To evaluate frequency-domain interference between orthogonally intersecting stripline geometries, a lumped mutual capacitance was incorporated into a circuit model, and then a simplified circuit was proposed in the previous paper. The circuit model was approximated from an investigation of the distribution of mutual capacitance but it has remained how the capacitance is approximated. In this paper, a technique using an error function is proposed for the problem. Then, the time-domain response in an analytical expression is studied using the simplified circuit model in a Laplace transformation to make the mechanism clear. Comparing the experimental and the computed results verifies the proposed models.

In this paper, we propose a continuous range query processing method over moving objects. To efficiently process continuous range queries, we design a main-memory-based query index that uses smaller storage and significantly reduces the query processing time. We show through performance evaluation that the proposed method outperforms the existing methods.

This paper presents a new methodology, Beacognition, for real-time discovery of the associations between a signal space and arbitrarily defined regions, termed as Semantically Meaningful Areas (SMAs), in the corresponding physical space. It lets the end users develop semantically meaningful location systems using standard 802.11 network beacons as they roam through their environment. The key idea is to discover the unique associations using a beacon popularity model. The popularity measurements are then used to localize the mobile devices. The beacon popularity is computed using an election' algorithm and a new recognition model is presented to perform the localization task. We have implemented such a location system in a five story campus building. The comparative results show significant improvement in localization by achieving on average 83% SMA and 88% Floor recognition rate in less than one minute per SMA training time.

The proliferation of research on target detection and tracking in wireless sensor networks has kindled development of monitoring continuous objects such as fires and hazardous bio-chemical material diffusion. In this paper, we propose an energy-efficient algorithm that monitors a moving event region by selecting only a subset of nodes near object boundaries. The paper also shows that we can effectively reduce report message size. It is verified with performance analysis and simulation results that total average report message size as well as the number of nodes which transmit the report messages to the sink can be greatly reduced, especially when the density of nodes over the network field is high.

This paper discusses an on-line Tasks Assignment and Routing Problem (TARP) for Autonomous Transportation Systems (ATSs) in manufacturing systems. The TARP is a constrained version of the Pickup and Delivery Problem with Time Windows (PDPTW). In our former study, a cooperative algorithm, called the triple loop method, with autonomous distributed agents has been proposed. The Improving initial Task Assignment and Avoiding Deadlock method (ITAAD) is a faster algorithm than the triple loop method. In this paper, we propose a new vehicle routing method for the ITAAD. Results of computational experiments show effectiveness of the proposed routing method.

We describe an evaluation system consisting of an evaluation and interpretation model to totally assess and interpret an end-user's computing capability. It includes four evaluation factors and eighteen items, the complex indicators, an evaluation process, and method. We verified the model construct was verified by factor analysis and reliability analysis through a pilot test. We confirmed the application of the developed system by applying the model to evaluating end-users in a computing business environment and presenting the results. This system contributes to developing a practical system for evaluating an end-user's computing capability and hence for improving computing capability of end-users.

This paper proposes a MAC protocol for presence information discovery in ubiquitous networks. The proposed protocol is designed for proactive discovery in which wireless devices periodically broadcast packets containing presence information. The protocol is based on Framed Aloha. The objective of the protocol is to assure the discovery time of single-hop neighbors considering wireless collisions and also power consumption. In this paper, we show that the proposed protocol is able to assure specified discovery time in distributed networks with random topology.

We propose a feasible V-BLAST detector based on modified Householder QRD (M-H-QRD) over spatially correlated fading channel, which can almost match the performance of the V-BLAST algorithm with much lower complexity and better numerical stability. Compared to the sorted QRD (S-QRD) detector, the proposed detector requires a smaller minimum word-length to reach the same value of error floor for fixed-point (FP) numerical precision despite no significant performance difference for floating-point machine precision. All these advantages make it attractive when implemented using FP arithmetic.

