329,931 research outputs found
Data for "Rapid seaward expansion of seaport footprints worldwide"
No full text[updated October 2023] This dataset comprises data and code used in "Rapid seaward expansion of seaport footprints worldwide" (Sengupta &amp; Lazarus, 2023; preprint: https://doi.org/10.31223/X5SD3T). The data include four csv files: 'Sengupta_Lazarus_REC_1990_2020_v05.csv' – annual time series of seaward expansion (km^2) between 1990–2020 through coastal reclamation for 66 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (Lloyd's List, 2021). Dataset includes Year, Seaport, Country, Region, Reclaimed area (km^2) [raw measurement], and Reclaimed area (km^2) [smoothed]. To produce the smoothed data, the raw data are passed through a Savitzky–Golay filter. 'Sengupta_Lazarus_REC_TEU_2011_2020_v03.csv' – annual time series of seaward expansion (km^2) and reported container throughput (millions TEU) between 2011–2020 for 43 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (Lloyd's List, 2021). Dataset includes Year, Seaport, Country, Region, Reclaimed area (km^2) [raw measurement], and Reclaimed area (km^2) [smoothed with a Savitzky–Golay filter], and TEU (millions), collated from archived Lloyd's List reports. 'Sengupta_Lazarus_REC_TEU_totals_v04.csv' – Simplified dataset listing total seaward expansion (km^2) and container throughput in 2020 for 66 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (Lloyd's List, 2021). Dataset includes Seaport, Country, Region, Reclaimed area (km^2) [smoothed with a Savitzky–Golay filter], ranked list of seaports by expansion extent, and TEU (millions) handled in 2020, and Lloyd's List rank in 2020 (Lloyd's List, 2021). 'Sengupta_Lazarus_2023_ports_excluded.csv' – contains list of 34 ports excluded from thus analysis because they are either not on an open coastline (e.g. estuarine, riverine) or expanded less than 1 km^2 seaward between 1990–2020. 'RECLAIM_port_trajectories_v11.ipynb' – Jupyter notebook for data wrangling and plotting figures presented in Sengupta &amp; Lazarus (2023). (Note that this notebook does not produce the map-based figures presented in that work.) The method for calculating reclaimed area over time in Google Earth Engine (GEE) is described in Sengupta et al. (2023), and the GEE code is available here: https://github.com/dhritirajsen/Seaport_reclamation These data and code are also available here: https://github.com/edlazarus/Seaports</span
Data for "Rapid seaward expansion of seaport footprints worldwide"
No full text[updated July 2023] This dataset comprises data and code used in "Rapid seaward expansion of seaport footprints worldwide" (Sengupta &amp; Lazarus, 2023; preprint: https://doi.org/10.31223/X5SD3T).
The data include four csv files:
'Sengupta_Lazarus_REC_1990_2020_v02.csv' – annual time series of seaward expansion (km^2) between 1990–2020 through coastal reclamation for 66 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (Lloyd's List, 2021). Dataset includes Year, Seaport, Country, Region, Reclaimed area (km^2) [raw measurement], and Reclaimed area (km^2) [smoothed]. To produce the smoothed data, the raw data are passed through a Savitzky–Golay filter.
'Sengupta_Lazarus_REC_TEU_2011_2020_v02.csv' – annual time series of seaward expansion (km^2) and reported container throughput (millions TEU) between 2011–2020 for 43 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (Lloyd's List, 2021). Dataset includes Year, Seaport, Country, Region, Reclaimed area (km^2) [raw measurement], and Reclaimed area (km^2) [smoothed with a Savitzky–Golay filter], and TEU (millions), collated from archived Lloyd's List reports.
Sengupta_Lazarus_REC_TEU_totals_v02.csv' – Simplified dataset listing total seaward expansion (km^2) and container throughput in 2020 for 66 of the world's top 100 container seaports in 2020, as ranked by reported container throughput (Lloyd's List, 2021). Dataset includes Seaport, Country, Region, Reclaimed area (km^2) [smoothed with a Savitzky–Golay filter], ranked list of seaports by expansion extent, and TEU (millions) handled in 2020, and Lloyd's List rank in 2020 (Lloyd's List, 2021).
'Sengupta_Lazarus_2023_ports_excluded.csv' – contains list of 34 ports excluded from thus analysis because they are either not on an open coastline (e.g. estuarine, riverine) or expanded less than 1 km^2 seaward between 1990–2020.
