EM-GPA: Generalized Procrustes analysis with hidden variables for 3D shape modeling

Aligning shapes is essential in many computer vision problems and generalized Procrustes analysis (GPA) is one of the most popular algorithms to align shapes. However, if some of the shape data are missing, GPA cannot be applied. In this paper, we propose EM-GPA, which extends GPA to handle shapes with hidden (missing) variables by using the expectation-maximization (EM) algorithm. For example, 2D shapes can be considered as 3D shapes with missing depth information due to the projection of 3D shapes into the image plane. For a set of 2D shapes, EM-GPA finds scales, rotations and 3D shapes along with their mean and covariance matrix for 3D shape modeling. A distinctive characteristic of EM-GPA is that it does not enforce any rank constraint often appeared in other work and instead uses GPA constraints to resolve the ambiguity in finding scales, rotations, and 3D shapes. The experimental results show that EM-GPA can recover depth information accurately even when the noise level is high and there are a large number of missing variables. By using the images from the FRGC database, we show that EM-GPA can successfully align 2D shapes by taking the missing information into consideration. We also demonstrate that the 3D mean shape and its covariance matrix are accurately estimated. As an application of EM-GPA, we construct a 2D + 3D AAM (active appearance model) using the 3D shapes obtained by EM-GPA, and it gives a similar success rate in model fitting compared to the method using real 3D shapes. EM-GPA is not limited to the case of missing depth information, but it can be easily extended to more general cases.     

An object-based middleware supporting efficient interoperability on a smart home network

This paper proposes a novel object-based home network middleware for supporting the interoperability among home devices and smart grid devices. This middleware provides various types of abstract objects for flexible representation of heterogeneous home devices, which are classified based on their characteristics. It is also flexible enough to allow addition of new protocols and message conversion between different protocols through an abstraction layer, which are not supported by existing home network middlewares so that various protocols can be supported. As a result, it can be utilized to provide the interoperability among a variety of devices from sensors to typical home appliances as well as smart grid devices such as a home electric generator and a battery, which adopt different protocols. In order to demonstrate that home devices and smart grid devices are interoperable through the proposed middleware, we implement applications based on the middleware on a target platform consisting of embedded boards, sensors and laptops for emulation purposes of a home network. According to our emulation, this middleware can provide efficient interoperability among home devices and smart grid devices for future energy efficient home.     

PASU: A personal area situation understanding system using wireless camera sensor networks

In this paper, we present a personal area situation understanding (PASU) system, a novel application of a smart device using wireless camera sensor networks. The portability of a PASU system makes it an attractive solution for monitoring and understanding the current situation of the personal area around a user. The PASU system allows its user to construct a 3D scene of the environment and view the scene from various vantage points for better understanding of the environment. The paper describes the architecture and implementation of the PASU system addressing limitations of wireless camera sensor networks, such as low bandwidth and limited computational capabilities. The capabilities of PASU are validated with extensive experiments. The PASU system demonstrates the potential of a portable system combining a smart device and a wireless camera sensor network for personal area monitoring and situation understanding.     

An optimized modular neural network controller based on environment classification and selective sensor usage for mobile robot reactive navigation

A new approach to the design of a neural network (NN) based navigator is proposed in which the mobile robot travels to a pre-defined goal position safely and efficiently without any prior map of the environment. This navigator can be optimized for any user-defined objective function through the use of an evolutionary algorithm. The motivation of this research is to develop an efficient methodology for general goal-directed navigation in generic indoor environments as opposed to learning specialized primitive behaviors in a limited environment. To this end, a modular NN has been employed to achieve the necessary generalization capability across a variety of indoor environments. Herein, each NN module takes charge of navigating in a specialized local environment, which is the result of decomposing the whole path into a sequence of local paths through clustering of all the possible environments. We verify the efficacy of the proposed algorithm over a variety of both simulated and real unstructured indoor environments using our autonomous mobile robot platform.     

Granular Neural Networks and Their Development Through Context-Based Clustering and Adjustable Dimensionality of Receptive Fields

In this study, we present a new architecture of a granular neural network and provide a comprehensive design methodology as well as elaborate on an algorithmic setup supporting its development. The proposed neural network relates to a broad category of radial basis function neural networks (RBFNNs) in the sense that its topology involves a collection of receptive fields. In contrast to the standard architectures encountered in RBFNNs, here we form individual receptive fields in subspaces of the original input space rather than in the entire input space. These subspaces could be different for different receptive fields. The architecture of the network is fully reflective of the structure encountered in the training data which are granulated with the aid of clustering techniques. More specifically, the output space is granulated with use of K-means clustering while the information granules in the multidimensional input space are formed by using the so-called context-based fuzzy C-means, which takes into account the structure being already formed in the output space. The innovative development facet of the network involves a dynamic reduction of dimensionality of the input space in which the information granules are formed in the subspace of the overall input space which is formed by selecting a suitable subset of input variables so that this subspace retains the structure of the entire space. As this search is of combinatorial character, we use the technique of genetic optimization [genetic algorithms (GAs), to be more specific] to determine the optimal input subspaces. A series of numeric studies exploiting synthetic data and data coming from the Machine Learning Repository, University of California at Irvine, provide a detailed insight into the nature of the algorithm and its parameters as well as offer some comparative analysis.     

