Fiber structure and order greatly impact the mechanical behavior of fibrous materials. In biological tissues, the nonlinear mechanics of fibrous scaffolds contribute to the functionality of the material. The nonlinear mechanical properties of the wavy structure (crimp) in collagen allow tissue flexibility while preventing over-extension. A number of approaches have tried to recreate this complex mechanical functionality. We generated microcrimped fibers by briefly heating electrospun parallel fibers over the glass transition temperature or by ethanol treatment. The crimp structure is similar to those of collagen fibers found in native aorta, intestines, or ligaments. Using poly-L-lactic acid fibers, we demonstrated that the bulk materials exhibit changed stress-strain behaviors with a significant increase in the toe region in correlation to the degree of crimp, similar to those observed in collagenous tissues. In addition to mimicking the stress-strain behavior of biological tissues, the microcrimped fibers are instructive in cell morphology and promote ligament phenotypic gene expression. This effect can be further enhanced by dynamic tensile loading, a physiological perturbation in vivo. This rapid and economical approach for microcrimped fiber production provides an accessible platform to study structure-function relationships and a novel functional scaffold for tissue engineering and cell mechanobiology studies.

This paper presents a novel distributed video coding (DVC) scheme using compressive sensing (CS) that achieves lowcomplexity for encoding and efficient signal sensing. Most CS recovery algorithms rely only on signal sparsity. Yet, under DVC architecture, additional statistical characterization of the signal is available, which offers the potential for more precise CS recovery. First, a set of random measurements are acquired and transmitted to the decoder. The decoder then exploits the statistical characterization of the signal and generates the side information (SI). Finally, utilizing the SI, a Bayesian inference using belief propagation (BP) decoding is performed for signal recovery. The proposed CS-DVC system offers a more direct way of signal acquisition and the potential for more precise estimation of the signal from random measurements. Experimental results indicate that SI can improve the signal reconstruction quality in comparison with a CS recovery algorithm that relies only on the sparsity.

People pay much attention to the technology of data mining recently and more and more research institutions begin to buy the databases to analyze. If it doesn't concern customer's secrets the enterprises would also like to sell their data warehouse to do the research. Therefore, it becomes an important subject to prove the integrity of the database. This paper discusses about using the digital watermarking and the public authentication mechanism to strengthen the verification of integrity of the database. First, MD5 hash algorithm is used to fetch a database feature. Second, making XOR operation of database feature and digital watermarking gets a certification number. At last, using the secret key encrypts the certification number and makes public in the network with the database. Before using this database, user needs to use database owner's public key to decrypt the ciphertext to get the certification number. Then making XOR operation of database feature fetched by MD5 algorithm and certification number gets a watermark. Finally, user can rely on the integrity of fetched watermark to understand whether the database is destroyed or not.

No abstract

No abstract

Muscular dystrophy is a group of genetic diseases that cause the loss of muscles and hence weakening the muscle strength. A typical treatment for muscular dystrophy patients is routinely performing weight exercise to slow down the loss in muscles. Thus, we propose a system MyoBuddy to help both physical therapists and patients to keep track of the weights in workout activities based on electromyography (EMG) sensors embedded in Myo armband. In our study, we collect 102 sessions of EMG data from barbell bicep curl exercise with a range of weights from 20 to 70 lbs with a 10-pound increment. Both Support Vector Machine and Random Forest algorithms are explored to classify which weight of barbells are lifted. At the end, we achieve 77.1% classification accuracy on average. † Equal contribution authors 1 The source of the statistics: https://www.cdc.gov/ ncbddd/musculardystrophy/research.html

License plate detection plays an important role in license plate recognition (LPR) system. If license plate can be detected exactly, the character segmentation and recognition can be implemented more precisely and efficiently. In our observation, the illumination affects the result of detecting the license plate. The aim of this paper is to extract more than one license plate from an image and to obtain the license plates in the image which is taken under different conditions. The proposed method includes six processes as follows: transferring color to grayscale, image equalization, edge detection, checking black pixel ratio, plate verification and output license plates. The experimental results show that the proposed method can gain 94% accuracy in GetRatio and 83% accuracy in GetRight.

No abstract