Academic literature on the topic 'Optical diversity metric'
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Journal articles on the topic "Optical diversity metric"
1
Imran, Hafiz Ali, Damiano Gianelle, Michele Scotton, Duccio Rocchini, Michele Dalponte, Stefano Macolino, Karolina Sakowska, Cristina Pornaro, and Loris Vescovo. "Potential and Limitations of Grasslands α-Diversity Prediction Using Fine-Scale Hyperspectral Imagery." Remote Sensing 13, no. 14 (July 6, 2021): 2649. http://dx.doi.org/10.3390/rs13142649.
Full textAbstract:
Plant biodiversity is an important feature of grassland ecosystems, as it is related to the provision of many ecosystem services crucial for the human economy and well-being. Given the importance of grasslands, research has been carried out in recent years on the potential to monitor them with novel remote sensing techniques. In this study, the optical diversity (also called spectral diversity) approach was adopted to check the potential of using high-resolution hyperspectral images to estimate α-diversity in grassland ecosystems. In 2018 and 2019, grassland species composition was surveyed and canopy hyperspectral data were acquired at two grassland sites: Monte Bondone (IT-MBo; species-rich semi-natural grasslands) and an experimental farm of the University of Padova, Legnaro, Padua, Italy (IT-PD; artificially established grassland plots with a species-poor mixture). The relationship between biodiversity (species richness, Shannon’s, species evenness, and Simpson’s indices) and optical diversity metrics (coefficient of variation-CV and standard deviation-SD) was not consistent across the investigated grassland plant communities. Species richness could be estimated by optical diversity metrics with an R = 0.87 at the IT-PD species-poor site. In the more complex and species-rich grasslands at IT-MBo, the estimation of biodiversity indices was more difficult and the optical diversity metrics failed to estimate biodiversity as accurately as in IT-PD probably due to the higher number of species and the strong canopy spatial heterogeneity. Therefore, the results of the study confirmed the ability of spectral proxies to detect grassland α-diversity in man-made grassland ecosystems but highlighted the limitations of the spectral diversity approach to estimate biodiversity when natural grasslands are observed. Nevertheless, at IT-MBo, the optical diversity metric SD calculated from post-processed hyperspectral images and transformed spectra showed, in the red part of the spectrum, a significant correlation (up to R = 0.56, p = 0.004) with biodiversity indices. Spatial resampling highlighted that for the IT-PD sward the optimal optical pixel size was 1 cm, while for the IT-MBo natural grassland it was 1 mm. The random pixel extraction did not improve the performance of the optical diversity metrics at both study sites. Further research is needed to fully understand the links between α-diversity and spectral and biochemical heterogeneity in complex heterogeneous ecosystems, and to assess whether the optical diversity approach can be adopted at the spatial scale to detect β-diversity. Such insights will provide more robust information on the mechanisms linking grassland diversity and optical heterogeneity.
2
Riemer, N., and M. West. "Quantifying aerosol mixing state with entropy and diversity measures." Atmospheric Chemistry and Physics Discussions 13, no. 6 (June 12, 2013): 15615–62. http://dx.doi.org/10.5194/acpd-13-15615-2013.
Full textAbstract:
Abstract. This paper presents the first quantitative metric for aerosol population mixing state, defined as the distribution of per-particle chemical species composition. This new metric, the mixing state index χ, is an affine ratio of the average per-particle species diversity Dα and the bulk population species diversity Dγ, both of which are based on information-theoretic entropy measures. The mixing state index χ enables the first rigorous definition of the spectrum of mixing states from so-called external mixture to internal mixture, which is significant for aerosol climate impacts, including aerosol optical properties and cloud condensation nuclei activity. We illustrate the usefulness of this new mixing state framework with model results from the stochastic particle-resolved model PartMC-MOSAIC. These results demonstrate how the mixing state metrics evolve with time for several archetypal cases, each of which isolates a specific process such as coagulation, emission, or condensation. Further, we present an analysis of the mixing state evolution for a complex urban plume case, for which these processes occur simultaneously. We additionally derive theoretical properties of the mixing state index and present a family of generalized mixing state indexes that vary in the importance assigned to low-mass-fraction species.
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Riemer, N., and M. West. "Quantifying aerosol mixing state with entropy and diversity measures." Atmospheric Chemistry and Physics 13, no. 22 (November 25, 2013): 11423–39. http://dx.doi.org/10.5194/acp-13-11423-2013.
Full textAbstract:
Abstract. This paper presents the first quantitative metric for aerosol population mixing state, defined as the distribution of per-particle chemical species composition. This new metric, the mixing state index χ, is an affine ratio of the average per-particle species diversity Dα and the bulk population species diversity Dγ, both of which are based on information-theoretic entropy measures. The mixing state index χ enables the first rigorous definition of the spectrum of mixing states from so-called external mixture to internal mixture, which is significant for aerosol climate impacts, including aerosol optical properties and cloud condensation nuclei activity. We illustrate the usefulness of this new mixing state framework with model results from the stochastic particle-resolved model PartMC-MOSAIC. These results demonstrate how the mixing state metrics evolve with time for several archetypal cases, each of which isolates a specific process such as coagulation, emission, or condensation. Further, we present an analysis of the mixing state evolution for a complex urban plume case, for which these processes occur simultaneously. We additionally derive theoretical properties of the mixing state index and present a family of generalized mixing state indexes that vary in the importance assigned to low-mass-fraction species.
