DSCALE_mod16: A Model for Disaggregating Microwave Satellite Soil Moisture with Land Surface Evapotranspiration Products and Gridded Meteorological Data

Improving the spatial resolution of microwave satellite soil moisture (SM) products is important for various applications. Most of the downscaling methods that fuse optical/thermal and microwave data rely on remotely sensed land surface temperature (LST) or LST-derived SM indexes (SMIs). However, these methods suffer from the problems of “cloud contamination”, “decomposing uncertainty”, and “decoupling effect”. This study presents a new downscaling method, referred to as DSCALE_mod16, without using LST and LST-derived SMIs. This model combines MODIS ET products and a gridded meteorological data set to obtain Land surface Evaporative Efficiency (LEE) as the main downscaling factor. A cosine-square form of downscaling function was adopted to represent the quantitative relationship between LEE and SM. Taking the central part of the United States as the case study area, we downscaled SMAP (Soil Moisture Active and Passive) SM products with an original resolution of 36km to a resolution of 500m. The study period spans more than three years from 2015 to 2018. In situ SM measurements from three sparse networks and three core validation sites (CVS) were used to evaluate the downscaling model. The evaluation results indicate that the downscaled SM values maintain the spatial dynamic range of original SM data while providing more spatial details. Moreover, the moisture mass is conserved during the downscaling process. The downscaled SM values have a good agreement with in situ SM measurements. The unbiased root-mean-square errors (ubRMSEs) of downscaled SM values is 0.035 m3/m3 at Fort Cobb, 0.026 m3/m3 at Little Washita, and 0.055 m3/m3 at South Fork, which are comparable to ubRMSEs of original SM estimates at these three CVS

Electrical source of surface plasmon polaritons based on hybrid Au-GaAs QW structures

In this paper, the electrical excitation of surface plasmon polaritons (SPPs) based on a hybrid metal-semiconductor quantum well (QW) structure is investigated by finite-difference time-domain The hybrid structure could serve as a plasmonic source for integrated plasmonic circuits

Surface electromyography characteristics of patients with anterior cruciate ligament injury in different rehabilitation phases

Background: Anterior cruciate ligament reconstruction (ACLR) is a common treatment for anterior cruciate ligament (ACL) injury. However, after ACLR, a significant proportion of patients do not return to pre-injury levels. Research on muscle function during movement has important implications in rehabilitation.Methods: Sixty patients with unilateral ACL injury were recruited for this study and assigned into three groups: group A, individuals with an ACL injury before 6 months; group B, individuals with ACLR from 6 months to 1 year; and group C, individuals with ACLR 1 year later. Surface electromyography (SEMG) signals were collected from the bilateral rectus femoris (RF), vastus medialis (VM), vastus lateralis (VL), biceps femoris (BF), and semitendinosus (ST). The tasks performed during the experiment included straight leg raising (SLR) training at 30°, SLR training at 60°, ankle dorsiflexion, walking, and fast walking.Results: In the maximum muscle strength test, the affected side of the BF in group A (199.4 ± 177.12) was significantly larger than in group B (53.91 ± 36.61, p = 0.02) and group C (75.08 ± 59.7, p = 0.023). In the walking test, the contralateral side of the RF in group B (347.53 ± 518.88) was significantly greater than that in group C (139.28 ± 173.78, p = 0.029). In the SLR training (60°) test, the contralateral side of the RF in group C (165.37 ± 183.06) was significantly larger than that in group A (115.09 ± 62.47, p = 0.023) and smaller than that in group B (226.21 ± 237.17, p = 0.046); In the ankle dorsiflexion training test, the contralateral side of the RF in group B (80.37 ± 87.9) was significantly larger than that in group C (45.61 ± 37.93, p = 0.046).Conclusion: This study showed the EMG characteristics of patients with ACL injury helped to determine which muscle requires more training and which exercise model would be best suited for intervention

Trigeneration: A new way for landfill gas utilization and its feasibility in Hong Kong

