Marco Caparrini

Unobtrusive biometric system based on electroencephalogram analysis

Features extracted from electroencephalogram (EEG) recordings have proved to be unique enough between subjects for biometric applications. We show here that biometry based on these recordings offers a novel way to robustly authenticate or identify subjects. In this paper, we present a rapid and unobtrusive authentication method that only uses 2 frontal electrodes referenced to another one placed at the ear lobe. Moreover, the system makes use of a multistage fusion architecture, which demonstrates to improve the system performance. The performance analysis of the system presented in this paper stems from an experiment with 51 subjects and 36 intruders, where an equal error rate (EER) of 3.4% is obtained, that is, true acceptance rate (TAR) of 96.6% and a false acceptance rate (FAR) of 3.4%. The obtained performance measures improve the results of similar systems presented in earlier work.

Airborne GNSS-R Polarimetric Measurements for Soil Moisture and Above-Ground Biomass Estimation

Soil moisture content (SMC) and above-ground biomass (AGB) are key parameters for the understanding of both the hydrological and carbon cycles. From an economical perspective, both SMC and AGB play a significant role in the agricultural sector, one of the most relevant markets worldwide. This paper assesses the sensitivity of Global Navigation Satellite System (GNSS) reflected signals to soil moisture and vegetation biomass from an experimental point of view. For that, three scientific flights were performed in order to acquire GNSS reflectometry (GNSS-R) polarimetric observations over a wide range of terrain conditions. The GNSS-R data were used to obtain the right-left and right-right reflectivity components, which were then georeferenced according to the transmitting GNSS satellite and receiver positions. It was determined that for low-altitude GNSS-R airborne platforms, the reflectivity polarization ratio provides a highly reliable observable for SMC due to its high stability with respect to surface roughness. A correlation coefficient of 0.93 and a sensitivity of 0.2 dB/SMC (%) were obtained for moderately vegetated fields with a surface roughness standard deviation below 3 cm. Similarly, the copolarized reflection coefficient shows a stable sensitivity to forest AGB with equal to 0.9 with a stable sensitivity of 1.5 dB/(100 t/ha) up to AGB values not detectable by other remote sensing systems.

Global Navigation Satellite Systems Reflectometry as a Remote Sensing Tool for Agriculture

The use of Global Navigation Satellite Systems (GNSS) signals for remote sensing applications, generally referred to as GNSS-Reflectometry (GNSS-R), is gaining increasing interest among the scientific community as a remote sensing tool for land applications. This paper describes a long term experimental campaign in which an extensive dataset of GNSS-R polarimetric measurements was acquired over a crop field from a ground-based stationary platform. Ground truth ancillary data were also continuously recorded during the whole experimental campaign. The duration of the campaign allowed to cover a full crop growing season, and as a consequence of seasonal rains on the experimental area, data could be recorded over a wide variety of soil conditions. This enabled a study on the effects of different land bio-geophysical parameters on GNSS scattered signals. It is shown that significant power variations in the measured GNSS reflected signals can be detected for different soil moisture and vegetation development conditions. In this work we also propose a technique based on the combination of the reflected signal’s polarizations in order to improve the integrity of the observables with respect to nuisance parameters such as soil roughness.

Potential Synergetic Use of GNSS-R Signals to Improve the Sea-State Correction in the Sea Surface Salinity Estimation: Application to the SMOS Mission

It is accepted that the best way to monitor sea surface salinity (SSS) on a global basis is by means of L-band radiometry. However, the measured sea surface brightness temperature (TB) depends not only on the SSS but also on the sea surface temperature (SST) and, more importantly, on the sea state, which is usually parameterized in terms of the 10-m-height wind speed (U10 ) or the significant wave height. It has been recently proposed that the mean-square slope (mss) derived from global navigation satellite system (GNSS) signals reflected by the sea surface could be a potentially appropriate sea-state descriptor and could be used to make the necessary sea state TB corrections to improve the SSS estimates. This paper presents a preliminary error analysis of the use of reflected GNSS signals for the sea roughness correction and was performed to support the European Space Agencys Soil Moisture and Ocean Salinity (SMOS) mission; the orbit and parameters for the SMOS instrument were assumed. The accuracy requirement for the retrieved SSS is 0.1 practical salinity units after monthly averaging over 2deg times 2degboxes. In this paper, potential improvements in salinity estimation are hampered mainly by the coarse sampling and by the requirements of the retrieval algorithm, particularly the need for a semiempirical model that relates TB and mss.

Tsunami Detection Using the PARIS Concept

On 26 December 2004 a tsunami generated by an earthquake with its epicentre in the Indian Ocean West of Indonesia caused a real human and material catastrophe in the region. After the event some proposals to establish a network of sensors for tsunami detection were put forward. This paper presents an alternative concept that can be applied from satellite, aircraft or from the coast, and which can complement such a network of sensors for fast tsunami detection. The concept makes use of GNSS signals reflected from the ocean’s surface to perform mesoscale ocean altimetry. The technique, designated PARIS (Passive Reflectometry and Interferometry System), aims at capturing fast topographic events happening on the ocean surface such as eddies and fronts. The paper includes details of some aircraft experiments whereby a PARIS altimeter was used to map a topographic signature with amplitude and wavelength similar to a tsunami in open ocean.

