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ICACT20250181 Question.12
Questioner: fbee5860@gmail.com    2025-02-19 오전 11:03:41 		ICACT20250181 Answer.12
Answer by Auhor 3120220183@bit.edu.cn   2025-02-19 오전 11:03:41
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The experimental part verifies the effectiveness of the algorithm in simulation and measured data, but does not mention the performance of different noise types (such as pulse noise, multipath interference). Is the improved method only applicable to Gaussian white noise scenarios? How robust is the algorithm in more complex real-world noise environments (such as urban multipath effects)? Current experiment scope: The experiments in this paper mainly focus on Gaussian white noise scenes, and verify the denoising performance of the algorithm under different signal-to-noise ratio (SNR). The results of simulation and measured data show that the improved method has remarkable denoising effect and localization accuracy improvement in Gaussian white noise environment. The applicability of complex noise environment: the paper does not explicitly test complex noise scenes such as pulse noise and multipath interference, but the wavelet threshold denoising method has strong universality and can theoretically be extended to other noise types. For complex noise such as multipath interference, it may be necessary to further adjust the wavelet basis function, decomposition layer number and threshold selection strategy to adapt to the non-stationary characteristics of the signal. Future work could include validating the robustness of the algorithm in more complex noise environments, such as multipath effects in urban environments or pulsed noise in industrial environments.
ICACT20250181 Question.19
Questioner: 1308374461@qq.com    2025-02-15 오후 3:59:29 		ICACT20250181 Answer.19
Answer by Auhor 3120220183@bit.edu.cn   2025-02-15 오후 3:59:29
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This paper presents an improved soft threshold function to solve the limitation of traditional hard/soft threshold function. Can you explain specifically how the mathematical design of this improved function balances the advantages of hard thresholds (retaining local features) and soft thresholds (reducing oscillations)? In the paper, there is an improved definition of the threshold function, λ is the threshold, α is the adjustment parameter, the function is close to the hard threshold when the wavelet coefficient is greater than λ (retain local characteristics, smooth transition when the wavelet coefficient is about equal to λ (reduce oscillation), so as to avoid the discontinuity of the hard threshold and the constant error of the soft threshold
ICACT20250181 Question.6
Questioner: 18801066623@163.com    2025-02-19 오전 11:06:02 		ICACT20250181 Answer.6
Answer by Auhor 3120220183@bit.edu.cn   2025-02-19 오전 11:06:02
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In this paper, the sampling rate of the base station is 61.44MHz, and multiple base stations need to collect signals synchronously. In practical engineering, high sampling rates and strict synchronization can increase hardware cost and complexity. Are the requirements of the algorithm on hardware devices (such as sampling rate and synchronization accuracy) likely to become the bottleneck of its large-scale deployment? Hardware requirements: The base station sampling rate used in the paper is 61.44MHz, which does put high requirements on hardware equipment, especially in large-scale deployment may increase costs. Multi-base station synchronous signal acquisition requires high precision time synchronization, which challenges the clock synchronization technology of the system. Optimization in practical applications: Sampling rate optimization: The requirement for sampling rate can be reduced through signal pre-processing (such as downsampling) or compressed sensing techniques, while maintaining the key characteristics of the signal. Synchronization technology: The adoption of high-precision clock synchronization protocols (such as IEEE 1588) or satellite-based synchronization technologies (such as GPS) can alleviate synchronization problems to some extent. Distributed computing: Through edge computing or distributed processing, part of the computing task is distributed to each base station, reducing the burden on the central node. Feasibility of large-scale deployment: Although high sampling rates and synchronization requirements increase hardware complexity, these challenges are gradually being overcome with the development of technologies such as 5G communications and high-performance embedded systems. For specific application scenarios (such as military or high-precision industrial inspection), hardware costs may be acceptable; However, for consumer applications, the algorithm may need to be further optimized to reduce hardware requirements.