Intelligent maintenance prediction system for LED wafer testing machine

Achieving high quality production of light-emitting diode (LED) wafers requires robust monitoring and the use of a stable test machine. In many factories, production continues 24 h a day. Stopping the manufacturing process at a factory is often difficult. Therefore, reducing inspection time and ensuring the stability of test machines are important. Traditionally, LED wafer factories examine their test machines during periodic maintenance. Standard lamp adjustments are performed to ensure their accuracy. This process interrupts the manufacturing process and requires extra manpower. It reduces productivity and increases production cost. Additionally, the accurate assessment of the aging of the components of the machine requires an experienced engineer. Correctly timing the maintenance and replacing the aging components of the LED wafer test machine are important. This work performed feature extraction to identify the working attributes of an LED wafer test machine. The intelligent maintenance prediction system then uses the radial basis function neural network and variability of the working attributes to predict the maintenance times and aging of the LED wafer test machines. Experimental results reveal that the accuracy of proposed system in predicting maintenance times exceeds 98 %.     

A microstrip wideband monopole antenna for multisystem integration by utilizing stepped‐impedance structure and L‐shaped slot

In this article, a coplanar‐waveguide (CPW)‐fed dual‐band antenna for applications of the multisystem integration has been demonstrated. The resonance analysis of the stepped‐impedance (SI) monopole is presented by using the transmission‐line analysis method. The frequency‐response characteristics of the SI‐monopole, such as the resonance condition and harmonic response, are systematically summarized. Furthermore, utilizing several simple techniques, such as bent feeding topology, asymmetric ground plane, and an L‐shaped slot etched in the ground plane, a right‐hand circularly polarized (RHCP) radiating wave at 1.57 GHz and a left‐hand circularly polarized (LHCP) radiating wave at 2.33 GHz are excited for the applications of the global positioning system (GPS) and the satellite digital audio radio (SDAR) service system. After optimization of the geometrical parameters of the proposed antenna, the measured impedance bandwidths of a reflection coefficient less than ?10 dB range from 1.40 to 2.98 GHz and from 4.48 to 6.27 GHz, and thus covers most of the commercial wireless communication systems, such as GPS, digital cellular system (DCS), personal communication system (PCS), international mobile telecommunications (IMT)?2000, wireless local area networks (WLAN), and long‐term evolution (LTE) 2300/2600. The measured 3‐dB axial ratio (AR) bandwidths are about 80 MHz at 1.57 GHz and 100 MHz at 2.33 GHz.     

An ideal multi-secret sharing scheme based on MSP

A multi-secret sharing scheme is a protocol to share m arbitrarily related secrets s₁, … , s_m among a set of n participants. In this paper, we propose an ideal linear multi-secret sharing scheme, based on monotone span programs, where each subset of the set of participants may have the associated secret. Our scheme can be used to meet the security requirement in practical applications, such as secure group communication and privacy preserving data mining etc. We also prove that our proposed scheme satisfies the definition of a perfect multi-secret sharing scheme.     

Comparison of multiple linear regression and artificial neural network in developing the objective functions of the orthopaedic screws

Optimizing the orthopaedic screws can greatly improve their biomechanical performances. However, a methodical design optimization approach requires a long time to search the best design. Thus, the surrogate objective functions of the orthopaedic screws should be accurately developed. To our knowledge, there is no study to evaluate the strengths and limitations of the surrogate methods in developing the objective functions of the orthopaedic screws. Three-dimensional finite element models for both the tibial locking screws and the spinal pedicle screws were constructed and analyzed. Then, the learning data were prepared according to the arrangement of the Taguchi orthogonal array, and the verification data were selected with use of a randomized selection. Finally, the surrogate objective functions were developed by using either the multiple linear regression or the artificial neural network. The applicability and accuracy of those surrogate methods were evaluated and discussed. The multiple linear regression method could successfully construct the objective function of the tibial locking screws, but it failed to develop the objective function of the spinal pedicle screws. The artificial neural network method showed a greater capacity of prediction in developing the objective functions for the tibial locking screws and the spinal pedicle screws than the multiple linear regression method. The artificial neural network method may be a useful option for developing the objective functions of the orthopaedic screws with a greater structural complexity. The surrogate objective functions of the orthopaedic screws could effectively decrease the time and effort required for the design optimization process.     

