The large signal performance and model for GaN FET devices was evaluated with DC, S-parameters, and large signal measurements. The large signal model was extended with bias and temperature dependence of access resistances, modified capacitance and charge equations, as well as breakdown models. The model was implemented in a commercial CAD tool and exhibits good overall accuracy.

This work presents a statistical model for the radiated susceptibility (RS) of an unshielded twisted-wire pair (TWP) running above ground, illuminated by a random electromagnetic field. The incident field is modeled as a superposition of elemental plane waves with random angular density, phase, and polarization. The statistical properties of both the differential-mode (DM) and the common-mode (CM) noise voltages induced across the terminal loads are derived and discussed.

This paper presents an approach to implementing simulation models for SAM fuzzy controllers without the use of external components. The approach represents a fuzzy controller as a composition of simple simulation models which involve only basic operations.

The success of peer-to-peer overlay networks depends on cooperation among participating peers. In this paper, we investigate the degree of cooperation among individual peers required to induce globally favorable properties in an overlay network. Specifically, we consider a resource pricing problem in a market-oriented overlay network where participating peers sell own resources (e.g., CPU cycles) to earn energy which represents some money or rewards in the network. In the resource pricing model presented in this paper, each peer sets the price for own resource based on the degree of cooperation; non-cooperative peers attempt to maximize their own energy gains, while cooperative peers maximize the sum of own and neighbors' energy gains. Simulation results are presented to demonstrate that the network topology is an important factor influencing the minimum degree of cooperation required to increase the network-wide global energy gain.

This paper describes a black-box model of mixed analog-digital VLSI circuits for the prediction of microcontroller electromagnetic emissions without disclosure of manufacturer data. The model is based on small-signal simulations performed at the analog and digital building-block level, considering also layout and technology parameters, and modeling the parasitic substrate coupling paths and the interconnects. The developed model allows system designers to predict the impact of microcontroller operation on the system-level EMEs by carrying out low-time consuming simulations in the early design phases of their products thus minimizing unnecessary costs and scheduling delays. In this paper, the black-box model of an 8-bit microcontroller is described and it is employed to predict the conducted emission delivered through the input-output ports.

Quad-core cpus have been a common desktop configuration for today's office. The increasing number of processors on a single chip opens new opportunity for parallel computing. Our goal is to make use of the multi-core as well as multi-processor architectures to speed up large-scale data mining algorithms. In this paper, we present a general parallel learning framework, Cut-And-Stitch, for training hidden Markov chain models. Particularly, we propose two model-specific variants, CAS-LDS for learning linear dynamical systems (LDS) and CAS-HMM for learning hidden Markov models (HMM). Our main contribution is a novel method to handle the data dependencies due to the chain structure of hidden variables, so as to parallelize the EM-based parameter learning algorithm. We implement CAS-LDS and CAS-HMM using OpenMP on two supercomputers and a quad-core commercial desktop. The experimental results show that parallel algorithms using Cut-And-Stitch achieve comparable accuracy and almost linear speedups over the traditional serial version.

Various observation models have been introduced into the object tracking community, and combining them has become a promising direction. This paper proposes a novel approach for estimating the confidences of different observation models, and then effectively combining them in the particle filter framework. In our approach, spatial Likelihood distribution is represented by three simple but efficient parameters, reflecting the overall similarity, distribution sharpness and degree of multi peak. The balance of these three aspects leads to good estimation of confidences, which helps maintain the advantages of each observation model and further increases robustness to partial occlusion. Experiments on challenging video sequences demonstrate the effectiveness of our approach.

Initial growth of GaP on Si substrates using metalorganic vapor phase epitaxy was studied. Si substrates were exposed to PH3 preflow for 15 s or 120 s at 830 after they were preheated at 925. Atomic force microscopy (AFM) revealed that the Si surface after preflow for 120 s was much rougher than that after preflow for 15 s. After 1.5 nm GaP deposition on the Si substrates at 830, GaP islands nucleated more uniformly on the Si substrate after preflow for 15 s than on the substrate after preflow for 120 s. After 3 nm GaP deposition, layer structures were observed on a fraction of Si surface after preflow for 15 s. Island-like structures remained on the Si surface after preflow for 120 s. After 6 nm GaP deposition, the continuity of GaP layers improved on both substrates. However, AFM shows pits that penetrated a Si substrate with preflow for 120 s. Transmission electron microscopy of a GaP layer on the Si substrate after preflow for 120 s revealed that V-shaped pits penetrated the Si substrate. The preflow for a long time roughened the Si surface, which facilitated the pit formation during GaP growth in addition to degrading the surface morphology of GaP at the initial growth stage. Even after 50 nm GaP deposition, pits with a density on the order of 107 cm-2 remained in the sample. A 50-nm-thick flat GaP surface without pits was achieved for the sample with PH3 preflow for 15 s. The PH3 short preflow is necessary to produce a flat GaP surface on a Si substrate.

