Applying Unbalanced RSA to Authentication and Key Distribution in 802.11

It is well known that the data confidentiality algorithm, called Wired Equivalent Privacy (WEP), offerred by the original IEEE 802.11 is not secure mainly due to its improper implementation of RC4 algorithm [3], [4]. The IEEE 802.11 Task Group ’I’ (TGi) has designed two options to address this problem. One is called Temporal Key Integrity Protocol (TKIP), intended to be used as a short-term patch for currently deployed equipment. The other one uses Advanced Encryption Standard (AES), a powerful block cipher recommended by NIST to replace DES in 2000, as a long-term solutio

Spatial heterogeneity in the nonlinear impact of built environment on commuting time of active users: A gradient boosting regression tree approach

Many studies provided evidence regarding the influence of built environment (BE) on commuting time. However, few studies have considered the spatial heterogeneity of such impacts. Using data from Nanjing, China, this study employs two-step clustering and gradient boosted regression trees (GBRT) to segment the neighborhoods into different types and investigate the effects of BE characteristics on the commuting time of active users. The results show a strong effect of BE characteristics on commuting time, involving active modes. The importance of BE characteristics varies among neighborhood types. For active commuters in the internal region of Nanjing, commuting time is affected mostly by the land use mix at the work end. The lowest impact of BE in internal regions is associated with metro station density. For active commuters in external region of the city, the relative importance of intersection density at the home end is the largest (as high as 5.76%). Moreover, other significant differences are found in the associations between BE characteristics and active commuting time in the two regions.</p

The Hα\alpha broadband photometric reverberation mapping of four Seyfert 1 galaxies

Broadband photometric reverberation mapping (PRM) have been investigated for AGNs in recent years, but mostly on accretion disk continuum RM. Due to the small fraction of broad emission lines in the broadband, PRM for emission lines is very challenging. Here we present an ICCF-Cut method for broadband PRM to obtain the H$\alpha$ broad line lag and apply it to four Seyfert 1 galaxies, MCG+08-11-011, NGC 2617, 3C 120 and NGC 5548. All of them have high quality broadband lightcurves with daily/sub-daily cadence, which enables us to extract H$\alpha$ lightcurves from the line band by subtracting the contributions from the continuum and host galaxy. Their extracted H$\alpha$ lightcurves are compared with the lagged continuum band lightcurves, as well as the lagged H$\beta$ lightcurves obtained by spectroscopic RM (SRM) at the same epochs. The consistency of these lightcurves and the comparison with the SRM H$\beta$ lags provide supports to the H$\alpha$ lags of these AGNs, in a range from 9 to 19 days, obtained by the ICCF-Cut, JAVELIN and $\chi^2$ methods. The simulations to evaluate the reliability of H$\alpha$ lags and the comparisons between SRM H$\beta$ and PRM H$\alpha$ lags indicate that the consistency of the ICCF-Cut, JAVELIN and $\chi^2$ results can ensure the reliability of the derived H$\alpha$ lags. These methods may be used to estimate the broad line region sizes and black hole masses of a large sample of AGNs in the large multi-epoch high cadence photometric surveys such as LSST in the future.Comment: 22 pages, 19 figures, accepted for publication in Ap

Largest Lyapunov Exponent Optimization for Control of a Bionic-Hand: A Brain Computer Interface Study

This paper introduces a brain control bionic-hand, and several methods have been developed for predicting and quantifying the behavior of a non-linear system such as a brain. Non-invasive investigations on the brain were conducted by means of electroencephalograph (EEG) signal oscillations. One of the prominent concepts necessary to understand EEG signals is the chaotic concept named the fractal dimension and the largest Lyapunov exponent (LLE). Specifically, the LLE algorithm called the chaotic quantifier method has been employed to compute the complexity of a system. The LLE helps us to understand how the complexity of the brain changes while making a decision to close and open a fist. The LLE has been used for a long time, but here we optimize the traditional LLE algorithm to attain higher accuracy and precision for controlling a bionic hand. In the current study, the main constant input parameters of the LLE, named the false nearest neighbor and mutual information, are parameterized and then optimized by means of the Water Drop (WD) and Chaotic Tug of War (CTW) optimizers. The optimized LLE is then employed to identify imaginary movement patterns from the EEG signals for control of a bionic hand. The experiment includes 21 subjects for recording imaginary patterns. The results illustrated that the CTW solution achieved a higher average accuracy rate of 72.31% in comparison to the traditional LLE and optimized LLE by using a WD optimizer. The study concluded that the traditional LLE required enhancement using optimization methods. In addition, the CTW approximation method has the potential for more efficient solutions in comparison to the WD method

Mastering Autonomous Assembly in Fusion Application with Learning-by-doing: a Peg-in-hole Study

Robotic peg-in-hole assembly is an essential task in robotic automation research. Reinforcement learning (RL) combined with deep neural networks (DNNs) lead to extraordinary achievements in this area. However, current RL-based approaches could hardly perform well under the unique environmental and mission requirements of fusion applications. Therefore, we have proposed a new designed RL-based method. Furthermore, unlike other approaches, we focus on innovations in the structure of DNNs instead of the RL model. Data from the RGB camera and force/torque (F/T) sensor as the input are fed into a multi-input branch network, and the best action in the current state is output by the network. All training and experiments are carried out in a realistic environment, and from the experiment result, this multi-sensor fusion approach has been shown to work well in rigid peg-in-hole assembly tasks with 0.1mm precision in uncertain and unstable environments