Statistical distribution of depth-integrated local horizontal momentum for second-order random ocean waves in finite water depth

Based on the second-order random wave solutions of water wave equations in finite water depth, statistical distributions of the depth- integrated local horizontal momentum components are derived by use of the characteristic function expansion method. The parameters involved in the distributions can be all determined by the water depth and the wave-number spectrum of ocean waves. As an illustrative example, a fully developed wind-generated sea is considered and the parameters are calculated for typical wind speeds and water depths by means of the Donelan and Pierson spectrum. The effects of nonlinearity and water depth on the distributions are also investigated.

Statistical distribution of wave-surface elevation for second-order random directional ocean waves in finite water depth

Based on the second-order random wave solutions of water wave equations in finite water depth, a statistical distribution of the wave-surface elevation is derived by using the characteristic function expansion method. It is found that the distribution, after normalization of the wave-surface elevation, depends only on two parameters. One parameter describes the small mean bias of the surface produced by the second-order wave-wave interactions. Another one is approximately proportional to the skewness of the distribution. Both of these two parameters can be determined by the water depth and the wave-number spectrum of ocean waves. As an illustrative example, we consider a fully developed wind-generated sea and the parameters are calculated for various wind speeds and water depths by using Donelan and Pierson spectrum. It is also found that, for deep water, the dimensionless distribution reduces to the third-order Gram-Charlier series obtained by Longuet-Higgins [J. Fluid Mech. 17 (1963) 459]. The newly proposed distribution is compared with the data of Bitner [Appl. Ocean Res. 2 (1980) 63], Gaussian distribution and the fourth-order Gram-Charlier series, and found our distribution gives a more reasonable fit to the data. (C) 2002 Elsevier Science B.V. All rights reserved.

Depth inversion in coastal water based on SAR image of waves

Wave-number spectrum technique is proposed to retrieve coastal water depths by means of Synthetic Aperture Radar (SAR) image of waves. Based on the general dispersion relation of ocean waves, the wavelength changes of a surface wave over varying water depths can be derived from SAR. Approaching the analysis of SAR images of waves and using the general dispersion relation of ocean waves, this indirect technique of remote sensing bathymetry has been applied to a coastal region of Xiapu in Fujian Province, China. Results show that this technique is suitable for the coastal waters especially for the near-shore regions with variable water depths.

Role of snow depth in spring of Tibetan Plateau in onset of South China Sea summer monsoon

The role of snow depth of Tibetan Plateau in the onset of South China Sea summer monsoon and the influence of ENSO on snow depth of Tibetan Plateau are investigated with use of data from ECMWF reanalysis and NCEP/NCAR reanalysis. The results are as follows: (1) The snow depth data from ECMWF reanalysis are tested and reliable, and can be used to study the influence of snow depth of Tibetan Plateau on the onset of South China Sea summer monsoon; (2) Anomaly of snow depth of Tibetan Plateau causes anomaly in air temperature and its contrast between the Indian Ocean and the continent resulting in easterly wind anomaly over 500 hPa and hence as well as in the atmospheric circulation in the lower layer. For the year of negative anomaly of snow depth a westerly wind anomaly with a cyclone pair takes place, while for positive anomaly of snow depth an easterly anomaly occurs with an anticyclone pair; (3) While positive anomaly of SST occurs in the eastern Pacific Ocean, positive anomaly of air pressure also takes place over the eastern Indian Ocean and the South China Sea, causing stronger meridional pressure gradient between the ocean and continent and then westerly wind anomaly. At the same time, the atmospheric pressure increases in the northern Tibetan Plateau, northerly wind gets stronger, and subtropical front strengthens. All of these are favorable for snowfall over Tibetan Plateau.

A new method for the estimation of oceanic mixed-layer depth using shipboard X-band radar images

Shipboard X-band radar images acquired on 24 June 2009 are used to study nonlinear internal wave characteristics in the northeastern South China Sea. The studied images show three nonlinear internal waves in a packet. A method based on the Radon Transform technique is introduced to calculate internal wave parameters such as the direction of propagation and internal wave velocity from backscatter images. Assuming that the ocean is a two-layer finite depth system, we can derive the mixed-layer depth by applying the internal wave velocity to the mixed-layer depth formula. Results show reasonably good agreement with in-situ thermistor chain and conductivity-temperature-depth data sets.