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Varotsos, George K., Hector E. Nistazakis, Konstantinos Aidinis, Fadi Jaber, Mohd Nasor, and Kanhira Kadavath Mujeeb Rahman. "Error Performance Estimation of Modulated Retroreflective Transdermal Optical Wireless Links with Diversity under Generalized Pointing Errors." Telecom 2, no. 2 (April 1, 2021): 167–80. http://dx.doi.org/10.3390/telecom2020011.
Full textAbstract:
Recent developments in both optical wireless communication (OWC) systems and implanted medical devices (IMDs) have introduced transdermal optical wireless (TOW) technology as a viable candidate for extremely high-speed in-body to out-of-body wireless data transmissions, which are growing in demand for many vital biomedical applications, including telemetry with medical implants, health monitoring, neural recording and prostheses. Nevertheless, this emerging communication modality is primarily hindered by skin-induced attenuation of the propagating signal bit carrier along with its stochastic misalignment-induced fading. Thus, by considering a typical modulated retroreflective (MRR) TOW system with spatial diversity and optimal combining (OC) for signal reception in this work, we focus, for the first time in the MRR TOW literature, on the stochastic nature of generalized pointing errors with non-zero boresight (NZB). Specifically, under these circumstances, novel analytical mathematical expressions were derived for the total average bit error rate (BER) of various system configurations. Their results revealed significant outage performance enhancements when spatial diversity was utilized. Moreover, taking into consideration the total transdermal pathloss along with the effects of stochastic NZB pointing errors, the critical average signal-to-noise ratio (SNR) metric was evaluated for typical power spectral-density values.
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Polley, H., Chenghai Yang, Brian Wilsey, and Philip Fay. "Spectral Heterogeneity Predicts Local-Scale Gamma and Beta Diversity of Mesic Grasslands." Remote Sensing 11, no. 4 (February 23, 2019): 458. http://dx.doi.org/10.3390/rs11040458.
Full textAbstract:
Plant species diversity is an important metric of ecosystem functioning, but field assessments of diversity are constrained in number and spatial extent by labor and other expenses. We tested the utility of using spatial heterogeneity in the remotely-sensed reflectance spectrum of grassland canopies to model both spatial turnover in species composition and abundances (β diversity) and species diversity at aggregate spatial scales (γ diversity). Shannon indices of γ and β diversity were calculated from field measurements of the number and relative abundances of plant species at each of two spatial grains (0.45 m2 and 35.2 m2) in mesic grasslands in central Texas, USA. Spectral signatures of reflected radiation at each grain were measured from ground-level or an unmanned aerial vehicle (UAV). Partial least squares regression (PLSR) models explained 59–85% of variance in γ diversity and 68–79% of variance in β diversity using spatial heterogeneity in canopy optical properties. Variation in both γ and β diversity were associated most strongly with heterogeneity in reflectance in blue (350–370 nm), red (660–770 nm), and near infrared (810–1050 nm) wavebands. Modeled diversity was more sensitive by a factor of three to a given level of spectral heterogeneity when derived from data collected at the small than larger spatial grain. As estimated from calibrated PLSR models, β diversity was greater, but γ diversity was smaller for restored grassland on a lowland clay than upland silty clay soil. Both γ and β diversity of grassland can be modeled by using spatial heterogeneity in vegetation optical properties provided that the grain of reflectance measurements is conserved.
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He, Xin, and Frank Y. Li. "Metric-Based Cooperative Routing in Multihop Ad Hoc Networks." Journal of Computer Networks and Communications 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/893867.
Full textAbstract:
Cooperative communication fully leverages the broadcast nature of wireless channels and exploits time/spatial diversity in a distributed manner, thereby achieving significant improvements in system capacity and transmission reliability. Cooperative diversity has been well studied from the physical layer perspective. Thereafter, cooperative MAC design has also drawn much attention recently. However, very little work has addressed cooperation at the routing layer. In this paper, we propose a simple yet efficient scheme for cooperative routing by using cooperative metrics including packet delivery ratio, throughput, and energy consumption efficiency. To make a routing decision based on our scheme, a node needs to first determine whether cooperation on each link is necessary or not, and if necessary, select the optimal cooperative scheme as well as the optimal relay. To do so, we calculate and compare cooperative routing metric values for each potential relay for each different cooperative MAC scheme (C-ARQ and CoopMAC in this study), and further choose the best value and compare it with the noncooperative link metric. Using the final optimal metric value instead of the traditional metric value at the routing layer, new optimal paths are set up in multihop ad hoc networks, by taking into account the cooperative benefits from the MAC layer. The network performance of the cooperative routing solution is demonstrated using a simple network topology.
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Huizing, Geert-Jan, Gabriel Peyré, and Laura Cantini. "Optimal transport improves cell–cell similarity inference in single-cell omics data." Bioinformatics 38, no. 8 (February 14, 2022): 2169–77. http://dx.doi.org/10.1093/bioinformatics/btac084.
Full textAbstract:
Abstract Motivation High-throughput single-cell molecular profiling is revolutionizing biology and medicine by unveiling the diversity of cell types and states contributing to development and disease. The identification and characterization of cellular heterogeneity are typically achieved through unsupervised clustering, which crucially relies on a similarity metric. Results We here propose the use of Optimal Transport (OT) as a cell–cell similarity metric for single-cell omics data. OT defines distances to compare high-dimensional data represented as probability distributions. To speed up computations and cope with the high dimensionality of single-cell data, we consider the entropic regularization of the classical OT distance. We then extensively benchmark OT against state-of-the-art metrics over 13 independent datasets, including simulated, scRNA-seq, scATAC-seq and single-cell DNA methylation data. First, we test the ability of the metrics to detect the similarity between cells belonging to the same groups (e.g. cell types, cell lines of origin). Then, we apply unsupervised clustering and test the quality of the resulting clusters. OT is found to improve cell–cell similarity inference and cell clustering in all simulated and real scRNA-seq data, as well as in scATAC-seq and single-cell DNA methylation data. Availability and implementation All our analyses are reproducible through the OT-scOmics Jupyter notebook available at https://github.com/ComputationalSystemsBiology/OT-scOmics. Supplementary information Supplementary data are available at Bioinformatics online.
