The increase in network access devices and demand for high quality of service (QoS) by the users have led to insufficient capacity for the network operators. Moreover, the existing control equipment and mechanisms are not flexible and agile enough for the dynamically changing environment of heterogeneous cellular networks (HetNets). This non-agile control plane is hard to scale with ever increasing traffic demand and has become the performance bottleneck. Furthermore, the new HetNet architecture requires tight coordination and cooperation for the densely deployed small cell base stations, particularly for interference mitigation and dynamic frequency reuse and sharing. These issues further complicate the existing control plane and can cause serious inefficiencies in terms of users' quality of experience and network performance. This article presents an SDN control framework for energy efficient downlink/uplink scheduling in HetNets. The framework decouples the control plane from data plane by means of a logically centralized controller with distributed agents implemented in separate entities of the network (users and base stations). The scheduling problem consists of three sub-problems: (i) user association, (ii) power control, (iii) resource allocation and (iv) interference mitigation. Moreover, these sub-problems are coupled and must be solved simultaneously. We formulate the DL/UL scheduling in HetNet as an optimization problem and use the Markov approximation framework to propose a distributed economical algorithm. Then, we divide the algorithm into three sub-routines for (i) user association, (ii) power control, (iii) resource allocation and (iv) interference mitigation. These sub-routines are then implemented on different agents of the SDN framework. We run extensive simulation to validate our proposal and finally, present the performance analysis.

This article introduces a self-organizing model which builds the topology of a DHT mapping system for ICN. Due to its self-organizing operation and low average degree of maintenance, the management overhead of the system is reduced dramatically, which yields inherent scalability. The proposed model can improve latency by around 10% compared to an existing approach which has a near optimal average distance when the number of nodes and degree are given. In particular, its operation is simple which eases maintenance concerns. Moreover, we analyze the model theoretically to provide a deeper understanding of the proposal.

Mobility load balancing (MLB) is a key technology for self-organization networks (SONs). In this paper, we explore the mobility load balancing problem and propose a unified cell specific offset adjusting algorithm (UCSOA) which more accurately adjusts the largely uneven load between neighboring cells and is easily implemented in practice with low computing complexity and signal overhead. Moreover, we evaluate the UCSOA algorithm in two different traffic conditions and prove that the UCSOA algorithm can get the lower call blocking rates and handover failure rates. Furthermore, the interdependency of the proposed UCSOA algorithm's performance and that of the inter-cell interference coordination (ICIC) algorithm is explored. A self-organization soft frequency reuse scheme is proposed. It demonstrates UCSOA algorithm and ICIC algorithm can obtain a positive effect for each other and improve the network performance in LTE system.

In this paper, an artificial sub-threshold oscillating spiking neuron is presented and its response phenomena to an input spike-train are analyzed. In addition, a dynamic parameter update rule of the neuron for achieving synchronizations to the input spike-train having various spike frequencies is presented. Using an analytical two-dimensional return map, local stability of the parameter update rule is analyzed. Furthermore, a pulse-coupled network of the neurons is presented and its basic self-organizing function is analyzed. Fundamental comparisons are also presented.

Topological structure of peer-to-peer (P2P) networks affects their operating performance. Thus, various models have been proposed to construct an efficient topology for the P2P networks. However, due to the simultaneous failures of peers and other disastrous events, it is difficult to maintain the originally designed topological structure that provides the network with some performance benefits. For this reason, in this paper we propose a simple local rewiring method that changes the network topology to have small diameter as well as highly clustered structure. Moreover, the presented evaluation study shows how these topological properties are involved with the performance of P2P networks.

A new generation network is requested to accommodate an enormous number of heterogeneous nodes and a wide variety of traffic and applications. To achieve higher scalability, adaptability, and robustness than ever before, in this paper we present new network architecture composed of self-organizing entities. The architecture consists of the physical network layer, service overlay network layer, and common network layer mediating them. All network entities, i.e. nodes and networks, behave in a self-organizing manner, where the global behavior emerges through their operation on local information and direct and/or indirect mutual interaction. The center of the architecture is so-called self-organization engines, which implement nonlinear self-organizing dynamics originating in biology, physics, and mathematics. In this paper, we also show some examples of self-organization engines.

The self-organized microwrinkles can serve as a surface alignment layer to align nematic liquid crystals, which is primarily based on the groove mechanism. The azimuthal anchoring energy is discussed and estimated from the groove topography and the actual twist angle in the twisted nematic cell.

This paper presents a middleware system for multi-agents on a distributed system as a general test-bed for bio-inspired approaches. The middleware is unique to other approaches, including distributed object systems, because it can maintain and migrate a dynamic federation of multiple agents on different computers. It enables each agent to explicitly define its own deployment policy as a relocation between the agent and another agent. This paper describes a prototype implementation of the middleware built on a Java-based mobile agent system and its practical applications that illustrates the utility and effectiveness of the approach in real distributed systems.

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.

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.

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.

We reported that a competitive learning neural network had the ability of self-organization in the classification of questionnaire survey data. In this letter, its self-organized learning was evaluated by means of mutual information. Mutual information may be useful to find efficently the network which can give optimal classification.