Factors influencing the climatological mixed layer depth in the South China Sea: numerical simulations

The mixed layer depth (MLD) in the upper ocean is an important physical parameter for describing the upper ocean mixed layer. We analyzed several major factors influencing the climatological mixed layer depth (CMLD), and established a numerical simulation in the South China Sea (SCS) using the Regional Ocean Model System (ROMS) with a high-resolution (1/12A degrees x1/12A degrees) grid nesting method and 50 vertical layers. Several ideal numerical experiments were tested by modifying the existing sea surface boundary conditions. Especially, we analyzed the sensitivity of the results simulated for the CMLD with factors of sea surface wind stress (SSWS), sea surface net heat flux (SSNHF), and the difference between evaporation and precipitation (DEP). The result shows that of the three factors that change the depth of the CMLD, SSWS is in the first place, when ignoring the impact of SSWS, CMLD will change by 26% on average, and its effect is always to deepen the CMLD; the next comes SSNHF (13%) for deepening the CMLD in October to January and shallowing the CMLD in February to September; and the DEP comes in the third (only 2%). Moreover, we analyzed the temporal and spatial characteristics of CMLD and compared the simulation result with the ARGO observational data. The results indicate that ROMS is applicable for studying CMLD in the SCS area.

Disparity Gradients and Depth Scaling

The binocular perception of shape and depth relations between objects can change considerably if the viewing direction is changed only by a small angle. We explored this effect psychophysically and found a strong depth reduction effect for large disparity gradients. The effect is found to be strongest for horizontally oriented stimuli, and stronger for line stimuli than for points. This depth scaling effect is discussed in a computational framework of stereo based on a Baysian approach which allows integration of information from different types of matching primitives weighted according to their robustness.

Spatial Reference Frames for Object Recognition: Tuning for Rotations in Depth

The inferior temporal cortex (IT) of monkeys is thought to play an essential role in visual object recognition. Inferotemporal neurons are known to respond to complex visual stimuli, including patterns like faces, hands, or other body parts. What is the role of such neurons in object recognition? The present study examines this question in combined psychophysical and electrophysiological experiments, in which monkeys learned to classify and recognize novel visual 3D objects. A population of neurons in IT were found to respond selectively to such objects that the monkeys had recently learned to recognize. A large majority of these cells discharged maximally for one view of the object, while their response fell off gradually as the object was rotated away from the neuron"s preferred view. Most neurons exhibited orientation-dependent responses also during view-plane rotations. Some neurons were found tuned around two views of the same object, while a very small number of cells responded in a view- invariant manner. For five different objects that were extensively used during the training of the animals, and for which behavioral performance became view-independent, multiple cells were found that were tuned around different views of the same object. No selective responses were ever encountered for views that the animal systematically failed to recognize. The results of our experiments suggest that neurons in this area can develop a complex receptive field organization as a consequence of extensive training in the discrimination and recognition of objects. Simple geometric features did not appear to account for the neurons" selective responses. These findings support the idea that a population of neurons -- each tuned to a different object aspect, and each showing a certain degree of invariance to image transformations -- may, as an assembly, encode complex 3D objects. In such a system, several neurons may be active for any given vantage point, with a single unit acting like a blurred template for a limited neighborhood of a single view.

The Coupled Depth/Slope Approach to Surface Reconstruction

Reconstructing a surface from sparse sensory data is a well known problem in computer vision. Early vision modules typically supply sparse depth, orientation and discontinuity information. The surface reconstruction module incorporates these sparse and possibly conflicting measurements of a surface into a consistent, dense depth map. The coupled depth/slope model developed here provides a novel computational solution to the surface reconstruction problem. This method explicitly computes dense slope representation as well as dense depth representations. This marked change from previous surface reconstruction algorithms allows a natural integration of orientation constraints into the surface description, a feature not easily incorporated into earlier algorithms. In addition, the coupled depth/ slope model generalizes to allow for varying amounts of smoothness at different locations on the surface. This computational model helps conceptualize the problem and leads to two possible implementations- analog and digital. The model can be implemented as an electrical or biological analog network since the only computations required at each locally connected node are averages, additions and subtractions. A parallel digital algorithm can be derived by using finite difference approximations. The resulting system of coupled equations can be solved iteratively on a mesh-pf-processors computer, such as the Connection Machine. Furthermore, concurrent multi-grid methods are designed to speed the convergence of this digital algorithm.