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Zhang, Lan. "Hybrid QPSO-NNIA2 Algorithm for Multi-Objective Optimization Problem." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 08 (June 25, 2019): 1959025. http://dx.doi.org/10.1142/s0218001419590250.
Full textAbstract:
To improve the convergence and distribution of a multi-objective optimization algorithm, a hybrid multi-objective optimization algorithm, based on the quantum particle swarm optimization (QPSO) algorithm and adaptive ranks clone and neighbor list-based immune algorithm (NNIA2), is proposed. The contribution of this work is threefold. First, the vicinity distance was used instead of the crowding distance to update the archived optimal solutions in the QPSO algorithm. The archived optimal solutions are updated and maintained by using the dynamic vicinity distance based m-nearest neighbor list in the QPSO algorithm. Secondly, an adaptive dynamic threshold of unfitness function for constraint handling is introduced in the process. It is related to the evolution algebra and the feasible solution. Thirdly, a new metric called the distribution metric is proposed to depict the diversity and distribution of the Pareto optimal. In order to verify the validity and feasibility of the QPSO-NNIA2 algorithm, we compare it with the QPSO, NNIA2, NSGA-II, MOEA/D, and SPEA2 algorithms in solving unconstrained and constrained multi-objective problems. The simulation results show that the QPSO-NNIA2 algorithm achieves superior convergence and superior performance by three metrics compared to other algorithms.
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Nunes, Laura A., Samuel T. Turvey, and James Rosindell. "The price of conserving avian phylogenetic diversity: a global prioritization approach." Philosophical Transactions of the Royal Society B: Biological Sciences 370, no. 1662 (February 19, 2015): 20140004. http://dx.doi.org/10.1098/rstb.2014.0004.
Full textAbstract:
The combination of rapid biodiversity loss and limited funds available for conservation represents a major global concern. While there are many approaches for conservation prioritization, few are framed as financial optimization problems. We use recently published avian data to conduct a global analysis of the financial resources required to conserve different quantities of phylogenetic diversity (PD). We introduce a new prioritization metric (ADEPD) that After Downlisting a species gives the Expected Phylogenetic Diversity at some future time. Unlike other metrics, ADEPD considers the benefits to future PD associated with downlisting a species (e.g. moving from Endangered to Vulnerable in the International Union for Conservation of Nature Red List). Combining ADEPD scores with data on the financial cost of downlisting different species provides a cost–benefit prioritization approach for conservation. We find that under worst-case spending $3915 can save 1 year of PD, while under optimal spending $1 can preserve over 16.7 years of PD. We find that current conservation spending patterns are only expected to preserve one quarter of the PD that optimal spending could achieve with the same total budget. Maximizing PD is only one approach within the wider goal of biodiversity conservation, but our analysis highlights more generally the danger involved in uninformed spending of limited resources.
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Yu, Hai-Tao. "Optimize What You Evaluate With: Search Result Diversification Based on Metric Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (June 28, 2022): 10399–407. http://dx.doi.org/10.1609/aaai.v36i9.21282.
Full textAbstract:
Most of the existing methods for search result diversification (SRD) appeal to the greedy strategy for generating diversified results, which is formulated as a sequential process of selecting documents one-by-one, and the locally optimal choice is made at each round. Unfortunately, this strategy suffers from the following shortcomings: (1) Such a one-by-one selection process is rather time-consuming for both training and inference. (2) It works well on the premise that the preceding choices are optimal or close to the optimal solution. (3) The mismatch between the objective function used in training and the final evaluation measure used in testing has not been taken into account. We propose a novel framework through direct metric optimization for SRD (referred to as MO4SRD) based on the score-and-sort strategy. Specifically, we represent the diversity score of each document that determines its rank position based on a probability distribution. These distributions over scores naturally give rise to expectations over rank positions. Armed with this advantage, we can get the differentiable variants of the widely used diversity metrics. Thanks to this, we are able to directly optimize the evaluation measure used in testing. Moreover, we have devised a novel probabilistic neural scoring function. It jointly scores candidate documents by taking into account both cross-document interaction and permutation equivariance, which makes it possible to generate a diversified ranking via a simple sorting. The experimental results on benchmark collections show that the proposed method achieves significantly improved performance over the state-of-the-art results.
Dissertations / Theses on the topic "Optical diversity metric"
1
Imran, Hafiz Ali. "Remote Sensing Tools for Monitoring Grassland Plant Leaf Traits and Biodiversity." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/329592.
Full textAbstract:
Grasslands are one of the most important ecosystems on Earth, covering approximately one-third of the Earth’s surface. Grassland biodiversity is important as many services provided by such ecosystems are crucial for the human economy and well-being. Given the importance of grasslands ecosystems, in recent years research has been carried out on the potential to monitor them with novel remote sensing techniques. Improved detectors technology and novel sensors providing fine-scale hyperspectral imagery have been enabling new methods to monitor plant traits (PTs) and biodiversity.
