Time-Division Multiplexing Architecture for Hybrid Filter Bank A/D converters

International audienceIn this paper, a new Hybrid Filter Bank (HFB) architecture called Time-Division Multiplexing (TDM) is proposed for HFB-based A/D Converters (ADC). The TDM HFB architecture is firstly extracted considering the TDM components of analog input as new input vector. The TDM HFB-based ADC is then simulated using simply-realizable analysis filters to approve the mathematical formulation of the TDM model. At last, the output resolutions of TDM and classical HFB-based ADC are compared considering practical analog filters including realization errors. It is shown that the TDM architecture is less sensitive to the realization errors than the classical HFB. Besides, the TDM HFB can exploit a blind technique to correct the realization errors in opposite to the classical HFB case

Thermosensitive Liposomes

Thermosensitive liposomes (TSLs) are a drug delivery system for targeted delivery that release the encapsulated drug when heated to fever temperatures (∼40–42°C). Combined with localized hyperthermia, TSLs allow precise drug delivery to a targeted region. While mostly investigated as cancer therapy, other applications including treatment of local infections and wound healing have been explored. Over the last ∼40 years, numerous TSL formulations and payloads have been investigated. As with other nanoparticles, the addition of targeting molecules to TSL has been examined to improve targeted delivery. TSL release kinetics and plasma stability are two important factors that affect efficacy, and new formulations often aim to further improve on these properties. The possibility of encapsulating a magnetic resonance (MR) contrast agent that is released together with the encapsulated drug allows for visualization of drug delivery with MR imaging. Various heating modalities have been examined in combination with TSL. Since the goal is to expose a defined tissue region to uniform temperatures within the range where TSLs release (typically ∼40–43°C), the choice of an appropriate heating modality has considerable impact on treatment efficacy. Several ongoing clinical trials with TSL as cancer therapy suggest the potential for clinical impact in the near future

Digital estimation of analog imperfections using blind equalization

International audienceThe analog electronic circuits are always subject to some imperfections. Analog imperfections cause deviations from nominal values of electronic elements. In the case of Linear Time-Invariant (LTI) circuits, the coefficients of the transfer function include some deviations from related typical values leading to the differences between the typical (i.e. design) and the actual transfer functions. In this paper, the analog imperfections are digitally estimated using only the output samples, without any access to the input signal nor to the analog system (blind method). Super Exponential Algorithm (SEA) is used as the blind equalization technique, since it provides rapid convergence. The only assumption is that the input is a non-Gaussian independent and identically distributed (i.i.d.) signal. Using this algorithm, the effects of analog imperfections in the analog circuits can be digitally estimated and possibly compensated without any dependance on the types and the sources of the analog imperfections. It provides the possibility to have an online compensation of the imperfections (realization errors, drifts, etc.). The analog imperfections have been estimated with a precision of §0:2% and §1:3% for the exemplary RC and RLC circuits respectively

Improved dynamic connection detection power in estimated dynamic functional connectivity considering multivariate dependencies between brain regions

To estimate dynamic functional connectivity (dFC), the conventional method of sliding window correlation (SWC) suffers from poor performance of dynamic connection detection. This stems from the equal weighting of observations, suboptimal time scale, nonsparse output, and the fact that it is bivariate. To overcome these limitations, we exploited the kernel-reweighted logistic regression (KELLER) algorithm, a method that is common in genetic studies, to estimate dFC in resting state functional magnetic resonance imaging (rs-fMRI) data. KELLER can estimate dFC through estimating both spatial and temporal patterns of functional connectivity between brain regions. This paper compares the performance of the proposed KELLER method with current methods (SWC and tapered-SWC (T-SWC) with different window lengths) based on both simulated and real rs-fMRI data. Estimated dFC networks were assessed for detecting dynamically connected brain region pairs with hypothesis testing. Simulation results revealed that KELLER can detect dynamic connections with a statistical power of 87.35% compared with 70.17% and 58.54% associated with T-SWC (p-value = .001) and SWC (p-value \u3c.001), respectively. Results of these different methods applied on real rs-fMRI data were investigated for two aspects: calculating the similarity between identified mean dynamic pattern and identifying dynamic pattern in default mode network (DMN). In 68% of subjects, the results of T-SWC with window length of 100 s, among different window lengths, demonstrated the highest similarity to those of KELLER. With regards to DMN, KELLER estimated previously reported dynamic connection pairs between dorsal and ventral DMN while SWC-based method was unable to detect these dynamic connections

Performance of subband HFB-based A/D converters

International audienceSubband Hybrid Filter Bank (HFB) structures may replace the classic ones in parallel A/D Converters (ADC). In this paper, subband and original HFB-based A/D converters are simulated and compared in the time domain. For this purpose, firstly a brief survey on subband HFB-based A/D converters is presented. According to the simulations, subband HFB-based ADC exhibits a lower sensitivity to realization errors than classic one. Besides, compensation techniques such as noise canceling may be used for correcting the analog imperfections of subband HFB. This is not possible for classic HFB. The output resolution of subband HFB-based ADC is shown to be better than the one related to classic architecture using FIR synthesis filters with the same order for some exemplary inputs. Computation complexity related to subband architecture is compared with the one due to classic structure. It may be seen that subband HFB-based ADC includes less computational complexity than the classic architecture if an adaptive compensation of analog imperfections is considered

