In automotive systems, a radar is a key component of autonomous driving. Using transmit and reflected radar signal by a target, we can capture the target range and velocity. However, when interference signals exist, noise floor increases and it severely affects the detectability of target objects. For these reasons, previous studies have been proposed to cancel interference or reconstruct original signals. However, the conventional signal processing methods for canceling the interference or reconstructing the transmit signals are difficult tasks, and also have many restrictions. In this work, we propose a novel approach to mitigate interference using deep learning. The proposed method provides high performance in various interference conditions and has low processing time. Moreover, we show that our proposed method achieves better performance compared to existing signal processing methods.

Ovarian cancer is the second most common gynaecological malignancy, and microRNAs (miRNAs) play important role in the cancer development. Here, we found that the level of miR-200b/200a/429 was significantly increased in serum and tumor tissues of patients with stage-I ovarian cancer. Consistent with these results, we detected increased expression levels of miR-200b/200a/429 in ovarian cancer cell lines compared with the human nontumorigenic ovarian epithelial cell line T80. The overexpression of miR-200b/200a/429 in T80 cells stimulated proliferation and caused their growth in soft agar and tumor formation in nude mice. Furthermore, we determined that miR-200b/200a/429 targets inhibitor of growth family 5 (ING5) and that the overexpression of ING5 can block miR-200b/200a/429-induced T80 cell transformation and tumorigenesis. Our findings suggest that miR-200b/200a/429 may be a useful biomarker for the early detection of ovarian cancer and that miR-200b/200a/429 significantly contributes to ovarian cancer development through ING5.

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