Cooperative adaptive control for synchronization of second‐order systems with unknown nonlinearities

This paper studies synchronization to a desired trajectory for multi‐agent systems with second‐order integrator dynamics and unknown nonlinearities and disturbances. The agents can have different dynamics and the treatment is for directed graphs with fixed communication topologies. The command generator or leader node dynamics is also nonlinear and unknown. Cooperative tracking adaptive controllers are designed based on each node maintaining a neural network parametric approximator and suitably tuning it to guarantee stability and performance. A Lyapunov‐based proof shows the ultimate boundedness of the tracking error. A simulation example with nodes having second‐order Lagrangian dynamics verifies the performance of the cooperative tracking adaptive controller. Copyright © 2010 John Wiley & Sons, Ltd.     

A local asynchronous distributed privacy preserving feature selection algorithm for large peer-to-peer networks

In this paper we develop a local distributed privacy preserving algorithm for feature selection in a large peer-to-peer environment. Feature selection is often used in machine learning for data compaction and efficient learning by eliminating the curse of dimensionality. There exist many solutions for feature selection when the data are located at a central location. However, it becomes extremely challenging to perform the same when the data are distributed across a large number of peers or machines. Centralizing the entire dataset or portions of it can be very costly and impractical because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, dynamic nature of the data/network, and privacy concerns. The solution proposed in this paper allows us to perform feature selection in an asynchronous fashion with a low communication overhead where each peer can specify its own privacy constraints. The algorithm works based on local interactions among participating nodes. We present results on real-world dataset in order to test the performance of the proposed algorithm.     

A genetic algorithm based heuristic for scheduling of virtual manufacturing cells (VMCs)

We present a genetic algorithm (GA) based heuristic approach for job scheduling in virtual manufacturing cells (VMCs). In a VMC, machines are dedicated to a part as in a regular cell, but machines are not physically relocated in a contiguous area. Cell configurations are therefore temporary, and assignments are made to optimize the scheduling objective under changing demand conditions. We consider the case where there are multiple jobs with different processing routes. There are multiple machine types with several identical machines in each type and are located in different locations in the shop floor. Scheduling objective is weighted makespan and total traveling distance minimization. The scheduling decisions are the (i) assignment of jobs to the machines, and (ii) the job start time at each machine. To evaluate the effectiveness of the GA heuristic we compare it with a mixed integer programming (MIP) solution. This is done on a wide range of benchmark problem. Computational results show that GA is promising in finding good solutions in very shorter times and can be substituted in the place of MIP model.     

Affine-based registration of CT and MR modality images of human brain using multiresolution approaches: comparative study on genetic algorithm and particle swarm optimization

We present a non-linear 2-D/2-D affine registration technique for MR and CT modality images of section of human brain. Automatic registration is achieved by maximization of a similarity metric, which is the correlation function of two images. The proposed method has been implemented by choosing a realistic, practical transformation and optimization techniques. Correlation-based similarity metric should be maximal when two images are perfectly aligned. Since similarity metric is a non-convex function and contains many local optima, choice of search strategy for optimization is important in registration problem. Many optimization schemes are existing, most of which are local and require a starting point. In present study we have implemented genetic algorithm and particle swarm optimization technique to overcome this problem. A comparative study shows the superiority and robustness of swarm methodology over genetic approach.     

Hadamard quantum broadcast channels

We consider three different communication tasks for quantum broadcast channels, and we determine the capacity region of a Hadamard broadcast channel for these various tasks. We define a Hadamard broadcast channel to be such that the channel from the sender to one of the receivers is entanglement-breaking and the channel from the sender to the other receiver is complementary to this one. As such, this channel is a quantum generalization of a degraded broadcast channel, which is well known in classical information theory. The first communication task we consider is classical communication to both receivers, the second is quantum communication to the stronger receiver and classical communication to other, and the third is entanglement-assisted classical communication to the stronger receiver and unassisted classical communication to the other. The structure of a Hadamard broadcast channel plays a critical role in our analysis: The channel to the weaker receiver can be simulated by performing a measurement channel on the stronger receiver’s system, followed by a preparation channel. As such, we can incorporate the classical output of the measurement channel as an auxiliary variable and solve all three of the above capacities for Hadamard broadcast channels, in this way avoiding known difficulties associated with quantum auxiliary variables.     

