选择性学习用于使用移位不变频谱稳定的低样本网络进行传感
Ankur Verma1, Ayush Goyal2, Sanjay Sarma3
1Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA, 16802, USA.
Scientific reports
|December 31, 2024
概括
本研究引入了对传感器数据的选择性学习方法,减少了数据收集需求,同时提高了准确性. 这种方法显著降低了实时传感应用的成本和计算需求.
科学领域:
- 科学计算科学计算
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 目前的传感器数据收集依赖于Shannon-Nyquist定理,导致大量的数据量和高的基础设施成本.
- 预计到2025年,全球传感器数据生成量将超过73万亿GB,这加剧了数据管理的挑战.
- 现有的方法在不断增加的成本和数据维护和计算所需的时间方面扎.
研究的目的:
- 引入一种选择性学习方法,减少对传感任务的数据收集要求.
- 开发能够处理实时传感问题的新型神经网络.
- 为了证明数据量,计算和相关成本的显著减少.
主要方法:
- 开发了新的转移不变和光谱稳定的神经网络.
- 制定实时感应问题作为分类或回归任务.
- 采用了选择性学习策略,数据收集依赖于问题.
主要成果:
- 证明可以收集更少的数据,同时保留重要信息.
- 证明测试准确性随着数据增强而提高,而不仅仅是增加原始数据收集.
- 证实神经网络可以学习最佳数据收集量,甚至低于单个数据点的尼奎斯特率.
结论:
- 选择性学习方法在数据收集,计算,功率,时间,带宽和延迟方面提供了数量级的降低.
- 这种方法对嵌入式应用有重大影响,从太空探索到水下车辆.
- 这些发现挑战了传统的信息理论方法,强调了智能数据选择的有效性.
相关概念视频
01:22Upsampling
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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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01:18Aliasing
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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
100
01:15Sampling Theorem
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In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
242
01:17Bandpass Sampling
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In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
133
01:20Downsampling
107
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
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01:10Reconstruction of Signal using Interpolation
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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
136