In this paper, a recursive Gauss-Newton (RGN) algorithm is first developed for adaptive tracking of the amplitude, frequency and phase of a real sinusoid signal in additive white noise. The derived algorithm is then simplified for computational complexity reduction as well as improved with the use of multiple forgetting factor (MFF) technique to provide a flexible way of keeping track of the parameters with different rates. The effectiveness of the simplified MFF-RGN scheme in sinusoidal parameter tracking is demonstrated via computer simulations.

This paper describes a vision-based and real-time system for detecting road signs from within a moving vehicle. The system architecture which is proposed in this paper consists of two parts, the learning and the detection part of road sign images. The proposed system has the standard architecture with adaboost algorithm. Adaboost is a popular algorithm which used to detect an object in real time. To improve the detection rate of adaboost algorithm, this paper proposes a new combination method of classifiers in every stage. In the case of detecting road signs in real environment, it can be ambiguous to decide to which class input images belong. To overcome this problem, we propose a method that applies fuzzy measure and fuzzy integral which use the importance and the evaluated values of classifiers within one stage. It is called fuzzy-boosting in this paper. Also, to improve the speed of a road sign detection algorithm using adaboost at the detection step, we propose a method which chooses several candidates by using MC generator. In this paper, as the sub-windows of chosen candidates pass classifiers which are made from fuzzy-boosting, we decide whether a road sign is detected or not. Using experiment result, we analyze and compare the detection speed and the classification error rate of the proposed algorithm applied to various environment and condition.

This paper suggests word voiceprint models to verify the recognition results obtained from a speech recognition system. Word voiceprint models have word-dependent information based on the distributions of phone-level log-likelihood ratio and duration. Thus, we can obtain a more reliable confidence score for a recognized word by using its word voiceprint models that represent the more proper characteristics of utterance verification for the word. Additionally, when obtaining a log-likelihood ratio-based word voiceprint score, this paper proposes a new log-scale normalization function using the distribution of the phone-level log-likelihood ratio, instead of the sigmoid function widely used in obtaining a phone-level log-likelihood ratio. This function plays a role of emphasizing a mis-recognized phone in a word. This individual information of a word is used to help achieve a more discriminative score against out-of-vocabulary words. The proposed method requires additional memory, but it shows that the relative reduction in equal error rate is 16.9% compared to the baseline system using simple phone log-likelihood ratios.

In next generation mobile multimedia communications, different wireless access networks are expected to cooperate. However, it is a challenging task to choose an optimal transmission path in this scenario. This paper focuses on the problem of selecting the optimal access network for multicast services in the cooperative mobile and broadcasting networks. An algorithm is proposed, which considers multiple decision factors and multiple optimization objectives. An analytic hierarchy process (AHP) method is applied to schedule the service queue and an artificial neural network (ANN) is used to improve the flexibility of the algorithm. Simulation results show that by applying the AHP method, a group of weight ratios can be obtained to improve the performance of multiple objectives. And ANN method is effective to adaptively adjust weight ratios when users' new waiting threshold is generated.

In this paper, we consider the broadcast storm problem in dense wireless ad hoc networks where interference among densely populated wireless nodes causes significant packet loss. To resolve the problem, we apply randomized network coding (RNC) to the networks. RNC is a completely different approach from existing techniques to resolve the problem, and it reduces the number of outstanding packets in the networks by encoding several packets into a single packet. RNC is a kind of linear network coding, and it is suited to wireless ad hoc networks because it can be implemented in a completely distributed manner. We describe a procedure for implementing the wireless ad hoc broadcasting with RNC. Further, with several simulation scenarios, we provide some insights on the relationship between the system parameters and performance and find that there is the optimal length of coding vectors for RNC in terms of packet loss probability. We also show a guideline for the parameter setting to resolve the broadcast storm problem successfully.