The aims of the work were to study different approaches to monitor key grassland PTs such as Leaf Area Index (LAI) and biodiversity-related traits. The thesis consists of 3 parts: 1) Evaluating the performance of remote sensing methods to estimate LAI in grassland ecosystems, 2) Estimating plant biodiversity by using the optical diversity approach in grassland ecosystems, and 3) Investigating the relationship between PTs variability with alpha and beta diversity for the applicability of the optical diversity approach in a subalpine grassland of the Italian Alps
To evaluate the performance of remote sensing methods to estimate LAI, temporal and spatial observations of hyperspectral reflectance and LAI were analyzed at a grassland site in Monte Bondone, Italy (IT-MBo). In 2018, ground temporal observations of hyperspectral reflectance and LAI were carried out at a grassland site in Neustift, Austria (AT-NEU). To estimate biodiversity, in 2018 and 2019 a floristics survey was conducted to determine species composition and hyperspectral data were acquired at two grassland sites: IT-MBo and University of Padova’s Experimental Farm, Legnaro, Padua, Italy (IT-PD) respectively. Furthermore, in 2018, biochemistry analysis of the biomass samples collected from the grassland site IT-MBo was carried out to determine the foliar biochemical PTs variability.
The results of the thesis demonstrated that the grassland spectral response across different spectral regions (Visible: VIS, red-edge: RE, Near-infrared: NIR) showed to be both site-specific and scale-dependent. In the first part of the thesis, the performance of spectral vegetation indices (SVIs) based on visible, red-edge (RE), and NIR bands alongside SVIs solely based or NIR-shoulder bands (wavelengths 750 - 900 nm) was evaluated. A strong correlation (R2 > 0.8) was observed between grassland LAI and both RE and NIR-shoulder SVIs on a temporal basis, but not on a spatial basis. Using the PROSAIL Radiative Transfer Model (RTM), it was demonstrated that grassland structural heterogeneity strongly affects the ability to retrieve LAI, with high uncertainties due to structural and biochemical PTs co-variation.
In the second part, the applicability of the spectral variability hypothesis (SVH) was questioned and highlighted the challenges to use high-resolution hyperspectral images to estimate biodiversity in complex grassland ecosystems. It was reported that the relationship between biodiversity (Shannon, Richness, Simpson, and Evenness) and optical diversity metrics (Coefficient of variation (CV) and Standard deviation (SD)) is not consistent across plant communities. The results of the second part suggested that biodiversity in terms of species richness could be estimated by optical diversity metrics with an R2 = 0.4 at the IT-PD site where the grassland plots were artificially established and are showing a lower structure and complexity from the natural grassland plant communities. On the other hand, in the natural ecosystems at IT-MBo, it was more difficult to estimate biodiversity indices, probably due to structural and biochemical PTs co-variation. The effects of canopy non-vegetative elements (flowers and dead material), shadow pixels, and overexposed pixels on the relationship between optical diversity metrics and biodiversity indices were highlighted.
In the third part, we examined the relationship between PTs variability (at both local and community scales, measured by standard deviation and by the Euclidean distances of the biochemical and biophysical PTs respectively) and taxonomic diversity (both α-diversity and β-diversity, measured by Shannon’s index and by Jaccard dissimilarity index of the species, families, and functional groups percent cover respectively) in Monte Bondone, Trentino province, Italy. The results of the study showed that the PTs variability metrics at alpha scale were not correlated with α-diversity. However, the results at the community scale (β-diversity) showed that some of the investigated biochemical and biophysical PTs variations metrics were associated with β-diversity. The SVH approach was also tested to estimate β-diversity and we found that spectral diversity calculated by spectral angular mapper (SAM) showed to be a better proxy of biodiversity in the same ecosystem where the spectral diversity failed to estimate alpha diversity, this leading to the conclusion that the link between functional and species diversity may be an indicator of the applicability of optical sampling methods to estimate biodiversity.
The findings of the thesis highlighted that grassland structural heterogeneity strongly affects the ability to retrieve both LAI and biodiversity, with high uncertainties due to structural and biochemical PTs co-variation at complex grassland ecosystems. In this context, the uncertainties of satellite-based products (e.g., LAI) in monitoring grassland canopies characterized by either spatially or temporally varying structure need to be carefully taken into account. The results of the study highlighted that the poor performance of optical diversity proxies in estimating biodiversity in structurally heterogeneous grasslands might be due to the complex relationships between functional diversity and biodiversity, rather than the impossibility to detect functional diversity with spectral proxies.
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IMRAN, HAFIZ ALI. "Remote sensing tools for monitoring grassland plant leaf traits and biodiversity." Doctoral thesis, 2022. http://hdl.handle.net/10449/74719.
Full textAbstract:
Grasslands are one of the most important ecosystems on Earth, covering approximately onethird of the Earth’s surface. Grassland biodiversity is important as many services provided by such
ecosystems are crucial for the human economy and well-being. Given the importance of grasslands
ecosystems, in recent years research has been carried out on the potential to monitor them with
novel remote sensing techniques. Improved detectors technology and novel sensors providing finescale hyperspectral imagery have been enabling new methods to monitor plant traits (PTs) and
biodiversity.
The aims of the work were to study different approaches to monitor key grassland PTs such
as Leaf Area Index (LAI) and biodiversity-related traits. The thesis consists of 3 parts: 1)
Evaluating the performance of remote sensing methods to estimate LAI in grassland ecosystems, 2)
Estimating plant biodiversity by using the optical diversity approach in grassland ecosystems, and
3) Investigating the relationship between PTs variability with alpha and beta diversity for the
applicability of the optical diversity approach in a subalpine grassland of the Italian Alps
To evaluate the performance of remote sensing methods to estimate LAI, temporal and
spatial observations of hyperspectral reflectance and LAI were analyzed at a grassland site in Monte
Bondone, Italy (IT-MBo). In 2018, ground temporal observations of hyperspectral reflectance and
LAI were carried out at a grassland site in Neustift, Austria (AT-NEU). To estimate biodiversity, in
2018 and 2019 a floristics survey was conducted to determine species composition and
hyperspectral data were acquired at two grassland sites: IT-MBo and University of Padova’s
Experimental Farm, Legnaro, Padua, Italy (IT-PD) respectively. Furthermore, in 2018,
biochemistry analysis of the biomass samples collected from the grassland site IT-MBo was carried
out to determine the foliar biochemical PTs variability.
