Enhancing the accuracy of WLAN-based location determination systems using predicted orientation information

Indoor location determination has emerged as a significant research topic due to the wide-spread deployment of wireless local area networks (WLANs) and the demand for context-aware services inside buildings. However, prediction accuracy remains a primary issue surrounding the practicality of WLAN-based location determination systems. This study proposes a novel scheme that utilizes mobile user orientation information to improve prediction accuracy. Theoretically, if the precise orientation of a user can be identified, then the location determination system can predict that user’s location with a high degree of accuracy by using the training data of this specific-orientation. In reality, a mobile user’s orientation can be estimated only by comparing variations in received signal strength; and nevertheless the predicted orientation may be incorrect. Incorrect orientation information causes the accuracy of the entire system to decrease. Therefore, this study presents an accumulated orientation strength algorithm which can utilize uncertain estimated orientation information to improve prediction accuracy. Implementation of this system is based on the Bayesian model, and the experimental results indeed show the effectiveness of our proposed approach.     

Effects of insulin therapy on non-insulin-dependent diabetics with secondary oral hypoglycemic agent failure

A recent trend among physicians is the categorisation of lung scans as normal [excludes pulmonary embolism (PE)], high probability (confirms PE) and non-diagnostic (no judgement on PE risk). The low probability scan is therefore being eliminated as a functional category. This occasional survey contends that such an approach is misguided. Correction of the original PIOPED data with certain assumptions provides a more reproducible, albeit restricted, low probability scan category which excludes PE in 97% of cases in the low pre-test clinical category. Patients with a low probability scan with risk factors for PE (i.e. medium clinical risk) will require further investigation. More important, the very low probability scan category excludes PE in 98% of patients with low and more than 92% of patients with medium pre-test clinical likelihood. The demise of "low probability" is premature.     

Elliptical trough reflector for the collection of light from linear sources

A trough reflector with a reflective, truncated elliptical surface was designed to efficiently collect freely propagating light from a linear source. The source was placed at one focus of the reflector, and light was collected through a rectangular aperture near the second focus. Collection efficiency was much greater than that of a spherical integrator and approximately 6.5x greater than that of an objective lens; as much as approximately 55% of the light could be captured from the full aperture. This reflector could be used to efficiently collect surface fluorescence excited by use of evanescent waves in fluorescence-based fiber optic or capillary waveguide sensors.     

Polarization-Dependent Periodic Pulse Oscillation in a Diode-Laser-Pumped and Intracavity Frequency-Doubled Nd:YVO 4 Laser

We demonstrate that two cross-polarized longitudinal modes can have 50% higher conversion efficiency than two parallel-polarized longitudinal modes in a diode-laser-pumped and intracavity frequency-doubled Nd:YVO(4) laser when operated under periodic pulse oscillation. Through simulations of the rate equations for primary frequency intensities and gains, we also verify that this effect can be attributed to gain competition and complementary conversion coefficient between second-harmonic and sum-frequency generations.     

Global exponential robust stability of reaction–diffusion interval neural networks with continuously distributed delays

This paper considers the existence of the equilibrium point and its global exponential robust stability for reaction-diffusion interval neural networks with variable coefficients and distributed delays by means of the topological degree theory and Lyapunov-functional method. The sufficient conditions on global exponential robust stability established in this paper are easily verifiable. An example is presented to demonstrate the effectiveness and efficiency of our results.     

Filtering Data Streams for Entity-Based Continuous Queries

The idea of allowing query users to relax their correctness requirements in order to improve performance of a data stream management system (e.g., location-based services and sensor networks) has been recently studied. By exploiting the maximum error (or tolerance) allowed in query answers, algorithms for reducing the use of system resources have been developed. In most of these works, however, query tolerance is expressed as a numerical value, which may be difficult to specify. We observe that in many situations, users may not be concerned with the actual value of an answer, but rather which object satisfies a query (e.g., "who is my nearest neighbor?”). In particular, an entity-based query returns only the names of objects that satisfy the query. For these queries, it is possible to specify a tolerance that is "nonvalue-based.” In this paper, we study fraction-based tolerance, a type of nonvalue-based tolerance, where a user specifies the maximum fractions of a query answer that can be false positives and false negatives. We develop fraction-based tolerance for two major classes of entity-based queries: 1) nonrank-based query (e.g., range queries) and 2) rank-based query (e.g., k-nearest-neighbor queries). These definitions provide users with an alternative to specify the maximum tolerance allowed in their answers. We further investigate how these definitions can be exploited in a distributed stream environment. We design adaptive filter algorithms that allow updates be dropped conditionally at the data stream sources without affecting the overall query correctness. Extensive experimental results show that our protocols reduce the use of network and energy resources significantly.     

An integrated fuzzy TOPSIS and MCGP approach to supplier selection in supply chain management

Supplier selection is an important issue in supply chain management. In recent years, determining the best supplier in the supply chain has become a key strategic consideration. However, these decisions usually involve several objectives or criteria, and it is often necessary to compromise among possibly conflicting factors. Thus, the multiple criteria decision making (MCDM) becomes a useful approach to solve this kind of problem. Considering both tangible and intangible criteria, this study proposes integrated fuzzy techniques for order preference by similarity to ideal solution (TOPSIS) and multi-choice goal programming (MCGP) approach to solve the supplier selection problem. The advantage of this method is that it allows decision makers to set multiple aspiration levels for supplier selection problems. The integrated model is illustrated by an example in a watch firm.     

On NFAs where all states are final,initial, or both

We examine questions involving nondeterministic finite automata where all states are final, initial, or both initial and final. First, we prove hardness results for the nonuniversality and inequivalence problems for these NFAs. Next, we characterize the languages accepted. Finally, we discuss some state complexity problems involving such automata.