Interpersonal Distance Tracking with mmWave Radar and IMUs

Tracking interpersonal distances is essential for real-time social distancing management and ex-post contact tracing to prevent spreads of contagious diseases. Bluetooth neighbor discovery has been employed for such purposes in combating COVID-19, but does not provide satisfactory spatiotemporal resolutions. This paper presents ImmTrack, a system that uses a millimeter wave radar and exploits the inertial measurement data from user-carried smartphones or wearables to track interpersonal distances. By matching the movement traces reconstructed from the radar and inertial data, the pseudo identities of the inertial data can be transferred to the radar sensing results in the global coordinate system. The re-identified, radar-sensed movement trajectories are then used to track interpersonal distances. In a broader sense, ImmTrack is the first system that fuses data from millimeter wave radar and inertial measurement units for simultaneous user tracking and re-identification. Evaluation with up to 27 people in various indoor/outdoor environments shows ImmTrack's decimeters-seconds spatiotemporal accuracy in contact tracing, which is similar to that of the privacy-intrusive camera surveillance and significantly outperforms the Bluetooth neighbor discovery approach. © 2023 Owner/Author.

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.

Extreme development of dragon fruit agriculture with nighttime lighting in Southern Vietnam

Dragon fruit is widely grown in Southeast Asia and other tropical or subtropical regions. As a high-value cash crop ideal for exportation, dragon fruit cultivation has boomed during the past decade in southern Vietnam. Light supplementing during the winter months using artificial lighting sources is a widely adopted cultivation technique to boost productivity in the major dragon fruit planting regions of Vietnam. The application of electric lighting at night leads to a significant increase of nighttime light (NTL) observable by satellite sensors. The strong seasonality signal of NTL in dragon fruit cultivation enables identifying dragon fruit plantations using NTL images. We employed Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) monthly nighttime imagery from 2012 to 2019 to extract the growing area of dragon fruit in Bình Thuan Province, the largest dragon fruit growing region of Vietnam. The Breakpoint for Additive Seasonal Trend (B-FAST) analysis was applied to calculate the seasonality of NTL inside the dragon fruit plantations and distinguish them from the background. The results indicated that the dragon fruit cultivation strongly increased after 2014 and reached a plateau after 2017. In recent years, dragon fruit cultivation has experienced a slight decrease due to market fluctuations. We applied a buffer analysis over the largest dragon fruit cultivation area in Bình Thuan to analyze the spatial trend of the expansion of dragon fruit planting. Our results suggest that the dragon fruit cultivation of Bình Thuan has expanded to cover most inter-hill plains, reaching a spatial extent capacity due to the topographical constraints, and thus has begun to encroach into the low-elevation foothill area. In the case of emergency lock-down orders in February 2020 during the COVID-19 pandemic, NTL used for dragon fruit cultivation changed heterogeneously in space and time, driven by market price and shipping limitations far away from the local restrictions. Under the dual rural-urban hot spot situation with strong and contemporary developments of both dragon fruit agriculture and the urban tourism industry, building structures were detected densely in the city and gradually dispersed well into the rural landscape in Bình Thuan. The outcomes of this study will be valuable for local policymakers to better understand of the available area for dragon fruit cultivation and achieve better-coordinated cultivation planning against future fluctuations of the global market while providing insights and new understanding into the dual hot-spot developments valuable for planning rural-urban change strategies. © Springer Nature Switzerland AG 2022. All rights reserved.

Improving Short-Term QPF Using Geostationary Satellite All-Sky Infrared Radiances: Real-Time Ensemble Data Assimilation and Forecast during the PRECIP 2020 and 2021 Experiments

The Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP) aims to improve our understanding of extreme rainfall processes in the East Asian summer monsoon. A convection-permitting ensemble-based data assimilation and forecast system (the PSU WRF-EnKF system) was run in real time in the summers of 2020–21 in advance of the 2022 field campaign, assimilating all-sky infrared (IR) radiances from the geostationary Himawari-8 and GOES-16 satellites, and providing 48-h ensemble forecasts every day for weather briefings and discussions. This is the first time that all-sky IR data assimilation has been performed in a real-time forecast system at a convection-permitting resolution for several seasons. Compared with retrospective forecasts that exclude all-sky IR radiances, rainfall predictions are statistically significantly improved out to at least 4–6 h for the real-time forecasts, which is comparable to the time scale of improvements gained from assimilating observations from the dense ground-based Doppler weather radars. The assimilation of all-sky IR radiances also reduced the forecast errors of large-scale environments and helped to maintain a more reasonable ensemble spread compared with the counterpart experiments that did not assimilate all-sky IR radiances. The results indicate strong potential for improving routine short-term quantitative precipitation forecasts using these high-spatiotemporal-resolution satellite observations in the future.Significance StatementDuring the summers of 2020/21, the PSU WRF-EnKF data assimilation and forecast system was run in real time in advance of the 2022 Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP), assimilating all-sky (clear-sky and cloudy) infrared radiances from geostationary satellites into a numerical weather prediction model and providing ensemble forecasts. This study presents the first-of-its-kind systematic evaluation of the impacts of assimilating all-sky infrared radiances on short-term qualitative precipitation forecasts using multiyear, multiregion, real-time ensemble forecasts. Results suggest that rainfall forecasts are improved out to at least 4–6 h with the assimilation of all-sky infrared radiances, comparable to the influence of assimilating radar observations, with benefits in forecasting large-scale environments and representing atmospheric uncertainties as well.

A Silicon-Based Radio Platform for Integrated Edge Sensing and Communication Toward Sustainable Healthcare

Faced with COVID-19 and the trend of aging, it is demanding to develop an online health metrics sensing solution for sustainable healthcare. An edge radio platform owning the function of integrated sensing and communications is promising to address the challenge. Radar demonstrates the capability for noncontact healthcare with high sensitivity and excellent privacy protection. Beyond conventional radar, this article presents a unique silicon-based radio platform for health status monitoring supported by coherent frequency-modulated continuous-wave (FMCW) radar at Ku-band and communication chip. The radar chip is fabricated by a 65-nm complementary metal–oxide–semiconductor (CMOS) process and demonstrates a 1.5-GHz chirp bandwidth with a 15-GHz center frequency in 220-mW power consumption. A specific small-volume antenna with modified Vivaldi architecture is utilized for emitting and receiving radar beams. Biomedical experiments were implemented based on the radio platform cooperating with the antenna and system-on-chip (SoC) field-programmable gate array (FPGA) edge unit. An industrial, scientific, and medical (ISM)-band frequency-shift keying (FSK) communication chip in 915-MHz center frequency with microwatt-level power consumption is used to attain communications on radar-detected health information. Through unified integration of radar chip, management software, and communication unit, the integrated radio platform featuring −72-dBm sensitivity with a 500-kb/s FSK data rate is exploited to drastically empower sustainable healthcare applications.

Contactless Human Respiratory Frequency Monitoring System Based on FMCW Radar

The COVID-19 virus pandemic (Coronavirus Disease 19) has become a hot topic of conversation due to this date. A disease that attacks the human respiratory system becomes a case of the spread of the disease that is increasing daily. The method for detecting the movement of the human chest usually uses a belt-shaped device attached to the chest to see the respiratory rate. However, chest-mounted use requires contact with other people and promotes less privacy and comfort due to such attachments. Radar systems are urgently needed as contactless devices to reduce the risk of spreading disease. The use of this radar is a Frequency Modulated Continuous Wave (FMCW) technique that can perform semi-real-time monitoring. A monitoring system designed to perform small calculations to detect small movements in chest breathing. This FMCW radar system research compares the RPM radar with manual calculations to get an error value of less than 5%. The results of testing the respiratory target dataset with radar detection obtained an average error value of 1.68%. The proposed research is aimed at the health sector on vital signs. © 2022 IEEE.