Oceanpal®: Monitoring sea state with a GNSS-R coastal instrument

Oceanpalreg is a coastal instrument developed at Starlab for operational remote sensing of the ocean surface, with potential direct applications to snow/ice mapping and soil moisture monitoring. The instrument is based on the exploitation of global navigation satellite systems (GNSS) and their augmentation systems (WAAS, EGNOS). The emitted signals provide an exceptional source of opportunity for passive remote sensing of the Earth. The use of GNSS reflections (GNSS-R) for sea-surface monitoring is a bistatic radar technique only requiring a receiving system. The concept has already been implemented for coastal platforms (few meters above the water), aircraft (1km to 10 km) and is being studied for space platforms (LEO, orbiting at 500-1000 km). The potential applications include sea-state, sea-surface altimetry and surface roughness, both for scientific and operational oceanography. We report on a recent long-term experimental and demonstration campaign, carried out at the Oceanpalreg Coeli station in the Barcelona Port during the period 2004-2007, with a real time web-based service. This campaign has been made possible through collaboration with the Barcelona Port Authority Environmental Monitoring Department (APB). The instrument was installed on a breakwater near the main entrance of the port, at 23 m over the sea-surface. We describe in this paper the successful long-term comparison between the data obtained by Oceanpal instrument and the observables recorded by two nearby buoys. Data used for this analysis cover a period of over one year, allowing a definitive evaluation of the performances of this GNSS-R based coastal instrument for SWH retrieval. We also review results from a weeklong phase altimetry campaign at the port of Vilagarcia.

Oceanpal: an instrument for remote sensing of the ocean and other water surfaces using GNSS reflections

Abstract This paper describes Oceanpal, an inexpensive, all-weather, passive instrument concept for remote sensing of the ocean and other water surfaces. Oceanpal is based on the use of reflected signals emitted from GNSS, and as such it is well grounded on the growing, long term GNSS infrastructure. As seen from the instrument, several GNSS emitters are simultaneously in view at any given time, providing separated multiple scattering points with different geometries. Reflected signals are affected by surface “roughness” and motion (i.e. sea state, orbital motion, and currents), mean surface height and dielectric properties (i.e. salinity and pollution). Oceanpal is envisaged to act as an accurate, “dry” tide gauge/surface monitoring system as a part of a future distributed ocean remote sensing network concept.

Modeling of the GNSS-R signal as a function of soil moisture and vegetation biomass

Very recently, it has been observed that GNSS-R can provide a significant contribution to agricultural and forestry applications, since the use of GNSS signals as sources of opportunity enables bistatic radar measurements at L-band, which showed to be sensitive to soil moisture and vegetation parameters. This perspective has been investigated in two experimental activities funded by the European Space Agency: the LEiMON and GRASS campaigns. This work has been carried out with the aim of interpreting the data collected during the two campaigns over land. This requires to model the coherent component associated to the mean surface, but at the same time the diffuse incoherent component due to roughness at wavelength scale. In presence of vegetation, both components must be taken into account. The paper presents the approach followed to develop a simulator of GNSS-R data over land, aiming to support potential applications of GNSS-R for soil moisture and biomass retrieval.

Non-space applications of GNSS-R: From research to operational services. Examples of water and land monitoring systems

The use of global navigation satellite signals for Earth Observation is recognized today as a complementary technique for remotely monitoring our planet. The current interest of this remote sensing technique is leading to the continuous development of new applications by the scientific community and to an increasing relevance in specific market niches. Starlab Barcelona has developed several GNSS-R applications that have reached over the last decade an operational maturity level in water-oriented services. These include sea applications, such as sea state monitoring and sea level monitoring, as well as inland water applications, namely water reservoir level monitoring. In addition, in the last years, there has also been increasing interest in soil applications, which has led to the establishment of new research lines targeting the measurement of land bio-geophysical parameters. These applications are reviewed in the current paper.

Grass: AN experiment on the capability of airborne GNSS-R sensors in sensing soil moisture and vegetation biomass

In this paper an experiment concerning the capabilities of GNSS-R sensors for land applications was described. An airborne campaign was performed in summer and fall 2011 over two areas close to Florence (Italy): an agricultural zone and a forest plot of poplars. A detailed comparison of the GNSS-R signals with ground truth data was performed. Both LR and RR reflection coefficients have been found to be sensitive to changes in the surface soil moisture, with a total variation of about 6 dB between dry and wet conditions. Regarding the sensitivity to vegetation, it was observed that the measured LR coefficients have a moderate power variation due to the presence of woody vegetation. It was observed that the LR coefficient experienced a monotonic decrease with increasing biomass, up to an estimated forest dry biomass of more than 150 t/ha.