The ratio of two probability densities is called the importance and its estimation has gathered a great deal of attention these days since the importance can be used for various data processing purposes. In this paper, we propose a new importance estimation method using Gaussian mixture models (GMMs). Our method is an extention of the Kullback-Leibler importance estimation procedure (KLIEP), an importance estimation method using linear or kernel models. An advantage of GMMs is that covariance matrices can also be learned through an expectation-maximization procedure, so the proposed method--which we call the Gaussian mixture KLIEP (GM-KLIEP)--is expected to work well when the true importance function has high correlation. Through experiments, we show the validity of the proposed approach.

Despite the prevalence of Java workloads across a variety of processor architectures, there is very little published data on the impact of the various processor design decisions on Java performance. We attribute the lack of data to the large design space resulting from the complexity of the modern superscalar processor and the additional complexities associated with executing Java bytecode using a virtual machine. To address this shortcoming, we use a statistically rigorous methodology to systematically quantify the the impact of the various processor microarchitecture parameters on Java execution performance. The adopted methodology enables efficient screening of significant factor effects in a large design space consisting of 35 factors (32-billion potential configurations) using merely 72 observations per benchmark application. We quantify and tabulate the significance of each of the 35 factors for 13 benchmark applications. While these tables provide various insights into Java performance, they consistently highlight the performance significance of the instruction delivery mechanism, especially the instruction cache and the ITLB design parameters. Furthermore, these tables enable the architect to identify processor bottlenecks for Java workloads by providing an estimate of the relative impact of various design decisions.

The purpose of this paper is to study sender authenticated key agreements by a third party, which uses the received parameters to verify the fact that a sender of a message knows his long-term private key. In particular, we propose a standard model for the protocol among three entities for the first time. The security of this protocol depends on the difficulty of solving two new problems related to one-way isomorphisms and the decision co-bilinear Diffie-Hellman problem on multiplicative cyclic groups. It is the first time that the security of a key agreement has been formally proven by using negligible probability. We believe that our contribution gives many applications in the cryptographic community.

When a store sells items to customers, the store wishes to determine the prices of the items to maximize its profit. Intuitively, if the store sells the items with low (resp. high) prices, the customers buy more (resp. less) items, which provides less profit to the store. So it would be hard for the store to decide the prices of items. Assume that the store has a set V of n items and there is a set E of m customers who wish to buy those items, and also assume that each item i ∈ V has the production cost di and each customer ej ∈ E has the valuation vj on the bundle ej ⊆ V of items. When the store sells an item i ∈ V at the price ri, the profit for the item i is pi=ri-di. The goal of the store is to decide the price of each item to maximize its total profit. We refer to this maximization problem as the item pricing problem. In most of the previous works, the item pricing problem was considered under the assumption that pi ≥ 0 for each i ∈ V, however, Balcan, et al. [In Proc. of WINE, LNCS 4858, 2007] introduced the notion of "loss-leader," and showed that the seller can get more total profit in the case that pi < 0 is allowed than in the case that pi < 0 is not allowed. In this paper, we derive approximation preserving reductions among several item pricing problems and show that all of them have algorithms with good approximation ratio.

A rotation invariant texture analysis technique is proposed with a novel combination of Radon Transform (RT) and Hidden Markov Models (HMM). Features of any texture are extracted during RT which due to its inherent property captures all the directional properties of a certain texture. HMMs are used for classification purpose. One HMM is trained for each texture on its feature vector which preserves the rotational invariance of feature vector in a more compact and useful form. Once all the HMMs have been trained, testing is done by picking any of these textures at any arbitrary orientation. The best percentage of correct classification (PCC) is above 98 % carried out on sixty texture of Brodatz album.

Conveying information about who, what, when and where is a primary purpose of some genres of documents, typically news articles. Statistical models that capture dependencies between named entities and topics can play an important role in handling such information. Although some relationships between who and where should be mentioned in such a document, no statistical topic models explicitly address the textual interactions between a who-entity and a where-entity. This paper presents a statistical model that directly captures the dependencies between an arbitrary number of word types, such as who-entities, where-entities and topics, mentioned in each document. We show that this multitype topic model performs better at making predictions on entity networks, in which each vertex represents an entity and each edge weight represents how a pair of entities at the incident vertices is closely related, through our experiments on predictions of who-entities and links between them. We also demonstrate the scale-free property in the weighted networks of entities extracted from written mentions.

