882 research outputs found

    Semi-Blind Cancellation of IQ-Imbalances

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    International audienceThe technical realization of modern wireless receivers yields significant interfering IQ-imbalances, which have to be compensated digitally. To cancel these IQ-imbalances, we propose an algorithm using iterative blind source separation (IBSS) as well as information about the modulation scheme used (hence the term semi-blind). The novelty of our approach lies in the fact that we match the nonlinearity involved in the IBSS algorithm to the probability density function of the source signals. Moreover, we use approximations of the ideal non-linearity to achieve low computational complexity. For severe IQ-mismatch, the algorithm leads to 0.2 dB insertion loss in an AWGN channel and with 16-QAM modulation

    The Influence of Instrumental Sources of Variance on Mass Spectral Comparison Algorithms

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    Current search algorithms for the identification of substances based only on their electron ionization mass spectra provide the correct compound as their top result approximately 80% of the time. One contributing factor to the ~20% deviation in the first-hit recognition rate is that traditional algorithms work by comparing the unknown spectrum to an ‘ideal’ or consensus spectrum of each reference compound. The inclusion of replicate reference spectra in a database has been shown to improve the probability of ranking the correct identity in the number one position, but the variance in ion abundances caused by different conditions or different instruments remains an intractable problem and the major source of uncertainty in mass spectral identification. To assess the relative contributions of different factors to the spectral variance of replicate spectra, this study initially considered the repeller voltage, focus lens voltage, and ion energy as primary parameters. A three-factor, three-level, full-factorial design of experiments was conducted using cocaine as a model compound. A library of cocaine spectra was collected with a gas chromatography-electron ionization-mass spectrometer (GC-EI-MS) by extracting each spectrum across the eluting peak. The 20 most abundant ions in the library of cocaine spectra were extracted to assess the contribution of each instrument parameter on the variance in ion abundances by performing multivariate analysis of variance (MANOVA). Results showed that these instrument parameters were responsible for only ~3% of the total variance in the normalized abundances. This initial finding prompted a subsequent study that monitored the branching ratios of cocaine during random fluctuations in the vacuum chamber pressure. Random changes in vacuum pressure accounted for ~90% of the natural variance in the relative ion abundances of the two most abundant peaks of cocaine (not including the base peak). The database of 389 cocaine spectra was then used to compare the traditional consensus approaches to spectral matching with two variants of a novel algorithm called the Expert Algorithm for Substance Identification (EASI). EASI uses multivariate linear modeling to predict the ion abundances of 20 ions in each spectrum, assuming that each of the 20 ion abundances is continuously dependent on the other 19 ion abundances. One variant of this model includes intercepts in the linear models, and the other does not. To assess the effect of spectral variance on spectral identifications, traditional measures of spectral similarity or dissimilarity were calculated between each query spectrum and the consensus cocaine spectrum, including the Pearson product-moment correlation (PPMC) coefficients, mean absolute residuals (MARs), Euclidean distances, and NIST scores. These metrics were then used as binary classifiers to obtain true positives, true negatives, false positives, and false negatives at a range of decision thresholds. The models were tested on a database of spectra that included more than 300 cocaine spectra from different laboratories, more than 700 spectra of 5 common drugs, and 10 spectra of cocaine diastereomers: allococaine, pseudococaine, and pseudoallococaine. The EASI models outperformed the consensus approach on every metric. EASI coupled with the PPMC values, MARs and Euclidean distances had accuracies greater than 90% with zero false positives, including spectra of cocaine diastereomers and cocaine collected on different instruments. The Mahalanobis distances to the training set as a binary classifier were also reported, and they were found to be as good or better than EASI at discriminating between cocaine and non-cocaine spectra. Each measure of spectral similarity was used to build receiver operating characteristic (ROC) curves and calculate the area under the ROC curve (AUC). When taking only the cocaine diastereomers as known negatives, the EASI without a constant had the highest area under the curve (AUC=0.925), followed by EASI including a constant (AUC=0.907), and lastly the consensus model with (AUC=0.829). This work shows that random variations in vacuum pressure are responsible for most of the short-term variance in replicate mass spectra and that a model (EASI) that accounts for cross-correlations between the different fragment ions allow superior compound identification to traditional algorithms

    Learning soft task priorities for safe control of humanoid robots with constrained stochastic optimization

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    Multi-task prioritized controllers are able to generate complex robot behaviors that concurrently satisfy several tasks and constraints. To perform, they often require a human expert to define the evolution of the task priorities in time. In a previous paper [1] we proposed a framework to automatically learn the task priorities thanks to a stochastic optimization algorithm (CMA-ES) maximizing the robot performance on a certain behavior. Here, we learn the task priorities that maximize the robot performance, ensuring that the optimized priorities lead to safe behaviors that never violate any of the robot and problem constraints. We compare three constrained variants of CMA-ES on several benchmarks, among which two are new robotics benchmarks of our design using the KUKA LWR. We retain (1+1)-CMA-ES with covariance constrained adaptation [2] as the best candidate to solve our problems, and we show its effectiveness on two whole-body experiments with the iCub humanoid robot

    Sub-clinical assessment of atopic dermatitis severity using angiographic optical coherence tomography

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    Measurement of sub-clinical atopic dermatitis (AD) is important for determining how long therapies should be continued after clinical clearance of visible AD lesions. An important biomarker of sub-clinical AD is epidermal hypertrophy, the structural measures of which often make optical coherence tomography (OCT) challenging due to the lack of a clearly delineated dermal-epidermal junction in AD patients. Alternatively, angiographic OCT measurements of vascular depth and morphology may represent a robust biomarker for quantifying the severity of clinical and sub-clinical AD. To investigate this, angiographic data sets were acquired from 32 patients with a range of AD severities. Deeper vascular layers within skin were found to correlate with increasing clinical severity. Furthermore, for AD patients exhibiting no clinical symptoms, the superficial plexus depth was found to be significantly deeper than healthy patients at both the elbow (p = 0.04) and knee (p < 0.001), suggesting that sub-clinical changes in severity can be detected. Furthermore, the morphology of vessels appeared altered in patients with severe AD, with significantly different vessel diameter, length, density and fractal dimension. These metrics provide valuable insight into the sub-clinical severity of the condition, allowing the effects of treatments to be monitored past the point of clinical remission

    Theoretical optimal trajectories for reducing the environmental impact of commercial aircraft operations

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    This work describes initial results obtained from an ongoing research involving the development of optimization algorithms which are capable of performing multi-disciplinary aircraft trajectory optimization processes. A short description of both the rationale behind the initial selection of a suitable optimization technique and the status of the optimization algorithms is firstly presented. The optimization algorithms developed are subsequently utilized to analyze different case studies involving one or more flight phases present in actual aircraft flight profiles. Several optimization processes focusing on the minimization of total flight time, fuel burned and oxides of nitrogen (NOx) emissions are carried out and their results are presented and discussed. When compared with others obtained using commercially available optimizers, results of these optimization processes show atisfactory level of accuracy (average discrepancies ~2%). It is expected that these optimization algorithms can be utilized in future to efficiently compute realistic, optimal and ‘greener’ aircraft trajectories, thereby minimizing the environmental impact of commercial aircraft operations
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