The results of the thesis demonstrated that the grassland spectral response across different
spectral regions (Visible: VIS, red-edge: RE, Near-infrared: NIR) showed to be both site-specific
and scale-dependent. In the first part of the thesis, the performance of spectral vegetation indices
(SVIs) based on visible, red-edge (RE), and NIR bands alongside SVIs solely based or NIRshoulder bands (wavelengths 750 - 900 nm) was evaluated. A strong correlation (R2 > 0.8) was
observed between grassland LAI and both RE and NIR-shoulder SVIs on a temporal basis, but not
on a spatial basis. Using the PROSAIL Radiative Transfer Model (RTM), it was demonstrated that
grassland structural heterogeneity strongly affects the ability to retrieve LAI, with high uncertainties
due to structural and biochemical PTs co-variation.
In the second part, the applicability of the spectral variability hypothesis (SVH) was
questioned and highlighted the challenges to use high-resolution hyperspectral images to estimate
biodiversity in complex grassland ecosystems. It was reported that the relationship between
biodiversity (Shannon, Richness, Simpson, and Evenness) and optical diversity metrics (Coefficient
of variation (CV) and Standard deviation (SD)) is not consistent across plant communities. The
results of the second part suggested that biodiversity in terms of species richness could be estimated
by optical diversity metrics with an R2 = 0.4 at the IT-PD site where the grassland plots were
artificially established and are showing a lower structure and complexity from the natural grassland
plant communities. On the other hand, in the natural ecosystems at IT-MBo, it was more difficult to
estimate biodiversity indices, probably due to structural and biochemical PTs co-variation. The
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effects of canopy non-vegetative elements (flowers and dead material), shadow pixels, and
overexposed pixels on the relationship between optical diversity metrics and biodiversity indices
were highlighted.
In the third part, we examined the relationship between PTs variability (at both local and
community scales, measured by standard deviation and by the Euclidean distances of the
biochemical and biophysical PTs respectively) and taxonomic diversity (both α-diversity and βdiversity, measured by Shannon’s index and by Jaccard dissimilarity index of the species, families,
and functional groups percent cover respectively) in Monte Bondone, Trentino province, Italy. The
results of the study showed that the PTs variability metrics at alpha scale were not correlated with
α-diversity. However, the results at the community scale (β-diversity) showed that some of the
investigated biochemical and biophysical PTs variations metrics were associated with β-diversity.
The SVH approach was also tested to estimate β-diversity and we found that spectral diversity
calculated by spectral angular mapper (SAM) showed to be a better proxy of biodiversity in the
same ecosystem where the spectral diversity failed to estimate alpha diversity, this leading to the
conclusion that the link between functional and species diversity may be an indicator of the
applicability of optical sampling methods to estimate biodiversity.
The findings of the thesis highlighted that grassland structural heterogeneity strongly affects
the ability to retrieve both LAI and biodiversity, with high uncertainties due to structural and
biochemical PTs co-variation at complex grassland ecosystems. In this context, the uncertainties of
satellite-based products (e.g., LAI) in monitoring grassland canopies characterized by either
spatially or temporally varying structure need to be carefully taken into account. The results of the
study highlighted that the poor performance of optical diversity proxies in estimating biodiversity in
structurally heterogeneous grasslands might be due to the complex relationships between functional
diversity and biodiversity, rather than the impossibility to detect functional diversity with spectral
proxies
Book chapters on the topic "Optical diversity metric"
1
Gittis, Andreas, Eric Vin, and Daniel J. Fremont. "Randomized Synthesis for Diversity and Cost Constraints with Control Improvisation." In Computer Aided Verification, 526–46. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13188-2_26.
Full textAbstract:
AbstractIn many synthesis problems, it can be essential to generate implementations which not only satisfy functional constraints but are also randomized to improve variety, robustness, or unpredictability. The recently-proposed framework of control improvisation (CI) provides techniques for the correct-by-construction synthesis of randomized systems subject to hard and soft constraints. However, prior work on CI has focused on qualitative specifications, whereas in robotic planning and other areas we often have quantitative quality metrics which can be traded against each other. For example, a designer of a patrolling security robot might want to know by how much the average patrol time needs to be increased in order to ensure that a particular aspect of the robot’s route is sufficiently diverse and hence unpredictable. In this paper, we enable this type of application by generalizing the CI problem to support quantitative soft constraints which bound the expected value of a given cost function, and randomness constraints which enforce diversity of the generated traces with respect to a given label function. We establish the basic theory of labelled quantitative CI problems, and develop efficient algorithms for solving them when the specifications are encoded by finite automata. We also provide an approximate improvisation algorithm based on constraint solving for any specifications encodable as Boolean formulas. We demonstrate the utility of our problem formulation and algorithms with experiments applying them to generate diverse near-optimal plans for robotic planning problems.
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Cheng, Shi, Yuhui Shi, and Quande Qin. "Population Diversity of Particle Swarm Optimizer Solving Single- and Multi-Objective Problems." In Emerging Research on Swarm Intelligence and Algorithm Optimization, 71–98. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-6328-2.ch004.
