Management Issues of a Mobile Agent-Based Service Environment

This paper introduces the concepts of a servicemanagement system for mobile agent-based services. Incontrast to several other approaches in whichagent-based applications are used to handletraditional network or service managementaspects, this paper describes a way to manage thedistributed agent environment itself. The proposedapproach was designed and developed by using OSI SystemsManagement concepts as a starting point, which are modified andadjusted in order to fulfill the new requirementsassociated with the mobile agent paradigm. However, thispaper does not cover integration or co-existence of the introduced agent management system (AMS)and traditional OSI management systems. Instead, theentire AMS environment is based on mobile agenttechnology. At present, this approach is validated inthe context of the European research project MobileAgent Environments in Intelligent Networks(MARINE).     

Automated Cellular Modeling and Prediction on a Large Scale

We describe CHAMP (CHurn Analysis, Modeling, andPrediction), an automated system for modeling cellularsubscriber churn that is predicting which customerswill discontinue cellular phone service. We describevarious issues related to developing and deployingthis system including automating data access from aremote data warehouse, preprocessing, featureselection, model validation, and optimization toreflect business tradeoffs. Using data from GTE'sdata warehouse for cellular phone customers, CHAMP iscapable of developing churn models customized byregion for over one hundred GTE cellular phone marketstotaling over 5 million customers. Every month churnfactors are identified for each geographic region andmodels are updated to generate churn scores predictingwho is likely to churn in the short term. Learningmethods such as decision trees and genetic algorithmsare used for feature selection and a cascade neuralnetwork is used for predicting churn scores. Inaddition to producing churn scores, CHAMP alsoproduces qualitative results in the form of rules andcomparison of market trends that are disseminatedthrough a web based interface.     

Can quantum discord increase in a quantum communication task?

Quantum teleportation of an unknown quantum state is one of the few communication tasks which has no classical counterpart. Usually the aim of teleportation is to send an unknown quantum state to a receiver. But is it possible in some way that the receiver’s state has more quantum discord than the sender’s state? We look at a scenario where Alice and Bob share a pure quantum state and Alice has an unknown quantum state. She performs joint measurement on her qubits and channel to prepare Bob’s qubits in a mixed state which has higher quantum discord than hers. We also observe an interesting feature in this scenario, when the quantum discord of Alice’s qubits increases, then the quantum discord of Bob’s prepared qubits decreases. Furthermore, we show that the fidelity of one-qubit quantum teleportation using Bob’s prepared qubits as the channel is higher than using Alice’s qubits.     

An Intelligent Transportation System Application for Smartphones Based on Vehicle Position Advertising and Route Sharing in Vehicular Ad-Hoc Networks

关于到来的紧急情况车辆和他们的线路警告司机能极大地在拥挤的城市里改进他们的旅行时间,当由于分心减少事故的风险时。这份报纸由建议耶稣,能够关于象救护车一样的到来的紧急情况车辆通知常规车辆的机器人应用,警察汽车和消防队贡献这个目标。这被创造能够直接在他们之间交流的车辆的一个网络使可能。因此,用户能由考虑到来的警戒以一种及时方式作开车决定。用我们的 GRCBox 硬件的支持,申请能在 5 GHz 乐队依靠车辆的特定的网络通讯, V2V (vehicle-to-vehicle ) 通讯通过基于机器人的 smartphone 和我们的 GRCBox 设备的联合正在被提供。申请与阻塞的不同层次在三种不同情形被测试,证明提供有能力第二在不到一个以内提醒起来注意 300 米,和通知车辆。     

The minimum positional error incurred by any connectivity-based positioning algorithm for mobile wireless systems

A very important problem in many wireless ad-hoc networks, including wireless sensor networks, is positioning or the determination of geographical locations of the wireless nodes. Positioning is used both in infrastructural aspects of sensor networks, like geographic routing and topology maintenance, and in applications like wildlife tracking. Connectivity-based positioning algorithms in mobile wireless systems are studied in this work. These algorithms compute node positions based only on the connectivity, i.e. the neighborhood information of each node. Many algorithms have been proposed for positioning in stationary node systems and bounds on positional error of algorithms have been derived. The design and analysis of positioning algorithms for mobile node systems is a more challenging problem. Node mobility increases the amount of positional information available to a positioning algorithm. The work in this paper establishes a bound on the positional error for connectivity-based algorithms in mobile systems. The formulation from the analysis is used to investigate the benefit of this additional positional information on reducing positional error. There is a limit to the usefulness of positional information from previous node positions due to movement. This captures an important performance tradeoff: historical positional information can yield reduced positional error but requires more connectivity information from the network which requires greater computational resources.     

