AirNet: A Calibration Model for Low-Cost Air Monitoring Sensors Using Dual Sequence Encoder Networks

Air pollution monitoring has attracted much attention in recent years. However, accurate and high-resolution monitoring of atmospheric pollution remains challenging. There are two types of devices for air pollution monitoring, i.e., static stations and mobile stations. Static stations can provide accurate pollution measurements but their spatial distribution is sparse because of their high expense. In contrast, mobile stations offer an effective solution for dense placement by utilizing low-cost air monitoring sensors, whereas their measurements are less accurate. In this work, we propose a data-driven model based on deep neural networks, referred to as AirNet, for calibrating low-cost air monitoring sensors. Unlike traditional methods, which treat the calibration task as a point-to-point regression problem, we model it as a sequence-to-point mapping problem by introducing historical data sequences from both a mobile station (to be calibrated) and the referred static station. Specifically, AirNet first extracts an observation trend feature of the mobile station and a reference trend feature of the static station via dual encoder neural networks. Then, a social-based guidance mechanism is designed to select periodic and adjacent features. Finally, the features are fused and fed into a decoder to obtain a calibrated measurement. We evaluate the proposed method on two real-world datasets and compare it with six baselines. The experimental results demonstrate that our method yields the best performance

Exemplar-free Class Incremental Learning via Discriminative and Comparable One-class Classifiers

The exemplar-free class incremental learning requires classification models to learn new class knowledge incrementally without retaining any old samples. Recently, the framework based on parallel one-class classifiers (POC), which trains a one-class classifier (OCC) independently for each category, has attracted extensive attention, since it can naturally avoid catastrophic forgetting. POC, however, suffers from weak discriminability and comparability due to its independent training strategy for different OOCs. To meet this challenge, we propose a new framework, named Discriminative and Comparable One-class classifiers for Incremental Learning (DisCOIL). DisCOIL follows the basic principle of POC, but it adopts variational auto-encoders (VAE) instead of other well-established one-class classifiers (e.g. deep SVDD), because a trained VAE can not only identify the probability of an input sample belonging to a class but also generate pseudo samples of the class to assist in learning new tasks. With this advantage, DisCOIL trains a new-class VAE in contrast with the old-class VAEs, which forces the new-class VAE to reconstruct better for new-class samples but worse for the old-class pseudo samples, thus enhancing the comparability. Furthermore, DisCOIL introduces a hinge reconstruction loss to ensure the discriminability. We evaluate our method extensively on MNIST, CIFAR10, and Tiny-ImageNet. The experimental results show that DisCOIL achieves state-of-the-art performance

A Power-Angle-Spectrum Based Clustering and Tracking Algorithm for Time-Varying Radio Channels

Radio channel modeling has been an important research topic, since the performance of any communication system depends on channel characteristics. So far, most existing clustering algorithms are conducted based on the multipath components (MPCs) extracted by using a high-resolution parameter estimation approach, e.g., SAGE or MUSIC, etc. However, most of the estimation approaches require prior information to extract MPCs. Moreover, the high-resolution estimation approaches usually result in relatively high complexity, and thus, the clusters can only be identified by using an offline approach after the measurements. Therefore, a power-angle-spectrum (PAS) based clustering and tracking algorithm (PASCT) is proposed in this paper. First, a PAS is extracted from measurement data by using a Bartlett beamformer. For each PAS, the potential targets are selected from the background and separated into clusters by using image processing approaches. The recognized clusters are characterized by the following three attributes: size, position, and shape feature, where an orientation histogram is developed to describe the shape feature of the clusters. Moreover, a cost minimizing tracking approach based on Kuhn-Munkres method is proposed to accurately identify the clusters in non-stationary channels. The proposed PASCT algorithm is validated based on both simulations and measurements. It is found that the dominating clusters in both line-of-sight and non-line-of-sight environments can be well recognized and tracked with the proposed algorithm. By using the proposed algorithm, the dynamic changes of the clusters in real-time channel measurements, e.g., number, birth-death process, and size of the clusters, can be well observed. Through the experiments, the proposed algorithm can achieve fairly good accuracy on the cluster identification with lower complexity compared to the conventional solution