Towards complex scenes: A deep learning-based camouflaged people detection method for snapshot multispectral images

Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems, including spectral, polarization, and infrared technologies, there is still a lack of effective real-time method for accurately detecting small-size and high-efficient camouflaged people in complex real-world scenes. Here, this study proposes a snapshot multispectral image-based camouflaged detection model, multispectral YOLO(MS-YOLO), which utilizes the SPD-Conv and Sim AM modules to effectively represent targets and suppress background interference by exploiting the spatial-spectral target information. Besides, the study constructs the first real-shot multispectral camouflaged people dataset(MSCPD), which encompasses diverse scenes, target scales, and attitudes. To minimize information redundancy, MS-YOLO selects an optimal subset of 12 bands with strong feature representation and minimal inter-band correlation as input. Through experiments on the MSCPD, MS-YOLO achieves a mean Average Precision of 94.31% and real-time detection at 65 frames per second, which confirms the effectiveness and efficiency of our method in detecting camouflaged people in various typical desert and forest scenes. Our approach offers valuable support to improve the perception capabilities of unmanned aerial vehicles in detecting enemy forces and rescuing personnel in battlefield.

Application and optimization design of non-obstructive particle damping-phononic crystal vibration isolator in viaduct structure-borne noise reduction

The problems associated with vibrations of viaducts and low-frequency structural noise radiation caused by train excitation continue to increase in importance.A new floating-slab track vibration isolator-non-obstructive particle damping-phononic crystal vibration isolator is proposed herein,which uses the particle damping vibration absorption technology and bandgap vibration control theory.The vibration reduction performance of the NOPD-PCVI was analyzed from the perspective of vibration control.The paper explores the structure-borne noise reduction performance of the NOPD-PCVIs installed on different bridge structures under varying service conditions encountered in practical engineering applications.The load transferred to the bridge is obtained from a coupled train-FST-bridge analytical model considering the different structural parameters of bridges.The vibration responses are obtained using the finite element method,while the structural noise radiation is simulated using the frequency-domain boundary element method.Using the particle swarm optimization algorithm,the parameters of the NOPD-PCVI are optimized so that its frequency bandgap matches the dominant bridge structural noise frequency range.The noise reduction performance of the NOPD-PCVIs is compared to the steel-spring isolation under different service conditions.

Design and Optimization of Omnidirectional Band Gap for One-Dimensional Periodic and Quasiperiodic Phononic Heterostructures

A new kind of one-dimensional multilayer phononie heterostructure is constructed to obtain a broad acoustic omnidirectional reflection （ODR） band. The heterostructure is formed by combining finite periodic phononic crystals （PnCs） and Fibonacci （or Thue-Morse） quasiperiodic PnCs. From the numerical results performed by the transfer matrix method, it is found that the ODR bands can be enlarged obviously by using the combination of periodic and quasi-periodic PnCs. Moreover, an application of particle swarm optimization in designing and optimizing acoustic ODR bands is reported. With regards to different thickness ratios and periodic numbers in the heterostructure, we give some optimization examples and finally achieve phononic heterostructure with a very broad ODR bandwidth. The result provides a new approach to achieve broad acoustic ODR bandwidth, and will be applied in design of omnidirectional acoustic mirrors.

Classification and spectrum optimization method of grease based on infrared spectrum

The infrared(IR)absorption spectral data of 63 kinds of lubricating greases containing six different types of thickeners were obtained using the IR spectroscopy.The Kohonen neural network algorithm was used to identify the type of the lubricating grease.The results show that this machine learning method can effectively eliminate the interference fringes in the IR spectrum,and complete the feature selection and dimensionality reduction of the high-dimensional spectral data.The 63 kinds of greases exhibit spatial clustering under certain IR spectrum recognition spectral bands,which are linked to characteristic peaks of lubricating greases and improve the recognition accuracy of these greases.The model achieved recognition accuracy of 100.00%,96.08%,94.87%,100.00%,and 87.50%for polyurea grease,calcium sulfonate composite grease,aluminum(Al)-based grease,bentonite grease,and lithium-based grease,respectively.Based on the different IR absorption spectrum bands produced by each kind of lubricating grease,the three-dimensional spatial distribution map of the lubricating grease drawn also verifies the accuracy of classification while recognizing the accuracy.This paper demonstrates fast recognition speed and high accuracy,proving that the Kohonen neural network algorithm has an efficient recognition ability for identifying the types of the lubricating grease.

Dynamic control approaches of spectrum sensing in multi-band cognitive radio networks

In this paper,the dynamic control approaches for spectrum sensing are proposed,based on the theory that prediction is synonymous with data compression in computational learning.Firstly,a spectrum sensing sequence prediction scheme is proposed to reduce the spectrum sensing time and improve the throughput of secondary users.We use Ziv-Lempel data compression algorithm to design the prediction scheme,where spectrum band usage history is utilized.In addition,an iterative algorithm to find out the optimal number of spectrum bands allowed to sense is proposed,with the aim of maximizing the expected net reward of each secondary user in each time slot.Finally,extensive simulation results are shown to demonstrate the effectiveness of the proposed dynamic control approaches of spectrum sensing.

Information extraction and dynamic evaluation of soil salinization with a remote sensing method in a typical county on the Huang-Huai-Hai Plain of China

Monitoring the dynamics of soil salinization is of great importance for agricultural production.This study selected Yucheng County,a typical county on the Huang-Huai-Hai Plain(HHHP)of China,as the study area and evaluated the spatial and temporal variation of soil salinization.Three methods,consisting of principal component analysis(PCA)transformation,tasseled cap(TC)transformation,and optimal band combination(OBC),were used to extract information from an early Landsat multispectral scanner(MSS)image from 1984,and their advantages were compared.In addition,OBC was used on a thematic mapper(TM)image from 2009.An iteratively self-organizing data analysis algorithm was used together with prior knowledge of likely classifications to interpret the MSS and TM images for data classification.Finally,a transfer matrix method was used to assess the spatial and temporal variability of soil salinization and analyze the driving factors of soil salinization.Compared to PCA transformation and OBC,TC transformation was a more effective method for extracting soil salinization information from the MSS sensor.The results indicate that a soil area of approximately 298 km^2was affected by salinity in 1984 in Yucheng County,of which 5.40%,11.96%,and 12.75%were classified as being subject to slight,moderate,and severe salinization,respectively.In 2009,the saline area was reduced to only 146 km^2,of which 10.70%and 3.75%were characterized by slight to moderate salinization and no severe salinization,respectively.The saline land decreased at an average rate of 6 km^2per year.This decrease was probably a result of lower groundwater depth,increased organic fertilizer or crop straw in soil,changed land use type,and increased vegetation coverage.