Composing choreography is challenging because it involves numerous iterative refinements. According to our video analysis and interviews, choreographers typically need to imagine dancers' movements to revise drafts on paper since testing new movements and formations with actual dancers takes time. To address this difficulty, we present an interactive group-dance simulation interface, DanceUnisoner, that assists choreographers in composing a group dance in a simulated environment. With DanceUnisoner, choreographers can arrange excerpts from solo-dance videos of dancers throughout a three-dimensional space. They can adjust various parameters related to the dancers in real time, such as each dancer's position and size and each movement's timing. To evaluate the effectiveness of the system's parametric, visual, and interactive interface, we asked seven choreographers to use it and compose group dances. Our observations, interviews, and quantitative analysis revealed their successful usage in iterative refinements and visual checking of choreography, providing insights to facilitate further computational creativity support for choreographers.

With the increase of IoT devices, P2P-based IoT platforms have been attracting attention because of their capabilities of building and maintaining their networks autonomously in a decentralized way. In particular, Skip Graph, which has a low network rebuilding cost and allows range search, is suitable for the platform. However, when data observed at geographically close points have similar values (i.e. when data have strong spatial autocorrelation), existing types of Skip Graph degrade their search performances. In this paper, we propose a query transfer method that enables efficient search even for spatially autocorrelated data by adaptively using two-types of Skip Graph depending on the key-distance to the target key. Simulation results demonstrate that the proposed method can reduce the query transfer distance compared to the existing method even for spatially autocorrelated data.

The choreography realization problem is a design challenge for systems based on service-oriented architecture. In our previous studies, we studied the problem on a case where choreography was given by one or two scenarios and was expressed by an acyclic relation of events; we introduced the notion of re-constructibility as a property of acyclic relations to be satisfied. However, when choreography is defined by multiple scenarios, the resulting behavior cannot be expressed by an acyclic relation. An event structure is composed of an acyclic relation and a conflict relation. Because event structures are a generalization of acyclic relations, a wider class of systems can be expressed by event structures. In this paper, we propose the use of event structures to express choreography, introduce the re-constructibility of event structures, and show a necessary condition for an event structure to be re-constructible.

For a service-oriented architecture-based system, the problem of synthesizing a concrete model (i.e., behavioral model) for each peer configuring the system from an abstract specification-which is referred to as choreography-is known as the choreography realization problem. A flow of interaction of peers is called a scenario. In our previous study, we showed conditions and an algorithm to synthesize concrete models when choreography is given by one scenario. In this paper, we extend the study for choreography given by two scenarios. We show necessary and sufficient conditions on the realizability of choreography under both cases where there exist conflicts between scenarios and no conflicts exist.

This paper describes the background noise estimation technique of the tensor product expansion with absolute error (TPE-AE) to estimate multiple sources. The electroencephalogram (EEG) signal produced by the saccadic eye movement is adopted to analyze relationship between a brain function and a human activity. The electrooculogram (EOG) generated by eye movements yields significant problems for the EEG analysis. The denoising of EOG artifacts is important task to perform an accurate analysis. In this paper, the two types of TPE-AE are proposed to estimates EOG and other components in EEG during eye movement. One technique estimates two outer products using median filter based TPE-AE. The another technique uses a reference signal to separate the two sources. We show that the proposed method is effective to estimate and separate two sources in EEG.

For a service-oriented architecture based system, the problem of synthesizing a concrete model, i.e., behavioral model, for each service configuring the system from an abstract specification, which is referred to as choreography, is known as the choreography realization problem. In this paper, we assume that choreography is given by an acyclic relation. We have already shown that the condition for the behavioral model is given by lower and upper bounds of acyclic relations. Thus, the degree of freedom for behavioral models increases; developing algorithms of synthesizing an intelligible model for users becomes possible. In this paper, we introduce several metrics for intelligibility of state machines, and study the algorithm of synthesizing Pareto efficient state machines.

An overview of the evolution of intelligent transport systems (ITS) supported by advances in information and communication technologies is presented. Focusing on a sensing platform as one of the ITS applications, this paper presents a survey on vehicular ad hoc network-based geographic routing. In addition to the minimum requirement of street-awareness based on street maps, traffic and packet-awareness are considered essential to achieve acceptable packet delivery performance. In particular, in addition to statistical information, real-time traffic and packet level information are indispensable for making routing protocols feasible and effective. Considering traffic conditions that are highly space- and time-dependent, static nodes can be used to assist with geographic routing, and a protocol workable under a partial deployment of static nodes is considered.

