A secure and efficient authenticated encryption for electronic payment systems using elliptic curve cryptography

The use of e-payment system for electronic trade is on its way to make daily life more easy and convenient. Contrarily, there are a number of security issues to be addressed, user anonymity and fair exchange have become important concerns along with authentication, confidentiality, integrity and non-repudiation. In a number of existing e-payment schemes, the customer pays for the product before acquiring it. Furthermore, many such schemes require very high computation and communication costs. To address such issues recently Yang et al. proposed an authenticated encryption scheme and an e-payment scheme based on their authenticated encryption. They excluded the need of digital signatures for authentication. Further they claimed their schemes to resist replay, man-in-middle, impersonation and identity theft attack while providing confidentiality, authenticity, integrity and privacy protection. However our analysis exposed that Yang et al.’s both authenticated encryption scheme and e-payment system are vulnerable to impersonation attack. An adversary just having knowledge of public parameters can easily masquerade as a legal user. Furthermore, we proposed improved authenticated encryption and e-payment schemes to overcome weaknesses of Yang et al.’s schemes. We prove the security of our schemes using automated tool ProVerif. The improved schemes are more robust and more lightweight than Yang et al.’s schemes which is evident from security and performance analysis.     

A stochastic model for forward–reverse logistics network design under risk

Attention with reverse logistics networks has increased during the last decade since their economic impact has been increasingly important and as environmental legislation has been becoming stricter. In this paper, A multi-period multi-echelon forward–reverse logistics network design under risk model is developed. The proposed network structure consists of three echelons in the forward direction, (suppliers, facilities and distribution centers) and two echelons, in the reverse direction (disassembly, and redistribution centers), first customer zones in which the demands are stochastic and second customer zones in which the demand is assumed to be deterministic, but it may also assumed to be stochastic. The problem is formulated in a stochastic mixed integer linear programming (SMILP) decision making form as a multi-stage stochastic program. The objective is to maximize the total expected profit.     

Partial eigenvalue assignment problem of high order control systems using orthogonality relations

In this paper, we present an explicit solution to the partial eigenvalue assignment problem of high order control system using orthogonality relations between eigenvectors of the matrix polynomial. Our solution can be implemented with only a partial knowledge of the spectrum and the corresponding left eigenvectors of the matrix polynomial. We show that the number of eigenvalues and eigenvectors that need to remain unchanged will not affected by feedback. A numerical example is given to illustrate the applicability and the practical usefulness of the proposed method.     

Using GMDH-based networks for improved spam detection and email feature analysis

Unsolicited or spam email has recently become a major threat that can negatively impact the usability of electronic mail. Spam substantially wastes time and money for business users and network administrators, consumes network bandwidth and storage space, and slows down email servers. In addition, it provides a medium for distributing harmful code and/or offensive content. In this paper, we explore the application of the GMDH (Group Method of Data Handling) based inductive learning approach in detecting spam messages by automatically identifying content features that effectively distinguish spam from legitimate emails. We study the performance for various network model complexities using spambase, a publicly available benchmark dataset. Results reveal that classification accuracies of 91.7% can be achieved using only 10 out of the available 57 attributes, selected through abductive learning as the most effective feature subset (i.e. 82.5% data reduction). We also show how to improve classification performance using abductive network ensembles (committees) trained on different subsets of the training data. Comparison with other techniques such as neural networks and naïve Bayesian classifiers shows that the GMDH-based learning approach can provide better spam detection accuracy with false-positive rates as low as 4.3% and yet requires shorter training time.     

GUIDE: Smartphone sensors‐based pedestrian indoor localization with heterogeneous devices

A plethora of indoor localization systems based on Wi‐Fi, radio frequency chips, ultra‐wide‐band, and bluetooth have been proposed, yet these systems do not work when the infrastructure is absent. On the other hand, infrastructure less systems benefit mostly from off‐the‐shelf smartphone sensors and do not need additional hardware. This study shows a similar indoor localization approach which turns smartphone built‐in sensors to good account. We take advantage of magnetic field strength fingerprinting approach to localize a pedestrian indoor. In addition, accelerometer and gyroscope sensors are utilized to find the pedestrian's traveled distance and heading estimation, respectively. Our aim is to solve the problem of device dependence by devising an approach that can perform localization using various smartphones in a similar fashion. We make the use of patterns of magnetic field strength to formulate the fingerprint database to achieve this goal. This approach solves two problems: need to update the database periodically and device dependence. We conduct experiments using Samsung Galaxy S8 and LG G6 for five different buildings with different dimensions in Yeungnam University, Republic of Korea. The evaluation is performed by following three different path geometries inside the buildings. The results show that the proposed localization approach can potentially be used for indoor localization with heterogeneous devices. The errors for path 1 and path 2 are very similar, however, localization error for path 3 is comparatively higher because of the complexity of the path 3. The mean and median errors for Galaxy S8 are 1.37 and 0.88 m while for LG G6, these are 1.84 and 1.21 m, respectively, while considering all buildings and all paths followed during the experiment. Overall, the proposed approach can potentially localize a pedestrian within 1.21 m at 50% and within 1.93 m at 75%, irrespective of the device used for localization. The performance of the proposed approach is compared with the K nearest neighbor (KNN) for evaluation. The proposed approach outperforms the KNN     

