Lab and Field Tests of a Low-Cost 3-Component Seismometer for Shallow Passive Seismic Applications

We performed laboratory tests and field surveys to evaluate the performance of a low-cost 3-component seismometer, consisting of three passive electromagnetic spring-mass sensors, whose 4.5 Hz natural frequency is extended down to 0.5 Hz thanks to hyper damping. Both lab and field datasets show that the −3 dB band of the seismometer ranges approximately from 0.7 to 39 Hz, in agreement with the nominal specifications. Median magnitude frequency response curves obtained from processing field data indicate that lower corner of the −3 dB band could be extended down to 0.55 Hz and the nominal sensitivity may be overestimated. Lab results confirm the non-linear behavior of the passive spring-mass sensor expected for high-level input signals (a few to tens of mm/s) and field data confirm relative timing accuracy is ±10 ms (1 sample). We found that absolute timing of data collected with USB GPS antennas can be affected by lag as large as +0.5 s. By testing two identical units, we noticed that there could be differences around 0.5 dB (i.e., about 6%) between the components of the same unit as well as between the same component of the two units. Considering shallow passive seismic applications and mainly focusing on unstable slope monitoring, our findings show that the tested seismometer is able to identify resonance frequencies of unstable rock pillars and to generate interferograms that can be processed to estimate subsurface velocity variations

Identification of Neural Circuits by Imaging Coherent Electrical Activity with FRET-Based Dyes

AbstractWe show that neurons that underlie rhythmic patterns of electrical output may be identified by optical imaging and frequency-domain analysis. Our contrast agent is a two-component dye system in which changes in membrane potential modulate the relative emission between a pair of fluorophores. We demonstrate our methods with the circuit responsible for fictive swimming in the isolated leech nerve cord. The output of a motor neuron provides a reference signal for the phase-sensitive detection of changes in fluorescence from individual neurons in a ganglion. We identify known and possibly novel neurons that participate in the swim rhythm and determine their phases within a cycle. A variant of this approach is used to identify the postsynaptic followers of intracellularly stimulated neurons

Adaptive Deep Brain Stimulation in Advanced Parkinson's Disease: Bridging the Gap beetween Concept and Clinical Application

Parkinson’s disease (PD) is a common neurodegenerative disorder. Recent evidence points towards increased synchronous neuronal oscillations of the cortico-thalamic-basal ganglia circuits in the beta band (12–30 Hz) as the main pathophysiological abnormality associated with PD. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for improving PD motor symptoms. However, the current DBS systems have several limitations, mainly related to the fixed and continuous application of stimulation. Especially in the long-term, DBS can only partially control clinical fluctuations and can exacerbate undesirable adverse effects often reversible with a change of stimulation parameters. A new strategy called adaptive DBS (aDBS) allows for continuous adaptation of STN stimulation to the patient’s clinical state by directly harnessing the recordings of the STN pathological oscillatory activity or local field potentials (LFPs). With this project, we aimed to accelerate the clinical translational process by suggesting a pathway to the clinical practice. To do so, we developed an external portable LFPs-based aDBS device for clinical investigations in acute experimental sessions. We then conducted a proof of concept study investigating the functioning of the device and comparing aDBS and conventional DBS (cDBS) and how they interacted with the concurrent pharmacological treatment. Then, we monitored the clinical and neurophysiological fluctuations over a period of eight hours with and without aDBS. We thus investigated the preservation of LFPs-clinical state correlation and the aDBS management of motor fluctuations during daily activities. Because in the clinical practice the DBS therapy is provided by means of implantable pulse generators (IPGs), we evaluated whether the proposed aDBS approach, based on real-time LFPs processing, fits the power constraints of implantable devices. Finally, we contextualized our results and proposed an overview of the possible pathways toward the clinical practice

Deteção espetral de banda larga para rádio cógnitivo

Doutoramento em TelecomunicaçõesEsta tese tem como objetivo o desenvolvimento de uma unidade autónoma de deteção espetral em tempo real. Para tal são analisadas várias implementações para a estimação do nível de ruído de fundo de forma a se poder criar um limiar adaptativo para um detetor com uma taxa constante de falso alarme. Além da identificação binária da utilização do espetro, pretende-se também obter a classificação individual de cada transmissor e a sua ocupação ao longo do tempo. Para tal são exploradas duas soluções baseadas no algoritmo, de agrupamento de dados, conhecido como maximização de expectativas que permite identificar os sinais analisados pela potência recebida e relação de fase entre dois recetores. Um algoritmo de deteção de sinal baseado também na relação de fase de dois recetores e sem necessidade de estimação do ruído de fundo é também demonstrado. Este algoritmo foi otimizado para permitir uma implementação eficiente num arranjo de portas programáveis em campo a funcionar em tempo real para uma elevada largura de banda, permitindo também estimar a direção da transmissão detetada.The purpose of this thesis is to develop an autonomous unit for real time spectrum sensing. For this purpose, several implementations for the estimation of the background noise level are analysed to create an adaptive threshold and ensure a constant false alarm rate detector. In addition to the binary identification of the spectrum usage, it is also intended to obtain an individual classification of each transmitter occupation and its spectrum usage over time. To do so, two solutions based on the expectation maximization data clustering, that allow to identify the analyzed signals by the received power and phase relation between two receivers, were explored. A signal detection algorithm, also based on the phase relationship between two receivers and with no need for noise estimation is also demonstrated. This algorithm has been optimized to allow an efficient implementation in a FPGA operating in real time for a high bandwidth and it also allows the estimation of the direction of arrival of the detected transmission