On Generalized Fuzzy Belief Functions in Infinite Spaces

Determined by a fuzzy implication operator, a general type of fuzzy belief structure and its induced dual pair of fuzzy belief and plausibility functions in infinite universes of discourse are first defined. Relationship between the belief-structure-based and the belief-space-based fuzzy Dempster-Shafer models is then established. It is shown that the lower and upper fuzzy probabilities induced by the fuzzy belief space yield a dual pair of fuzzy belief and plausibility functions. For any fuzzy belief structure, there must exist a fuzzy belief space such that the fuzzy belief and plausibility functions defined by the given fuzzy belief structure are just the lower and upper fuzzy probabilities induced by the fuzzy belief space, respectively. Essential properties of the fuzzy belief and plausibility functions are also examined. The fuzzy belief and plausibility functions are, respectively, a fuzzy monotone Choquet capacity and a fuzzy alternating Choquet capacity of infinite order.     

Description and evaluation of a new 3-D computerized treatment planning dose compensator system

Evaluation has been performed of compensators generated by means of a computerized three-dimensional treatment planning system that can utilize either digitized slice profiles or CT scans. Two methods of calculating compensator thickness are used: the modified Batho power law (dSAR) method for digitized profiles and the equivalent TAR (eqTAR) method for CT scans. This system not only compensates for patient surface contours but also compensates for internal inhomogeneities. In addition, any required wedging will be incorporated in the compensator generation. This system has been tested for a number of extreme cases with inhomogeneities and sloping contours. Good agreement was obtained between the measured and computer calculated dose profiles especially along the central axis of the beam. A "Profile Uniformity Index" was defined to quantify the goodness of dose compensation in three dimensions. Compensation using this system can achieve good dose uniformity within the target volume in all clinical cases and is definitely an improvement over systems based solely on tissue deficit.     

An integrated dual sensor system automatically optimized by target rate histogram

The use of combined sensors and advanced algorithms using different principles can improve rate performance over a single sensor system. Combinations of sensors and more sophisticated algorithms, however, invariably increase the complexity of pacemaker programming. An automatically optimized combined minute ventilation and activity DDDR pacemaker was developed to minimize repeated sensor adjustment. The device used subthreshold (below cardiac stimulation threshold) lead impedance to detect lead configuration at implantation automatically, followed by "implant management," including setting of lead polarity and initiation of DDDR pacing. Automatic sensor adaptation was achieved by programming a "target rate histogram" based on the patient's activity level and frequency of exertion, and the rate profile optimization process matched the recorded integrated sensor response to the target rate histogram profile. In nine patients implanted with the DX2 pacemakers, the implant management gave 100% accuracy in the detection of lead polarity. Rate profile optimization automatically increased the pacing rate during exercise between discharge and 3-month follow-up (hall walk: 78 +/- 3 vs 98 +/- 3 beats/min, and maximal treadmill exercise: 89 +/- 6 vs 115 +/- 5 beats/min, P < 0.001) with a significant increase in exercise duration during maximal exercise (7.18 +/- 1 min vs 9.56 +/- 2 min, P = 0.05). The accuracy of rate profile optimization versus manual programming was assessed at 1 month, and there was no significant difference between pacing rate kinetics and maximal pacing rate between the two methods of programming. In conclusion, pacemaker automaticity can be initiated at implantation and the self-optimized rate adaptive response appeared to be comparable to that derived from a manual programming procedure, which may reduce the need to perform time consuming sensor programming.     

Recognition of handwritten Chinese characters by critical region analysis

The problem of recognizing offline handwritten Chinese characters has been investigated extensively. One difficulty is due to the existence of characters with very similar shapes. In this paper, we propose a “critical region analysis” technique which highlights the critical regions that distinguish one character from another similar character. The critical regions are identified automatically based on the output of the Fisher's discriminant. Additional features are extracted from these regions and contribute to the recognition process. By incorporating this technique into the character recognition system, a record high recognition rate of 99.53% on the ETL-9B database is obtained.     

