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The paper presents a novel human lower limb simulation test system, which can be used to replace real human lower limb in experiment and evaluate performance of lower limb exoskeletons, especially gravity-counteracting exoskeletons. Based on wire driving, the test system can simulate the rotation of the hip joint and knee joint of human lower limb to realize the simulation of human gait, which can provide more reliable and safer test conditions compared with human-involved tests. Employing the test system, offsetting efficiency can be obtained from the perspective of energy consumption. A human lower limb simulation test system prototype was designed and manufactured accordingly. Experiment results validate the proposed method and show huge potential for multiple exoskeleton test.

Wildfire, one of the most serious disasters in the world, making a huge threat to local economic development and residents' life and property safety. Therefore, it is crucial to realize near real time wildfire detection in specific high wildfire risk area. Himawari-8 is a state of art Geostationary Orbit Satellites (GOS), which can realize near-real time wildfire detection and dynamic monitoring of wildfire changes. In this study, three features strategies: spectral, spectral calculated and spectral with spatial were used to extract features from Himawari-8 and Topography data. Next, Random Forest models were trained by these features. And then, we evaluated models on Himawari-8 data generated in March 29, 2020 6:10 UTC, March 30, 2020 5:50 UTC, March 31, 2020 5:30 UTC and April 1,2020 5:50 UTC to detect specific wildfire events in Southwestern China. The results showed an overall precision of 64.24%, 64.08% and 68.07%, and an overall F1-score of 0.737, 0.677, 0.724 based on three different features strategies. This study proved that our models have the ability to detect wildfire point accurately, and model with strategy.3 performed the best when considering precision, omission and F1-score.

Text image super-resolution technique is widely used to improve the text image quality in different resolution without distortion. However, the existed algorithms for text image super-resolution fail to work under many conditions, such as image blurring, edge discontinuity, edge artifact, etc. In this paper, we propose a novel algorithm framework to solve these above problems, in which we firstly obtain a high resolution text image with a sharper edge by using edge map as the priori condition, and then the edge signals of high-resolution image compensating edge residual is computed by iterative back-projection, finally the false edges and sharpen edges are suppressed at the same time. Experimental results demonstrate the effectiveness of our proposed algorithm compared to previously reported methods.

Lumped parameter model was used to explain the working principle of magnetically coupled resonant wireless power transmission (MCR-WPT) system. A resonant magnetic coupling two-coil model with Series-Series (SS) structure was established based on the equivalent circuit theory. The mathematical expression of output power and transmission efficiency of the MCR-WPT system was obtained. The relationship between transmission efficiency and system frequency was analyzed, as well as the relationship between transmission efficiency and transmission distance. A magnetically coupled resonant WPT system was built. The experimental results verified the theoretical analysis.

We describe a novel image fusion method for remote sensing based on structured dictionary learning and joint sparse representation which improves both quality and execution time. First, structured dictionaries are separately trained from the panchromatic (PAN) image and multispectral (MS) image by the double-sparsity model. Then, we use the joint sparse model to obtain the innovation components of the PAN image that are absent from the MS bands. Finally, the fused image is constructed by injecting the innovation components of PAN image into the low-resolution multispectral image by injection model. Experimental results show that our method has better performance than other state-of-the-art methods in terms of the spectral information, spatial resolution and computational efficiency.

With maturity of micro-electro-mechanical system (MEMS) technology, varieties of electronic products have entered into the era of integration, miniaturization and low power consumption. It is noted that the traditional battery power supply method restricted the development of electronic products. Vibration energy harvesting is one of the most promising solutions. Because the vibration frequency in normal circumstance is about 1–100Hz, the lower resonant frequency of piezoelectric harvester is demanded. Based on the piezoelectric effect, the characteristics of a novel kind of Double-Fork piezoelectric energy harvester (DFPEH) was studied. A model of the DFPEH was built in the finite element analysis software ANSYS, and the corresponding static analysis, modal analysis, harmonic response analysis were made sequentially. Results show that the first three order resonance frequencies of the harvester are 21Hz, 47Hz and 91Hz separately, which are suitable for low-frequency vibration environment. The frequency gap between the first-order resonant frequency and the second-order resonant frequency is 26Hz. Besides, the 3dB bandwidth of the first-order resonant frequency is 8Hz under condition of acceleration value 9.9m/s2 and the damping factor 0.025, which is applied on the fixed end of the DFPEH. Consequently, the DFPEH has shown the distinguished broadband characteristic.

