A reversible data hiding scheme using complementary embedding strategy

Obtaining good visual quality and high hiding capacity with reversible data hiding systems is a technically challenging problem. In this paper, we propose a simple reversible data hiding scheme that uses a complementary hiding strategy. The proposed method embeds one secret bit horizontally and vertically into one cover pixel of a grayscale cover image by decreasing odd-valued pixels and increasing even-valued pixels by one. Experimental results show that the hiding capacity measured by bit per pixel (bpp) of the proposed scheme is at least 1.21 bpp with a PSNR (peak signal-to-noise ratio) value greater than 52 dB for all standard test images. Especially in the case of four-layer embedding, the PSNR value of the proposed method is still greater than 51 dB at a hiding capacity of about 5 bpp for all standard test images. In addition, the proposed method is quite simple because it primarily uses additions and subtractions. These results indicate that the proposed scheme is superior to many existing reversible data hiding schemes introduced in the literature.     

Dissipative particle dynamics simulations for fibre suspensions in newtonian and viscoelastic fluids

A versatile model of fibre suspensions in Newtonian and viscoelastic fluids has been developed using dissipative particle dynamics method. The viscoelastic fluid is modelled by linear chains with linear connector spring force (the Oldroyd-B model), which is known to be a reasonable model for the so-called Boger fluid (a dilute suspension of polymer in a highly viscous solvent). The numerical results are in excellent agreement with the analytical results of the Oldroyd-B model in simple shear flow. An effective meso-scale model of fibre in DPD is proposed and then incorporated with simple Newtonian fluid and our Boger fluid to enable entirely study rheological properties of fibre suspensions in both Newtonian and viscoelastic solvents. The numerical results are well compared with available experimental data and other numerical models.     

Improving traversability of quadruped walking robots using body movement in 3D rough terrains

This paper presents a study on improving the traversability of a quadruped walking robot in 3D rough terrains. The key idea is to exploit body movement of the robot. The position and orientation of the robot are systematically adjusted and the possibility of finding a valid foothold for the next swing is maximized, which makes the robot have more chances to overcome the rough terrains. In addition, a foothold search algorithm that provides the valid foothold while maintaining a high traversability of the robot, is investigated and a gait selection algorithm is developed to help the robot avoid deadlock situations. To explain the algorithms, new concepts such as reachable area, stable area, potential search direction, and complementary kinematic margin are introduced, and the effectiveness of the algorithms is validated via simulations and experiments.     

A dispatching method for automated lifting vehicles in automated port container terminals

In automated container terminals, containers are transported from the marshalling yard to a ship and vice versa by automated vehicles. The automated vehicle type studied in this paper is an automated lifting vehicle (ALV) that is capable of lifting a container from the ground by itself. This study discusses how to dispatch ALVs by utilizing information about pickup and delivery locations and time in future delivery tasks. A mixed-integer programming model is provided for assigning optimal delivery tasks to ALVs. A procedure for converting buffer constraints into time window constraints and a heuristic algorithm for overcoming the excessive computational time required for solving the mathematical model are suggested. Numerical experiments are reported to compare the objective values and computational times by a heuristic algorithm with those by an optimizing method and to analyze the effects of dual cycle operation, number of ALVs, and buffer capacity on the performance of ALVs.     

Tool path smoothing of a redundant machine: Application to Automated Fiber Placement

Automated Tape Laying and Fiber Placement of composite materials are the two principal automated processes used for fabrication of large composite structures in aeronautical industry. The aluminum parts produced by High Speed Machining tend to be replaced by carbon fiber composite parts realized with these processes. However, structural parts present reinforcement zones which disturb the tool path follow-up and generate an increase of the manufacturing time. Thus, this paper deals with the optimization of tool paths of a 7-axis machine tool of Fiber Placement with the objective of reducing the manufacturing time while ensuring the requested quality of the final part. In this paper, two complementary methods are detailed. The first method takes advantage of the degree of redundancy of the machine tool to decrease the kinematic loads of the control joints. The second method aims to smooth the orientation of the machine head along the tool path while ensuring quality constraints. These two methods are then applied on a test tool path and bring to a significant decrease of the manufacturing time (32.9%).     

