Embedded Electrotactile Feedback System for Hand Prostheses Using Matrix Electrode and Electronic Skin.

Hyperspectral images (HSI) have high spectral and low spatial resolutions. However, multispectral images (MSI) usually have low spectral and high spatial resolutions. In various applications HSI with high spectral and spatial resolutions are required. In this paper, a new method for spatial resolution enhancement of HSI using high resolution MSI based on sparse coding and linear spectral unmixing (SCLSU) is introduced. In the proposed method (SCLSU), high spectral resolution features of HSI and high spatial resolution features of MSI are fused. In this case, the sparse representation of some high resolution MSI and linear spectral unmixing (LSU) model of HSI and MSI is simultaneously used in order to construct high resolution HSI (HRHSI). The fusion process of HSI and MSI is formulated as an ill-posed inverse problem. It is solved by the Split Augmented Lagrangian Shrinkage Algorithm (SALSA) and an orthogonal matching pursuit (OMP) algorithm. Finally, the proposed algorithm is applied to the Hyperion and ALI datasets. Compared with the other state-of-the-art algorithms such as Coupled Nonnegative Matrix Factorization (CNMF) and local spectral unmixing, the SCLSU has significantly increased the spatial resolution and in addition the spectral content of HSI is well maintained.

To design a more accurate and reproducible technique for the measurement of blood-brain barrier (BBB) permeability in gadolinium-enhancing multiple sclerosis (MS) lesions. Four MS patients were scanned using a new dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) protocol based on an uninterrupted two-part acquisition consisting of an initial part at high temporal and low spatial resolutions and a second part at low temporal and high spatial resolutions. The method preserves both the high spatial resolution needed for the often small size of lesions and the high temporal resolution required during the first minute after injection to sufficiently sample the first-pass bolus. Simulations compared the performance of this new protocol with the conventional one at low temporal and high spatial resolutions throughout. The BBB permeability estimates changed by up to 33% between the two protocols. The new protocol led to simulated error on K(trans) of 7%-10%, versus 7%-30% with the conventional protocol, and was more robust with respect to offsets between acquisition and injection start times, differences in shape of the first-pass peak, and permeability values. The dual-temporal resolution protocol produces improved BBB permeability estimates and provides a more complete view of active inflammatory MS lesion pathology.

Hyperspectral remote sensing images (HSIs) are characterized by having a low spatial resolution and a high spectral resolution, whereas multispectral images (MSIs) are characterized by low spectral and high spatial resolutions. These complementary characteristics have stimulated active research in the inference of images with high spatial and spectral resolutions from HSI-MSI pairs. In this paper, we formulate this data fusion problem as the minimization of a convex objective function containing two data-fitting terms and an edge-preserving regularizer. The data-fitting terms are quadratic and account for blur, different spatial resolutions, and additive noise; the regularizer, a form of vector Total Variation, promotes aligned discontinuities across the reconstructed hyperspectral bands. The optimization described above is rather hard, owing to its non-diagonalizable linear operators, to the non-quadratic and non-smooth nature of the regularizer, and to the very large size of the image to be inferred. We tackle these difficulties by tailoring the Split Augmented Lagrangian Shrinkage Algorithm (SALSA) — an instance of the Alternating Direction Method of Multipliers (ADMM) — to this optimization problem. By using a convenient variable splitting and by exploiting the fact that HSIs generally “live” in a low-dimensional subspace, we obtain an effective algorithm that yields state-of-the-art results, as illustrated by experiments.

Hyperspectral images (HSIs) usually have high spectral and low spatial resolution. Conversely, multispectral images (MSIs) usually have low spectral and high spatial resolution. The fusion of HSI and MSI aims to create spectral images with high spectral and spatial resolution. In this paper, we propose a fusion algorithm by combining linear spectral unmixing with the local low-rank property. By taking advantage of the local low-rank property, we first partition the corresponding spectral image into patches. For each patch pair, we cast the fusion problem as a coupled spectral unmixing problem that extracts the abundance and the endmembers of MSI and HSI, respectively. It then updates the abundance and the endmember through an alternating update algorithm. In fact, the convergence of the alternative update algorithm can be mathematically and empirically supported. We also propose a multiscale postprocessing procedure to combine fusion results obtained under different patch sizes. In experiments on three data sets, the proposed fusion algorithms outperformed state-of-the-art fusion algorithms in both spatial and spectral domains.