INNOVATION IN TIMES OF A PANDEMIC: THE CASE OF A CONSTRUCTION INDUSTRY

Objetivo do estudo: Evidenciar as inovaçöes realizadas no período da pandemia por uma industria de transformaçâo, que fornece produtos para a construçâo civil. Originalidade/ Relevancia: Descreve de forma qualitativa as inovaçöes realizadas com base no radar de inovaçâo em uma industria, em momento adverso de pandemia. Metodologia: Caracteriza-se como um relato técnico, descritivo, de caráter qualitativo e aplicado, realizado por meio da técnica de análise do conteúdo. Principais resultados: Os resultados apontam que a industria foi diretamente afetada pela falta de matéria prima no período de pandemia e encontrou soluçöes inovando, sobretudo, nas dimensöes: oferta, soluçöes, agregaçâo de valor, organizaçâo, cadeia de fornecimento e processos. Contributes: Como contribuiçöes foram apontadas informaçöes relacionadas ao ambiente empresarial voltado a inovaçâo em tempos de crise, em especial na pandemia mundial da covid19, e o avanço na mensuraçâo de inovaçöes empresariais de forma qualitativa utilizando a ferramenta radar da inovaçâo.Alternate :Objetivo del estudio: Destacar las innovaciones realizadas durante el período de pandemia por una industria manufacturera, que provee productos para la construcción. Originalidad/Relevancia: Describe cualitativamente las innovaciones realizadas con base en el radar de innovación en una industria en un momento adverso de pandemia. Metodología: Se caracteriza por ser un informe técnico, descriptivo, cualitativo a través del análisis de contenido. Principales resultados: Los resultados mostraron que la industria se vio directamente afectada por la falta de materia prima durante el período de pandemia, y encontró soluciones innovando, sobre todo, en las dimensiones de abastecimiento, soluciones, agregación de valor, organización, cadena de suministro y procesos. Aportes: Como aportes al estudio, información relacionada con el entorno empresarial enfocada a la innovación en tiempos de crisis y especialmente en la pandemia mundial del covid-19, y el avance en la medición de las innovaciones empresariales de forma cualitativa utilizando la herramienta radar de innovación.Alternate :Objective of the study: Highlight the innovations achieved in the pandemic period by a transformation industry that supplies products for civil construction. Originality/Relevance: Qualitatively describes how innovations are carried out based on the innovation radar in an industry at an adverse time of a pandemic. Methodology/Approach: It is characterized as a technical, descriptive, qualitative report through content analysis. Main results: The results showed that the industry was directly affected by the lack of raw material during the pandemic period, and found solutions by innovating, mainly in the dimensions supply, solutions, value addition, organization, supply chain, and processes. Contribution: As contributions of the study, information related to the business environment aimed at innovation in times of crisis and in the covid-19 world pandemic was pointed out, and the advance in measuring business innovations in a qualitative way using the innovation radar tool.

Automatic detection and classification of land subsidence in deltaic metropolitan areas using distributed scatterer InSAR and Oriented R-CNN

Multi-temporal interferometric synthetic aperture radar (InSAR) is an effective tool for measuring large-scale land subsidence. However, the measurement points generated by InSAR are too many to be manually analyzed, and automatic subsidence detection and classification methods are still lacking. In this study, we developed an oriented R-CNN deep learning network to automatically detect and classify subsidence bowls using InSAR measurements and multi-source ancillary data. We used 541 Sentinel-1 images acquired during 2015–2021 to map land subsidence of the Guangdong-Hong Kong-Macao Greater Bay Area by resolving persistent and distributed scatterers. Multi-source data related to land subsidence, including geological and lithological, land cover, topographic, and climatic data, were incorporated into deep learning, allowing the local subsidence to be classified into seven categories. The results showed that the oriented R-CNN achieved an average precision (AP) of 0.847 for subsidence detection and a mean AP (mAP) of 0.798 for subsidence classification, which outperformed the other three state-of-the-art methods (Rotated RetinaNet, R3Det, and ReDet). An independent effect analysis showed that incorporating all datasets improved the AP by 11.2% for detection and the mAP by 73.9% for classification, respectively, compared with using InSAR measurements only. Combining InSAR measurements with globally available land cover and digital elevation model data improved the AP for subsidence detection to 0.822, suggesting that our methods can be potentially transferred to other regions, which was further validated this using a new dataset in Shanghai. These results improve the understanding of deltaic subsidence and facilitate geohazard assessment and management for sustainable environments. • Land subsidence of the GBA from 2015 to 2021 was measured by PS/DS detection. • The oriented R-CNN was applied to automatically identify local subsidence. • Incorporating multi-source data improved the performance of subsidence detection. • COVID-19 lockdown ceased groundwater extraction and decelerated subsidence. [ABSTRACT FROM AUTHOR] Copyright of Remote Sensing of Environment is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Simulating Changes in Aircraft Noise at Heathrow Airport during the 2020 Covid-19 Lockdown

