Analysis and evaluations of logical instructions called in parallel digital optical operations based on optical array logic

The authors have analysed and evaluated a two-dimensional instruction set of parallel operation based on optical array logic (OAL), which is a digital optical computing paradigm, to clarify efficient composition of an optical computing system based on OAL. To evaluate parallel operation based on OAL, the author have introduced new indices and evaluated a logical instruction set of various parallel operations with the indices, so that a guideline for composing a simple and efficient OAL computing system is clarified. Also, the authors have proposed the reduced operation kernel set correlation technique to perform parallel operations more efficiently by a simple OAL computing system. It has been clarified that the technique can reduce the required hardware necessary for an OAL computing system for efficient general-purpose processing.     

Study of Cuprate Superconductivity Using the Particle Number Conserving Bogoliubov-de Gennes Equations: ARPES and STS Images From Surface Plus Bulk Layers Model

Journal of Superconductivity and Novel Magnetism - We theoretically study the superconductivity in hole-doped cuprate superconductors by employing a model composed of surface and bulk CuO $$_2$$...     

Realization of an extremely low reflectance surface based on InP porous nanostructures for application to photoelectrochemical solar cells

Extremely low reflectance was obtained from InP porous nanostructures in UV, visible, and near-infrared ranges. Porous samples were electrochemically prepared on which 130-nm-diameter nanopores were formed in a straight, vertical direction and were laterally separated by 50-nm-thick InP nanowalls. The reflectance strongly depended on the surface morphology. The lowest reflectance of 0.1% in the visible light range was obtained after the irregular top layer had been completely removed. Superior photoelectrochemical properties were obtained on the InP porous structures due to two unique features: the large surface area inside pores, and the large photon absorption enhanced on the low reflectance surface.     

Composite of Au nanoparticles and molecularly imprinted polymer as a sensing material

A molecularly imprinted polymer with immobilized Au nanoparticles (Au-MIP) is reported as a novel type of sensing material. The sensing mechanism is based upon the variable proximity of the Au nanoparticles immobilized in the imprinted polymer, which exhibits selective binding of a given analyte accompanied by swelling that causes a blue-shift in the plasmon absorption band of the immobilized Au nanoparticles. Using adrenaline as the model analyte, it was shown that molecular imprinting effectively enhanced the sensitivity and selectivity, and accordingly, Au-MIP selectively detects the analyte at 5 microM. The combination of molecular imprinting and the Au nanoparticle-based sensing system was shown to be a general strategy for constructing sensing materials in a tailor-made fashion due to wide applicability of the imprinting technique and the independence of the sensing mechanism from the analyte recognition system.     

Efficient distribution-free population learning of simple concepts

We consider a variant of the ‘population learning model’ proposed by Kearns and Seung [8], in which the learner is required to be ‘distribution-free’ as well as computationally efficient. A population learner receives as input hypotheses from a large population of agents and produces as output its final hypothesis. Each agent is assumed to independently obtain labeled sample for the target concept and output a hypothesis. A polynomial time population learner is said to PAC-learn a concept class, if its hypothesis is probably approximately correct whenever the population size exceeds a certain bound which is polynomial, even if the sample size for each agent is fixed at some constant. We exhibit some general population learning strategies, and some simple concept classes that can be learned by them. These strategies include the ‘supremum hypothesis finder’, the ‘minimum superset finder’ (a special case of the ‘supremum hypothesis finder’), and various voting schemes. When coupled with appropriate agent algorithms, these strategies can learn a variety of simple concept classes, such as the ‘high–low game’, conjunctions, axis-parallel rectangles and others. We give upper bounds on the required population size for each of these cases, and show that these systems can be used to obtain a speed up from the ordinary PAC-learning model [11], with appropriate choices of sample and population sizes. With the population learner restricted to be a voting scheme, what we have is effectively a model of ‘population prediction’, in which the learner is to predict the value of the target concept at an arbitrarily drawn point, as a threshold function of the predictions made by its agents on the same point. We show that the population learning model is strictly more powerful than the population prediction model. Finally, we consider a variant of this model with classification noise, and exhibit a population learner for the class of conjunctions in this model. This revised version was published online in June 2006 with corrections to the Cover Date.     

Learning commutative deterministic finite state automata in polynomial time

We consider the problem of learning the commutative subclass of regular languages in the on-line model of predicting {0,1∼-valued functions from examples and reinforcements due to Littlestone [7,4]. We show that the entire class of commutative deterministic finite state automata (CDFAs) of an arbitrary alphabet sizek is predictable inO(s ^k) time with the worst case number of mistakes bounded above byO(s ^kk logs), wheres is the number of states in the target DFA. As a corollary, this result implies that the class of CDFAs is also PAC-learnable from random labeled examples in timeO(s ^k) with sample complexity, using a different class of representations. The mistake bound of our algorithm is within a polynomial, for a fixed alphabet size, of the lower boundO(s+k) we obtain by calculating the VC-dimension of the class. Our result also implies the predictability of the class of finite sets of commutative DFAs representing the finite unions of the languages accepted by the respective DFAs. Part of this work was supported by the Office of Naval Research under contract number N00014-87-K-0401 while the author was at the Department of Computer and Information Science, University of Pennsylvania, and N0014-86-K-0454 while at the Department of Computer and Information Sciences, U.C. Santa Cruz. The author’s email address is abe@IBL.CL.nec.co.jp     

Return Link Systems for Wireless Mobile Camera Using 42 GHz-Band with Frequency and Space Diversity Techniques

In this paper, return link systems for wireless mobile camera using 42 GHz-band in multipath fading environments are proposed. The return link systems for wireless mobile camera require wireless transmission with high reliability as same as the conventional wired camera systems. The proposed return link systems achieve transmission with high reliability by taking into consideration frequency and space diversity techniques. The proposed systems can be classified as 3 three types of return link systems according to diversity techniques： FD （frequency diversity） systems, SD （space diversity） systems and FD＋SD system （frequency and space diversity）. Computer simulations are carried out in order to confirm the effectiveness of the proposed return link systems. It is confirmed that the proposed three types of return link systems have an advantage about BER performances and effectively exploit frequency and space resources.