Probing environmental and tectonic changes underneath Mexico City with the urban seismic field

The sediments underneath Mexico City have unique mechanical properties that give rise to strong site effects. We investigated temporal changes in the seismic velocity at strong-motion and broadband seismic stations throughout Mexico City, including sites with different geologic characteristics ranging from city center locations situated on lacustrine clay to hillside locations on volcanic bedrock. We used autocorrelations of urban seismic noise, enhanced by waveform clustering, to extract subtle seismic velocity changes by coda wave interferometry. We observed and modeled seasonal, co- and post-seismic changes, as well as a long-term linear trend in seismic velocity. Seasonal variations can be explained by self-consistent models of thermoelastic and poroelastic changes in the subsurface shear wave velocity. Overall, sites on lacustrine clay-rich sediments appear to be more sensitive to seasonal surface temperature changes, whereas sites on alluvial and volcaniclastic sediments and on bedrock are sensitive to precipitation. The 2017 Mw 7.1 Puebla and 2020 Mw 7.4 Oaxaca earthquakes both caused a clear drop in seismic velocity, followed by a time-logarithmic recovery that may still be ongoing for the 2017 event at several sites or that may remain incomplete. The slope of the linear trend in seismic velocity is correlated with the downward vertical displacement of the ground measured by interferometric synthetic aperture radar, suggesting a causative relationship and supporting earlier studies on changes in the resonance frequency of sites in the Mexico City basin due to groundwater extraction. Our findings show how sensitively shallow seismic velocity and, in consequence, site effects react to environmental, tectonic and anthropogenic processes. They also demonstrate that urban strong-motion stations provide useful data for coda wave monitoring given sufficiently high-amplitude urban seismic noise.

Breathing detection using thermal facial landmark with DICOM file

To limit the spread of COVID-19, thermal screening cameras were installed everywhere. These cameras observe many thermal faces. These thermal face data are generally used to monitor strange temperatures for COVID-19 screening or to maintain social distancing. Big data of Thermal face generated everywhere should be used in the more practical functions. We proposed a method to measure non-contact breathing signals using thermal face data. In addition, breathing signals data estimated from thermal face data was converted to DICOM waveform Information Object Definitions (IODs) for interoperability management of medical data. The proposed method was tested on a golden reference (chest belt) with a mean accuracy of 93.52 %. a proposed method that can extract breathing signals using thermal screening cameras that are widely available around the world and manage data as healthcare interoperability information can show important potential in the public, telemedicine field in the future. © 2022 IEEE.

A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing

Wavelet transform is a widespread and effective method in seismic waveform analysis and processing. Choosing a suitable wavelet has also aroused many scholars’ research interest and produced many effective strategies. However, with the convenience of seismic data acquisition, the existing wavelet selection methods are unsuitable for the big dataset. Therefore, we proposed a novel wavelet selection method considering the big dataset for seismic signal intelligent processing. The relevance r is calculated using the seismic waveform’s correlation coefficient and variance contribution rate. Then values of r are calculated from all seismic signals in the dataset to form a set. Furthermore, with a mean value μ and variance value σ2 of that set, we define the decomposition stability w as μ/σ2. Then, the wavelet that maximizes w for this dataset is considered to be the optimal wavelet. We applied this method in automatic mining-induced seismic signal classification and automatic seismic P arrival picking. In classification experiments, the mean accuracy is 93.13% using the selected wavelet, 2.22% more accurate than other wavelets generated. Additionally, in the picking experiments, the mean picking error is 0.59 s using the selected wavelet, but is 0.71 s using others. Moreover, the wavelet packet decomposition level does not affect the selection of wavelets. These results indicate that our method can really enhance the intelligent processing of seismic signals.

Remote Prototyping of FPGA-Based Devices in the IoT Concept during the COVID-19 Pandemic

This paper presents a system for the remote design and testing of electronic circuits and devices with FPGAs during COVID-19 and similar lockdown periods when physical access to laboratories is not permitted. The system is based on the application of the IoT concept, in which the final device is a test board with an FPGA chip. The system allows for remote visual inspection of the board and the devices linked to it in the laboratory. The system was developed for remote learning taking place during the lockdown periods at Poznan University of Technology (PUT) in Poland. The functionality of the system is confirmed by two demonstration tasks (the use of the temperature and humidity DHT11 sensor and the design of a generator of sinusoidal waveforms) for students in the fundamentals of digital design and synthesis courses. The proposed solution allows, in part, to bypass the time-consuming simulations, and accelerate the process of prototyping digital circuits by remotely accessing the infrastructure of the microelectronics laboratory.

Implementation of Various Modulation Techniques using Scilab: A User-Friendly Solution

COVID 19 situation is a big blow to students specially undergraduates wherein they severely suffered and are restricted to the online classes. Core subjects in electronics and communication engineering requires hardware laboratories wherein the theoretical explanation is made clearer by practical implementations. Analog communication is supposed to be the fundamental core subject of electronics with hardware lab wherein student can have a clear idea of various modulated waveforms. In this situation when all are restricted to homes a user-friendly solution came up, an open-source software tool wherein students and faculties can implement all hardware related experiments easily. Scilab is a replacement for licensed software such as MATLAB with simple syntax which can be easily understood. In this paper scilab codes are developed for various modulation techniques such as amplitude modulation (AM), frequency modulation (FM) and pulse amplitude modulation (PAM) which can be used to make the students comfortable with the concepts of modulation. A feedback survey was taken by the undergraduate students of electronics and communication engineering and it clearly shows that students are interested and comfortable in using scilab programming and wished to use this tool while implementing major project. © 2022 IEEE.

The method of GF-7 satellite laser altimeter on-orbit geometric calibration without field site

On-orbit geometric calibration without field site is a key problem for future multi-beam laser altimetry satellites. In view of the linear system full waveform laser altimeter loaded on the GF-7 satellite, a non-field step by step calibration method based on terrain and waveform matching is proposed. Based on the analysis of the characteristics of the GF-7 satellite laser altimeter, a rigorous geometric positioning model is constructed. The field-free on orbit geometric calibration test is carried out by using the open topographic reference data and the basic geographic information of DOM and LiDAR DSM in a certain area, which has greatly improved the accuracy of the laser altimetry data. With this method, during the first half of 2020, the calibration parameter configuration and data processing of GF-7 satellite laser altimeter was not affected, even the field calibration can't be implemented due to the negative impact of the COVID-19. The accuracy is compared with the field calibration results after the COVID-19, and the results show that the plane error of the non-field calibration is 11.597±3.693 m and the minimum value is 7.115 m. The elevation accuracy of flat area is better than 0.3 m, although it is slightly lower than the results of field calibration, it can basically meet the requirements of 1: 10 000 elevation control points. © 2022, Surveying and Mapping Press. All right reserved.

A Photoplethysmography Wearable with Long-term Heart Rate Variability Detection Algorithm

This paper presents a wearable device that extracts and utilizes a photopfethysmogram waveform to measure and estimate various vital signs via a mobile application's custom-designed algorithms. These vital signs include peripheral oxygen saturation, heart rate, respiratory rate, and short/fong term heart rate variability. The device wirelessly transmits accumulated data to a mobile phone and a personal computer over Bluetooth Low Energy. Moreover, this paper explores the proposed device as an emerging technology with the Coronavirus Disease 19 (COVID-19) pandemic's contemporary concerns. The peripheral oxy gen saturation measurements would give an early indicator of degrading respiratory health before the apparent manifestation of symptoms. The convenient use of this device in a mobile setting is especially relevant to current isolation precautions in place and its critical role in improving at-risk patients' care. © 2020 IEEE.