO simulation consists of a "movement"-phase and a "detection"-phase in which the distance-calculation between MOs requires a heavy load, and DTSS utilizes the distance values to calculate Δt. In this paper, we also discuss a suitable HLA based time management mechanism to implement DTSS on a distributed computing environment. In the performance evaluation of DTSS, the calculation cost of DTSS is implemented by using the HLA suitable time management mechanism. The results show that DTSS can be executed within the ideal time plus its 1% over-cost when a basic scenario of war-game simulation is employed. Therefore if the ratio of SoD to the total simulation is small, the execution time is expected to decrease to nearly this ratio. We also introduce the criterion for determining when DTSS is superior to the conventional method by using the performance evaluation results. The results presented in this paper are effectively utilized when DTSS is applied to practical applications.

In this paper, we propose a new vertex adjustment method which is based on the size ratio of an object and that of a polygon. In the conventional polygonal approximation methods, the sizes of an object and an approximating polygon are quite different, therefore there are so many error pixels between them. The proposed method reduces the size of error regions by adjusting the size of the polygon to that of an object. Simulation results show outstanding performance of the proposed method.

Design of optimal controllers satisfying performance criteria of minimum tracking error and disturbance level for an interval system using a multi-objective evolutionary approach is proposed in this paper. Based on a worst-case design philosophy, the design problem is formulated as a minimax optimization problem, subsequently solved by a proposed two-phase multi-objective genetic algorithm (MOGA). By using two sets of interactive genetic algorithms where the first one determines the maximum (worst-case) cost function values for a given set of controller parameters while the other one minimizes the maximum cost function values passed from the first genetic algorithm, the proposed approach evolutionarily derives the optimal controllers for the interval system. To suitably assess chromosomes for their fitness in a population, root locations of the 32 generalized Kharitonov polynomials will be used to establish a constraints handling mechanism, based on which a fitness function can be constructed for effective evaluation of the chromosomes. Because of the time-consuming process that genetic algorithms generally exhibit, particularly the problem nature of minimax optimization, a parallel computation scheme for the evolutionary approach in the MATLAB-based working environment is also proposed to accelerate the design process.

An improved algorithm of Guided Local Search called objective function adjustment algorithm is proposed for combinatorial optimization problems. The performance of Guided Local Search is improved by objective function adjustment algorithm using multipliers which can be adjusted during the search process. Moreover, the idea of Tabu Search is introduced into the objective function adjustment algorithm to further improve the performance. The simulation results based on some TSPLIB benchmark problems showed that the objective function adjustment algorithm could find better solutions than Local Search, Guided Local Search and Tabu Search.

A multi-stage approach -- which is fast, robust and easy to train -- for a face-detection system is proposed. Motivated by the work of Viola and Jones [1], this approach uses a cascade of classifiers to yield a coarse-to-fine strategy to reduce significantly detection time while maintaining a high detection rate. However, it is distinguished from previous work by two features. First, a new stage has been added to detect face candidate regions more quickly by using a larger window size and larger moving step size. Second, support vector machine (SVM) classifiers are used instead of AdaBoost classifiers in the last stage, and Haar wavelet features selected by the previous stage are reused for the SVM classifiers robustly and efficiently. By combining AdaBoost and SVM classifiers, the final system can achieve both fast and robust detection because most non-face patterns are rejected quickly in earlier layers, while only a small number of promising face patterns are classified robustly in later layers. The proposed multi-stage-based system has been shown to run faster than the original AdaBoost-based system while maintaining comparable accuracy.

An effective human-robot interaction is essential for wide penetration of service robots into the market. Such robot needs a vision system to recognize objects. It is, however, difficult to realize vision systems that can work in various conditions. More robust techniques of object recognition and image segmentation are essential. Thus, we have proposed to use the human user's assistance for objects recognition through speech. This paper presents a system that recognizes objects in occlusion and/or multicolor cases using geometric and photometric analysis of images. Based on the analysis results, the system makes a hypothesis of the scene. Then, it asks the user for confirmation by describing the hypothesis. If the hypothesis is not correct, the system generates another hypothesis until it correctly understands the scene. Through experiments on a real mobile robot, we have confirmed the usefulness of the system.