Stability radius of implicit dynamic equations with constant coefficients on time scales

This paper deals with the stability radii of implicit dynamic equations on time scales when the structured perturbations act on both the coefficient of derivative and the right-hand side. Formulas of the stability radii are derived as a unification and generalization of some previous results. A special case where the real stability radius and the complex stability radius are equal is studied. Examples are derived to illustrate results.     

Random multispace quantization as an analytic mechanism for BioHashing of biometric and random identity inputs

Biometric analysis for identity verification is becoming a widespread reality. Such implementations necessitate large-scale capture and storage of biometric data, which raises serious issues in terms of data privacy and (if such data is compromised) identity theft. These problems stem from the essential permanence of biometric data, which (unlike secret passwords or physical tokens) cannot be refreshed or reissued if compromised. Our previously presented biometric-hash framework prescribes the integration of external (password or token-derived) randomness with user-specific biometrics, resulting in bitstring outputs with security characteristics (i.e., noninvertibility) comparable to cryptographic ciphers or hashes. The resultant BioHashes are hence cancellable, i.e., straightforwardly revoked and reissued (via refreshed password or reissued token) if compromised. BioHashing furthermore enhances recognition effectiveness, which is explained in this paper as arising from the random multispace quantization (RMQ) of biometric and external random inputs     

Local Convergence of the Lavrentiev Method for the Cauchy Problem via a Carleman Inequality

The purpose is to perform a sharp analysis of the Lavrentiev method applied to the regularization of the ill-posed Cauchy problem, set in the Steklov-Poincaré variational framework. Global approximation results have been stated earlier that demonstrate that the Lavrentiev procedure yields a convergent strategy. However, no convergence rates are available unless a source condition is assumed on the exact Cauchy solution. We pursue here bounds on the approximation (bias) and the noise propagation (variance) errors away from the incomplete boundary where instabilities are located. The investigation relies on a Carleman inequality that enables enhanced local convergence rates for both bias and variance errors without any particular smoothness assumption on the exact solution. These improved results allows a new insight on the behavior of the Lavrentiev solution, look similar to those established for the Quasi-Reversibility method in [Inverse Problems 25, 035005, 2009]. There is a case for saying that this sort of ??super-convergence?? is rather inherent to the nature of the Cauchy problem and any reasonable regularization procedure would enjoy the same locally super-convergent behavior.     

AH-64D main transmission accessory drive spur gear installation fault detections

The US Army has launched an aggressive program to implement condition based maintenance on its rotary wing assets. Condition Based Maintenance takes advantage of technology developments in the areas of machinery monitoring, signal processing and fault modeling to reduce the cost of ownership through improved maintenance procedures. Documenting the reduction in maintenance burden, cost savings and increased safety through early detection of helicopter faults is an important step in justifying the program. This paper describes a novel technique used to detect a serious fault in the accessory section of the AH-64D Apache main transmission. The technique determines if the primary or secondary clutch system is driving the aircraft accessories through a tachometer speed ratio. The method has identified three aircraft operating on the secondary system allowing Army maintenance crews to replace the affected transmissions and quickly restore the aircraft to operational status.     

An effective and interpretable method for document classification

As the number of documents has been rapidly increasing in recent time, automatic text categorization is becoming a more important and fundamental task in information retrieval and text mining. Accuracy and interpretability are two important aspects of a text classifier. While the accuracy of a classifier measures the ability to correctly classify unseen data, interpretability is the ability of the classifier to be understood by humans and provide reasons why each data instance is assigned to a label. This paper proposes an interpretable classification method by exploiting the Dirichlet process mixture model of von Mises–Fisher distributions for directional data. By using the labeled information of the training data explicitly and determining automatically the number of topics for each class, the learned topics are coherent, relevant and discriminative. They help interpret as well as distinguish classes. Our experimental results showed the advantages of our approach in terms of separability, interpretability and effectiveness in classification task of datasets with high dimension and complex distribution. Our method is highly competitive with state-of-the-art approaches.