Tropical peatland across Southeast Asia is drained extensively for production of pulpwood, palm oil and other food crops. Associated increases in peat decomposition have led to widespread subsidence, deterioration of peat condition and CO2 emissions. However, quantification of subsidence and peat condition from these processes is challenging due to the scale and inaccessibility of dense tropical peat swamp forests. The development of satellite interferometric synthetic aperture radar (InSAR) has the potential to solve this problem. The Advanced Pixel System using Intermittent Baseline Subset (APSIS, formerly ISBAS) modelling technique provides improved coverage across almost all land surfaces irrespective of ground cover, enabling derivation of a time series of tropical peatland surface oscillations across whole catchments. This study aimed to establish the extent to which APSIS-InSAR can monitor seasonal patterns of tropical peat surface oscillations at North Selangor Peat Swamp Forest, Peninsular Malaysia. Results showed that C-band SAR could penetrate the forest canopy over tropical peat swamp forests intermittently and was applicable to a range of land covers. Therefore the APSIS technique has the potential for monitoring peat surface oscillations under tropical forest canopy using regularly acquired C-band Sentinel-1 InSAR data, enabling continuous monitoring of tropical peatland surface motion at a spatial resolution of 20 m.
Cardiovascular diagnostics relies heavily on the ECG (ECG), which reveals significant information about heart rhythm and function. Despite their significance, traditional ECG measures employing electrodes have limitations. As a result of extended electrode attachments, patients may experience skin irritation or pain, and motion artifacts may interfere with signal accuracy. Additionally, ECG monitoring usually requires highly trained professionals and specialized equipment, which increases the treatment's complexity and cost. In critical care scenarios, such as continuous monitoring of hospitalized patients, wearable sensors for collecting ECG data may be difficult to use. Although there are issues with ECG, it remains a valuable tool for diagnosing and monitoring cardiac disorders due to its non-invasive nature and the detailed information it provides about the heart. The goal of this study is to present an innovative method for generating continuous ECG waveforms from non-contact radar data by using Deep Learning. The method can eliminate the need for invasive or wearable biosensors and expensive equipment to collect ECGs. In this paper, we propose the MultiResLinkNet, a one-dimensional convolutional neural network (1D CNN) model for generating ECG signals from radar waveforms. With the help of a publicly accessible radar benchmark dataset, an end-to-end DL architecture is trained and assessed. There are six ports of raw radar data in this dataset, along with ground truth physiological signals collected from 30 participants in five distinct scenarios: Resting, Valsalva, Apnea, Tilt-up, and Tilt-down. By using strong temporal and spectral measurements, we assessed our proposed framework's ability to convert ECG data from Radar signals in three distinct scenarios, namely Resting, Valsalva, and Apnea (RVA). ECG segmentation performed better by MultiResLinkNet than by state-of-the-art networks in both combined and individual cases. As a result of the simulations, the resting, valsalva, and RVA scenarios showed the highest average temporal values, respectively: 66.09523 ± 19.33, 60.13625 ± 21.92, and 61.86265 ± 21.37. In addition, it exhibited the highest spectral correlation values (82.4388 ± 18.42 (Resting), 77.05186 ± 23.26 (Valsalva), 74.65785 ± 23.17 (Apnea), and 79.96201 ± 20.82 (RVA)), along with minimal temporal and spectral errors in almost every case. The qualitative evaluation revealed strong similarities between generated and actual ECG waveforms. As a result of our method of forecasting ECG patterns from remote radar data, we can monitor high-risk patients, especially those undergoing surgery.
