Search Results (4)

Carbon dioxide (CO₂) assisted oxidative dehydrogenation of propane over Ga-modified catalysts is highly sensitive to the identity of support, but the underlying cause of support effects has not been well established. In this article, SSZ-13, SSZ-39, ZSM-5, silica and γ-Al₂O₃ were used to load Ga species by incipient wet impregnation. The structure, textural properties, acidity of the Ga-based catalysts and the process of CO₂-assisted oxidative dehydrogenation of propane were examined by X-ray diffraction (XRD), nitrogen physisorption (N₂ physisorption), ammonia temperature-programmed desorption (NH₃-TPD), pyridine chemisorbed Fourier transform infrared spectra (Py-FTIR), OH-FTIR and in situ FTIR. Evaluation of the catalytic performance combined with detailed catalyst characterization suggests that their dehydrogenation activity is positively associated with the number of acid sites in middle strength, confirming that the Lewis acid sites generated by Ga cations are the active species in the reaction. Ga/Na-SSZ-39(9) also has feasible acidic strength and a unique channel structure, which is conducive to the dissociative adsorption of propane and desorption of olefins. The Ga/Na-SSZ-39(9) catalysts showed superior olefins selectivity and catalytic stability at 600 ℃ compared to any other catalysts. This approach to quantifying support acid strength, and channel structure and applying it as a key catalytic descriptor of support effects is a useful tool to enable the rational design of next-generation CO₂-assisted oxidative dehydrogenation catalysts. Full article

Gesture recognition has been studied for decades and still remains an open problem. One important reason is that the features representing those gestures are not sufficient, which may lead to poor performance and weak robustness. Therefore, this work aims at a comprehensive and discriminative feature for hand gesture recognition. Here, a distinctive Fingertip Gradient orientation with Finger Fourier (FGFF) descriptor and modified Hu moments are suggested on the platform of a Kinect sensor. Firstly, two algorithms are designed to extract the fingertip-emphasized features, including palm center, fingertips, and their gradient orientations, followed by the finger-emphasized Fourier descriptor to construct the FGFF descriptors. Then, the modified Hu moment invariants with much lower exponents are discussed to encode contour-emphasized structure in the hand region. Finally, a weighted AdaBoost classifier is built based on finger-earth mover’s distance and SVM models to realize the hand gesture recognition. Extensive experiments on a ten-gesture dataset were carried out and compared the proposed algorithm with three benchmark methods to validate its performance. Encouraging results were obtained considering recognition accuracy and efficiency. Full article

The screen-cam process, which is taking pictures of the content displayed on a screen with mobile phones or cameras, is one of the main ways that image information is leaked. However, traditional image watermarking methods are not resilient to screen-cam processes with severe distortion. In this paper, a screen-cam robust watermarking scheme with a feature-based synchronization method is proposed. First, the distortions caused by the screen-cam process are investigated. These distortions can be summarized into the five categories of linear distortion, gamma tweaking, geometric distortion, noise attack, and low-pass filtering attack. Then, a local square feature region (LSFR) construction method based on a Gaussian function, modified Harris–Laplace detector, and speeded-up robust feature (SURF) orientation descriptor is developed for watermark synchronization. Next, the message is repeatedly embedded in each selected LSFR by an improved embedding algorithm, which employs a non-rotating embedding method and a preprocessing method, to modulate the discrete Fourier transform (DFT) coefficients. In the process of watermark detection, we fully utilize the captured information and extract the message based on a local statistical feature. Finally, the experimental results are presented to illustrate the effectiveness of the method against common attacks and screen-cam attacks. Compared to the previous schemes, our scheme has not only good robustness against screen-cam attack, but is also effective against screen-cam with additional common desynchronization attacks. Full article

An unsupervised approach to classify surface defects in wire rod manufacturing is developed in this paper. The defects are extracted from an eddy current signal and classified using a clustering technique that uses the dynamic time warping distance as the dissimilarity measure. The new approach has been successfully tested using industrial data. It is shown that it outperforms other classification alternatives, such as the modified Fourier descriptors. Full article