Photocrosslinked hydrogels reinforced by microfibrillated cellulose (MFC) were prepared from a methacrylate-functionalized fish elastin polypeptide and MFC dispersed in dimethylsulfoxide (DMSO). First, a water-soluble elastin peptide with a molecular weight of ca. 500 g/mol from the fish bulbus arteriosus was polymerized by N,N′-dicyclohexylcarbodiimide (DCC), a condensation reagent, and then modified with 2-isocyanatoethyl methacrylate (MOI) to yield a photocrosslinkable fish elastin polypeptide. The product was dissolved in DMSO and irradiated with UV light in the presence of a radical photoinitiator. We obtained hydrogels successfully by substitution of DMSO with water. The composite gel with MFC was prepared by UV irradiation of the photocrosslinkable elastin polypeptide mixed with dispersed MFC in DMSO, followed by substitution of DMSO with water. The tensile test of the composite gels revealed that the addition of MFC improved the tensile properties, and the shape of the stress–strain curve of the composite gel became more similar to the typical shape of an elastic material with an increase of MFC content. The rheology measurement showed that the elastic modulus of the composite gel increased with an increase of MFC content. The cell proliferation test on the composite gel showed no toxicity.

There is a strong demand for super-resolution time of arrival (TOA) estimation techniques for radar applications that can that can exceed the theoretical limits on range resolution set by frequency bandwidth. One of the most promising solutions is the use of compressed sensing (CS) algorithms, which assume only the sparseness of the target distribution but can achieve super-resolution. To preserve the reconstruction accuracy of CS under highly correlated and noisy conditions, we introduce a random resampling approach to process the received signal and thus reduce the coherent index, where the frequency-domain-based CS algorithm is used as noise reduction preprocessing. Numerical simulations demonstrate that our proposed method can achieve super-resolution TOA estimation performance not possible with conventional CS methods.

Super-resolution time of arrival estimation methods have attracted much attention in radar signal processing. Many studies have used compressed sensing (CS)-based approaches to attain the super-resolution property because they assume sparseness of temporal distribution of target signal. However, this approach still suffer from accuracy degradation when decomposing highly correlated signals in heavily noise-contaminated situations. To resolve this problem, this study introduces an enhanced CS method by exploiting the sparseness of the signals in terms of both time and frequency domains. Numerical simulation and comparison with results obtained by conventional methods demonstrate that the proposed method considerably enhances the reconstruction accuracy for multiple highly correlated signals in lower signal-to-noise ratio situations.