In this paper, we propose an image completion algorithm which takes advantage of the countless number of images available on Internet photo sharing sites to replace occlusions in an input image. The algorithm 1) automatically selects the most suitable images from a database of downloaded images and 2) seamlessly completes the input image using the selected images with minimal user intervention. Experimental results on input images captured at various locations and scene conditions demonstrate the effectiveness of the proposed technique in seamlessly reconstructing user-defined occlusions.

This paper performs application-level QoS and user-level QoS assessment of audio-video streaming in cross-layer designed wireless ad hoc networks. In order to achieve high QoS at the user-level, we employ link quality-based routing in the network layer and media synchronization control in the application layer. We adopt three link quality-based routing protocols: OLSR-SS (Signal Strength), AODV-SS, and LQHR (Link Quality-Based Hybrid Routing). OLSR-SS is a proactive routing protocol, while AODV-SS is a reactive one. LQHR is a hybrid protocol, which is a combination of proactive and reactive routing protocols. For application-level QoS assessment, we performed computer simulation with ns-2 where an IEEE 802.11b mesh topology network with 24 nodes was assumed. We also assessed user-level QoS by a subjective experiment with 30 assessors. From the assessment results, we find AODV-SS the best for networks with long inter-node distances, while LQHR outperforms AODV-SS for short inter-node distances. In addition, we also examine characteristics of the three schemes with respect to the application-level QoS in random topology networks.

Rapid advances in semiconductor manufacturing technology have led to higher chip power densities, which places greater emphasis on packaging and temperature control during testing. For system-on-chips, peak power-based scheduling algorithms have been used to optimize tests under specified power constraints. However, imposing power constraints does not always solve the problem of overheating due to the non-uniform distribution of power across the chip. This paper presents a TAM/Wrapper co-design methodology for system-on-chips that ensures thermal safety while still optimizing the test schedule. The method combines a simplified thermal-cost model with a traditional bin-packing algorithm to minimize test time while satisfying temperature constraints. Furthermore, for temperature checking, thermal simulation is done using cycle-accurate power profiles for more realistic results. Experiments show that even a minimal sacrifice in test time can yield a considerable decrease in test temperature as well as the possibility of further lowering temperatures beyond those achieved using traditional power-based test scheduling.

Tin-doped indium oxide (ITO) thin films were prepared on a polyethylene terephthalate (PET) foil by bias sputtering. In the absence of a substrate bias, films having a high resistivity of 210-2 Ωcm were formed. On the other hand, by the application of an rf substrate bias, films having a low resistivity of 2.610-4 Ωcm were formed. The energy of ions that bombarded the substrate during bias sputtering was estimated by a simulation of the ion acceleration. The optimum ion-energy required for the reduction of resistivity was found to be approximately 50 eV.

This paper describes a method for recognizing romanized Japanese words in learner English. They become noise and problematic in a variety of systems and tools for language learning and teaching including text analysis, spell checking, and grammatical error detection because they are Japanese words and thus mostly unknown to such systems and tools. A problem one encounters when recognizing romanized Japanese words in learner English is that the spelling rules of romanized Japanese words are often violated. To address this problem, the described method uses a clustering algorithm reinforced by a small set of rules. Experiments show that it achieves an F-measure of 0.879 and outperforms other methods. They also show that it only requires the target text and an English word list of reasonable size.

An indirectly reactive sputtering coater has been developed to deposit various high quality metallic and metal oxide films at high deposition rate. In this letter, several kinds of filters such as antireflection (AR) coating, IR-cut filter, and Rugate filter were deposited for the benchmark test of implemental capabilities. Our coater was established to be a powerful tool for both discrete multilayer and Rugate filters due to high stability and reproducibility of the refractive index and the deposition rate.

This letter presents an efficient adaptive beamformer to deal with the multipath environments created by signal source scatterings. To improve the performance possible with the fixed forgetting factor, the regular adaptive forgetting factor algorithm is derived and applied to the subarray recursive least squares (RLS) beamforming. Simulations confirm that the proposed scheme has better performance than not only the conventional RLS algorithm but also the subarray RLS and adaptive forgetting factor RLS algorithms.