Due to technical and budget limitations, there are inevitably some trade-offs in the design of remote sensing instruments, making it difficult to acquire high spatiotemporal resolution remote sensing images simultaneously. To address this problem, this paper proposes a new data fusion model named the deep convolutional spatiotemporal fusion network (DCSTFN), which makes full use of a convolutional neural network (CNN) to derive high spatiotemporal resolution images from remotely sensed images with high temporal but low spatial resolution (HTLS) and low temporal but high spatial resolution (LTHS). The DCSTFN model is composed of three major parts: the expansion of the HTLS images, the extraction of high frequency components from LTHS images, and the fusion of extracted features. The inputs of the proposed network include a pair of HTLS and LTHS reference images from a single day and another HTLS image on the prediction date. Convolution is used to extract key features from inputs, and deconvolution is employed to expand the size of HTLS images. The features extracted from HTLS and LTHS images are then fused with the aid of an equation that accounts for temporal ground coverage changes. The output image on the prediction day has the spatial resolution of LTHS and temporal resolution of HTLS. Overall, the DCSTFN model establishes a complex but direct non-linear mapping between the inputs and the output. Experiments with MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Operational Land Imager (OLI) images show that the proposed CNN-based approach not only achieves state-of-the-art accuracy, but is also more robust than conventional spatiotemporal fusion algorithms. In addition, DCSTFN is a faster and less time-consuming method to perform the data fusion with the trained network, and can potentially be applied to the bulk processing of archived data.

Most modern satellites can provide two types of images: 1) panchromatic (PAN) image and 2) multispectral (MS) image. The former has high spatial resolution and low spectral resolution, while the latter has high spectral resolution and low spatial resolution. To obtain images with both high spectral and spatial resolution, pansharpening has emerged to fuse the spatial information of the PAN image and the spectral information of the MS image. However, most pansharpening methods fail to preserve spatial and spectral information simultaneously. In this article, we propose a framelet-based convolutional neural network (CNN) for pansharpening which makes it possible to pursue both high spectral and high spatial resolution. Our network consists of three subnetworks: 1) feature embedding net; 2) feature fusion net; and 3) framelet prediction net. Different from conventional CNN methods directly inferring high-resolution MS images, our approach learns to predict their framelet coefficients from available PAN and MS images. The introduction of multilevel feature aggregation and hybrid residual connection makes full use of spatial information of PAN image and spectral information of MS image. Quantitative and qualitative experiments at reduced- and full-resolution demonstrate that the proposed method achieves more appealing results than other state-of-the-art pansharpening methods. The source code and trained models are available at https://github.com/TingMAC/FrMLNet.

Nowadays, robots have been widely used in industrial, medical and service fields. With more and more applications of robots, safety in human-robot interaction becomes more important. As an excellent sensing element that can be attached onto mechanical arms of robots, electronic skin (e-skin) has attracted wide interests and been recognized having promising application prospects. However, existing electronic skins relying on multi-sensor array require complex structures and conditioning circuits, as well as complicated fabrication and assembly, which limits their applications to robots. In this paper, we propose an electronic skin based on thermosensation that has a simple structure and can be easily installed on a robotic arm to measure both collision force and position of collision point simultaneously and independently in human-robot interaction. Importantly, the e-skin has a high sensitivity and a broad measuring range, which demonstrates its promising potential in practical robotic application.

MODIS and ASTER synergy for characterizing thermal volcanic activity

Recently, many convolutional networks have been built to fuse a low spatial resolution (LR) hyperspectral image (HSI) and a high spatial resolution (HR) multispectral image (MSI) to obtain HR HSIs. However, most deep learning-based methods are supervised methods, which require sufficient HR HSIs for supervised training. Collecting plenty of HR HSIs is laborious and time-consuming. In this paper, a self-supervised spectral-spatial residual network (SSRN) is proposed to alleviate dependence on a mass of HR HSIs. In SSRN, the fusion of HR MSIs and LR HSIs is considered a pixel-wise spectral mapping problem. Firstly, this paper assumes that the spectral mapping between HR MSIs and HR HSIs can be approximated by the spectral mapping between LR MSIs (derived from HR MSIs) and LR HSIs. Secondly, the spectral mapping between LR MSIs and LR HSIs is explored by SSRN. Finally, a self-supervised fine-tuning strategy is proposed to transfer the learned spectral mapping to generate HR HSIs. SSRN does not require HR HSIs as the supervised information in training. Simulated and real hyperspectral databases are utilized to verify the performance of SSRN.

In this paper, we present a novel spatial and spectral fusion model (SASFM) that uses sparse matrix factorization to fuse remote sensing imagery with different spatial and spectral properties. By combining the spectral information from sensors with low spatial resolution (LSaR) but high spectral resolution (HSeR) (hereafter called HSeR sensors), with the spatial information from sensors with high spatial resolution (HSaR) but low spectral resolution (LSeR) (hereafter called HSaR sensors), the SASFM can generate synthetic remote sensing data with both HSaR and HSeR. Given two reasonable assumptions, the proposed model can integrate the LSaR and HSaR data via two stages. In the first stage, the model learns from the LSaR data a spectral dictionary containing pure signatures, and in the second stage, the desired HSaR and HSeR data are predicted using the learned spectral dictionary and the known HSaR data. The SASFM is tested with both simulated data and actual Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) acquisitions, and it is also compared to other representative algorithms. The experimental results demonstrate that the SASFM outperforms other algorithms in generating fused imagery with both the well-preserved spectral properties of MODIS and the spatial properties of ETM+. Generated imagery with simultaneous HSaR and HSeR opens new avenues for applications of MODIS and ETM+.

