Compact Statistical Model Research Articles

Statistical Modeling of Lower Limb Kinetics During Deep Squat and Forward Lunge.

PurposeModern statistics and higher computational power have opened novel possibilities to complex data analysis. While gait has been the utmost described motion in quantitative human motion analysis, descriptions of more challenging movements like the squat or lunge are currently lacking in the literature. The hip and knee joints are exposed to high forces and cause high morbidity and costs. Pre-surgical kinetic data acquisition on a patient-specific anatomy is also scarce in the literature. Studying the normal inter-patient kinetic variability may lead to other comparable studies to initiate more personalized therapies within the orthopedics.MethodsTrials are performed by 50 healthy young males who were not overweight and approximately of the same age and activity level. Spatial marker trajectories and ground reaction force registrations are imported into the Anybody Modeling System based on subject-specific geometry and the state-of-the-art TLEM 2.0 dataset. Hip and knee joint reaction forces were obtained by a simulation with an inverse dynamics approach. With these forces, a statistical model that accounts for inter-subject variability was created. For this, we applied a principal component analysis in order to enable variance decomposition. This way, noise can be rejected and we still contemplate all waveform data, instead of using deduced spatiotemporal parameters like peak flexion or stride length as done in many gait analyses. In addition, this current paper is, to the authors’ knowledge, the first to investigate the generalization of a kinetic model data toward the population.ResultsAverage knee reaction forces range up to 7.16 times body weight for the forwarded leg during lunge. Conversely, during squat, the load is evenly distributed. For both motions, a reliable and compact statistical model was created. In the lunge model, the first 12 modes accounts for 95.26% of inter-individual population variance. For the maximal-depth squat, this was 95.69% for the first 14 modes. Model accuracies will increase when including more principal components.ConclusionOur model design was proved to be compact, accurate, and reliable. For models aimed at populations covering descriptive studies, the sample size must be at least 50.

Image quality eigenfunctions for the human eye.

This work presents a compact statistical model of the retinal image quality in a large population of human eyes following two objectives. The first was to develop a general modal representation of the optical transfer function (OTF) in terms of orthogonal functions and construct a basis composed of cross-correlations between pairs of complex Zernike polynomials. That basis was not orthogonal and highly redundant, requiring the application of singular value decomposition (SVD) to obtain an orthogonal basis with a significantly lower dimensionality. The first mode is the OTF of the perfect system, and hence the modal representation, is highly compact for well-corrected optical systems, and vice-versa. The second objective is to apply this modal representation to the OTFs of a large population of human eyes for a pupil diameter of 5 mm. This permits an initial strong data compression. Next, principal component analysis (PCA) is applied to obtain further data compression, leading to a compact statistical model of the initial population. In this model each OTF is approximated by the sum of the population mean plus a linear combination of orthogonal eigenfunctions (eigen-OTF) accounting for a selected percentage (90%) of the population variance. This type of models can be useful for Monte Carlo simulations among other applications.

Fusing PDTDFB magnitude and relative phase modeling for geometrical correction-based image watermarking

Robustness, which refers to the ability that watermark signal survives various attacks, like additive noise, compression, rotation, etc., has played extremely important role in the multiple applications of digital watermarking. As one of the most difficult kinds of signal processing operations for a digital watermark to survive, geometric distortions have become a central problem in image watermarking research. Therefore, developing a greatly robust digital image watermarking approach, which can withstand geometrical distortions, remains a quite challenging work. In this paper, a new geometrical correction-based image watermarking approach using PDTDFB magnitude and relative phase modeling is proposed. This approach consists of digital watermark embedding, geometric distortions correction, and digital watermark extraction. In the watermark embedding process, PDTDFB (Pyramidal dual-tree directional filter bank) decomposition is performed on the original host image, followed by the low-pass subband partitioning. Watermark bit is inserted into low-pass subband block by modifying (quantization index modulation, QIM) the set of low-pass PDTDFB coefficients. In the geometric correction, the PDTDFB magnitude and relative phase are modeled by using Weibull distribution and Vonn distribution, respectively. Utilizing the compact statistical model parameters, the LS-SVR (Least squares support vector regression) correction is performed to estimate the geometrical distortions parameters. After LS-SVR geometrical correction, the watermark bits are extracted from the watermarked low-pass subband by employing the inverse QIM. Experimental results confirm that, under various well-known practical attacks, including common signal processing operations and geometrical distortions, the proposed approach performs well compared to conventional image watermarking methods.

