Compact non-binary fast adders using single-electron devices

This paper proposes compact adders that are based on non-binary redundant number systems and single-electron (SE) devices. The adders use the number of single electrons to represent discrete multiple-valued logic state and manipulate single electrons to perform arithmetic operations. These adders have fast speed and are referred as fast adders. We develop a family of SE transfer circuits based on MOSFET-based SE turnstile. The fast adder circuit can be easily designed by directly mapping the graphical counter tree diagram (CTD) representation of the addition algorithm to SE devices and circuits. We propose two design approaches to implement fast adders using SE transfer circuits the threshold approach and the periodic approach. The periodic approach uses the voltage-controlled single-electron transfer characteristics to efficiently achieve periodic arithmetic functions. We use HSPICE simulator to verify fast adders operations. The speeds of the proposed adders are fast. The numbers of transistors of the adders are much smaller than conventional approaches. The power dissipations are much lower than CMOS and multiple-valued current-mode fast adders. (C) 2009 Elsevier Ltd. All rights reserved.

A 1,000 Frames/s Programmable Vision Chip with Variable Resolution and Row-Pixel-Mixed Parallel Image Processors

A programmable vision chip with variable resolution and row-pixel-mixed parallel image processors is presented. The chip consists of a CMOS sensor array, with row-parallel 6-bit Algorithmic ADCs, row-parallel gray-scale image processors, pixel-parallel SIMD Processing Element (PE) array, and instruction controller. The resolution of the image in the chip is variable: high resolution for a focused area and low resolution for general view. It implements gray-scale and binary mathematical morphology algorithms in series to carry out low-level and mid-level image processing and sends out features of the image for various applications. It can perform image processing at over 1,000 frames/s (fps). A prototype chip with 64 x 64 pixels resolution and 6-bit gray-scale image is fabricated in 0.18 mu m Standard CMOS process. The area size of chip is 1.5 mm x 3.5 mm. Each pixel size is 9.5 mu m x 9.5 mu m and each processing element size is 23 mu m x 29 mu m. The experiment results demonstrate that the chip can perform low-level and mid-level image processing and it can be applied in the real-time vision applications, such as high speed target tracking.

Characterization of entanglement transformation via group representation theory

Entanglement transformation of composite quantum systems is investigated in the context of group representation theory. Representation of the direct product group SL(2, C) circle times SL(2, C), composed of local operators acting on the binary composite system, is realized in the four-dimensional complex space in terms of a set of novel bases that are pseudo-orthonormalized. The two-to-one homomorphism is then established for the group SL(2, C) circle times SL(2, C) onto the SO(4, C). It is shown that the resulting representation theory leads to the complete characterization for the entanglement transformation of the binary composite system.

Fast Haar Transform Based Feature Extraction for Face Representation and Recognition

Subspace learning is the process of finding a proper feature subspace and then projecting high-dimensional data onto the learned low-dimensional subspace. The projection operation requires many floating-point multiplications and additions, which makes the projection process computationally expensive. To tackle this problem, this paper proposes two simple-but-effective fast subspace learning and image projection methods, fast Haar transform (FHT) based principal component analysis and FHT based spectral regression discriminant analysis. The advantages of these two methods result from employing both the FHT for subspace learning and the integral vector for feature extraction. Experimental results on three face databases demonstrated their effectiveness and efficiency.

An integrated aurora image retrieval system: AuroraEye

With the digital all-sky imager (ASI) emergence in aurora research, millions of images are captured annually. However, only a fraction of which can be actually used. To address the problem incurred by low efficient manual processing, an integrated image analysis and retrieval system is developed. For precisely representing aurora image, macroscopic and microscopic features are combined to describe aurora texture. To reduce the feature dimensionality of the huge dataset, a modified local binary pattern (LBP) called ALBP is proposed to depict the microscopic texture, and scale-invariant Gabor and orientation-invariant Gabor are employed to extract the macroscopic texture. A physical property of aurora is inducted as region features to bridge the gap between the low-level visual features and high-level semantic description. The experiments results demonstrate that the ALBP method achieves high classification rate and low computational complexity. The retrieval simulation results show that the developed retrieval system is efficient for huge dataset. (c) 2010 Elsevier Inc. All rights reserved.

A Unified Tensor Level Set for Image Segmentation

This paper presents a new region-based unified tensor level set model for image segmentation. This model introduces a three-order tensor to comprehensively depict features of pixels, e.g., gray value and the local geometrical features, such as orientation and gradient, and then, by defining a weighted distance, we generalized the representative region-based level set method from scalar to tensor. The proposed model has four main advantages compared with the traditional representative method as follows. First, involving the Gaussian filter bank, the model is robust against noise, particularly the salt-and pepper-type noise. Second, considering the local geometrical features, e. g., orientation and gradient, the model pays more attention to boundaries and makes the evolving curve stop more easily at the boundary location. Third, due to the unified tensor pixel representation representing the pixels, the model segments images more accurately and naturally. Fourth, based on a weighted distance definition, the model possesses the capacity to cope with data varying from scalar to vector, then to high-order tensor. We apply the proposed method to synthetic, medical, and natural images, and the result suggests that the proposed method is superior to the available representative region-based level set method.