The passive bistatic radar (PBR) system can utilize the illuminator of opportunity to enhance radar capability. By utilizing the forward scattering technique and procedure into the specific mode of PBR can provide an improvement in target detection and classification. The system is known as passive Forward Scattering Radar (FSR). The passive FSR system can exploit the peculiar advantage of the enhancement in forward scatter radar cross section (FSRCS) for target detection. Thus, the aim of this paper is to show the feasibility of passive FSR for moving target detection and classification by experimental analysis and results. The signal source is coming from the latest technology of 4G Long-Term Evolution (LTE) base station. A detailed explanation on the passive FSR receiver circuit, the detection scheme and the classification algorithm are given. In addition, the proposed passive FSR circuit employs the self-mixing technique at the receiver; hence the synchronization signal from the transmitter is not required. The experimental results confirm the passive FSR system's capability for ground target detection and classification. Furthermore, this paper illustrates the first classification result in the passive FSR system. The great potential in the passive FSR system provides a new research area in passive radar that can be used for diverse remote monitoring applications.
Wind turbines are massive electrical structures. They produce large returns when illuminated by radar waves. These
scatterings have a great impact on the operation of surveillance, air traffic control and weather radars. This paper presents
two geometric modelling methods for reshaping wind turbine towers so that the Radar Cross Section (RCS) of wind turbines
is reduced. In the proposed reshaping methods, bump structures are created on the surface of the conventional cylinder
wind turbine tower. When a reshaped tower is illuminated by radar waves, the bump structures scatter incident radar
waves into insignificant directions so that the strength of back-scattering is declined and the RCS of the wind turbine is
decreased. The test results confirmed that the proposed methodssignificantly reduce bi-static RCS values of wind turbines.
The proposed reshaping methods are practical, flexible and effective in alleviating the scatterings of wind turbines.
Recently, rapid advances in radio detection and ranging (radar) technology applications have been implemented in various fields. In particular, micro-Doppler radar has been widely developed to perform certain tasks, such as detection of buried victims in natural disaster, drone system detection, and classification of humans and animals. Further, micro-Doppler radar can also be implemented in medical applications for remote monitoring and examination. This paper proposes a human respiration rate detection system using micro-Doppler radar with quadrature architecture in the industrial, scientific, and medical (ISM) frequency of 5.8 GHz. We use a mathematical model of human breathing to further explore any insights into signal processes in the radar. The experimental system is designed using the USRP B200 mini-module as the main component of the radar and the Vivaldi antennas working at 5.8 GHz. The radar system is integrated directly with the GNU Radio Companion software as the processing part. Using a frequency of 5.8 GHz and USRP output power of 0.33 mW, our proposed method was able to detect the respiration rate at a distance of 2 m or less with acceptable error. In addition, the radar system could differentiate different frequency rates for different targets, demonstrating that it is highly sensitive. We also emphasize that the designed radar system can be used as a portable device which offers flexibility to be used anytime and anywhere.
Microstrip couplers play a crucial role in signal processing and transmission in various applications, including RF and wireless communication, radar systems, and satellites. In this work, a novel microstrip 180° coupler is designed, fabricated and measured. The layout configuration of this coupler is completely new and different from the previously reported Rat-race, branch-line and directional couplers. To obtain the proposed coupler, the meandrous coupled lines are used and analyzed mathematically. To improve the performance of our coupler, an optimization method is used. The designed coupler is very compact with an overall size of 0.014λg2. The obtained values of S21 and S31 are -3.45 dB and -3.75 dB, respectively at the operating frequency, while the fractional bandwidth (FBW) is 56.2%. It operates at fo = 1.61 GHz (suitable for 5G applications) and can suppress harmonics up to 2.17fo. Another advantage of this coupler is its low phase imbalance, while the phase difference between S21 and S31 is 180°± 0.023°. Therefore, our device is a balanced coupler with ±0.3 dB magnitude unbalance at its operating frequency. It is important to note that it is very difficult to find a coupler that has all these advantages at the same time. The proposed 180° coupler is fabricated and measured. The comparison shows that the measurement and simulation results are in good agreement. Therefore, the proposed coupler can be easily used in designing high-performance 5G communication systems.
In this paper, a new compact wideband monopole antenna is presented for wireless communication applications. This antenna comprises of a new radiating patch, a new arc-shaped strip, microstrip feed line, and a notched ground plane. The proposed radiating patch is combined with a rectangular and semi-circular patch and is integrated with a partial ground plane to provide a wide impedance bandwidth. The new arc-shaped strip between the radiating patch and microstrip feed line creates an extra surface on the patch, which helps further widen the bandwidth. Inserting one step notch on the ground plane further enhances the bandwidth. The antenna has a compact size of 16×20×1.6mm3. The measured result indicated that the antenna achieves a 127% bandwidth at VSWR≤2, ranging from 4.9GHz to 22.1GHz. Stable radiation patterns with acceptable gain are achieved. Also, a measured bandwidth of 107.7% at VSWR≤1.5 (5.1-17GHz) is obtained, which is suitable for UWB outdoor propagation. This antenna is compatible with a good number of wireless standards, including UWB band, Wimax 5.4 GHz band, MVDDS (12.2-12.7GHz), and close range radar and satellite communication in the X-band (8-12GHz), and Ku band (12-18GHz).
The SAR has the ability of all-weather and all-time data acquisition, it can penetrate the cloud and is not affected by extreme weather conditions, and the acquired images have better contrast and rich texture information. This paper aims to investigate the use of an object-oriented classification approach for flood information monitoring in floodplains using backscattering coefficients and interferometric coherence of Sentinel-1 data under time series. Firstly, the backscattering characteristics and interference coherence variation characteristics of SAR time series are used to analyze whether the flood disaster information can be accurately reflected and provide the basis for selecting input classification characteristics of subsequent SAR images. Subsequently, the contribution rate index of the RF model is used to calculate the importance of each index in time series to convert the selected large number of classification features into low dimensional feature space to improve the classification accuracy and reduce the data redundancy. Finally, the SAR image features in each period after multi-scale segmentation and feature selection are jointly used as the input features of RF classification to extract and segment the water in the study area to monitor floods' spatial distribution and dynamic characteristics. The results showed that the various attributes of backscatter coefficients and interferometric coherence under time series could accurately correspond with the actual flood risk, and the combined use of backscattering coefficient and interferometric coherence for flood extraction can significantly improve the accuracy of flood information extraction. Overall, the object-based random forest method using the backscattering coefficient and interference coherence of Sentinel-1 time series for flood extraction advances our understanding of flooding's temporal and spatial dynamics, essential for the timely adoption of adaptation and mitigation strategies for loss reduction.
This paper analyses electromagnetic signal scattered from the target crossing the Forward Scattering
Radar (FSR) system baseline. The aim of the analysis was to extract the Doppler signal of a target under the influence of high ground clutter and noise interference. The extraction was used for the
automatic target detection (ATD) in the FSR system. Two extraction methods, namely Hilbert Transform and Wavelet Technique, were analyzed. The detection using the Hilbert Transform is only applicable for some conditions; however, the detection using the Wavelet Technique is more robust to any clutter and noise level. From 55 sets of signal, only 4% of false alarm was detected or occurred when the Wavelet Technique was applied as a detection scheme. Two sets of field experimentation were carried out and the target’s signal under the influence of high clutter had successfully been detected using the proposed method.
Digital elevation model (DEM) generation from stereo images is an effective and economical method in topography mapping. This paper used the stereo pair methodology to generate the digital elevation model (DEM) from PRISM (Panchromatic Remote-Sensing Instrument Satellite) sensor which is onboard of ALOS (Advanced Land Observing Satellite). The pair of forward-backward is used as stereoscopic imagery in this study. Ten ground control points (GCPs) are collected with residual error 0.49 pixels to generate an absolute DEM. This generated DEM with 2.5 m spatial resolution is then matched with the 90 m spatial resolution of
SRTM (Space Shuttle Radar Topography Mission) DEM to compare the result. Although SRTM-DEM has a much coarser resolution, the positional accuracy of the matching is found. The difference of the height from the mean sea level (MSL) between the SRTM-DEM and the PRISM-DEM is analyzed and the correlation between the two DEMs is R²=0.8083. The accuracy of the DEM generated is given by the RMSE value of 0.8991 meter.
