The social brain : how social stimuli are translated into neuroendocrine signals

Tese de doutoramento, Biologia (Ecofisiologia), Universidade de Lisboa, Faculdade de Ciências, 2014

The effect of clock jitter on the performance of continuous-time delta-sigma ADC for GNSS signals

Next generation Global Navigation Satellite System (GNSS) receivers will operate in multiple navigation bands. An efficient way to achieve this with lower power and cost is to employ BandPass Sampling (BPS); nevertheless, the sampling operation injects large amounts of jitter noise, which degrades the performance of the receiver. Continuous–Time (CT) Delta–Sigma (ΔΣ) modulators are capable of suppressing this noise but the impact of clock jitter at the output of the Digital– to–Analog Converter (DAC) in the feedback path of the modulator should be taken into account. This paper presents an analytical approach for describing clock jitter in GNSS receivers when a CT–ΔΣ modulator is utilized for Analog–to–Digital Conversion (ADC). The validity of the presented approach is verified through time–domain simulations using a behavioural model of the fourth–order CT–ΔΣ modulator with 1–bit NRZ DAC feedback pulse.

Narrow-band variable center frequency single-loop and multistage sigma-delta modulators for bandpass signals

Oversampled narrow-band single-loop and multistage resonator-based bandpass sigma-delta (Σ-Δ) modulators that can accommodate different passband center to sampling frequency ratios are reported. These tunable bandpass configurations are designed by analytically determining and subsequently verifying through detailed empirical simulations the required compensation hardware to deliver enhanced noise-shaping. It is demonstrated that comparatively superior in-band signal-to-noise ratios and dynamic ranges are attributed to the inclusion of appropriate digital feedforward and feedback compensators within these structures.

A comparative study of complex multiple-band noise-shaping sigma-delta modulators for multi-tone input signals

This paper presents a comparative study of complex single-bit and multi-bit sigma-delta modulators that are capable of providing concurrent multiple-band noise-shaping for multi-tone narrow-band input signals. The concepts applied for the three design methodologies are based on the noise transfer functions of complex comb, complex slink and complex multi-notch filters.

Practical simulation of GNSS signals in the presence of ionospheric scintillaton

We propose a low complexity technique to generate amplitude correlated time-series with Nakagami-m distribution and phase correlated Gaussian-distributed time-series, which is useful in the simulation of ionospheric scintillation effects during the transmission of GNSS signals. The method requires only the knowledge of parameters S4 (scintillation index) and σΦ (phase standard deviation) besides the definition of models for the amplitude and phase power spectra. The Zhang algorithm is used to produce Nakagami-distributed signals from a set of Gaussian autoregressive processes.

A Review on the Characterization of Signals and Systems by Power Law Distributions

Power laws, also known as Pareto-like laws or Zipf-like laws, are commonly used to explain a variety of real world distinct phenomena, often described merely by the produced signals. In this paper, we study twelve cases, namely worldwide technological accidents, the annual revenue of America׳s largest private companies, the number of inhabitants in America׳s largest cities, the magnitude of earthquakes with minimum moment magnitude equal to 4, the total burned area in forest fires occurred in Portugal, the net worth of the richer people in America, the frequency of occurrence of words in the novel Ulysses, by James Joyce, the total number of deaths in worldwide terrorist attacks, the number of linking root domains of the top internet domains, the number of linking root domains of the top internet pages, the total number of human victims of tornadoes occurred in the U.S., and the number of inhabitants in the 60 most populated countries. The results demonstrate the emergence of statistical characteristics, very close to a power law behavior. Furthermore, the parametric characterization reveals complex relationships present at higher level of description.

Using artificial neural networks to generate trading signals for crude oil, copper and gold futures

In this thesis, a feed-forward, back-propagating Artificial Neural Network using the gradient descent algorithm is developed to forecast the directional movement of daily returns for WTI, gold and copper futures. Out-of-sample back-test results vary, with some predictive abilities for copper futures but none for either WTI or gold. The best statistically significant hit rate achieved was 57% for copper with an absolute return Sharpe Ratio of 1.25 and a benchmarked Information Ratio of 2.11.

Membrane potential dynamics of neocortical projection neurons driving target-specific signals.

Primary sensory cortex discriminates incoming sensory information and generates multiple processing streams toward other cortical areas. However, the underlying cellular mechanisms remain unknown. Here, by making whole-cell recordings in primary somatosensory barrel cortex (S1) of behaving mice, we show that S1 neurons projecting to primary motor cortex (M1) and those projecting to secondary somatosensory cortex (S2) have distinct intrinsic membrane properties and exhibit markedly different membrane potential dynamics during behavior. Passive tactile stimulation evoked faster and larger postsynaptic potentials (PSPs) in M1-projecting neurons, rapidly driving phasic action potential firing, well-suited for stimulus detection. Repetitive active touch evoked strongly depressing PSPs and only transient firing in M1-projecting neurons. In contrast, PSP summation allowed S2-projecting neurons to robustly signal sensory information accumulated during repetitive touch, useful for encoding object features. Thus, target-specific transformation of sensory-evoked synaptic potentials by S1 projection neurons generates functionally distinct output signals for sensorimotor coordination and sensory perception.

Specific targeting of pro-death NMDA receptor signals with differing reliance on the NR2B PDZ ligand.

