Deep Learning-Based Image Denoising Approach for the Identification of Structured Light Modes in Dusty Weather

Structured light is gaining importance in free-space communication. Classifying spatially-structured light modes is challenging in a dusty environment because of the distortion on the propagating beams. This article addresses this challenge by proposing a deep learning convolutional autoencoder algorithm for modes denoising followed by a neural network for modes classification. The input to the classifier was set to be either the denoised image or the latent code of the convolutional autoencoder. This code is a low-dimensional representation of the inputted images. The proposed machine learning (ML) models were trained and tested using laboratory-generated mode data sets from the Laguerre and Hermite Gaussian mode bases. The results show that the two proposed approaches achieve an average classification accuracy exceeding 98%, and both are better than the classification accuracy reported recently (83–91%) in the literature

Photoplethysmography Data Reduction Using Truncated Singular Value Decomposition and Internet of Things Computing

Biometric-based identity authentication is integral to modern-day technologies. From smart phones, personal computers, and tablets to security checkpoints, they all utilize a form of identity check based on methods such as face recognition and fingerprint-verification. Photoplethysmography (PPG) is another form of biometric-based authentication that has recently been gaining momentum, because it is effective and easy to implement. This paper considers a cloud-based system model for PPG-authentication, where the PPG signals of various individuals are collected with distributed sensors and communicated to the cloud for authentication. Such a model incursarge signal traffic, especially in crowded places such as airport security checkpoints. This motivates the need for a compression–decompression scheme (or a Codec for short). The Codec is required to reduce the data traffic by compressing each PPG signal before it is communicated, i.e., encoding the signal right after it comes off the sensor and before it is sent to the cloud to be reconstructed (i.e., decoded). Therefore, the Codec has two system requirements to meet: (i) produce high-fidelity signal reconstruction; and (ii) have a computationallyightweight encoder. Both requirements are met by the Codec proposed in this paper, which is designed using truncated singular value decomposition (T-SVD). The proposed Codec is developed and tested using a publicly available dataset of PPG signals collected from multiple individuals, namely the CapnoBase dataset. It is shown to achieve a 95% compression ratio and a 99% coefficient of determination. This means that the Codec is capable of delivering on the first requirement, high-fidelity reconstruction, while producing highly compressed signals. Those compressed signals do not require heavy computations to be produced as well. An implementation on a single-board computer is attempted for the encoder, showing that the encoder can average 300 milliseconds per signal on a Raspberry Pi 3. This is enough time to encode a PPG signal prior to transmission to the cloud

Double Directional Channel Measurements for THz Communications in an Urban Environment

While mm-wave systems are a mainstay for 5G communications, the inexorable increase of data rate requirements and user densities will soon require the exploration of next-generation technologies. Among these, Terahertz (THz) band communication seems to be a promising direction due to availability of large bandwidth in the electromagnetic spectrum in this frequency range, and the ability to exploit its directional nature by directive antennas with small form factors. The first step in the analysis of any communication system is the analysis of the propagation channel, since it determines the fundamental limitations it faces. While THz channels have been explored for indoor, short-distance communications, the channels for wireless access links in outdoor environments are largely unexplored. In this paper, we present the - to our knowledge - first set of double-directional outdoor propagation channel measurements for the THz band. Specifically, the measurements are done in the 141 - 148.5 GHz range, which is one of the frequency bands recently allocated for THz research by the Federal Communication Commission (FCC). We employ double directional channel sounding using a frequency domain sounding setup based on RF-over-Fiber (RFoF) extensions for measurements over 100 m distance in urban scenarios. An important result is the surprisingly large number of directions (i.e., direction-of-arrival and direction-of-departure pairs) that carry significant energy. More generally, our results suggest fundamental parameters that can be used in future THz Band analysis and implementations