Joint Rigid Registration of Multiple Generalized Point Sets With Anisotropic Positional Uncertainties in Image-Guided Surgery

In medical image analysis (MIA) and computer-assisted surgery (CAS), aligning two multiple point sets (PSs) together is an essential but also a challenging problem. For example, rigidly aligning multiple point sets into one common coordinate frame is a prerequisite for statistical shape modelling (SSM). Accurately aligning the pre-operative space with the intra-operative space in CAS is very crucial to successful interventions. In this article, we formally formulate the multiple generalized point set registration problem (MGPSR) in a probabilistic manner, where both the positional and the normal vectors are used. The six-dimensional vectors consisting of both positional and normal vectors are called as generalized points. In the formulated model, all the generalized PSs to be registered are considered to be the realizations of underlying unknown hybrid mixture models (HMMs). By assuming the independence of the positional and orientational vectors (i.e., the normal vectors), the probability density function (PDF) of an observed generalized point is computed as the product of Gaussian and Fisher distributions. Furthermore, to consider the anisotropic noise in surgical navigation, the positional error is assumed to obey a multi-variate Gaussian distribution. Finally, registering PSs is formulated as a maximum likelihood (ML) problem, and solved under the expectation maximization (EM) technique. By using more enriched information (i.e., the normal vectors), our algorithm is more robust to outliers. By treating all PSs equally, our algorithm does not bias towards any PS. To validate the proposed approach, extensive experiments have been conducted on surface points extracted from CT images of (i) a human femur bone model; (ii) a human pelvis bone model. Results demonstrate our algorithm's high accuracy, robustness to noise and outliers

Generalized Coherent Point Drift With Multi-Variate Gaussian Distribution and Watson Distribution

This letter introduces a novel rigid point set registration (PSR) approach that accurately aligns the pre-operative space and the intra-operative space together in the scenario of computer-Assisted orthopedic surgery (CAOS). Motivated by considering anisotropic positional localization noise and utilizing undirected normal vectors in the point sets (PSs), the multi-variate Gaussian distribution and the Watson distribution are utilized to model positional and normal vectors' error distributions respectively. In the proposed approach, with the above probability distributions, the PSR problem is then formulated as a maximum likelihood estimation (MLE) problem and solved under the expectation maximization (EM) framework. Our contributions are three folds. First, the rigid registration problem of aligning generalized points with undirected normal vectors is formally formulated in a probabilistic manner. Second, the MLE problem is solved under the EM framework. Third, the gradients of associated objective functions with respect to desired parameters are computed and provided. Experimental results on both the human pelvis and femur models demonstrate the potential clinical values and that the proposed approach owns significantly improved performances compared with existing methods

Technical Report Series on Global Modeling and Data Assimilation

The Data Assimilation Office (DAO) at Goddard Space Flight Center and the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) have produced multi-year global assimilations of historical data employing fixed analysis systems. These "reanalysis" products are ideally suited for studying short-term climatic variations. The availability of multiple reanalysis products also provides the opportunity to examine the uncertainty in the reanalysis data. The purpose of this document is to provide an updated estimate of seasonal and interannual variability based on the DAO and NCEP/NCAR reanalyses for the 15-year period 1980-1995. Intercomparisons of the seasonal means and their interannual variations are presented for a variety of prognostic and diagnostic fields. In addition, atmospheric potential predictability is re-examined employing selected DAO reanalysis variables

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Standard level N-channel MOSFET in TO220 package qualified to 175 °C. This product is designed and qualified for use in a wide range of industrial, communications and domestic equipment. 2. Features and benefits • High efficiency due to low switching and conduction losses • Suitable for standard level gate drive source

Enabling Simultaneous Extreme Ultra Low-k in Stiff, Resilient, and Thermally Stable Nano-Architected Materials

Low dielectric constant (low-k) materials have gained increasing popularity because of their critical role in developing faster, smaller, and higher performance devices. Their practical use has been limited by the strong coupling among mechanical, thermal, and electrical properties of materials and their dielectric constant; a low-k is usually attained by materials that are very porous, which results in high compliance, that is, silica aerogels; high dielectric loss, that is, porous polycrystalline alumina; and poor thermal stability, that is, Sr-based metal–organic frameworks. We report the fabrication of 3D nanoarchitected hollow-beam alumina dielectrics which k is 1.06–1.10 at 1 MHz that is stable over the voltage range of −20 to 20 V and a frequency range of 100 kHz to 10 MHz. This dielectric material can be used in capacitors and is mechanically resilient, with a Young’s modulus of 30 MPa, a yield strength of 1.07 MPa, a nearly full shape recoverability to its original size after >50% compressions, and outstanding thermal stability with a thermal coefficient of dielectric constant (TCK) of 2.43 × 10^(-5) K^(-1) up to 800 °C. These results suggest that nanoarchitected materials may serve as viable candidates for ultra low-k materials that are simultaneously mechanically resilient and thermally and electrically stable for microelectronics and devices

Anisotropic Generalized Bayesian Coherent Point Drift for Point Set Registration

Registration is highly demanded in many real-world scenarios such as robotics and automation. Registration is challenging partly due to the fact that the acquired data is usually noisy and has many outliers. In addition, in many practical applications, one point set (PS) usually only covers a partial region of the other PS. Thus, most existing registration algorithms cannot guarantee theoretical convergence. This article presents a novel, robust, and accurate three-dimensional (3D) rigid point set registration (PSR) method, which is achieved by generalizing the state-of-the-art (SOTA) Bayesian coherent point drift (BCPD) theory to the scenario that high-dimensional point sets (PSs) are aligned and the anisotropic positional noise is considered. The high-dimensional point sets typically consist of the positional vectors and normal vectors. On one hand, with the normal vectors, the proposed method is more robust to noise and outliers, and the point correspondences can be found more accurately. On the other hand, incorporating the registration into the BCPD framework will guarantee the algorithm's theoretical convergence. Our contributions in this article are three folds. First, the problem of rigidly aligning two general PSs with normal vectors is incorporated into a variational Bayesian inference framework, which is solved by generalizing the BCPD approach while the anisotropic positional noise is considered. Second, the updated parameters during the algorithm's iterations are given in closed-form or with iterative solutions. Third, extensive experiments have been done to validate the proposed approach and its significant improvements over the BCPD

Novel Packet Switching for Green IP Networks

A green technology for reducing energy consumption has become a critical factor in ICT industries. However, for the telecommunications sector in particular, most network elements are not usually optimized for power efficiency. Here, we propose a novel energy-efficient packet switching method for use in an IP network for reducing unnecessary energy consumption. As a green networking approach, we first classify the network nodes into either header or member nodes. The member nodes then put the routing-related module at layer 3 to sleep under the assumption that the layer in the OSI model can operate independently. The entire set of network nodes is then partitioned into clusters consisting of one header node and multiple member nodes. Then, only the header node in a cluster conducts IP routing and its member nodes conduct packet switching using a specially designed identifier, a tag. To investigate the impact of the proposed scheme, we conducted a number of simulations using wellknown real network topologies and achieved a more energy-efficient performance than that achieved in previous studies.Peer reviewe

Dynamic characteristics

Standard level N-channel enhancement mode Field-Effect Transistor (FET) in a plastic package using TrenchMOS technology. This product is designed and qualified for use in computing, communications, consumer and industrial applications only. 1.2 Features and benefits � Low conduction losses due to low on-state resistance � Suitable for high frequency applications due to fast switching characteristics 1.3 Application