Adapted Compressed Sensing: A Game Worth Playing

Mangia, Mauro; Pareschi, Fabio; Rovatti, Riccardo; Setti, Gianluca

doi:10.1109/mcas.2019.2961727

Cited by 13 publications

(6 citation statements)

References 54 publications

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“…We note that the relevant developments are very rich in the CS field. For example, (1) characteristics of feasible acquisition hardware and objective signals could be exploited to achieve better applicability and sampling efficiency [100,101]; (2) performance of the reconstruction algorithm could be further improved (sometimes coupled with the sampling design), in terms of computational efficiency and restoration quality [102]; (3) theories could be extended to more general cases [103]; (4) advances in machine learning may be adopted to enhance CS [104]. It is out of the scope of this paper to discuss them in detail.…”

Section: Compressed Sensingmentioning

confidence: 99%

Data-Driven Intelligent 3D Surface Measurement in Smart Manufacturing: Review and Outlook

et al. 2021

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High-fidelity characterization and effective monitoring of spatial and spatiotemporal processes are crucial for high-performance quality control of many manufacturing processes and systems in the era of smart manufacturing. Although the recent development in measurement technologies has made it possible to acquire high-resolution three-dimensional (3D) surface measurement data, it is generally expensive and time-consuming to use such technologies in real-world production settings. Data-driven approaches that stem from statistics and machine learning can potentially enable intelligent, cost-effective surface measurement and thus allow manufacturers to use high-resolution surface data for better decision-making without introducing substantial production cost induced by data acquisition. Among these methods, spatial and spatiotemporal interpolation techniques can draw inferences about unmeasured locations on a surface using the measurement of other locations, thus decreasing the measurement cost and time. However, interpolation methods are very sensitive to the availability of measurement data, and their performances largely depend on the measurement scheme or the sampling design, i.e., how to allocate measurement efforts. As such, sampling design is considered to be another important field that enables intelligent surface measurement. This paper reviews and summarizes the state-of-the-art research in interpolation and sampling design for surface measurement in varied manufacturing applications. Research gaps and future research directions are also identified and can serve as a fundamental guideline to industrial practitioners and researchers for future studies in these areas.

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Section: Compressed Sensingmentioning

confidence: 99%

Data-Driven Intelligent 3D Surface Measurement in Smart Manufacturing: Review and Outlook

et al. 2021

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“…The energy consumption of high-frequency sampling processes can be reduced by novel event-triggered 63 and adapted compressed sensing paradigms. 64 Alternatively, emerging technologies might distribute the complex and energy-consuming machine-learning computations among distributed levels of machine learning, combining both smart wearables or edge artificial intelligence and intermediate server levels at home (ie, fog computing). Such technology might improve >10× the system lifetime with respect to data transmitted to the central cloud medical system, and reduce the system latency by up to 60%.…”

Section: Detectionmentioning

confidence: 99%

“…Thus, seizure detection and forecasting algorithms should ideally be integrated into the same system. All of the above developments require significant improvement in several core aspects of wearable hardware and software technologies, including low power requirements. The energy consumption of high‐frequency sampling processes can be reduced by novel event‐triggered 63 and adapted compressed sensing paradigms 64 . Alternatively, emerging technologies might distribute the complex and energy‐consuming machine‐learning computations among distributed levels of machine learning, combining both smart wearables or edge artificial intelligence and intermediate server levels at home (ie, fog computing).…”

Section: The Future Of Fs Detectionmentioning

confidence: 99%

Noninvasive detection of focal seizures in ambulatory patients

et al. 2020

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Reliably detecting focal seizures without secondary generalization during daily life activities, chronically, using convenient portable or wearable devices, would offer patients with active epilepsy a number of potential benefits, such as providing more reliable seizure count to optimize treatment and seizure forecasting, and triggering alarms to promote safeguarding interventions. However, no generic solution is currently available to reach these objectives. A number of biosignals are sensitive to specific forms of focal seizures, in particular heart rate and its variability for seizures affecting the neurovegetative system, and accelerometry for those responsible for prominent motor activity. However, most studies demonstrate high rates of false detection or poor sensitivity, with only a minority of patients benefiting from acceptable levels of accuracy. To tackle this challenging issue, several lines of technological progress are envisioned, including multimodal biosensing with cross‐modal analytics, a combination of embedded and distributed self‐aware machine learning, and ultra–low‐power design to enable appropriate autonomy of such sophisticated portable solutions.

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“…Initially, mostly random Gaussian measurement kernels have been investigated [11], since for those, theoretical guarantees could be established ensuring that the reconstruction is robust and stable. This entails that mostly theoretical results exist in the ultrasound literature, as the design of a generic CS hardware is a challenging task [12].…”

Section: Introduction a State Of The Artmentioning

confidence: 99%