Determine OWA operator weights using kernel density estimation

Some subjective methods should divide input values into local clusters before determining the ordered weighted averaging (OWA) operator weights based on the data distribution characteristics of input values. However, the process of clustering input values is complex. In this paper, a novel probability density based OWA (PDOWA) operator is put forward based on the data distribution characteristics of input values. To capture the local cluster structures of input values, the kernel density estimation (KDE) is used to estimate the probability density function (PDF), which fits to the input values. The derived PDF contains the density information of input values, which reflects the importance of input values. Therefore, the input values with high probability densities (PDs) should be assigned with large weights, while the ones with low PDs should be assigned with small weights. Afterwards, the desirable properties of the proposed PDOWA operator are investigated. Finally, the proposed PDOWA operator is applied to handle the multicriteria decision making problem concerning the evaluation of smart phones and it is compared with some existing OWA operators. The comparative analysis shows that the proposed PDOWA operator is simpler and more efficient than the existing OWA operator

RIS-Aided Wireless Communications: Prototyping, Adaptive Beamforming, and Indoor/Outdoor Field Trials

The prospects of using a Reconfigurable Intelligent Surface (RIS) to aid wireless communication systems have recently received much attention from academia and industry. Most papers make theoretical studies based on elementary models, while the prototyping of RIS-aided wireless communication and real-world field trials are scarce. In this paper, we describe a new RIS prototype consisting of 1100 controllable elements working at 5.8 GHz band. We propose an efficient algorithm for configuring the RIS over the air by exploiting the geometrical array properties and a practical receiver-RIS feedback link. In our indoor test, where the transmitter and receiver are separated by a 30 cm thick concrete wall, our RIS prototype provides a 26 dB power gain compared to the baseline case where the RIS is replaced by a copper plate. A 27 dB power gain was observed in the short-distance outdoor measurement. We also carried out long-distance measurements and successfully transmitted a 32 Mbps data stream over 500 m. A 1080p video was live-streamed and it only played smoothly when the RIS was utilized. The power consumption of the RIS is around 1 W. Our paper is vivid proof that the RIS is a very promising technology for future wireless communications.Comment: 13 pages, 18 figures, submitte

Effects of Multisensory Integration through Spherical Video-Based Immersive Virtual Reality on Students’ Learning Performances in a Landscape Architecture Conservation Course

Many courses are transitioning from offline to online instruction in the wake of the COVID-19 pandemic. Landscape architecture conservation courses face problems such as reduced interest in learning, poor learning attitudes and low learning efficiency among students. At the same time, due to the nature of landscape architecture conservation courses, students need more experience to learn well, and many landscape architecture courses do not meet this requirement. Online education also lacks the necessary education scenarios and is not very immersive, making it difficult to meet students’ learning needs. Continued advances in technology have provided new ways for people to connect with nature, increasing awareness and adoption of sustainable landscape architecture practices. To solve the above problems, this study uses multisensory spherical video-based immersive virtual reality technology to develop a VR learning system for landscape architecture conservation courses based on the senses of sight, sound and smell. This system is simple to operate, but interactive and immersive. A quasi-experimental study was also conducted to test the effectiveness of the system. Analyzing the results of the study, students in the experimental group outperformed students in the control group in terms of learning achievements, learning model satisfaction, technology acceptance, flow experience and learning attitudes, which suggests that the use of multisensory spherical video-based immersive virtual reality technology in a landscape architecture conservation course is effective in improving students’ learning performances, and that the study can provide input for the development of other courses

Highly-Efficient and Visible Light Photocatalytical Degradation of Organic Pollutants Using TiO2-Loaded on Low-Cost Biomass Husk

A composite composing of TiO2 nanoparticles load on biomass rice husk (RH) is developed by directly growing TiO2 nanoparticles on RH. The in-situ growth of the nanocrystals on RH is achieved by a low-cost and one-step homogeneous precipitation. Rapid hydrolysis proceeds at 90 °C by using ammonium fluotitanate and urea to facilitate the selective growth of TiO2. The method provides an easy access to the TiO2-RH composite with a strong interaction between TiO2 nanoparticles and the underlying RH. The structure and composition of TiO2-RH are characterized by using X-ray diffraction, X-ray photoelectron spectroscopy, Fourier-transform infrared spectroscopy, and UV-vis absorption spectroscopy. TiO2 nanoparticles-RH exhibits a good photocatalytic degradation of methyl orange. The results show that 92% of methyl orange (20 mg L−1) can be degraded within three hours in visible light. The catalytic activity of the composite is not reduced after 6 cycles, and it still reaches 81% after 6 cycles. The enhanced performance is ascribed to the suitable particle size the good dispersibility. It is expected that the high photocatalytical performance and the cost-effective composite presented here will inspire the development of other high-performance photocatalysts

Effects of Multisensory Integration through Spherical Video-Based Immersive Virtual Reality on Students’ Learning Performances in a Landscape Architecture Conservation Course

