On the effects of memoryless nonlinearities on M-QAM and DQPSK OFDM signals

Published versio

Teaching old sensors New tricks: archetypes of intelligence

In this paper a generic intelligent sensor software architecture is described which builds upon the basic requirements of related industry standards (IEEE 1451 and SEVA BS- 7986). It incorporates specific functionalities such as real-time fault detection, drift compensation, adaptation to environmental changes and autonomous reconfiguration. The modular based structure of the intelligent sensor architecture provides enhanced flexibility in regard to the choice of specific algorithmic realizations. In this context, the particular aspects of fault detection and drift estimation are discussed. A mixed indicative/corrective fault detection approach is proposed while it is demonstrated that reversible/irreversible state dependent drift can be estimated using generic algorithms such as the EKF or on-line density estimators. Finally, a parsimonious density estimator is presented and validated through simulated and real data for use in an operating regime dependent fault detection framework

A spectral model for RF oscillators with power-law phase noise

Published versio

Use of the Extended Kalman Filter for State Dependent Drift Estimation in Weakly Nonlinear Sensors

A number of mechanisms are responsible for the generation of reversible or irreversible drift in the response of a sensor. In this letter, we discuss three approaches for the identification of reversible state dependent drift in sensors through the use of the Extended Kalman Filter. We compare their performance by simulation and demonstrate their validity by estimating the drift of an accelerometer, modeled as a weakly nonlinear system

An overview of optimal and sub-optimal detection techniques for a non orthogonal spectrally efficient FDM

Spectrally Efficient non orthogonal Frequency Division Multiplexing (SEFDM) Systems occupy less bandwidth than equivalent orthogonal FDM (OFDM). However, enhanced spectral efficiency comes at the expense of an increased complexity in the signal detection. In this work, we present an overview of different detection techniques that trade the error performance optimality for the signal recovery computational effort. Linear detection methods like Zero Forcing (ZF) and Minimum Mean Squared Error (MMSE) offer fixed complexity but suffer from a significant degradation of the Bit Error Rate (BER). On the other hand optimal receivers like Sphere Decoders (SD) achieve the optimal solution in terms of error performance. Notwithstanding, their applicability is severely constrained by the SEFDM signal dimension, the frequency separation between the carriers as well as the noise level in the system

Strong secrecy in wireless network coding systems with M-QAM modulators

We investigate the possibility of developing physical layer network coding (PNC) schemes with embedded strong secrecy based on standard QAM modulators. The proposed scheme employs a triple binning approach at the QAM front-end of the wireless PNC encoders. A constructive example of a strong secrecy encoder is presented when a BPSK and an 8-PAM modulator are employed at the wireless transmitters and generalized to arbitrary M-QAM modulators, assuming channel inversion is attainable at the first cycle of the transmission. Our preliminary investigations demonstrate the potential of using such techniques to increase the throughput while in parallel not compromise the confidentiality of the exchanged data

On the impact of network-state knowledge on the Feasibility of secrecy

In this paper, the impact of network-state knowledge is studied in the context of decentralized active non-colluding eavesdropping. The main contribution is a formal proof of a paradoxical effect that might appear when increasing the available knowledge at each of the network components. Using a broadcast channel similar to the time-division downlink of a single-cell cellular system, it is shown that providing more knowledge to both the transmitter and the receivers negatively affects their performance. Eavesdroppers become more conservative in their attacks, which makes them harmless in terms of information leakage, whereas the transmitter becomes more careful and less willing to transmit, which reduces the expected secrecy capacity of this channel. Finally, it is shown that this counter-intuitive effect vanishes in the high SNR regime, in which the system becomes resilient to active attacks. © 2013 IEEE

Non-Parametric Approximations for Anisotropy Estimation in Two-dimensional Differentiable Gaussian Random Fields

Spatially referenced data often have autocovariance functions with elliptical isolevel contours, a property known as geometric anisotropy. The anisotropy parameters include the tilt of the ellipse (orientation angle) with respect to a reference axis and the aspect ratio of the principal correlation lengths. Since these parameters are unknown a priori, sample estimates are needed to define suitable spatial models for the interpolation of incomplete data. The distribution of the anisotropy statistics is determined by a non-Gaussian sampling joint probability density. By means of analytical calculations, we derive an explicit expression for the joint probability density function of the anisotropy statistics for Gaussian, stationary and differentiable random fields. Based on this expression, we obtain an approximate joint density which we use to formulate a statistical test for isotropy. The approximate joint density is independent of the autocovariance function and provides conservative probability and confidence regions for the anisotropy parameters. We validate the theoretical analysis by means of simulations using synthetic data, and we illustrate the detection of anisotropy changes with a case study involving background radiation exposure data. The approximate joint density provides (i) a stand-alone approximate estimate of the anisotropy statistics distribution (ii) informed initial values for maximum likelihood estimation, and (iii) a useful prior for Bayesian anisotropy inference.Comment: 39 pages; 8 figure

Metastasis to the breast from an adenocarcinoma of the lung with extensive micropapillary component: a case report and review of the literature

Breast metastasis from extra-mammary malignancy is rare. Based on the literature an incidence of 0.4-1.3% is reported. The primary malignancies most commonly metastasizing to the breast are leukemia-lymphoma, and malignant melanoma. We present a case of metastasis to the breast from a pulmonary adenocarcinoma, with extensive micropapillary component, diagnosed concomitantly with the primary tumor. A 73-year-old female presented with dyspnea and dry cough of 4 weeks duration and a massive pleural effusion was found on a chest radiograph. Additionally, on physical examination a poorly defined mass was noted in the upper outer quadrant of the left breast. The patient underwent bronchoscopy, excisional breast biopsy and medical thoracoscopy. By cytology, histology and immunohistochemistry primary lung adenocarcinoma with metastasis to the breast and parietal pleura was diagnosed. Both the primary and metastatic anatomic sites demonstrated histologically extensive micropapillary component, which is recently recognized as an important prognostic factor. The patient received chemotherapy but passed away within 7 months. Accurate differentiation of metastatic from primary carcinoma is of crucial importance because the treatment and prognosis differ significantly