Medical IoT devices are rapidly becoming part of management ecosystems for pandemics such as COVID-19. Existing research shows that deep learning (DL) algorithms have been successfully used by ...researchers to identify COVID-19 phenomena from raw data obtained from medical IoT devices. Some examples of IoT technology are radiological media, such as CT scanning and X-ray images, body temperature measurement using thermal cameras, safe social distancing identification using live face detection, and face mask detection from camera images. However, researchers have identified several security vulnerabilities in DL algorithms to adversarial perturbations. In this article, we have tested a number of COVID-19 diagnostic methods that rely on DL algorithms with relevant adversarial examples (AEs). Our test results show that DL models that do not consider defensive models against adversarial perturbations remain vulnerable to adversarial attacks. Finally, we present in detail the AE generation process, implementation of the attack model, and the perturbations of the existing DL-based COVID-19 diagnostic applications. We hope that this work will raise awareness of adversarial attacks and encourages others to safeguard DL models from attacks on healthcare systems.
Oil thickness in oil spills involving sea ice is a key parameter required for an effective oil spill response; however, quantifying it from radar backscatter data remains a difficult task. We ...investigated a possible solution for estimating oil slick thickness by using electromagnetic (EM) forward and inverse scattering models of oil-covered newly formed sea ice (NI). Our forward model employs a first-order approximation of a multilayered small perturbation method (SPM) to predict two copolarization C-band radar backscatters of NI covered by an oil slick with thicknesses ranging from 0 to 7 mm. The results showed that the backscatter decreases as slick thickness increases, which we attributed to signal attenuation within the saline-oil layer. Our inverse model relies on the particle swarm optimization (PSO) algorithm to determine the slick thickness on NI using synthetic backscatter data, and it requires the input of several important ice and oil physical parameters (thickness, dielectrics, and roughness). Moreover, the estimated slick thickness was validated using scatterometer data from an oil-on-ice experiment at the University of Manitoba’s Sea-ice Environmental Research Facility (SERF). With synthetic data, the 5 mm oil slick thickness was overestimated by 25%, while with experimental data, it was overestimated by 8%. Overall, our findings have laid the groundwork for future inversion studies to identify the thickest oil spill zone from current and future C-band radar satellites for immediate response.
Opposition-based learning (OBL) is an effective strategy to enhance many optimization methods among which opposition-based differential evolution (ODE) is one of the successful variants. However, ODE ...is a strict point-to-point algorithm, which may cause those opposite solutions to be ignored who are close to, however, have a gap to more promising solutions in the neighborhood. It usually provides a relatively narrow search channel for the candidate solutions and cannot maintain well population diversity. Hence, it is necessary to broaden the search neighborhood of the opposite solutions to increase the possibility of seeking out an even better solution. Thus, a new approach, GODE, is proposed to implement a Gaussian perturbation operation around the opposite point to expand its search neighborhood. Three different self-adaptive standard deviation models are then proposed and compared in the Gaussian perturbation strategy. Subsequently, a multi-stage perturbation strategy with different sized neighborhood is adopted to balance exploration and exploitation during different evolutionary stages. GODE is firstly compared with DE and ODE on CEC2014 benchmark suite with dimension of 30, 50 and 100. Many recent state-of-the-art algorithms using OBL strategy are further conducted comparison with GODE. The experimental results and statistical comparison analysis demonstrated that GODE has better or equal competitive performance against the classical and recent competitors.
We consider a class of continuous-time hybrid dynamical systems that correspond to subgradient flows of a piecewise linear and convex potential function with finitely many pieces, and which includes ...the fluid-level dynamics of the Max-Weight scheduling policy as a special case. We study the effect of an external disturbance/perturbation on the state trajectory, and establish that the magnitude of this effect can be bounded by a constant multiple of the integral of the perturbation.
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