A Compact Statistical Model for the Low-Frequency Noise in Halo-Implanted MOSFETs: Large RTN Induced by Halo Implants

In this paper, we propose a novel compact statistical model for the low-frequency noise (LFN) of MOS devices with halo implants. The compact model is suited for the incorporation in modern models, such as BSIM, PSP, and EKV, and can be used to predict the dependence of the LFN of halo-implanted MOSFETs with bias, temperature, geometry, and technological parameters. This compact model is based on the physics-based random telegraph noise (RTN) model, previously published by our group. The previous model was simplified in analytical expressions dependent on parameters and on physical quantities already calculated in modern compact models. Following the physics-based model, the LFN compact model predicts the large bias dependence of the LFN statistics induced by the halo implants in long-channel devices. Moreover, we show for the first time that the halo implants also induce a large temperature dependence of the LFN statistics for devices operated near the weak inversion or saturation, and the proposed compact model predicts this dependence.

Geodesic shape regression with multiple geometries and sparse parameters.

Many problems in medicine are inherently dynamic processes which include the aspect of change over time, such as childhood development, aging, and disease progression. From medical images, numerous geometric structures can be extracted with various representations, such as landmarks, point clouds, curves, and surfaces. Different sources of geometry may characterize different aspects of the anatomy, such as fiber tracts from DTI and subcortical shapes from structural MRI, and therefore require a modeling scheme which can include various shape representations in any combination. In this paper, we present a geodesic regression model in the large deformation (LDDMM) framework applicable to multi-object complexes in a variety of shape representations. Our model decouples the deformation parameters from the specific shape representations, allowing the complexity of the model to reflect the nature of the shape changes, rather than the sampling of the data. As a consequence, the sparse representation of diffeomorphic flow allows for the straightforward embedding of a variety of geometry in different combinations, which all contribute towards the estimation of a single deformation of the ambient space. Additionally, the sparse representation along with the geodesic constraint results in a compact statistical model of shape change by a small number of parameters defined by the user. Experimental validation on multi-object complexes demonstrate robust model estimation across a variety of parameter settings. We further demonstrate the utility of our method to support the analysis of derived shape features, such as volume, and explore shape model extrapolation. Our method is freely available in the software package deformetrica which can be downloaded at www.deformetrica.org.

Variability and reliability analysis in self-assembled multichannel carbon nanotube field-effect transistors

Carbon nanotubes (CNTs) have been widely studied as a channel material of scaled transistors for high-speed and low-power logic applications. In order to have sufficient drive current, it is widely assumed that CNT-based logic devices will have multiple CNTs in each channel. Understanding the effects of the number of CNTs on device performance can aid in the design of CNT field-effect transistors (CNTFETs). We have fabricated multi-CNT-channel CNTFETs with an 80-nm channel length using precise self-assembly methods. We describe compact statistical models and Monte Carlo simulations to analyze failure probability and the variability of the on-state current and threshold voltage. The results show that multichannel CNTFETs are more resilient to process variation and random environmental fluctuations than single-CNT devices.

A statistical parametric approach to video-realistic text-driven talking avatar

This paper proposes a statistical parametric approach to video-realistic text-driven talking avatar. We follow the trajectory HMM approach where audio and visual speech are jointly modeled by HMMs and continuous audiovisual speech parameter trajectories are synthesized based on the maximum likelihood criterion. Previous trajectory HMM approaches only focus on mouth animation, which synthesizes simple geometric mouth shapes or video-realistic effects of the lip motion. Our approach uses trajectory HMM to generate visual parameters of the lower face and it realizes video-realistic animation of the whole face. Specifically, we use active appearance model (AAM) to model the visual speech, which offers a convenient and compact statistical model of both the shape and the appearance variations of the face. To realize video-realistic effects with high fidelity, we use Poisson image editing technique to stitch the synthesized lower-face image to a whole face image seamlessly. Objective and subjective experiments show that the proposed approach can produce natural facial animation.

