Achievable Accuracy and Cramer Rao Bounds for SSB Based LEO Positioning in NR NTN

Advanced Low Earth Orbit (LEO) satellite networks, such as Starlinks Mobile Satellite Service (MSS), will adopt the 5G New Radio (NR) Non-Terrestrial Network (NTN) standard. This enables the use of ubiquitous Synchronization Signal Blocks (SSBs) for opportunistic receiver positioning based on pseudorange and Doppler measurements. In this work, we characterize the estimation theoretic limits of SSB-based positioning by deriving single SSB Cramer Rao lower bounds (CRLBs) for delay and carrier frequency observables associated with pseudorange and Doppler. These bounds are obtained using the full SSB time-frequency energy distribution and extended into a multi epoch, multi satellite Fisher information framework that jointly bounds stationary user position, clock bias, and clock drift. Each SSB contribution is weighted according to a range dependent CRLB determined by the received SNR, so the estimator and the bound share a common noise model. In simulation, the resulting bound closely matches achievable performance, and a physically weighted least squares estimator approaches the CRLB at realistic operating SNR. Using a Starlink based constellation, we analyze the operating SNR experienced by a ground user and demonstrate sub-meter positioning accuracy.

Opportunistic Positioning with LEO Satellites based on SSB from NR NTN

Forthcoming Low Earth Orbit (LEO) satellite networks such as Starlink's Mobile Satellite Service (MSS) will incorporate the New Radio (NR) Non-Terrestrial Network (NTN) standard. The Synchronization Signal Block (SSB) specified as part of NR is periodically broadcast for cell search and initial access. We propose to exploit the SSB for opportunistic receiver positioning. Doppler shift measurements are modeled and pseudoranges are derived from SSB while also taking into account the receiver's clock bias and drift. The resulting per satellite integer ambiguity in the pseudorange is resolved by geometry alone, without inter-satellite differencing or an a-priori position. Measurements are taken from SSBs of multiple satellites and at multiple occasions per satellite, whereby the SSBs are subject to different transmission timings and varying propagation delays. Finally, a simulation model is developed for positioning based on the actual Starlink constellation and the NR NTN standard to evaluate the positioning accuracy to be expected. The proposed approach achieves a mean positioning error of less than 10m without requiring any modification of the NR NTN standard.

Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey

Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI-powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.

AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an App

Inability to test at scale has become Achille's heel in humanity's ongoing war against COVID-19 pandemic. An agile, scalable and cost-effective testing, deployable at a global scale, can act as a game changer in this war. To address this challenge, building on the promising results of our prior work on cough-based diagnosis of a motley of respiratory diseases, we develop an Artificial Intelligence (AI)-based test for COVID-19 preliminary diagnosis. The test is deployable at scale through a mobile app named AI4COVID-19. The AI4COVID-19 app requires 2-second cough recordings of the subject. By analyzing the cough samples through an AI engine running in the cloud, the app returns a preliminary diagnosis within a minute. Unfortunately, cough is common symptom of over two dozen non-COVID-19 related medical conditions. This makes the COVID-19 diagnosis from cough alone an extremely challenging problem. We solve this problem by developing a novel multi-pronged mediator centered risk-averse AI architecture that minimizes misdiagnosis. At the time of writing, our AI engine can distinguish between COVID-19 patient coughs and several types of non-COVID-19 coughs with over 90% accuracy. AI4COVID-19's performance is likely to improve as more and better data becomes available. This paper presents a proof of concept to encourage controlled clinical trials and serves as a call for labeled cough data. AI4COVID-19 is not designed to compete with clinical testing. Instead, it offers a complementing tele-testing tool deployable anytime, anywhere, by anyone, so clinical-testing and treatment can be channeled to those who need it the most, thereby saving more lives.

Can Machine Learning Be Used to Recognize and Diagnose Coughs?

5G is bringing new use cases to the forefront, one of the most prominent being machine learning empowered health care. Since respiratory infections are one of the notable modern medical concerns and coughs being a common symptom of this, a system for recognizing and diagnosing infections based on raw cough data would have a multitude of beneficial research and medical applications. In the literature, machine learning has been successfully used to detect cough events in controlled environments. In this work, we present a novel system that utilizes Convolutional Neural Networks (CNNs) to detect cough within environment audio and diagnose three potential illnesses (i.e., Bronchitis, Bronchiolitis, and Pertussis) based on their unique cough audio features. Our detection model achieves an accuracy of 90.17% and a specificity of 89.73%, whereas the diagnosis model achieves an accuracy of about 94.74% and an F1 score of 93.73%. These results clearly show that our system is successfully able to detect and separate cough events from background noise. Moreover, our single diagnosis model is capable of distinguishing between different illnesses without the need of separate models.