Full textAbstract:
Premature convergence occurs in swarm intelligence algorithms searching for optima. A swarm intelligence algorithm has two kinds of abilities: exploration of new possibilities and exploitation of old certainties. The exploration ability means that an algorithm can explore more search places to increase the possibility that the algorithm can find good enough solutions. In contrast, the exploitation ability means that an algorithm focuses on the refinement of found promising areas. An algorithm should have a balance between exploration and exploitation, that is, the allocation of computational resources should be optimized to ensure that an algorithm can find good enough solutions effectively. The diversity measures the distribution of individuals' information. From the observation of the distribution and diversity change, the degree of exploration and exploitation can be obtained. Another issue in multiobjective is the solution metric. Pareto domination is utilized to compare two solutions; however, solutions are almost Pareto non-dominated for multiobjective problems with more than ten objectives. In this chapter, the authors analyze the population diversity of a particle swarm optimizer for solving both single objective and multiobjective problems. The population diversity of solutions is used to measure the goodness of a set of solutions. This metric may guide the search in problems with numerous objectives. Adaptive optimization algorithms can be designed through controlling the balance between exploration and exploitation.
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Zeltni, Kamel, Souham Meshoul, and Heyam H. Al-Baity. "On the Convergence and Diversity of Pareto Fronts Using Swarm Intelligence Metaheuristics for Constrained Search Space." In Robotic Systems, 1573–93. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1754-3.ch075.
Full textAbstract:
This article reviews existing constraint-handling techniques then presents a new design for Swarm Intelligence Metaheuristics (SIM) to deal with constrained multi-objective optimization problems (CMOPs). This new design aims to investigate potential effects of leader concepts that characterize the dynamic of SIM in the hope to help the population to reach Pareto optimal solutions in a constrained search space. The new leader-based constraint handling mechanism is incorporated in Constrained Multi-Objective Cuckoo Search (C-MOCS) and Constrained Multi-Objective Particle Swarm Optimization (C-MOPSO) as specific instances of a more general class of SIMs. The experimental results are carried out using a set of six well-known test functions and two performance metrics. The convergence and diversity of C-MOCS and C-MOPSO are analysed and compared to the well-known Multi-Objective Evolutionary Algorithm (MOEA) NSGA-II and discussed based on the obtained results.
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Dey, Sandip, Siddhartha Bhattacharyya, and Ujjwal Maulik. "Optimum Gray Level Image Thresholding using a Quantum Inspired Genetic Algorithm." In Advances in Computational Intelligence and Robotics, 349–77. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9474-3.ch012.
Full textAbstract:
In this article, a genetic algorithm inspired by quantum computing is presented. The novel algorithm referred to as quantum inspired genetic algorithm (QIGA) is applied to determine optimal threshold of two gray level images. Different random chaotic map models exhibit the inherent interference operation in collaboration with qubit and superposition of states. The random interference is followed by three different quantum operators viz., quantum crossover, quantum mutation and quantum shifting produce population diversity. Finally, the intermediate states pass through the quantum measurement for optimization of image thresholding. In the proposed algorithm three evaluation metrics such as Brinks's, Kapur's and Pun's algorithms have been applied to two gray level images viz., Lena and Barbara. These algorithms have been applied in conventional GA and Han et al.'s QEA. A comparative study has been made between the proposed QIGA, Han et al.'s algorithm and conventional GA that indicates encouraging avenues of the proposed QIGA.
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Garg, Ritu. "Guided Search-Based Multi-Objective Evolutionary Algorithm for Grid Workflow Scheduling." In Exploring Critical Approaches of Evolutionary Computation, 166–95. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5832-3.ch009.
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The computational grid provides the global computing infrastructure for users to access the services over a network. However, grid service providers charge users for the services based on their usage and QoS level specified. Therefore, in order to optimize the grid workflow execution, a robust multi-objective scheduling algorithm is needed considering economic cost along with execution performance. Generally, in multi-objective problems, simulations rely on running large number of evaluations to obtain the accurate results. However, algorithms that consider the preferences of decision maker, convergence to optimal tradeoff solutions is faster. Thus, in this chapter, the author proposed the preference-based guided search mechanism into MOEAs. To obtain solutions near the pre-specified regions of interest, the author has considered two MOEAs, namely R-NSGA-II and R-ε-MOEA. Further, to improve the diversity of solutions, a modified form called M-R-NSGA-II is used. Finally, the experimental settings and performance metrics are presented for the evaluation of the algorithms.
Conference papers on the topic "Optical diversity metric"
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Bolcar, Matthew R., and James R. Fienup. "A Comparison of Regularized Metrics for Phase Diversity." In Frontiers in Optics. Washington, D.C.: OSA, 2008. http://dx.doi.org/10.1364/fio.2008.fmm1.
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Scheepers, Christiaan, and Andries P. Engelbrecht. "Misleading Pareto optimal front diversity metrics: Spacing and distribution." In 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2016. http://dx.doi.org/10.1109/ssci.2016.7850218.
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Farhang-Mehr, Ali, and Shapour Azarm. "On the Entropy of Multi-Objective Design Optimization Solution Sets." In ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/detc2002/dac-34122.
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In this paper, an entropy-based metric is presented for quality assessment of non-dominated solution sets obtained from a multiobjective optimization technique. This metric quantifies the ‘goodness’ of a solution set in terms of its distribution quality over the Pareto-optimal frontier. Therefore, it can be useful in comparison studies of different multi-objective optimization techniques, such as Multi-Objective Genetic Algorithms (MOGAs), wherein the capabilities of such techniques to produce and maintain diversity among different solution points are desired to be compared on a quantitative basis. An engineering test example, the multiobjective design optimization of a speed-reducer, is presented in order to demonstrate an application of the proposed entropy metric.
