The stream of life: from frozen babies to the birth of psychic life

This article describes the work with a flexible model having Esther Bick’s infant observation model in mind. Having to solve the problem of the many babies in orphanages who had been abandoned and were often sick, I worked out with the psychologist a model of intervention using the knowledge the psychologist had of the approach of infant observation, but adapting it to the context of the orphanage. I helped the psychologist observer to use her countertransference to contain the babies’ distress and interact with them in order to give them the possibility of feeling loved and understood, in order to make space in their minds for introjecting a good object. On behalf of the observations that the psychologist, whom I will call Ling, had with babies Bea and Sally, we can say that the dramatic distress of their being abandoned by their families has been worked through because the psychologist was able to give a loving space with significant interaction. The sessions reported in this article give a portrait of the inside world of despairing babies, and document the evolution towards being able to keep in mind a good enough object that can help them survive.

Incorporating the field border effect to reduce the predicted uncertainty of pollen dispersal model in Asia

The presence of the field border (FB), such as roadways or unplanted areas, between two fields is common in Asian farming system. This study evaluated the effect of the FB on the cross-pollination (CP) and predicted the CP rate in the field considering and not considering FB. Three experiments including 0, 6.75, and 7.5 m width of the FB respectively were conducted to investigate the effect of distance and the FB on the CP rate. The dispersal models combined kernel and observation model by calculating the parameter of observation model from the output of kernel. These models were employed to predict the CP rate at different distances. The Bayesian method was used to estimate parameters and provided a good prediction with uncertainty. The highest average CP rates in the field with and without FB were 74.29% and 36.12%, respectively. It was found that two dispersal models with the FB effect displayed a higher ability to predict average CP rates. The correlation coefficients between actual CP rates and CP rates predicted by the dispersal model combined zero-inflated Poisson observation model with compound exponential kernel and modified Cauchy kernel were 0.834 and 0.833, respectively. Furthermore, the predictive uncertainty was reducing using the dispersal models with the FB effect.

Analysis of the stability of automatic tracking of super maneuvering air objects by radio technical tracking systems of the multichannel radar

Multichannel tracking radars with phased antenna arrays are widely used to track air targets. The use of a phased array in combination with digital computing technology allows to control the radar radiation pattern and track several targets in the time distribution mode. Air target tracking in a multichannel radar is provided by subsystems for measuring range, radial velocity and angular coordinates, in most cases, without adaptation to the external influence characteristics. When tracking supermaneuverable air targets, such as 5th and so-called 4++ generation fighters, there is a decrease in the accuracy and stability of tracking relative to the area without maneuver. If the tracking system algorithms are tuned to a low intensity of maneuvering or its absence, a significant increase in the error of tracking the aircraft in the maneuvering section will lead to disruption of auto tracking due to a significant dynamic component of the error. The stability of auto-tracking of maneuvering targets by subsystems of range, radial velocity, and angular coordinates with fixed parameters for the case when the setting of the parameters of the tracking system algorithms coincide with the characteristics of the external influence is analyzes in the paper. The influence of the observation model parameters, the stochastic model of the target movement with exponentially correlated values of the target acceleration, and the measurement period of the target coordinates on the potential tracking accuracy by radio technical tracking systems of the multichannel radar is investigated. To assess the stability of auto-tracking, it is proposed to use the equivalent aperture size of the discriminating characteristic. The influence of the parameters of the target movement stochastic model, the observation model, and the measurement period of the target coordinates on the stability of auto-tracking in terms of range, radial velocity, and angular coordinates is estimated. It is shown that the "weak link" is the radial velocity tracking system. As a result of the research carried out, it becomes possible to further assess the feasibility of adapting the auto-tracking systems to the target maneuvering characteristics and to develop recommendations for choosing the measurement period of the target coordinates.