In order to improve the robustness of the existing character recognition systems and increase their recognition ratio under various bad illumination conditions, we proposed a fast image enhancement algorithm based on illumination-reflectance model, by using an approximate edge-preserved filter. Experimental results showed that our algorithm can greatly help increase the character recognition ratio. In addition, our algorithm can be expected to achieve real-time processing.

A haze removal method for single image on complex imaging background is proposed. The basic assumption of dark channel prior and the selecting principle of atmospheric light value are studied theoretically, and it is found that the template scale plays a key role in the determination of atmospheric light value and the quality of transmission image, which are two crucial parameters for getting high-precision images. Based on this, a multi-scale model is ingeniously proposed in order to obtain the most desirable two crucial parameters simultaneously. The advantages of the multi-scale model for single image haze removal on complex imaging background are also experimentally demonstrated.

To improve the performance of sparsity-based single image super-resolution (SR), we propose a joint SR framework of structure prior based sparse representation (SPSR). The proposed SPSR algorithm exploits the multi-scale spatial structural self-similarities, the gradient prior and nonlocally centralized sparse representation to formulate a constrained optimization problem for high-resolution image recovery. The high-resolution image is firstly initialized by exploiting cross-scale patch redundancy in an image pyramid from single input low-resolution image. Then the sparse modeling of the image SR problem is proposed to refine it further, where the gradient histogram preservation is incorporated as a regularization term. Finally, an iterative solution is provided to solve the problem of model parameter estimation and sparse representation. Experimental results on image super-resolution validate the generality, effectiveness and robustness of the proposed SPSR algorithm.

Sparse dictionary learning has attracted enormous interest in image processing and data representation in recent years. To improve the performance of dictionary learning, we propose an efficient block-structured incoherent K-SVD algorithm for the sparse representation of signals. Without relying on any prior knowledge of the group structure for the input data, we develop a two-stage agglomerative hierarchical clustering method for block sparse representations. This clustering method adaptively identifies the underlying block structure of the dictionary under the restricted conditions of both a maximal block size and a minimal distance between the blocks. Furthermore, to meet the constraints of both the upper bound and the lower bound of the mutual coherence of dictionary atoms, we introduce a regularization term for the objective function to suppress the block coherence of the overcomplete dictionary. The experiments on synthetic data and real images demonstrate that the proposed algorithm has lower representation error, higher visual quality and better reconstructed results than other state-of-the-art methods.

The observed images are usually noisy due to data acquisition and transmission process. Therefore, image denoising is a necessary procedure prior to post-processing applications. The proposed algorithm exploits the self-similarity based low rank technique to approximate the real-world image in the multivariate analysis sense. It consists of two successive steps: adaptive dimensionality reduction of similar patch groups, and the collaborative filtering. For each target patch, the singular value decomposition (SVD) is used to factorize the similar patch group collected in a local search window by block-matching. Parallel analysis automatically selects the principal signal components by discarding the nonsignificant singular values. After the inverse SVD transform, the denoised image is reconstructed by the weighted averaging approach. Finally, the collaborative Wiener filtering is applied to further remove the noise. Experimental results show that the proposed algorithm surpasses the state-of-the-art methods in most cases.