Statistical Functional Yield Estimation and Enhancement of CNFET-Based VLSI Circuits

Carbon nanotube field effect transistors (CNFETs) show great promise as extensions to silicon CMOS. However, imperfections, which are mainly related to carbon nanotubes (CNTs) growth process, result in metallic and nonuniform CNTs leading to significant functional yield reduction. This paper presents a comprehensive technique for statistical functional yield estimation and enhancement of CNFET-based VLSI circuits. Based on experimental data extracted from aligned CNTs, we propose a compact statistical model to estimate the failure probability of a CNFET. Using the proposed failure model, we show that enhancing the CNT synthesis process alone cannot achieve acceptable functional yield for upcoming CNFET-based VLSI circuits. We propose a technique which is based on replacing each transistor by series-parallel transistor structures to reduce the failure probability of CNFETs in the presence of metallic and nonuniform CNTs. The technique is adapted to use single directional independence, which is inherent in aligned CNTs, to enhance the functional yield as validated by theoretical analysis and simulation results. Tradeoffs between failure probability reduction and design overheads such as area and current drive are explored. As demonstrated by extensive simulation results, the proposed technique achieves 80% functional yield in CNFET technology at the cost of 7.5X area and 34% current drive overheads if the CNT density and the fraction of semiconducting CNTs are improved to 200 CNTs per μm and 99.99%, respectively.

AUTOMATIC SEGMENTATION OF CARDIAC MAGNETIC RESONANCE IMAGES USING ACTIVE APPEARANCE MODELS AND HAUSDORFF DISTANCE

Active Appearance Models (AAM), have been introduced by Cootes et al. [IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001], and are used to learn objects characteristics during a training phase by building a compact statistical model representing shape and texture variation of the object. This Model is used to find the object location and shape-appearance parameters, in a test set. The selection of the initial position of the construct model in a test image is a very important task in this context. The goal of this work is to propose an automatic segmentation method applied to cardiovascular MR images using an AAM based segmentation approach. The AAM model was constructed using 20 end-diastolic and end-systolic short axis cardiac magnetic resonance images (MRI). Once the model is constructed, we select the best position in order to start the search step manually in the test image. That is why; in this paper, the localization of the left ventricular cavity in the test image is used to select the initial position of the construct model developed from the training images. So we propose an automatic approach to detect this spatial position by using two methods: (1) the circular Hough transform (CHT) and (2) the evaluation of the Hausdorff distance.

A Compact Statistical Model of the Song Syntax in Bengalese Finch

Songs of many songbird species consist of variable sequences of a finite number of syllables. A common approach for characterizing the syntax of these complex syllable sequences is to use transition probabilities between the syllables. This is equivalent to the Markov model, in which each syllable is associated with one state, and the transition probabilities between the states do not depend on the state transition history. Here we analyze the song syntax in Bengalese finch. We show that the Markov model fails to capture the statistical properties of the syllable sequences. Instead, a state transition model that accurately describes the statistics of the syllable sequences includes adaptation of the self-transition probabilities when states are revisited consecutively, and allows associations of more than one state to a given syllable. Such a model does not increase the model complexity significantly. Mathematically, the model is a partially observable Markov model with adaptation (POMMA). The success of the POMMA supports the branching chain network model of how syntax is controlled within the premotor song nucleus HVC, but also suggests that adaptation and many-to-one mapping from the syllable-encoding chain networks in HVC to syllables should be included in the network model.

A new path-gain/delay-spread propagation model for digital cellular channels

We derive a statistical model for the distribution of RMS delay spread (/spl tau//sub rms/) within a cellular environment, including the effects of base-to-mobile distance, environment type (urban, suburban, rural, and mountainous areas), and the correlation between delay spread and shadow fading. We begin with intuitive arguments that /spl tau//sub rms/ should be lognormally distributed at any given distance d; that the median of this distribution should grow as some (weak) power of d and that the variation about the median should be negatively correlated with shadow fading gain. We then present empirical evidence, drawn from a wide array of published reports, which gives strong support to these conjectures. Finally, we combine our findings with the widely used model for path gain in a cellular environment. The result is a compact statistical model for the joint distribution of path gain and delay spread. The model lends itself readily to Monte Carlo simulation and is useful for performance studies of cellular systems with bandwidths up to tens of kilohertz.