In this paper, a dual-band metamaterial absorber (MMA) ring with a mirror reflexed C-shape is introduced for X and Ku band sensing applications. The proposed metamaterial consists of two square ring resonators and a mirror reflexed C-shape, which reveals two distinctive absorption bands in the electromagnetic wave spectrum. The mechanism of the two-band absorber particularly demonstrates two resonance frequencies and absorption was analyzed using a quasi-TEM field distribution. The absorption can be tunable by changing the size of the metallic ring in the frequency spectrum. Design and analysis of the proposed meta-absorber was performed using the finite-integration technique (FIT)-based CST microwave studio simulation software. Two specific absorption peaks value of 99.6% and 99.14% are achieved at 13.78 GHz and 15.3 GHz, respectively. The absorption results have been measured and compared with computational results. The proposed dual-band absorber has potential applications in sensing techniques for satellite communication and radar systems.
RADARSAT data have a potential role for coastal pollution monitoring. This study presents a new approach to detect and forecast oil slick trajectory movements. The oil slick trajectory movements is based on the tidal current effects and Fay's algorithm for oil slick spreading mechanisms. The oil spill trajectory model contains the integration between Doppler frequency shift model and Lagrangian model. Doppler frequency shift model implemented to simulate tidal current pattern from RADARSAT data while the Lagrangian model used to predict oil spill spreading pattern. The classical Fay's algorithm was implemented with the two models to simulate the oil spill trajectory movements. The study shows that the slick lengths are effected by tidal current V component with maximum velocity of 1.4 m/s. This indicates that oil slick trajectory path is moved towards the north direction. The oil slick parcels are accumulated along the coastline after 48 h. The analysis indicated that tidal current V components were the dominant forcing for oil slick spreading.
The increase in drone misuse by civilian apart from military applications is alarming and need to be addressed. This drone is characterized as a low altitude, slow speed, and small radar cross-section (RCS) (LSS) target and is considered difficult to be detected and classified among other biological targets, such as insects and birds existing in the same surveillance volume. Although several attempts reported the successful drone detection on radio frequency-based (RF), thermal, acoustic, video imaging, and other non-technical methods, however, there are also many limitations. Thus, this paper investigated a micro-Doppler analysis from drone rotating blades for detection in a special Forward Scattering Radar (FSR) geometry. The paper leveraged the identified benefits of FSR mode over conventional radars, such as improved radar cross-section (RCS) value irrespective of radar absorbing material (RAM), direct signal perturbation, and high resolutions. To prove the concept, a received signal model for micro-Doppler analysis, a simulation work, and experimental validation are elaborated and explained in the paper. Two rotating blades aspect angle scenarios were considered, which are (i) when drone makes a turn, the blade cross-sectional area faces the receiver and (ii) when drone maneuvers normally, the cross-sectional blade faces up. The FSR system successfully detected a commercial drone and extracted the micro features of a rotating blade. It further verified the feasibility of using a parabolic dish antenna as a receiver in FSR geometry; this marked an appreciable achievement towards the FSR system performance, which in future could be implemented as either active or passive FSR system.
In mining process, the height of water flowing fractured zone is important significance to prevent mine of water and gas, in order to further research the failure characteristic of the overlying strata. Taking certain coal mine with 5.82 m mining height as the experimental face, by using the equipment which is sealed two ends by capsules in borehole, affused measurable water between the two capsules and borehole televiewer system, ground penetrating radar, microseismic monitoring system in underground coal mine, the height of water flowing fractured zone of fully-mechanized top caving are monitored, a numerical simulation experiment on the failure process was conducted, a similarity simulation experiment on the cracks evolution was conducted, at the same time, empirical formula of traditional was modified, The results showed that the height of caving and fractured zones were respectively 43.1 and 86.7 m in fully mechanized sub-level caving mining. The data difference of each test method of caving, fractured and water flowing fractured zones were respectively less than 4.5%, 7.1% and 9.0%. The degree of fracture development was low before mining, the number of fissures was obviously increased after mining, the degree of fracture development increased. The fractures cluster region mainly focuses near the coal wall. The fractures density distribution curves of overlying strata like sanke-shapes. The new and adapt to certain coal mine geological conditions empirical formula of water flowing fractured zone height is proposed.
