High-resolution Representation Research Articles

Noise-assisted instantaneous coherence analysis of brain connectivity.

Characterizing brain connectivity between neural signals is key to understanding brain function. Current measures such as coherence heavily rely on Fourier or wavelet transform, which inevitably assume the signal stationarity and place severe limits on its time-frequency resolution. Here we addressed these issues by introducing a noise-assisted instantaneous coherence (NAIC) measure based on multivariate mode empirical decomposition (MEMD) coupled with Hilbert transform to achieve high-resolution time frequency representation of neural coherence. In our method, fully data-driven MEMD, together with Hilbert transform, is first employed to provide time-frequency power spectra for neural data. Such power spectra are typically sparse and of high resolution, that is, there usually exist many zero values, which result in numerical problems for directly computing coherence. Hence, we propose to add random noise onto the spectra, making coherence calculation feasible. Furthermore, a statistical randomization procedure is designed to cancel out the effect of the added noise. Computer simulations are first performed to verify the effectiveness of NAIC. Local field potentials collected from visual cortex of macaque monkey while performing a generalized flash suppression task are then used to demonstrate the usefulness of our NAIC method to provide highresolution time-frequency coherence measure for connectivity analysis of neural data.

An alternative map of the United States based on an n-dimensional model of geographic space

Geographic features have traditionally been visualized with fairly high amount of geometric detail, while relationships among these features in attribute space have been represented at a much coarser resolution. This limits our ability to understand complex high-dimensional relationships and structures existing in attribute space. In this paper, we present an alternative approach aimed at creating a high-resolution representation of geographic features with the help of a self-organizing map (SOM) consisting of a large number of neurons. In a proof-of-concept implementation, we spatialize 200,000+ U.S. Census block groups using a SOM consisting of 250,000 neurons. The geographic attributes considered in this study reflect a more holistic representation of geographic reality than in previous studies. The study includes 69 attributes regarding population statistics, land use/land cover, climate, geology, topography, and soils. This diversity of attributes is informed by our desire to build a comprehensive two-dimensional base map of n-dimensional geographic space. The paper discusses how standard GIS methods and neural network processing are combined towards the creation of an alternative map of the United States.

Application of an unstructured mesh model to storm surge propagation in the Mersey estuary region of the Irish Sea

The storm surge period of 13–16 November 1977 when there was a major positive surge followed by a negative surge in the Irish Sea is investigated using a two-dimensional unstructured mesh model of the west coast of Britain. The model accounts for tidal and external surge forcing across its open boundaries which are situated in the Celtic Sea and off the west coast of Scotland. Although this period has been examined previously using a uniform finite-difference model, and a finite element model, neither of these could resolve the Mersey estuary which is the focus of the present study. By using a finite element model with very high mesh resolution within the Mersey, the spatial variability of surge elevations and currents within the Mersey to rapidly changing surge dynamics can be examined. The mesh in the model varies from about 7 km in deep water, to the order of 100 m in the Mersey, with the largest mesh length reaching 17 km in deep offshore regions, and smallest of order 26 m occurring in shallow coastal regions of the Mersey estuary. The model accounts for wetting/drying which occurs in shallow water coastal areas. Calculations showed that during the positive surge period, the amplitude and speed of propagation of the surge was largest in the deep water channels. This gave rise to significant spatial variability of surge elevations and currents within the estuary. As wind stresses decreased over the Irish Sea, a negative surge occurred over Liverpool Bay and at the entrance to the Mersey. However, within the Mersey there was a local positive surge which continued to propagate down the estuary. This clearly showed that although the large scale response of the Irish Sea to changing wind fields occurred rapidly, the response in the Mersey was much slower. These calculations with a west coast variable mesh model that included a high-resolution representation of the Mersey revealed for the first time how elevations and currents within the Mersey responded to Irish Sea surges that rapidly changed from positive to negative.

Integrated marine gravity field in the Brazilian coast from altimeter-derived sea surface gradient and shipborne gravity

Abstract Sea surface gradients derived from the Geosat and ERS-1 satellite altimetry geodetic missions were integrated with marine gravity data from the National Geophysical Data Center and Brazilian national surveys. Using the least squares collocation method, models of free-air gravity anomaly and geoid height were calculated for the coast of Brazil with a resolution of 2′ × 2′. The integration of satellite and shipborne data showed better statistical results in regions near the coast than using satellite data only, suggesting an improvement when compared to the state-of-the-art global gravity models. Furthermore, these results were obtained with considerably less input information than was used by those reference models. The least squares collocation presented a very low content of high-frequency noise in the predicted gravity anomalies. This may be considered essential to improve the high resolution representation of the gravity field in regions of ocean–continent transition.

