Sonar and radar SAR processing for parking lot detection

In this paper, SAR processing algorithms for automotive applications are presented and illustrated on data from non-trivial test scenes. The chosen application is parking lot detection. Laboratory results obtained with a teaching sonar experiment emphasize the resolution improvement introduced with range-Doppler SAR processing. A similar improvement is then confirmed through full scale measurements performed with an automotive radar prototype operating at 77GHz in very close range conditions, typical of parking lot detection. The collected data allows a performance comparison between different SAR processing algorithms for realistic targets

Improving subband spectral estimation using modified AR model

It has already been shown that spectral estimation can be improved when applied to subband outputs of an adapted filterbank rather than to the original fullband signal. In the present paper, this procedure is applied jointly to a novel predictive autoregressive (AR) model. The model exploits time-shifting and is therefore referred to as time-shift AR (TSAR) model. Estimators are proposed for the unknown TS-AR parameters and the spectrum of the observed signal. The TS-AR model yields improved spectrum estimation by taking advantage of the correlation between subseries that after decimation. Simulation results on signals with continuous and line spectra that demonstrate the performance of the proposed method are provided

Hazards caused by natural and anthropogenic changes of catchment area in karst

International audienceDetermination of the catchment area is the starting point in most hydrological analyses. It serves as a basis for many hydrological and water resources management calculations. The catchment boundaries and areas in karst regions are often fragmented and not accurately known. They can change over time due to natural and anthropogenic causes. Natural and man-made processes cause changes of catchment area on different time and space scales. Human intervention, especially construction of dams and reservoirs, as well as interbasin water transfer through long tunnels and pipelines can introduce instantaneous, definite and hazardous change. This paper presents seven examples of natural and anthropogenic factors which influenced changes of catchment area in the Dinaric karst: 1) Closing of the Obod Estavelle in the Fatniceko polje; 2) Operation of the Zakucac hydroelectric power plant on the Cetina River; 3) Problems caused by Salakovac Reservoir; 4) Catastrophic flooding in the Cetinje Polje; 5) Regulation and canalization of the Trebi?njica River; 6) Building of the underground hydroelectric power plant Ombla; 7) An earthquake in Southern Croatia and Western Herzegovina

Stochastic Block Models with Multiple Continuous Attributes

The stochastic block model (SBM) is a probabilistic model for community structure in networks. Typically, only the adjacency matrix is used to perform SBM parameter inference. In this paper, we consider circumstances in which nodes have an associated vector of continuous attributes that are also used to learn the node-to-community assignments and corresponding SBM parameters. While this assumption is not realistic for every application, our model assumes that the attributes associated with the nodes in a network's community can be described by a common multivariate Gaussian model. In this augmented, attributed SBM, the objective is to simultaneously learn the SBM connectivity probabilities with the multivariate Gaussian parameters describing each community. While there are recent examples in the literature that combine connectivity and attribute information to inform community detection, our model is the first augmented stochastic block model to handle multiple continuous attributes. This provides the flexibility in biological data to, for example, augment connectivity information with continuous measurements from multiple experimental modalities. Because the lack of labeled network data often makes community detection results difficult to validate, we highlight the usefulness of our model for two network prediction tasks: link prediction and collaborative filtering. As a result of fitting this attributed stochastic block model, one can predict the attribute vector or connectivity patterns for a new node in the event of the complementary source of information (connectivity or attributes, respectively). We also highlight two biological examples where the attributed stochastic block model provides satisfactory performance in the link prediction and collaborative filtering tasks

Analytic pulse design for selective population transfer in many-level quantum systems: maximizing amplitude of population oscillations

State selective preparation and manipulation of discrete-level quantum systems such as atoms, molecules or quantum dots is a the ultimate tool for many diverse fields such as laser control of chemical reactions, atom optics, high-precision metrology and quantum computing. Rabi oscillations are one of the simplest, yet potentially quite useful mechanisms for achieving such manipulation. Rabi theory establishes that in the two-level systems resonant drive leads to the periodic and complete population oscillations between the two system levels. In this paper an analytic optimization algorithm for producing Rabi-like oscillations in the general discrete many-level quantum systems is presented.Comment: Published in Phys.Rev.A. This is the final published versio