EVM (Error Vector Measurement) is used to measure the end-to-end quality of digital communication links. It comes from noise, linear and non-linear distortion, and interference if any. I propose a method to discriminate between random noise that is independent of the signal and distortion that depends on the signal. Interference is more complex to discriminate as it is not random but can be either synchronous with the signal or not. Echoes such as multipath cause linear distortion if they are static. However, variable echoes, such as those created in a reverberation chamber must be treated specifically.

Since time-delay and phase estimation is a fundamental task in a plethora of engineering fields, several CRB and MLE expressions have been derived for the past decades. In all these previous works, a common hypothesis is that the wave transmission process introduces an unknown phase which prevents from estimating both delay and transmission phase components. By revisiting this problem, including the derivation of the MLE and the associated CRB, we show that this well-admitted assertion is not true strictly: both informations can be estimated, but generally with a sub-optimal achievable MSE in the asymptotic region. Moreover, since practical problems exist where the transmission phase can be estimated apart, adding this additionnal measure to the observation model provides a setting allowing to explore the contribution of each signal component (carrier frequency, baseband signal and transmission phase measure) to the achievable MSE of time-delay and phase estimation in the asymptotic region.

A fusion method was recently proposed for ultrasound and magnetic resonance images for endometriosis diagnosis. This method combined the advantages of each modality, i.e., the good contrast and signal to noise ratio of the MR image and the good spatial resolution of the US image. The method was based on an inverse problem, performing a superresolution of the MR image and a denoising of the US image. A polynomial function was introduced to model the relationships between the gray levels of the MR and US images. This paper studies the potential interest of replacing this polynomial function by a non-parametric transformation built using the theory of reproducing kernel Hilbert spaces. Simulations conducted on a phantom and synthetic data allow the performance of the resulting fusion method to be appreciated.

In this work, we investigate the problem of neuralbased error correction decoding, and more specifically, the new so-called syndrome-based decoding technique introduced to tackle scalability in the training phase for larger code sizes. We improve on previous works in terms of allowing full decoding of the message rather than codewords, allowing thus the application to nonsystematic codes, and proving that the single-message training property is still viable. The suggested system is implemented and tested on polar codes of sizes (64,32) and (128,64), and a BCH of size (63,51), leading to a significant improvement in both Bit Error Rate (BER) and Frame Error Rate (FER), with gains between 0.3dB and 1dB for the implemented codes in the high Signal-to-Noise Ratio (SNR) regime.

The world’s forests have undergone substantial changes in the last decades. In the tropics, 17% of moist forests disappeared between 1990 and 2019, through deforestation and forest degradation [7]. These changes contribute greatly to biodiversity loss through habitat destruction, soil erosion, terrestrial water cycle disturbances, and anthropogenic CO2 emissions. Continuous monitoring of global deforestation is a fundamental tool to support preservation actions and to stop further destruction of vegetation. Several forest disturbance detection systems have already been developed, mainly based on space-borne optical remote sensing [4] which is severely limited by cloud coverage in the tropics. Contrarily to optical imagery, SAR products have the great potential of being insensitive to the presence of clouds. In recent years, several SAR-based systems have been developed and are now operational in different dense forest areas across the tropics [2], [3], [5], [6]. Despite the extensive coverage and temporal density of acquisitions, C-band SAR data like Sentinel-1 are not ideal for deforestation monitoring since the returned backscatter can be altered by variations in soil moisture and others. In this work, we investigate a new method to monitor forest loss in a near real-time manner exploiting the principle of Bayesian inference. In particular, forest loss is treated as a change-point detection problem within a univariate time series (i.e. Sentinel-1 single polarization), in which each new observation contributes to the probability of having or not deforestation in a Bayesian-like manner [1]. Detection delay and false alarm reduction have been investigated through the extension of the algorithm to the multivariate case of dual-polarization Sentinel-1 acquisitions. Given the synchronous nature of VV, VH acquisitions, such a modification allows an increase in the equivalent number of looks on a pixel on the ground, hence augmenting the level of confidence of an issued alert. A validation campaign has been conducted to assess the performance of the method. The test sites are located in French Guiana and Brazil where deforestation takes place constantly and near real-time monitoring is fundamental for law enforcement practices. Additionally, a comparison with a well-known deforestation monitoring technique, namely Maximum Likelihood Ratio Test, has been performed to further evaluate the proposed method. Conclusively, the potential of extending the current method to asynchronous data sources such as Sentinel-2 optical data is addressed.

Intrinsic lower bounds on the intrinsic mean square error are of major importance to characterize the best achievable estimation performance of any unbiased estimator on smooth manifold as Lie group (LG). When the parameter is described by a LG model and the observation noise is with unknown variance, it is also necessary to determine a bound both on the LG parameter and this variance. In this communication, we propose an intrinsic generic Fisher information matrix taking into account this problem. To achieve that, we derive an intrinsic Slepian-Bangs formula on the LG product of the unknown parameter of interest and the LG of positive scalar values in which the variance intrinsically lies. The proposed bound is validated on a Gaussian observation model for unknown parameter lying to SE (3) and variance noise on R+.

In this article, we first derive a general intrinsic Barankin bound (IBB) for unknown parameters lying on Lie groups (LGs), and its intrinsic McAulay-Seidman bound (IMSB) approximation. Second, the IMSB expression is used to revisit the intrinsic Cramér-Rao bound (ICRB) on LGs. Indeed, an analytic expression of the ICRB, which is a special IMSB case, is obtained from the latter. Finally, closed-form expressions for both IMSB and ICRB are obtained for Euclidean and LG observation models depending on parameters lying in SO(3) and SE(3). The validity of the these IMSB and ICRB expressions, with respect to the intrinsic mean square error, is shown via numerical simulations to support the discussion.