Measuring the composition of AlGaN layers in GaN based structures grown on 150 mm Si substrates using (2 0 5) reciprocal space maps

A critical element for the successful growth of GaN device layers on Si is accurate control of the AlGaN buffer layers used to manage strain. Here we present a method for measuring the composition of the AlGaN buffer layers in device structures which makes use of a one-dimensional x-ray detector to provide efficient measurement of a reciprocal space map which covers the full compositional range from AlN to GaN. Combining this with a suitable x-ray reflection with low strain sensitivity it is possible to accurately determine the Al fraction of the buffer layers independent of their relaxation state. © 2013 IOP Publishing Ltd.

Mass chains with passive interconnection: Complex iterative maps and scalability

This paper introduces the problem of passive control of a chain of N identical masses in which there is an identical passive connection between neighbouring masses and a similar connection to a movable point. The problem arises in the design of multi-storey buildings which are subjected to earthquake disturbances, but applies in other situations, for example vehicle platoons. The paper will study the scalar transfer functions from the disturbance to a given intermass displacement. It will be shown that these transfer functions can be conveniently represented in the form of complex iterative maps and that these maps provide a method to establish boundedness in N of the H ∞-norm of these transfer functions for certain choices of interconnection impedance. © 2013 IEEE.

structuring and manipulating hand-drawn concept maps

Concept maps are an important tool to knowledge organization,representation, and sharing. Most current concept map tools do not provide full support for hand-drawn concept map creation and manipulation, largely due to the lack of methods to recognize hand-drawn concept maps. This paper proposes a structure recognition method. Our algorithm can extract node blocks and link blocks of a hand-drawn concept map by combining dynamic programming and graph partitioning and then build a concept-map structure by relating extracted nodes and links. We also introduce structure-based intelligent manipulation technique of hand-drawn concept maps. Evaluation shows that our method has high structure recognition accuracy in real time, and the intelligent manipulation technique is efficient and effective.

AFLP-based genetic linkage maps of the Pacific oyster Crassostrea gigas Thunberg

Amplified fragment length polymorphisms (AFLPs) were used for genome mapping in the Pacific Oyster Crassostrea gigas Thunberg. Seventeen selected primer combinations produced 1106 peaks, of which 384 (34.7%) were polymorphic in a backcross family. Among the polymorphic markers, 349 were segregating through either the female or the male parent. Chi-square analysis indicated that 255 (73.1%) of the markers segregated in a Mendelian ratio, and 94 (26.9%) showed significant (P < 0.05) segregation distortion. Separate genetic linkage maps were constructed for the female and male parents. The female framework map consisted of 119 markers in 11 linkage groups, spanning 1030.7 cM, with an average interval of 9.5 cM per marker. The male map contained 96 markers in 10 linkage groups, covering 758.4 cM, with 8.8 cM per marker. The estimated genome length of the Pacific oyster was 1258 cM for the female and 933 cM for the male, and the observed coverage was 82.0% for the female map and 81.3% for the male map. Most distorted markers were deficient for homozygotes and closely linked to each other on the genetic map, suggesting the presence of major recessive deleterious genes in the Pacific oyster.

Phylogenetic relationships of five pika species (genus Ochotona) based on mitochondrial DNA restriction maps

Restriction site mapping of mitochondrial DNA (mtDNA) with 16 restriction endonucleases was used to examine the phylogenetic relationships of Ochotona cansus, O. huangensis, O. thibetana, O. curzoniae and O. erythrotis. A 1-kb length variation between O. erythrotis of subgenus Pika and other four species of subgenus Ochotona was observed, which may be a useful genetic marker for identifying the two subgenera. The phylogenetic tree constructed using PAUP based on 61 phylogenetically informative sites suggests that O. erythrotis diverged first, followed by O. cansus, while O. curzoniae and O. huangensis are sister taxa related to O. thibetana, The results indicate that both O. cansus and O. huangensis should be treated as independent species. If the base substitution rate of pikas mtDNA was 2% per million years, then the divergence time of the two subgenera, Pika and Ochotana, is about 8.8 Ma ago of late Miocence, middle Bao-dian of Chinese mammalian age, and the divergence of the four species in subgenus Ochotona would have occurred about 2.5 - 4.2 Ma ago, Yushean of Chinese mammalian age. This calculation appears to be substantiated by the fossil record.

Visual Navigation: Constructing and Utilizing Simple Maps of an Indoor Environment

The goal of this work is to navigate through an office environmentsusing only visual information gathered from four cameras placed onboard a mobile robot. The method is insensitive to physical changes within the room it is inspecting, such as moving objects. Forward and rotational motion vision are used to find doors and rooms, and these can be used to build topological maps. The map is built without the use of odometry or trajectory integration. The long term goal of the project described here is for the robot to build simple maps of its environment and to localize itself within this framework.

