Performance Comparison of Detection Methods for Combined STBC and SM Systems

2008 ◽  
Vol E91-B (6) ◽  
pp. 1734-1742 ◽  
Author(s):  
X. N. TRAN ◽  
H. C. HO ◽  
T. FUJINO ◽  
Y. KARASAWA
Keyword(s):  
Performance Comparison ◽  
Detection Methods

scholarly journals Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2509 ◽  
Author(s):  
Kamran Shaukat ◽  
Suhuai Luo ◽  
Vijay Varadharajan ◽  
Ibrahim A. Hameed ◽  
Shan Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Performance Comparison ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Modern World ◽  
The Internet ◽  
Cyber Threats ◽  
Risk Detection ◽  
Learning Techniques ◽  
Made In

Cyberspace has become an indispensable factor for all areas of the modern world. The world is becoming more and more dependent on the internet for everyday living. The increasing dependency on the internet has also widened the risks of malicious threats. On account of growing cybersecurity risks, cybersecurity has become the most pivotal element in the cyber world to battle against all cyber threats, attacks, and frauds. The expanding cyberspace is highly exposed to the intensifying possibility of being attacked by interminable cyber threats. The objective of this survey is to bestow a brief review of different machine learning (ML) techniques to get to the bottom of all the developments made in detection methods for potential cybersecurity risks. These cybersecurity risk detection methods mainly comprise of fraud detection, intrusion detection, spam detection, and malware detection. In this review paper, we build upon the existing literature of applications of ML models in cybersecurity and provide a comprehensive review of ML techniques in cybersecurity. To the best of our knowledge, we have made the first attempt to give a comparison of the time complexity of commonly used ML models in cybersecurity. We have comprehensively compared each classifier’s performance based on frequently used datasets and sub-domains of cyber threats. This work also provides a brief introduction of machine learning models besides commonly used security datasets. Despite having all the primary precedence, cybersecurity has its constraints compromises, and challenges. This work also expounds on the enormous current challenges and limitations faced during the application of machine learning techniques in cybersecurity.

Performance Comparison of Wideband Radar Target Detection Methods

2014 ◽  
Vol 971-973 ◽  
pp. 1680-1683
Author(s):  
Miao He ◽  
Li Yu Tian ◽  
Xiong Jun Fu ◽  
Yun Chen Jiang
Keyword(s):  
Target Detection ◽  
Pulse Train ◽  
Performance Comparison ◽  
The Other ◽  
Detection Methods ◽  
Radar Target ◽  
Random Parameters ◽  
Related Model ◽  
Performance Curves ◽  
Wideband Radar

In wideband radar situation, target-spread and all scattering points back wave could be considered as the pulse train of random parameters. The wideband radar target and built the related model. Then it gave two methods of target detection, one is Energy Accumulation and the other is the IPTRP. It also presented the simulation result of these two methods performance curves. It showed that the IPTRP improved by more than 3dB in the same SNR.

Performance comparison of detection methods for weak target based on two-dimensional fractal sea surface

Optik ◽  
2014 ◽  
Vol 125 (12) ◽  
pp. 2963-2969
Author(s):  
Chungang Wang ◽  
Wenquan Feng ◽  
Chunsheng Li
Keyword(s):  
Performance Comparison ◽  
Sea Surface ◽  
Detection Methods ◽  
Two Dimensional ◽  
Weak Target

scholarly journals ANOMALY DETECTION PERFORMANCE COMPARISON ON ANOMALY-DETECTION BASED CHANGE DETECTION ON MARTIAN IMAGE PAIRS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1437-1441
Author(s):  
A. R. D. Putri ◽  
P. Sidiropoulos ◽  
J.-P. Muller
Keyword(s):  
High Resolution ◽  
Anomaly Detection ◽  
Change Detection ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Performance Comparison ◽  
Detection Methods ◽  
Large Area ◽  
Visible Wavelengths ◽  
Image Pairs

<p><strong>Abstract.</strong> The surface of Mars has been imaged in visible wavelengths for more than 40 years since the first flyby image taken by Mariner 4 in 1964. With higher resolution from orbit from MOC-NA, HRSC, CTX, THEMIS, and HiRISE, changes can now be observed on high-resolution images from different instruments, including spiders (Piqueux et al., 2003) near the south pole and Recurring Slope Lineae (McEwen et al., 2011) observable in HiRISE resolution. With the huge amount of data and the small number of datasets available on Martian changes, semi-automatic or automatic methods are preferred to help narrow down surface change candidates over a large area.</p><p>To detect changes automatically in Martian images, we propose a method based on a denoising autoencoder to map the first Martian image to the second Martian image. Both images have been automatically coregistered and orthorectified using ACRO (Autocoregistration and Orthorectification) (Sidiropoulos and Muller, 2018) to the same base image, HRSC (High-Resolution Stereo Camera) (Neukum and Jaumann, 2004; Putri et al., 2018) and CTX (Context Camera) (Tao et al., 2018) orthorectified using their DTMs (Digital Terrain Models) to reduce the number of false positives caused by the difference in instruments and viewing conditions. Subtraction of the codes of the images are then inputted to an anomaly detector to look for change candidates. We compare different anomaly detection methods in our change detection pipeline: OneClassSVM, Isolation Forest, and, Gaussian Mixture Models in known areas of changes such as Nicholson Crater (dark slope streak), using image pairs from the same and different instruments.</p>

scholarly journals Two-Step Real-Time Night-Time Fire Detection in an Urban Environment Using Static ELASTIC-YOLOv3 and Temporal Fire-Tube

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2202 ◽  
Author(s):  
MinJi Park ◽  
Byoung Chul Ko
Keyword(s):  
Urban Areas ◽  
State Of The Art ◽  
Fire Detection ◽  
Classification Performance ◽  
Performance Comparison ◽  
Detection Methods ◽  
Night Time ◽  
Detection Systems ◽  
Ambient Lighting ◽  
Fast Classification

While the number of casualties and amount of property damage caused by fires in urban areas are increasing each year, studies on their automatic detection have not maintained pace with the scale of such fire damage. Camera-based fire detection systems have numerous advantages over conventional sensor-based methods, but most research in this area has been limited to daytime use. However, night-time fire detection in urban areas is more difficult to achieve than daytime detection owing to the presence of ambient lighting such as headlights, neon signs, and streetlights. Therefore, in this study, we propose an algorithm that can quickly detect a fire at night in urban areas by reflecting its night-time characteristics. It is termed ELASTIC-YOLOv3 (which is an improvement over the existing YOLOv3) to detect fire candidate areas quickly and accurately, regardless of the size of the fire during the pre-processing stage. To reflect the dynamic characteristics of a night-time flame, N frames are accumulated to create a temporal fire-tube, and a histogram of the optical flow of the flame is extracted from the fire-tube and converted into a bag-of-features (BoF) histogram. The BoF is then applied to a random forest classifier, which achieves a fast classification and high classification performance of the tabular features to verify a fire candidate. Based on a performance comparison against a few other state-of-the-art fire detection methods, the proposed method can increase the fire detection at night compared to deep neural network (DNN)-based methods and achieves a reduced processing time without any loss in accuracy.

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