430 research outputs found
Automatic Target Recognition Strategy for Synthetic Aperture Radar Images Based on Combined Discrimination Trees
A strategy is introduced for achieving high accuracy in synthetic aperture radar (SAR) automatic target recognition (ATR) tasks. Initially, a novel pose rectification process and an image normalization process are sequentially introduced to produce images with less variations prior to the feature processing stage. Then, feature sets that have a wealth of texture and edge information are extracted with the utilization of wavelet coefficients, where more effective and compact feature sets are acquired by reducing the redundancy and dimensionality of the extracted feature set. Finally, a group of discrimination trees are learned and combined into a final classifier in the framework of Real-AdaBoost. The proposed method is evaluated with the public release database for moving and stationary target acquisition and recognition (MSTAR). Several comparative studies are conducted to evaluate the effectiveness of the proposed algorithm. Experimental results show the distinctive superiority of the proposed method under both standard operating conditions (SOCs) and extended operating conditions (EOCs). Moreover, our additional tests suggest that good recognition accuracy can be achieved even with limited number of training images as long as these are captured with appropriately incremental sample step in target poses
Analysis of Features for Synthetic Aperture Radar Target Classification
Considering two classes of vehicles, we aim to identify the physical elements of the vehicles with the most impact on identifying the class of the vehicle in synthetic aperture radar (SAR) images. We classify vehicles using features, from polarimetric SAR images, corresponding to the structure of physical elements. We demonstrate a method which determines the most impactful features to classification by applying subset selection on the features. Determination of the most impactful elements of the vehicles is beneficial to the development of low observables, target models, and automatic target recognition (ATR) algorithms. We show how previous work with features from individual pixels is applied to a greater number of target states. At a greater number of target states, the previous work has poor classification performance. Additionally, the nature of the features from pixels limits the identification of the most impactful elements of vehicles. We apply concepts from optical sensing to reduce the limitation on identification of physical elements. We draw from optical sensing feature extraction with the use of Histogram of Oriented Gradients (HOG). From the cells of HOG, we form features from frequency and polarization attributes of SAR images. Using a subset set of features, we achieve a classification performance of 96.10 percent correct classification. Using the features from HOG and the cells, we identify the features with the most impact. Using backward selection, a process for subset selection, we identify the features with the most impact to classification. The execution of backward selection removes the features which induce the most error
Reporting the novel synthetic cathinone 5-PPDI through its analytical characterization by mass spectrometry and nuclear magnetic resonance
Introduction:
User
surveys
indicate
that
expectations
of
higher
drug
purity
are
a
key
reason
for
cryptomarket
use.
In
2014–2015,
Spain’s
NGO
Energy
Control
conducted
a
1-year
pilot
project
to
provide
a
testing
service
to
cryptomarket
drug
users
using
the
Transnational
European
Drug
Information
(TEDI)
guidelines.
In
this
paper,
we
present
content
and
purity
data
from
the
trial.
Methods:
219
samples
were
analyzed
by
gas
chromatography
associated
with
mass
spectrometry
(GC/
MS).
Users
were
asked
to
report
what
substance
they
allegedly
purchased.
Results:
40
different
advertised
substances
were
reported,
although
77.6%
were
common
recreational
drugs
(cocaine,
MDMA,
amphetamines,
LSD,
ketamine,
cannabis).
In
200
samples
(91.3%),
the
main
result
of
analysis
matched
the
advertised
substance.
Where
the
advertised
compound
was
detected,
purity
levels
(m
SD)
were:
cocaine
71.6
19.4%;
MDMA
(crystal)
88.3
1.4%;
MDMA
(pills)
133.3
38.4
mg;
Amphetamine
(speed)
51.3
33.9%;
LSD
123.6
40.5
m
g;
Cannabis
resin
THC:
16.5
7.5%
CBD:
3.4
1.5%;
Ketamine
71.3
38.4%.
39.8%
of
cocaine
samples
contained
the
adulterant
levamisole
(11.6
8%).
No
adulterants
were
found
in
MDMA
and
LSD
samples.
Discussion:
The
largest
collection
of
test
results
from
drug
samples
delivered
from
cryptomarkets
are
reported
in
this
study.
Most
substances
contained
the
advertised
ingredient
and
most
samples
were
of
high
purity.
