Machine Learning/Artificial Intelligence for Sensor Data Fusion–Opportunities and Challenges

Blasch, Erik; Pham, Tien; Chong, Chee-Yee; Koch, Wolfgang; Leung, Henry; Braines, Dave; Abdelzaher, Tarek

doi:10.1109/maes.2020.3049030

Cited by 69 publications

(20 citation statements)

References 20 publications

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“…The ω vector represents the network parameters learned in the training phase. The D3F T ω is the natural generalization of (13) for IID observations, and plays the role of the LLR (39).…”

Section: B Dependent Observations: Target Detection In Binary Imagesmentioning

confidence: 99%

“…Given the tremendous success of AI, the opportunities and challenges of merging AI and sensor data fusion are under investigation by several research groups with special focus on computational efficiency, improved decision making, security, multi-domain operations, and human-machine teaming [39]. Ethical aspects concerning the AI are also of paramount importance; in this regard, digital ethics for AI and information fusion in the context of the defense domain is discussed in [40].…”

Section: Introductionmentioning

confidence: 99%

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Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis

Braca¹,

Millefiori²,

Aubry³

et al. 2022

Preprint

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We study the performance-and specifically the rate at which the error probability converges to zero-of Machine Learning (ML) classification techniques. Leveraging the theory of large deviations, we provide the mathematical conditions for a ML classifier to exhibit error probabilities that vanish exponentially, say ∼ exp (−n I + o(n)), where n is the number of informative observations available for testing (or another relevant parameter, such as the size of the target in an image) and I is the error rate. Such conditions depend on the Fenchel-Legendre transform of the cumulant-generating function of the Data-Driven Decision Function (D3F, i.e., what is thresholded before the final binary decision is made) learned in the training phase. As such, the D3F and, consequently, the related error rate I, depend on the given training set, which is assumed of finite size. Interestingly, these conditions can be verified and tested numerically exploiting the available dataset, or a synthetic dataset, generated according to the available information on the underlying statistical model. In other words, the classification error probability convergence to zero and its rate can be computed on a portion of the dataset available for training. Coherently with the large deviations theory, we can also establish the convergence, for n large enough, of the normalized D3F statistic to a Gaussian distribution. This property is exploited to set a desired asymptotic false alarm probability, which empirically turns out to be accurate even for quite realistic values of n. Furthermore, approximate error probability curves ∼ ζn exp (−n I) are provided, thanks to the refined asymptotic derivation (often referred to as exact asymptotics), where ζn represents the most representative sub-exponential terms of the error probabilities. Leveraging the refined asymptotic, we are able to compute an analytically accurate approximation of the classification performance for both the regimes of small and large values of n. Theoretical findings are corroborated by extensive numerical simulations. Real-world data, acquired by an X-band maritime radar system for surveillance, are exploited to assess the validity of the proposed theory.

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“…The ω vector represents the network parameters learned in the training phase. The D3F T ω is the natural generalization of (13) for IID observations, and plays the role of the LLR (39).…”

Section: B Dependent Observations: Target Detection In Binary Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis

Braca¹,

Millefiori²,

Aubry³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Machine learning has been widely explored recently [1], [3], [6], [26], [35], [38] to improve accuracy by recognizing patterns and features based on data from distinct sensors. For example, it is widely deployed in body sensor networks [26] to recognize data patterns from different sensors carried by an user such as an accelerometer and heart-rate monitor, to accurately match a user's activities with a specific data pattern (e.g., sports, sleeping, or walking).…”

Section: Multisensor Integrationmentioning

confidence: 99%

FusIon Data Tracking System (FITS)

Rodrigues¹,

Scheid²,

Willems³

et al. 2022

IEEE Sensors J.

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The field of generating movement profiles of individuals is valuable in many real-world applications (e.g., controlling disease spread or evaluating marketing engagement). Existing solutions often rely on GPS (Global Positioning System) or similar systems, primarily targeted at outdoor use cases. However, indoor tracking capabilities of current solutions either lack precision or are available in closed buildings only. The literature proposes sensor fusion approaches, but many of those are based on specific sensors. These approaches do not reveal implementation details or data to allow for their independent evaluation. Therefore, this paper presents FITS (FusIon Data Tracking System) as an approach and proof-of-concept to facilitate the correlation of data from different indoor sensors to movement profiles of different individuals. Functionally, FITS does this by generating synthetic sensor measurement data based on real-world movement data and correlating objects tracked from distinct sensors by effectively solving clustering and position prediction tasks. This correlation is evaluated based on different metrics (MOTA and MOTP -Multiple Object Tracker Accuracy/Precision) in four different scenarios e.g., sparse data, high density of sensors, low density of sensors, and a base case. Lastly, FITS's performance was evaluated by increasing the load test (dataset up to 100,000 measurements and 1,000 visitors) to assess whether near real-time processing is feasible under a high workload.

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“…As shown in Figure 16, fusion of heterogeneous sources data for insight (i.e., contacts of an infected individual, stay points of an individual, and co-relation of the symptoms with underlying diseases, etc.) finding is a promising avenue for research in the near future [254]. During the pandemic, there is an emerging need to secure all phases of the data lifecycle in order to prevent privacy breaches [255].…”

Section: Future Research Directionsmentioning

confidence: 99%

Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

Majeed

Hwang

2021

Symmetry

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This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.

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Machine Learning/Artificial Intelligence for Sensor Data Fusion–Opportunities and Challenges

Cited by 69 publications

References 20 publications

Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis

Statistical Hypothesis Testing Based on Machine Learning: Large Deviations Analysis

FusIon Data Tracking System (FITS)

Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

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