Síntesi de la molècula ML-10 i del seu precursor immediat per poder obtenir 18F-ML-10, d'ús en tècniques PET

El 18F-ML-10 és un radiofàrmac utilitzat com a traçador de cèl·lules apoptòtiques mitjançant la tècnica del PET (Positron Emission Tomography). En el present treball de recerca s’ha dut a terme la síntesi de dos precursors del 18F-ML-10, i del producte final no marcat isotòpicament, el ML-10. S’ha plantejat una síntesi convergent per l’obtenció del compost final 3, partint del malonat de di-tert-butil i l’1,5-pentandiol. Tant el precursor tosilat 1 com el mesilat 2 s’han sintetitzat en 3 etapes amb un 29% i un 36% de rendiment, respectivament. El compost 3, utilitzat com a referència en la síntesi del 18F-ML-10, s’ha sintetitzat en 2 etapes a partir del precursor 2 amb un rendiment total de la síntesi del 31%. El procés de radiomarcació s’està portant a terme a l’Institut d’Alta Tecnologia – Parc de Recerca Biomèdica de Barcelona (IAT–PRBB), on posteriorment es faran estudis neurològics i oncològics on el seguiment de processos apoptòtics és clau.

ML approaches to channel estimation for pilot-aided multi-rate DS/CDMA systems

This paper analyzes the asymptotic performance of maximum likelihood (ML) channel estimation algorithms in wideband code division multiple access (WCDMA) scenarios. We concentrate on systems with periodic spreading sequences (period larger than or equal to the symbol span) where the transmitted signal contains a code division multiplexed pilot for channel estimation purposes. First, the asymptotic covariances of the training-only, semi-blind conditional maximum likelihood (CML) and semi-blind Gaussian maximum likelihood (GML) channelestimators are derived. Then, these formulas are further simplified assuming randomized spreading and training sequences under the approximation of high spreading factors and high number of codes. The results provide a useful tool to describe the performance of the channel estimators as a function of basicsystem parameters such as number of codes, spreading factors, or traffic to training power ratio.

ML estimator and hybrid beamformer for multipath and interference mitigation in GNSS receivers

This paper addresses the estimation of the code-phase(pseudorange) and the carrier-phase of the direct signal received from a direct-sequence spread-spectrum satellite transmitter. Thesignal is received by an antenna array in a scenario with interferenceand multipath propagation. These two effects are generallythe limiting error sources in most high-precision positioning applications.A new estimator of the code- and carrier-phases is derivedby using a simplified signal model and the maximum likelihood(ML) principle. The simplified model consists essentially ofgathering all signals, except for the direct one, in a component withunknown spatial correlation. The estimator exploits the knowledgeof the direction-of-arrival of the direct signal and is much simplerthan other estimators derived under more detailed signal models.Moreover, we present an iterative algorithm, that is adequate for apractical implementation and explores an interesting link betweenthe ML estimator and a hybrid beamformer. The mean squarederror and bias of the new estimator are computed for a numberof scenarios and compared with those of other methods. The presentedestimator and the hybrid beamforming outperform the existingtechniques of comparable complexity and attains, in manysituations, the Cramér–Rao lower bound of the problem at hand.