Design and performance study of phase‐locked loop using fractional‐order loop filter

The present work reports the realization of an analog fractional‐order phase‐locked loop (FPLL) using a fractional capacitor. The expressions for bandwidth, capture range, and lock range of the FPLL have been derived analytically and then compared with the experimental observations using LM565 IC. It has been observed that bandwidth and capture range can be extended by using FPLL. It has also been found that FPLL can provide faster response and lower phase error at the time of switching compared to its integer‐order counterpart. Copyright © 2014 John Wiley & Sons, Ltd.     

Optimum design for buckling of plain and stiffened composite axisymmetric shell panels/shells

The buckling of plain and discretely stiffened composite axisymmetric shell panels/shells made of repeated sublaminate construction is studied using the finite element method. In repeated sublaminate construction, a full laminate is obtained by repeating a basic sublaminate, which has a smaller number of plies. The optimum design for buckling is obtained by determining the layup sequence of the plies in the sublaminate by ranking, so as to achieve maximum buckling load for a specified thickness. For this purpose, a four-noded 48-dof quadrilateral composite thin shell element, together with fully compatible two-noded 16-dof composite meridional and parallel circle stiffener elements are used.     

Neural networks for contract bridge bidding

The objective of this study is to explore the possibility of capturing the reasoning process used in bidding a hand in a bridge game by an artificial neural network. We show that a multilayer feedforward neural network can be trained to learn to make an opening bid with a new hand. The game of bridge, like many other games used in artificial intelligence, can easily be represented in a machine. But, unlike most games used in artificial intelligence, bridge uses subtle reasoning over and above the agreed conventional system, to make a bid from the pattern of a given hand. Although it is difficult for a player to spell out the precise reasoning process he uses, we find that a neural network can indeed capture it. We demonstrate the results for the case of one-level opening bids, and discuss the need for a hierarchical architecture to deal with bids at all levels.     

用广播与选择型OADM实现低成本透明光传输

互联网业务和数据业务的不断增长，使长途骨干网中的业务传送模式发生了显著变化。目前，互联网业务流比传统的话音业务流的传送距离远得多。研究表明，从2000年到2005年，北美地区的互联网业务流的平均传输距离将从目前的740公里增长到2700公里。业务传送模式的根本性变化，要求网络设计与运营模式也作相应的更新。今后，网络节点必须能提供更多的带宽和更长的电路连接。如果采用不透明的光网络，所有的信号都必须在每个中间节点进行再生，对于60%的传输距离不超过600公里的话音业务而言是不成问题的；但对于60%的传输距离超过3000公里的互…     

A finite range failure model

In the present article the characterstics of a finite range failure time distribution, that includes the rectangular distribution as a particular case, are studied. When shape parameter surpass unity then the distribution is IFR otherwise it always remains IFRA. A graphic shape of the distribution is also formed for certain admissible values of the constants.     

Thermal stability in bulk cryomilled ultrafine-grained 5083 Al alloy

Thermal stability in bulk ultrafine-grained (UFG) 5083 Al that was processed by gas atomization followed by cryomilling, consolidation, and extrusion, and that exhibited an average grain size of 305 nm, was investigated in the temperature range of 473 to 673 K (0.55 to 0.79 T _m , where T _m is the melting temperature of the material) for different annealing times. Appreciable grain growth was observed at temperatures > 573 K, whereas there was limited grain growth at temperatures < 573 K even after long annealing times. The values of the grain growth exponent, n, deduced from the grain growth data were higher than the value of 2 predicted from elementary grain growth theories. The discrepancy was attributed to the operation of strong pinning forces on boundaries during the annealing treatment. An examination of the microstructure of the alloy suggests that the origin of the pinning forces is most likely related to the presence of dispersion particles, which are mostly introduced during cryomilling. Two-grain growth regimes were identified: the low-temperature region (<573 K) and the high-temperature region (>573 K). For temperatures lower than 573 K, the activation energy of 25 ± 5 kJ/mol was determined. It is suggested that this low activation energy represents the energy for the reordering of grain boundaries in the UFG material. For temperatures higher than 573 K, an activation energy of 124 ± 5 kJ/mol was measured. This value of activation energy, 124 ± 5 kJ/mol, lies between that for grain boundary diffusion and lattice diffusion in analogous aluminum polycrystalline systems. The results show that the strength and ductility of bulk UFG 5083 Al, as obtained from tensile tests, correlate well with substructural changes introduced in the alloy by the annealing treatment.     

I-convergence in probabilistic n-normed space

In this article we introduce the notion of I-Cauchy sequence and I-convergent sequence in probabilistic n-normed space. The concept of I*-Cauchy sequence and I*-convergence in probabilistic n-normed space are also introduced and some of their properties related to these notions have been established.     

Energy-Efficient Thermal-Aware Autonomic Management of Virtualized HPC Cloud Infrastructure

Virtualized datacenters and clouds are being increasingly considered for traditional High-Performance Computing (HPC) workloads that have typically targeted Grids and conventional HPC platforms. However, maximizing energy efficiency and utilization of datacenter resources, and minimizing undesired thermal behavior while ensuring application performance and other Quality of Service (QoS) guarantees for HPC applications requires careful consideration of important and extremely challenging tradeoffs. Virtual Machine (VM) migration is one of the most common techniques used to alleviate thermal anomalies (i.e., hotspots) in cloud datacenter servers as it reduces load and, hence, the server utilization. In this article, the benefits of using other techniques such as voltage scaling and pinning (traditionally used for reducing energy consumption) for thermal management over VM migrations are studied in detail. As no single technique is the most efficient to meet temperature/performance optimization goals in all situations, an autonomic approach that performs energy-efficient thermal management while ensuring the QoS delivered to the users is proposed. To address the problem of VM allocation that arises during VM migrations, an innovative application-centric energy-aware strategy for Virtual Machine (VM) allocation is proposed. The proposed strategy ensures high resource utilization and energy efficiency through VM consolidation while satisfying application QoS by exploiting knowledge obtained through application profiling along multiple dimensions (CPU, memory, and network bandwidth utilization). To support our arguments, we present the results obtained from an experimental evaluation on real hardware using HPC workloads under different scenarios.     

Realization of a temperature sensor using both two- and three-dimensional photonic structures through a machine learning technique

A temperature sensor based on photonic crystal structures with two- and three-dimensional geometries is proposed, and its measurement performance is estimated using a machine learning technique. The temperature characteristics of the photonic crystal structures are studied by mathematical modeling. The physics of the structure is investigated based on the effective electrical permittivity of the substrate (silicon) and column (air) materials for a signal at 1200 nm, whereas the mathematical principle of its operation is studied using the plane-wave expansion method. Moreover, the intrinsic characteristics are investigated based on the absorption and reflection losses as frequently considered for such photonic structures. The output signal (transmitted energy) passing through the structures determines the magnitude of the corresponding temperature variation. Furthermore, the numerical interpretation indicates that the output signal varies nonlinearly with temperature for both the two- and three-dimensional photonic structures. The relation between the transmitted energy and the temperature is found through polynomial-regression-based machine learning techniques. Moreover, rigorous mathematical computations indicate that a second-order polynomial regression could be an appropriate candidate to establish this relation. Polynomial regression is implemented using the Numpy and Scikit-learn library on the Google Colab platform.