Quantified Derandomization: How to Find Water in the Ocean

Tell, Roei

doi:10.1561/0400000108

Cited by 2 publications

(2 citation statements)

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“…The changing surface flow field affects the change of its roughness by modulating the microscale waves at the sea surface, which in turn affects the ability of the sea surface to reflect energy, i.e. the strength of backward scattering, making the grey scale value of SAR images change, thus enabling the observation of ocean internal waves through SAR images [9][10].…”

Section: Principle Of Intra-oceanic Wave Imaging From Sar Imagesmentioning

confidence: 99%

“…In Equation (10), μ is the mean value of the image; T(i, j) is a random variable representing the value of the texture function at location (i, j) that depicts the elevation of the topography of the detection area, with T(i, j) ≥ 0 for any (i, j) and satisfying μT = E{T) = 1; F(i, J) represents the speckle noise process and, as with T(i, j), at F(i, J) ≥ 0 at any (i, j), μF = E{R}-1, and T(i, j) and F(i, J) are independent of each other.…”

Section: Noise Characteristicsmentioning

confidence: 99%

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Ocean Turbulence Observation Techniques and Data Processing Algorithms based on SAR Sequence Images

2023

J. Phys.: Conf. Ser.

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Waves in the ocean are a widespread phenomenon and their effective detection requires the use of high-resolution, large-area detection devices. Synthetic aperture radar (SAR), with its all-weather performance and high resolution, is often used for the detection and imaging of both land and ocean, as well as large-area, high-resolution SAR and interferometric SAR, which are all based on SAR enhancements. This has become an important area of research in marine sensors, and the use of multiple-input multiple-output (MIMO) reception modes for detection and imaging of large-area, high-resolution SAR data has become one of the research priorities for obtaining large-area, high-resolution SAR data. Oceanic internal waves generate internal mixing of seawater through unstable fragmentation, which is an important mechanism for maintaining the meridional overturning circulation within the ocean, and the strong currents and strong mixing processes generated by large amplitude internal waves carry enormous energy. The main objective of this paper is to investigate ocean turbulence observation techniques and data processing algorithms based on SAR sequence images. The research is of great significance for the development of marine science, and the technical areas studied are of great research significance for the development of the marine economy, breakthroughs in marine research technology, and the development of the marine industry.

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Section: Principle Of Intra-oceanic Wave Imaging From Sar Imagesmentioning

confidence: 99%

Section: Noise Characteristicsmentioning

confidence: 99%

Ocean Turbulence Observation Techniques and Data Processing Algorithms based on SAR Sequence Images

2023

J. Phys.: Conf. Ser.

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On the Complexity of Avoiding Heavy Elements

Lu,

Oliveira,

Ren

et al. 2024

2024 IEEE 65th Annual Symposium on Foundations of Computer Science (FOCS)

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We introduce and study the following natural total search problem, which we call the heavy element avoidance (Heavy Avoid) problem: for a distribution on N bits specified by a Boolean circuit sampling it, and for some parameter δ(N ) ≥ 1/poly(N ) fixed in advance, output an N -bit string that has probability less than δ(N ). We show that the complexity of Heavy Avoid is closely tied to frontier open questions in complexity theory about uniform randomized lower bounds and derandomization. Among other results, we show:1. For a wide range of circuit classes C, including ACC 0 , TC 0 , NC 1 and general Boolean circuits, EXP does not have uniform randomized C-circuits if and only if Heavy Avoid for uniform implicit C-samplers has efficient deterministic algorithms infinitely often. This gives the first algorithmic characterization of lower bounds for EXP against uniform randomized low-depth circuits. We show similar algorithmic characterizations for lower bounds in PSPACE, NP and EXP NP .2. Unconditionally, there are polynomial-time pseudodeterministic algorithms that work infinitely often for several variants of Heavy Avoid, such as for uniform samplers of small randomness complexity. In contrast, the existence of a similar algorithm that solves Heavy Avoid for arbitrary polynomial-time samplers would solve a long-standing problem about hierarchies for probabilistic time.3. If there is a time and depth efficient deterministic algorithm for Heavy Avoid, then BPP = P. Without the depth-efficiency requirement in the assumption, we still obtain a non-trivial form of infinitely-often deterministic simulation of randomized algorithms. These results are shown using non-black-box reductions, and we argue that the use of non-black-box reductions is essential here.

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Quantified Derandomization: How to Find Water in the Ocean

Cited by 2 publications

References 0 publications

Ocean Turbulence Observation Techniques and Data Processing Algorithms based on SAR Sequence Images

Ocean Turbulence Observation Techniques and Data Processing Algorithms based on SAR Sequence Images

On the Complexity of Avoiding Heavy Elements

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