This paper presents a robust object tracking method under pose variation and partial occlusion. In practical environment, the appearance of objects is changed dynamically by pose variation or partial occlusion. Therefore, the robustness to them is required for practical applications. However, it is difficult to be robust to various changes by only one tracking model. Therefore, slight robustness to variations and the easiness of model update are required. For this purpose, Kernel Principal Component Analysis (KPCA) of local parts is used. KPCA of local parts is proposed originally for the purpose of pose independent object recognition. Training of this method is performed by using local parts cropped from only one or two object images. This is good property for tracking because only one target image is given in practical applications. In addition, the model (subspace) of this method can be updated easily by solving a eigen value problem. Performance of the proposed method is evaluated by using the test face sequence captured under pose, partial occlusion, scaling and illumination variations. Effectiveness and robustness of the proposed method are demonstrated by the comparison with template matching based tracker. In addition, adaptive update rule using similarity with current subspace is also proposed. Effectiveness of adaptive update rule is shown by experiment.

The present research examines the relationship between texture processing and object processing in human vision. Recent computational studies have suggested a difference between the stages of processing. Texture processing can be performed by using statistical parameterization of the response of primary spatial filters. Object processing requires more complex and elaborate computation at a higher stage than texture processing. Our psychophysical experiments are conducted to clarify the relationship of the stages of texture processing and object processing, by focusing on same-object effect which facilitates and speeds attention shifts within the same object and also costs and delays attention shifts if the attention focus moves from one object to another. Texture is composed of lines parallel to, perpendicular to or inside of elongated rectangles used as objects. The same-object effect is measured with reaction time in a cued detection task. Vertical rectangles are used in xperiment 1 and horizontal ones are used in Experiment 2. Experiment 1 shows that the texture lines interrupt the same-object effect and that the interruption is nearly equal if texture lines are added both to the background and the inside of the objects. Experiment 2 yields the result same as Experiment 1. The interruption of the same-object effect by adding texture lines suggests that texture processing affects object processing.

Real-time object detection or recognition technology becomes more important for various intelligent vision systems. Processing models for object detection or recognition from natural images should tolerate pattern deformations and pattern position shifts. The hierarchical convolutional neural networks are considered as a promising model for robust object detection/recognition. This model requires huge computational power for a large number of multiply-and-accumulation operations. In order to apply this model to robot vision or various intelligent real-time vision systems, its LSI implementation is essential. This paper proposes a new algorithm for reducing multiply-and-accumulation operation by sorting neuron outputs by magnitude. We also propose an LSI architecture based on this algorithm. As a proof of concept for our LSI architecture, we have designed, fabricated and tested two test LSIs: a sorting LSI and an image-filtering LSI. The sorting LSI is designed based on the content addressable memory (CAM) circuit technology. The image-filtering LSI is designed for parallel processing by analog circuit array based on the merged/mixed analog-digital approach. We have verified the validity of our LSI architecture by measuring the LSIs.

Since a human object is an important element of the moving pictures being processed by mobile terminals, establishing a human object extraction method encourages dissemination of new applications. In accordance with the requirement of mobile applications, this paper proposes a low-cost human object extraction method, which consists of a face object and a hair object extraction based on their color information and a simple body extraction utilizing the position information of the face object. In the proposed method, skin color and hair color are estimated through color space segmentation, and a human object is effectively extracted by using a radial active contour model. Simulation results of the human object extraction with the use of XScale processor claims that QCIF 15 fps video sequences can be processed in real time.

In this paper, we propose a hybrid fine-tuned multi-objective memetic algorithm hybridizing different solution fitness evaluation methods for global exploitation and exploration. To search across all regions in objective space, the algorithm uses a widely diversified set of weights at each generation, and employs a simulated annealing to optimize each utility function. For broader exploration, a grid-based technique is adopted to discover the missing nondominated regions on existing tradeoff surface, and a Pareto-based local perturbation is performed to reproduce incrementing solutions trying to fill up the discontinuous areas. Additional advanced feature is that the procedure is made dynamic and adaptive to the online optimization conditions based on a function of improvement ratio to obtain better stability and convergence of the algorithm. Effectiveness of our approach is shown by applying it to multi-objective 0/1 knapsack problem (MOKP).

Inference attacks mean that a user derives information on the execution results of unauthorized queries from the execution results of authorized queries. Most of the studies on inference attacks so far have focused on only inference of positive information (i.e., what value is the execution result of a given unauthorized query). However, negative information (i.e., what value is never the execution result of a given unauthorized query) is also sensitive in many cases. This paper presents the following results on the security against inference attacks on negative information in object-oriented databases. First, inference of negative information is formalized under a model of object-oriented databases called method schemas. Then, the following two types of security problems are defined: (1) Is a given database instance secure against inference attacks on given negative information? (2) Are all of the database instances of a given database schema secure against inference attacks on given negative information? It is shown that the first problem is decidable in polynomial time in the description size of the database instance while the second one is undecidable. A decidable sufficient condition for any database instance of a given database schema to be secure is also proposed. Finally, it is shown that for a monadic schema (i.e., every method has exactly one parameter), this sufficient condition is also a necessary one.