Comparison of Classifiers for Radar Emitter Type Identification

ARTMAP neural network classifiers are considered for the identification of radar emitter types from their waveform parameters. These classifiers can represent radar emitter type classes with one or more prototypes, perform on-line incremental learning to account for novelty encountered in the field, and process radar pulse streams at high speed, making them attractive for real-time applications such as electronic support measures (ESM). The performance of four ARTMAP variants- ARTMAP (Stage 1), ARTMAP-IC, fuzzy ARTMAP and Gaussian ARTMAP - is assessed with radar data gathered in the field. The k nearest neighbor (kNN) and radial basis function (RDF) classifiers are used for reference. Simulation results indicate that fuzzy ARTMAP and Gaussian ARTMAP achieve an average classification rate consistently higher than that of the other ARTMAP classifers and comparable to that of kNN and RBF. ART-EMAP, ARTMAP-IC and fuzzy ARTMAP require fewer training epochs than Gaussian ARTMAP and RBF, and substantially fewer prototype vectors (thus, smaller physical memory requirements and faster fielded performance) than Gaussian ARTMAP, RBF and kNN. Overall, fuzzy ART MAP performs at least as well as the other classifiers in both accuracy and computational complexity, and better than each of them in at least one of these aspects of performance. Incorporation into fuzzy ARTMAP of the MT- feature of ARTMAP-IC is found to be essential for convergence during on-line training with this data set.Defense Advanced Research Projects Agency and the Office of Naval Research (N000I4-95-1-409 (S.G. and M.A.R.); National Science Foundation (IRI-97-20333) (S.G.); Natural Science and Engineering Research Council of Canada (E.G.); Office of Naval Research (N00014-95-1-0657

Using activity-oriented design methods (AODM) to investigatemobile learning

The past few years have witnessed significant interest and developments in researching mobile learning, with a lot of important contributions being made towards understanding and defining mobile learning. However, current research efforts are being redirected towards a new agenda to establish appropriate methods for investigating mobile learning, as this book testifies. This chapter contributes to this research effort by articulating how to adapt Activity-Oriented Design Methods (AODM – see Mwanza, 2002) for use in mobile learning research

Familiarity Discrimination of Radar Pulses

The ARTMAP-FD neural network performs both identification (placing test patterns in classes encountered during training) and familiarity discrimination (judging whether a test pattern belongs to any of the classes encountered during training). The performance of ARTMAP-FD is tested on radar pulse data obtained in the field, and compared to that of the nearest-neighbor-based NEN algorithm and to a k > 1 extension of NEN

Digitalisation and Enterprise Knowledge (net)Working

Social media and emerging mobile technologies have forever changed the landscape of human interaction. Furthermore, they already play a pivotal role also in enterprises as a part of the organisational Knowledge Management System. Almost all large organisations have already implemented at least one Enterprise Social Media tool since they enable collaboration, provide easy access to information, and are available at reasonable costs. The effects of the decoupling of the real and the virtual world (as a result of Social Media use) on the construct knowledge and on knowledge management are still not sufficiently investigated. Against this background, the paper presents an exploratory approach of the development of a specific morphological tableau as an instrument for the analysis of employees’ behavior in context of knowledge management related ESM use. Furthermore, the application of the tableau is exemplary illustrated and further research steps are explained

Simulation of Water Distribution Systems

In this paper a software package offering a means of simulating complex water distribution systems is described. It has been developed in the course of our investigations into the applicability of neural networks and fuzzy systems for the implementation of decision support systems in operational control of industrial processes with case-studies taken from the water industry. Examples of how the simulation package have been used in a design and testing of the algorithms for state estimation, confidence limit analysis and fault detection are presented. Arguments for using a suitable graphical visualization techniques in solving problems like meter placement or leakage diagnosis are also given and supported by a set of examples

Deep Learning Techniques in Radar Emitter Identification

In the field of electronic warfare (EW), one of the crucial roles of electronic intelligence is the identification of radar signals. In an operational environment, it is very essential to identify radar emitters whether friend or foe so that appropriate radar countermeasures can be taken against them. With the electromagnetic environment becoming increasingly complex and the diversity of signal features, radar emitter identification with high recognition accuracy has become a significantly challenging task. Traditional radar identification methods have shown some limitations in this complex electromagnetic scenario. Several radar classification and identification methods based on artificial neural networks have emerged with the emergence of artificial neural networks, notably deep learning approaches. Machine learning and deep learning algorithms are now frequently utilized to extract various types of information from radar signals more accurately and robustly. This paper illustrates the use of Deep Neural Networks (DNN) in radar applications for emitter classification and identification. Since deep learning approaches are capable of accurately classifying complicated patterns in radar signals, they have demonstrated significant promise for identifying radar emitters. By offering a thorough literature analysis of deep learning-based methodologies, the study intends to assist researchers and practitioners in better understanding the application of deep learning techniques to challenges related to the classification and identification of radar emitters. The study demonstrates that DNN can be used successfully in applications for radar classification and identification.   &nbsp

Comprensión de textos como una situación de solución de problemas

La investigación en la comprensión de textos ha dado detalles de cómo las características del texto y los procesos cognitivos interactúan con el fin de consituir la comprensión y generar significado. Sin embargo, no existe un vínculo explícito entre los procesos cognitivos desplegados durante la comprensión de textos y su lugar en la cognición de orden superior, como en la resolución de problemas. El propósito de este trabajo es proponer un modelo cognitivo en el que la comprensión de textos se hace similar a una resolución de problemas y la situación que se basa en la investigación actual sobre los procesos cognitivos conocidos como la generación de la inferencia, la memoria y las simulaciones. La característica clave del modelo es que incluye explícitamente la formulación de las preguntas como un componente que aumenta la potencia de representación. Otras características del modelo se especifican y sus extensiones a la investigación básica y en la comprensión de textos y de orden superior los procesos cognitivos se describen aplican.Research in text comprehension has provided details as to how text features and cognitive processes interact in order to build comprehension and generate meaning. However, there is no explicit link between the cognitive processes deployed during text comprehension and their place in higher-order cognition, as in problem solving. The purpose of this paper is to propose a cognitive model in which text comprehension is made analogous to a problem solving situation and that relies on current research on well-known cognitive processes such as inference generation, memory, and simulations. The key characteristic of the model is that it explicitly includes the formulation of questions as a component that boosts representational power. Other characteristics of the model are specified and its extensions to basic and applied research in text comprehension and higher-order cognitive processes are outlined.Fil: Marmolejo Ramos, Fernando. University of Adelaide; AustraliaFil: Yomha Cevasco, Jazmin. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin