Obstructive sleep apnea (OSA) is one of the major sleep disorders, which has been demonstrated to be a high-risk factor for cardiovascular disease, hypertension, and motor vehicle accidents. Pressure sensors in a contactless manner are a promising way to monitor sleep conditions outside of the hospital. However, previous studies mainly based on limited sensors are often subjected to noise contamination and constrained by the sleeper position to pressure sensors. The acquired pressure signals are of poor quality or even lost, which are not appropriate for the downstream task of OSA event detection. To address this issue, we designed a sensitive piezoelectric ceramic sensor array (PCSA) by aligning sixteen sensors embedded into a mat covering the chest and abdomen area, which can capture the changes of weak pressure signals under a sleeping mattress with a thickness of up to 30 cm. Based on PCSA, we recruited 36 adult volunteers from the Peking Union Medical College Hospital and conducted a pilot study to acquire overnight pressure signals along with polysomnography recordings. Subsequently, we developed an automated OSA event detection method named DRFNet. The main advantage of DRFNet is that it can well capture the time-domain and frequency-domain features from different views by fusing ResNet18 and DenseNet121 networks. Experiment results showed that DRFNet can achieve 75.19 % sensitivity, 87.78 % specificity, and 81.48 % accuracy, which is competitive with existing state-of-the-art methods. Combined with PCSA, it can be potentially deployed into an embedded device and provide contactless sleep monitoring service in home settings.

Classic McEliece is a code-based quantum-resistant public-key scheme characterized with relative high encapsulation/decapsulation speed and small ciphertexts, with an in-depth analysis on its security. However, slow key generation with large public key size make it hard for wider applications. Based on this observation, Mckeycutter, a high-throughput key generator in hardware, is proposed to accelerate the key generation in Classic McEliece based on algorithm-hardware co-design. Meanwhile the storage overhead caused by large-size keys is also minimized. First, compact large-size GF(2) Gauss elimination method is presented by adopting naive processing array and memory-friendly scheduling strategy. Second, an optimized constant-time hardware sorter is proposed to support regular memory accesses with less comparators and storage. Third, algorithmlevel pipeline is enabled for high-throughput processing, allowing for concurrent key generations. Our FPGA implementation results achieve around 4× improvements in throughput with 9~14× less memory-time product compared with the existing FPGA solutions.

Obstructive sleep apnea (OSA) is considered one of the most common sleep disorders, causing multiple organ multiple system dysfunction and leading to a series of complications such as depression, insomnia, stroke, and so on. Piezoelectric ceramic sensor array (PCSA) with the merit of easily embedding into a mattress is a quite promising tool to monitor OSA events at home. Previous efforts achieved promising results for OSA detection based on PCSA, however, there are two major challenges still on open: (1) how to screen out the high-quality PCSA signals; (2) how to alleviate the difference among PCSA signals from different individuals. To address these challenges, we propose a cross-domain deep neural network for OSA event detection named CDDNNet. To obtain the high-quality PCSA signals, we propose a new dynamic channel-selection algorithm with maximizing energy minimizing variance of PCSA signals. To make CDDNNet with the cross-domain learning ability, we employ the gradient reverse learning (GRL) technique to alleviate the difference among PCSA signals from different individuals. What's more, we conduct a pilot study to collect overnight PCSA signals from the Peking Union Medical College Hospital. Experiment results show that CDDNNet can achieve competitive results of 54.20% sensitivity, 90.30% specificity, and 84.68% accuracy for OSA event detection.