For a service-oriented architecture-based system, the problem of synthesizing a concrete model (i.e., a behavioral model) for each peer configuring the system from an abstract specification — which is referred to as choreography — is known as the choreography realization problem. In this paper, we consider the condition for the behavioral model when choreography is given by an acyclic relation. A new notion called re-constructible decomposition of acyclic relations is introduced, and a necessary and sufficient condition for a decomposed relation to be re-constructible is shown. The condition provides lower and upper bounds of the acyclic relation for the behavioral model. Thus, the degree of freedom for behavioral models increases; developing algorithms for synthesizing an intelligible model for users becomes possible. It is also expected that the condition is applied to the case where choreography is given by a set of acyclic relations.

This paper reports an assessment of the feasibility and the practicality of a creation support system for contemporary dance e-learning. We developed a Body-part Motion Synthesis System (BMSS) that allows users to create choreographies by synthesizing body-part motions to increase the effect of learning contemporary dance choreography. Short created choreographies can be displayed as animation using 3DCG characters. The system targets students who are studying contemporary dance and is designed to promote the discovery learning of contemporary dance. We conducted a series of evaluation experiments for creating contemporary dance choreographies to verify the learning effectiveness of our system as a support system for discovery learning. As a consequence of experiments with 26 students who created contemporary dances, we verified that BMSS is a helpful creation training tool to discover new choreographic methods, new dance movements, and new awareness of their bodies.

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.

Much work in cooperative communication has been done from the perspective of the physical and network layers. However, the exact impact of signal error rate performance on cooperative routing discovery still remains unclear in multihop ad hoc networks. In this paper, we show the symbol error rate (SER) performance improvement obtained from cooperative commutation, and examine how to incorporate the factor of SER into the distributed routing discovery scheme called DGCR (Dynamic Geographic Cooperative Routing). For a single cooperative communication hop, we present two types of metric to specify the degree that one node is suitable for becoming the relay node. One metric is the potential of a node to relay with optimal SER performance. The other metric is the distance of a node to the straight line that passes through the last forwarding node and the destination. Based on location knowledge and contention scheme, we combine the two metrics into a composite metric to choose the relay node. The forwarding node is chosen dynamically according to the positions of the actual relay node and the destination. Simulation results show that our approach outperforms non-cooperative geographic routing significantly in terms of symbol error rate, and that DGCR's SER performance is better than traditional geographic cooperative routing with slight path length increase.

In classical routing protocols, geographical distances/locations are typically used as the metric to select the best route, under the assumption that shorter distances exhibit lower energy consumption and nodes within the communication range of the sender can receive packets with a certain success probability. However, in underwater acoustic sensor networks (UASNs), sound propagation in the ocean medium is more complex than that in the air due to many factors, including sound speed variations and the interaction of sound waves with the sea surface and floor, causing the sound rays to bend. Therefore, propagation of sound is anisotropic in water, and may cause a phenomenon called shadow zone where nodes in the communication range of the sender cannot hear any signal. This renders conventional routing protocols no longer energy-efficient. In this paper, we make use of the ray-model to account for the environment-dependent behavior of the underwater channel, re-define nodes' one-hop neighbors based on signal attenuation rather than geographical distance, and design a distributed energy-efficient routing protocol for UASNs. Results show that our ray-model-based routing policy consistently outperforms the shortest path policy, and performs very close to the optimal one in several scenarios.

In this paper, we propose a geographic location-based distributed routing (GDR) system. The GDR system provides information lookup based on latitude and longitude coordinates. Each node of the GDR system utilizes the coordinates as an identifier (ID), and manages an overlay routing table. An ID is generated to reflect the geographical location without using Space Filling Curve (SFC). The ID is in cartesian format (x, y), which represents the longitude x and latitude y. In a system with N nodes, each node has a routing table of size log N and a search is possible in O(log N). We evaluate the routing performance of GDR and other systems based on Chord, Kademlia and CAN. We show that in both the ID is in cartesian format and the ID is generated by using SFC, GDR, Chord and Kademlia have the same mean and the same variance of the path length, while the mean and the variance of the relay length of GDR are smaller than those of Chord and Kademlia. Furthermore, while GDR and CAN have the same mean and the same variance of the relay length, the mean and the variance of the path length of GDR are smaller than those of CAN.