Earthquake Recurrence in Simulated Fault Systems

We employ a computationally efficient fault system earthquake simulator, RSQSim, to explore effects of earthquake nucleation and fault system geometry on earthquake occurrence. The simulations incorporate rate- and state-dependent friction, high-resolution representations of fault systems, and quasi-dynamic rupture propagation. Faults are represented as continuous planar surfaces, surfaces with a random fractal roughness, and discontinuous fractally segmented faults. Simulated earthquake catalogs have up to 106 earthquakes that span a magnitude range from ∼M4.5 to M8. The seismicity has strong temporal and spatial clustering in the form of foreshocks and aftershocks and occasional large-earthquake pairs. Fault system geometry plays the primary role in establishing the characteristics of stress evolution that control earthquake recurrence statistics. Empirical density distributions of earthquake recurrence times at a specific point on a fault depend strongly on magnitude and take a variety of complex forms that change with position within the fault system. Because fault system geometry is an observable that greatly impacts recurrence statistics, we propose using fault system earthquake simulators to define the empirical probability density distributions for use in regional assessments of earthquake probabilities.

A High-Resolution Coupled Riverine Flow, Tide, Wind, Wind Wave, and Storm Surge Model for Southern Louisiana and Mississippi. Part I: Model Development and Validation

Abstract A coupled system of wind, wind wave, and coastal circulation models has been implemented for southern Louisiana and Mississippi to simulate riverine flows, tides, wind waves, and hurricane storm surge in the region. The system combines the NOAA Hurricane Research Division Wind Analysis System (H*WIND) and the Interactive Objective Kinematic Analysis (IOKA) kinematic wind analyses, the Wave Model (WAM) offshore and Steady-State Irregular Wave (STWAVE) nearshore wind wave models, and the Advanced Circulation (ADCIRC) basin to channel-scale unstructured grid circulation model. The system emphasizes a high-resolution (down to 50 m) representation of the geometry, bathymetry, and topography; nonlinear coupling of all processes including wind wave radiation stress-induced set up; and objective specification of frictional parameters based on land-cover databases and commonly used parameters. Riverine flows and tides are validated for no storm conditions, while winds, wind waves, hydrographs, and high water marks are validated for Hurricanes Katrina and Rita.

Application of a finite element model to the computation of tides in the Mersey Estuary and Eastern Irish Sea

A variable mesh finite element model of the Irish and Celtic Sea regions with/without the inclusion of the Mersey estuary is used to examine the influence of grid resolution and the Mersey upon the higher harmonics of the tide in the region. Comparisons are made with observations and published results from finite difference models of the area. Although including a high resolution representation of the Mersey had little effect upon computed tides in the western Irish Sea it had a significant effect upon tidal currents in the eastern Irish Sea. In addition the higher harmonics of the M 2 tide in near-shore regions of the eastern Irish Sea particularly the Solway and Mersey estuary together with Morecambe Bay showed significant small scale variability. The Mersey was used to test the sensitivity to including estuaries because high resolution accurate topography was available. The results presented here suggest that comparable detailed topographic data sets are required in all estuaries and near-shore regions. In addition comparisons clearly show the need for an unstructured grid model of the region that can include all the estuaries. Such an unstructured grid solution was developed here within a finite element approach, although other methods in particular the finite volume, or coordinate transformations/curvilinear grids and nesting could be applied.

Detailed sensory memory, sloppy working memory

Visual short-term memory (VSTM) enables us to actively maintain information in mind for a brief period of time after stimulus disappearance. According to recent studies, VSTM consists of three stages – iconic memory, fragile VSTM, and visual working memory – with increasingly stricter capacity limits and progressively longer lifetimes. Still, the resolution (or amount of visual detail) of each VSTM stage has remained unexplored and we test this in the present study. We presented people with a change detection task that measures the capacity of all three forms of VSTM, and we added an identification display after each change trial that required people to identify the “pre-change” object. Accurate change detection plus pre-change identification requires subjects to have a high-resolution representation of the “pre-change” object, whereas change detection or identification only can be based on the hunch that something has changed, without exactly knowing what was presented before. We observed that people maintained 6.1 objects in iconic memory, 4.6 objects in fragile VSTM, and 2.1 objects in visual working memory. Moreover, when people detected the change, they could also identify the pre-change object on 88% of the iconic memory trials, on 71% of the fragile VSTM trials and merely on 53% of the visual working memory trials. This suggests that people maintain many high-resolution representations in iconic memory and fragile VSTM, but only one high-resolution object representation in visual working memory.

