Web-services have become most common IT enablers today. Cyber attacks such as the distributed denial of service (DDoS) attacks pose availability concerns which may result into service outages and consequently financia...
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The ephemeris and timing errors of low Earth orbit (LEO) satellites are modeled, leading to an approach to disambiguate these errors from pseudorange-type measurements. First, a model is derived describing the ephemer...
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The ephemeris and timing errors of low Earth orbit (LEO) satellites are modeled, leading to an approach to disambiguate these errors from pseudorange-type measurements. First, a model is derived describing the ephemeris error's impact on ranging measurements from LEO space vehicles (SVs) with imprecise ephemerides. A simulation study is presented comparing the impact of ephemeris error on ranging error for five LEO constellations (Statlink, OneWeb, Orbcomm, Iridium, and Globalstar) and five medium Earth orbit (MEO) constellations (GPS, GLONASS, Galileo, BeiDou-3, and O3B). Second, it is shown that for a particular SV position, the ephemeris error has no effect on range measurements. Next, the ephemeris and timing errors are parametrized by the 3-D ephemeris error magnitude and its direction angle from the in-track axis. This parametrization is exploited in a proposed algorithm to disambiguate the ephemeris and timing errors from the LEO SVs' pseudorange measurements at a reference receiver. The two parameters can be communicated to any unknown receiver listening to the same LEO SVs to correct for ephemerides ranging error, leading to improved positioning, navigation, and timing precision. Monte Carlo simulation results are presented demonstrating the efficacy of the proposed algorithm. The simulations considered a reference receiver tracking via pseudorange measurements 22 Starlink and 4 OneWeb LEO SVs with poorly known ephemerides [obtained from two-line element (TLE) files, propagated with SGP4]. The proposed algorithm reduced the 3-D position error of all SVs from a few kilometers to less than 120 m. The parameters were communicated to an unknown receiver to correct the LEO ephemerides, after which the receiver estimated its position by fusing its LEO pseudoranges via an extended Kalman filter, resulting in a horizontal position error of 0.91 m, as compared to 213 m utilizing TLE+SGP4 ephemerides. Two sets of experimental results are presented where the ephemeris
Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare *** patient data pro-cessing from remote places may lead to severe privacy ***,the existing cl...
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Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare *** patient data pro-cessing from remote places may lead to severe privacy ***,the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to offloading of live patient data to remote cloud *** the privacy *** proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption *** can help maintain the privacy preservation and confidentiality of patients’medical data during diagnosis of Parkinson’s *** addition,the energy and delay aware computational offloading scheme is proposed to minimize the uncertainty and energy consumption of end-user *** proposed research maintains the better privacy and robustness of live video data processing during prediction and diagnosis compared to existing health-care systems.
Breast cancer remains a significant global health issue, primarily affecting females and requiring advanced detection methods to improve patient outcomes. While computer-aided diagnostic methods have progressed, limit...
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The growing concerns over mitigating climate change effects resulted in power system planning and generation expansion strategies that aim in increasing penetration of intermittent renewable energy sources (RES) to fu...
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The concept of Network-as-a-Service involves deploying and reconfiguring next-generation networks, in a flexible and dynamic manner, to always cater to the needs of the respective stakeholders. It presents a complex c...
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Background: The population of Fontan patients, patients born with a single functioningventricle, is growing. There is a growing need to develop algorithms for this population that can predicthealth outcomes. Artiffcia...
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Background: The population of Fontan patients, patients born with a single functioningventricle, is growing. There is a growing need to develop algorithms for this population that can predicthealth outcomes. Artiffcial intelligence models predicting short-term and long-term health outcomes forpatients with the Fontan circulation are needed. Generative adversarial networks (GANs) provide a solutionfor generating realistic and useful synthetic data that can be used to train such models. Methods: Despitetheir promise, GANs have not been widely adopted in the congenital heart disease research communitydue, in some part, to a lack of knowledge on how to employ them. In this research study, a GAN was usedto generate synthetic data from the Pediatric Heart Network Fontan I dataset. A subset of data consistingof the echocardiographic and BNP measures collected from Fontan patients was used to train the *** sets of synthetic data were created to understand the effect of data missingness on synthetic datageneration. Synthetic data was created from real data in which the missing values were imputed usingMultiple Imputation by Chained Equations (MICE) (referred to as synthetic from imputed real samples). Inaddition, synthetic data was created from real data in which the missing values were dropped (referred to assynthetic from dropped real samples). Both synthetic datasets were evaluated for ffdelity by using visualmethods which involved comparing histograms and principal component analysis (PCA) plots. Fidelitywas measured quantitatively by (1) comparing synthetic and real data using the Kolmogorov-Smirnovtest to evaluate the similarity between two distributions and (2) training a neural network to distinguishbetween real and synthetic samples. Both synthetic datasets were evaluated for utility by training aneural network with synthetic data and testing the neural network on its ability to classify patients thathave ventricular dysfunction using echocardiograph measures an
Membrane proteins provide a significant part in cellular activities. The role of membrane proteins is inevitable in drug interactions and in all living organisms. Membrane protein classification is used to identify th...
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In [1], two implicit transmission techniques, namely, implicit transmission with bit flipping (ITBF) and implicit transmission with collection decoding (ITCD) have been introduced to transmit an independent second cod...
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Coherent multiple-input–multiple-output (MIMO) radar could significantly improve the weak moving target detection ability by accumulating multichannel and multiframe echo signal. However, due to the target motion and...
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