In 3D computer graphics, mesh parameterization is a key technique for digital geometry processings such as morphing, shape blending, texture mapping, re-meshing and so on. Most of the previous approaches made use of an identical primitive domain to parameterize a mesh model. In recent works of mesh parameterization, more flexible and attractive methods that can create direct mappings between two meshes have been reported. These mappings are called "cross-parameterization" and typically preserve semantic feature correspondences between target meshes. This paper proposes a novel approach for parameterizing a mesh into another one directly. The main idea of our method is to combine a competitive learning and a least-square mesh techniques. It is enough to give some semantic feature correspondences between target meshes, even if they are in different shapes or in different poses.

This paper provides insights on the status of broadband optical access market and technologies in Europe and on the expected trends for the next generation optical access networks. The final target for most operators, cities or any other player is of course FTTH (Fibre To The Home) deployment although we can expect intermediate steps with copper or wireless technologies. Among the two candidate architectures for FTTH, PON (Passive Optical Network) is by far the most attractive and cost effective solution. We also demonstrate that Ethernet based optical access network is very adequate to all-IP networks without any incidence on the level of quality of service. Finally, we provide feedback from a FTTH pilot network in Colmar (France) based on Gigabit Ethernet PON technology. The interest of this pilot lies on the level of functionality required for broadband optical access networks but also on the development of new home network configurations.

This paper proposes a fast, efficient detection algorithm of missing data (also referred to as blotches) based on Markov Random Field (MRF) models with less computational load and a lower false alarm rate than the existing MRF-based blotch detection algorithms. The proposed algorithm can reduce the computational load by applying fast block-matching motion estimation based on the diamond searching pattern and restricting the attention of the blotch detection process to only the candidate bloch areas. The problem of confusion of the blotches is frequently seen in the vicinity of a moving object due to poorly estimated motion vectors. To solve this problem, we incorporate a weighting function with respect to the pixels, which are accurately detected by our moving edge detector and inputed into the formulation. To solve the blotch detection problem formulated as a maximum a posteriori (MAP) problem, an iterated conditional modes (ICM) algorithm is used. The experimental results show that our proposed method results in fewer blotch detection errors than the conventional blotch detectors, and enables lower computational cost and the more efficient detecting performance when compared with existing MRF-based detectors.

This paper addresses the problem of view invariant action recognition using 2D trajectories of landmark points on human body. It is a challenging task since for a specific action category, the 2D observations of different instances might be extremely different due to varying viewpoint and changes in speed. By assuming that the execution of an action can be approximated by dynamic linear combination of a set of basis shapes, a novel view invariant human action recognition method is proposed based on non-rigid matrix factorization and Hidden Markov Models (HMMs). We show that the low dimensional weight coefficients of basis shapes by measurement matrix non-rigid factorization contain the key information for action recognition regardless of the viewpoint changing. Based on the extracted discriminative features, the HMMs is used for temporal dynamic modeling and robust action classification. The proposed method is tested using real life sequences and promising performance is achieved.

The performance of recently developed Large Signal (LS) HBT model was evaluated with extensive LS measurements like Power spectrum, Load pull and Inter-modulation investigations. Proposed model has adopted temperature dependent leakage resistance and a simplified capacitance models. The model was implemented in ADS as SDD. Important feature of the model is that the main model parameters are taken directly from measurements in rather simple and understandable way. Results show good accuracy despite the simplicity of the model. To our knowledge the HBT model is one of a few HBT models which can handle high current & Power HBT devices, with significantly less model parameters with good accuracy.

Humans have a remarkable ability to flexibly control various objects such as tools. Much evidence suggests that the internal models acquired in the central nervous system (CNS) support flexible control. Internal models are neural mechanisms that mimic the input-output properties of controlled objects. In a series of functional magnetic resonance imaging (fMRI) studies, we demonstrate how the CNS acquires and switches internal models for dexterous use of many tools. In the first study, we investigated human cerebellar activity when human subjects learned how to use a novel tool (a rotated computer mouse, where the cursor appears in a rotated position) and found that activity reflecting an internal model of the novel tool increases in the lateral cerebellum after learning how to use the tool. In the second study, we investigated the internal-model activity after sufficient training in the use of two types of novel tools (the rotated mouse and a velocity mouse, where the cursor's velocity is proportional to mouse's position) and found that the cerebellar activities for the two tools were spatially segregated. In the third study, we investigated brain activity associated with the flexible switching of tools. We found that the activity related to switching internal models was in the prefrontal lobe (area 46 and the insula), the parietal lobe, and the cerebellum. These results suggest that internal models in the cerebellum represent input-output properties of the tools as modulators of continuous signals. The cerebellar abilities in adaptive modulation of signals can be used to enhance the control signals in communications between the brain and computers.