21487383
Coordinated Collective Motion of Groups of Autonomous Mobile Robots with Directed Interconnected Topology
Coordinated collective motion of groups of autonomous mobile robots is studied. A qualitative analysis for the collective dynamics of multiple autonomous robots with directed interconnected topology using nearest neighbor rules is given. A necessary and sufficient graphical condition is proposed to guarantee that the headings of all robots converge to the same heading. The graph having a globally reachable node plays a crucial role in convergence analysis. Furthermore, we show that the globally reachable node having no neighbors serves as a group leader as a special case
31796281
Reliable Robust Controller Design for Nonlinear State-Delayed Systems Based on Neural Networks
An approach is investigated for the adaptive guaranteed cost control design for a class of nonlinear state-delayed systems. The nonlinear term is approximated by a linearly parameterized neural networks( LPNN). A linear state feedback H∞ control law is presented. An adaptive weight adjustment mechanism for the neural networks is developed to ensure H∞ regulation performance. It is shown that the control gain matrices and be transformed into a standard linear matrix inequality problem and solved via a developed recurrent neural network.
10763979
Flocking Motion of Mobile Agents with a Leader Based on Distance-Dependent Adjacency Matrix
In this study, a novel procedure is presented for control and analysis of multiple autonomous agents with point mass dynamics achieving flocking motion with a time variant leader. Two control laws are proposed for a group of autonomous agents to achieve flocking formations related to two different centers (mass center and geometric center) of the flock. The first one is designed for flocking motion based on mass center and the second for geometric center. Distance-dependent adjacency matrix is used to quantify the way agents influence each other. Theoretical analysis is established to indicate the stability (cohesiveness, collision avoidance and velocity matching) of the control systems using classical Lyapunov theory. Finally, a simulation example is demonstrated to validate the theoretical results.
15077294
Fudan University: hierarchical video retrieval with adaptive multi-modal fusion
This paper describes Fudan University's interactive video retrieval system. The system uses an adaptive multi-modal fusion method and enables the user to browse the results hierarchically across different levels of temporal granularity.
28329707
Modified ASDPS for Geochemical Data Processing
Geochemical data is firstly processed using various smoothing approaches to reduce random and/or systematic errors in the survey and/or analysis. In theory, a moving window varying from 2 × 2 to 9 × 9 can be used to smoothen the data with either a linear model or curved surface model. However, how to determine the ’best’ outcome from many smoothened datasets still depends on the analyser’s experience. In this paper, we propose a slightly modified ASDPS scheme to determine the likely ’best’ outcome among many smoothened datasets. This is achieved by introducing a statistics-based selector to determine the ’best’ smoothened result. The examples of processing Pb and other elements demonstrate that this modified ASDPS scheme is useful for geochemical data processing. The proposed statistical selector quantifies the determination of smoothing processing of geochemical data. This selection model also reveals that for geochemical data smoothing the 3× 3 surface model produces the closest result to its original data. However, the 5 × 5 surface model would become the right choice if the result of the 3×3 surface model is considered too ’fine’ whereas that of the 5× 5 linear model is too ’coarse’.
6172514
Dynamic Subchannel and Power Allocation in OFDMA-Based DF Cooperative Relay Networks
This paper focuses on subchannel and power allocation problem in OFDMA-based decode-and-forward (DF) cooperative relay networks. The objective of this problem is to maximize the total data rate under the constraints of joint total transmission power and subchannels occupation, while maintaining the maximum fairness among multiple relay nodes. We transform this mixed integer programming problem to a standard convex optimization one through Lagrangian relaxation method. Accordingly, the optimal solution can be achieved by using Karush-Kuhn-Tucker (KKT) conditions. In addition, we propose two suboptimal subchannel allocation schemes with and without subchannel permutation (SP), which reduce significantly the computational complexity with small performance loss. The simulation results show that the performance of the proposed suboptimal algorithms with SP approaches asymptotically that of the optimal algorithm and with the increment of the number of relay nodes.
18580310
1-Persistent Collision-Free CSMA Protocols for Opportunistic Optical Hyperchannels
Recently, a new WDM optical network infrastructure named SMART [1], which is capable of dynamically setting up, modifying, and tearing down optical connections, was proposed. The performance of SMART is determined by the performance of its hyperchannels, which are essentially optical buses or light-trails. Previously proposed CSMA protocols for unidirectional optical buses can be used as distributed medium access control protocols for hyperchannels. However, these protocols are either unfair, or not work-conserving. In this paper, we propose a class of work-conserving, collision-free, and fair CSMA protocols for opportunistic hyperchannels which are tailored hyperchannels for SMART. We show that the performances of our proposed protocols are much better than that of p i -persistent CSMA and that of priority-based CSMA protocol in terms of throughput and fairness.
18521632
Mining Most General Multidimensional Summarization of Probably Groups in Data Warehouses
Data summarization is an important data analysis task in data warehousing and online analytic processing. In this paper, we consider a novel type of summarization queries, probable group queries , uch as “What are the groups of patients that have a50% or more opportunity to get lung cancer than the average?” An aggregate cell satisfying the requirement is called aprobable group . To make the answer succinct and effective, we propose that only the most general probable groups should be mined. For example, if both groups (smoking, drinking) and (smoking, *) are probable, then the former groups should not be returned. The problem of mining the most general probable groups is challenging since the probable groups can be widely scattered in the cube lattice, and do not present any monotonicity in group containment order. We extend the state-of-the-art BUC algorithm to tackle the problem, and develop techniques and heuristics to speed up the search. An extensive performance study is reported to illustrate the effect of our approach.