A Molecular Dynamics Study of Description Models for Shear Viscosity in Nanochannels: Mixtures and Effect of Temperature

Equilibrium molecular dynamics simulation was utilized to calculate the shear viscosity of monoatomic molecules liquid flow confined in a nanochannel in the pure state and their binary mixtures. The pure flow viscosity data of argon, krypton, and xenon were used to predict their binary mixture viscosities using prediction methods proposed in the literature. Each method was evaluated by comparison of its results with those obtained by validated molecular dynamics simulation at different temperatures. In addition, correlative methods for mixture viscosity estimation and temperature dependence models were evaluated and corresponding model parameters were obtained. Moreover, the relations representing mixture parameter values as a function of the pure ones were determined and evaluated according to their accuracy.     

Rotation-invariant multiresolution texture analysis using radon and wavelet transforms.

A new rotation-invariant texture-analysis technique using Radon and wavelet transforms is proposed. This technique utilizes the Radon transform to convert the rotation to translation and then applies a translation-invariant wavelet transform to the result to extract texture features. A kappa-nearest neighbors classifier is employed to classify texture patterns. A method to find the optimal number of projections for the Radon transform is proposed. It is shown that the extracted features generate an efficient orthogonal feature space. It is also shown that the proposed features extract both of the local and directional information of the texture patterns. The proposed method is robust to additive white noise as a result of summing pixel values to generate projections in the Radon transform step. To test and evaluate the method, we employed several sets of textures along with different wavelet bases. Experimental results show the superiority of the proposed method and its robustness to additive white noise in comparison with some recent texture-analysis methods.     

An information fidelity criterion for image quality assessment using natural scene statistics.

Measurement of visual quality is of fundamental importance to numerous image and video processing applications. The goal of quality assessment (QA) research is to design algorithms that can automatically assess the quality of images or videos in a perceptually consistent manner. Traditionally, image QA algorithms interpret image quality as fidelity or similarity with a "reference" or "perfecft" image in some perceptual space. Such "full-referenc" QA methods attempt to achieve consistency in quality prediction by modeling salient physiological and psychovisual features of the human visual system (HVS), or by arbitrary signal fidelity criteria. In this paper, we approach the problem of image QA by proposing a novel information fidelity criterion that is based on natural scene statistics. QA systems are invariably involved with judging the visual quality of images and videos that are meant for "human consumption." Researchers have developed sophisticated models to capture the statistics of natural signals, that is, pictures and videos of the visual environment. Using these statistical models in an information-theoretic setting, we derive a novel QA algorithm that provides clear advantages over the traditional approaches. In particular, it is parameterless and outperforms current methods in our testing. We validate the performance of our algorithm with an extensive subjective study involving 779 images. We also show that, although our approach distinctly departs from traditional HVS-based methods, it is functionally similar to them under certain conditions, yet it outperforms them due to improved modeling. The code and the data from the subjective study are available at.     

Effects of feeding an aquatic weed Lemna trisulaca as partial replacement of fish meal on the performance of growing ducklings

Growing ducklings were fed diets containing an aquatic weed Lemna trisulaca meal (LTM) replacing, on a protein basis, either 40, 60 or 80 g kg^?1 of the fish meal (FM) from a control diet which contained 120 g FM kg^?1. Partial replacement of FM (40 and 60 g kg^?1 of the FM) by LTM on a protein basis showed good growth and low food consumption but food conversion efficiency was found to be comparable. It was concluded that LTM could be considered as a protein feed supplement for growing ducklings and also as a part replacement of animal protein (FM) in the nutrition of growing ducklings without deleterious effect on performance.     

Reliability and Mobility Load Balancing in Next Generation Self-organized Networks: Using Stochastic Learning Automata

Self-organizing networking (SON) is an automation technology designed to make the planning, configuration, management, optimization and healing of mobile radio access networks simpler and faster. Most current self-organization networking functions apply rule-based recommended systems to control network resources which seem too complicated and time-consuming to design in practical conditions. This research proposes a cognitive cellular network empowered by an efficient self-organization networking approach which enables SON functions to separately learn and find the best configuration setting. An effective learning approach is proposed for the functions of the cognitive cellular network, which exhibits how the framework is mapped to SON functions. One of the main functions applied in this framework is mobility load balancing. In this paper, a novel Stochastic Learning Automata has been suggested as the load balancing function in which approximately the same quality level is provided for each subscriber. This framework can also be effectively extended to cloud-based systems, where adaptive approaches are needed due to unpredictability of total accessible resources, considering cooperative nature of cloud environments. The results demonstrate that the function of mobility robustness optimization not only learns to optimize HO performance, but also it learns how to distribute excess load throughout the network. The experimental results demonstrate that the proposed scheme minimizes the number of unsatisfied subscribers (N_us) by moving some of the edge users served by overloaded cells towards one or more adjacent target cells. This solution can also guarantee a more balanced network using cell load sharing approach in addition to increase cell throughput outperform the current schemes.