Bounds on the Power of Constant-Depth Quantum Circuits

We show that if a language is recognized within certain error bounds by constant-depth quantum circuits over a finite family of gates, then it is computable in (classical) polynomial time. In particular, our results imply EQNC^0 ⊆ P, where EQNC^0 is the constant-depth analog of the class EQP. On the other hand, we adapt and extend ideas of Terhal and DiVincenzo [?] to show that, for any family

Small Depth Quantum Circuits

Small depth quantum circuits have proved to be unexpectedly powerful in comparison to their classical counterparts. We survey some of the recent work on this and present some open problems.

Boundary, Brightness, and Depth Interactions During Preattentive Representation and Attentive Recognition of Figure and Ground

This article applies a recent theory of 3-D biological vision, called FACADE Theory, to explain several percepts which Kanizsa pioneered. These include 3-D pop-out of an occluding form in front of an occluded form, leading to completion and recognition of the occluded form; 3-D transparent and opaque percepts of Kanizsa squares, with and without Varin wedges; and interactions between percepts of illusory contours, brightness, and depth in response to 2-D Kanizsa images. These explanations clarify how a partially occluded object representation can be completed for purposes of object recognition, without the completed part of the representation necessarily being seen. The theory traces these percepts to neural mechanisms that compensate for measurement uncertainty and complementarity at individual cortical processing stages by using parallel and hierarchical interactions among several cortical processing stages. These interactions are modelled by a Boundary Contour System (BCS) that generates emergent boundary segmentations and a complementary Feature Contour System (FCS) that fills-in surface representations of brightness, color, and depth. The BCS and FCS interact reciprocally with an Object Recognition System (ORS) that binds BCS boundary and FCS surface representations into attentive object representations. The BCS models the parvocellular LGN→Interblob→Interstripe→V4 cortical processing stream, the FCS models the parvocellular LGN→Blob→Thin Stripe→V4 cortical processing stream, and the ORS models inferotemporal cortex.

From Stereograms to Surface: How the Brain Sees the World in Depth

When we look at a scene, how do we consciously see surfaces infused with lightness and color at the correct depths? Random Dot Stereograms (RDS) probe how binocular disparity between the two eyes can generate such conscious surface percepts. Dense RDS do so despite the fact that they include multiple false binocular matches. Sparse stereograms do so even across large contrast-free regions with no binocular matches. Stereograms that define occluding and occluded surfaces lead to surface percepts wherein partially occluded textured surfaces are completed behind occluding textured surfaces at a spatial scale much larger than that of the texture elements themselves. Earlier models suggest how the brain detects binocular disparity, but not how RDS generate conscious percepts of 3D surfaces. A neural model predicts how the layered circuits of visual cortex generate these 3D surface percepts using interactions between visual boundary and surface representations that obey complementary computational rules.

Measurements of Epidural Space Depth Using Preexisting CT Scans Correlate with Loss of Resistance Depth during Thoracic Epidural Catheter Placement.

Background. Thoracic epidural catheters provide the best quality postoperative pain relief for major abdominal and thoracic surgical procedures, but placement is one of the most challenging procedures in the repertoire of an anesthesiologist. Most patients presenting for a procedure that would benefit from a thoracic epidural catheter have already had high resolution imaging that may be useful to assist placement of a catheter. Methods. This retrospective study used data from 168 patients to examine the association and predictive power of epidural-skin distance (ESD) on computed tomography (CT) to determine loss of resistance depth acquired during epidural placement. Additionally, the ability of anesthesiologists to measure this distance was compared to a radiologist, who specializes in spine imaging. Results. There was a strong association between CT measurement and loss of resistance depth (P < 0.0001); the presence of morbid obesity (BMI > 35) changed this relationship (P = 0.007). The ability of anesthesiologists to make CT measurements was similar to a gold standard radiologist (all individual ICCs > 0.9). Conclusions. Overall, this study supports the examination of a recent CT scan to aid in the placement of a thoracic epidural catheter. Making use of these scans may lead to faster epidural placements, fewer accidental dural punctures, and better epidural blockade.