Spatio-temporal analysis of air quality and its relationship with COVID-19 lockdown over Dublin.

Air pollution has become one of the biggest challenges for human and environmental health. Major pollutants such as Nitrogen Dioxide (NO 2 ), Sulphur Dioxide (SO 2 ), Ozone (O 3 ), Carbon Monoxide (CO), and Particulate matter (PM10 and PM2.5) are being ejected in a large quantity every day. Initially, authorities did not implement the strictest mitigation policies due to pressures of balancing the economic needs of people and public safety. Still, after realizing the effect of the COVID-19 pandemic, countries around the world imposed a complete lockdown to contain the outbreak, which had the unexpected benefit of causing a drastic improvement in air quality. The present study investigates the air pollution scenarios over the Dublin city through satellites (Sentinel-5P and Moderate Resolution Imaging Spectroradiometer) and ground-based observations. An average of 28% reduction in average NO 2 level and a 27.7% improvement in AQI (Air Quality Index) was experienced in 2020 compared to 2019 during the lockdown period (27 March-05 June). We found that PM10 and PM2.5 are the most dominating factor in the AQI over Dublin.

LAMSkyCam: A low-cost and miniature ground-based sky camera.

Ground-based sky imagers (GSIs) are increasingly becoming popular amongst the remote sensing analysts. This is because such imagers offer fantastic alternatives to satellite measurements for the purpose of earth observations. In this paper, we propose an extremely low-cost and miniature ground-based sky camera for atmospheric study. Built using 3D printed and off-the-shelf components, our sky camera is lightweight and robust for use in diverse climatic conditions. With a 63 ° field of view angle, the camera captures high resolution sky/cloud images for both day and night times at 5 min intervals. The camera is designed to be mounted on a pole-like architecture and with its compact form, it can be installed at any location without requiring any change in the existing infrastructure. For remote areas, the camera also has a local backup facility from which data can be easily accessed manually. We have open-sourced the hardware design of our sky camera, and therefore researchers can easily manufacture and deploy these cameras for their respective use cases.

ANNet: A Lightweight Neural Network for ECG Anomaly Detection in IoT Edge Sensors.

In this paper, we propose a lightweight neural network for real-time electrocardiogram (ECG) anomaly detection and system level power reduction of wearable Internet of Things (IoT) Edge sensors. The proposed network utilizes a novel hybrid architecture consisting of Long Short Term Memory (LSTM) cells and Multi-Layer Perceptrons (MLP). The LSTM block takes a sequence of coefficients representing the morphology of ECG beats while the MLP input layer is fed with features derived from instantaneous heart rate. Simultaneous training of the blocks pushes the overall network to learn distinct features complementing each other for making decisions. The network was evaluated in terms of accuracy, computational complexity, and power consumption using data from the MIT-BIH arrhythmia database. To address the class imbalance in the dataset, we augmented the dataset using SMOTE algorithm for network training. The network achieved an average classification accuracy of 97% across several records in the database. Further, the network was mapped to a fixed point model, retrained in a bit accurate fixed-point environment to compensate for the quantization error, and ported to an ARM Cortex M4 based embedded platform. In laboratory testing, the overall system was successfully demonstrated, and a significant saving of ≅ 50% power was achieved by gating the wireless transmission using the classifier. Wireless transmission was enabled only to transmit the beats deemed anomalous by the classifier. The proposed technique compares favourably with current methods in terms of computational complexity and has the advantage of stand-alone operation in the edge node, without the need for always-on wireless connectivity making it ideal for IoT wearable devices.

Predicting Stroke from Electronic Health Records.

Studies have identified various risk factors associated with the onset of stroke in an individual. Data mining techniques have been used to predict the occurrence of stroke based on these factors by using patients' medical records. However, there has been limited use of electronic health records to study the inter-dependency of different risk factors of stroke. In this paper, we perform an analysis of patients' electronic health records to identify the impact of risk factors on stroke prediction. We also provide benchmark performance of the state-of-art machine learning algorithms for predicting stroke using electronic health records.