'RECLAIM_port_trajectories_v7.ipynb' – Jupyter notebook for data wrangling and plotting figures presented in Sengupta &amp; Lazarus (2023). (Note that this notebook does not produce the map-based figures presented in that work.) The method for calculating reclaimed area over time in Google Earth Engine (GEE) is described in Sengupta et al. (2023), and the GEE code is available here: https://github.com/dhritirajsen/Mapping_Coastal_land_reclamation These data and code are also available here: https://github.com/edlazarus/Seaports</span
Data for "Rapid seaward expansion of seaport footprints worldwide"
No full textData used in "Rapid seaward expansion of seaport footprints worldwide" (Sengupta &amp; Lazarus, 2023: doi xxxxxxx). Data include five csv files: 'Sengupta_Lazarus_2023_F1_b' – total reclaimed area (km^2) in 2020, ranked by magnitude 'Sengupta_Lazarus_2023_F2_a' – normalised reclaimed area versus time for 68 seaports 'Sengupta_Lazarus_2023_F2_b' – series of ranks by different metrics: Lloyd's List 2020 (Lloyd's, 2021), total reclaimed area in 2020, area under normalised curves of reclaimed area through time, and coastal &amp; cyclone hazard (Verschuur et al., 2023). 'Sengupta_Lazarus_rec_TEU_2011_2020' – data for container volume (in millions TEU) relative to reclamation area, 2011-2020; data for container volume sourced from UNCTD and the World Shipping Council. 'Sengupta_Lazarus_time_series_rec_data' – data (raw and smoothed) for seaport reclamation (km^2), 1990–2020 The method for calculating reclaimed area over time in Google Earth Engine (GEE) is described in Sengupta et al. (2023), and the GEE code is available here: https://github.com/dhritirajsen/Mapping_Coastal_land_reclamation Code for plotting these data are available here: https://github.com/edlazarus/Seaports</span
Filtered Audio Clips from Approximate FIR Filters Designed Using the SABER Algorithm
No full textThe results of using accurate and approximate finite impulse response (FIR) filters on noisy song clips are summarized here.
The names of the clips are listed along with its description.
For each song (e.g., Country), there are four versions:
a. Country_1NOISY - Noisy version of the song downloaded from the GTZAN Genre Collection (marsyas.info/downloads/datasets.html - use of audio clips permitted by collection creator and falls under fair use).
b. Country_2EXACTfilter - Filtered version using exact hardware of the FIR filter
c. Country_3APPROXifilterBudget100K - Filtered version using approximate hardware of the FIR filter with error variance 100,000.
d. Country_4APPROXifilterBudget200K - Filtered version using approximate hardware of the FIR filter with error variance 200,000.
e. Country_5APPROXifilterBudget400K - Filtered version using approximate hardware of the FIR filter with error variance 400,000.
The original songs were processed with low pass filter and 6 seconds of each data was selected for our analysis from each of the eight genres specified.
Colored noise (high frequency) was added to the 6 seconds data to generate the noisy signal. This signal was then passed through four different versions of an order-33 FIR filter to obtained the filtered versions. Each version of the filter corresponds to the exact, and approximate configurations with three different error variance budgets, and designed using the Selection of Approximate Bits for the Design of Error Tolerant Circuits (SABER) algorithm.
While the filtered signal still has some noise, it is within acceptable auditory range of human ears as compared to the noisy signal. Our paper shows how using the approximate version of the FIR filter can lead to power savings compared to the exact version, with minimal compromise on the user experience in terms of the quality of the output.We developed an algorithm, Selection of Approximate Bits for the Design of Error Tolerant Circuits (SABER), to generate an approximate circuit with the aim of maximizing the number of approximate bits in a circuit (which translates to power/area minimization) so that it uses minimal resources under a specified error budget. Our work demonstrates results on fixed-point integer arithmetic operations. The key ingredient of any methodology based on approximate design is an accurate quantification of the error injected into a computation by the approximation scheme. We use the variance of this error as the error metric to be constrained within a user-specified budget. We use an analytical expression of this error variance as a function of the total approximation in a circuit.The Doctoral Dissertation Fellowship, University of MinnesotaAwards CCF-1162267, CCF-1525925, and CCF-1525749, National Science FoundationSengupta, Deepashree; Snigdha, Farhana, S; Hu, Jiang; Sapatnekar, Sachin S. (2017). Filtered Audio Clips from Approximate FIR Filters Designed Using the SABER Algorithm. Retrieved from the University Digital Conservancy, https://doi.org/10.13020/D6BP4X
Semantic Mapping of Road Scenes
No full textThe problem of understanding road scenes has been on the fore-front in the computer vision community
for the last couple of years. This enables autonomous systems to navigate and understand
the surroundings in which it operates. It involves reconstructing the scene and estimating the objects
present in it, such as ‘vehicles’, ‘road’, ‘pavements’ and ‘buildings’. This thesis focusses on these
aspects and proposes solutions to address them.
