Optimal mean-square prediction of the mobile-radio fading envelope

Long-range prediction of the mobile-radio fading envelope is an enabling technology for many fading compensation approaches. Because the fading envelope is well modeled as a bandlimited process, it has special predictability properties. In this paper, we find a linear predictor that is optimal in the mean-square sense when the predictor impulse response is energy constrained. This solution may be used to determine the minimum mean squared error of a prediction based on past values that are corrupted with estimation errors.     

Experimental real-time detector of GSM terminals

The authors present an experimental real-time GSM terminal detector, to be installed in a restricted area. The detector triggers terminal signaling, which can be captured.     

Switched-Current Filters Revisited: Square-Root Domain Sampled-Data Filters

In this brief, the well-known switched-current (SI) filtering technique is revisited using the concept of the square-root domain (SRD) filtering. It is proved that SI filters are a subclass of the SRD filters, where sampled-data signal processing is performed. This is achieved by considering typical lossless and lossy SRD sampled-data integrator configurations, using a set of complementary SRD operators which are based on the quadratic I-V relationship of MOS transistor operated in the saturation. Circuit examples are given, where linear-domain integrator and third-order filter configurations were derived using appropriate SRD sampled-data building blocks     

Fracture response of fibre-reinforced materials with macro/microcrack damage using the Boundary Element Technique

The Boundary Element Method (BEM) incorporating the Embedded Cell Approach (ECA) has been used to analyse the effects of constituent material properties, fibre spatial distribution and microcrack damage on the localised behaviour of transversely fractured, unidirectional fibre-reinforced composites. Three specific composites, i.e., glass fibre reinforced polyester, carbon fibre reinforced epoxy and a glass-carbon hybrid, are considered. The geometrical structures examined were perfectly periodic, uniformly spaced fibre arrangements in square and hexagonal embedded cells. In addition, numerical simulations were also conducted using embedded cells containing randomly distributed fibres. The models involve both elastic fibres and matrix, with the interfaces between the different phases being fully bonded. The results indicate that the constituent material properties (two phase composite) and spatial distribution have a significant effect on the localised stress distributions around the primary crack tip. However, the strain energy release rate associated with crack propagation is predominantly influenced by the material composition. The three-phase hybrid composite exhibited an apparent intermediate fracture toughness value, compared to the all-glass and all-carbon models. Furthermore, the strain energy release rate for the macrocrack lowers as it enters a zone of localised damage (microcracking). The presence of microcracks relaxes the stress field, which can result in a significant reduction in the energetics of the primary crack.     

Surface effects in nanocrystalline silicon

Exposure to ammonia (NH₃) increases the dark current (DC) in nanocrystalline silicon. Light soaking (LS) for short periods also enhances the dark current, which remains at a high value for a long time. Pumping alone is unable to restore the initial annealed state, but annealing brings it back. The final state obtained by LS and NH₃ exposure depends on the order in which they are performed. Evaporated selenium (Se) deposited on nanocrystalline silicon decreases the DC. These effects cannot be explained entirely by the presence of a-Si : H alone, in our sample. DC and photoluminescence (PL) measurements indicate the presence of two types of center in our sample, which behave differently when exposed to NH₃.     

An introduction of the condition class space with continuous value discretization and rough set theory

The granularity of an information system has an incumbent effect on the efficacy of the analysis from many machine learning algorithms. An information system contains a universe of objects characterized and categorized by condition and decision attributes. To manage the concomitant granularity, a level of continuous value discretization (CVD) is often undertaken. In the case of the rough set theory (RST) methodology for object classification, the granularity contributes to the grouping of objects into condition classes with the same condition attribute values. This article exposits the effect of a level of CVD on the subsequent condition classes constructed, with the introduction of the condition class space—the domain within which the condition classes exist. This domain elucidates the association of the condition classes to the related decision outcomes—reflecting the inexactness incumbent when a level of CVD is undertaken. A series of measures is defined that quantify this association. Throughout this study and without loss of generality, the findings are made through the RST methodology. This further offers a novel exposition of the relationship between all the condition attributes and the RST‐related reducts (subsets of condition attributes). © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 173–191, 2006.     

Learning in multiple model adaptive control switch

Including a learning mechanism in SMMAC (switched, multiple-model adaptive control) avoids the need for a priori knowledge of the model set of the plant to control and leads to a significant performance improvement with respect to the sole inclusion of an adaptive control channel in combination with switched fixed local controllers.