Characterization of the sleep architecture in two species of fruit bat

Bats (Chiroptera) are the second-most abundant mammalian order in the world, occupying a diverse range of habitats and exhibiting many different life history traits. In order to contribute to this highly underrepresented group we describe the sleep architecture of two species of frugivorous bat, the greater short-nosed fruit bat (Cynopterus sphinx) and the lesser dawn fruit bat (Eonycteris spelaea). Electroencephalogram (EEG) and electromyogram (EMG) data were recorded from multiple individuals (>= 5) by telemetry over a 72-h period in a laboratory setting with light/dark cycles equivalent to those found in the wild. Our results show that over a 24-h period both species spent more time asleep than awake (mean 15 h), less than previous reported for Chiroptera (20 h). C sphinx spent significantly more of its non-rapid eye movement sleep (NREM) and rapid eye movement sleep (REM) quotas during the light phase, while E. spelaea divided its sleep-wake architecture equally between both light and dark phases. Comparing the sleep patterns of the two species found that C. sphinx had significantly fewer NREM and REM episodes than E. spelaea but each episode lasted for a significantly longer period of time. Potential hypotheses to explain the differences in the sleep architecture of C. sphinx with E. spelaea, including risk of predation and social interaction are discussed. (C) 2010 Published by Elsevier B.V.

A new species of bamboo bat (Chiroptera : Vespertilionidae : Tylonycteris) from southwestern China

Bamboo bats are a group of small bats with unique skull and morphology. They roost inside hollow bamboo stems in tropical and subtropical Asia and the Ambon Islands (Moluccas). We examined 53 specimens of Tylonycteris from southern and southwestern China. Comparisons of skull and external characteristics, pelage color, shapes of thumbpads and footpads, and statistical analysis of cranial measurements revealed that specimens from Damenglong, Jinghong County, Xishuang-banna, Yunnan, are distinctly different from the other two species of Tylonycteris described so far. The Yunnan specimens are the smallest in size; have dark blackish brown pelage color; and have larger upper premolars, smaller first lower premolars, and longer C-M-3. They are sympatric with the previously described species. Here we review the genus Tylonycteri and describe a new species, Tylonycteris pygmaeus, from the Yunnan material.

Correcting the systematic error of the density functional theory calculation: the alternate combination approach of genetic algorithm and neural network

The alternate combinational approach of genetic algorithm and neural network (AGANN) has been presented to correct the systematic error of the density functional theory (DFT) calculation. It treats the DFT as a black box and models the error through external statistical information. As a demonstration, the AGANN method has been applied in the correction of the lattice energies from the DFT calculation for 72 metal halides and hydrides. Through the AGANN correction, the mean absolute value of the relative errors of the calculated lattice energies to the experimental values decreases from 4.93% to 1.20% in the testing set. For comparison, the neural network approach reduces the mean value to 2.56%. And for the common combinational approach of genetic algorithm and neural network, the value drops to 2.15%. The multiple linear regression method almost has no correction effect here.

The multi-weight neuron with geometry algorithm and its application

A novel approach for multi-dimension signals processing, that is multi-weight neural network based on high dimensional geometry theory, is proposed. With this theory, the geometry algorithm for building the multi-weight neuron is mentioned. To illustrate the advantage of the novel approach, a Chinese speech emotion recognition experiment has been done. From this experiment, the human emotions are classified into 6 archetypal classes: fear, anger, happiness, sadness, surprise and disgust. And the amplitude, pitch frequency and formant are used as the feature parameters for speech emotion recognition. Compared with traditional GSVM model, the new method has its superiority. It is noted that this method has significant values for researches and applications henceforth.

Iris recognition algorithm based on point covering of high-dimensional space and neural network

In this paper, we constructed a Iris recognition algorithm based on point covering of high-dimensional space and Multi-weighted neuron of point covering of high-dimensional space, and proposed a new method for iris recognition based on point covering theory of high-dimensional space. In this method, irises are trained as "cognition" one class by one class, and it doesn't influence the original recognition knowledge for samples of the new added class. The results of experiments show the rejection rate is 98.9%, the correct cognition rate and the error rate are 95.71% and 3.5% respectively. The experimental results demonstrate that the rejection rate of test samples excluded in the training samples class is very high. It proves the proposed method for iris recognition is effective.

The design algorithm of an associative memory neural network

A design algorithm of an associative memory neural network is proposed. The benefit of this design algorithm is to make the designed associative memory model can implement the hoped situation. On the one hand, the designed model has realized the nonlinear association of infinite value pattern from n dimension space to m dimension space. The result has improved the ones of some old associative memory neural network. On the other hand, the memory samples are in the centers of the fault-tolerant. In average significance the radius of the memory sample fault-tolerant field is maximum.

An algorithm for removing facial makeup disturbances based on high dimensional imaginal geometry

In this paper, a novel algorithm for removing facial makeup disturbances as a face detection preprocess based on high dimensional imaginal geometry is proposed. After simulation and practical application experiments, the algorithm is theoretically analyzed. Its apparent effect of removing facial makeup and the advantages of face detection with this pre-process over face detection without it are discussed. Furthermore, in our experiments with color images, the proposed algorithm even gives some surprises.

