Optimal pilot waveform assisted modulation for ultrawideband communications

Ultrawideband (UWB) transmissions induce pronounced frequency-selective fading effects in their multipath propagation. Multipath diversity gains can be collected to enhance performance, provided that the underlying channel can be estimated at the receiver. To this end, we develop a novel pilot waveform assisted modulation (PWAM) scheme that is tailored for UWB communications. We select our PWAM parameters by jointly optimizing channel estimation performance and information rate. The resulting transmitter design maximizes the average capacity, which is shown to be equivalent to minimizing the mean-square channel estimation error, and thereby achieves the Crame/spl acute/r-Rao lower bound. Application of PWAM to practical UWB systems is promising because it entails simple integrate-and-dump operations at the frame rate. Equally important, it offers a flexible UWB channel estimator, capable of striking desirable rate-performance tradeoffs depending on the channel coherence time.     

Object and texture classification using higher order statistics

The problem of the detection and classification of deterministic objects and random textures in a noisy scene is discussed. An energy detector is developed in the cumulant domain by exploiting the noise insensitivity of higher order statistics. An efficient implementation of this detector is described, using matched filtering. Its performance is analyzed using asymptotic distributions in a binary hypothesis-testing framework. The object and texture discriminant functions are minimum distance classifiers in the cumulant domain and can be efficiently implemented using a bank of matched filters. They are immune to additive Gaussian noise and insensitive to object shifts. Important extensions, which can handle object rotation and scaling, are also discussed. An alternative texture classifier is derived from a ML viewpoint and is statistically efficient at the expense of complexity. The application of these algorithms to the texture-modeling problem is indicated, and consistent parameter estimates are obtained     

Spatio-Temporal Short-Term Urban Traffic Volume Forecasting Using Genetically Optimized Modular Networks

Abstract:   Current interest in short-term traffic volume forecasting focuses on incorporating temporal and spatial volume characteristics in the forecasting process. This article addresses the problem of integrating and optimizing predictive information from multiple locations of an urban signalized arterial and proposes a modular neural predictor consisting of temporal genetically optimized structures of multilayer perceptrons (MLP) that are fed with volume data from sequential locations to improve the accuracy of short-term forecasts. The results show that the proposed methodology provides more accurate forecasts compared to the conventional statistical methodologies applied, as well as to the static forms of neural networks.     

Joint coding-precoding with low-complexity turbo-decoding

We combine error-control coding with linear precoding (LP) for flat-fading channels, as well as for wireless orthogonal frequency-division multiplexing transmissions through frequency-selective fading channels. The performance is analyzed and compared with the corresponding error-control-coded system without precoding. By wedding LP with conventional error-control coding, the diversity order becomes equal to the error-control code's minimum Hamming distance times the precoder size. We also derive a low-complexity turbo-decoding algorithm for joint coded-precoded transmissions. We analyze the decoding complexity and compare it with an error-control-coded system without LP. Extensive simulations with convolutional and turbo codes for HiperLan/2 channels support the analysis and demonstrate superior performance of the proposed system.     

Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks

UWB technology provides an excellent means for wireless positioning due to its high resolution capability in the time domain. Its ability to resolve multipath components makes it possible to obtain accurate location estimates without the need for complex estimation algorithms. In this article, theoretical limits for TOA estimation and TOA-based location estimation for UWB systems have been considered. Due to the complexity of the optimal schemes, suboptimal but practical alternatives have been emphasized. Performance limits for hybrid TOA/SS and TDOA/SS schemes have also been considered. Although the fundamental mechanisms for localization, including AOA-, TOA-, TDOA-, and SS-based methods, apply to all radio air interface, some positioning techniques are favored by UWB-based systems using ultrawide bandwidths.     

Higher-order statistics-based input/output system identification and application to noise cancellation

Higher-than-second-order statistics-based input/output identification algorithms are proposed for linear and nonlinear system identification. The higher-than-second-order cumulant-based linear identification algorithm is shown to be insensitive to contamination of the input data by a general class of noise including additive Gaussian noise of unknown covariance, unlike its second-order counterpart. The nonlinear identification is at least as optimal as any linear identification scheme. Recursive-least-squares-type algorithms are derived for linear/nonlinear adaptive identification. As applications, the problems of adaptive noise cancellation and time-delay estimation are discussed and simulated. Consistency of the adaptive estimator is shown. Simulations are performed and compared with the second-order design.Part of the results of this paper were presented at the workshop on HOSA, Vail, CO, June 1989, and at the International Conference on ASSP, Albuquerque, NM, April 1990. The work of G. B. Giannakis in this paper was supported by LabCom Contract 5-25254.     