In this paper, we present Greedy Routing for Maximum Lifetime (GRMax) [1],[2] which can use the limited energy available to nodes in a Wireless Sensor Network (WSN) in order to delay the dropping of packets, thus extend the network lifetime. We define network lifetime as the time period until a source node starts to drop packets because it has no more paths to the destination [3]. We introduce the new concept of Network Connectivity Aiming (NCA) node. The primary goal of NCA nodes is to maintain network connectivity and avoid network partition. To evaluate GRMax, we compare its performance with Geographic and Energy Aware Routing (GEAR) [4], which is an energy efficient geographic routing protocol and Greedy Perimeter Stateless Routing (GPSR) [5], which is a milestone among geographic routing protocol. We evaluate and compare the performance of GPSR, GEAR, and GRMax using OPNET Modeler version 15. The results show that GRMax performs better than GEAR and GPSR with respect to the number of successfully delivered packets and the time period before the nodes begin to drop packets. Moreover, with GRMax, there are fewer dead nodes in the system and less energy is required to deliver packets to destination node (sink).

Geographic routing uses the geographical location information provided by nodes to make routing decisions. However, the nodes can not obtain accurate location information due to the effect of measurement error. A new routing strategy using maximum expected distance and angle (MEDA) algorithm is proposed to improve the performance and promote the successive transmission rate. We firstly introduce the expected distance and angle, and then we employ the principal component analysis to construct the object function for selecting the next hop node. We compare the proposed algorithm with maximum expectation within transmission range (MER) and greedy routing scheme (GRS) algorithms. Simulation results show that the proposed MEDA algorithm outperforms the MER and GRS algorithms with higher successive transmission rate.

In this paper, a Spectrum-Aware Routing (SAR) protocol for cognitive radio ad hoc networks, (CRAHN), is proposed which is robust to primary user activity and node failures. The protocol allows nodes to collect spectrum information during a spectrum management interval followed by a transmission period. Cognitive users discover routes by joint channel and next hop selection (synchronization) in the transmission intervals. A restricted geographical routing approach is adopted to avoid performance degradation specially due to routing overhead. We also add spectrum mobility capabilities to routes in our proposed method to provide robustness to primary user activity. SAR protocol performance is investigated through simulations of different scenarios and is compared with the most similar work, CAODV protocol. The results indicate that SAR can achieve significant reduction in control overhead as well as improved throughput.

To provide scalability against group size, Global Location Search based Hierarchical Geographic Multicast Protocols (GLS-HGMPs) have recently been proposed for wireless sensor networks. To reduce the communication overhead imposed by the global location search and prevent the multicast data detour imposed by the hierarchical geographic multicasting in GLS-HGMPs, this letter proposes Local Location Search based Progressive Geographic Multicast Protocol (LLS-PGMP). Simulation results show that LLS-PGMP is superior to GLS-HGMPs.

This paper proposes a method for reducing redundant greedy-perimeter transitions in beacon-less geographic routing for wireless sensor networks (WSNs). Our method can be added to existing routing methods. Using a bloom filter, each node can detect a routing loop, and then the node stores the information as “failure history”. In the next forwarding the node can avoid such bad neighbors based on the failure history. Simulation results demonstrate the benefit of our method.

Fractal structures emerge from statistical and hierarchical processes in urban development or network evolution. In a class of efficient and robust geographical networks, we derive the size distribution of layered areas, and estimate the fractal dimension by using the distribution without huge computations. This method can be applied to self-similar tilings based on a stochastic process.

Recently in ad hoc networks, routing schemes using location information which is provided by GPS (Global Position System) have been proposed. However, many routing schemes using location information assume that a source node has already known the location information of the destination node and they do not adapt to large ad hoc networks. On another front, the autonomous clustering scheme has been proposed to construct the hierarchical structure in ad hoc networks and adapt to large ad hoc networks. However, even when the hierarchical structure is introduced, there is some problem. The data delivery ratio becomes lower as the node speed becomes higher, and clusterheads have much overhead in the hierarchical routing scheme based on the autonomous clustering scheme. In order to cope with these problems, this paper proposes a new Hierarchical Geographical Routing with Alternative Paths (Hi-GRAP) using the autonomous clustering scheme and shows the effectiveness of the proposed hierarchical geographical routing in comparison with GPSR, Hi-AODV and AODV through simulation experiments with respect to the amount of control packets and the data delivery ratio.