End Group Tuning in Acceptor–Donor–Acceptor Nonfullerene Small Molecules for High Fill Factor Organic Solar Cells

High fill factors have only recently become commonplace in nonfullerene‐based organic solar cells, with the balance of charge carrier mobilities often cited as the contributing factor. Here an end‐group modification to a commonly used nonfullerene acceptor (O‐IDTBR) is reported, in which the rhodanine end groups are replaced with dicyano moieties, resulting in the acceptor O‐IDTBCN. This new acceptor affords significant improvement in the fill factor (73%) and photocurrent (19.8 mA cm^?2) in organic solar cells with the low bandgap polymer PTB7‐Th. A narrowing of the bandgap, as a result of greater push–pull hybridization, allows complementary absorption to the donor and thus improved photon harvesting. Additionally, the measurement of charge carrier mobilities and lifetimes in both systems reveal that the PTB7‐Th:O‐IDTBCN blend possesses more balanced charge carrier mobilities, and longer lifetimes. Morphology studies reveal a slightly greater degree of molecular mixing of the O‐IDTBCN when blended with PTB7‐Th, despite the greater and more balanced charge carrier mobilities in this blend.     

A new chaotic multi-verse optimization algorithm for solving engineering optimization problems

Multi-verse optimization algorithm (MVO) is one of the recent meta-heuristic optimization algorithms. The main inspiration of this algorithm came from multi-verse theory in physics. However, MVO like most optimization algorithms suffers from low convergence rate and entrapment in local optima. In this paper, a new chaotic multi-verse optimization algorithm (CMVO) is proposed to overcome these problems. The proposed CMVO is applied on 13 benchmark functions and 7 well-known design problems in the engineering and mechanical field; namely, three-bar trust, speed reduce design, pressure vessel problem, spring design, welded beam, rolling element-bearing and multiple disc clutch brake. In the current study, a modified feasible-based mechanism is employed to handle constraints. In this mechanism, four rules were used to handle the specific constraint problem through maintaining a balance between feasible and infeasible solutions. Moreover, 10 well-known chaotic maps are used to improve the performance of MVO. The experimental results showed that CMVO outperforms other meta-heuristic optimization algorithms on most of the optimization problems. Also, the results reveal that sine chaotic map is the most appropriate map to significantly boost MVO’s performance.     

Detection of metallic and nonmetallic concealed targets based on millimeter‐wave inverse scattering approach

A millimeter‐wave radar based on active invers scattering approach for two dimensional screening of metallic and nonmetallic concealed targets is presented. The perceived challenges of detecting a nonmetallic target exhibiting poor dynamic range for measurement systems are analyzed and discussed by comparing the performance of three different antenna sensors. A short time duration pulse with frequency sweep covering 27 to 33 GHz band is used to feed the antenna sensors. In our experimental test, we buried a concealed target consists of metallic or dielectric strips under a dielectric layer that simulates the human body model. Waveguide and printed antipodal Vivaldi antennas are considered to study the target detectability and the quality of the measured millimeter‐wave images. The use of proposed AVA resulted in a better‐quality image with lower noise effect for both metallic and nonmetallic cases.     

Secure color image cryptosystem based on chaotic logistic in the FrFT domain

Multimedia Tools and Applications - Recently, the digital multimedia security technology has been an interesting research theme due to fast advancement in employing real time multimedia through...     

Identification based adaptive iterative learning controller

In recent years, more research in the control field has been in the area of self‐learning and adaptable systems, such as a robot that can teach itself to improve its performance. One of the more promising algorithms for self‐learning control systems is Iterative Learning Control (ILC), which is an algorithm capable of tracking a desired trajectory within a specified error limit. Conventional ILC algorithms have the problem of relatively slow convergence rate and adaptability. This paper suggests a novel approach by combining system identification techniques with the proposed ILC approach to overcome the aforementioned problems. The ensuing design procedure is explained and results are accrued from a number of simulation examples. A key point in the proposed scheme is the computation of gain matrices using the steepest descent approach. It has been found that the learning rule can be guaranteed to converge if certain conditions are satisfied. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society