504 research outputs found
Statistical similarity between the compression of a porous material and earthquakes
It has been long stated that there are profound analogies between fracture
experiments and earthquakes; however, few works attempt a complete
characterization of the parallelisms between these so separate phenomena. We
study the Acoustic Emission events produced during the compression of Vycor
(SiO2). The Gutenberg-Richter law, the modified Omori's law, and the law of
aftershock productivity are found to hold for a minimum of 5 decades, are
independent of the compression rate, and keep stationary for all the duration
of the experiments. The waiting-time distribution fulfills a unified scaling
law with a power-law exponent close to 2.45 for long times, which is explained
in terms of the temporal variations of the activity rate.Comment: 4 pages and a bit more, 4 figure
Loop-induced photon spectral lines from neutralino annihilation in the NMSSM
We have computed the loop-induced processes of neutralino annihilation into
two photons and, for the first time, into a photon and a Z boson in the
framework of the NMSSM. The photons produced from these radiative modes are
monochromatic and possess a clear "smoking gun" experimental signature. This
numerical analysis has been done with the help of the SloopS code, initially
developed for automatic one-loop calculation in the MSSM. We have computed the
rates for different benchmark points coming from SUGRA and GMSB soft SUSY
breaking scenarios and compared them with the MSSM. We comment on how this
signal can be enhanced, with respect to the MSSM, especially in the low mass
region of the neutralino. We also discuss the possibility of this observable to
constrain the NMSSM parameter space, taking into account the latest limits from
the FERMI collaboration on these two modes.Comment: 18 pages, 3 figures. Minor clarifications added in the text. Typing
mistakes and references corrected. Matches published versio
A Novel Detection Refinement Technique for Accurate Identification of Nephrops norvegicus Burrows in Underwater Imagery
With the evolution of the convolutional neural network (CNN), object detection in the
underwater environment has gained a lot of attention. However, due to the complex nature of the
underwater environment, generic CNN-based object detectors still face challenges in underwater
object detection. These challenges include image blurring, texture distortion, color shift, and scale
variation, which result in low precision and recall rates. To tackle this challenge, we propose a
detection refinement algorithm based on spatial–temporal analysis to improve the performance of
generic detectors by suppressing the false positives and recovering the missed detections in underwater
videos. In the proposed work, we use state-of-the-art deep neural networks such as Inception,
ResNet50, and ResNet101 to automatically classify and detect the Norway lobster Nephrops norvegicus
burrows from underwater videos. Nephrops is one of the most important commercial species in
Northeast Atlantic waters, and it lives in burrow systems that it builds itself on muddy bottoms.
To evaluate the performance of proposed framework, we collected the data from the Gulf of Cadiz.
From experiment results, we demonstrate that the proposed framework effectively suppresses false
positives and recovers missed detections obtained from generic detectors. The mean average precision
(mAP) gained a 10% increase with the proposed refinement technique.Versión del edito
Automatic Detection of Nephrops norvegicus Burrows in Underwater Images Using Deep Learning
Autonomous Underwater Vehicles and Remotely
Operated Vehicles equipped with HD cameras are used by
the scientist to capture the underwater footages efficiently and
accurately. The abundance of the Norway Lobster Nephrops
norvegicus stock in the Gulf of Cadiz is assessed based on
the identification and counting of the burrows where they live,
using underwater videos. The Instituto Espa˜ nol de Oceanograf´ıa
(IEO) conducts an annual standard underwater television survey
(UWTV) to generate burrow density estimates of Nephrops within
a defined area, with a coefficient of variation (CV) or relative
standard error of less than 20%. Currently, the identification
and counting of the Nephrops burrows are carried out manually
by the experts. This is quite hectic and time consuming job.
Computer Vision and Deep learning plays a vital role now a
days in detection and classification of objects.
The proposed system introduces a deep learning based automated
way to identify and classify the Nephrops burrows. The
proposed work is using current state of the art Faster RCNN
models Inception v2 and MobileNet v2 for objects detection
and classification. Tensorflow is used to evaluate the Inception
and MobileNet performance with different numbers of training
images. The average mean precision of Inception is more than
75% as compared to MobileNet which is 64%. The results show
the comparison of Inception and MobileNet detections, as well
as the calculation of True Positive and False Positive detections
along with undetected burrows.Universidad de Málaga, IEEE, Sir SYED University Karachi-Pakistán, Mehran University Jamshoro-Pakistán, Riphah International Universit
Automatic Detection of Nephrops Norvegicus Burrows from Underwater Imagery Using Deep Learning
The Norway lobster, Nephrops norvegicus, is one of the main commercial
crustacean fisheries in Europe. The abundance of Nephrops norvegicus
stocks is assessed based on identifying and counting the burrows where they
live from underwater videos collected by camera systems mounted on sledges.
The Spanish Oceanographic Institute (IEO) andMarine Institute Ireland (MIIreland)
conducts annual underwater television surveys (UWTV) to estimate
the total abundance of Nephrops within the specified area, with a coefficient
of variation (CV) or relative standard error of less than 20%. Currently, the
identification and counting of the Nephrops burrows are carried out manually
by the marine experts. This is quite a time-consuming job. As a solution, we
propose an automated system based on deep neural networks that automatically
detects and counts the Nephrops burrows in video footage with high
precision. The proposed system introduces a deep-learning-based automated
way to identify and classify the Nephrops burrows. This research work uses the
current state-of-the-art Faster RCNN models Inceptionv2 and MobileNetv2
for object detection and classification. We conduct experiments on two data
sets, namely, the Smalls Nephrops survey (FU 22) and Cadiz Nephrops survey
(FU 30), collected by Marine Institute Ireland and Spanish Oceanographic
Institute, respectively. From the results, we observe that the Inception model
achieved a higher precision and recall rate than theMobileNetmodel. The best
mean Average Precision (mAP) recorded by the Inception model is 81.61%
compared toMobileNet, which achieves the best mAP of 75.12%.Versión del edito
Comment on "Identifying Molecular Orientation of Individual C<sub>60</sub> on a Si(111)-(7x7) Surface"
A Comment on the Letter by J. G. Hou, et al., Phys. Rev. Lett. 83, 3001 (1999)
- …