Primary focal segmental glomerulosclerosis: clinical course and response to therapy

Primary focal segmental glomerulosclerosis (FSGS) is a lesion associated with a poor prognosis and results in end-stage renal disease after 5 to 10 years. Based on past experience, many nephrologists have considered primary FSGS a lesion that is steroid resistant and therefore are reluctant to offer steroids as treatment. Recent data, however, have demonstrated that patients with primary FSGS have a response to steroid therapy that is considerably better than had been described. Thus, it may be that nephrologists have been more "steroid reluctant" than the lesion is steroid resistant. To better understand this issue we review the clinical course and response to therapy in patients with primary FSGS.     

Fabrication of silicon membrane using fusion bonding and two-step electrochemical etch-stopping

A new type of silicon membrane structure was fabricated using wafer fusion bonding and two-step electrochemical etch-stopping methods. An active wafer of p-type epi/n-type epi/p-type substrate was first elctrochemically etched to form a shallow cavity on the p-type epitaxial layer. Then, the cavity-formed side was fusionally bonded with p-type silicon working wafer and, afterwards, the p-type substrate of the active wafer part was removed by a second electrochemical etch-stopping leaving only the n-type membrane on the shallow cavity. Using the new membrane structure in mechanical sensors, more precise control of cavity depth and membrane thickness was achievable and the influence of crystalline imperfections on the sensing circuits located near the bonding seam was avoidable.     

Preparation of PbTiO3 thin films using an alkoxide-alkanolamine sol-gel system

The stable range of PbTiO₃ sol and the processing conditions of uniform thin films were investigated using a solution of titanium isopropoxide, three kinds of alkanolamines (monoethanolamine, diethanolamine, triethanolamine), lead acetate trihydrate and isopropanol. Depending on the sol state with various alkanolamine/alkoxide molar ratios, diethanolamine (DEA) was very effective in preparing uniform and dense oxide films through room-temperature reaction, owing to its superior stability during the hydrolysis and condensation reaction. Perovskite PbTiO₃ thin films were obtained on oxidized silicon wafer above 550 °C and completely pure films were obtained at 650 °C using DEA as a complexing agent. The dielectric constant and loss tangent of these thin films fired at 650 °C for 30 min were found to be 240 and 0.01 at 1 kHz, respectively.     

Significant reduction of medical costs by differentiation therapy with all-trans retinoic acid during remission induction of newly diagnosed patients with acute promyelocytic leukemia. The Japan Adult Leukemia Study Group

OBJECTIVE: For resuscitation of hemorrhagic hypovolemia, we compared the effectiveness of (1) isotonic lactated Ringer's solution (LRS), (2) 2400 mOsm of 7.5% NaCl:6% dextran 70 (HSD), and (3) 2400 mOsm of 7.9% sodium acetate:1.9% NaCl:6% dextran 70 (HAD). DESIGN: In six randomized, blinded experiments for each solution, conscious instrumented adult sheep were hemorrhaged by removing approximately 1.8 L (42 +/- 3 mL/kg) of blood, while maintaining the mean arterial pressure (MAP) at 50 mm Hg for 2 hours. METHODS: Test solutions were infused as needed to restore the cardiac index to baseline. RESULTS: Volume requirements with HAD (236 +/- 29 mL) and HSD (244 +/- 39 mL) were significantly less (p < 0.05) than LRS (3463 +/- 234 mL). Mean arterial pressure was normalized with HSD and LRS, but not with HAD, which resulted in MAPs of 20 to 25 mm Hg less than baseline resulting from a reduced peripheral resistance. Oxygen delivery, however, was significantly higher with HAD during the resuscitation period. Acid-base balance (pH) and oxygen consumption were normalized within 5 minutes of infusion only with HAD. CONCLUSIONS: Small-volume infusion with HAD resulting in "high-flow-low-pressure" resuscitation may offer unique hemodynamic and metabolic advantages for the initial treatment of hemorrhage from trauma.     

Correlated Quantum Phenomena of Spin–Orbit Coupled Perovskite Oxide Heterostructures: Cases of SrRuO3 and SrIrO3 Based Artificial Superlattices

Unexpected, yet useful functionalities emerge when two or more materials merge coherently. Artificial oxide superlattices realize atomic and crystal structures that are not available in nature, thus providing controllable correlated quantum phenomena. This review focuses on 4d and 5d perovskite oxide superlattices, in which the spin–orbit coupling plays a significant role compared with conventional 3d oxide superlattices. Modulations in crystal structures with octahedral distortion, phonon engineering, electronic structures, spin orderings, and dimensionality control are discussed for 4d oxide superlattices. Atomic and magnetic structures, J_eff = 1/2 pseudospin and charge fluctuations, and the integration of topology and correlation are discussed for 5d oxide superlattices. This review provides insights into how correlated quantum phenomena arise from the deliberate design of superlattice structures that give birth to novel functionalities.