Probabilistic approximations of ODEs based bio-pathway dynamics

Bio-chemical networks are often modeled as systems of ordinary differential equations (ODEs). Such systems will not admit closed form solutions and hence numerical simulations will have to be used to perform analyses. However, the number of simulations required to carry out tasks such as parameter estimation can become very large. To get around this, we propose a discrete probabilistic approximation of the ODEs dynamics. We do so by discretizing the value and the time domain and assuming a distribution of initial states w.r.t. the discretization. Then we sample a representative set of initial states according to the assumed initial distribution and generate a corresponding set of trajectories through numerical simulations. Finally, using the structure of the signaling pathway we encode these trajectories compactly as a dynamic Bayesian network.This approximation of the signaling pathway dynamics has several advantages. First, the discretized nature of the approximation helps to bridge the gap between the accuracy of the results obtained by ODE simulation and the limited precision of experimental data used for model construction and verification. Second and more importantly, many interesting pathway properties can be analyzed efficiently through standard Bayesian inference techniques instead of resorting to a large number of ODE simulations. We have tested our method on ODE models of the EGF-NGF signaling pathway [1] and the segmentation clock pathway [2]. The results are very promising in terms of accuracy and efficiency.     

Operational Regimes and Physics Present in Optoelectronic Tweezers

Optoelectronic tweezers (OET) are a powerful light-based technique for the manipulation of micro- and nanoscopic particles. In addition to an optically patterned dielectrophoresis (DEP) force, other light-induced electrokinetic and thermal effects occur in the OET device. In this paper, we present a comprehensive theoretical and experimental investigation of various fluidic, optical, and electrical effects present during OET operation. These effects include DEP, light-induced ac electroosmosis, electrothermal flow, and buoyancy-driven flow. We present finite-element modeling of these effects to establish the dominant mode for a given set of device parameters and bias conditions. These results are confirmed experimentally and present a comprehensive outline of the operational regimes of the OET device.     

Trustworthy keyword search for compliance storage

Intense regulatory focus on secure retention of electronic records has led to a need to ensure that records are trustworthy, i.e., able to provide irrefutable proof and accurate details of past events. In this paper, we analyze the requirements for a trustworthy index to support keyword-based search queries. We argue that trustworthy index entries must be durable—the index must be updated when new documents arrive, and not periodically deleted and rebuilt. To this end, we propose a scheme for efficiently updating an inverted index, based on judicious merging of the posting lists of terms. Through extensive simulations and experiments with two real world data sets and workloads, we demonstrate that the scheme achieves online update speed while maintaining good query performance. We also present and evaluate jump indexes, a novel trustworthy and efficient index for join operations on posting lists for multi-keyword queries. Jump indexes support insert, lookup and range queries in time logarithmic in the number of indexed documents.     

Image Retrieval With Relevance Feedback Based on Graph-Theoretic Region Correspondence Estimation

This paper presents a graph-theoretic approach for interactive region-based image retrieval. When dealing with image matching problems, we use graphs to represent images, transform the region correspondence estimation problem into an inexact graph matching problem, and propose an optimization technique to derive the solution. We then define the image distance in terms of the estimated region correspondence. In the relevance feedback steps, with the estimated region correspondence, we propose to use a maximum likelihood method to re-estimate the ideal query and the image distance measurement. Experimental results show that the proposed graph-theoretic image matching criterion outperforms the other methods incorporating no spatially adjacent relationship within images. Furthermore, our maximum likelihood method combined with the estimated region correspondence improves the retrieval performance in feedback steps.     

A confidence-based framework for business to consumer (B2C) mobile commerce adoption

The Technology Acceptance Model (TAM) has been considered to be fundamental in determining the acceptance of new technology in the past decades. The two beliefs, ease of use and usefulness, in the model may not, however, fully explain the consumers’ behavior in an emerging environment, such as mobile commerce (m-commerce). This paper aims to develop a framework for m-commerce adoption in consumer decision-making processes. In this paper TAM has been adopted and extended to analyze successful m-commerce adoption. The key elements of the proposed confidence-based framework for B2C m-commerce adoption include psychological and behavioral factors. Psychological factors include history-based confidence, institution-based confidence and personality-based confidence. Behavioral factors include perceived ease of use and perceived usefulness of the mobile application and technology.