16132997
Manifold of facial expression
We propose the concept of manifold of facial expression based on the observation that images of a subject's facial expressions define a smooth manifold in the high dimensional image space. Such a manifold representation can provide a unified framework for facial expression analysis. We first apply active wavelet networks (AWN) on the image sequences for facial feature localization. To learn the structure of the manifold in the feature space derived by AWN, we investigated two types of embeddings from a high dimensional space to a low dimensional space: locally linear embedding (LLE) and Lipschitz embedding. Our experiments show that LLE is suitable for visualizing expression manifolds. After applying Lipschitz embedding, the expression manifold can be approximately considered as a super-spherical surface in the embedding space. For manifolds derived from different subjects, we propose a nonlinear alignment algorithm that keeps the semantic similarity of facial expression from different subjects on one generalized manifold. We also show that nonlinear alignment outperforms linear alignment in expression classification.
8192681
Image Retrieval Using Schwarz Representation of One-Dimensional Feature
Retrieval efficiency and accuracy are two important issues in designing a content based database retrieval system. In order to retrieve efficiently, we must extract feature to build index. Recently intensive research focused on how to extract one-dimensional features and calculate the distance between them, such as color histogram, Fourier descriptor, image shape spectrum (ISS). We develop a new method to match one-dimensional feature function in multiscale space using Schwarz representation. It can obtain closed form match function and similarity measure instead of traditional optimization. Thus we can calculate the global distance when the local information of feature function is matched. In this paper, we use the center distance function of shape as the feature functions. We calculate their Schwarz representation as indices, and calculate the optimal distance as similarity measure to sort the images. Experimental results show its efficiency and accuracy.
13451430
Non-negative matrix factorization framework for face recognition
Non-negative Matrix Factorization (NMF) is a part-based image representation method which adds a non-negativity constraint to matrix factorization. NMF is compatible with the intuitive notion of combining parts to form a whole face. In this paper, we propose a framework of face recognition by adding NMF constraint and classifier constraints to matrix factorization to get both intuitive features and good recognition results. Based on the framework, we present two novel subspace methods: Fisher Non-negative Matrix Factorization (FNMF) and PCA Non-negative Matrix Factorization (PNMF). FNMF adds both the non-negative constraint and the Fisher constraint to matrix factorization. The Fisher constraint maximizes the between-class scatter and minimizes the within-class scatter of face samples. Subsequently, FNMF improves the capability of face recognition. PNMF adds the non-negative constraint and characteristics of PCA, such as maximizing the variance of output coordinates, orthogonal bases, etc. to matrix factorization. Therefore, we can get intuitive features and desirable PCA characteristics. Our experiments show that FNMF and PNMF achieve better face recognition performance than NMF and Local NMF.
1952111
Manifold Based Analysis of Facial Expression
We propose a novel approach for modeling, tracking and recognizing facial expressions. Our method works on a low dimensional expression manifold, which is obtained by Isomap embedding. In this space, facial contour features are first clustered, using a mixture model. Then, expression dynamics are learned for tracking and classification. We use ICondensation to track facial features in the embedded space, while recognizing facial expressions in a cooperative manner, within a common probabilistic framework. The image observation likelihood is derived from a variation of the Active Shape Model (ASM) algorithm. For each cluster in the low-dimensional space, a specific ASM model is learned, thus avoiding incorrect matching due to non-linear image variations. Preliminary experimental results show that our probabilistic facial expression model on manifold significantly improves facial deformation tracking and expression recognition.
206993144
Charge-based core and the model architecture of BSIM5
The paper outlines the charge-based core and the architecture of the BSIM5 MOSFET model for sub-100 nm CMOS circuit simulation. The BSIM5 model is a continuous, completely symmetric and accurate non charge-sheet based MOS transistor model derived from the basic device physics, including various physics effects. Comparison of the inversion charge between the BSIM5 prediction and self-consistent numerical solution shows good agreement. The demonstration of fully symmetric characteristics of BSIM5, such as channel current and its high-order derivative in the Gummel symmetry test, and charge and trans-capacitances in a SPICE simulation, also implies BSIM5 is the physically symmetric MOSFET model valid for RF-analog circuit simulations.
2350863
Generating and Applying Rules for Interval Valued Fuzzy Observations
One of the objectives of intelligent data engineering and automated learning is to develop algorithms that learn the environment, generate rules, and take possible courses of actions. In this paper, we report our work on how to generate and apply such rules with a rule matrix model. Since the environments can be interval valued and rules often fuzzy, we further study how to obtain and apply rules for interval valued fuzzy observations.
6208845
Efficient generation of delay change curves for noise-aware static timing analysis
In this paper, we explore the concept of using analytical models to efficiently generate delay change curves (DCCs) that can then be used to characterize the impact of noise on any victim/aggressor configuration. Such an approach captures important noise considerations such as the possibility of delay change even when the switching windows of neighboring gates do not overlap. The technique is model-independent, which we demonstrate by using several crosstalk noise models to obtain results. Furthermore, we extend an existing noise model to more accurately handle multiple aggressors in the timing analysis framework. DCC results from the analytical approach closely match those from time-consuming SPICE simulations, making timing analysis using DCCs efficient as well as accurate.
863906
Per-Flow Queueing by Dynamic Queue Sharing
Per-flow queuing is believed to be able to guarantee advanced Quality of Service (QoS) for each flow. With the dramatic increase of link speed and number of traffic flows, per-flow queuing faces a great challenge since millions of queues need to be maintained for implementation in a traditional sense. In this paper, by setting only a small number of physical queues, we propose a Dynamic Queue Sharing (DQS) mechanism to achieve an equal performance to the pure per-flow queuing with a lower cost. The proposed mechanism is based on an interesting fact that the number of simultaneous active flows in the router buffer is far less than that of in-progress flows. In DQS, a physical queue is dynamically created on-demand when a new flow comes and then dynamically released when the flow temporarily pauses. Hashing and binary sorting tree (or linked list) are combined to manage the mapping between flows and queues, so as to isolate flows in different queues. Theoretical analysis and traces experiments are conducted to evaluate DQS. The results demonstrate that when the parameters are well set, the operation delay is less than two time cycles in average with an extra memory of 16k bits.
16131402
A mobile agent-based active network architecture
Active networks enable the customization of network functionality without the lengthy standard-mediated committee processes. Most of the works in the literature utilize capsules or active packets as the means to transfer code information across active networks. In this paper, we propose an active network infrastructure based on mobile agent technologies. In our prototype implementation, mobile agents are the building blocks carrying functional customizations, and the active nodes offer software application layers, caled agent servers, to process mobile agent-specific customizations to facilitate network functionality. Both integrated and discrete operational models of network customizations are supported. In addition, for the application-specific protocol development and deployment, an abstract protocol structure and a protocol loading mechanism are presented. Furthermore, we provide an agent management/control mechanism and devise a protocol management/control mechanism. As a result, improved network functionality can be achieved.