A synopsis on node compromise detection in wireless sensor networks using sequential analysis (Invited Review Article)

By exploiting the unattended nature of the wireless sensor networks, an attacker can physically capture and compromise sensor nodes and then launch a variety of attacks. He can additionally create many replicas of a few compromised nodes and spread these replicas over the network, thus launching further attacks with their help. In order to minimize the damage incurred by compromised and replicated nodes, it is very important to detect such malicious nodes as quickly as possible. In this review article, we synthesize our previous works on node compromise detection in sensor networks while providing the extended analysis in terms of performance comparison to the related work. More specifically, we use the methodology of the sequential analysis to detect static and mobile compromised nodes, as well as mobile replicated nodes in sensor networks. With the help of analytical and simulation results, we also demonstrate that our schemes provide robust and efficient node compromise detection capability.     

Automatic Clustering Using an Improved Differential Evolution Algorithm

Differential evolution (DE) has emerged as one of the fast, robust, and efficient global search heuristics of current interest. This paper describes an application of DE to the automatic clustering of large unlabeled data sets. In contrast to most of the existing clustering techniques, the proposed algorithm requires no prior knowledge of the data to be classified. Rather, it determines the optimal number of partitions of the data "on the run." Superiority of the new method is demonstrated by comparing it with two recently developed partitional clustering techniques and one popular hierarchical clustering algorithm. The partitional clustering algorithms are based on two powerful well-known optimization algorithms, namely the genetic algorithm and the particle swarm optimization. An interesting real-world application of the proposed method to automatic segmentation of images is also reported.     

Lossless compression of medical images using two-dimensional multiplicative autoregressive models

Presents new methods for lossless predictive coding of medical images using two dimensional multiplicative autoregressive models. Both single-resolution and multi-resolution schemes are presented. The performances of the proposed schemes are compared with those of four existing techniques. The experimental results clearly indicate that the proposed schemes achieve higher compression compared to the lossless image coding techniques considered.     

A study of the oxidation kinetics of CuFeO2 in the Cu-Fe-O system

The oxidation kinetics of CuFeO₂ in the Cu-Fe-O system have been studied between 500 and 900° C in an atmosphere containing 1 vol% oxygen in a nitrogen stream using thermogravimetric analysis (TGA). It was found that addition of Fe₂O₃ to the CuFeO₂ caused a decrease in the oxidation rate while addition of CuO caused an increase. On increasing the concentration of Fe₂O₃ the activation energy was found to increase from ~ 18 kcal mol^–1 to ~ 45 kcal mol^–1 and the exponent n in Avrami's equationf=1-exp (–kt ⁿ) was also observed to increase, from 1.3 to 2.3. On adding CuO to the CuFeO₂ in the Cu-Fe-O system the activation energy decreased from ~ 18 kcal mol^–1 to ~ 8 kcal mol^–1. The variation in both values indicates changes in the oxidation mechanisms. The microstructural changes associated with oxidation have been studied using optical microscopy. A model has been proposed to explain the results.     

Application of polymeric flocculant for enhancing settling of the pond ash particles and water drainage from hydraulically stowed pond ash

Delayed settling of the ash particles and poor drainage of water from the pond ash are the major problems faced during the hydraulic stowing of pond ash. In this study the effect of polymeric flocculant on settling of the ash particles and drainage of water during pond ash stowing are investigated. In addition, the parameters, viz. drainage and absorption of water during pond ash stowing are quantified by stowing a mine goaf model with pond ash slurries of five different concentrations added with and without flocculant. The study revealed that addition of only 5 × 10^?6 of Sodium Carboxymethyl Cellulose (Na-CMC) flocculant with the pond ash slurries during stowing offers best result in terms of quicker settling of the ash particles and enhanced water drainage from the hydraulically stowed pond ash. Besides, it resulted in drainage of more than 85% of the total water used in the initial 45 min of stowing. The improvement in drainage is caused due to coagulation and flocculation of the pond ash particles because of charge neutralization and particle–particle bridging. This study may provide a basis for estimating the drainage and absorption of water during the real pond ash stowing operation in underground mines.