SENSITIVITY OF TIME-DIVISION MULTIPLEXING PARALLEL A/D CONVERTERS TO ANALOG IMPERFECTION

International audienceIn this paper, the Time-Division Multiplexing (TDM) architecture for the Hybrid Filter Bank (HFB) A/D Converters (ADCs) is studied in the time domain. Giving a brief survey on the TDM structure, the classical and TDM HFB-based ADCs are compared in terms of the output resolution for some input signals. To study the sensitivity to the realization errors, both structures are simulated assuming the same realization errors in the analysis filter banks. The TDM HFBbased ADC exhibits a better performance either in the presence or in the absence of realization errors than the classical one. Besides, the input-output relationship is demonstrated to be Linear Time-Invariant (LTI) for the TDM HFB, but it is non-LTI in the the classical HFB case. Thus, it is possible only in the TDM case that a blind deconvolution method is employed for adaptively compensating the realization errors

ERREURS ANALOGIQUES DANS LES CAN A BANCS DE FILTRES HYBRIDES<br />"Méthodes d'estimation et nouvelles structures"

Hybrid Filter Bank (HFB) A/D converters are a good candidate for realizing the future versatile, intelligent and wide-band communication systems. However, the HFB structures exhibit a large sensitivity to the analog non-idealities of analysis part so that the classical HFB ADCs are not practically useful unless these errors are corrected. The efforts have been made in this thesis to more profoundly study this problem and to propose a group of possible solutions. Firstly, the design phase of related HFBs is described in the matrix form. Considering the simply realizable first- and second-order analog circuits as analysis filter bank and FIR digital synthesis filters, the HFB structures are designed for A/D conversion in this thesis. Simulating some exemplary HFB ADCs, it is shown that the sensitivity of HFB to analog errors is so large because the related analysis matrix is ill-conditioned, particularly in the case of oversampling process. Using Second-Order and Higher-Order Statistics, it is shown that the analog imperfections of analog circuits may digitally be estimated through the output samples. However, these techniques appear not to be applicable to the conventional HFB structure because of undersampling process included at each branch of HFB-based ADC. Thus, for exploiting the digital techniques to estimate and then correct the analog imperfections of analysis filter bank, new Multiple-Input Multiple-Output (MIMO) HFB structures are proposed so that there exist no under-sampling operation anymore between the related input-output signals. The simulations show that the MIMO (TDM and subband) HFB architectures provide not only a better output resolution but a less sensitivity to the realization errors of analysis filter bank than the classical HFB. Finally, using the TDM and subband MIMO HFBs, it is proposed to use the blind methods such as blind deconvoluton or Automatic Noise Cancelation (ANC) for compensating and then reducing the sensitivity to the analog non-idealities.Les Convertisseurs Analogique-Numérique (CAN) à Bancs de Filtres Hybrides (BFH) sont de bons candidats pour répondre aux exigences des futurs systèmes de communication devant être versatile, intelligent et à large-bande. Cependant, les BFH montrent une grande sensibilité aux non-idéalités analogiques du banc d'analyse, de sorte que les CAN à BFH classiques ne seraient pas pratiquement utilisables à moins que ces erreurs ne soient corrigées. Les efforts, dans cette thèse, ont porté sur l'étude de ce problème afin de proposer des pistes de solutions. A cet égard, la conception des BFH est, d'abord, décrite sous la forme de matrice. Puis, en utilisant des circuits analogiques simplement réalisables ainsi que des Filtres numériques à Réponse Impulsionnelle Finie (RIF), les BFH sont conçus pour la conversion A/N. Selon la simulation des CAN à BFH, nous montrons que la sensibilité de ceux-ci aux erreurs analogiques est très élevée puisque la matrice d'analyse associée est mal-conditionnée, surtout dans le cas oµu le suréchantillonnage est utilisé. Pour estimer numériquement les imperfections des circuits analogiques, nous proposons l'utilisation de méthodes d'estimation aveugle, basées sur des statistiques de seconde-ordre ou d'ordre supérieur. Cependant, ces techniques semblent ne pas être applicables aux BFH classiques en raison du sous- échantillonnage inclus µa chaque branche du CAN à BFH. Ainsi, pour exploiter les techniques numériques pour la correction des imperfections analogiques des Filtres d'analyse, nous proposons de nouvelles structures µa Entrée-sortie Multiple (ESM). Dans ces structures, il n'existe plus aucune opération de sous-échantillonnage entre les entrée-sortie associées. Les simulations prouvent que les BFH µa ESM (à sous-bande et à multiplexage temporel) mènent non seulement à une meilleure résolution mais aussi µa une sensibilité moins élevée par rapport aux BFH classique. En conclusion, en utilisant les BFH à ESM, les méthodes aveugles telles que la déconvolution ou l'annulation du bruit peuvent être employées afin de réduire encore la sensibilité aux non-idéalités analogiques