Weakly-supervised Semantic Segmentation (WSSS) aims to provide a precise semantic segmentation results without expensive pixel-wise segmentation labels. With the supervision gap between classification and segmentation, Image-level WSSS mainly relies on Class Activation Maps (CAMs) from the classification model to emulate the pixel-wise annotations. However, CAMs often fail to cover the entire object region because classification models tend to focus on narrow discriminative regions in an object. Towards accurate CAM coverage, Existing WSSS methods have tried to boost feature representation learning or impose consistency regularization to the classification models, but still there are limitation in activating non-discriminative area, where the focus of the models is weak. To tackle this issue, we propose FSAE framework, which provides explicit supervision of non-discriminative area, encouraging the CAMs to activate on various object features. We leverage weak-strong consistency with pseudo-label expansion strategy for reliable supervision and enhance learning of non-discriminative object boundaries. Specifically, we use strong perturbation to make challenging inference target, and focus on generating reliable pixel-wise supervision signal for broad object regions. Extensive experiments on the WSSS benchmark datasets show that our method boosts initial seed quality and segmentation performance by large margin, achieving new state-of-the-art performance on benchmark WSSS datasets. Our public code is available at https://github.com/obeychoi0120/FSAE.

Human respiration rate (RR) estimation is essential for various health care applications, such as sleep apnea detection and chronic obstructive pulmonary disease early diagnose. Recently, radio frequency based RR estimation has achieved high accuracy for single-person RR detection. However, multi-person RR estimation is still the obstacle blocking the wide commercialization of RF sensing based RR solution. In this paper, a novel multi-person RR estimation algorithm that can overcome the frequency resolution limit is present. The proposed algorithm is not only analytically justified but also verified in a real test-bed involving commercial off-the-shelf WiFi devices. Extensive experiment results show a 98% accuracy in people-counting and a root mean square error (RMSE) of 0.13 breath per minute (bpm) on RR detection. To the best of our knowledge, this is the first WiFi sensing work that can detect different people who share the same RR by only using a single pair of transmit and receive antenna.

In this paper, we develop a contention window (CW) control scheme for practical IEEE 802.11 wireless local area networks (WLANs) that have node heterogeneity in terms of the traffic load, transmission rate, and packet size. We introduce activity probability, i.e., the probability that a node contends for medium access opportunities at a given time. We then newly develop a performance analysis model that enables analytic estimation on the contention status including the collision probability, collision time, back-off time, and throughput with comprehensive consideration of node heterogeneity. Based on the newly developed model, we derive the theoretically ideal contention status, and develop a CW control scheme that achieves the ideal contention status in an average sense. We perform extensive NS-3 simulations and real testbed experiments for evaluation of both the proposed performance analysis model and CW control scheme. The results show that the proposed model provides accurate prediction on the contention status, and the proposed CW control scheme achieves considerable throughput improvement compared to the existing schemes which do not comprehensively consider node heterogeneity.