Shape Recipes: Scene Representations that Refer to the Image

The goal of low-level vision is to estimate an underlying scene, given an observed image. Real-world scenes (e.g., albedos or shapes) can be very complex, conventionally requiring high dimensional representations which are hard to estimate and store. We propose a low-dimensional representation, called a scene recipe, that relies on the image itself to describe the complex scene configurations. Shape recipes are an example: these are the regression coefficients that predict the bandpassed shape from bandpassed image data. We describe the benefits of this representation, and show two uses illustrating their properties: (1) we improve stereo shape estimates by learning shape recipes at low resolution and applying them at full resolution; (2) Shape recipes implicitly contain information about lighting and materials and we use them for material segmentation.

Multimodal lexicography: The representation of meaning in electronic dictionaries

One finding of user studies is that information on meaning tends to be what diction¬ary users want most from their dictionaries. This is consistent with the traditional image of the dictionary as a repository of meanings of words, and this is also borne out in definitions of the item DICTIONARY itself as given in dictionaries. While this popular view has not changed much, the growing role of electronic dictionaries can change the lexicographers' approach to meaning repre¬sentation. Traditionally, paper dictionaries have explained words with words, using either a defi¬nition or an equivalent, and occasionally a line-drawn picture. However, a prominent feature of the electronic medium is its multimodality, and this offers potential for the description of meaning. While it is much easier to include pictorial content, electronic dictionaries can also hold media objects which paper cannot carry, such as audio, animation or video. Publishers are drawn by the attraction of these new options, but are they always functionally useful for the dictionary users? In this article, the existing evidence is examined, and informed guesses are offered where evidence is not yet available.

Estimating 3D Hand Pose from a Cluttered Image

A method is proposed that can generate a ranked list of plausible three-dimensional hand configurations that best match an input image. Hand pose estimation is formulated as an image database indexing problem, where the closest matches for an input hand image are retrieved from a large database of synthetic hand images. In contrast to previous approaches, the system can function in the presence of clutter, thanks to two novel clutter-tolerant indexing methods. First, a computationally efficient approximation of the image-to-model chamfer distance is obtained by embedding binary edge images into a high-dimensional Euclide an space. Second, a general-purpose, probabilistic line matching method identifies those line segment correspondences between model and input images that are the least likely to have occurred by chance. The performance of this clutter-tolerant approach is demonstrated in quantitative experiments with hundreds of real hand images.

Combining Textual and Visual Cues for Content-based Image Retrieval on the World Wide Web

Some WWW image engines allow the user to form a query in terms of text keywords. To build the image index, keywords are extracted heuristically from HTML documents containing each image, and/or from the image URL and file headers. Unfortunately, text-based image engines have merely retro-fitted standard SQL database query methods, and it is difficult to include images cues within such a framework. On the other hand, visual statistics (e.g., color histograms) are often insufficient for helping users find desired images in a vast WWW index. By truly unifying textual and visual statistics, one would expect to get better results than either used separately. In this paper, we propose an approach that allows the combination of visual statistics with textual statistics in the vector space representation commonly used in query by image content systems. Text statistics are captured in vector form using latent semantic indexing (LSI). The LSI index for an HTML document is then associated with each of the images contained therein. Visual statistics (e.g., color, orientedness) are also computed for each image. The LSI and visual statistic vectors are then combined into a single index vector that can be used for content-based search of the resulting image database. By using an integrated approach, we are able to take advantage of possible statistical couplings between the topic of the document (latent semantic content) and the contents of images (visual statistics). This allows improved performance in conducting content-based search. This approach has been implemented in a WWW image search engine prototype.

A Neuromorphic Model for Achromatic and Chromatic Surface Representation of Natural Images

This study develops a neuromorphic model of human lightness perception that is inspired by how the mammalian visual system is designed for this function. It is known that biological visual representations can adapt to a billion-fold change in luminance. How such a system determines absolute lightness under varying illumination conditions to generate a consistent interpretation of surface lightness remains an unsolved problem. Such a process, called "anchoring" of lightness, has properties including articulation, insulation, configuration, and area effects. The model quantitatively simulates such psychophysical lightness data, as well as other data such as discounting the illuminant, the double brilliant illusion, and lightness constancy and contrast effects. The model retina embodies gain control at retinal photoreceptors, and spatial contrast adaptation at the negative feedback circuit between mechanisms that model the inner segment of photoreceptors and interacting horizontal cells. The model can thereby adjust its sensitivity to input intensities ranging from dim moonlight to dazzling sunlight. A new anchoring mechanism, called the Blurred-Highest-Luminance-As-White (BHLAW) rule, helps simulate how surface lightness becomes sensitive to the spatial scale of objects in a scene. The model is also able to process natural color images under variable lighting conditions, and is compared with the popular RETINEX model.