Image modeling using inverse filtering criteria with application totextures

Statistical approaches to image modeling have largely relied upon random models that characterize the 2-D process in terms of its first- and second-order statistics, and therefore cannot completely capture phase properties of random fields that are non-Gaussian. This constrains the parameters of noncausal image models to be symmetric and, therefore, the underlying random field to be spatially reversible. Research indicates that this assumption may not be always valid for texture images. In this paper, higher- than second-order statistics are used to derive and implement two classes of inverse filtering criteria for parameter estimation of asymmetric noncausal autoregressive moving-average (ARMA) image models with known orders. Contrary to existing approaches, FIR inverse filters are employed and image models with zeros on the unit bicircle can be handled. One of the criteria defines the smallest set of cumulant lags necessary for identifiability of these models to date, Consistency of these estimators is established, and their performance is evaluated with Monte Carlo simulations as well as texture classification and synthesis experiments.     

A Holistic Evolutionary and 3D Pharmacophore Modelling Study Provides Insights into the Metabolism,Function, and Substrate Selectivity of the Human Monocarboxylate Transporter 4 (hMCT4)

Monocarboxylate transporters (MCTs) are of great research interest for their role in cancer cell metabolism and their potential ability to transport pharmacologically relevant compounds across the membrane. Each member of the MCT family could potentially provide novel therapeutic approaches to various diseases. The major differences among MCTs are related to each of their specific metabolic roles, their relative substrate and inhibitor affinities, the regulation of their expression, their intracellular localization, and their tissue distribution. MCT4 is the main mediator for the efflux of L-lactate produced in the cell. Thus, MCT4 maintains the glycolytic phenotype of the cancer cell by supplying the molecular resources for tumor cell proliferation and promotes the acidification of the extracellular microenvironment from the co-transport of protons. A promising therapeutic strategy in anti-cancer drug design is the selective inhibition of MCT4 for the glycolytic suppression of solid tumors. A small number of studies indicate molecules for dual inhibition of MCT1 and MCT4; however, no selective inhibitor with high-affinity for MCT4 has been identified. In this study, we attempt to approach the structural characteristics of MCT4 through an in silico pipeline for molecular modelling and pharmacophore elucidation towards the identification of specific inhibitors as a novel anti-cancer strategy.     

The mechanical property and phase structures of wheat proteins/polyvinyl alcohol blends studied by high-resolution solid-state NMR

The phase structures of thermally processed wheat proteins (WP) and polyvinyl alcohol (PVOH) blends were studied by solid-state high-resolution NMR spectroscopy. The intermolecular interactions among the multi-component systems and the behavior of each component in the blends on scales of nanometers were examined. The mechanical properties of the blends were also measured and related to the phase structure studies. The results indicated that the polymer chains of WP could be homogeneously mobilized when thermally processed with glycerol and water as plasticisers, but the glycerol predominately associated with WP rather than PVOH in the blends. The intermolecular hydrogen bonding interactions between WP and PVOH caused some extent of miscibility in the system on scales of nanometers especially when the PVOH content was low. The tensile strength and modulus of the blends were improved as compared to WP. However, the intermolecular interactions were relatively weak and could not be further enhanced by increasing PVOH component in the blends. The particle miscible WP/PVOH blends contained plasticised WP and PVOH phases in conjunction with the miscible WP/PVOH phase. Increasing the PVOH content in the blends did not result in an increase of the percentage of the miscible phase and the blends tented to be immiscible while the elongation of the blends was reduced when increasing the PVOH content in the blends.     

Applying storm water management in Greek cities: learning from the European experience

This paper is based on the studies forming part of the European research project DayWater, which aims at the promotion of stormwater source control (participating countries: UK, France, Sweden, Denmark, Germany, The Netherlands and Greece). Source control techniques include all techniques (e.g. retention, infiltration, reuse) of stormwater treatment near its source, in contrast to end-of-pipe. In this paper we present: the different stormwater management issues across Europe; the specific stormwater management issues in Greece; source control techniques applied in European cities; Public policies for promoting source control techniques; Strategies of application of the aforementioned techniques and policies in Greek cities. In Greece, the question of stormwater treatment has not yet attracted a lot of attention. Wastewater management, flood prevention and fresh water scarcity issues are given priority instead. However source control stormwater management can contribute to the solution of all these problems. In many European cities source control techniques have been chosen for stormwater treatment because: a) they are less expensive than massive end-of-pipe treatment installations, b) they are more compatible with the natural water cycle, c) they reduce the overall stormwater flow and thus permit the expansion of cities without requesting reconstruction of the existing sewer networks. Different policy instruments (taxes, specific regulations and controls, information campaigns) have been applied in order to promote source control techniques. The main objective of this work is to discuss the possibility of applying similar techniques and policies in Greek cities.