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IEEE ICAS 2021 is a premier international forum for presenting the technological advances and research results in the fields of theoretical, experimental, and applied Autonomous Systems (AS). IEEE International Conference on Autonomous Systems (ICAS 2021) is sponsored by IEEE Signal Processing Society (SPS) through IEEE SPS Autonomous Systems Initiative.

In human-machine systems (HMS), trust placed by humans on machines is a complex concept and attracts increasingly research efforts. Herein, we reviewed recent studies on building and measuring trust in HMS. The review was based on one comprehensive model of trust – IMPACTS, which has 7 features of intention, measurability, performance, adaptivity, communication, transparency, and security. The review found that, in the past 5 years, HMS fulfill the features of intention, measurability, communication, and transparency. Most of the HMS consider the feature of performance.


Human perceptual sensitivity of various types of forces, e.g., stiffness and friction, is important for surgeons during robotic surgeries such as needle insertion and palpation. However, force feedback from robot end-effector is usually a combination of desired and undesired force components which could have an effect on the perceptual sensitivity of the desired one. In presence of undesired forces, to improve perceptual sensitivity of desired force could benefit robotic surgical outcomes.


The automatic diagnosis of lung infections using chest computed
tomography (CT) scans has been recently obtained remarkable significance,
particularly during the COVID-19 pandemic that the early
diagnosis of the disease is of utmost importance. In addition, infection
diagnosis is the main building block of most automated diagnostic/
prognostic frameworks. Recently, due to the devastating effects
of the radiation on the body caused by the CT scan, there has been
a surge in acquiring low and ultra-low-dose CT scans instead of the