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Dong, Guang, and John Cooper. "Particle Swarm Optimization With Crossover and Mutation Operators Using the Diversity Criteria." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-13593.
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Particle Swarm Optimization is a population based globalized search algorithm that mimics the behavior of swarms. It belongs to the larger class of evolutionary algorithms as widely used stochastic technique in the global optimization field. Since the PSO is population based, it requires no auxiliary information, such as the gradient of the problem. It is known that each particle in the PSO uses only two pieces of information, called the personal best position and the global best position, to update its moving velocity and position by generations. One disadvantage of this algorithm is that it can be easily trapped into some local optimal solutions because of the premature convergence. This may be an issue when solving complex multi-modal functions with multiple local minimums. Hence, the global optimization algorithm should have the ability to prevent being trapped into local optima by keeping wide search space and maintaining the population diversity. In order to improve the performance of the PSO for complex global optimization problems, this paper introduces both crossover and mutation operators to the basic PSO algorithm. The proposed algorithm uses the mechanism that all the particles in the current iteration will have crossover and mutation operations if the diversity criteria of the particles is reduced to be smaller than a predefined limit value. Therefore, the PSO using both crossover and mutation operators can maintain the diversity of population and enhance the search ability as to get better results while solving complex problems. This study adopts the average distance around the swarm center as the diversity measure, and extends the distance metrics to both L1 norm distance and L∞ norm distance. To verify the usability and effectiveness of the proposed algorithm, it is applied to 12 widely used nonlinear benchmark functions. These examples show that the proposed PSO with crossover and mutation operators using the diversity criteria has better optimization performance than the basic PSO by maintaining the swarm diversity. Moreover, the PSO using the L1 norm distance diversity gives better results than both L2 and L∞ norm distance for most cases.
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Shi, Yu, and Rolf D. Reitz. "Assessment of Multi-Objective Genetic Algorithms With Different Niching Strategies and Regression Methods for Engine Optimization and Design." In ASME 2009 Internal Combustion Engine Division Spring Technical Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/ices2009-76015.
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In previous study [1] the Non-dominated Sorting Genetic Algorithm II (NSGA II) [2] performed better than other popular Multi-Objective Genetic Algorithms (MOGA) in engine optimization that sought optimal combinations of the piston bowl geometry, spray targeting, and swirl ratio. NSGA II is further studied in this paper using different niching strategies that are applied to the objective-space and design-space, which diversify the optimal objectives and design parameters accordingly. Convergence and diversity metrics are defined to assess the performance of NSGA II using different niching strategies. It was found that use of the design niching achieved more diversified results with respect to design parameters, as expected. Regression was then conducted on the design datasets that were obtained from the optimizations with two niching strategies. Four regression methods, including K-nearest neighbors (KN), Kriging (KR), Neural Networks (NN), and Radial Basis Functions (RBF), were compared. The results showed that the dataset obtained from optimization with objective niching provided a more fitted learning space for the regression methods. The KN, KR, outperformed the other two methods with respect to the prediction accuracy. Furthermore, a log transformation to the objective-space improved the prediction accuracy for the KN, KR, and NN methods but not the RBF method. The results indicate that it is appropriate to use a regression tool to partly replace the actual CFD evaluation tool in engine optimization designs using the genetic algorithm. This hybrid mode saves computational resources (processors) without losing optimal accuracy. A Design of Experiment (DoE) method (the Optimal Latin Hypercube method) was also used to generate a dataset for the regression processes. However, the predicted results were much less reliable than results that were learned using the dynamically increasing datasets from the NSGA II generations. Applying the dynamical learning strategy during the optimization processes allows computationally expensive CFD evaluations to be partly replaced by evaluations using the regression techniques. The present study demonstrates the feasibility of applying the hybrid mode to engine optimization problems, and the conclusions can also extend to other optimization studies (numerical or experimental) that feature time-consuming evaluations and have highly non-linear objective-spaces.
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Meng, Ronghua, Yunqing Rao, and Qiang Luo. "Optimizing Bi-Criteria Permutation Flow Shop Scheduling Problem by Improved NSGA III." In ASME 2018 13th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/msec2018-6493.
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This paper addresses a bi-objective distribution permutation flow shop scheduling problem (FSP) with setup times aiming to minimize the makespan and the total tardiness. It is very difficult to obtain an optimal solution by using traditional approaches in reasonable computational time. This paper presents an appropriate non-dominated sorting Genetic Algorithm III based on the reference point. The NEH strategy is applied into the generation of the initial solution set. To validate the performance of the NEH strategy improved NSGA III (NNSGA III) on solution quality and diversity level, various test problems are carried out. Three algorithms, including NSGA II, NEH strategy improved NSGA II(NNSGA II) and NNSGA III are utilized to solve this FSP. Experimental results suggest that the proposed NNSGA III outperforms the other algorithms on the Inverse Generation Distance metric, and the distribution of Pareto solutions are improved excellently.
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Dam, Nhan, Quan Hoang, Trung Le, Tu Dinh Nguyen, Hung Bui, and Dinh Phung. "Three-Player Wasserstein GAN via Amortised Duality." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/305.
