Flexibility, adaptation and distribution have been regarded as major challenges of modern interorganizational workflow. To address these issues, this paper proposes an interorganizational workflow execution framework based on process agents and ECA rules. In our framework, an interorganizational workflow is modeled as a multiagent system with a process agent for each organization. The whole execution is divided into two parts: the intra-execution, which means execution within a same organization, and the inter-execution, which represents interaction between organizations. For intra-execution, we use the method of transforming the graph-based local workflow into block-based workflow to design general ECA rules. ECA rules are used to control internal state transitions and process agents are used to control external state transitions of tasks in the local workflows. Inter-execution is realized by process agent interaction protocols. The proposed approach can provide flexible execution of interorganizational workflow with distributed organizational autonomy and adaptation. A case study of offshore software development is illustrated for the proposed approach.

Cache performance optimization is an important design consideration in building high-performance embedded processors. Unlike general-purpose microprocessors, embedded processors can take advantages of application-specific information in optimizing the cache performance. One of such examples is to use modified cache index bits (over conventional index bits) based on memory access traces from key target embedded applications so that the number of conflict misses can be reduced. In this paper, we present a novel fine-grained cache reconfiguration technique which allows an intra-program reconfiguration of cache index bits, thus better reflecting the changing characteristics of a program execution. The proposed technique, called dynamic reconfiguration of index bits (DRIB), dynamically changes cache index bits in the function level. This compiler-directed and fine-grained approach allows each function to be executed using its own optimal index bits with no additional hardware support. In order to avoid potential performance degradation by frequent cache invalidations from reconfiguring cache index bits, we describe an efficient algorithm for selecting target functions whose cache index bits are reconfigured. Our algorithm ensures that the number of cache misses reduced by DRIB outnumbers the number of cache misses increased from cache invalidations. We also propose a new cache architecture, Two-Level Indexing (TLI) cache, which further reduces the number of conflict misses by intelligently dividing indexing steps into two stages. Our experimental results show that the DRIP approach combined with the TLI cache reduces the number of cache misses by 35% over the conventional cache indexing technique.

A one-dimensional (1-D) full search variable block size motion estimation (VBSME) architecture is presented in this paper. By properly choosing the partial sum of absolute differences (SAD) registers and scheduling the addition operations, the architecture can be implemented with simple control logic and regular workflow. Moreover, only one single-port SRAM is used to store the search area data. The design is realized in TSMC 0.18 µm 1P6M technology with a hardware cost of 67.6K gates. In typical working conditions (1.8 V, 25), a clock frequency of 266 MHz can be achieved.

This paper presents a method for tuning the structure of a causal network (CN) to evaluate a learner's profile for a learning assistance system that employs hierarchically structured learning material. The method uses as an initial CN structure causally related inter-node paths that explicitly define the learning material structure. Then, based on this initial structure other inter-node paths (sideway paths) not present in the initial CN structure are inferred by referring to the learner's database generated through the use of a learning assistance system. An evaluation using simulation indicates that the method has an inference probability of about 63% and an inference accuracy of about 30%.

The requirement for achieving the smoothness of mode transit between track seeking and track following has become a challenging issue for hard disk drive (HDD) motion control. In this paper, a random-optimization-based self-organizing neuro-fuzzy controller (RO-SNFC) for HDD servo system is presented. The proposed controller is composed of three designs. First, the concept of pseudo-errors is used to detect the potential dynamics of the unknown plant for rule extraction. Second, the propensity of the obtained pseudo-errors is specified by a cubic regression model, with which the cluster-based self-organization is implemented to generate clusters. The generated clusters are regarded as the antecedents of the T-S fuzzy "IF-THEN" rules. The initial knowledge base of the RO-SNFC is established. Third, the well-known random optimization (RO) algorithm is used to evolve the controller parameters for control efficiency and robustness. In this paper, a motion reference curve for HDD read/write head is employed. With the reference velocity curve, the RO-SNFC is used to achieve the optimal positioning control. From the illustrations, the feasibility of the proposed approach for HDD servo systems is demonstrated. Through the comparison to other approaches, the excellent performance by the proposed approach in access time and positioning smoothness is observed.

This work explores generative models of handwritten digit images using natural elastic nets. The analysis aims to extract global features as well as distributed local features of handwritten digits. These features are expected to form a basis that is significant for discriminant analysis of handwritten digits and related analysis of character images or natural images.

Energy consumption has become one of the most critical constraints in the design of portable multimedia systems. For media applications, address buses between processor and data memory consume a considerable amount of energy due to their large capacitance and frequent accesses. This paper studies impacts of memory data organization on the address bus energy. Our experiments show that the address bus activity is significantly reduced by 50% through exploring memory data organization and encoding address buses.