Introduction: Most health advisories related to outdoor physical activity during haze are general in nature. The advisories normally advise everyone to reduce or limit prolonged exertion or heavy exertion without mentioning the acceptable duration for performing outdoor physical activity causing difficulty for public to decide to stop or cancel a particular outdoor or sport event. The aim of this paper is to determine the acceptable duration for performing outdoor physical activity pattern during haze based on API level.
Methods: Health risk assessment approach that comprises of hazard identification, exposure assessment, dose-response, and risk characterization steps was used to determine the potential inhaled dose and risk associated with performing the physical activity during haze. We have considered many factors that include time spent for physical activity patterns for Malaysian adult, age and physical intensity-specific inhalation rate (m3/min), and the indoor/outdoor ratio of PM10. A hypothetical exposure scenario of PM10 was created using the breakpoints of PM10 concentration for the calculation of respective API levels during haze.
Results: The association between physical activity pattern, API level and risk quotient were presented in the form of risk radar diagram. Based on the 50th percentile inhalation rate, all prolonged exertion and heavy exertion should be avoided when API reach >201 (very unhealthy) and >175 (unhealthy) respectively. Below the said API, the duration for performing prolonged exertion and heavy exertion should be reduced according to the API level. When API reaches 140, high intensity physical activity should be limited to < 90 minutes. A football match which requires 90 minutes, should be postponed of cancelled if API > 140. Whereas, for the same API level, prolonged exertion (moderate intensity physical activity) should be limited to 4 hours.
Conclusions: Reducing the physical activity is an effective strategy to lower the dose of inhaled pollutants and reduce the health risk during poor air quality. Based on the assessment, taking into account the uncertainty of risk assessment methodology, we proposed all prolonged exertion should be avoided when API reach very unhealthy status (>201). Below the said API level, outdoor physical activity should be reduced according to the level of API respectively. The recommendation is not applicable for the sensitive groups. The computed risk radar provide a valuable guide for the public to organize or considering postponing an outdoor event during haze.
Three techniques to retrieve information on sea ice thickness from both active and passive radar backscatter data are presented. The first inversion model is a combination of the radiative transfer theory with dense medium phase and amplitude correction theory (DMPACT), and the Levenberg-Marquardt optimization algorithm. The radiative transfer theory was applied as the forward model to generate radar backscatter data, while the DMPACT was included to account for the close spacing effect among the scatterers within the medium. The Levenberg-Marquardt optimization algorithm was then applied to reduce the error between the model generated radar backscatter data and the measured radar backscatter data from satellite images so that the sea ice thickness could be estimated. The second method presented was the neural network inversion method which utilizes a chain of neurons with variable weights. Once the network was fully operational it would be possible to predict the sea ice thickness, provided sufficient training data are given. The last method was the genetic algorithm which is a search technique used in order to predict the approximate sea ice thickness from the measured data. Data from ground truth measurements carried out in Ross Island, Antarctica, together with radar backscatter data extracted from purchased satellite images were used as input to verify the models. All three models were tested and successfully predicted sea ice thickness from actual terrain using the ground truth measurement data, with several constraints and assumptions placed to avoid problems during the retrieval process. While the models still have their own limitations, the potential use of the models for actual sea ice thickness retrieval was confirmed.