Application of landfill gas (LFG) means a synergy between environmental protection and energy production. This paper presents a review of the status of LFG application. To more efficiently utilize the LFG in Hong Kong, a trigeneration scheme is proposed as a new way of LFG utilization. The feasibility of LFG trigeneration in Hong Kong is evaluated from the views of primary energy-saving and greenhouse gas (GHG) emission reduction as well as economic benefit. The proposed scenario is compared with the conventional scenarios of LFG treatment and utilization. It is shown that LFG for trigeneration has a higher energy saving and GHG emission reduction potentials. The new scheme is also more economical than the conventional way of LFG utilization. Some policy recommendations are also given to promote the biomass energy utilization from waste landfills in Hong Kong.Landfill gas application Trigeneration Greenhouse gas emission

Hyperspectral Image Denoising Based on Spectral Dictionary Learning and Sparse Coding

Processing and applications of hyperspectral images (HSI) are limited by the noise component. This paper establishes an HSI denoising algorithm by applying dictionary learning and sparse coding theory, which is extended into the spectral domain. First, the HSI noise model under additive noise assumption was studied. Considering the spectral information of HSI data, a novel dictionary learning method based on an online method is proposed to train the spectral dictionary for denoising. With the spatial–contextual information in the noisy HSI exploited as a priori knowledge, the total variation regularizer is introduced to perform the sparse coding. Finally, sparse reconstruction is implemented to produce the denoised HSI. The performance of the proposed approach is better than the existing algorithms. The experiments illustrate that the denoising result obtained by the proposed algorithm is at least 1 dB better than that of the comparison algorithms. The intrinsic details of both spatial and spectral structures can be preserved after significant denoising

Developing a Coordinated Signal Control System for Urban Ring Road Under the Vehicle-Infrastructure Connected Environment

Ring roads have been widely built in many cities, especially in the central districts with excessively heavy traffic demands and frequently generated congestion. In order to improve the operations and reduce traffic delay on urban ring roads, this paper developed a coordinated signal control system for urban ring roads under vehicle-infrastructure connected environment. The speed guidance would be provided to motorists utilizing four sub-systems including detection, communication, signal control, and expected speed calculation in the system. The signal timing parameters such as cycle length, green split, and offset, would be adjusted based on the artificial bee colony-shuffled frog leaping algorithm. The proposed signal control system had been test using VISSIM simulation model and the simulation results showed that the average delay, number of stops, and queue length were significantly improved compared with the conventional traffic control system

Tracking-based moving object detection

We present a novel approach for multi-object detection in aerial videos based on tracking. The proposed method mainly involves three steps. Firstly, the spatial-temporal saliency is employed to detect moving objects. Secondly, the detected objects are tracked by mean shift in the subsequent frames. Finally, the saliency results are fused with the weight map generated by tracking to get refined detection results, and in turn the modified detection results are used to update the tracking models. The proposed algorithm is evaluated on VIVID aerial videos, and the results show that our approach can reliably detect moving objects even in challenging situations. Meanwhile, the proposed method can process videos in real time, without the effect of time delay

Interferometric Phase Image Estimation via Sparse Coding in the Complex Domain

This paper addresses interferometric phase image estimation – that is, the estimation of phase modulo-2π images from sinusoidal 2π-periodic and noisy observations. These degradation mechanisms make interferometric phase image estimation a quite challenging problem. We tackle this challenge by reformulating the true estimation problem as a sparse regression, often termed sparse coding, in the complex domain. Following the standard procedure in patch-based image restoration, the image is partitioned into small overlapping square patches and the vector corresponding to each patch is modeled as a sparse linear combination of vectors, termed atoms, taken from a set called dictionary. Aiming at optimal sparse representations, and thus at optimal noise removing capabilities, the dictionary is learned from the data it represents via matrix factorization with sparsity constraints on the code (i.e., the regression coefficients) enforced by the ℓ1 norm. The effectiveness of the new sparse coding based approach to interferometric phase estimation, termed SpInPHASE, is illustrated in a series of experiment