Linearized motion estimation for articulated planes

In this paper, we describe the explicit application of articulation constraints for estimating the motion of a system of articulated planes. We relate articulations to the relative homography between planes and show that these articulations translate into linearized equality constraints on a linear least-squares system, which can be solved efficiently using a Karush-Kuhn-Tucker system. The articulation constraints can be applied for both gradient-based and feature-based motion estimation algorithms and to illustrate this, we describe a gradient-based motion estimation algorithm for an affine camera and a feature-based motion estimation algorithm for a projective camera that explicitly enforces articulation constraints. We show that explicit application of articulation constraints leads to numerically stable estimates of motion. The simultaneous computation of motion estimates for all of the articulated planes in a scene allows us to handle scene areas where there is limited texture information and areas that leave the field of view. Our results demonstrate the wide applicability of the algorithm in a variety of challenging real-world cases such as human body tracking, motion estimation of rigid, piecewise planar scenes, and motion estimation of triangulated meshes.     

Microfabrication and characterization of metal-embedded thin-film thermomechanical microsensors for applications in hostile manufacturing environments

Effective monitoring and diagnosis of manufacturing processes is of critical importance. If critical manufacturing process conditions are continuously monitored, problems can be detected and solved during the processing cycle. However, current technology still evidently lags behind practical needs. Microfabricated thin-film thermocouples and strain gauges are attractive for their small size and fast response. It is challenging to fabricate and embed these sensors into metallic components that are widely used in manufacturing. This paper investigates the fabrication, embedding, and characterization of metal embedded thin-film thermocouples and strain gauges. The materials (dielectric and metallic) constituting a complete microsensor were characterized and optimized. The results obtained from characterization and optimization of materials are presented and discussed. Thin-film thermocouples on stainless steel substrates (before and after embedding) were calibrated to elevated temperatures. The behavior of thin-film strain gauges was also studied. The metal embedded sensors demonstrated good accuracy, sensitivity, and linearity that matched the performance of commercial thermocouples and strain gauges well. The metal embedded microsensors are promising for in situ monitoring in hostile manufacturing environments.     

Bridging the gap: effect of text query reformulation in multimodal retrieval

Multimedia Tools and Applications - Multimodal Retrieval provides new paradigms and methods aimed at effectively searching through the enormous volume of data. Multimodal retrieval is a well...     

Self-adaptive differential evolution for feature selection in hyperspectral image data

Hyperspectral images are captured from hundreds of narrow and contiguous bands from the visible to infrared regions of electromagnetic spectrum. Each pixel of an image is represented by a vector where the components of the vector constitute the reflectance value of the surface for each of the bands. The length of the vector is equal to the number of bands. Due to the presence of large number of bands, classification of hyperspectral images becomes computation intensive. Moreover, higher correlation among neighboring bands increases the redundancy among them. As a result, feature selection becomes very essential for reducing the dimensionality. In the proposed work, an attempt has been made to develop a supervised feature selection technique guided by evolutionary algorithms. Self-adaptive differential evolution (SADE) is used for feature subset generation. Generated subsets are evaluated using a wrapper model where fuzzy k-nearest neighbor classifier is taken into consideration. Our proposed method also uses a feature ranking technique, ReliefF algorithm, for removing duplicate features. To demonstrate the effectiveness of the proposed method, investigation is carried out on three sets of data and the results are compared with four other evolutionary based state-of-the-art feature selection techniques. The proposed method shows promising results compared to others in terms of overall classification accuracy and Kappa coefficient.