Quantum coherence control of solid-state charge qubit by means of a suboptimal feedback algorithm

The quantum coherence control of a solid-state charge qubit is studied by using a suboptimal continuous feedback algorithm within the Bayesian feedback scheme. For the coherent Rabi oscillation, the present algorithm suggests a simple bang-bang control protocol, in which the control parameter is modulated between two values. For the coherence protection of the idle state, the present approach is applicable to arbitrary states, including those lying on the equator of the Bloch sphere which are out of control in the previous Markovian feedback scheme.

A novel image restoration algorithm based on high-dimensional space geometry

A novel image restoration approach based on high-dimensional space geometry is proposed, which is quite different from the existing traditional image restoration techniques. It is based on the homeomorphisms and "Principle of Homology Continuity" (PHC), an image is mapped to a point in high-dimensional space. Begin with the original blurred image, we get two further blurred images, then the restored image can be obtained through the regressive curve derived from the three points which are mapped form the images. Experiments have proved the availability of this "blurred-blurred-restored" algorithm, and the comparison with the classical Wiener Filter approach is presented in final.

A VSLMS Style Tap-length Learning Algorithm for Structure Adaptation

Compared with the ordinary adaptive filter, the variable-length adaptive filter is more efficient (including smaller., lower power consumption and higher computational complexity output SNR) because of its tap-length learning algorithm, which is able to dynamically adapt its tap-length to the optimal tap-length that best balances the complexity and the performance of the adaptive filter. Among existing tap-length algorithms, the LMS-style Variable Tap-Length Algorithm (also called Fractional Tap-Length Algorithm or FT Algorithm) proposed by Y.Gong has the best performance because it has the fastest convergence rates and best stability. However, in some cases its performance deteriorates dramatically. To solve this problem, we first analyze the FT algorithm and point out some of its defects. Second, we propose a new FT algorithm called 'VSLMS' (Variable Step-size LMS) Style Tap-Length Learning Algorithm, which not only uses the concept of FT but also introduces a new concept of adaptive convergence slope. With this improvement the new FT algorithm has even faster convergence rates and better stability. Finally, we offer computer simulations to verify this improvement.

Two-dimensional photonic crystals with a large photonic band gap designed by a rapid genetic algorithm

We used Plane Wave Expansion Method and a Rapid Genetic Algorithm to design two-dimensional photonic crystals with a large absolute band gap. A filling fraction controlling operator and Fourier transform data storage mechanism had been integrated into the genetic operators to get desired photonic crystals effectively and efficiently. Starting from randomly generated photonic crystals, the proposed RGA evolved toward the best objectives and yielded a square lattice photonic crystal with the band gap (defined as the gap to mid-gap ratio) as large as 13.25%. Furthermore, the evolutionary objective was modified and resulted in a satisfactory PC for better application to slab system.

A face detection algorithm based on high dimensional space geometry

In this paper, a face detection algorithm which is based on high dimensional space geometry has been proposed. Then after the simulation experiment of Euclidean Distance and the introduced algorithm, it was theoretically analyzed and discussed that the proposed algorithm has apparently advantage over the Euclidean Distance. Furthermore, in our experiments in color images, the proposed algorithm even gives more surprises.

An algorithm of analysis tools on points distribution in high dimension space: The distance of a point and an infinite sub-space

This paper discusses the algorithm on the distance from a point and an infinite sub-space in high dimensional space With the development of Information Geometry([1]), the analysis tools of points distribution in high dimension space, as a measure of calculability, draw more attention of experts of pattern recognition. By the assistance of these tools, Geometrical properties of sets of samples in high-dimensional structures are studied, under guidance of the established properties and theorems in high-dimensional geometry.

A novel image restoration algorithm based on high-dimensional space geometry

A novel image restoration approach based on high-dimensional space geometry is proposed, which is quite different from the existing traditional image restoration techniques. It is based on the homeomorphisms and "Principle of Homology Continuity" (PHC), an image is mapped to a point in high-dimensional space. Begin with the original blurred image, we get two further blurred images, then the restored image can be obtained through the regressive curve derived from the three points which are mapped form the images. Experiments have proved the availability of this "blurred-blurred-restored" algorithm, and the comparison with the classical Wiener Filter approach is presented in final.

A Novel Geometric Algorithm for Blind Image Restoration Based on High-Dimensional Space

A novel geometric algorithm for blind image restoration is proposed in this paper, based on High-Dimensional Space Geometrical Informatics (HDSGI) theory. In this algorithm every image is considered as a point, and the location relationship of the points in high-dimensional space, i.e. the intrinsic relationship of images is analyzed. Then geometric technique of "blurring-blurring-deblurring" is adopted to get the deblurring images. Comparing with other existing algorithms like Wiener filter, super resolution image restoration etc., the experimental results show that the proposed algorithm could not only obtain better details of images but also reduces the computational complexity with less computing time. The novel algorithm probably shows a new direction for blind image restoration with promising perspective of applications.