Global restrictions on domestic and international travel introduced in March 2020 as a result of the Covid-19 pandemic resulted in a significant reduction in air traffic movements around the world. This paper presents the findings of research carried out at London Heathrow Airport exploring the day-by-day changes in aircraft noise exposure and event levels over the period March 2020 to June 2020. The research was carried out using validated modelling of aircraft procedures and noise profiles alongside radar data obtained from the airport. This allowed trends in metrics such as LAeq, N65, and overflight to be considered in the form of contours, and at community locations. This was facilitated using geospatial databases and interactive dynamic reporting toolkits. The research has allowed estimates to be made of the point where aircraft noise at Heathrow Airport reached a minimum. It also provides some helpful insight as to the potential of generating daily noise exposure data and the advantages, and disadvantages of modelling using radar data. © 2022 Internoise 2022 - 51st International Congress and Exposition on Noise Control Engineering. All rights reserved.

Surface Subsidence Characteristics and Causes in Beijing (China) before and after COVID-19 by Sentinel-1A TS-InSAR

Surface subsidence is a serious threat to human life, buildings and traffic in Beijing. Surface subsidence is closely related to human activities, and human activities in Beijing area showed a decreasing trend during the Corona Virus Disease 2019 (COVID-19). To study surface subsidence in Beijing before and after the COVID-19 outbreak and its causes, a total of 51 Sentinel-1A SAR images covering Beijing from January 2018 to April 2022 were selected to derive subsidence information by Time Series Interferometry Synthetic Aperture Radar (TS-InSAR). The results of surface subsidence in Beijing demonstrate that Changping, Chaoyang, Tongzhou and Daxing Districts exhibited the most serious subsidence phenomenon before the COVID-19 outbreak. The four main subsidence areas form an anti-Beijing Bay that surrounds other important urban areas. The maximum subsidence rate reached −57.0 mm/year. After the COVID-19 outbreak, the main subsidence area was separated into three giant subsidence funnels and several small subsidence funnels. During this period, the maximum subsidence rate was reduced to −43.0 mm/year. Human activity decrease with the COVID-19 outbreak. This study effectively analysed the influence of natural factors on surface subsidence after excluding most of the human factors. The following conclusions are obtained from the analysis: (1) Groundwater level changes, Beijing's geological structure and infrastructure construction are the main reasons for surface subsidence in Beijing. (2) Seasonal changes in rainfall and temperature indirectly affect groundwater level changes, thereby affecting surface subsidence in the area. (3) The COVID-19 outbreak in early 2020 reduced the payload of Beijing's transportation facilities. It also slowed down the progress of various infrastructure construction projects in Beijing. These scenarios affected the pressure on the soft land base in Beijing and reduced the surface subsidence trend to some extent. © 2023 by the authors.

GIS-based spatial distribution analysis of water quality assessment using water pollution index of Yamuna river at Delhi

Pollution is one of the leading risk factors for the deterioration of the environment, mankind's poor health, and endangerment of the plant kingdom. The exploration of water pollution levels through a new remote sensing model "Water Pollution Index” makes this study unique, which is derived from the weighted overlay technique using land surface temperature, Chlorophyll Index, NCAI, and backscattering values from Sentinel 1, Sentinel 2, and Landsat 8 data sets. This chapter is concerned with the qualitative study of water pollution of the Yamuna river stretch, Delhi. To substantiate the results, sources are taken from different published papers and ground surveys. The objective is to define the pollution level and its contributing factors, algae blooming, sewage debris, coronavirus disease 2019 (COVID-19) shutdown impact, and rain in different seasons for two consecutive years, 2019 and 2020. A noticeable difference is found in the annual result indicating less pollution in 2020 especially in premonsoon data compared to 2019. © 2023 Elsevier Inc. All rights reserved.