Aligning Coupled Manifolds for Face Hallucination

Many learning-based super-resolution methods are based on the manifold assumption, which claims that point-pairs from the low-resolution representation manifold (LRM) and the corresponding high-resolution representation manifold (HRM) possess similar local geometry. However, the manifold assumption does not hold well on the original coupled manifolds (i.e., LRM and HRM) due to the nonisometric one-to-multiple mappings from low-resolution (LR) image patches to high-resolution (HR) ones. To overcome this limitation, we propose a solution from the perspective of manifold alignment. In this context, we perform alignment by learning two explicit mappings which project the point-pairs from the original coupled manifolds into the embeddings of the common manifold (CM). For the task of SR reconstruction, we treat HRM as target manifold and employ the manifold regularization to guarantee that the local geometry of CM is more consistent with that of HRM than LRM is. After alignment, we carry out the SR reconstruction based on neighbor embedding between the new couple of the CM and the target HRM. Besides, we extend our method by aligning the multiple coupled subsets instead of the whole coupled manifolds to address the issue of the global nonlinearity. Experimental results on face image super-resolution verify the effectiveness of our method.

Storm surge computations in estuarine and near-coastal regions: the Mersey estuary and Irish Sea area

An unstructured grid storm surge model of the west coast of Britain, incorporating a high-resolution representation of the Mersey estuary is used to examine storm surge dynamics in the region. The focus of the study is the major surge that occurred during the period 11–14 November 1977, which has been investigated previously using uniform grid finite difference models and a finite element model of the west coast of Britain. However, none of these models included the Mersey estuary. Comparison of solutions in the eastern Irish Sea with those computed with these earlier models showed that, away from the Liverpool Bay region, the inclusion of the Mersey estuary had little effect. However, at the entrance to the Mersey, its inclusion did influence the solution. By including a detailed representation of the Mersey estuary within the model, it was possible to conduct a detailed study of storm surge propagation in the Mersey, which had never previously been performed. This detailed study showed for the first time that the surge’s temporal variability within the estuary is influenced by surge elevation at its entrance. This varies with time as a function of spatial and temporal variations of wind stress over the west coast of Britain. Within the Mersey, calculations show that the spatial variability is mainly determined by changes in bottom topography, which had not been included in earlier finite difference models. However, since water depth is influenced by variations in tidal elevation, this, together with tide surge interaction through bottom friction and momentum advection, influences the surge. The ability of the finite element model to vary the mesh in near-shore regions to such an extent that it can resolve the Mersey and hence the impact of the Mersey estuary upon the Liverpool Bay circulation shows that it has distinct advantages over earlier finite difference models. In the absence of detailed measurements within the Mersey at the time of the surge, it was not possible to validate predicted surge elevations within the Mersey. However, significant insight into physical processes influencing the surge propagation down the estuary, its reflection and spatial/temporal variability could be gained.

A Modeling and Observational Framework for Diagnosing Local Land–Atmosphere Coupling on Diurnal Time Scales

AbstractLand–atmosphere interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface temperature and moisture states. The degree of coupling between the land surface and PBL in numerical weather prediction and climate models remains largely unexplored and undiagnosed because of the complex interactions and feedbacks present across a range of scales. Furthermore, uncoupled systems or experiments [e.g., the Project for the Intercomparison of Land-Surface Parameterization Schemes (PILPS)] may lead to inaccurate water and energy cycle process understanding by neglecting feedback processes such as PBL-top entrainment. In this study, a framework for diagnosing local land–atmosphere coupling is presented using a coupled mesoscale model with a suite of PBL and land surface model (LSM) options along with observations during field experiments in the U.S. Southern Great Plains. Specifically, the Weather Research and Forecasting Model (WRF) has been coupled to the Land Information System (LIS), which provides a flexible and high-resolution representation and initialization of land surface physics and states. Within this framework, the coupling established by each pairing of the available PBL schemes in WRF with the LSMs in LIS is evaluated in terms of the diurnal temperature and humidity evolution in the mixed layer. The coevolution of these variables and the convective PBL are sensitive to and, in fact, integrative of the dominant processes that govern the PBL budget, which are synthesized through the use of mixing diagrams. Results show how the sensitivity of land–atmosphere interactions to the specific choice of PBL scheme and LSM varies across surface moisture regimes and can be quantified and evaluated against observations. As such, this methodology provides a potential pathway to study factors controlling local land–atmosphere coupling (LoCo) using the LIS–WRF system, which will serve as a test bed for future experiments to evaluate coupling diagnostics within the community.

