The effect of atmospheric plasma treatment of recycled carbon fiber at different plasma powers on recycled carbon fiber and its polypropylene composites

To increase the mechanical properties of recycled carbon fiber-reinforced polypropylene (PP) composites, recycled carbon fibers (RCF) were subjected to atmospheric plasma treatment at different plasma powers (100, 200, and 300 W). The changes on surface topography and roughness of RCF were examined by atomic force microscopy. Plasma treatment of RCF increased the roughness value of RCF. The variation of surface elemental compositions and tensile strength of RCF were determined by using X-ray photoelectron spectroscopy and tensile test, respectively. Plasma-treated RCF-reinforced PP composites were fabricated using high speed thermo-kinetic mixer. Plasma treatment of RCF at 100 W increased the tensile and flexural strength values of RCF-reinforced PP composites considerably by 17 and 11%, respectively. However, plasma treatment of RCF at higher plasma powers (200 W and 300 W) decreased tensile and flexural strength values of composites because of the etching of RCF. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47131.     

Real-time implementation of order-statistics-based directional filters

Vector filters based on order-statistics have proved successful in removing impulsive noise from colour images while preserving edges and fine image details. Among these filters, the ones that involve the cosine distance function (directional filters) have particularly high computational requirements, which limits their use in time-critical applications. In this paper, we introduce two methods to speed up these filters. Experiments on a diverse set of colour images show that the proposed methods provide substantial computational gains without significant loss of accuracy.     

Shape-driven segmentation of the arterial wall in intravascular ultrasound images

Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its 3-D reconstruction, and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition to a shape prior, we utilize an intensity prior through a nonparametric probability-density-based image energy, with global image measurements rather than pointwise measurements used in previous methods. Furthermore, a detection step is included to address the challenges introduced to the segmentation process by side branches and calcifications. All these features greatly enhance our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach.     

Design and experimental testing of a tactile sensor for self-compensation of contact error in soft tissue stiffness measurement

The measurement of viscoelastic properties of soft tissues has become a research interest with applications in the stiffness estimation of soft tissues, sorting and quality control of postharvest fruit, and fruit ripeness estimation. This paper presents a tactile sensor configuration to estimate the stiffness properties of soft tissues, using fruit as case study. Previous stiffness-measuring tactile sensor models suffer from unstable and infinite sensor outputs due to irregularities and inclination angles of soft tissue surfaces. The proposed configuration introduces two low stiffness springs at the extreme ends of the sensor with one high stiffness spring in-between. This study also presents a closed form mathematical model that considers the maximum inclination angle of the tissue’s (fruit) surface, and a finite element analysis to verify the mathematical model, which yielded stable sensor outputs. A prototype of the proposed configuration was fabricated and tested on kiwifruit samples. The experimental tests revealed that the sensor’s output remained stable, finite, and independent on both the inclination angle of the fruit surface and applied displacement of the sensor. The sensor distinguished between kiwifruit at various stiffness and ripeness levels with an output error ranging between 0.18 % and 3.50 %, and a maximum accuracy of 99.81 %, which is reasonable and competitive compared to previous design concepts.

     

Battery power loss compensated fractional order sliding mode control of a quadrotor UAV

This work considers the fractional order control of a lightweight quadrotor under loss in the battery voltage. Since the outdoor brushless motors are driven via a pulse width modulation (pwm) scheme, handshaking between the dynamic model and controller is established at the pwm level and this constitutes a major contribution of the paper. The attitude control is achieved via fractional order sliding mode control (FSMC) scheme. Necessary stability considerations are presented and it is seen that FSMC is a good alternative for the control of unmanned aerial vehicles. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Parametric least squares approximation using gamma bases

We study the problem of linear approximation of a signal using the parametric gamma bases in L² space. These bases have a time scale parameter, which has the effect of modifying the relative angle between the signal and the projection space, thereby yielding an extra degree of freedom in the approximation. Gamma bases have a simple analog implementation that is a cascade of identical lowpass filters. We derive the normal equation for the optimum value of the time scale parameter and decouple it from that of the basis weights. Using statistical signal processing tools, we further develop a numerical method for estimating the optimum time scale     

Theoretical Limits for Time Delay Based Location Estimation in Cooperative Relay Networks

Node localization in wireless networks is crucial for supporting advanced location-based services and improving the performance of network algorithms such as routing schemes. In this paper, we study the fundamental limits for time delay based location estimation in cooperative relay networks. The theoretical limits are investigated by obtaining Cramer–Rao Lower Bound (CRLB) expressions for the unknown source location under different relaying strategies when the location of the destination is known and unknown. More specifically, the effects of amplify-and-forward and decode-and-forward relaying strategies on the location estimation accuracy are studied. Furthermore, the CRLB expressions are derived for the cases where the location of only source as well as both source and destination nodes are unknown considering the relays as reference nodes. In addition, the effects of the node topology on the location estimation accuracy of the source node are investigated. The results reveal that the relaying strategy at relay nodes, the number of relays, and the node topology can have significant impacts on the location accuracy of the source node. Additionally, knowing the location of the destination node is crucial for achieving accurate source localization in cooperative relay networks.     

Improved prediction methods for scalable predictive animated mesh compression

Animated meshes represented as sequences of static meshes sharing the same connectivity require efficient compression. Among the compression techniques, layered predictive coding methods efficiently encode the animated meshes in a structured way such that the successive reconstruction with an adaptable quality can be performed. The decoding quality heavily depends on how well the prediction is performed in the encoder. Due to this fact, in this paper, three novel prediction structures are proposed and integrated into a state of the art layered predictive coder. The proposed structures are based on weighted spatial prediction with its weighted refinement and angular relations of triangles between current and previous frames. The experimental results show that compared to the state of the art scalable predictive coder, up to 30% bitrate reductions can be achieved with the combination of proposed prediction schemes depending on the content and quantization level.     

Ranging Accuracy in Dynamic Spectrum Access Networks

Theoretical analysis of time-of-arrival (TOA)-based high-precision ranging algorithm for the dynamic spectrum access networks (DSANs) is performed. The asymptotic frequency-domain Cramer-Rao bound (CRB) of the ranging algorithm that takes the frequency-dependent feature (FDF) and phase of multipath components (MPCs) into account is derived through Whittle formula. The effects of FDF-MPCs and related parameters such as absolute bandwidth and operating center frequency on the ranging accuracy are investigated. The results show that the impacts of the FDF-MPCs on the ranging accuracy can be significant and it is recommended utilizing as large absolute bandwidth as possible at low operating center frequencies to obtain high-precision distance information of the users in the DSANs     

Optimal scheduling of compute-intensive tasks on a network ofworkstations

In a network of high performance workstations, many workstations are underutilized by their owners. The problem of using these idle cycles for solving computationally intensive tasks by executing a large task on many workstations has been addressed before and algorithms with O(N²) time and O(N) space for choosing the optimal subset of workstations out of N workstations were presented. We improve these algorithms to reduce the running time to O(N log N), while keeping the space requirement the same. The proposed algorithms are particularly useful for SPMD parallelism where computation is the same for all workstations and the data space is partitioned between the workstations