10th International Conference on Inverse Problems in Engineering ICIPE 22

May 15-19, 2022 Francavilla al Mare (Chieti), ITALYIntroductionThis Proceedings of the Journal of Physics: Conference Series contains a subset of papers presented at the 10th International Conference on Inverse Problems in Engineering (ICIPE), hosted by the Department of Industrial and Information Engineering and Economics, University of L'Aquila, Italy, and held in Francavilla al Mare (Chieti), May 15 – 19, 2022.Due to Coronavirus emergency and to protect the health and safety to all our participants, the 10th Edition, scheduled during May 18-21, 2020, and then May 16-20, 2021, was postponed to May 15-19, 2022, and termed as ICIPE 22.Since the first ICIPE in 1993, this conference has served as the main international venue for collaboration and interaction between applied mathematicians who develop inverse analysis tools, and engineers who use these tools in many different disciplines of science such as manufacturing and machining processes, medical imaging, oil exploration, radar, sonar and seismology, space applications, non-destructive testing and so on. The 2022 meeting continued this tradition, with more than 50 delegates from many sub-disciplines of engineering, science, and applied mathematics. The number of participants was lower than the standard number of 80 – 120 due to both covid travel restrictions in some Asian and American countries and conflict in Ukraine.List of Dedication, References, Organizing Committee, Local Committee, Scientific Committee, List of Registrants, Sponsors and Logos are available in this pdf.

I/Q Channel Selection for 24 GHz CW Radar to Non-Contact Vital Signs Detection for Health Check System

With the increasing improvement of quality of life (QOL), health has become an item of concern. However, owing to Covid-19, most organizations cannot do annual health check-ups because they require contact with people and it is difficult to maintain social distance. Consequently, in an era of increasing epidemics, non-contact methods are paramount. In this paper, we present a non-contact breathing and heart rate measurement system integrated into an application using 24 GHz medical radar to support the health check work. In this system, we solve the problem of imbalance between the two signal channels of the radar to increase the accuracy of the breathing and heart rate extraction. © 2022 IEEE.

Study on the Simultaneous Measurement of Multiple Positions of the Human Body Using Two-Dimensional Millimeter-Wave Array Radar

Radar-based non contact measurement of physiological signals and vital signs has been of great interest, partly because of the COVID-19 pandemic. Existing studies reported that different physiological signals can be extracted from different positions of the human body. In this study, we demonstrate the measurement of multiple positions of the human body using a radar system with a two-dimensional antenna array. Using a 79-GHz 48-channel multiple-input multiple-output antenna array, we image multiple body parts of participants and separate the echoes using array signal processing. We present experimental results to show the feasibility of the proposed approach. © 2022 The Institute of Electronics Information and Communication Engineers (IEICE) of Japan.

A Novel Scheme Based on Variational Mode Decomposition and Multiple Denoising Technologies for Heartbeat Rate Estimation

In the face of the serious aging of the global population and the sudden outbreak of COVID-19, monitoring human vital signs such as heart rate is very important to save lives. For more accurate heartbeat detection, we propose a heartbeat detection scheme based on variational mode decomposition (VMD) and multiple technologies of noise and interference suppression. First, a filter is designed to suppress the impulse noise and reduce the loss of useful signal information. Then, VMD is performed to decompose the pre-processed vital signs into a series of intrinsic mode function (IMF) components. Thirdly, much attention is paid on denoising of IMF components corresponding to the heartbeat signals, an improved wavelet threshold denoising method is proposed to process these IMF components and reconstruct the heartbeat signal. Finally, an adaptive notch filter is used to process the residual respiratory harmonics in the reconstructed heartbeat signal. To verify the heartbeat detection accuracy of our method, the results are compared with a reliable reference sensor. Our results show that the mean average absolute error (AAE) of heart rate estimated by the proposed method is 1.06 bpm, which is 7.51 bpm better than the original method. © 2022 IEEE.

A Silicon-Based Radio Platform for Integrated Edge Sensing and Communication Toward Sustainable Healthcare

Faced with COVID-19 and the trend of aging, it is demanding to develop an online health metrics sensing solution for sustainable healthcare. An edge radio platform owning the function of integrated sensing and communications is promising to address the challenge. Radar demonstrates the capability for noncontact healthcare with high sensitivity and excellent privacy protection. Beyond conventional radar, this article presents a unique silicon-based radio platform for health status monitoring supported by coherent frequency-modulated continuous-wave (FMCW) radar at Ku-band and communication chip. The radar chip is fabricated by a 65-nm complementary metal–oxide–semiconductor (CMOS) process and demonstrates a 1.5-GHz chirp bandwidth with a 15-GHz center frequency in 220-mW power consumption. A specific small-volume antenna with modified Vivaldi architecture is utilized for emitting and receiving radar beams. Biomedical experiments were implemented based on the radio platform cooperating with the antenna and system-on-chip (SoC) field-programmable gate array (FPGA) edge unit. An industrial, scientific, and medical (ISM)-band frequency-shift keying (FSK) communication chip in 915-MHz center frequency with microwatt-level power consumption is used to attain communications on radar-detected health information. Through unified integration of radar chip, management software, and communication unit, the integrated radio platform featuring <inline-formula> <tex-math notation="LaTeX">$-$</tex-math> </inline-formula>72-dBm sensitivity with a 500-kb/s FSK data rate is exploited to drastically empower sustainable healthcare applications. IEEE

