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- Naha, Arunava, et al.
(författare)
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Bayesian Quickest Change-Point Detection With an Energy Harvesting Sensor and Asymptotic Analysis
- 2024
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Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 72, s. 565-579
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Tidskriftsartikel (refereegranskat)abstract
- This paper studies the problem of the quickest change-point detection by a sensor powered by randomly available energy harvested from the environment under a Bayesian framework. In particular, the sensor observes a stochastic process by taking and processing samples at discrete times. We assume the distribution of the sampled data changes at an unknown random time, and the pre and post-change distributions are stationary and known. In the proposed framework, the sensor takes a new sample if there is enough evidence of a change. Otherwise, the sensor saves energy for the future and does not take new samples. The optimal policy is obtained by dynamic programming that minimizes the average detection delay for fixed upper bounds on the false alarm rate and an average number of samples taken before the change point. We model the test statistics as a perturbed random walk and study the asymptotic performance of the proposed method applying non-linear renewal theory under two different scenarios. First, (H) over bar >= E-s, where (H) over bar is the average harvested energy in one sampling period and E-s is the energy needed to take and process a new sample, and second, (H) over bar < E-s. For the first scenario, the optional policy turns out to be greedy, i.e., the sensor takes samples when sufficient energy is available. However, under the second scenario, the proposed method performs better than the greedy approach since it prepares for the future and uses available energy parsimoniously. We have provided several numerical results to support the derived theory.
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| 2. |
- Zhao, Ziwen, et al.
(författare)
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Automated Analysis of Nano-Impact Single-Entity Electrochemistry Signals Using Unsupervised Machine Learning and Template Matching
- 2024
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Ingår i: ADVANCED INTELLIGENT SYSTEMS. - : John Wiley & Sons. - 2640-4567. ; 6:1
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Tidskriftsartikel (refereegranskat)abstract
- Nano-impact (NIE) (also referred to as collision) single-entity electrochemistry is an emerging technique that enables electrochemical investigation of individual entities, ranging from metal nanoparticles to single cells and biomolecules. To obtain meaningful information from NIE experiments, analysis and feature extraction on large datasets are necessary. Herein, a method is developed for the automated analysis of NIE data based on unsupervised machine learning and template matching approaches. Template matching not only facilitates downstream processing of the NIE data but also provides a more accurate analysis of the NIE signal characteristics and variations that are difficult to discern with conventional data analysis techniques, such as the height threshold method. The developed algorithm enables fast automated processing of large experimental datasets recorded with different systems, requiring minimal human intervention and thereby eliminating human bias in data analysis. As a result, it improves the standardization of data processing and NIE signal interpretation across various experiments and applications. Nano-impact (NIE) electrochemistry is an emerging technique for studying individual entities. Analyzing large NIE datasets, often with low signal-to-noise ratios, is challenging. Herein, an automated approach is introduced using unsupervised machine learning and template matching for accurate feature extraction from spike-shaped NIE signals. It improves data processing, accuracy and standardization, reducing human bias in signal interpretation across experiments.image (c) 2023 WILEY-VCH GmbH
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