Discrete fixed-resolution representations in visual working memory

Limits on the storage capacity of working memory significantly affect cognitive abilities in a wide range of domains, but the nature of these capacity limits has been elusive. Some researchers have proposed that working memory stores a limited set of discrete, fixed-resolution representations, whereas others have proposed that working memory consists of a pool of resources that can be allocated flexibly to provide either a small number of high-resolution representations or a large number of low-resolution representations. Here we resolve this controversy by providing independent measures of capacity and resolution. We show that, when presented with more than a few simple objects, human observers store a high-resolution representation of a subset of the objects and retain no information about the others. Memory resolution varied over a narrow range that cannot be explained in terms of a general resource pool but can be well explained by a small set of discrete, fixed-resolution representations.

Iterative projection reconstruction of time‐resolved images using highly‐constrained back‐projection (HYPR)

Highly-constrained back-projection (HYPR) is a technique for the reconstruction of sparse, highly-undersampled time-resolved image data. A novel iterative HYPR (I-HYPR) algorithm is presented and validated in computer simulations. The reconstruction method is then applied to cerebral perfusion MRI simulated as a radial acquisition and contrast-enhanced angiography of the head to assess feasibility in accelerating acquisitions requiring high temporal resolution and accurate representation of contrast kinetics. The I-HYPR algorithm is shown to be more robust than standard HYPR in these applications in which the sparsity condition is not met or in which quantitative information is required. Specifically, iterative reconstruction of undersampled perfusion and contrast-enhanced angiography data improved accuracy of the representation of contrast kinetics and increased the temporal separation of arterial and venous contrast kinetics. The I-HYPR reconstruction may have important diagnostic applications in settings requiring high temporal resolution and quantitative signal dynamics. Because I-HYPR allows relaxation of the sparsity requirements for the composite frame, the iterative reconstruction can enable novel acquisition strategies that independently optimize the quality of the composite and temporal resolution of the dynamic frames.

An integrated study of economic effects of and vulnerabilities to global warming on the Barents Sea cod fisheries

The Barents Sea area is characterised by a highly fluctuating physical environment causing substantial variations in the ecosystems and fisheries depending upon this. Simulations assuming different management regimes have been carried out to study how physical and biological effects of global warming influence the Barents Sea cod fisheries. A regional, high-resolution representation of the B2 world region (OECD90) scenario from the Intergovernmental Panel on Climate Change was used to calculate water temperatures and plankton biomasses by hydrodynamic modelling. These results were included in simulations performed by a multi-fleet, multi-species model, by which a fully integrated model linking to the global circulation model to the Barents Sea fisheries through a regional downscaling to the Barents Sea area is constructed. One factor of particular importance for the natural annual biological variations is the occasional inflow of young herring into the Barents Sea area. The herring inflow is difficult to predict and links to dynamical systems outside the Barents Sea area, complex recruitment mechanisms and oceanographic conditions. These processes are in the study represented by a stochastic representation of herring inflow based on historical observations. According to the performed simulations the biomass fluctuations may slightly increase over the next 25 years, possibly caused by changes in temperature patterns. Six different management regimes have been included in the study and the results support earlier studies claiming that the choice of management regime potentially has a greater importance for biological and economic performance in the Barents Sea fisheries than impacts which derive from global warming over the next 25 years. A basic assumption for this conclusion is however that the Barents Sea ecosystem essentially preserves its structure and composition of today. Possible, unpredictable significant shifts in the ecosystem structure are not considered.

Interactions between higher and lower visual areas improve shape selectivity of higher level neurons—Explaining crowding phenomena

Recent theories of visual perception propose that feedforward cortical processing enables rapid and automatic object categorizations, yet incorporates a limited amount of detail. Subsequent feedback processing highlights high-resolution representations in early visual areas and provides spatial detail. To verify this hypothesis, we separate the contributions of feedforward and feedback signals to the selectivity of cortical neurons in a neural network simulation that is modeled after the hierarchical feedforward–feedback organization of cortical areas. We find that in such a network the responses of high-level neurons can initially distinguish between low-resolution aspects of objects but are ‘blind’ to differences in detail. After several feedback–feedforward cycles of processing, however, they can also distinguish between objects that differ in detail. Moreover, we find that our model captures recent paradoxical results of crowding phenomena, showing that spatial detail that is lost in visual crowding is nevertheless able to evoke specific adaptation effects. Our results thus provide an existence proof of the feasibility of novel theoretical models and provide a mechanism to explain various psychophysical and physiological results.

