Synthesis and characterization of UV photocrosslinkable hydrogels with poly(N-vinyl-2-pyrrolidone): Determination of the network mesh size distribution

Hydrogels of poly(n-vinyl-2-pyrrolidone) were produced by UV irradiation of aqueous solutions of the polymer in presence of hydrogen peroxide, used as initiator. The mechanical and the nanostructural properties of the gels were characterized by a combination of experimental techniques including rheology, low field nuclear magnetic resonance spectroscopy (LF-NMR), and small angle X-ray scattering. Different irradiation doses as well as polymer and initiator concentrations were tested in the characterization. The study elucidates the relationship between different methods to estimate the mesh size of the gel polymeric network. Moreover, a novel correlation model was developed based on Chui and Scherer theories for the interpretation of LF-NMR dataset of polymer solutions and networks.     

The UCSC Kestrel parallel processor

The architectural landscape of high-performance computing stretches from superscalar uniprocessor to explicitly parallel systems, to dedicated hardware implementations of algorithms. Single-purpose hardware can achieve the highest performance and uniprocessors can be the most programmable. Between these extremes, programmable and reconfigurable architectures provide a wide range of choice in flexibility, programmability, computational density, and performance. The UCSC Kestrel parallel processor strives to attain single-purpose performance while maintaining user programmability. Kestrel is a single-instruction stream, multiple-data stream (SIMD) parallel processor with a 512-element linear array of 8-bit processing elements. The system design focuses on efficient high-throughput DNA and protein sequence analysis, but its programmability enables high performance on computational chemistry, image processing, machine learning, and other applications. The Kestrel system has had unexpected longevity in its utility due to a careful design and analysis process. Experience with the system leads to the conclusion that programmable SIMD architectures can excel in both programmability and performance. This work presents the architecture, implementation, applications, and observations of the Kestrel project at the University of California at Santa Cruz.     

Are fuzzy definitions of basic attributes of image objects reallyuseful?

Computer vision applications often involve measuring properties of objects in images. Typically, thresholding or segmentation techniques are used to obtain crisp object boundaries before object properties are computed. In this correspondence, we explore the possibility of using fuzzy definitions for measuring object properties without having to make crisp decisions about object boundaries prematurely. We present theorems which indicate that the use of fuzzy definitions to measure properties in intensity-based image analysis almost always gives accurate results. We also present experimental evidence and reasoning which show that fuzzy definitions are not always useful in feature-based methods     

Quantum Computer Development with Single Ion Implantation

Spins of single donor atoms are attractive candidates for large scale quantum information processing in silicon. Formation of devices with a few qubits is crucial for validation of basic ideas and development of a scalable architecture. We describe our development of a single ion implantation technique for placement of single atoms into device structures. Collimated highly charged ion beams are aligned with a scanning probe microscope. Enhanced secondary electron emission due tohigh ion charge states (e.g., ³¹P¹³⁺, or ¹²⁶Te³³⁺)allows efficient detection of single ion impacts. Studies of electrical activation of low dose, low energy implants of ³¹P in silicon show a drastic effect of dopant segregation to the SiO₂/Si interface,while Si₃N₄/Si retards ³¹P segregation. We discuss resolution limiting factors in ion placement, and process challenges forintegration of single atom arrays with control gates and single electron transistors. PACS: 03.67.Lx, 34.50.Dy, 85.35.Gv, 73.23, 61.72, 86.40.py, 07.79.-v     

Office task effects on comfort and body dynamics in five dynamic office chairs

In the present study, we investigated the effect of office tasks on posture and movements in field settings, and the comfort rating for chair characteristics and correlation with type of task. The tasks studied were: computer work, telephoning, desk work and conversation. Postures, movements, chair part inclinations and comfort rating data were collected from 12 subjects. Computer work showed the lowest physical activity, together with upright trunk and head position and low backrest inclination. Conversation shows the highest activity of head legs and low back together with the highest cervical spine extension. In contrast, desk work provoked the most cervical spine flexion and showed the second lowest activity. The telephoning tasks showed medium activity and the highest kyphosis. Conversation showed the highest backrest inclination. Positive comfort relations were found for computer work and a "swing system" chair, for telephoning and an active longitudinal seat rotation, and for desk work and a chair with a three-dimensionally moveable seat.     

Analysis and efficient implementation of a linguistic fuzzy c-means

The paper is concerned with a linguistic fuzzy c-means (FCM) algorithm with vectors of fuzzy numbers as inputs. This algorithm is based on the extension principle and the decomposition theorem. It turns out that using the extension principle to extend the capability of the standard membership update equation to deal with a linguistic vector has a huge computational complexity. In order to cope with this problem, an efficient method based on fuzzy arithmetic and optimization has been developed and analyzed. We also carefully examine and prove that the algorithm behaves in a way similar to the FCM in the degenerate linguistic case. Synthetic data sets and the iris data set have been used to illustrate the behavior of this linguistic version of the FCM.     

Efficient high order matching

We present a computational approach to high-order matching of data sets in IR(d). Those are matchings based on data affinity measures that score the matching of more than two pairs of points at a time. High-order affinities are represented by tensors and the matching is then given by a rank-one approximation of the affinity tensor and a corresponding discretization. Our approach is rigorously justified by extending Zass and Shashua's hypergraph matching to high-order spectral matching. This paves the way for a computationally efficient dual-marginalization spectral matching scheme. We also show that, based on the spectral properties of random matrices, affinity tensors can be randomly sparsified while retaining the matching accuracy. Our contributions are experimentally validated by applying them to synthetic as well as real data sets.     

Evidence aggregation networks for fuzzy logic inference

Fuzzy logic has been applied in many engineering disciplines. The problem of fuzzy logic inference is investigated as a question of aggregation of evidence. A fixed network architecture employing general fuzzy unions and intersections is proposed as a mechanism to implement fuzzy logic inference. It is shown that these networks possess desirable theoretical properties. Networks based on parameterized families of operators (such as Yager's union and intersection) have extra predictable properties and admit a training algorithm which produces sharper inference results than were earlier obtained. Simulation studies corroborate the theoretical properties.