The
representativeness
of
these
results
is
unknow
Semi-Supervised SAR ATR Framework with Transductive Auxiliary Segmentation
Convolutional neural networks (CNNs) have achieved high performance in
synthetic aperture radar (SAR) automatic target recognition (ATR). However, the
performance of CNNs depends heavily on a large amount of training data. The
insufficiency of labeled training SAR images limits the recognition performance
and even invalidates some ATR methods. Furthermore, under few labeled training
data, many existing CNNs are even ineffective. To address these challenges, we
propose a Semi-supervised SAR ATR Framework with transductive Auxiliary
Segmentation (SFAS). The proposed framework focuses on exploiting the
transductive generalization on available unlabeled samples with an auxiliary
loss serving as a regularizer. Through auxiliary segmentation of unlabeled SAR
samples and information residue loss (IRL) in training, the framework can
employ the proposed training loop process and gradually exploit the information
compilation of recognition and segmentation to construct a helpful inductive
bias and achieve high performance. Experiments conducted on the MSTAR dataset
have shown the effectiveness of our proposed SFAS for few-shot learning. The
recognition performance of 94.18\% can be achieved under 20 training samples in
each class with simultaneous accurate segmentation results. Facing variances of
EOCs, the recognition ratios are higher than 88.00\% when 10 training samples
each class
Mid-price prediction based on machine learning methods with technical and quantitative indicators
Stock price prediction is a challenging task, but machine learning methods
have recently been used successfully for this purpose. In this paper, we
extract over 270 hand-crafted features (factors) inspired by technical and
quantitative analysis and tested their validity on short-term mid-price
movement prediction. We focus on a wrapper feature selection method using
entropy, least-mean squares, and linear discriminant analysis. We also build a
new quantitative feature based on adaptive logistic regression for online
learning, which is constantly selected first among the majority of the proposed
feature selection methods. This study examines the best combination of features
using high frequency limit order book data from Nasdaq Nordic. Our results
suggest that sorting methods and classifiers can be used in such a way that one
can reach the best performance with a combination of only very few advanced
hand-crafted features
SAR Target Image Generation Method Using Azimuth-Controllable Generative Adversarial Network
Sufficient synthetic aperture radar (SAR) target images are very important
for the development of researches. However, available SAR target images are
often limited in practice, which hinders the progress of SAR application. In
this paper, we propose an azimuth-controllable generative adversarial network
to generate precise SAR target images with an intermediate azimuth between two
given SAR images' azimuths. This network mainly contains three parts:
generator, discriminator, and predictor. Through the proposed specific network
structure, the generator can extract and fuse the optimal target features from
two input SAR target images to generate SAR target image. Then a similarity
discriminator and an azimuth predictor are designed. The similarity
discriminator can differentiate the generated SAR target images from the real
SAR images to ensure the accuracy of the generated, while the azimuth predictor
measures the difference of azimuth between the generated and the desired to
ensure the azimuth controllability of the generated. Therefore, the proposed
network can generate precise SAR images, and their azimuths can be controlled
well by the inputs of the deep network, which can generate the target images in
different azimuths to solve the small sample problem to some degree and benefit
the researches of SAR images. Extensive experimental results show the
superiority of the proposed method in azimuth controllability and accuracy of
SAR target image generation
A Hybrid Templated-Based Composite Classification System
An automatic target classification system contains a classifier which reads a feature as an input and outputs a class label. Typically, the feature is a vector of real numbers. Other features can be non-numeric, such as a string of symbols or alphabets. One method of improving the performance of an automatic classification system is through combining two or more independent classifiers that are complementary in nature. Complementary classifiers are observed by finding an optimal method for partitioning the problem space. For example, the individual classifiers may operate to identify specific objects. Another method may be to use classifiers that operate on different features. We propose a design for a hybrid composite classification system, which exploits both real-numbered and non-numeric features with a template matching classification scheme. This composite classification system is made up of two independent classification systems.These two independent classification systems, which receive input from two separate sensors are then combined over various fusion methods for the purpose of target identification. By using these two separate classifiers, we explore conditions that allow the two techniques to be complementary in nature, thus improving the overall performance of the classification system. We examine various fusion techniques, in search of the technique that generates the best results. We investigate different parameter spaces and fusion rules on example problems to demonstrate our classification system. Our examples consider various application areas to help further demonstrate the utility of our classifier. Optimal classifier performance is obtained using a mathematical framework, which takes into account decision variables based on decision-maker preferences and/or engineering specifications, depending upon the classification problem at hand
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