In this paper, we present a wireless ECG-derived Respiration Rate (RR) estimation using an autoencoder with a DCT Layer. The wireless wearable system records the ECG data of the subject and the respiration rate is determined from the variations in the baseline level of the ECG data. A straightforward Fourier analysis of the ECG data obtained using the wireless wearable system may lead to incorrect results due to uneven breathing. To improve the estimation precision, we propose a neural network that uses a novel Discrete Cosine Transform (DCT) layer to denoise and decorrelates the data. The DCT layer has trainable weights and soft-thresholds in the transform domain. In our dataset, we improve the Mean Squared Error (MSE) and Mean Absolute Error (MAE) of the Fourier analysis-based approach using our novel neural network with the DCT layer.

Optical camera communication (OCC) has attracted increasing attention recently thanks to the wide usage of LED and high-resolution cameras. The lens-image sensor structure enables the camera distinguish light from various source, which is ideal for spatial MIMO. Hence, OCC can be applied to several emerging application scenarios, such as vehicle and drone communications. However, distance is a major bottleneck for OCC system, because the increase in distance makes it difficult for the camera to distinguish adjacent LEDs, which we call LED spatial mixing.In this paper, we propose a novel hierarchical coding scheme named as OnionCode to support dynamic range of channel capacity in one-to-many OCC scenario. OnionCode adopts a multi-priority receiving scheme, i.e., the receivers can dynamically discard the low-priority bit streams according to the measured channel capacity. OnionCode achieves this based on a key insight that, the luminance level of a mix-LED is distinguishable. We prototype a LED-based OCC system to evaluate the efficacy of OnionCode and the results show that OnionCode achieves a higher coding efficiency and overall throughput compared with the existing hierarchical coding.

Quick response (QR) codes are becoming pervasive due to their rapid readability and the popularity of smartphones with built-in cameras. QR codes are also gaining importance in the retail sector as a convenient mobile payment method. However, researchers have concerns regarding the security of QR codes, which leave users susceptible to financial loss or private information leakage. In this study, we address this issue by developing a novel QR code (called mQR code), which exploits patterns presenting a specific spatial frequency as a form of camouflage. When the targeted receiver holds a camera in a designated position (e.g., directly in front at a distance of 30 cm from the camouflaged QR code), the original QR code is revealed in form of a Moiré pattern. From any other position, only the camouflaged QR code can be seen. In experiments, the decryption rate of mQR codes is $> 98%$. The decryption rate for cameras positioned $20\degree$ off axis or $> 10cm$ from the designated location drops to $0%$, indicating that any attackers will be unable to steal a usable image.