Highlights
a light CNN for efficient detection of COVID-19 from chest CT scans is proposed
the accuracy is comparable with that of more complex CNN designs
the efficiency is 10 times better than more complex CNNs using pre-processing
no GPU acceleration is required and can be executed on middle class computers
A fascinating challenge in the field of human–robot interaction is the possibility to endow robots with emotional intelligence in order to make the interaction more intuitive, genuine, and natural. To achieve this, a critical point is the capability of the robot to infer and interpret human emotions. Emotion recognition has been widely explored in the broader fields of human–machine interaction and affective computing. Here, we report recent advances in emotion recognition, with particular regard to the human–robot interaction context. Our aim is to review the state of the art of currently adopted emotional models, interaction modalities, and classification strategies and offer our point of view on future developments and critical issues. We focus on facial expressions, body poses and kinematics, voice, brain activity, and peripheral physiological responses, also providing a list of available datasets containing data from these modalities.
The use of pH-sensitive nitroxides, in conjunction with low-frequency EPR, offers a unique opportunity for non-invasive assessment of pH values (in the range 0 to 14) in living animals. In the present study, we have investigated the potential use of pH-sensitive nitroxide free radicals in conjunction with EPR imaging techniques at low and very low frequencies (280 MHz-2.1 GHz). In particular, we have measured the hyperfine splitting (hfs) of a pH-sensitive probe at three different EPR frequencies: 280 MHz, 1.1 GHz and 2.1 GHz. We have also developed EPR imaging experiments with phantoms simulating in vivo conditions, using pH-sensitive probes at 280 MHz (spatial-spatial) and 1.1 GHz (spectral-spatial). Finally, we discuss the actual sensitivity/resolution limits of the EPR imaging techniques at low frequencies. Practical applications of this method in the biomedical field are suggested for the continuous and non-invasive localization of pH in vivo.
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