Multi-Port Network Approach for the Analysis of Dual Band Fractal Microstrip Antennas

The multiport network approach is extended to analyze the behavior of microstrip fractal antennas. The capacitively fedmicrostrip square ring antenna has the side opposite to the feed arm replaced with a fractal Minkowski geometry. Dual frequency operation is achieved by suitably choosing the indentation of this fractal geometry. The width of the two sides adjacent to this is increased to further control the resonant characteristics and the ratio of the two resonance frequencies of this antenna. The impedance matrix for the multiport network model of this antenna is simplified exploiting self-similarity of the geometry with greater accuracy and reduced analysis time. Experimentally validated results confirm utility of the approach in analyzing the input characteristics of similar multi-frequency fractal microstrip antennas with other fractal geometries.

Smoothed functional and Quasi-Newton algorithms for routing in multi-stage queueing network with constraints

We consider the problem of optimal routing in a multi-stage network of queues with constraints on queue lengths. We develop three algorithms for probabilistic routing for this problem using only the total end-to-end delays. These algorithms use the smoothed functional (SF) approach to optimize the routing probabilities. In our model all the queues are assumed to have constraints on the average queue length. We also propose a novel quasi-Newton based SF algorithm. Policies like Join Shortest Queue or Least Work Left work only for unconstrained routing. Besides assuming knowledge of the queue length at all the queues. If the only information available is the expected end-to-end delay as with our case such policies cannot be used. We also give simulation results showing the performance of the SF algorithms for this problem.

Multi-port network modeling for fractal shape microstrip antenna

In this paper we demonstrate the use of multi-port network modeling to analyze one such antenna with fractal shaped parts. Based on simulation and experimental studies, it has been demonstrated that model can accurately predict the input characteristics of antennas with Minkowski geometry replacing a side micro strip square ring.

Simulation of Deformation Texture Evolution During Multi Axial Forging of Interstitial Free Steel

Bulk texture measurement of multi-axial forged body center cubic interstitial free steel performed in this study using x-ray and neutron diffraction indicated the presence of a strong {101}aOE (c) 111 > single texture component. Viscoplastic self-consistent simulations could successfully predict the formation of this texture component by incorporating the complicated strain path followed during this process and assuming the activity of {101}aOE (c) 111 > slip system. In addition, a first-order estimate of mechanical properties in terms of highly anisotropic yield locus and Lankford parameter was also obtained from the simulations.

Boosting-based transfer learning for multi-view head-pose classification from surveillance videos

This work proposes a boosting-based transfer learning approach for head-pose classification from multiple, low-resolution views. Head-pose classification performance is adversely affected when the source (training) and target (test) data arise from different distributions (due to change in face appearance, lighting, etc). Under such conditions, we employ Xferboost, a Logitboost-based transfer learning framework that integrates knowledge from a few labeled target samples with the source model to effectively minimize misclassifications on the target data. Experiments confirm that the Xferboost framework can improve classification performance by up to 6%, when knowledge is transferred between the CLEAR and FBK four-view headpose datasets.

Time variant reliability model updating in instrumented dynamical systems based on Girsanov's transformation

The problem of updating the reliability of instrumented structures based on measured response under random dynamic loading is considered. A solution strategy within the framework of Monte Carlo simulation based dynamic state estimation method and Girsanov's transformation for variance reduction is developed. For linear Gaussian state space models, the solution is developed based on continuous version of the Kalman filter, while, for non-linear and (or) non-Gaussian state space models, bootstrap particle filters are adopted. The controls to implement the Girsanov transformation are developed by solving a constrained non-linear optimization problem. Numerical illustrations include studies on a multi degree of freedom linear system and non-linear systems with geometric and (or) hereditary non-linearities and non-stationary random excitations.

