Hand pose estimation based on improved NSRM network

He, Duo; Li, Qi; Wang, Jinhua; Li, Dexin

doi:10.1186/s13634-023-00970-y

Cited by 6 publications

(5 citation statements)

References 32 publications

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“…Specifically, different phase alignments lead to constructive or destructive summation of these waves at the receiver, thereby shaping the final observed amplitude variation of the signal. Figure 1 presents a schematic diagram illustrating the multipath interference effects on radar signals propagating over the sea surface [16]. The direct propagation path A → D and the reflected paths A → B i → D contribute to the multipath interference, where i indicates the multiple reflected paths.…”

Section: Radar Multipath Effectsmentioning

confidence: 99%

Deep Anomaly Detection with Attention (DADA): A Novel Approach for Identifying Multipath Interference in Radar Signals

Yan,

Jin,

Huang

et al. 2024

IET Signal Processing

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Multipath interference in radar signals caused by sea, ground, and other environments poses significant challenges to the target detection, tracking, and classification capabilities of radar systems. Existing methods for radar signal identification require labeled samples and focus mainly on the classification of normal signals. However, in practice, anomalous samples (multipath interference signals) may be scarce and highly imbalanced (i.e., mostly normal samples). To address this problem, we propose a deep anomaly detection with attention (DADA) for semisupervised detection of multipath radar signals. The method transforms radar signals into time–frequency images and is trained exclusively on normal samples. The autoencoder architecture is extended with a feature extractor network to capture latent sample features. CBAM attention is introduced to improve feature extraction. By learning the distribution of normal samples in high-dimensional image space and low-dimensional feature space, a two-dimensional feature space representing normal samples is constructed. A one-class SVM then learns the boundary of normal samples for anomaly detection. Extensive experiments on radar signal datasets validate the effectiveness of the proposed approach.

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Section: Radar Multipath Effectsmentioning

confidence: 99%

Deep Anomaly Detection with Attention (DADA): A Novel Approach for Identifying Multipath Interference in Radar Signals

Yan,

Jin,

Huang

et al. 2024

IET Signal Processing

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“…MFCC features [22] are widely used in audio forensics for gunshot recognition. MFCC is a technique that represents the spectral envelope of an audio signal by extracting the Mel-scale frequency bands and calculating the cepstral coefficients of these bands.…”

Section: B Mel Frequency Cepstral Coefficients Feature Extractionmentioning

confidence: 99%

Preventing Crimes Through Gunshots Recognition Using Novel Feature Engineering and Meta-Learning Approach

Raza,

Rustam,

Mallampati

et al. 2023

IEEE Access

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Gunshot sounds are common in crimes, particularly those involving threats, harassment, or killing. The gunshot sounds in crimes can create fear and panic among victims, often leading to psychological trauma. Gunshot sounds are associated with a significant mortality rate, especially in cases of gun violence. The sound of gunshots can serve as evidence in criminal investigations, allowing law enforcement officials to determine the number of shots fired, the caliber of the gun used, and the distance from which the shots were fired. Efficient gunshot detection is necessary to address the issue of gun violence in society. This study aims to detect gunshot sounds using an efficient approach to prevent crimes. The frequency-time domain spectrum analysis is performed to understand the patterns of signals related to each target class. A novel Discrete Wavelet Transform Random Forest Probabilistic (DWT-RFP) feature engineering approach is proposed, which takes Mel-frequency cepstral coefficients (MFCC) extracted from gunshot sound data as input for feature extraction. A novel meta-learning-based Meta-RF-KN (MRK) is proposed to detect gunshots based on newly created ensemble features with a DWT-RFP approach. For experiments, the gunshot sounds dataset containing 851 audio clips collected from public videos on YouTube from eight kinds of gun models, is used. Advanced machine learning and deep learning techniques are applied in comparison to evaluate the performance of the proposed approach. Extensive experiments show that the proposed MRK approach achieves 99% k-fold accuracy for detecting gunshots and outperforms state-of-the-art approaches. The proposed approach can potentially be used for accurate gunshot detection and to help prevent crimes.

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“…Pre-processing generally refers to audio down-sampling [ 11 , 12 ] and amplitude normalization [ 13 , 14 ]. Signal processing methods are also applied, and some specific filters are designed to meet the research needs [ 15 ] or improve signal quality [ 16 , 17 , 18 ]. Regarding features for classification and recognition, the power spectrum was often used in early research [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…Xue et al added the channel attention mechanism to their model to extract more useful feature information [ 15 ]. Yang Jirui, et al improved channel attention mechanism to better adapt to the characteristics of underwater acoustic signals [ 17 ]. However, both 1D convolution and RNNs can only model changes between adjacent time points, thus failing to discover long-term dependencies of sequences.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Underwater Acoustic Target Recognition: Introduce a Recent Temporal 2D Modeling Method

Tang,

Gao,

et al. 2024

Sensors

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In recent years, the application of deep learning models for underwater target recognition has become a popular trend. Most of these are pure 1D models used for processing time-domain signals or pure 2D models used for processing time-frequency spectra. In this paper, a recent temporal 2D modeling method is introduced into the construction of ship radiation noise classification models, combining 1D and 2D. This method is based on the periodic characteristics of time-domain signals, shaping them into 2D signals and discovering long-term correlations between sampling points through 2D convolution to compensate for the limitations of 1D convolution. Integrating this method with the current state-of-the-art model structure and using samples from the Deepship database for network training and testing, it was found that this method could further improve the accuracy (0.9%) and reduce the parameter count (30%), providing a new option for model construction and optimization. Meanwhile, the effectiveness of training models using time-domain signals or time-frequency representations has been compared, finding that the model based on time-domain signals is more sensitive and has a smaller storage footprint (reduced to 30%), whereas the model based on time-frequency representation can achieve higher accuracy (1–2%).

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Hand pose estimation based on improved NSRM network

Cited by 6 publications

References 32 publications

Deep Anomaly Detection with Attention (DADA): A Novel Approach for Identifying Multipath Interference in Radar Signals

Deep Anomaly Detection with Attention (DADA): A Novel Approach for Identifying Multipath Interference in Radar Signals

Preventing Crimes Through Gunshots Recognition Using Novel Feature Engineering and Meta-Learning Approach

Deep Learning Based Underwater Acoustic Target Recognition: Introduce a Recent Temporal 2D Modeling Method

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