Extended stigmergy in collective construction

We've described a scheme by which a swarm of robots can automatically assemble solid structures of square building blocks in two dimensions according to a high-level user-specified design. We discuss the use of extended stigmergy: augmenting the basic notion of stigmergy by increasing the capabilities of environmental elements. In this construction setting, the elements are the building blocks, the basic information they carry is the simple fact of their presence at a location, and extensions include cases where they can store additional information, perform computations, and/or communicate with physically attached neighbors. Benefits can include increased robustness and faster completion of a desired structure. We review three variants of that construction scheme that use extended stigmergy to different degrees. We focus on analysis of the algorithms and their comparative performance, mainly theoretical but also through simulation experiments.     

Robust regulation with 2 performance

We consider the problem of minimizing the ₂ norm of a feedback system subject to the constraint that the controller robustly regulate against constants and sinusoids. We show that, for any ε > 0, there exists a controller that both achieves robust regulation and renders the closed-loop ₂ norm within ε of the optimal norm (the norm achievable without the robust regulation constraint). We also provide conditions that are both necessary and sufficient for there to exist a robustly regulating controller that achieves the optimal ₂ norm. All proofs are constructive.     

A Feature Reduced Intrusion Detection System with Optimized SVM Using Big Bang Big Crunch Optimization

The swift proliferation in traffic across computer networks has led to certain types of attacks and intrusions, raising a serious global concern of information security. Attack detection is possible by monitoring and observing occurrences in intrusion detection systems, however these systems tend to suffer from problem of curse of dimensionality, high false alarm rate, high time complexity and low detections. In order to overcome these limitations, we propose a feature reduced intrusion detection system employing optimized SVM as a classifier. Feature Reduction has been performed by fusing ranked features from information gain and chi square in a way that it has helped in retaining only important features and discarding the rest. The study further proposes an optimized version of SVM classifier using Big Bang Big Crunch (BBBC) optimization that simulates the big bang and big crunch theory of evolution of universe. BBBC has helped in finding an optimal set of SVM parameters quickly that are further used for classification. We also experimented with a number of fitness functions for gauging the performance of IDS and propose a new fitness function based on the weighted F1 score of various traffic classes. KDD-99 dataset has been used for experimentation and analysis. The paper further experiments the effects of under-sampling and oversampling of various traffic classes on the proposed IDS performance and recommends that maintaining a desired ratio for a mix of under-sampling and over-sampling of desired classes produces the best results.

     

Effect of Ag addition on glass transition and crystallization in Se80Te20 glass

The present paper reports on the differential scanning calorimetric (DSC) behaviour of glassy Se₈₀Te₂₀ containing Ag up to 10 at%. DSC runs were taken at different heating rates. Well‐defined endothermic and exothermic peaks were obtained at glass transition and crystallization temperatures. The glass transition temperature (T_g) and crystallization temperature (T_c) increase with the increase in heating rate. T_g and T_c both decrease with increase in Ag concentration. The activation energies for glass transition and crystallization also decrease with Ag incorporation in the Se₈₀Te₂₀ binary alloy. Copyright © 2000 John Wiley & Sons, Ltd.     

Experimental Results for and Theoretical Analysis of a Self-Organizing Global Coordinate System for Ad Hoc Sensor Networks

We present an algorithm for achieving robust and reasonably accurate localization in a randomly placed wireless sensor network, without the use of global control, globally-accessible beacon signals, or accurate estimates of inter-sensor distances. We present theoretical analysis, simulation results and recent experimental results. The theoretical analysis shows that there is a critical minimum average neighborhood size of 15 for good accuracy, and simulation results show that position accuracy to within 20% of the local radio range can be achieved, even with upto 10% variation in the radio ranges.