Many courses are transitioning from offline to online instruction in the wake of the COVID-19 pandemic. Landscape architecture conservation courses face problems such as reduced interest in learning, poor learning attitudes and low learning efficiency among students. At the same time, due to the nature of landscape architecture conservation courses, students need more experience to learn well, and many landscape architecture courses do not meet this requirement. Online education also lacks the necessary education scenarios and is not very immersive, making it difficult to meet students’ learning needs. Continued advances in technology have provided new ways for people to connect with nature, increasing awareness and adoption of sustainable landscape architecture practices. To solve the above problems, this study uses multisensory spherical video-based immersive virtual reality technology to develop a VR learning system for landscape architecture conservation courses based on the senses of sight, sound and smell. This system is simple to operate, but interactive and immersive. A quasi-experimental study was also conducted to test the effectiveness of the system. Analyzing the results of the study, students in the experimental group outperformed students in the control group in terms of learning achievements, learning model satisfaction, technology acceptance, flow experience and learning attitudes, which suggests that the use of multisensory spherical video-based immersive virtual reality technology in a landscape architecture conservation course is effective in improving students’ learning performances, and that the study can provide input for the development of other courses

Virtual image-based cloud removal for Landsat images

The inevitable thick cloud contamination in Landsat images has severely limited the usability and applications of these images. Developing cloud removal algorithms has been a hot research topic in recent years. Many previous algorithms used one or multiple cloud-free image(s) in the same area acquired on other date(s) as reference image(s) to reconstruct missing pixel values. However, it remains challenging to determine the optimal reference image(s). In addition, abrupt land cover change can substantially degrade the reconstruction accuracies. To address these issues, we present a new cloud removal algorithm called Virtual Image-based Cloud Removal (VICR). For each cloud region, VICR reconstructs the missing surface reflectance by three steps: virtual image within cloud region construction based on time-series reference images, similar pixel selection using the newly proposed temporally weighted spectral distance (TWSD), and residual image estimation. By establishing two buffer zones around the cloud region, VICR allows automatic selection of the optimal set of time-series reference images. The effectiveness of VICR was validated at four testing sites with different landscapes (i.e. urban, croplands, and wetlands) and land change patterns (i.e. phenological change, abrupt change caused by flooding and tidal inundation), and the performances were compared with mNSPI (modified neighborhood similar pixel interpolator), WLR (weighted linear regression) and ARRC (AutoRegression to Remove Clouds). Experimental results showed that VICR outperformed the other algorithms and achieved higher Correlation Coefficients and lower Root Mean Square Errors in surface reflectance estimation at the four sites. The improvement is particularly noticeable at the sites with abrupt land change. By considering the difference in the contributions from the reference images, TWSD can select more reliable similar pixels to improve the prediction of abrupt change in surface reflectance. Moreover, VICR is more robust to different cloud sizes and to changing reference images. VICR is also computationally much faster than ARRC. The framework for time-series image cloud removal by VICR has great potential to be applied for large datasets processing

Reconstruction of spatially continuous time-series land subsidence based on PS-InSAR and improved MLS-SVR in Beijing Plain area

Beijing has undergone severe settlement in recent years. Persistent Scatterers Interferometric Synthetic Aperture Radar (PS-InSAR) technique has been widely used to derive time-series land deformation. However, existing studies have faced two challenges: (1) the nonlinear characteristics of time-series subsidence has not been fully investigated; (2) since PS points are normally distributed in urban areas with high building density, measurement gaps usually exist in nonurban areas. To address the challenges, we presented a new method to reconstruct spatially continuous time-series deformation. First, PS-InSAR was used to retrieve the deformation based on 135 scenes of Envisat ASAR and Radarsat-2 images from 2003 to 2020. Polynomial Curve Fitting (PCF) was then used to model nonlinear time-series deformation for the PS points. In the PS measurement gaps, Iterative Self-Organizing Data Analysis Technique (ISODATA) and Multi-output Least Squares Support Vector Regression (MLS-SVR) were used to estimate the PCF coefficients and then time-series deformation considering 40 features including thickness of the compressible layers, annual groundwater level, etc. The major results showed that (1) compared to linear, quadratic, and quartic models, cubic polynomial model generated better fit for the time-series deformation (R2 ≈0.99), suggesting obvious nonlinear temporal pattern of deformation; (2) the time-series deformation over measurement gaps reconstructed by ISODATA and MLS-SVR had satisfactory accuracy (R2 = 0.92, MAPE < 15%) and yielded higher accuracy (R2 = 0.947) than IDW (R2 = 0.687) and Ordinary Kriging (R2 = 0.688) interpolation methods. The reconstructed results maintain the nonlinear characteristics and ensure the high spatial resolution (120 m) of time-series deformation. Among the 40 predictor variables, ground water level datasets are the most influential predictors of time-series deformation