Understanding the echo clues used by dolphins to remotely identify the target characteristics of submerged elastic shells

1996 ◽  
Author(s):  
Guillermo C. Gaunaurd ◽  
Donald Brill ◽  
H. Huang ◽  
Patrick W. Moore ◽  
Hans C. Strifors
Keyword(s):  
Elastic Shells ◽  
Target Characteristics

scholarly journals Classification of Space Objects by Using Deep Learning with Micro-Doppler Signature Images

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4365
Author(s):  
Kwangyong Jung ◽  
Jae-In Lee ◽  
Nammoon Kim ◽  
Sunjin Oh ◽  
Dong-Wook Seo
Keyword(s):  
Transfer Learning ◽  
Data Augmentation ◽  
Doppler Frequency ◽  
Extraction Methods ◽  
Classification Performance ◽  
Reconstruction Method ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Space Objects ◽  
Target Characteristics

Radar target classification is an important task in the missile defense system. State-of-the-art studies using micro-doppler frequency have been conducted to classify the space object targets. However, existing studies rely highly on feature extraction methods. Therefore, the generalization performance of the classifier is limited and there is room for improvement. Recently, to improve the classification performance, the popular approaches are to build a convolutional neural network (CNN) architecture with the help of transfer learning and use the generative adversarial network (GAN) to increase the training datasets. However, these methods still have drawbacks. First, they use only one feature to train the network. Therefore, the existing methods cannot guarantee that the classifier learns more robust target characteristics. Second, it is difficult to obtain large amounts of data that accurately mimic real-world target features by performing data augmentation via GAN instead of simulation. To mitigate the above problem, we propose a transfer learning-based parallel network with the spectrogram and the cadence velocity diagram (CVD) as the inputs. In addition, we obtain an EM simulation-based dataset. The radar-received signal is simulated according to a variety of dynamics using the concept of shooting and bouncing rays with relative aspect angles rather than the scattering center reconstruction method. Our proposed model is evaluated on our generated dataset. The proposed method achieved about 0.01 to 0.39% higher accuracy than the pre-trained networks with a single input feature.

scholarly journals Evolution of Cancer Vaccines—Challenges, Achievements and Future Directions

Vaccines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 535
Author(s):  
Ban Qi Tay ◽  
Quentin Wright ◽  
Rahul Ladwa ◽  
Christopher Perry ◽  
Graham Leggatt ◽  
...  
Keyword(s):  
Cancer Vaccines ◽  
New Drugs ◽  
Delivery Methods ◽  
Future Directions ◽  
The Past ◽  
New Directions ◽  
Tumour Antigens ◽  
Target Characteristics ◽  
Toxic Responses ◽  
Shed Light

The development of cancer vaccines has been intensively pursued over the past 50 years with modest success. However, recent advancements in the fields of genetics, molecular biology, biochemistry, and immunology have renewed interest in these immunotherapies and allowed the development of promising cancer vaccine candidates. Numerous clinical trials testing the response evoked by tumour antigens, differing in origin and nature, have shed light on the desirable target characteristics capable of inducing strong tumour-specific non-toxic responses with increased potential to bring clinical benefit to patients. Novel delivery methods, ranging from a patient’s autologous dendritic cells to liposome nanoparticles, have exponentially increased the abundance and exposure of the antigenic payloads. Furthermore, growing knowledge of the mechanisms by which tumours evade the immune response has led to new approaches to reverse these roadblocks and to re-invigorate previously suppressed anti-tumour surveillance. The use of new drugs in combination with antigen-based therapies is highly targeted and may represent the future of cancer vaccines. In this review, we address the main antigens and delivery methods used to develop cancer vaccines, their clinical outcomes, and the new directions that the vaccine immunotherapy field is taking.

A Target Selection Algorithm of Seeker Based on the Prior Information and SVM

2013 ◽  
Vol 734-737 ◽  
pp. 3071-3074
Author(s):  
Guo Dong Zhang ◽  
Zhong Liu
Keyword(s):  
Prior Information ◽  
Simulation Experiment ◽  
Target Selection ◽  
Support Vector ◽  
Selection Algorithm ◽  
Distribution Law ◽  
Surface Ship ◽  
False Target ◽  
Target Characteristics ◽  
Selection Of

Aiming at the phenomenon that the chaff and corner reflector released by surface ship can influence the selection of missile seeker, this paper proposed a multi-target selection method based on the prior information of false targets distribution and Support Vector Machine (SVM). By analyzing the false targets distribution law we obtain two classification principles, which are used to train the SVM studies the true and false target characteristics. The trained SVM is applied to the seeker in the target selection. This method has advantages of simple programming and high classification accuracy, and the simulation experiment in this paper confirms the correctness and effectiveness of this method.

scholarly journals The Pennsylvania—Toruń Planet Search: Target Characteristics and Recent Results

2011 ◽  
Author(s):  
Paweł Zieliński ◽  
Andrzej Niedzielski ◽  
Aleksander Wolszczan ◽  
Grzegorz Nowak ◽  
Monika Adamów ◽  
...  
Keyword(s):  
Target Characteristics
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