Effect of different surface treatments on polypropylene composites reinforced with yerba mate fibers: Physical,mechanical, chemical,and morphological properties

This study presents the preparation of post-consumer polypropylene (r-PP) composites filled with 30 wt% yerba mate (YM) stick particles. To improve the fiber–matrix adhesion, three surface treatments were performed: alkaline treatment with sodium hydroxide (NaOH) and use of 3-aminopropyltriethoxysilane (APTS) and maleic anhydride graft polypropylene copolymer (PP-g-MA) as coupling agents. Mechanical properties including tensile, flexural, and impact resistance were determined, and chemical (Fourier transform infrared spectroscopy [FTIR]), physical (water absorption), and morphological analyses were performed. The main findings show that the treatments were efficient in improving the mechanical properties of the composites, with emphasis on the r-PP/YM30/APTS and r-PP/YM30/PP-g-MA composites, which proved to be superior in tensile, flexion and impact strength and absorption of water compared to the untreated composite. The morphological analysis showed a better interaction between the fiber and the polymeric matrix for the composites with YM/APTS and YM/PP-g-MA, which corroborates the results of tensile and flexural strength, as well as with the spectra of FTIR in which the chemical modification of the fibers is observed. However, the results show that these treatments are promising in obtaining composites with recycled matrix with better properties.     

Efficiently finding the optimum number of clusters in a dataset with a new hybrid differential evolution algorithm: DELA

Clustering algorithms, a fundamental base for data mining procedures and learning techniques, suffer from the lack of efficient methods for determining the optimal number of clusters to be found in an arbitrary dataset. The few methods existing in the literature always use some sort of evolutionary algorithm having a cluster validation index as its objective function. In this article, a new evolutionary algorithm, based on a hybrid model of global and local heuristic search, is proposed for the same task, and some experimentation is done with different datasets and indexes. Due to its design, independent of any clustering procedure, it is applicable to virtually any clustering method like the widely used \(k\)-means algorithm. Moreover, the use of non-parametric statistical tests over the experimental results, clearly show the proposed algorithm to be more efficient than other evolutionary algorithms currently used for the same task.     

Measuring the role of age in user performance during interaction with computers

The influence of aging on computer interaction has been widely analyzed in human–computer interaction research literature. Despite this, there are no age-based user maps that could support the user-interface customization. Studying the specific needs and constraints of these groups is crucial in order to adapt a user interface to the user’s interaction requirements. This work studies the performance of a sample of participants on three different basic tasks (pointing, dragging and dropping, and text selection) and the influence of age for each of them. It is concluded that this influence differs between specific activities. A group profile map that can support automatic classification in the future has been obtained.     

Real-time lane tracking using Rao-Blackwellized particle filter

A novel approach to real-time lane modeling using a single camera is proposed. The proposed method is based on an efficient design and implementation of a particle filter which applies the concepts of the Rao-Blackwellized particle filter (RBPF) by separating the state into linear and non-linear parts. As a result the dimensionality of the problem is reduced, which allows the system to perform in real-time in embedded systems. The method is used to determine the position of the vehicle inside its own lane and the curvature of the road ahead to enhance the performance of advanced driver assistance systems. The effectiveness of the method has been demonstrated implementing a prototype and testing its performance empirically on road sequences with different illumination conditions (day and nightime), pavement types, traffic density, etc. Results show that our proposal is capable of accurately determining if the vehicle is approaching the lane markings (Lane Departure Warning), and the curvature of the road ahead, achieving processing times below 2 ms per frame for laptop CPUs, and 12 ms for embedded CPUs.     

Detecting end-effectors on 2.5D data using geometric deformable models: Application to human pose estimation

End-effectors are usually related to the location of limbs, and their reliable detection enables robust body tracking as well as accurate pose estimation. Recent innovation in depth cameras has re-stated the pose estimation problem. We focus on the information provided by these sensors, for which we borrow the name 2.5D data from the Graphics community. In this paper we propose a human pose estimation algorithm based on topological propagation. Geometric Deformable Models are used to carry out such propagation, implemented according to the Narrow Band Level Set approach. A variant of the latter method is proposed, including a density restriction which helps preserving the topological properties of the object under analysis. Principal end-effectors are extracted from a directed graph weighted with geodesic distances, also providing a skeletal-like structure describing human pose. An evaluation against reference methods is performed with promising results. The proposed solution allows a frame-wise end-effector detection, with no temporal tracking involved, which may be generalized to the tracking of other objects beyond human body.     

Contention-based forwarding with multi-hop connectivity awareness in vehicular ad-hoc networks

Recent vehicular routing proposals use real-time road traffic density estimates to dynamically select forwarding paths. Estimating the traffic density in vehicular ad hoc networks requires the transmission of additional dedicated messages increasing the communications load. These proposals are generally based on unicast sender-based forwarding schemes. The greedy nature of sender-based forwarding can result in the selection of forwarders with weak radio links that might compromise the end-to-end performance. To overcome these limitations, this paper presents TOPOCBF, a novel contention-based broadcast forwarding protocol that dynamically selects forwarding paths based on their capability to route packets between anchor points. Such capability is estimated by means of a multi-hop connectivity metric. The obtained results demonstrate that TOPOCBF can provide good packet delivery ratios while reducing the communications load compared to unicast sender-based forwarding schemes using road traffic density estimates.     

Design with shape grammars and reinforcement learning

Shape grammars are a powerful and appealing formalism for automatic shape generation in computer-based design systems. This paper presents a proposal complementing the generative power of shape grammars with reinforcement learning techniques. We use simple (naive) shape grammars capable of generating a large variety of different designs. In order to generate those designs that comply with given design requirements, the grammar is subject to a process of machine learning using reinforcement learning techniques. Based on this method, we have developed a system for architectural design, aimed at generating two-dimensional layout schemes of single-family housing units. Using relatively simple grammar rules, we learn to generate schemes that satisfy a set of requirements stated in a design guideline. Obtained results are presented and discussed.     

Variable Structure Control with chattering elimination and guaranteed stability for a generalized T-S model

In this paper, a fuzzy based Variable Structure Control (VSC) with guaranteed stability is presented. The main objective is to obtain an improved performance of highly non-linear unstable systems. The main contribution of this work is that, firstly, new functions for chattering reduction and error convergence without sacrificing invariant properties are proposed, which is considered the main drawback of the VSC control. Secondly, the global stability of the controlled system is guaranteed.The well known weighting parameters approach, is used in this paper to optimize local and global approximation and modeling capability of T-S fuzzy model.A one link robot is chosen as a nonlinear unstable system to evaluate the robustness, effectiveness and remarkable performance of optimization approach and the high accuracy obtained in approximating nonlinear systems in comparison with the original T-S model. Simulation results indicate the potential and generality of the algorithm. The application of the proposed FLC-VSC shows that both alleviation of chattering and robust performance are achieved with the proposed FLC-VSC controller. The effectiveness of the proposed controller is proven infront of disturbances and noise effects.     

On the security of public key cryptosystems with a double decryption mechanism

In public key encryption schemes with a double decryption mechanism (DD-PKE), decryption can be done in either of two ways: by the user owning the secret/public key pair corresponding to the ciphertext, or by a trusted party holding a sort of master secret-key. In this note we argue that the classical security notion for standard public key encryption schemes does not suffice for DD-PKE schemes, and propose a new natural definition. Additionally, we illustrate the usefulness of the new security definition by showing that a DD-PKE scheme presented in the workshop Selected Areas in Cryptography 2005 is insecure under this augmented security notion.