     

Dependability evaluation of Altera FPGA-based embedded systems subjected to SEUs

Dependability evaluation of embedded systems due to the integration of hardware and software parts is difficult to analyze. In this paper, we have proposed an experimental method to determine sensitivity to soft errors in an embedded system exploiting Altera SRAM-based FPGAs. The evaluation is performed using both the hardware and software parts of the embedded system in a single framework. To do this, the HDL hardware model of the target system as well as the C-written software codes of the target system, are required. Both permanent and transient faults are injected into the partially- or fully-synthesizable hardware of the target system and this can be performed during the design cycle of the system. The fault injection is composed of injecting SEUs into user design memory, and used configuration memory of the exploited FPGA. Using the experimental results, the sensitivity of Altera FPGAs to SEU faults are analyzed and derived. The analytical results reveal that the configuration memory is more significant than design memory to the SEUs due to the relative number of SRAM bits. Moreover, in this framework, in the case of injecting SEUs into user memory, the fault injection experiments are accelerated by the cooperation between a simulator and the FPGA.     

Optimal visual sensor placement for coverage based on target location profile

The proper placement of visual sensors across a sensor field for covering targets with arbitrary location and orientation is a mission-critical decision in surveillance applications. The specifics of sensor deployment in these applications not only determine the maximum achievable coverage, but it also affects the quality of the target’s appearance in cameras for subsequent use in vision tasks. However, the inaccuracies inherent in localization techniques and the lack of knowledge regarding the target orientation render existing proposals insufficient for real-life scenarios. In this paper, we address both challenges. First, we extend the conventional point representation of targets with a circular model to account for full-angle coverage of targets with known location yet with unknown orientation from all directions. We then assume, in the absence of precise location information, a trajectory profile of targets could instead be generated through the importance sampling of the environment, indicating spots where the target is most likely located. This profile-based abstraction enables us to capture the uncertainty in target’s location by encircling every agglomeration of proximal samples within one cluster. Each cluster can then be viewed as a virtual macroscopic circular target for which we formulate the coverage problem in terms of a Binary Integer Programming (BIP) model. We have also taken into account the presence of obstruction in between multiple targets by calculating the angles of view of the sensors in an occlusion-dependant manner, effectively determining optimal placement for maximal instead of full-angle coverage. Evaluation results derived from our simulation experiments reveal that the proposed mechanism can effectively achieve high coverage accuracy with minimum number of deployed sensors.     

Photoactive Electrodes Incorporating Electrosprayed Bacterial Reaction Centers

Highly efficient light absorption and charge separation within the photosystem and reaction center (RC) complexes of photosynthetic plants and bacteria are of great interest for solar cell and photo detector applications, since they offer almost unity quantum yield and expected ultimate power conversion efficiencies of more than 18% and 12%, respectively. In addition, the charge separated states created by these protein complexes are very long lived compared to conventional semiconductor solar cells. In this work, a novel technique is presented for the deposition of photosynthetic protein complexes, by electrospraying RCs of Rhodobacter sphaeroides onto highly ordered pyrolytic graphite (HOPG) electrodes. Remarkably, it is shown that the RCs not only survive exposure to the high electric fields but also yield peak photocurrent densities of up to 7 μA cm^?2, which is equal to the highest value reported to date.     

Local oscillator chain for 1.55 to 1.75THz with 100-/spl mu/W peak power

We report on the design and performance of a fix-tuned /spl times/2/spl times/3/spl times/3 frequency multiplier chain that covers 1.55-1.75THz. The chain is nominally pumped with 100mW at W-band. At 120K the measured output power is larger than 4/spl mu/W across the band with a peak power of 100/spl mu/W at 1.665THz. A similar chain operated at room temperature produced a peak power of 21/spl mu/W. These power levels now make it possible to deploy multipixel heterodyne imaging arrays in this frequency range.