This paper presents a novel low-energy memory design technique based on variable analysis for on-chip data memory (RAM) in application-specific systems, which called VAbM technique. It targets the exploitation of both data locality and effective data width of variables to reduce energy consumed by data transfer and storage. Variables with higher access frequency and smaller effective data width are assigned into a smaller low-energy memory with fewer bit lines and word lines, placed closer the processor. Under constraints of the number of memory banks, VAbM technique use variable analysis results to perform allocating and assigning on-chip RAM into multiple banks, which have different size with different number of word lines and different number of bit lines tailored to each application requirements. Experimental results with several real embedded applications demonstrate significant energy reduction up to 64.8% over monolithic memory, and 27.7% compared to memory designed by memory banking technique.

The group organization used for group learning in a knowledge intensive domain like software development affects educational achievement. This paper proposes a group organization system for software engineering education done through group learning. The organizational problem itself is defined and why a Genetic Algorithm (GA) is an appropriate means of solving this problem is explained. This system is a Web application developed with open source software and runs on an open source software platform. Based on the group organization data collected from actual classes, we generated various group organizations by using different strategy parameter values. We then gave a questionnaire to actual students asking them which solution produced the fairest group organization. The replies received revealed that the candidate solution that set greater weight on leadership capability and system analysis capability was the fairest.

Although there are many projects focusing on multiagent systems, there are only a few focusing on systematic design of large scale multiagent system. In this paper we formalize the knowledge representation and sharing of agents, using symbol structures, define agencies as organizations (i. e. , a coalition of agents), propose a formalism to represent organizational Intelligence, devise a basic configuration for generalized agents (AG), and use them in a large scale multiagent system design. Private knowledge of an AG agent is represented by a symbol structure (SS) and AG agents can share their knowledge using combination, specialization and generalization methods that operate on the SS. Opposite to the other works, organizational knowledge, is defined as a property of at least a pair of AG agents.

Signal conservation logic (SCL) is a model of logic for the physical world subject to the matter conservation law. This letter proves that replication, complementary replication, and computational universality called elemental universality are equivalent in SCL. Since intelligence has a close relation to computational universality, the presented theorem may mean that life under the matter conservation law eventually acquires some kind of intelligence.

This paper focuses on the problems how to organize and retrieve video data in an effective manner. First we identify several issues to be solved for the problems. Next, we overview our current research results together with a brief survey in the research area of video databases. We especially describe the following research results obtained by the the Japanese Ministry of Education under Grant-in-Aid for Scientific Research on Priority Area: "Advanced Databases" concerned with organization and retrieval of video data: Instance-Based Video Annotation Models, Self-Organization of Video Data, and A Query Model for Fragmentally Indexed Video.

A fuzzy Kohonen clustering network was proposed which integrates the Fuzzy c-means (FCM) model into the learning rate and updating strategies of the Kohonen network. This yields an optimization problem related to FCM, and the numerical results show improved convergence as well as reduced labeling error. However, the clusters may be either hyperspherical-shaped or hyperellipsoidal-shaped, we use a generalized objective function involving a collection of linear varieties. In this way the model is distributed and consists of a series of `local' linear-type models (based on the revealed clusters). We propose a method to generalize the fuzzy Kohonen clustering networks. Anderson's IRIS data and the artificial data set are used to illustrate this method; and results are compared with the standard Kohonen approach and the fuzzy Kohonen clustering networks.

The hippocampus is thought to play an important role in the transformation from short-term memory into long-term memory, which is called consolidation. The physiological phenomenon of synaptic change called LTP or LTD has been studied as a basic mechanism for learning and memory. The neural network mechanism of the consolidation, however, is not clarified yet. The authors' approach is to construct information processing theory in learning and memory, which can explain the physiological data and behavioral data. This paper proposes a dynamical hippocampal model which can store and recall spatial input patterns. The authors assume that the primary functions of hippocampus are to store episodic information of sensory signals and to keep them for a while until the neocortex stores them as a long-term memory. On the basis of the hippocampal architecture and hypothetical synaptic dynamics of LTP/LTD, the authors construct a hippocampal model. This model considers: (1) divergent connections, (2) the synaptic dynamics of LTP and LTD based on pre- and postsynaptic coincidence, and (3) propagation of LTD. Computer simulations show that this model can store and recall its input spatial pattern by self-organizing closed activating pathways. By the backward propagation of LTD, the synaptic pathway for a specific spatial input pattern can be selected among the divergent closed connections. In addition, the output pattern also suggests that this model is sensitive to the temporal timing of input signals. This timing sensitivity suggests the applicability to spatio-temporal input patterns of this model. Future extensions of this model are also discussed.

