2692717
End-to-end delay boundary prediction using maximum entropy principle (MEP) for internet-based teleoperation
Since data packets may get lost somewhere in the Internet connections, for real-time applications such as Internet-based teleoperation, delay boundary prediction plays an important role in determining properly whether a packet is lost or not. The predictors currently employed are lowpass filters based on the autoregressive and moving average (ARMA) models. However, recent studies and the results of the experiments in this paper show that the traditional ARMA model is not suitable because sometimes delays develop with quick and evident variation. In this paper, we present a novel adaptive algorithm for delay boundary prediction based on the maximum entropy principle (MEP). The results of our 3 successive working day experiments on 9 links which consists of academic, commercial and governmental ones among Northern America, Asia and Europe show that the MEP algorithm proposed has a better performance than the traditional ARMA method.
22089214
A neural network based torque controller for collision-free navigation of mobile robots
In this paper, a neural network based torque controller is proposed for real-time collision-free navigation of nonholonomic mobile robots. A torque resulted from the obstacles is incorporated in the control design based on the artificial potential technique, which locally pushes the robot away from the obstacles to avoid collisions. All the needed environment information can be obtained from on-board robot sensors that have limited visibility range only. A torque from a simply single-layer neural network is employed to learn the completely unknown robot dynamics. The system stability is guaranteed by a Lyapunov stability theory. The real-time fine control of mobile robots is achieved through the on-line learning of the neural network. The effectiveness of the proposed controller is demonstrated by simulation studies in both static and dynamic environments.
1275297
Bridging Physical and Virtual Worlds: Complex Event Processing for RFID Data Streams
Advances of sensor and RFID technology provide significant new power for humans to sense, understand and manage the world. RFID provides fast data collection with precise identification of objects with unique IDs without line of sight, thus it can be used for identifying, locating, tracking and monitoring physical objects. Despite these benefits, RFID poses many challenges for data processing and management: i) RFID observations contain duplicates, which have to be filtered; ii) RFID observations have implicit meanings, which have to be transformed and aggregated into semantic data represented in their data models; and iii) RFID data are temporal, streaming, and in high volume, and have to be processed on the fly. Thus, a general RFID data processing framework is needed to automate the transformation of physical RFID observations into the virtual counterparts in the virtual world linked to business applications. In this paper, we take an event-oriented approach to process RFID data, by devising RFID application logic into complex events. We then formalize the specification and semantics of RFID events and rules. We demonstrate that traditional ECA event engine cannot be used to support highly temporally constrained RFID events, and develop an RFID event detection engine that can effectively process complex RFID events. The declarative event-based approach greatly simplifies the work of RFID data processing, and significantly reduces the cost of RFID data integration.
7683416
Digital filter design using multiple Pareto fronts
Evolutionary approaches have been used in a large variety of design domains, from aircraft engineering to the designs of analog filters. Many of these approaches use measures to improve the variety of solutions in the population. In this paper, clustering and Pareto optimisation are combined into a single evolutionary design algorithm. The objective of this is to prevent the system from converging prematurely to a local minimum and to encourage a number of different designs that fulfil the design criteria. Our approach is demonstrated in the domain of digital filter design. Using a polar coordinate based pole-zero representation, two different lowpass filter design problems are explored. The results are compared to designs created by a human expert. The results demonstrate that the evolutionary process is able to create designs that are competitive with those created using a conventional design process by a human expert. They also demonstrate that each evolutionary run can produce a number of different designs with similar fitness values, but very different characteristics.
26752053
Structural Attribute Feature Code Representation and Recognition of Multifont Printed Chinese Characters
In this paper, a new structural representation and fuzzy matching scheme are proposed for multifont printed Chinese character recognition. A Chinese character is decomposed into eight stroke types. A complete structural attribute feature codes among different types of strokes are defined and extracted, which consist of weak and strong primary codes and secondary codes. Weak and strong primary feature codes depict the global and local spatial relationships among different types of strokes respectively, and they are used for a detailed match. A fuzzy matching scheme is used for detailed match between an input character and candidate characters. An experiment on 3755 Chinese characters used daily in multifonts and multisizes shows that our method is robust and can achieve high recognition accuracy.
227525
Integrated RFID data modeling: an approach for querying physical objects in pervasive computing
Radio frequency identiﬂcation(RFID) technology uses radio-frequency waves to transfer data between readers and mov-able tagged objects without line of sight. RFID holds thepromise of real-time identifying, locating, tracking and mon-itoring physical objects, and can be used for a wide range ofpervasive computing applications. To achieve these goals,RFID data have to be collected, transformed and expres-sively modeled as their virtual counterparts in the virtualworld. RFID data, however, have their own unique char-acteristics { including aggregation, location, temporal andhistory-oriented { which have to be fully considered and inte-grated into the data model. The diversity of RFID applica-tions pose further challenges to a generalized framework forRFID data modeling. In this paper, we explore the funda-mental characteristics of RFID data and classify RFID ap-plications into a set of typical scenarios. We then propose ageneralized RFID data modeling framework with constructsfor each typical scenario. These constructs can be combinedto model most RFID applications in real world.
11533048
RFID Data Processing with a Data Stream Query Language
RFID technology provides significant advantages over traditional object-tracking technologies and is increasingly adopted and deployed in real applications. RFID applications generate large volume of streaming data, which have to be automatically filtered, processed, and transformed into semantic data, and integrated into business applications. Indeed, RFID data are highly temporal, and RFID observations form complex temporal event patterns which can be very different for various RFID applications. Thus, it is desirable to have a general RFID data processing framework with a powerful language, for the end users to express a variety of queries on RFID data streams, as well as detecting complex events patterns. While data stream management systems (DSMSs) are emerging for optimized stream data processing, they usually lack the language construct support for temporal event detection. In this paper, we discuss a stream query language to provide comprehensive temporal event detection, through temporal operators and extension of sliding-window constructs. With the integration of temporal event detection, a DSMS has the capability to serve as a powerful system for RFID data processing.