Multipath channel identification by using global optimization in ambiguity function domain

A new transform domain array signal processing technique is proposed for identification of multipath communication channels. The received array element outputs are transformed to delay-Doppler domain by using the cross-ambiguity function (CAF) for efficient exploitation of the delay-Doppler diversity of the multipath components. Clusters of multipath components can be identified by using a simple amplitude thresholding in the delay-Doppler domain. Particle swarm optimization (PSO) can be used to identify parameters of the multipath components in each cluster. The performance of the proposed PSO-CAF technique is compared with the space alternating generalized expectation maximization (SAGE) technique and with a recently proposed PSO based technique at various SNR levels. Simulation results clearly quantify the superior performance of the PSO-CAF technique over the alternative techniques at all practically significant SNR levels.     

Detection of interdependent primary systems using wideband cognitive radios

Cognitive radios (CRs) may be sharing multiple frequency bands with primary systems if the CR is a wideband or an ultra wideband (UWB) system. In that case, the CR should ensure all the coexisting primary systems in these bands are detected before it can start data transmission. In this work, we study the primary system detection performance of a wideband CR assuming that there are multiple coexisting primary systems and that these primary systems may be jointly active. Accordingly, we consider the implementation of energy detection scheme in multiple bands followed by two detection methods: (i) a maximum-a-posteriori (MAP) based detection (i.e., joint detection) that takes into account the statistics of simultaneously operating systems in independent bands and (ii) a Neyman–Pearson (NP) test based detection that optimizes the threshold values independently in each band (i.e., independent detection). For a simpler implementation of the independent detection, we show that the threshold values obtained from joint detection can be used in order to achieve the optimum NP test based independent detection results. In addition to quantifying the gain of joint detection over independent detection in terms of probabilities of false alarm and detection for practical scenarios, we also present the operation capability of CRs in terms of the fractions of time the CR can access the channel without interfering with the primary systems. The results are important for the practical implementation of multiband detection when the primary systems are known to be interdependent.     

Effects of porosity and related interstitial phase morphology difference on the grindability of clinkers

The effects of regional clinker porosity and interstitial phase morphology differences on grindability of clinkers have been investigated within the scope of this study. Porosity and phase characteristics of two clinkers from different furnaces which are using the same raw materials were determined by the application of image processing techniques on SEM images of polished surfaces. Comparison of interstitial phase compositions of two clinkers showed that, clinkers with similar phase compositions may have different grindability due to the porosity and phase morphology differences. Porous clinkers usually exhibit a dendritic interstitial phase morphology with an improved grindability. Increasing porosity also leads to greater incidence of the dendritic morphology in porous regions and lower incidence of alkali in the aluminate in dendritic regions.     

From engineering diagrams to engineering models: Visual recognition and applications

We present a computational recognition approach to convert network-like, image-based engineering diagrams into engineering models with which computations of interests, such as CAD modeling, simulation, information retrieval and semantic-aware editing, are enabled. The proposed approach is designed to work on diagrams produced using computer-aided drawing tools or hand sketches, and does not rely on temporal information for recognition. Our approach leverages a Convolutional Neural Network (CNN) as a trainable engineering symbol recognizer. The CNN is capable of learning the visual features of the defined symbol categories from a few user-supplied prototypical diagrams and a set of synthetically generated training samples. When deployed, the trained CNN is applied either to the entire input diagram using a multi-scale sliding window or, where applicable, to each isolated pixel cluster obtained through Connected Component Analysis (CCA). Then the connectivity between the detected symbols are analyzed to obtain an attributed graph representing the engineering model conveyed by the diagram. We evaluate the performance of the approach with benchmark datasets and demonstrate its utility in different application scenarios, including the construction and simulation of control system or mechanical vibratory system models from hand-sketched or camera-captured images, content-based image retrieval for resonant circuits and sematic-aware image editing for floor plans.     

Continuous models in nonlinear thermomechanics of binary systems

We study some general problems connected with the construction of thermodynamic models in the mechanics of two-component elastic bodies. Principal relations of the model are established on the basis of fundamental concepts and approaches of nonlinear continuum mechanics and nonequilibrium thermodynamics. We also demonstrate that systems of this sort can be described without using chemical potentials in the explicit form.Center of Mathematical Simulation. Pidstryhach Institute for Applied Problems in Mathematics and Mechanics, Ukrainian Academy of Sciences, L'viv. Translated from Fiziko-Khimicheskaya Mekhanika Materialov, Vol. 31, No. 4, pp. 7–15, July – August, 1995.