First, we propose a solution to generate a dense semantic map from multiple street-level images.
This map can be imagined as the bird’s eye view of the region with associated semantic labels for
ten’s of kilometres of street level data. We generate the overhead semantic view from street level
images. This is in contrast to existing approaches using satellite/overhead imagery for classification
of urban region, allowing us to produce a detailed semantic map for a large scale urban area. Then
we describe a method to perform large scale dense 3D reconstruction of road scenes with associated
semantic labels. Our method fuses the depth-maps in an online fashion, generated from the
stereo pairs across time into a global 3D volume, in order to accommodate arbitrarily long image
sequences. The object class labels estimated from the street level stereo image sequence are used to
annotate the reconstructed volume. Then we exploit the scene structure in object class labelling by
performing inference over the meshed representation of the scene. By performing labelling over the
mesh we solve two issues: Firstly, images often have redundant information with multiple images
describing the same scene. Solving these images separately is slow, where our method is approximately
a magnitude faster in the inference stage compared to normal inference in the image domain.
Secondly, often multiple images, even though they describe the same scene result in inconsistent
labelling. By solving a single mesh, we remove the inconsistency of labelling across the images.
Also our mesh based labelling takes into account of the object layout in the scene, which is often
ambiguous in the image domain, thereby increasing the accuracy of object labelling. Finally, we perform
labelling and structure computation through a hierarchical robust PN Markov Random Field
defined on voxels and super-voxels given by an octree. This allows us to infer the 3D structure and
the object-class labels in a principled manner, through bounded approximate minimisation of a well
defined and studied energy functional. In this thesis, we also introduce two object labelled datasets
created from real world data. The 15 kilometre Yotta Labelled dataset consists of 8,000 images per
camera view of the roadways of the United Kingdom with a subset of them annotated with object
class labels and the second dataset is comprised of ground truth object labels for the publicly available
KITTI dataset. Both the datasets are available publicly and we hope will be helpful to the vision
research community
Assessment of dopaminergic neuron degeneration in a C. elegans model of Parkinson's disease
No full textTransgenic Caenorhabditis elegans that expresses the full-length wild-type human α-synuclein in dopaminergic neurons provides a well-established Parkinson's disease (PD) nematode model. Here, we present a detailed protocol to monitor and dissect the molecular underpinnings of age-associated neurodegeneration using this PD nematode model. This protocol includes preparation of nematode growth media and bacterial food sources, as well as procedures for nematode growth, synchronization, and treatment. We then describe procedures to assess dopaminergic neuronal death in vivo using fluorescence imaging. For complete details on the use and execution of this protocol, please refer to SenGupta et al. (2021). © 2022 The Author(s
Spatio-temporal growth of disturbances in a boundary layer and energy based receptivity analysis
No full textIn fluid dynamical systems, it is not known a priori whether disturbances grow either in space or in time or as spatio-temporal structures. However, for boundary layers, it is customary to treat it as a spatial problem and some limited comparison between prediction and laboratory experiments exist. In the present work, the receptivity problem of a zero pressure gradient boundary layer excited by a localized harmonic source is investigated under the general spatio-temporal framework, using the Bromwich contour integral method. While this approach has been shown to be equivalent to the spatial study, for unstable systems excited by a single frequency source [T. K. Sengupta, M. Ballav, and S. Nijhawan, Phys. Fluids6, 1213 (1994)], here we additionally show, how the boundary layer behaves when it is excited (i) at a single frequency that corresponds to a stable condition (given by spatial normal-mode analysis) and (ii) by wideband frequencies, that shows the possibility of flow transition due to a spatio-temporally growing forerunner or wave front. An energy based receptivity analysis tool is also developed as an alternative to traditional instabilitytheory. Using this, we reinterpret the concept of critical layer that was originally postulated to explain the mathematical singularity of inviscid disturbance field in traditional instabilitytheory of normal modes
An efficient plate element for the vibration of composite plates
No full textAbstract not availableP. Dey, S. Haldar, D. Sengupta, A.H. Sheik
The Core Can Be Accessed in a Bounded Number of Steps
No full textThis paper strengthens the result of Sengupta and Sengupta (1996). We show that for the class of games with nonempty cores the core can be reached in a bounded number of proposals and counterproposals. Our result is more general than this: the boundedness holds for any two imputations with an indirect dominance relation between them.dynamic cooperative game, indirect dominance, core.
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