AEROSAT - a space-borne sensor for continental aerosols: evaluation of the conceptual model

Even though satellite observations are the most effective means to gather global information in a short span of time, the challenges in this field still remain over continental landmass, despite most of the aerosol sources being land-based. This is a hurdle in global and regional aerosol climate forcing assessment. Retrieval of aerosol properties over land is complicated due to irregular terrain characteristics and the high and largely uncertain surface reflection which acts as `noise' to the much smaller amount of radiation scattered by aerosols, which is the `signal'. In this paper, we describe a satellite sensor the - `Aerosol Satellite (AEROSAT)', which is capable of retrieving aerosols over land with much more accuracy and reduced dependence on models. The sensor, utilizing a set of multi-spectral and multi-angle measurements of polarized components of radiation reflected from the Earth's surface, along with measurements of thermal infrared broadband radiance, results in a large reduction of the `noise' component (compared to the `signal). A conceptual engineering model of AEROSAT has been designed, developed and used to measure the land-surface features in the visible spectral band. Analysing the received signals using a polarization radiative transfer approach, we demonstrate the superiority of this method. It is expected that satellites carrying sensors following the AEROSAT concept would be `self-sufficient', to obtain all the relevant information required for aerosol retrieval from its own measurements.

GYROSONICS: SIGNATURE ANALYSIS AND REDUCED-ORDER MODELS

In this paper, the authors study the structure of a novel binaural sound with a certain phase and amplitude modulation and the response to this excitation when it is applied to natural rewarding circuit of human brain through auditory neural pathways. This novel excitation, also referred to as gyrosonic excitation in this work, has been found to have interesting effects such as stabilization effects on the left and right hemispheric brain signaling as captured by Galvanic Skin Resistance (GSR) measurements, control of cardiac rhythms (observed from ECG signals), mitigation of psychosomatic syndrome, and mitigation of migraine pain. Experimental data collected from human subjects are presented, and these data are examined to categorize the extent of systems disorder and reinforcement reward due to the gyrosonic stimulus. A multi-path reduced-order model has been developed to analyze the GSR signals. The filtered results are indicative of complicated reinforcing reward patterns due to the gyrosonic stimulation when it is used as a control input for patients with psychosomatic and cardiac disorders.

Interfacial growth as a model of tube-width heterogeneities in concentrated solutions of stiff polymers

Recent experimental measurements of the distribution P(w) of transverse chain fluctuations w in concentrated solutions of F-actin filaments B. Wang, J Guan, S. M. Anthony, S. C. Bae, K. S. Schweizer, and S. Granick, Phys. Rev. Lett. 104, 118301 (2010); J. Glaser, D. Chakraborty, K. Kroy, I. Lauter, M. Degawa, N. Kirchgessner, B. Hoffmann, R. Merkel, and M. Giesen, Phys. Rev. Lett. 105, 037801 (2010)] are shown to be well-fit to an expression derived from a model of the conformations of a single harmonically confined weakly bendable rod. The calculation of P(w) is carried out essentially exactly within a path integral approach that was originally applied to the study of one-dimensional randomly growing interfaces. Our results are generally as successful in reproducing experimental trends as earlier approximate results obtained from more elaborate many-chain treatments of the confining tube potential.

Interfacial growth as a model of tube-width heterogeneities in concentrated solutions of stiff polymers

Recent experimental measurements of the distribution P(w) of transverse chain fluctuations w in concentrated solutions of F-actin filaments B. Wang, J Guan, S. M. Anthony, S. C. Bae, K. S. Schweizer, and S. Granick, Phys. Rev. Lett. 104, 118301 (2010); J. Glaser, D. Chakraborty, K. Kroy, I. Lauter, M. Degawa, N. Kirchgessner, B. Hoffmann, R. Merkel, and M. Giesen, Phys. Rev. Lett. 105, 037801 (2010)] are shown to be well-fit to an expression derived from a model of the conformations of a single harmonically confined weakly bendable rod. The calculation of P(w) is carried out essentially exactly within a path integral approach that was originally applied to the study of one-dimensional randomly growing interfaces. Our results are generally as successful in reproducing experimental trends as earlier approximate results obtained from more elaborate many-chain treatments of the confining tube potential. (C) 2013 AIP Publishing LLC.

Time variant reliability model updating in instrumented dynamical systems based on Girsanov’s transformation

The problem of updating the reliability of instrumented structures based on measured response under random dynamic loading is considered. A solution strategy within the framework of Monte Carlo simulation based dynamic state estimation method and Girsanov’s transformation for variance reduction is developed. For linear Gaussian state space models, the solution is developed based on continuous version of the Kalman filter, while, for non-linear and (or) non-Gaussian state space models, bootstrap particle filters are adopted. The controls to implement the Girsanov transformation are developed by solving a constrained non-linear optimization problem. Numerical illustrations include studies on a multi degree of freedom linear system and non-linear systems with geometric and (or) hereditary non-linearities and non-stationary random excitations.

Multi-task learning using shared and task specific information

Multi-task learning solves multiple related learning problems simultaneously by sharing some common structure for improved generalization performance of each task. We propose a novel approach to multi-task learning which captures task similarity through a shared basis vector set. The variability across tasks is captured through task specific basis vector set. We use sparse support vector machine (SVM) algorithm to select the basis vector sets for the tasks. The approach results in a sparse model where the prediction is done using very few examples. The effectiveness of our approach is demonstrated through experiments on synthetic and real multi-task datasets.

Dynamic multi-relational Chinese restaurant process for analyzing influences on users in social media

We study the problem of analyzing influence of various factors affecting individual messages posted in social media. The problem is challenging because of various types of influences propagating through the social media network that act simultaneously on any user. Additionally, the topic composition of the influencing factors and the susceptibility of users to these influences evolve over time. This problem has not been studied before, and off-the-shelf models are unsuitable for this purpose. To capture the complex interplay of these various factors, we propose a new non-parametric model called the Dynamic Multi-Relational Chinese Restaurant Process. This accounts for the user network for data generation and also allows the parameters to evolve over time. Designing inference algorithms for this model suited for large scale social-media data is another challenge. To this end, we propose a scalable and multi-threaded inference algorithm based on online Gibbs Sampling. Extensive evaluations on large-scale Twitter and Face book data show that the extracted topics when applied to authorship and commenting prediction outperform state-of-the-art baselines. More importantly, our model produces valuable insights on topic trends and user personality trends beyond the capability of existing approaches.

Multi-port network modeling of stub loaded Microstrip ring antenna for dual-band operations

A square ring microstrip antenna can be modified for dual-band operations by appropriately attaching an open ended stub. The input impedance of this antenna is analyzed here using multi-port network modeling (MNM) approach. The coupled feed is included by defining additional terms in the model. A prototype antenna is fabricated and tested to validate these computations.

Generalized model predictive static programming and its application to 3D impact angle constrained guidance of air-to-surface missiles

A new `generalized model predictive static programming (G-MPSP)' technique is presented in this paper in the continuous time framework for rapidly solving a class of finite-horizon nonlinear optimal control problems with hard terminal constraints. A key feature of the technique is backward propagation of a small-dimensional weight matrix dynamics, using which the control history gets updated. This feature, as well as the fact that it leads to a static optimization problem, are the reasons for its high computational efficiency. It has been shown that under Euler integration, it is equivalent to the existing model predictive static programming technique, which operates on a discrete-time approximation of the problem. Performance of the proposed technique is demonstrated by solving a challenging three-dimensional impact angle constrained missile guidance problem. The problem demands that the missile must meet constraints on both azimuth and elevation angles in addition to achieving near zero miss distance, while minimizing the lateral acceleration demand throughout its flight path. Both stationary and maneuvering ground targets are considered in the simulation studies. Effectiveness of the proposed guidance has been verified by considering first order autopilot lag as well as various target maneuvers.