The What-And-Where Filter: A Spatial Mapping Neural Network for Object Recognition and Image Understanding

The What-and-Where filter forms part of a neural network architecture for spatial mapping, object recognition, and image understanding. The Where fllter responds to an image figure that has been separated from its background. It generates a spatial map whose cell activations simultaneously represent the position, orientation, ancl size of all tbe figures in a scene (where they are). This spatial map may he used to direct spatially localized attention to these image features. A multiscale array of oriented detectors, followed by competitve and interpolative interactions between position, orientation, and size scales, is used to define the Where filter. This analysis discloses several issues that need to be dealt with by a spatial mapping system that is based upon oriented filters, such as the role of cliff filters with and without normalization, the double peak problem of maximum orientation across size scale, and the different self-similar interpolation properties across orientation than across size scale. Several computationally efficient Where filters are proposed. The Where filter rnay be used for parallel transformation of multiple image figures into invariant representations that are insensitive to the figures' original position, orientation, and size. These invariant figural representations form part of a system devoted to attentive object learning and recognition (what it is). Unlike some alternative models where serial search for a target occurs, a What and Where representation can he used to rapidly search in parallel for a desired target in a scene. Such a representation can also be used to learn multidimensional representations of objects and their spatial relationships for purposes of image understanding. The What-and-Where filter is inspired by neurobiological data showing that a Where processing stream in the cerebral cortex is used for attentive spatial localization and orientation, whereas a What processing stream is used for attentive object learning and recognition.

A What-and-Where Neural Network for Invariant Image Preprocessing

A feedforward neural network for invariant image preprocessing is proposed that represents the position1 orientation and size of an image figure (where it is) in a multiplexed spatial map. This map is used to generate an invariant representation of the figure that is insensitive to position1 orientation, and size for purposes of pattern recognition (what it is). A multiscale array of oriented filters followed by competition between orientations and scales is used to define the Where filter.

Legal myth-making: Medea and the legal representation of the feminine ‘other'

The Law operates by, and through, the creation of ideal benchmarks of conduct that are deemed to be representative of the behavioural norm. It is in this sense that it could be contended that the Law utilises, and relies on, myths in the same way as do other disciplines, notably psycho-analysis. It is possible to go even further and argue that the use of a created narrative mythology is essential to the establishment of a defined legal benchmark of behaviour by which the female defendant is assessed, judged and punished. While mythology expresses and symbolizes cultural and political behaviour, it is the Law that embodies and prescribes punitive sanctions. This element represents a powerful literary strand in classical mythology. This may be seen, for instance, in Antigone’s appeal to the Law as justification for her conduct, as much as in Medea’s challenge to the Law though her desire for vengeance. Despite its image of neutral, objective rationality, the Law, in creating and sustaining the ideals of legally-sanctioned conduct, engages in the same literary processes of imagination, reason and emotion that are central to the creation and re-creation of myth. The (re-)presentation of the Medea myth in literature (especially in theatre) and in art, finds its echo in the theatre of the courtroom where wronged women who have refused to passively accept their place, have instead responded with violence. Consequently, the Medea myth, in its depiction of the (un)feminine, serves as a template for the Law’s judgment of ‘conventional’ feminine conduct in the roles of wife and mother. Medea is an image of deviant femininity, as is Lady Macbeth and the countless other un-feminine literary and mythological women who challenge the power of the dominant culture and its ally, the Law. These women stand opposed to the other dominant theme of both literature and Law: the conformist woman, the passive dupe, who are victims of male oppression – women such as Ariadne of Naxos and Tess of the D’Ubervilles – and who are subsequently consumed by the Law, much as Semele is consumed by the fire of Jupiter’s gaze upon her. All of these women, the former as well as the latter, have their real-life counterparts in the pages of the Law Reports. As Fox puts it, “these women have come to bear the weight of the cultural stereotypes and preconceptions about women who kill.”

Proof of polar ejection from the close-binary core of the planetary nebula Abell 63

We present the first detailed kinematical analysis of the planetary nebula Abell 63, which is known to contain the eclipsing close-binary nucleus UU Sge. Abell 63 provides an important test case in investigating the role of close-binary central stars on the evolution of planetary nebulae. Longslit observations were obtained using the Manchester echelle spectrometer combined with the 2.1-m San Pedro Martir Telescope. The spectra reveal that the central bright rim of Abell 63 has a tube-like structure. A deep image shows collimated lobes extending from the nebula, which are shown to be high-velocity outflows. The kinematic ages of the nebular rim and the extended lobes are calculated to be 8400 +/- 500 and 12900 +/- 2800 yr, respectively, which suggests that the lobes were formed at an earlier stage than the nebular rim. This is consistent with expectations that disc-generated jets form immediately after the common envelope phase. A morphological-kinematical model of the central nebula is presented and the best-fitting model is found to have the same inclination as the orbital plane of the central binary system; this is the first proof that a close-binary system directly affects the shaping of its nebula. A Hubble-type flow is well-established in the morphological-kinematical modelling of the observed line profiles and imagery. Two possible formation models for the elongated lobes of Abell 63 are considered, (i) a low-density, pressure-driven jet excavates a cavity in the remnant asymptotic giant branch (AGB) envelope; (ii) high-density bullets form the lobes in a single ballistic ejection event.