EVALUATION OF SKIN AGEING THROUGH WRINKLE ANALYSIS IN CAPACITIVE IMAGES

Quantitative evaluation of the changes in skin topographic structures are of great importance in the dermocosmetic field to assess subjects response to medical or cosmetic treatments. Although many devices and methods are known to measure these changes, they are not suitable for a routine approach and most of them are invasive. Moreover, it has always been difficult to give a measure of the skin health status as well as of the human aging process by simply analyzing the skin surface appearance. This work describes how a portable capacitive device could be utilized to achieve measurements of skin ageing in vivo and routinely. The capacitive images give a high resolution representation of the skin micro-relief, both in terms of skin surface tissue and wrinkles. In a previous work we dealt with the former; here we have addressed the latter. The algorithm we have developed allowed us to extract two original features from wrinkles: the first is based on photometric properties while the second has been achieved through the multiresolution analysis of the wavelet transform. Accurate experiments accomplished on 87 subjects show how the features we conceived are related to skin ageing.

The Application of Microarray Technology to Neuropathology: Cutting Edge Tool With Clinical Diagnostics Potential or Too Much Information?

Microarray technology is a tremendously powerful method for simultaneously monitoring the expression of thousands of species of nucleic acids, usually cellular mRNA, producing a high-resolution representation of the genes encoded or expressed in a cell. As such, microarray technology has great potential for impacting research and clinical approaches to treatment. However, this complex technology has been challenging to apply as a result of difficulties discerning biologic variation from technologic issues, therefore slowing the application of the technology to human diagnostics. Nevertheless, significant advances in microarray technology, improvements that avoid potential pitfalls, and a wider spectrum of application are making this technology easier to apply. Indeed, microarray technology has provided valuable insights into mechanisms involving gene regulation and expression in Alzheimer disease, and it remains a powerful tool to identify biomarkers for disease diagnosis. Ultimately, the most robust markers will enable the application of more specific treatments particular to disease stages or subcategories. Currently, no widely applicable molecular test is available to identify those at risk for developing Alzheimer disease or those who have early markers of pathology but show discernible cognitive impairment. The progression of this technology will lead to earlier detection of the disease through enhanced understanding of disease onset and progression.

Single-Frame Image Super-resolution through Contourlet Learning

We propose a learning-based, single-image super-resolution reconstruction technique using the contourlet transform, which is capable of capturing the smoothness along contoursmaking use of directional decompositions. The contourlet coefficients at finer scales of the unknown high-resolution image are learned locally from a set of high-resolution training images, the inverse contourlet transform of which recovers the super-resolved image. In effect, we learn the high-resolution representation of an oriented edge primitive from the training data. Our experiments show that the proposed approach outperforms standard interpolation techniques as well as a standard (Cartesian) wavelet-based learning both visually and in terms of the PSNR values, especially for images with arbitrarily oriented edges.

Fusion and super-resolution in multispectral data using neural networks for improved RGB representation

A new method, which aims at creating a high-quality RGB fused representation with increased spatial resolution from multispectral data, is proposed. For this purpose, a feed-forward neural network is employed, and a successive training procedure is applied in order to incorporate into the network structure knowledge about recovering folded frequencies and, therefore, obtain output colour images with fine resolution. The successive training of the network takes place in three consecutive steps. The training data used in each step are of higher resolution than those used in the previous step. A decimation procedure, based on a generally acceptable super-resolution model, is employed to create from the original multispectral bands all the required down-sampled series of input training data vectors. The output (target) training vectors are obtained by fusing the multispectral images. Fusion is carried out by means of segmented principal component transformation in order to adjust the type of spectral characteristics transferred to the final RGB image components. MERIS multispectral data from the ENVISAT satellite are employed to demonstrate the performance of the proposed method. The method can be applied to obtaining high-resolution multispectral data representation, suitable for monitoring and inspection by humans. Furthermore, it can be applied without substantial modification to other types of multi-modal data, such as medical images, for yielding improved RGB representation to specialists.

重畳型マルチプロジェクションシステムによる流れの可視化の高解像度表示について

重畳型マルチプロジェクションシステムによる流れの可視化の高解像度表示について