Kinematics in the Terminal Swing Phase of Unilateral Transfemoral Amputees: Microprocessor-Controlled Versus Swing-Phase Control Prosthetic Knees

Particle image velocimetry (PIV) techniques have a limited field of view of the flow field and can only capture high-resolution flow fields in localized areas. To obtain a larger measurement range, multiple cameras must be used to capture the flow field simultaneously and then stitch the parts together. However, this method can be expensive. We propose the local-global fusion convolutional neural network (LGF-CNN) for reconstructing large-field flow fields with high spatial resolution based only on two flow data types: local small-field high spatial resolution wake velocity fields and global large-field low spatial resolution velocity fields. The core of the model consists of convolutional neural network (CNN) architecture to learn the mapping relationship between the small field of view with high spatial resolution and the large field of view with low spatial resolution. Using the effectively trained LGF-CNN model, we demonstrate its ability to reconstruct high-resolution velocity fields around the circular cylinder. The LGF-CNN is rigorously validated on a number of representative datasets, including simulated data for Reynolds numbers of 200 and 500, as well as experimental data for a Reynolds number of 3.3 × 104 with the steady jet at the rear stagnation point of the cylinder. The results demonstrate the ability of LGF-CNN to generate accurate velocity fields with high spatial resolution, including reliable detection of high-frequency components. The proposed method could reduce the number of cameras required for large-field, high spatial resolution PIV measurements, thereby reducing experimental costs.

<p>Integration of a high share of renewables into the energy system comes with its implications. In order to study long and short-term effects on the electrical system, long time series of power generation with high spatial resolution are necessary. In recent years, reanalysis data have become a popular resource for obtaining these power generation datasets, however with the drawback of a rather coarse spatial resolution of several kilometres (MERRA-2: approx. 50km, ERA5: approx. 31 km). In order to overcome this limitation, reanalysis datasets can be combined with other datasets with a higher spatial resolution.</p><p>In the present study, we assess whether applying the Global Wind Atlas (GWA) developed by the Technical University of Denmark with a spatial resolution of 1 km can improve wind power generation simulated from two reanalysis (MERRA-2 and ERA5)  datasets when compared to observed power generation. Furthermore, these two reanalysis datasets are compared to determine how different spatial resolution of underlying reanalysis datasets affects the resulting time series. Wind power generation is simulated from reanalysis wind speeds based on a physical model. For wind speed bias correction to specific locations, mean wind speeds are approximated to GWA wind speeds. A turbine-specific power curve model scaled by the turbine specific power is applied to account for different technical performance. The analysis is conducted for different regions of the world (USA, Brazil, Austria, South Africa) and for different spatial and temporal levels, to determine how different datasets perform on different spatio-temporal scales.</p><p>Preliminary results show that bias correction with the GWA has a positive impact on simulation results for MERRA-2, the dataset with lower spatial resolution, while the effect for ERA5 is ambiguous. The error between simulated and observed wind power generation time series can be decreased by spatial and temporal aggregation and a positive, but not very strong correlation between system size (defined by a wind-correlation indicator) and simulation quality (higher correlation, lower error measures) could be identified.</p><p>Based on these results, we recommend applying additional wind speed bias correction on datasets with rather coarse spatial resolution, while the quality of newer datasets with high spatial resolution may be sufficient to be used without additional bias correction.</p>

Flexible pressure sensors as electronic skins have attracted wide attention to their potential applications for healthcare and intelligent robotics. However, the tradeoff between their sensitivity and pressure range restricts their practical applications in various healthcare fields. Herein, a cost-effective flexible pressure sensor with an ultrahigh sensitivity over an ultrawide pressure-range is developed by combining a sandpaper-molded multilevel microstructured polydimethylsiloxane and a reduced oxide graphene film. The unique multilevel microstructure via a two-step sandpaper-molding method leads to an ultrahigh sensitivity (2.5-1051 kPa-1 ) and can detect subtle and large pressure over an ultrawide range (0.01-400 kPa), which covers the overall pressure regime in daily life. Sharp increases in the contact area and additional contact sites caused by the multilevel microstructures jointly contribute to such unprecedented performance, which is confirmed by in situ observation of the gap variations and the contact states of the sensor under different pressures. Examples of the flexible pressure sensors are shown in potential applications involving the detection of various human physiological signals, such as breathing rate, vocal-cord vibration, heart rate, wrist pulse, and foot plantar pressure. Another object manipulation application is also demonstrated, where the material shows its great potential as electronic skin intelligent robotics and prosthetic limbs.

Balance control on a moving platform in unilateral lower limb amputees