scholarly journals Formulation of a multi-scale watershed ecological model using a statistical approach

2020 ◽  
Author(s):  
Bruce Pruitt ◽  
K. Killgore ◽  
William Slack ◽  
Ramune Matuliauskaite
Keyword(s):  
Aquatic Ecosystems ◽  
Statistical Approach ◽  
Ecological Model ◽  
Multiple Scales ◽  
Cost Benefit ◽  
Condition Index ◽  
Multi Scale ◽  
Stream Condition ◽  
Ecosystem Studies ◽  
General Statistical

The purpose of this special report is to provide a statistical stepwise process for formulation of ecological models for application at multiple scales using a stream condition index (SCI). Given the global variability of aquatic ecosystems, this guidance is for broad application and may require modification to suit specific watersheds or stream reaches. However, the general statistical treatise provided herein applies across physiographies and at multiple scales. The Duck River Watershed Assessment in Tennessee was used, in part, to develop and test this multiscale, statistical approach; thus, it is considered a case example and referenced throughout this report. The findings of this study can be utilized to (1) prioritize water-sheds for restoration, enhancement, and conservation; (2) plan and conduct site-specific, intensive ecosystem studies; and (3) assess ecosystem outcomes (that is, ecological lift) applicable to future with and without restoration actions including alternative, feasibility, and cost-benefit analyses and adaptive management.

scholarly journals DCPNet: A Densely Connected Pyramid Network for Monocular Depth Estimation

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6780
Author(s):  
Zhitong Lai ◽  
Rui Tian ◽  
Zhiguo Wu ◽  
Nannan Ding ◽  
Linjian Sun ◽  
...  
Keyword(s):  
Multiple Scales ◽  
Feature Fusion ◽  
State Of The Art ◽  
Depth Estimation ◽  
Multi Scale ◽  
Pyramid Structure ◽  
Benchmark Datasets ◽  
The Common ◽  
Monocular Depth ◽  
Multiple Stages

Pyramid architecture is a useful strategy to fuse multi-scale features in deep monocular depth estimation approaches. However, most pyramid networks fuse features only within the adjacent stages in a pyramid structure. To take full advantage of the pyramid structure, inspired by the success of DenseNet, this paper presents DCPNet, a densely connected pyramid network that fuses multi-scale features from multiple stages of the pyramid structure. DCPNet not only performs feature fusion between the adjacent stages, but also non-adjacent stages. To fuse these features, we design a simple and effective dense connection module (DCM). In addition, we offer a new consideration of the common upscale operation in our approach. We believe DCPNet offers a more efficient way to fuse features from multiple scales in a pyramid-like network. We perform extensive experiments using both outdoor and indoor benchmark datasets (i.e., the KITTI and the NYU Depth V2 datasets) and DCPNet achieves the state-of-the-art results.

A statistical approach to sparse multi-scale phase-based stereo

2007 ◽  
Vol 40 (9) ◽  
pp. 2504-2520 ◽  
Author(s):  
I. Ulusoy ◽  
E.R. Hancock
Keyword(s):  
Statistical Approach ◽  
Multi Scale

Electron Probe Evaluation of Heterogeneity in the Certification of NIST Standard Reference Materials for Microanalysis

1998 ◽  
Vol 4 (S2) ◽  
pp. 242-243
Author(s):  
R. B. Marinenko ◽  
S. Leigh
Keyword(s):  
Electron Beam ◽  
Reference Material ◽  
Electron Probe ◽  
Statistical Approach ◽  
Standard Reference Materials ◽  
Standard Material ◽  
Certification Process ◽  
The Past ◽  
Physical Form ◽  
General Statistical

The certification process for a microanalysis standard can be quite lengthy. In addition to certifying the reference material for composition, the extent of heterogeneity between specimens and within specimens must be determined. NIST (or formerly, NBS) 260 Special Publications have been used in the past to describe procedures used for individual SRM certifications. Some of these publications describe in detail how the extent of microheterogeneity (or, microhomogeneity) as well as the specimen to specimen heterogeneity can be determined. The intent here is to describe a general statistical approach used at NIST to determine and report the extent of heterogeneity. This approach can be readily used by other laboratories either for certification or for evaluation of standards.When evaluating a material for use as a standard, there are several physical characteristics which must be satisfied. The material must be robust under the electron beam at the voltages and currents to which it will be subjected in its proposed use. When mounted and polished, it should be stable on exposure to the atmosphere. However, if the material will not be mounted and polished in use, it should be in the same physical form as the final certified standard material.