Constructing extensions of CP-maps via tensor dilations with rhe help of von Neumann modules

Gohm, Rolf; Skeide, M., (2005) 'Constructing extensions of CP-maps via tensor dilations with rhe help of von Neumann modules', Infinite Dimensional Analysis, Quantum Probability and Related Topics 8(2) pp.291-305 RAE2008

A probabilistic index for completely positive maps and an application

Gohm, Rolf, (2003) 'A probabilistic index for completely positive maps and an application', Journal of Operator Theory 54(2) pp.339-361 RAE2008

The jubilee story of the China Inland Mission : with portraits, illustrations & maps / by Marshall Broomhall.

http://www.archive.org/details/jubileechinamis00broouoft

The Encyclopaedia of missions : descriptive, historical, biographical, statistical : with a full assortment of maps, a complete bibliography, and lists of Bible versions, missionary societies, mission stations, and a general index / edited by Edwin Munsell Bliss.

http://www.archive.org/details/encyclopaediamis02unknuoft

Speaker Normalization Using Cortical Strip Maps: A Neural Model for Steady State Vowel Identification

Auditory signals of speech are speaker-dependent, but representations of language meaning are speaker-independent. Such a transformation enables speech to be understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitchindependent representation of speech sounds, while also preserving information about speaker identity. This speaker-invariant representation is categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be categorized, or chunked, into syllable and word representations. The proposed model fits into an emerging model of auditory streaming and speech categorization. The auditory streaming and speaker normalization parts of the model both use multiple strip representations and asymmetric competitive circuits, thereby suggesting that these two circuits arose from similar neural designs. The normalized speech items are rapidly categorized and stably remembered by Adaptive Resonance Theory circuits. Simulations use synthesized steady-state vowels from the Peterson and Barney [J. Acoust. Soc. Am. 24, 175-184 (1952)] vowel database and achieve accuracy rates similar to those achieved by human listeners. These results are compared to behavioral data and other speaker normalization models.

Vector Associative Maps: Unsupervised Real-time Error-based Learning and Control of Movement Trajectories

This article describes neural network models for adaptive control of arm movement trajectories during visually guided reaching and, more generally, a framework for unsupervised real-time error-based learning. The models clarify how a child, or untrained robot, can learn to reach for objects that it sees. Piaget has provided basic insights with his concept of a circular reaction: As an infant makes internally generated movements of its hand, the eyes automatically follow this motion. A transformation is learned between the visual representation of hand position and the motor representation of hand position. Learning of this transformation eventually enables the child to accurately reach for visually detected targets. Grossberg and Kuperstein have shown how the eye movement system can use visual error signals to correct movement parameters via cerebellar learning. Here it is shown how endogenously generated arm movements lead to adaptive tuning of arm control parameters. These movements also activate the target position representations that are used to learn the visuo-motor transformation that controls visually guided reaching. The AVITE model presented here is an adaptive neural circuit based on the Vector Integration to Endpoint (VITE) model for arm and speech trajectory generation of Bullock and Grossberg. In the VITE model, a Target Position Command (TPC) represents the location of the desired target. The Present Position Command (PPC) encodes the present hand-arm configuration. The Difference Vector (DV) population continuously.computes the difference between the PPC and the TPC. A speed-controlling GO signal multiplies DV output. The PPC integrates the (DV)·(GO) product and generates an outflow command to the arm. Integration at the PPC continues at a rate dependent on GO signal size until the DV reaches zero, at which time the PPC equals the TPC. The AVITE model explains how self-consistent TPC and PPC coordinates are autonomously generated and learned. Learning of AVITE parameters is regulated by activation of a self-regulating Endogenous Random Generator (ERG) of training vectors. Each vector is integrated at the PPC, giving rise to a movement command. The generation of each vector induces a complementary postural phase during which ERG output stops and learning occurs. Then a new vector is generated and the cycle is repeated. This cyclic, biphasic behavior is controlled by a specialized gated dipole circuit. ERG output autonomously stops in such a way that, across trials, a broad sample of workspace target positions is generated. When the ERG shuts off, a modulator gate opens, copying the PPC into the TPC. Learning of a transformation from TPC to PPC occurs using the DV as an error signal that is zeroed due to learning. This learning scheme is called a Vector Associative Map, or VAM. The VAM model is a general-purpose device for autonomous real-time error-based learning and performance of associative maps. The DV stage serves the dual function of reading out new TPCs during performance and reading in new adaptive weights during learning, without a disruption of real-time operation. YAMs thus provide an on-line unsupervised alternative to the off-line properties of supervised error-correction learning algorithms. YAMs and VAM cascades for learning motor-to-motor and spatial-to-motor maps are described. YAM models and Adaptive Resonance Theory (ART) models exhibit complementary matching, learning, and performance properties that together provide a foundation for designing a total sensory-cognitive and cognitive-motor autonomous system.