15972722
Optimal Approximation Algorithms for Multi-agent Combinatorial Problems with Discounted Price Functions
Submodular functions are an important class of functions in combinatorial optimization which satisfy the natural properties of decreasing marginal costs. The study of these functions has led to strong structural properties with applications in many areas. Recently, there has been significant interest in extending the theory of algorithms for optimizing combinatorial problems (such as network design problem of spanning tree) over submodular functions. Unfortunately, the lower bounds under the general class of submodular functions are known to be very high for many of the classical problems. 
In this paper, we introduce and study an important subclass of submodular functions, which we call discounted price functions. These functions are succinctly representable and generalize linear cost functions. In this paper we study the following fundamental combinatorial optimization problems: Edge Cover, Spanning Tree, Perfect Matching and Shortest Path, and obtain tight upper and lower bounds for these problems. 
The main technical contribution of this paper is designing novel adaptive greedy algorithms for the above problems. These algorithms greedily build the solution whist rectifying mistakes made in the previous steps.
14475706
Efficiency, Fairness and Competitiveness in Nash Bargaining Games
Recently, [8] defined the class of Linear Nash Bargaining Games (LNB) and obtained combinatorial, polynomial time algorithms for several games in this class. [8] also defines two natural subclasses within LNB, UNB and SNB, which contain a number of natural Nash bargaining games. In this paper we define three basic game theoretic properties of Nash bargaining games: price of bargaining, fairness and full competitiveness. We show that for each of these properties, a game in UNB has this property iff it is in SNB.
47527050
Approximability of Combinatorial Problems with Multi-agent Submodular Cost Functions
Abstract— Applications in complex systems such as the Internet have spawned recent interest in studying situations involving multiple agents with their individual cost or utility functions. In this paper, we introduce an algorithmic framework for studying combinatorial problems in the presence of multiple agents with submodular cost functions. We study several fundamental covering problems (Vertex Cover, Shortest Path, Perfect Matching, and Spanning Tree) in this setting and establish tight upper and lower bounds for the approximability of these problems
14777107
Cooperative OFDM for energy-efficient wireless sensor networks
This paper presents a new energy-efficient cooperative sensor network scheme. Cooperative nodes shift the carrier frequency of the source node, and the destination node receives the signal from the source node and the cooperative nodes as a standard orthogonal frequency division multiplexing (OFDM) receiver. The maximal ratio combining (MRC) approach is applied to achieve full diversity with low complexity. The proposed scheme is scalable with respect to the number of nodes and is robust to time synchronization. A clustered network topology and cooperation protocol are proposed to realize multi-hop long haul signal transmission. The effect of the cluster scale is studied thoroughly to obtain the optimal size of clusters subject to the cooperation overhead. Simulation results show that the proposed cooperation scheme can consistently reduce the transmission power with the increase of cooperation scale. However, there is an optimal cluster scale where a good tradeoff between the diversity gain and cooperation cost can be established to minimize the total power consumption of the whole network.
14161644
A Fast Algorithm for Creating a Compact and Discriminative Visual Codebook
In patch-based object recognition, using a compact visual codebook can boost computational efficiency and reduce memory cost. Nevertheless, compared with a large-sized codebook, it also risks the loss of discriminative power. Moreover, creating a compact visual codebook can be very time-consuming, especially when the number of initial visual words is large. In this paper, to minimize its loss of discriminative power, we propose an approach to build a compact visual codebook by maxi- mally preserving the separability of the object classes. Furthermore, a fast algorithm is designed to accomplish this task effortlessly, which can hierarchically merge 10,000 visual words down to 2 in ninety seconds. Experimental study shows that the compact visual codebook created in this way can achieve excellent classification performance even after a considerable reduction in size.
34094601
DNA-Based Algorithm for 0-1 Planning Problem
Biochemical reaction theory based DNA computation is of much better performance in solving a class of intractable computational problems such as NP-complete problems, it is important to study the DNA computation. A novel algorithm based on DNA computation is proposed, which solves a special category of 0-1 planning problem by using the surface-based fluorescence labeling technique. The analysis show that our new algorithm is of significant advantages such as simple encoding, low cost and short operating time, etc.
41493711
Car Plate Localization Using Modified PCNN in Complicated Environment
Car plate Localization, which remains a difficult problem under complicated environment, is the key problem in many traffic related applications. In this paper we describe a new method based on modified Pulse Coupled Neural Network (PCNN) with adaptive threshold, which can capture relatively complete objects in human perception. After inverse filtering, PCNN processing is applied to produce a firing time sequence image. Then car plates' position and rotated angle can be extracted from the firing image. Experiment results show that the correct car plate locating rate reaches 98%, which is higher than other Localization methods on the same image database.
44863002
Comparative Study on Bionic Optimization Algorithms for Sewer Optimal Design
Sewer network as a necessary urban infrastructure plays an important role in people’s daily life. Conventional optimization techniques have significant limitations on solving the problems of sewer optimal design. Because as a high-dimensional discrete complex optimization problem, sewer optimal design is characterized by its discrete objective function and, as an integer discrete variable, its decision variable amount keeps the same pace with engineering scales. Over the last decade, various kinds of modern bionic optimization algorithms with their special advantages have been created and applied into sewer optimal design successfully. Based on previous studies, this paper analyses and compares the solution performances of Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Ant Colony Algorithms (ACA) from the three aspects respectively, they are convergence, speed and complexity of algorithm. The research result shows that compared with the other two algorithms, the ACA manifests its superiority for better convergence, satisfactory speed and relatively small algorithm complexity, which are very suitable for solving the problems of sewer optimal design.