Satellite imagery reveals flowstripes on Foundation Ice Stream parallel to ice flow, and meandering features on the ice-shelf that cross-cut ice flow and are thought to be formed by water exiting a well-organised subglacial system. Here, ice-penetrating radar data show flow-parallel hard-bed landforms beneath the grounded ice, and channels incised upwards into the ice shelf beneath meandering surface channels. As the ice transitions to flotation, the ice shelf incorporates a corrugation resulting from the landforms. Radar reveals the presence of subglacial water alongside the landforms, indicating a well-organised drainage system in which water exits the ice sheet as a point source, mixes with cavity water and incises upwards into a corrugation peak, accentuating the corrugation downstream. Hard-bedded landforms influence both subglacial hydrology and ice-shelf structure and, as they are known to be widespread on formerly glaciated terrain, their influence on the ice-sheet-shelf transition could be more widespread than thought previously.
Drought is considered one of the costliest natural disasters that result in water scarcity and crop damage almost every year. Drought monitoring and forecasting are essential for the efficient management of water resources and sustainability in agriculture. However, the design of a consistent drought prediction model based on the dynamic relationship of the drought index with its antecedent values remains a challenging task. In the present research, the SVR (support vector regression) model was hybridized with two different optimization algorithms namely; Particle Swarm Optimization (PSO) and Harris Hawks Optimization (HHO) for reliable prediction of effective drought index (EDI) 1 month ahead, at different locations of Uttarakhand State of India. The inputs of the models were selected through partial autocorrelation function (PACF) analysis. The output produced by the SVR-HHO and SVR-PSO models was compared with the EDI estimated from observed data using five statistical indicators, i.e., RMSE (Root Mean Square Error), MAE (Mean Absolute Error), COC (Coefficient of Correlation), NSE (Nash-Sutcliffe Efficiency), WI (Willmott Index), and graphical inspection of radar-chart, time-variation plot, box-whisker plot, and Taylor diagram. Appraisal of results indicates that the SVR-HHO model (RMSE = 0.535-0.965, MAE = 0.363-0.622, NSE = 0.558-0.860, COC = 0.760-0.930, and WI = 0.862-0.959) outperformed the SVR-PSO model (RMSE = 0.546-0.967, MAE = 0.372-0.625, NSE = 0.556-0.855, COC = 0.758-0.929, and WI = 0.861-0.956) in predicting EDI. Visual inspection of model performances also showed a better performance of SVR-HHO compared to SVR-PSO in replicating the median, inter-quartile range, spread, and pattern of the EDI estimated from observed rainfall. The results indicate that the hybrid SVR-HHO approach can be utilized for reliable EDI predictions in the study area.
The rapid growth of electronic systems and devices operating within the gigahertz (GHz) frequency range has increased electromagnetic interference. In order to eliminate or reduce the spurious electromagnetic radiation levels more closely in different applications, there is strong research interest in electromagnetic absorber technology. Moreover, there is still a lack of ability to absorb electromagnetic radiation in a broad frequency range using thin thickness. Thus, this study examined the effect of incorporating magnetic and dielectric materials into the polymer matrix for the processing of radar absorbing materials. The experiment evaluated the sample preparation with different weight percentages of multi-walled carbon nanotubes (MWCNT) mixed with Ni0.5Zn0.5Fe2O4 (Nickel-Zinc-Ferrite) loaded into epoxy (P) as a matrix. The prepared samples were analysed by examining the reflectivity measurements in the 8 – 18 GHz frequency range and conducting a morphological study using scanning electron microscopy analyses. The correlation of the results showed that different amounts of MWCNT influenced the performance of the microwave absorber. As the amount of MWCNTs increased, the reflection loss (RL) peak shifted towards a lower frequency range and the trend was similar for all thicknesses. The highest RL was achieved when the content of MWCNTs was 2 wt% with a thickness of 2 mm with an RL of – 14 dB at 16 GHz. The 2.5 GHz bandwidth corresponded to the RL below -10 dB (90% absorption) in the range of 14.5 – 17 GHz. This study showed that the proposed experimental route provided flexible absorbers with suitable absorption values by mixing only 2 wt% of MWCNTs.