Sparse Representation Combined with Edge Computing in Target Recognition of Live Work Using Target Recognition Algorithm

Signal decomposition is crucial in signal processing, but orthogonal decomposition can be problematic for signals with a broad time-frequency field. To address this issue, sparse representation is used to compress high-resolution range profile data and extract functions. A redundant structural dicti...

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Bibliographic Details
Published in2024 International Conference on Smart Systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC) pp. 763 - 767
Main Authors Hussian, Ahmed, Jaaywel, Waleed Sadeq, Alabdeli, Haider, Ahmed, Ibrahem, Ismail, Laith S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 28.06.2024
Subjects
Accuracy
Dictionaries
Edge computing
loT
Sparse approximation
sparse representation
Target recognition
Target tracking
Training
Uncertainty
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Summary:Signal decomposition is crucial in signal processing, but orthogonal decomposition can be problematic for signals with a broad time-frequency field. To address this issue, sparse representation is used to compress high-resolution range profile data and extract functions. A redundant structural dictionary and a fast sparse representation approach are introduced for radar target detection. This research uses a unique Target Recognition (TR) method with a fast sparse decomposition technique for objectively recognizing SAR images. The study distinguishes malicious loT -Edge Computer (EC) network traffic from compromised loT equipment using the loT botnet attack detection (SRF -loTAD) method with rehabilitation error threshold criteria. The TR-EC method extracts the generalized two-dimensional main element evaluating properties of training samples to construct sub- dictionaries. The orthogonal matching tracking method computes coefficients for sparse displays of test samples across sub-dictionaries. The results of an actual loT -based network dataset were compared to those obtained using an autoencoder approach, revealing the highest analysis, comparison, accuracy, and performance ratios compared to other approaches.
DOI:10.1109/ICSSEECC61126.2024.10649530