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We propose a new formulation for learning generative adversarial networks (GANs) using optimal transport cost (the general form of Wasserstein distance) as the objective criterion to measure the dissimilarity between target distribution and learned distribution. Our formulation is based on the general form of the Kantorovich duality which is applicable to optimal transport with a wide range of cost functions that are not necessarily metric. To make optimising this duality form amenable to gradient-based methods, we employ a function that acts as an amortised optimiser for the innermost optimisation problem. Interestingly, the amortised optimiser can be viewed as a mover since it strategically shifts around data points. The resulting formulation is a sequential min-max-min game with 3 players: the generator, the critic, and the mover where the new player, the mover, attempts to fool the critic by shifting the data around. Despite involving three players, we demonstrate that our proposed formulation can be trained reasonably effectively via a simple alternative gradient learning strategy. Compared with the existing Lipschitz-constrained formulations of Wasserstein GAN on CIFAR-10, our model yields significantly better diversity scores than weight clipping and comparable performance to gradient penalty method.
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Lúcio, Yan Lieven Souza, Luiza Scapinello Aquino, and Leandro dos Santos Coelho. "Marine Predators Algorithm Approaches on a Multivariable Fractional PID Controller Tuning." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-39.
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In this paper, a performance comparison between the Marine Predators Algorithm (MPA), a metaheuristic paradigm, and two other designed variants for the tuning of a fractional proportional-integrative-derivative (PID) controller in a multiple-input multiple-output (MIMO) application is presented. The practical system plant corresponds to a ball mill pulverizing system, whose structure presents two inputs and two outputs. To encounter the optimal response on the MIMO control of this system a MPA approach applied to PID tuning is suitable, as it presents both the capability to diversify the search space (exploration) and to improve the quality of current solutions (exploitation) in search space. The MPA is a metaheuristic inspired by the extensive hunting strategy of ocean predators called Lévy and Brownian movements, it focuses on an optimal confront rate procedure in natural interaction between predator and prey in the marine ecosystem. The original MPA itself presents a satisfactory performance, in terms of statistical metrics. Nevertheless, it can be improved through the modification and addition of distinct techniques. In order to achieve those modifications, three variants are implemented exploring different procedures namely the oppositional-based learning and application of quantum mechanics. The optimal parameter values for the PID controller are analyzed by minimizing the integral time squared error (ITSE) index of the system’s response. The simulations are performed using the SIMULINK/MATLAB computational environment. Statistical measures including best, mean, median and standard deviation of the system response error for the tuned controllers are evaluated and compared over fifty runs. The obtained results suggest that the use of the mentioned proposals has an advantage in enhancing the tuning efficiency of the MPA in this application.
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Nejat, Amir, Pooya Mirzabeygi, and Masoud Shariat-Panahi. "Aerodynamic Shape Optimization Using Improved Territorial Particle Swarm Algorithm." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-88828.
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In this paper, a new robust optimization technique with the ability of solving single and multi-objective constrained design optimization problems in aerodynamics is presented. This new technique is an improved Territorial Particle Swarm Optimization (TPSO) algorithm in which diversity is actively preserved by avoiding overcrowded clusters of particles and encouraging broader exploration. Adaptively varying “territories” are formed around promising individuals to prevent many of the lesser individuals from premature clustering and encouraged them to explore new neighborhoods based on a hybrid self-social metric. Also, a new social interaction scheme is introduced which guided particles towards the weighted average of their “elite” neighbors’ best found positions instead of their own personal bests which in turn helps the particles to exploit the candidate local optima more effectively. The TPSO algorithm is developed to take into account multiple objective functions using a Pareto-Based approach. The non-dominated solutions found by swarm are stored in an external archive and nearest neighbor density estimator method is used to select a leader for the individual particles in the swarm. Efficiency and robustness of the proposed algorithm is demonstrated using multiple traditional and newly-composed optimization benchmark functions and aerodynamic design problems. In final airfoil design obtained from the Multi Objective Territorial Particle Swarm Optimization algorithm, separation point is delayed to make the airfoil less susceptible to stall in high angle of attack conditions. The optimized airfoil also reveals an evident improvement over the test case airfoil across all objective functions presented.
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Ambrose, Ivor, and Katerina Papamichail. "INFORMATION TOOLS FOR CULTURAL TOURISM DESTINATIONS: MANAGING ACCESSIBILITY." In Tourism in Southern and Eastern Europe 2021: ToSEE – Smart, Experience, Excellence & ToFEEL – Feelings, Excitement, Education, Leisure. University of Rijeka, Faculty of Tourism and Hospitality Management, 2021. http://dx.doi.org/10.20867/tosee.06.2.
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Purpose – This paper is prepared in connection with the H2020 IMPACTOUR project on “Improving Sustainable Development Policies and Practices to access, diversify and foster Cultural Tourism (CT) in European regions and areas”. It addresses the development of indicators for the management of accessibility in European CT destinations, responding to the growing accessible tourism market as a driver of sustainable tourism strategies. Methodology – The paper describes the development of tools, indicators and metrics for gathering accessibility information, which DMOs may use as part of the IMPACTOUR CT destination management system. It reports on global and European destination management systems and tools, and describes key requirements for accessibility indicators, namely: 1) Validity, 2) Reliability, 3) Universality, 4) Availability, 5) Scalability and 6) Operability. Findings – A set of “core indicators” and additional “optional indicators” are selected for initial testing in the IMPACTOUR Destination Pilot Sites in various EU countries. Pilot destinations and representative groups of citizens and visitors will be engaged in testing and validating the accessibility parameters of the tool and demonstrating how tourists with access requirements can be suitably catered for within the overall framework of sustainable destination management. Contribution – The paper describes the development of information tools supporting CT destinations in managing the demands of the growing accessible tourism market. The use of accessibility indicators in destination management is part of the holistic, data-driven approach promoted by IMPACTOUR, aiming to ensure inclusive cultural tourism for all visitors and citizens in the host communities.