Characterization of RF sputtered thin film potassium sodium niobate (KNN) with silicon and nickel electrodes

A low cost recipe for thin film deposition of Potassium Sodium Niobate, (Na,K)NbO₃ (KNN) is pursued. The use of expensive noble metals as electrodes was avoided and instead highly doped silicon was used for both the structural layer and the bottom electrode. Nickel was used for the top electrode. In order to evaluate the outcome, the films were studied in terms of stoichiometry, crystal structure and leakage current density. RF sputtering of thin films of KNN at room temperature was successfully done. Proper crystal structure (Perovskite structure) was achieved after post deposition annealing. Though the leakage current density exhibited high dependency on the polarity of the applied voltage, a leakage current density of 1 × 10⁻⁶ A/cm² at 100 kV/cm was measured. A stoichiometry study revealed that the relative ratio of the volatile elements (Na and K) in the samples was within the acceptable range, however, a total loss of about 25–33 % was observed.

     

Impaired lipoprotein metabolism in obese offspring of streptozotocin-induced diabetic rats

The time course of changes in lipoprotein metabolism of obese offspring of mildly diabetic rats was studied with respect to serum lipoprotein composition as well as LCAT and tissue lipoprotein lipase (LPL) activities. Mild hyperglycemia in pregnant rats was induced by intraperitoneal injection of streptozotocin on day 5 of gestation. Control pregnant rats were injected with citrate buffer. At birth, obese pups had higher serum glucose, insulin, and lipoprotein (VLDL, LDL-HDL₁, HDL_2–3) levels than control pups. After 1 mon of life, all of these parameters in obese rats became similar to those of controls. However, LCAT, adipose tissue LPL, and hepatic triacylglycerol lipase activities were high. At 2 mon of age, VLDL-TAG levels were higher in obese females than in controls. By the age of 3 mon, obese offspring had developed insulin resistance with hyperglycemia, hyperinsulinemia, and higher serum lipoprotein concentrations. Indeed, qualitative abnormalities of lipoproteins were seen and were typical of obese and diabetic human beings. Fetal hyperinsulinemia should be considered as a risk factor for later metabolic diseases, including dyslipoproteinemia.     

CMAC神经网络结构参数及其结构优化的研究

CMAC神经网络具有学习算法简单、收敛速度快、局域泛化等优点,被广泛应用于机器人控制、信号处理、模式识别以及自适用控制等领域。但是网络的训练过程需要大量的存储单元,最优结构参数的选取是CMAC网络设计中一个重要问题。文中通过对函数逼近问题的研究,说明了量化精度和泛化参数如何影响网络对函数的逼近质量。仿真结果表明,通过对结构参数的调整,可以达到最小的逼近误差。而通过对网络结构的优化不但可以节约网络的训练时间而且可以大幅度减少存储单元的数量。     

A machine learning based intelligent vision system for autonomous object detection and recognition

Existing object recognition techniques often rely on human labeled data conducting to severe limitations to design a fully autonomous machine vision system. In this work, we present an intelligent machine vision system able to learn autonomously individual objects present in real environment. This system relies on salient object detection. In its design, we were inspired by early processing stages of human visual system. In this context we suggest a novel fast algorithm for visually salient object detection, robust to real-world illumination conditions. Then we use it to extract salient objects which can be efficiently used for training the machine learning-based object detection and recognition unit of the proposed system. We provide results of our salient object detection algorithm on MSRA Salient Object Database benchmark comparing its quality with other state-of-the-art approaches. The proposed system has been implemented on a humanoid robot, increasing its autonomy in learning and interaction with humans. We report and discuss the obtained results, validating the proposed concepts.     

BSS: block-based sharing scheme for secure data storage services in mobile cloud environment

For the last few years, academia and research organizations are continuously investigating and resolving the security and privacy issues of mobile cloud computing environment. The additional consideration in designing security services for mobile cloud computing environment should be the resource-constrained mobile devices. The execution of computationally intensive security services on mobile device consumes battery’s charging quickly. In this regard, the study presents a novel energy-efficient block-based sharing scheme that provides confidentiality and integrity services for mobile users in the cloud environment. The block-based sharing scheme is compared with the existing schemes on the basis of energy consumption, CPU utilization, memory utilization, encryption time, decryption time, and turnaround time. The experimental results show that the block-based sharing scheme consumes less energy, reduces the resources utilization, improves response time, and provides better security services to the mobile users in the presence of fully untrusted cloud server(s) as compared to the existing security schemes.     

