High resolution lunar radar studies: Preliminary results

High resolution radar data for the lunar surface were acquired over 14 sites in June and November 1990 using the new 10 MHz data taking system at the Arecibo Observatory. The raw data collected for each site covers an area approximately 100 by 400 km and will be processed using delay-Doppler techniques into images of backscatter cross section with three fo four independent looks. All observations transmitted a circularly polarized signal and both senses of circular polarization were received containing the polarized and depolarized component of the backscatter signal. The relative power in these two polarizations provides useful information on properties of the surface, in particular surface roughness. The effort to date focused on the initial data analysis with new software written to perform a full synthetic aperture focusing on the raw radar data. This analysis will involve the use of complementary high resolution optical and topographic data sets to aid interpretation of surface scattering mechanisms

Exploring the temporal dynamics of speech production with EEG and group ICA

Speech production is a complex skill whose neural implementation relies on a large number of different regions in the brain. How neural activity in these different regions varies as a function of time during the production of speech remains poorly understood. Previous MEG studies on this topic have concluded that activity proceeds from posterior to anterior regions of the brain in a sequential manner. Here we tested this claim using the EEG technique. Specifically, participants performed a picture naming task while their naming latencies and scalp potentials were recorded. We performed group temporal Independent Component Analysis (group tICA) to obtain temporally independent component timecourses and their corresponding topographic maps. We identified fifteen components whose estimated neural sources were located in various areas of the brain. The trial-by-trial component timecourses were predictive of the naming latency, implying their involvement in the task. Crucially, we computed the degree of concurrent activity of each component timecourse to test whether activity was sequential or parallel. Our results revealed that these fifteen distinct neural sources exhibit largely concurrent activity during speech production. These results suggest that speech production relies on neural activity that takes place in parallel networks of distributed neural sources

Extensions of independent component analysis for natural image data

An understanding of the statistical properties of natural images is useful for any kind of processing to be performed on them. Natural image statistics are, however, in many ways as complex as the world which they depict. Fortunately, the dominant low-level statistics of images are sufficient for many different image processing goals. A lot of research has been devoted to second order statistics of natural images over the years. Independent component analysis is a statistical tool for analyzing higher than second order statistics of data sets. It attempts to describe the observed data as a linear combination of independent, latent sources. Despite its simplicity, it has provided valuable insights of many types of natural data. With natural image data, it gives a sparse basis useful for efficient description of the data. Connections between this description and early mammalian visual processing have been noticed. The main focus of this work is to extend the known results of applying independent component analysis on natural images. We explore different imaging techniques, develop algorithms for overcomplete cases, and study the dependencies between the components by using a model that finds a topographic ordering for the components as well as by conditioning the statistics of a component on the activity of another. An overview is provided of the associated problem field, and it is discussed how these relatively small results may eventually be a part of a more complete solution to the problem of vision.reviewe

SiSeRHMap v1.0: A simulator for mapped seismic response using a hybrid model

SiSeRHMap is a computerized methodology capable of drawing up prediction maps of seismic response. It was realized on the basis of a hybrid model which combines different approaches and models in a new and non-conventional way. These approaches 5 and models are organized in a code-architecture composed of five interdependent modules. A GIS (Geographic Information System) Cubic Model (GCM), which is a layered computational structure based on the concept of lithodynamic units and zones, aims at reproducing a parameterized layered subsoil model. A metamodeling process confers a hybrid nature to the methodology. In this process, the one-dimensional linear 10 equivalent analysis produces acceleration response spectra of shear wave velocitythickness profiles, defined as trainers, which are randomly selected in each zone. Subsequently, a numerical adaptive simulation model (Spectra) is optimized on the above trainer acceleration response spectra by means of a dedicated Evolutionary Algorithm (EA) and the Levenberg–Marquardt Algorithm (LMA) as the final optimizer. In the fi15 nal step, the GCM Maps Executor module produces a serial map-set of a stratigraphic seismic response at different periods, grid-solving the calibrated Spectra model. In addition, the spectra topographic amplification is also computed by means of a numerical prediction model. This latter is built to match the results of the numerical simulations related to isolate reliefs using GIS topographic attributes. In this way, different sets 20 of seismic response maps are developed, on which, also maps of seismic design response spectra are defined by means of an enveloping technique

Inter-areal coordination of columnar architectures during visual cortical development

The occurrence of a critical period of plasticity in the visual cortex has long been established, yet its function in normal development is not fully understood. Here we show that as the late phase of the critical period unfolds, different areas of cat visual cortex develop in a coordinated manner. Orientation columns in areas V1 and V2 become matched in size in regions that are mutually connected. The same age trend is found for such regions in the left and right brain hemisphere. Our results indicate that a function of critical period plasticity is to progressively coordinate the functional architectures of different cortical areas - even across hemispheres.Comment: 30 pages, 1 table, 6 figure