This paper presents a new self-organization classification algorithm for remote-sensing images. Kohonen and other scholars have proposed self-organization algorithms. Kohonen's model easily converges to the local minimum by tuning the elaborate parameters. In addition to others, S. C. Amatur and Y. Takefuji have also proposed self-organization algorithm model. In their algorithm, the maximum neuron model (winner-take-all neuron model) is used where the parameter-tuning is not needed. The algorithm is able to shorten the computation time without a burden on the parameter-tuning. However, their model has a tendency to converge to the local minimum easily. To remove these obstacles produced by the two algorithms, we have proposed a new self-organization algorithm where these two algorithms are fused such that the advantages of the two algorithms are combined. The number of required neurons is the number of pixels multiplied by the number of clusters. The algorithm is composed of two stages: in the first stage we use the maximum self-organization algorithm until the state of the system converges to the local-minimum, then, the Kohonen self-organization algorithm is used in the last stage in order to improve the solution quality by escaping from the local minimum of the first stage. We have simulated a LANDSAT-TM image data with 500 pixel 100 pixel image and 8-bit gray scaled. The results justifies all our claims to the proposed algorithm.

We have recently discovered a novel phenomenon for the fabrication of nanostructures. A self-organization phenomenon of a strained InGaAs/AlGaAs system on a GaAs (311)B substrate during metal-organic vapor phase epitaxial growth is described, and nano-scale confinement lasers with self-organized InGaAs quantum disks are mentioned. Low-threshold operation of strained InGaAs quantum disk lasers is achieved under a continuous-wave condition at room temperature. The threshold current is around 20 mA, which is consider-ably lower than that of a reference double-quantum-well laser on a GaAs (100) substrate grown side-by-side. However, the light output versus the driving current exhibits a pronounced tendency towards a saturation compared to that of the (100) quantum well laser. We also discuss new methods using self-organization for nanofabrication to produce high-quality low-dimensional optical devices, considering requirements and the current status for next-generation optical devices.

This paper discusses our newly developed technology for making GaAs/InGaAs/GaAs Tetrahedral-Shaped Recess (TSR) quantum dots. The heterostructures were grown by low-pressure MOVPE in tetrahedral-shaped recesses created on a (111) B oriented GaAs substrate using anisotropic chemical etching. We examined these structures by using cathodoluminescence (CL) measurements, and observed lower energy emissions from the bottoms of, and higher energy emissions from the walls of the TSRs. This suggests carrier confinement at the bottoms with the lowest potential energy. We carried out microanlaysis of the structures by using TEM and EDX, and found an In-rich region that had grown vertically from the bottom of the TSR with a (111)B-like bond configuration. We also measured a smaller diamagnetic shift of the lower energy photoluminecscence (PL) peak in the structure. Based on these results, we have concluded that the quantum dots are formed at the bottoms of TSRs, mainly because of the dependence of InAs composition on the local crystalline structure in this system. We also studied the lateral distribution and vertical alignment of TSR quantum dots by CL and PL measurements respectively. The advantages of TSR quantum dot technology can be summarized as follows: (i) better control in dot positioning in the lateral direction, (ii) realization of dot sizes exceeding limitations posed by lithography, (iii) high uniformity of dot size, and (iv) vertical alignment of quantum dots.

A self-organizing neural network model of spatio-temporal visual receptive fields is proposed. It consists of a one-layer linear learning network with multiple temporal input channels, and each temporal channel has different impulse response. Every weight of the learning network is modified according to a Hebb-type learning algorithm proposed by Sanger. It is shown by simulation studies that various types of spatio-temporal receptive fields are self-organized by the network with random noise inputs. Some of them have similar response characteristics to X- and Y-type cells found in mammalian retina. The properties of receptive fields obtained by the network are analyzed theoretically. It is shown that only circularly symmetric receptive fields change their spatio-temporal characteristics depending on the bias of inputs. In particular, when the inputs are non-zero mean, the temporal properties of center-surround type receptive fields become heterogeneous and alter depending on the positions in the receptive fields.

This paper describes a new hierarchical bit line organization utilizing a T-shaped bit line(H-BLT) and its practical implementation in a 4-Mb SRAM using a 0.4µm CMOS process. The H-BLT has reduced the number of I/O circuits for multiplexers, sense amplifiers and write drivers, resulting in an efficient multiple blockdivision of the memory cell array. The size of the SRAM die was reduced by 14% without an access penalty. The active current is 30mA at 5 V and 10 MHz. The typical address access time is 35 ns with a 4.5 V supply voltage and a 30 pF load capacitance. The operating voltage range is 2.5 V to 6.0 V. H-BLT is a bright and useful architecture for the high density SRAMs of the future.

Using fundamental device and circuits, we have realized three functions required for synaptic connections in self-organizing neural networks: long term memory of synaptic weights, fixed total amount of synaptic weights in a neuron, and lateral inhibition. The first two functions have been condensed into an optical adaptive device and circuits with floating gates. Lateral inhibition has been realized by a winner-take-all circuit and a following lateral excitatory connection circuit. We have fabricated these devices and circuits using CMOS technology and confirmed the three functions. In addition, topological mapping, which is essential for feature extraction, has been formed in a primitive network constructed with the fundamental device and circuits.