While research on adversarial examples in machine learning for images has been prolific, similar attacks on deep learning (DL) for radio frequency (RF) signals and their mitigation strategies are scarcely addressed in the published work, with only one recent publication in the RF domain [1]. RF adversarial examples (AdExs) can cause drastic, targeted misclassification results mostly in spectrum sensing/ survey applications (e.g. BPSK mistaken for OFDM) with minimal waveform perturbation. It is not clear if the RF AdExs maintain their effects in the physical world, i.e., when AdExs are delivered over-theair (OTA). Our research on deep learning AdExs and proposed defense mechanisms are RF-centric, and incorporate physicalworld, OTA effects. We here present defense mechanisms based on statistical tests. One test to detect AdExs utilizes Peak-to-Average-Power-Ratio (PAPR) of the DL data points delivered OTA, while another statistical test uses the Softmax outputs of the DL classfier, which corresponds to the probabilities the classifier assigns to each of the trained classes. The former test leverages the RF nature of the data, and the latter is universally applicable to AdExs regardless of their origin. Both solutions are shown as viable mitigation methods to subvert adversarial attacks against communications and radar sensing systems.

This essay considers the significance of students’ emotional experiences during online engineering instruction by reviewing the evidence of factors that make for more efficient and effective online instructional practices during the pandemic period. The engineering courses, particularly those with labs and activities, were especially disrupted when they were re-designed for online context. The continuation of these instructional changes may cause substantially increased stress levels for students that ultimately may impact enrollment. Therefore, the success of engineering education for both the near and long-term future depends on providing students positive learning experiences which are associated with academic emotions during the establishment of this new normal of online technical engineering education.

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