Mapping of linear road features with the inverse visual detector observation model

The paper proposes an algorithm for mapping linear features detected on the roadway — road marking lines, curbs, road boundaries. The algorithm is based on a mapping method with an inverse observation model. An inverse observation model is proposed to take into account the spatial error of the linear feature visual detector. The influence of various parameters of the model on the resulting quality of mapping was studied. The mapping algorithm was tested on data recorded on an autonomous vehicle while driving at the test site. The quality of the mapping algorithm was assessed according to several quality metrics known from the literature. In addition, the mapping problem was considered as a binary classification problem, in which each map cell may or may not contain the desired feature, and the ROC curve and AUC-ROC metric were used to assess the quality. As a naive solution, a map was built containing all detected linear features without any additional filtering. For the map built on the basis of the raw data, the AUC-ROC was 0.75, and as a result of applying the algorithm, the value of 0.81 was reached. The experimental results have confirmed that the proposed algorithm can effectively filter noise and false-positive detections of the detector, which confirms the applicability of the proposed algorithm and the inverse observation model for solving practical problems. Key words Linear features, mapping, inverse observation model, road map, autonomous vehicle, digital road map.

A method for optimal linear super-resolution image restoration

In this paper, we propose a super-resolution (pixel grid refinement) method for digital images. It is based on the linear filtering of a zero-padded discrete signal. We introduce a continuous-discrete observation model to create a reconstruction system. The proposed observation model is typical of real-world imaging systems - an initially continuous signal first undergoes linear (dynamic) distortions and then is subjected to sampling and the effect of additive noise. The proposed method is optimal in the sense of mean square recovery error minimization. In the theoretical part of the article, a general scheme of the linear super-resolution of the signal is presented and expressions for the impulse and frequency responses of the optimal reconstruction system are derived. An expression for the error of such restoration is also derived. For the sake of brevity, the entire description is presented for one-dimensional signals, but the obtained results can easily be generalized for the case of two-dimensional images. The experimental section of the paper is devoted to the analysis of the super-resolution reconstruction error depending on the parameters of the observation model. The significant superiority of the proposed method in terms of the reconstruction error is demonstrated in comparison with linear interpolation, which is usually used to refine the grid of image pixels.

Conditional and Unconditional Deterministic Bounds on the MSE of the Non-Uniform Linear Co-centered Orthogonal Loop and Dipole Array

The co-centered orthogonal loop and dipole (COLD) array exhibits some interesting properties, which makes it ubiquitous in the context of polarized source localization. In the literature, one can find a plethora of estimation schemes adapted to the COLD array. Nevertheless, their ultimate performance in terms the so-called threshold region of mean square error (MSE), have not been fully investigated. In order to fill this lack, we focus, in this paper, on conditional and unconditional bounds that are tighter than the well known Cramér-Rao Bound (CRB). More precisely, we give some closed form expressions of the McAulay-Hofstetter, the Hammersley-Chapman-Robbins, the McAulaySeidman bounds and the recent Todros-Tabrikian bound, for both the conditional and unconditional observation model. Finally, numerical examples are provided to corroborate the theoretical analysis and to reveal a number of insightful properties.

A Rigorous Observation Model for the Risley Prism-Based Livox Mid-40 Lidar Sensor

Modern lidar sensors are continuing to decrease in size, weight, and cost, but the demand for fast, abundant, and high-accuracy lidar observations is only increasing. The Livox Mid-40 lidar sensor was designed for use within sense-and-avoid navigation systems for autonomous vehicles, but has also found adoption within aerial mapping systems. In order to characterize the overall quality of the point clouds from the Mid-40 sensor and enable sensor calibration, a rigorous model of the sensor’s raw observations is needed. This paper presents the development of an angular observation model for the Mid-40 sensor, and its application within an extended Kalman filter that uses the sensor’s data to estimate the model’s operating parameters, systematic errors, and the instantaneous prism rotation angles for the Risley prism optical steering mechanism. The analysis suggests that the Mid-40’s angular observations are more accurate than the specifications provided by the manufacturer. Additionally, it is shown that the prism rotation angles can be used within a planar constrained least-squares adjustment to theoretically improve the accuracy of the angular observations of the Mid-40 sensor.

Modified Extended Object Tracker for Lidar Data Using Random Matrix Model

Random Matrix (RM) model is an effective method for modeling extended objects, and has been widely used in extended object tracking. However, the existing RM based tracking methods usually assume that the measurement models obey Gaussian distribution, which will lead to the decrease of accuracy when applied to Lidar system. This paper proposed a new observation model and used it to modified the RM smoother by considering the characteristics of Lidar data. Simulation results show that the proposed approach achieved better performance compared with the original RM tracker in Lidar system.