A Novel Multi-Scale Feature Fusion Method for Region Proposal Network in Fast Object Detection

2020 ◽  
Vol 16 (3) ◽  
pp. 132-145
Author(s):  
Gang Liu ◽  
Chuyi Wang
Keyword(s):  
Object Detection ◽  
Multiple Scales ◽  
Feature Fusion ◽  
Uniform Space ◽  
Fusion Method ◽  
Well Performance ◽  
Feature Maps ◽  
Neural Network Models ◽  
Scale Feature ◽  
Multi Scale

Neural network models have been widely used in the field of object detecting. The region proposal methods are widely used in the current object detection networks and have achieved well performance. The common region proposal methods hunt the objects by generating thousands of the candidate boxes. Compared to other region proposal methods, the region proposal network (RPN) method improves the accuracy and detection speed with several hundred candidate boxes. However, since the feature maps contains insufficient information, the ability of RPN to detect and locate small-sized objects is poor. A novel multi-scale feature fusion method for region proposal network to solve the above problems is proposed in this article. The proposed method is called multi-scale region proposal network (MS-RPN) which can generate suitable feature maps for the region proposal network. In MS-RPN, the selected feature maps at multiple scales are fine turned respectively and compressed into a uniform space. The generated fusion feature maps are called refined fusion features (RFFs). RFFs incorporate abundant detail information and context information. And RFFs are sent to RPN to generate better region proposals. The proposed approach is evaluated on PASCAL VOC 2007 and MS COCO benchmark tasks. MS-RPN obtains significant improvements over the comparable state-of-the-art detection models.

A multi-scale statistical approach to assess the effects of connectivity of road and stream networks on geomorphic channel condition

2014 ◽  
pp. n/a-n/a ◽  
Author(s):  
Alison M. Pechenick ◽  
Donna M. Rizzo ◽  
Leslie A. Morrissey ◽  
Kerrie M. Garvey ◽  
Kristen L. Underwood ◽  
...  
Keyword(s):  
Statistical Approach ◽  
Stream Networks ◽  
Channel Condition ◽  
Multi Scale

A statistical approach to multi-scale edge detection

2003 ◽  
Vol 21 (1) ◽  
pp. 37-48 ◽  
Author(s):  
Scott Konishi ◽  
Alan Yuille ◽  
James Coughlan
Keyword(s):  
Edge Detection ◽  
Statistical Approach ◽  
Multi Scale

scholarly journals Quantifying the multi-scale performance of network inference algorithms

2014 ◽  
Vol 13 (5) ◽  
Author(s):  
Chris J. Oates ◽  
Richard Amos ◽  
Simon E.F. Spencer
Keyword(s):  
Graphical Models ◽  
Network Inference ◽  
Multiple Scales ◽  
Biological Systems ◽  
Higher Order ◽  
Wisdom Of Crowds ◽  
Multi Scale ◽  
Reverse Engineer ◽  
Multiple Length Scales ◽  
Inference Algorithms

AbstractGraphical models are widely used to study complex multivariate biological systems. Network inference algorithms aim to reverse-engineer such models from noisy experimental data. It is common to assess such algorithms using techniques from classifier analysis. These metrics, based on ability to correctly infer individual edges, possess a number of appealing features including invariance to rank-preserving transformation. However, regulation in biological systems occurs on multiple scales and existing metrics do not take into account the correctness of higher-order network structure. In this paper novel performance scores are presented that share the appealing properties of existing scores, whilst capturing ability to uncover regulation on multiple scales. Theoretical results confirm that performance of a network inference algorithm depends crucially on the scale at which inferences are to be made; in particular strong local performance does not guarantee accurate reconstruction of higher-order topology. Applying these scores to a large corpus of data from the DREAM5 challenge, we undertake a data-driven assessment of estimator performance. We find that the “wisdom of crowds” network, that demonstrated superior local performance in the DREAM5 challenge, is also among the best performing methodologies for inference of regulation on multiple length scales.

scholarly journals MSICA: multi-scale signal decomposition based on independent component analysis with application to denoising and reliable multi-channel

2020 ◽  
Vol 1 (1) ◽  
pp. 25-35
Author(s):  
Abolfazl Hajisami ◽  
Dario Pompili
Keyword(s):  
Independent Component Analysis ◽  
Multiple Scales ◽  
Digital Signal ◽  
Component Analysis ◽  
Scale Space ◽  
Independent Component ◽  
Signal Denoising ◽  
Multi Scale ◽  
Information Preservation ◽  
Bank Structure

Multi-scale decomposition is a signal description method in which the signal is decomposed into multiple scales, which has been shown to be a valuable method in information preservation. Much focus on multi-scale decomposition has been based on scale-space theory and wavelet transform. In this article, a new powerful method to perform multi-scale decomposition exploiting Independent Component Analysis (ICA), called MSICA, is proposed to translate an original signal into multiple statistically independent scales. It is proven that extracting the independent components of the even and odd samples of a digital signal results in the decomposition of the same into approximation and detail. It is also proven that the whitening procedure in ICA is equivalent to a filter bank structure. Performance results of MSICA in signal denoising are presented; also, the statistical independency of the approximation and detail is exploited to propose a novel signal-denoising strategy for multi-channel noisy transmissions aimed at improving communication reliability by exploiting channel diversity.

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