Scheduling jobs with equal processing times subject to machine eligibility constraints

We consider the problem of nonpreemptively scheduling a set of n jobs with equal processing times on m parallel machines so as to minimize the makespan. Each job has a prespecified set of machines on which it can be processed, called its eligible set. We consider the most general case of machine eligibility constraints as well as special cases of nested and inclusive eligible sets. Both online and offline models are considered. For offline problems we develop optimal algorithms that run in polynomial time, while for online problems we focus on the development of optimal algorithms of a new and more elaborate structure as well as approximation algorithms with good competitive ratios.     

Rule acquisition and attribute reduction in real decision formal contexts

Formal Concept Analysis of real set formal contexts is a generalization of classical formal contexts. By dividing the attributes into condition attributes and decision attributes, the notion of real decision formal contexts is introduced. Based on an implication mapping, problems of rule acquisition and attribute reduction of real decision formal contexts are examined. The extraction of “if–then” rules from the real decision formal contexts, and the approach to attribute reduction of the real decision formal contexts are discussed. By the proposed approach, attributes which are non-essential to the maximal s rules or l rules (to be defined later in the text) can be removed. Furthermore, discernibility matrices and discernibility functions for computing the attribute reducts of the real decision formal contexts are constructed to determine all attribute reducts of the real set formal contexts without affecting the results of the acquired maximal s rules or l rules.     

Data race: tame the beast

Data races hamper parallel programming and threaten the reliability of future software. This paper proposes the data race prevention scheme View-Oriented Data race Prevention (VODAP), which can prevent data races in the View-Oriented Parallel Programming (VOPP) model. VOPP is a novel shared-memory data-centric parallel programming model, which uses views to bundle mutual exclusion with data access. We have implemented the data race prevention scheme with a memory protection mechanism. Experimental results show that the extra overhead of memory protection is trivial in our applications. The performance is evaluated and compared with modern programming models such as OpenMP and Cilk.     

Access and service convergence over the mobile internet – A survey

Next generation heterogeneous wireless networks are expected to interwork with Internet Protocol (IP)-based infrastructures. Conventional network services operate like silos in that a specific set of services are offered over a specific type of access network. It is desirable for users to be able to roam between fixed and mobile networks that employ different access technologies. Therefore, mobility management with quality of service (QoS) support is of particular importance and one of the driving forces of convergence. Since service providers often provide more than one service to their subscribers, it is important to facilitate convergence of network charging architecture through a common charging framework. One of the main issues of IP-based convergence is security and privacy. This requires coordination of different security policies in diverse networks that have different security levels and capabilities. The business case for migration to an IP-based platform motivates operators to deliver more powerful services for customers as well as a better user experience. This paper provides an overview of converged mobile Internet architectures and their implications on QoS, charging/billing and security, as well as emerging business models for telecommunication services.     

Kernel Width Optimization for Faulty RBF Neural Networks with Multi-node Open Fault

Many researches have been devoted to select the kernel parameters, including the centers, kernel width and weights, for fault-free radial basis function (RBF) neural networks. However, most are concerned with the centers and weights identification, and fewer focus on the kernel width selection. Moreover, to our knowledge, almost no literature has proposed the effective and applied method to select the optimal kernel width for faulty RBF neural networks. As is known that the node faults inevitably take place in real applications, which results in a great many of faulty networks, it will take a lot of time to calculate the mean prediction error (MPE) for the traditional method, i.e., the test set method. Thus, the letter derives a formula to estimate the MPE of each candidate width value and then use it to select the optimal one with the lowest MPE value for faulty RBF neural networks with multi-node open fault. Simulation results show that the chosen optimal kernel width by our proposed MPE formula is very close to the actual one by the conventional method. Moreover, our proposed MPE formula outperforms other selection methods used for fault-free neural networks.