Autonomous Navigation of an AMR using Deep Reinforcement Learning in a Warehouse Environment

Mobile robots have been used in warehouses worldwide as a means for distribution of goods and gained demand after the Covid19 labor issue. This paper proposes an Autonomous Mobile Robot (AMR) to navigate in a warehouse environment to its target location using LIDAR. The method used to solve this problem is a deep reinforcement learning algorithm called deep Q-network (DQN) to detect and avoid obstacles and reach the target location. DQN is used as it is desired for solving complex tasks. Training of the DQN algorithm is carried out in ROS Gazebo environment using LIDAR-based robot model. The LIDAR sensor detects the obstacles and the odometer sensor helps to find the distance between the target location are used as inputs for training the algorithm and optimal actions are taken based on the two inputs. A reward policy is awarded when an obstacle is avoided and reaches the target location. The results show that mobile robot can successfully navigate in an unknown environment through simulation and real life. © 2022 IEEE.

DCPC: Drug Candidates for the Prevention of COVID-19 Database

The world immediately studied Coronavirus Disease 2019 (COVID-19) and raced towards finding the cure and developing an effective treatment. An automated approach is needed to discover drug candidates and provide those data to facilitate clinical trials in saving time and only focusing on the candidates which potentially become the cure for COVID-19. We propose the Drug Candidates for the Prevention of COVID-19 (DCPC) Database. DCPC Database provides a list of candidates of potential drugs for the prevention of COVID-19 based on disease-drug associations which are automatically discovered from biomedical literature. DCPC database is an integrative structural database, which involves a chemical database repository, such as PubChem and DrugBank to ensure that drug compound candidates have a standard representation of compounds. The database provides keyword-chosen categories and a determination of minimum supported articles for search, a list of drug candidates in the sorted table followed by the detail for each candidate, and a download feature. The keyword category consists of three keywords, they are Chinese herbal compounds, Indian medicinal plants/and Indian medicinal plants & diabetic treatment herbs. Each candidate links to an article in the biomedical literature and to a page of the compound structure visualization. DCPC is freely available at https://dcpc.brin.go.id/dcpc/. © 2022 IEEE.

Real-Time Medical Radar-based Vital Sign Monitoring System Implemented with Signal Quality Classification Algorithm

Owing to the Covid-19 epidemic, medical radar has become a potential non-contact method in patient monitoring. However, this radar type is sensitive to external interference. The output signal obtained by the radar when a patient makes random body movements can significantly reduce the accuracy of vital sign detection algorithms. In addition, algorithms should be developed for actual application. In this study, we present an improved model of the 24-GHz radar signal quality classification system and a technique to enhance the resolution of respiration rate (RR) and heart rate (HR) for short time interval signals. Moreover, a complete system including signal quality assessment and vital signs extraction is implemented in real time on the Lab-VIEW software. The signal quality classification was evaluated on the measured signals of 10 healthy subjects. Accordingly, the obtained results indicate that with specific features, the accuracy of signal quality classification reaches 89.8%-100% while real-time RR and HR extraction results demonstrate significant agreement between radar measurement and the contact-type sensor. © 2022 IEEE.

Time-Series Ground Subsidence in Bali Before and During the Covid-19 Pandemic Monitored by PS-INSAR Method

Some researchers have reported subsidence in Bali. However, the main factor and the subsidence mechanism remain unclear due to a lack of data. One possibility is due to groundwater extraction. During the Covid-19 pandemic, the number of visitors to Bali decreased significantly. Using the persistent interferometric synthetic aperture radar (PS-InSAR) and the trend model method, we found a strong relationship between the number of visitors and the subsidence velocity. The subsidence velocity decreased rapidly during the Covid-19 pandemic. It may cause a decrease in groundwater extraction. It may be proven that groundwater extraction is one factor of subsidence in Bali. However, a detailed field investigation, such as groundwater table measurement, is required to validate this new finding. © 2022 IEEE.