NMDA receptors (NMDARs) mediate ischemic brain damage, for which interactions between the C termini of NR2 subunits and PDZ domain proteins within the NMDAR signaling complex (NSC) are emerging therapeutic targets. However, expression of NMDARs in a non-neuronal context, lacking many NSC components, can still induce cell death. Moreover, it is unclear whether targeting the NSC will impair NMDAR-dependent prosurvival and plasticity signaling. We show that the NMDAR can promote death signaling independently of the NR2 PDZ ligand, when expressed in non-neuronal cells lacking PSD-95 and neuronal nitric oxide synthase (nNOS), key PDZ proteins that mediate neuronal NMDAR excitotoxicity. However, in a non-neuronal context, the NMDAR promotes cell death solely via c-Jun N-terminal protein kinase (JNK), whereas NMDAR-dependent cortical neuronal death is promoted by both JNK and p38. NMDAR-dependent pro-death signaling via p38 relies on neuronal context, although death signaling by JNK, triggered by mitochondrial reactive oxygen species production, does not. NMDAR-dependent p38 activation in neurons is triggered by submembranous Ca(2+), and is disrupted by NOS inhibitors and also a peptide mimicking the NR2B PDZ ligand (TAT-NR2B9c). TAT-NR2B9c reduced excitotoxic neuronal death and p38-mediated ischemic damage, without impairing an NMDAR-dependent plasticity model or prosurvival signaling to CREB or Akt. TAT-NR2B9c did not inhibit JNK activation, and synergized with JNK inhibitors to ameliorate severe excitotoxic neuronal loss in vitro and ischemic cortical damage in vivo. Thus, NMDAR-activated signals comprise pro-death pathways with differing requirements for PDZ protein interactions. These signals are amenable to selective inhibition, while sparing synaptic plasticity and prosurvival signaling.

Anisotropie de la photoluminescence dans des nanostructures organiques chirales autoassemblées

Nous investiguons dans ce travail la dynamique des excitons dans une couche mince d’agrégats H autoassemblés hélicoïdaux de molécules de sexithiophène. Le couplage intermoléculaire (J=100 meV) place ce matériau dans la catégorie des semi-conducteurs à couplage de type intermédiaire. Le désordre énergétique et la forte interaction électronsphonons causent une forte localisation des excitons. Les espèces initiales se ramifient en deux états distincts : un état d’excitons autopiégés (rendement de 95 %) et un état à transfert de charge (rendement de 5%). À température de la pièce (293K), les processus de sauts intermoléculaires sont activés et l’anisotropie de la fluorescence décroît rapidement à zéro en 5 ns. À basse température (14K), les processus de sauts sont gelés. Pour caractériser la dynamique de diffusion des espèces, une expérience d’anisotropie de fluorescence a été effectuée. Celle-ci consiste à mesurer la différence entre la photoluminescence polarisée parallèlement au laser excitateur et celle polarisée perpendiculairement, en fonction du temps. Cette mesure nous donne de l’information sur la dépolarisation des excitons, qui est directement reliée à leur diffusion dans la structure supramoléculaire. On mesure une anisotropie de 0,1 après 20 ns qui perdure jusqu’à 50ns. Les états à transfert de charge causent une remontée de l’anisotropie vers une valeur de 0,15 sur une plage temporelle allant de 50 ns jusqu’à 210 ns (période entre les impulsions laser). Ces résultats démontrent que la localisation des porteurs est très grande à 14K, et qu’elle est supérieure pour les espèces à transfert de charge. Un modèle numérique simple d’équations différentielles à temps de vie radiatif et de dépolarisation constants permet de reproduire les données expérimentales. Ce modèle a toutefois ses limitations, notamment en ce qui a trait aux mécanismes de dépolarisation des excitons.

Non-negative matrix decomposition approaches to frequency domain analysis of music audio signals

On étudie l’application des algorithmes de décomposition matricielles tel que la Factorisation Matricielle Non-négative (FMN), aux représentations fréquentielles de signaux audio musicaux. Ces algorithmes, dirigés par une fonction d’erreur de reconstruction, apprennent un ensemble de fonctions de base et un ensemble de coef- ficients correspondants qui approximent le signal d’entrée. On compare l’utilisation de trois fonctions d’erreur de reconstruction quand la FMN est appliquée à des gammes monophoniques et harmonisées: moindre carré, divergence Kullback-Leibler, et une mesure de divergence dépendente de la phase, introduite récemment. Des nouvelles méthodes pour interpréter les décompositions résultantes sont présentées et sont comparées aux méthodes utilisées précédemment qui nécessitent des connaissances du domaine acoustique. Finalement, on analyse la capacité de généralisation des fonctions de bases apprises par rapport à trois paramètres musicaux: l’amplitude, la durée et le type d’instrument. Pour ce faire, on introduit deux algorithmes d’étiquetage des fonctions de bases qui performent mieux que l’approche précédente dans la majorité de nos tests, la tâche d’instrument avec audio monophonique étant la seule exception importante.

Dynamique de recombinaison radiative dans les nanofils InGaN/GaN : étude détaillée de la photoluminescence

Mesures effectuées dans le laboratoire de caractérisation optique des semi-conducteurs du Prof. Richard Leonelli du département de physique de l'université de Montréal. Les nanofils d'InGaN/GaN ont été fournis par le groupe du Prof. Zetian Mi du département de génie électrique et informatique de l'université McGill.