We investigate geometries for efficient coupling of single ions to fiber-coupled light fields for applications in quantum sensing, quantum metrology, and quantum information processing. Specifically, we discuss the integration of fiber-tip microcavities into radio-frequency ion traps. The distortions of the trapping fields induced by the presence of the optical fibers are simulated for a range of ion trap geometries and the most promising arrangements are identified. Finally, we investigate the use of fiber-tip microcavities with non-spherical mirrors for enhanced ion-light coupling at the center of the trap by appropriate shaping of the cavity modes.
We study various non-Gaussian states generated by photon subtrastion from a squeezed light source. The source is a cw beam generated by optical parametric oscillator. The photon subtraction
is made by tapping a small fraction of the squeezed light source and by guiding it into two Si-APDs, which enable the subtraction of one to two photons. Trigger photon clicks specify a certain temporally
localized mode in the remaining squeezed beam. By filtering the remaining squeezed beam through an appropriate mode function, one can generate a variety of non-Gaussian states. This includes single and two photon states, the NOON state (N = 2), Schrödinger kitten states of both odd and even parities, and their arbitrarily desired superposition.
We report on the novel nonequilibrium microwave emissions from quasiparticle-injected high-Tc superconductors. The phenomena have been observable for the current-injected YBa2Cu3O7- y(YBCO)/I/Au or Bi2Sr2CaCu2Oy(BSCCO)/I/Au thin- film tunnel junctions and BSCCO single-crystal mesa samples. For the thin-film tunnel junctions, the emitted radiation appears as broadband. The different emission characteristics between the YBCO and BSCCO tunnel junctions strongly suggest the possibility of Josephson plasma emission. On the other hand, for the mesa samples, the radiation appears as three different modes depending on the bias point in the hysteretic current-voltage characteristics: Josephson self-emission, nonequilibrium broad emission and sharp emission. The latter two emission are identified as Josephson plasma emission.
A method for obtaining intelligent behavior and influencing the shapes of artificial creatures following an evolutionary model is described. The creatures obtain intelligent behavior by interacting with the variable conditions in the environment where they live. Our algorithm proposes a way for the creatures to shirk from enemies, overcome obstacles, search for a mate and look after their own needs by using the five senses. The evolutional model used in our research is based on Genetic Algorithms (GA) and offers a novel way to generate new shapes from intelligent behaviors. This paper proposes a method to generate intelligent behaviors for and evolve the shape of artificial creatures by expanding our previously proposed evolutionary model.
Proc. SPIE. 3422, Input/Output and Imaging Technologies
KEYWORDS: Principal component analysis, Data compression, Image compression, Visualization, Magnetic resonance imaging, 3D modeling, Computed tomography, 3D displays, 3D visualizations, Visual compression
This paper presents one part of our work on 'hierarchical visualization.' Here, we propose an efficient hierarchical structure for the visualization of 3D volumetric data, where for instance, data compression techniques are embedded into the hierarchical structure. The hierarchical structure consists of two layers. The first hierarchy roughly visualizes the whole lossy compressed data so that we can quickly understand and interpret the outline of the whole 3D volumetric dataset. This step allows users to choose the desired parts of the 3D volumetric data. The second hierarchy is used for scrutinizing the detail of the chosen parts using lossless compressed data. To implement the hierarchical structure we propose a partitioning algorithm for 3D volumetric data based on principal component analysis. With principal component analysis, the original 3D volumetric space can be divided into a number of 3D volumetric blocks such that each block contains similar data. Analyzing these specific blocks and taking advantage of an octree hierarchy, from the whole 3D volumetric dataset we can easily access only the parts which are required for display. The proposed partitioning algorithm should be very useful in reducing the amount of rendering, which means that we will need only to precisely render the blocks required for display, and not the whole 3D volumetric dataset.
This paper proposes a hair volume algorithm that is effective in producing realistic hair model for individual. The hair volume is produced from three images of the head taken from the right, back and top view. This hair volume represents the real space volume where hairs are found and guides the hair strands to move in the desired direction. Hair strands are randomly generated on the skull. The outline region acquired through image processing together with the hair volume ensures that the randomly generated hair strands fall neatly into the hair volume to produce hair model resembling the input images. This hair model can find many applications in the generation of synthetic humans and creatures in movies, multimedia and computer game productions.
Recent efforts in image morphing research aim at improving both user interface and warping results. To specify feature points in tow images, the user interface takes up much time and it allows the warping specification by the user which represents very tedious work. In this paper, we propose a semi-automatic algorithm based on active contour model to specify the feature correspondence between two given images. It allows a user to extract a contour that defines a facial features such as lips, mouth, profile, etc., by specifying only endpoints of the contour around the feature that serve as the extremities of a contour. The proposal algorithm uses these two points as anchor points, and automatically computes the image information around these endpoints to provide boundary conditions. Then we optimize the contour by taking this information into account close to its extremities. During the iterative optimization process, the image forces are moving progressively from the contours extremities towards its center to define the feature. Once the feature correspondence points are paired, the intermediate images are generated by interpolating the positions of feature points linearly. The proposal algorithm helps the user to define easily the exact position of a feature. It may also reduce the time taken to establish feature correspondence between two images.
This report describes a GA (Genetic Algorithms) method that evolves multi-layered feedforward neural network architectures for specific mappings. The network is represented as a genotype that has six kinds of genes. They are a learning rate, a slant of sigmoid function, a coefficient of momentum term, an initializing weights range, the number of layers and the unit numbers of each layer. Genetic operators affect populations of these genotypes to produce adaptive networks with higher fitness values. We define three kinds of fitness functions that evaluate networks generated by the GA method. Their fitnesses are assessed for the generated network trained with BP (Back Propagation) algorithm by several network performances. In our experiments, we train the networks for the XOR mapping. They are designed systematically and easily using the GA method. These generated networks require fewer training cycles then networks used until now, and a rate of convergence is improved.
A method of pattern recognition using a three layered feedforward neural network is described. Experiments were carried out for handwritten katakana in a frame using neural network. Handwritten characters have varieties of scales, positions, and orientations. In a neural network, however, if the input patterns are shifted in position, rotated, and varied in scales, it does not function well. So we describe a method to solve the problems of these variations using three layered feedforward neural network. We used two kinds of moment values that are invariant for these variations. One is regular moments and the other is Zernike moment, which gives a set of orthogonal complex moments of an image known as Zernike moments. We also describe the problem of the structure of neural networks and the relation between the recognition rate and data sets for similar and different patterns.