Related references
Note: Only part of the references are listed.
Article
Engineering, Biomedical
Ao Chen et al.
Summary: Computer-Aided Sperm Analysis (CASA) is a widely studied topic in male reproductive health, but there is a lack of publicly available large-scale image datasets. To address this issue, researchers provide the Sperm Videos and Images Analysis (SVIA) dataset for testing and evaluating different computer vision techniques in CASA. Through experimental analyses and comparisons, the dataset demonstrates its ability to assess various functions and contribute to the development of CASA.
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Boban Sazdic-Jotic et al.
Summary: The research indicates that the use of drones has significantly improved and expanded, and a potential deep learning algorithm has been proposed as an anti-drone solution. The results show that the proposed algorithm has great potential in detecting and identifying drones, and exhibits high accuracy in detecting multiple drones.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Environmental Sciences
Frank Kulwa et al.
Summary: This study proposes a novel technique for analyzing microorganisms, which utilizes pairwise deep learning features to improve the accuracy of microorganism identification in the environment. The results show a significant improvement compared to non-paired deep learning features.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Chemistry, Analytical
Yongguang Mo et al.
Summary: This paper proposes a multi-stage deep learning-based method for UAV identification and detection, which samples UAV communication signals using compressed sensing technique and utilizes neural network structures for detection and classification. The experimental results demonstrate the effectiveness and accuracy of this method.
Article
Computer Science, Information Systems
Olusiji . O. Medaiyese et al.
Summary: In this study, we conducted a comprehensive comparative analysis on a radio frequency-based drone detection and identification system using machine learning algorithms and a pre-trained convolutional neural network called SqueezeNet. We considered wireless interference such as WiFi and Bluetooth and explored the performance of different models built using wavelet transforms under various signal-to-noise ratio levels.
PERVASIVE AND MOBILE COMPUTING
(2022)
Article
Computer Science, Information Systems
Thien Huynh-The et al.
Summary: This paper proposes a radio-frequency surveillance solution for detecting and classifying drones, and recognizing their operation modes using a convolutional neural network. The proposed network, RF-UAVNet, achieves high accuracy through the use of grouped one-dimensional convolution and a novel multi-level skip-connection structure.
Article
Engineering, Electrical & Electronic
Olusiji O. Medaiyese et al.
Summary: This study proposes a radio frequency-based UAV detection and identification system that can accurately detect UAVs or UAV control signals in the presence of other wireless signals, and extract features for further identification.
IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION
(2022)
Article
Computer Science, Artificial Intelligence
Lahbib Khrissi et al.
Summary: This article presents a new image segmentation approach based on the principle of clustering optimized by the meta-heuristic algorithm SCA (Algorithm Sinus Cosine). The approach addresses drawbacks in classic clustering techniques, providing satisfactory results compared to other methods.
EVOLUTIONARY INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Rui Fu et al.
Summary: This paper introduces a drone classification method based on LSTM and ALRO models, which achieves better drone detection accuracy by reducing computational overhead and using adaptive learning rates.
Article
Computer Science, Hardware & Architecture
Igor Bisio et al.
Summary: Remotely piloted vehicles have become increasingly popular due to their increased availability on the market. However, drones can pose a threat to public safety due to their potential misuse, which motivates the need for specific methods and techniques for monitoring sensitive areas and deploying effective drone surveillance systems. This article conducts a study on the literature related to wireless target localization based on RF and WiFi techniques, providing a thorough analysis of their potential deployment in drone surveillance applications.
Article
Remote Sensing
Pietro Casabianca et al.
Summary: With the widespread use of multirotor UAVs in commercial and public sectors, the associated security risks are increasing. This paper explores the use of deep learning methods to detect UAVs using acoustic signals. Convolutional Neural Networks have been found to outperform other network models, with late fusion methods achieving the highest accuracy.
Article
Multidisciplinary Sciences
M. H. D. Saria Allahham et al.
Proceedings Paper
Computer Science, Artificial Intelligence
Mark Coletti et al.
PROCEEDINGS OF 2019 IEEE/ACM THIRD WORKSHOP ON DEEP LEARNING ON SUPERCOMPUTERS (DLS)
(2019)
Article
Computer Science, Information Systems
Jiguang Dai et al.
Article
Computer Science, Information Systems
Bin Wang et al.
Article
Remote Sensing
Seamus Coveney et al.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2017)