Ultra-fast sampling of terahertz pulses from a quantum cascade laser using superconducting antenna-coupled NbN and YBCO detectors

We demonstrate the ultra-fast detection of terahertz pulses from a quantum cascade laser (QCL) using superconducting NbN and YBCO detectors. This has enabled both the intrapulse and interpulse dynamics of a THz QCL to be measured directly, including interpulse heating effects on sub-μs timescales

Wind Power Integration with Smart Grid and Storage System: Prospects and Limitations

Wind power generation is playing a pivotal role in adopting renewable energy sources in many countries. Over the past decades, we have seen steady growth in wind power generation throughout the world. This article aims to summarize the operation, conversion and integration of the wind power with conventional grid and local microgrids so that it can be a one-stop reference for early career researchers. The study is carried out primarily based on the horizontal axis wind turbine and the vertical axis wind turbine. Afterward, the types and methods of storing this electric power generated are discussed elaborately. On top of that, this paper summarizes the ways of connecting the wind farms with conventional grid and microgrid to portray a clear picture of existing technologies. Section-wise, the prospects and limitations are discussed and opportunities for future technologies are highlighted. It is envisaged that, this paper will help researchers and engineering professionals to grasp the fundamental concepts related to wind power generation concisely and effectively

Diffuse-reflectance spectroscopy using a frequency-switchable terahertz quantum cascade laser

We demonstrate diffuse-reflectance (DR) spectroscopy of powders using a discretely-tunable terahertz-frequency quantum cascade laser (THz QCL) with a heterogeneous active region. DR signatures were obtained at frequencies of 3.06, 3.21, 3.28 and 3.35 THz, and the relative absorption coefficients were inferred at each frequency using a Kubelka–Munk (KM) scattering model. The spectral lineshapes reproduce the absolute Beer–Lambert (BL) absorption spectra of a range of materials, which were also measured using conventional transmission-mode THz time-domain spectroscopy. It is shown that the DR technique works reliably for materials that include pharmaceutical compounds and foodstuffs, with BL absorption coefficients in the range 2–10 mm−1. This method is potentially suitable for automated material identification, without any requirement for a priori knowledge of the refractive index or scattering properties of the sampled material

COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network

The reliable and rapid identification of the COVID-19 has become crucial to prevent the rapid spread of the disease, ease lockdown restrictions and reduce pressure on public health infrastructures. Recently, several methods and techniques have been proposed to detect the SARS-CoV-2 virus using different images and data. However, this is the first study that will explore the possibility of using deep convolutional neural network (CNN) models to detect COVID-19 from electrocardiogram (ECG) trace images. In this work, COVID-19 and other cardiovascular diseases (CVDs) were detected using deep-learning techniques. A public dataset of ECG images consisting of 1937 images from five distinct categories, such as normal, COVID-19, myocardial infarction (MI), abnormal heartbeat (AHB), and recovered myocardial infarction (RMI) were used in this study. Six different deep CNN models (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and MobileNetv2) were used to investigate three different classification schemes: (i) two-class classification (normal vs COVID-19); (ii) three-class classification (normal, COVID-19, and other CVDs), and finally, (iii) five-class classification (normal, COVID-19, MI, AHB, and RMI). For two-class and three-class classification, Densenet201 outperforms other networks with an accuracy of 99.1%, and 97.36%, respectively; while for the five-class classification, InceptionV3 outperforms others with an accuracy of 97.83%. ScoreCAM visualization confirms that the networks are learning from the relevant area of the trace images. Since the proposed method uses ECG trace images which can be captured by smartphones and are readily available facilities in low-resources countries, this study will help in faster computer-aided diagnosis of COVID-19 and other cardiac abnormalities