scholarly journals Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

10.3791/60120 ◽  
2019 ◽  
Author(s):  
Elie Rassi ◽  
Marco Fuscà ◽  
Nathan Weisz ◽  
Gianpaolo Demarchi
Keyword(s):  
Object Perception ◽  
Source Level ◽  
Stimulus Source

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

scholarly journals A Reference Spectrum Model for Estimating Source Levels of Marine Shipping Based on Automated Identification System Data

2021 ◽  
Vol 9 (4) ◽  
pp. 369 ◽  
Author(s):  
Alexander MacGillivray ◽  
Christ de Jong
Keyword(s):  
Objective Assessment ◽  
Noise Pollution ◽  
Specific Reference ◽  
Identification System ◽  
Reference Spectrum ◽  
Underwater Sound ◽  
The North ◽  
Joint Monitoring ◽  
Source Level ◽  
Spectrum Model

Underwater sound mapping is increasingly being used as a tool for monitoring and managing noise pollution from shipping in the marine environment. Sound maps typically rely on tracking data from the Automated Information System (AIS), but information available from AIS is limited and not easily related to vessel noise emissions. Thus, robust sound mapping tools not only require accurate models for estimating source levels for large numbers of marine vessels, but also an objective assessment of their uncertainties. As part of the Joint Monitoring Programme for Ambient Noise in the North Sea (JOMOPANS) project, a widely used reference spectrum model (RANDI 3.1) was validated against statistics of monopole ship source level measurements from the Vancouver Fraser Port Authority-led Enhancing Cetacean Habitat and Observation (ECHO) Program. These validation comparisons resulted in a new reference spectrum model (the JOMOPANS-ECHO source level model) that retains the power-law dependence on speed and length but incorporates class-specific reference speeds and new spectrum coefficients. The new reference spectrum model calculates the ship source level spectrum, in decidecade bands, as a function of frequency, speed, length, and AIS ship type. The statistical uncertainty (standard deviation of the deviation between model and measurement) in the predicted source level spectra of the new model is estimated to be 6 dB.

scholarly journals Hindcasting Soundscapes before and during the COVID-19 Pandemic in Selected Areas of the North Sea and the Adriatic Sea

2021 ◽  
Vol 9 (7) ◽  
pp. 702
Author(s):  
Hüseyin Özkan Sertlek
Keyword(s):  
North Sea ◽  
Adriatic Sea ◽  
Propagation Model ◽  
The North ◽  
Fishing Vessels ◽  
Water Test ◽  
The North Sea ◽  
Monthly Changes ◽  
Source Level ◽  
Sound Maps

The national measures in several European countries during the COVID-19 pandemic also affected offshore human activities, including shipping. In this work, the temporal and spatial variations of shipping sound are calculated for the years before and during the pandemic in selected shallow water test areas from the Southern North Sea and the Adriatic Sea. First, the monthly sound pressure level maps of ships and wind between 2017 and 2020 are calculated for frequencies between 100 Hz to 10 kHz. Next, the monthly changes in these maps are compared. The asymptotic approximation of the hybrid flux-mode propagation model reduces the computational requirements for sound mapping simulations and facilitates the production of a large number of sound maps for different months, depths, frequencies, and ship categories. After the strictest COVID-19 measures were applied in April 2020, the largest decline was observed for the fishing, passenger and recreational ships. Although the changes in the number of fishing vessels are large, their contribution to the soundscape is minor due to their low source level. In both test areas, the spatial exceedance levels and acoustic energies were decreased in 2020 compared to the average of the previous three years.

Feminist multimodality—A retrospective account of an exhibition on speculative urbanism

2021 ◽  
pp. 263497952110276
Author(s):  
Hemangini Gupta
Keyword(s):  
Object Perception ◽  
Central Place ◽  
Urban Residents ◽  
Research Project ◽  
The Future ◽  
Retrospective Account ◽  
Forward Looking

This essay offers a retrospective account of a multimodal public exhibit at the end of a multi-year research project on speculative urbanism. While the registers of speculation are invariably forward-looking, our research presented us with the central place of memory as a frame through which urban residents in Bengaluru, India, negotiate their present and imagine the possibilities of the future. This essay examines four ways in which we created space for memory in our exhibit, understanding our approach as situating an archive-optic, drawing on approaches of critical fabulation, object perception, and submerged perspectives. I suggest that these forms of engagement are multimodal and that they offer feminist and decolonial ways to unmaster linear narratives and situate our research affectively.

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