An efficient playout smoothing mechanism for layered streaming in P2P networks

Layered video streaming in peer-to-peer (P2P) networks has drawn great interest, since it can not only accommodate large numbers of users, but also handle peer heterogeneity. However, there’s still a lack of comprehensive studies on chunk scheduling for the smooth playout of layered streams in P2P networks. In these situations, a playout smoothing mechanism can be used to ensure the uniform delivery of the layered stream. This can be achieved by reducing the quality changes that the stream undergoes when adapting to changing network conditions. This paper complements previous efforts in throughput maximization and delay minimization for P2P streaming by considering the consequences of playout smoothing on the scheduling mechanisms for stream layer acquisition. The two main problems to be considered when designing a playout smoothing mechanism for P2P streaming are the fluctuation in available bandwidth between peers and the unreliability of user-contributed resources—particularly peer churn. Since the consideration of these two factors in the selection and scheduling of stream layers is crucial to maintain smooth stream playout, the main objective of our smoothing mechanism becomes the determination of how many layers to request from which peers, and in which order. In this work, we propose a playout smoothing mechanism for layered P2P streaming. The proposed mechanism relies on a novel scheduling algorithm that enables each peer to select appropriate stream layers, along with appropriate peers to provide them. In addition to playout smoothing, the presented mechanism also makes efficient use of network resources and provides high system throughput. An evaluation of the performance of the mechanism demonstrates that the proposed mechanism provides a significant improvement in the received video quality in terms of lowering the number of layer changes and useless chunks while improving bandwidth utilization.     

Sliding‐mode control for automated lane guidance of heavy vehicle

In the present work, an active steering assistance system for heavy vehicles is developed to prevent lane departure and let the vehicle follow the road's centre line. The controller is based on the super‐twisting algorithm. An estimator based on the sliding mode observer is also developed to estimate the vehicle dynamics. The lateral positions and speeds are then controlled to keep the vehicle in the centre of the lane. The lateral offset and the relative yaw angle are supposed to be measured and the road curvature to be known. Some simulation results are given to show the quality of this steering assistance system. Copyright © 2011 John Wiley & Sons, Ltd.     

New Cloud Consolidation Architecture for Electrical Energy Consumption Management

Cloud computing is taking more extensive space in the research field. Cloud architectures will need to worry about energy in its various forms, to be profitable, on the one hand, and comply with environmental constraints （energy consumption and CO2 emission） on the other hand. VM （virtual machines） consolidation （VMs in this document）, among other techniques, must take into account the consumption of electrical energy, for example, while providing a level of performance that meets the requirements of SLAs （service level agreements）. In our work, we focus on an architecture configuration to manage virtual machines in a data center, in order to optimize the consumption of energy, and meet SLAs＇s constraints at the same time, by grafting a tracing component of the multiple consolidation plans that leads to an optimal configuration to finally give the order of the migration machinery to a minimum number of servers switched on, knowing that VMs （virtual machines） that coexist in the same server, are at risk of congestion and interference.     

Hybrid credit ranking intelligent system using expert system and artificial neural networks

The main goal of all commercial banks is to collect the savings of legal and real persons and allocate them as credit to industrial, services and production companies. Non repayment of such credits cause many problems to the banks such as incapability to repay the central bank’s loans, increasing the amount of credit allocations comparing to credit repayment and incapability to allocate more credits to customers. The importance of credit allocation in banking industry and it’s important role in economic growth and employment creation leads the development of many models to evaluate the credit risk of applicants. But many of these models are classic and are incapable to do credit evaluation completely and efficiently. Therefore the demand to use artificial intelligence in this field has grown up. In this paper after providing appropriate credit ranking model and collecting expert’s knowledge, we design a hybrid intelligent system for credit ranking using reasoning-transformational models. Expert system as symbolic module and artificial neural network as non-symbolic module are components of this hybrid system. Such models provide the unique features of each components, the reasoning and explanation of expert system and the generalization and adaptability of artificial neural networks. The results of this system demonstrate hybrid intelligence system is more accurate and powerful in credit ranking comparing to expert systems and traditional banking models.     

A human-like visual-attention-based artificial vision system for wildland firefighting assistance

In this work we contribute to development of a “Human-like Visual-Attention-based Artificial Vision” system for boosting firefighters’ awareness about the hostile environment in which they are supposed to move along. Taking advantage from artificial visual-attention, the investigated system’s conduct may be adapted to firefighter’s way of gazing by acquiring some kind of human-like artificial visual neatness supporting firefighters in interventional conditions’ evaluation or in their appraisal of the rescue conditions of people in distress dying out within the disaster. We achieve such a challenging goal by combining a statistically-founded bio-inspired saliency detection model with a Machine-Learning-based human-eye-fixation model. Hybridization of the two above-mentioned models leads to a system able to tune its parameters in order to fit human-like gazing of the inspected environment. It opens appealing perspectives in computer-aided firefighters’ assistance boosting their awareness about the hostile environment in which they are supposed to evolve. Using as well various available wildland fires images’ databases as an implementation of the investigated concept on a 6-wheeled mobile robot equipped with communication facilities, we provide experimental results showing the plausibility as well as the efficiency of the proposed system.