An Approach to Mining Picture Objects Based on Textual Cues

Abstract. The task of extracting knowledge from text is an important research problem for information processing and document understanding. Approaches to capture the semantics of picture objects in documents constitute subjects of great interest in the domain of document mining recently. In this paper, we present an approach to extracting information about picture objects in a document using cues from the text written about them. The goal of this work is to mine a document and understand the content of picture objects in the document based on meaning inferred from the texts written about such objects. We apply some Natural Language Processing techniques to extract semantic information about picture objects in a document and process texts written about them. The mining algorithms were developed and implemented as a working system and gone through testing and experimentations. Results and future extensions of the work are discussed in this paper

Abstract Exchange strategies for multiple Ant Colony System

this paper we apply the concept of parallel processing to enhance the performance of the Ant Colony System algorithm. New exchange strategies based on a weighting scheme are introduced under three different types of interactions. A search assessment technique based on a team consensus methodology is developed to study the influence of these strategies on the search behavior. This technique demonstrates the influence of these strategies in terms of search diversity. The performance of the Multiple Ant Colony System algorithm, applied to the Vehicle Routing Problem with Time Windows as well as the Traveling Salesman Problem, is investigated and evaluated with respect to solution quality and computational effort. The experimental studies demonstrate that the Multiple Ant Colony System outperforms the sequential Ant Colony System. The studies also indicate that the weighting scheme improves performance, particularly in strategies that share pheromone information among all colonies. A considerable improvement is also obtained by combining the Multiple Ant Colony System with a local search procedure

Multi-hop Interference-Aware Routing Protocol for Wireless Sensor Networks

AbstractWireless Sensor Networks (WSN) have gained much attention in recent years, however, these networks suffer from limited energy supply and noisy wireless links. Thus, efficient energy management and noise handling are key requirements in designing WSNs. This paper proposes an interference-aware and energy-aware routing algorithm such that power dissipation is uniform among all sensors. The proposed algorithm utilizes time synchronization and traffic scheduling to avoid interference. This work mathematically models the problem as node clustering optimization. Simulation results show the optimized proportions of packets sent by nodes to ensure uniform energy dissipation, as well as, reduced interference within clusters

A Low-Cost Lane-Determination System Using GNSS/IMU Fusion and HMM-Based Multistage Map Matching

This paper presents a low-cost real-time lane-determination system that fuses micro-electromechanical systems inertial sensors (accelerometers and gyroscopes), global navigation satellite system (GNSS), and commercially available road network maps. The system can be used for intelligent transportation systems, telematics applications, and autonomous driving. The system does not depend on visual markings or highly precise GNSS technology, such as DGPS or RTK, and it does not need explicit lane-level resolution maps. High-resolution estimation of the vehicle's position, velocity, and orientation is implemented by fusing inertial sensors with GNSS in a loosely coupled mode using extended Kalman filter. A curve-to-curve road-level map-matching is implemented using a hidden Markov model followed by a least-square regression step that estimates the vehi