A long-standing practical problem lies in achieving magnetic resonance thermometry (MRT) with high spatiotemporal resolution because the amount of data required increases exponentially as the physical dimension increases. To solve this problem, a novel method based on a partial separable function (PSF) model was proposed by exploiting the data redundancy. In this PSF model, two datasets (image data and navigating data) are applied for image reconstruction, which determine the spatial and temporal resolution respectively. After the phase information was extracted from the images reconstructed by the PS model, high spatial and temporal resolution MRT was realized by using the reference (proton resonance frequency) PRF shift technique. The simulation and experiment results of this novel method show that the spatial and dynamic characteristics of MRT images were accurately realized by use of PSF model in MRT. This method also has a smaller distortion of the temperature measurement than the conventional MRT. The proposed data acquisition and reconstruction method may facilitate the use of MR-monitored thermal ablations as an effective treatment option especially in moving tissues, such as liver and kidney.

The goal of image segmentation in imaging science is to solve the problem of partitioning an image into smaller disjoint homogeneous regions that share similar attributes. The novel technique of the expectation-maximization (EM) algorithm based on principal component analysis (PCA) with adaptively selecting dominant factors for color image segmentation in color spaces is studied here. And simultaneously, the final segmentation is completed by a simple labeling scheme. Then the comparative study of the refined EM algorithm is done in multiple color spaces. The experimental results from Berkeley segmentation dataset, illustrate that the improved EM algorithm with or without PCA has good segmentation results with fine adaptability in RGB, CIE XYZ, HSV, YCbCr, and YIQ(NTSC) color spaces where the results of test image changes little. Moreover, the optimized EM algorithm without PCA usually has better segmentation than the one with PCA. Nevertheless, these color spaces, i.e. CIE L∗a∗b∗, and h1h2h3, usually produce poor segmentation on the reliability and accuracy of a set of test images by performance analysis with subjective and objective evaluation indicators.

Single-sensor digital cameras capture images by covering the sensor surface with a color filter array (CFA) such that only one color sample is taken at the location of each pixel. To render a full-color image, color CFA interpolation or demosaicing using neighboring samples as a very important part of video/image processing, is required to estimate values of the other colors at each pixel. However, if demosaicing is not performed appropriately, images suffer from highly visible color artifacts. In this study, a novel adaptive improved edge preserving, edge directed interpolating technique is proposed for color reproduction from Bayer mosaic images along with self-regulating window size. It can take full advantage of not only inter-channel but also intra-channel correlation effectively. Experimental results show that the proposed method for producing high quality color images outperforms the existing state-of-the-art demosaicing techniques, both visually and in terms of peak signal-to-noise ratio(PSNR).

By analyzing the data obtained by GPS in dynamic deformation, processing analog GPS data mixed with random interference in the experiments through adopting various wavelet functions and different rank of the wavelet, and contrasting their analysis results, the suitable Daubechies function with the 'N' of six (db6) could be found. And it was used to filter GPS data of dynamic deformation, distill deformation character and separate different deformation frequency. From the processing results of actual data for large dam, we can realize db6 wavelet is advantaged to improve GPS data accuracy of dynamic deformation and its effect is notable.

In this paper, we report on a process-based approach to estimate leaf chlorophyll content from hyperspectral remote sensing imagery. Extensive field and laboratory measurements were conducted for ten sites in black spruce (Picea mariana) forests near Sudbury, Ontario, Canada in 2003 and 2004. Leaf optical spectra and chlorophyll content, leaf and canopy biophysical parameters, and forest background optical properties were collected. Hyperspectral remote sensing images were acquired by the compact airborne spectrographic imager (CASI) over the study sites within one week of ground measurements. Using measured data as inputs, a geometrical- optical model 4-Scale was investigated to estimate forest canopy reflectance. The simulated canopy reflectance agrees well with the CASI measured reflectance. A look-up-table approach was developed to provide the probabilities of viewing sunlit foliage and background, and to determine a spectral multiple scattering factor as functions of leaf area index, view zenith angle, and solar zenith angle. With the look-up-tables, leaf reflectance spectra were inverted from hyperspectral remote sensing imagery. Leaf chlorophyll content was estimated from the retrieved leaf reflectance spectra using the modified leaf-level PROSPECT inversion model.