Electroplating of nickel films at ultra low electrolytic temperature

In this paper, we have demonstrated a successful electroplating process of nickel (Ni) in a sulfamate electrolyte bath at ultra low electrolytic temperatures of 273–278 K. The potentiostatic mode is essential for the electroplating process rather than galvanostatic mode. The reason is that diffusion-limited current can be easily obtained by applying a specific potential which is higher than reduction potential. On the contrary, galvanostatic mode can not identify the diffusion-limited current and the reduction will suspend while the setting current is higher than diffusion-limited current ranged in hundreds of μA. The microstructure, morphology and hardness of the Ni electrodeposits were characterized using grazing incidence x-ray diffractometer, atomic force microscopy and nano-indentation test were performed. The hardness of Ni film was much enhanced to around 6.37 GPa at 273 K to 6.18 GPa at 278 K compared with that around 4.11 GPa at 288 K to 4.01 GPa at 293 K. The normal hardness of pure nickel is about 4 GPa. The enhanced hardness of Ni at ultra low temperature is attributed to both mechanisms of reduced grain size strengthening and residual compressive stress hardening.     

Application of neural processing paradigm in visual landmark recognition and autonomous robot navigation

This article addresses the issue of visual landmark recognition in autonomous robot navigation along known routes, by intuitively exploiting the functions of the human visual system and its navigational ability. A feedforward–feedbackward architecture has been developed for recognising visual landmarks in real time. It integrates the theoretical concepts from the pre-attentive and attentive stages in the human visual system, the selective attention adaptive resonance theory neural network and its derivatives, and computational approaches towards object recognition in computer vision. The architecture mimics the pre-attentive and attentive stages in the context of object recognition, embedding neural network processing paradigm into a computational template-matching approach in computer vision. The real-time landmark recognition capability is achieved by mimicking the pre-attentive stage, where it models a selective attention mechanism for optimal computational resource allocation, focusing only on the regions of interest to address the computational restrictive nature of current computer processing power. Similarly, the recognition of visual landmarks in both clean and cluttered backgrounds is implemented in the attentive stage by developing a memory feedback modulation (MFM) mechanism that enables knowledge from the memory to interact and enhance the efficiency of earlier stages in the architecture. Furthermore, it also incorporates both top-down and bottom-up facilitatory and inhibition pathways between the memory and the earlier stages to enable the architecture to recognise a 2D landmark, which is partially occluded by adjacent features in the surroundings. The results show that the architecture is able to recognise objects in cluttered backgrounds using real-images in both indoor and outdoor scenes. Furthermore, the architecture application in autonomous robot navigation has been demonstrated through a number of real-time trials in both indoor and outdoor environments.     

Stacking graphic elements to avoid over-plotting

An ongoing challenge for information visualization is how to deal with over-plotting forced by ties or the relatively limited visual field of display devices. A popular solution is to represent local data density with area (bubble plots, treemaps), color (heatmaps), or aggregation (histograms, kernel densities, pixel displays). All of these methods have at least one of three deficiencies:1) magnitude judgments are biased because area and color have convex downward perceptual functions, 2) area, hue, and brightness have relatively restricted ranges of perceptual intensity compared to length representations, and/or 3) it is difficult to brush or link to individual cases when viewing aggregations. In this paper, we introduce a new technique for visualizing and interacting with datasets that preserves density information by stacking overlapping cases. The overlapping data can be points or lines or other geometric elements, depending on the type of plot. We show real-dataset applications of this stacking paradigm and compare them to other techniques that deal with over-plotting in high-dimensional displays.     

Performance analysis of the IEEE 802.11 DCF in the presence of the hidden stations

In this paper, we present an analytical model to evaluate the hidden station effect on the performance of the IEEE 802.11 Distributed Coordination Function (DCF) in both non-saturation and saturation condition. DCF is a random channel-access scheme based on Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) method and the exponential backoff procedure. DCF is widely used and can support both wireless network with an access point and ad hoc wireless network because of its random channel-access method. On the other hand, this method unavoidably suffers the hidden station effect that causes significant performance degradation. As shown in this paper, hidden stations occur frequently in real-world settings, and the performance impact on the 802.11 DCF is a significant concern, but it has not been adequately studied. We study this problem through a spatial–temporal analysis and a Markov chain model. Our model generalizes the existing work on the performance modeling of 802.11 DCF for both non-saturation and saturation conditions. The scenario of no hidden station can be considered as a special case in our model. The performance of our model is evaluated by comparison with ns-2 simulations.     

Optimization of forging processes using Finite Element simulations

During the last decades, simulation software based on the Finite Element Method (FEM) has significantly contributed to the design of feasible forming processes. Coupling FEM to mathematical optimization algorithms offers a promising opportunity to design optimal metal forming processes rather than just feasible ones. In this paper Sequential Approximate Optimization (SAO) for optimizing forging processes is discussed. The algorithm incorporates time-consuming nonlinear FEM simulations. Three variants of the SAO algorithm—which differ by their sequential improvement strategies—have been investigated and compared to other optimization algorithms by application to two forging processes. The other algorithms taken into account are two iterative algorithms (BFGS and SCPIP) and a Metamodel Assisted Evolutionary Strategy (MAES). It is essential for sequential approximate optimization algorithms to implement an improvement strategy that uses as much information obtained during previous iterations as possible. If such a sequential improvement strategy is used, SAO provides a very efficient algorithm to optimize forging processes using time-consuming FEM simulations.     

Maskless writing of microfluidics: Rapid prototyping of 3D microfluidics using scratch on a polymer substrate

We present a new rapid prototyping method designed for simple fabrication of 3D microfluidics using a maskless direct writing technique on polymer substrates. The entire process is enabled by a commercial cutter plotter with 10 μm resolution precision and high speed. A CAD design of top and bottom microstructures is directly written on a polymer substrate using a cutter plotter after setting up the suitable force. The smallest channel width of 20 μm was obtained with the minimum force and 100 μm from the maximum. Also the written depth increased linearly with force from 30 to 130 μm. Several 3D microfluidic devices are demonstrated using a maskless writing technique. The entire fabrication process from CAD layout to a final 3D device can be completed in 30 min outside the clean room facilities.     

Multiwalled carbon nanotubes grafted chitosan nanobiocomposite: A prosperous functional nanomaterials for glucose biosensor application

Multiwalled carbon nanotubes (MWNTs) grafted chitosan (CS) nanowire (NW) was prepared by phase separation method. Glucose oxidase (GOx) was sequentially immobilized into MWNT-CS-NW to obtain MWNT-CS-NW/GOx biosensor. Field emission scanning electron microscopy (FESEM) images of MWNT-CS-NW/GOx reveals the existence of MWNT and CS. Cyclic voltammetry and amperometry were used to evaluate the electrochemical determination of glucose. The MWNT-CS-NW/GOx biosensor shows an excellent performance for glucose at +0.34 V with a high sensitivity (5.03 μA/mM) and lower response time (3 s) in a wide concentration range of 1-10 mM (correlation coefficient of 0.9988). In addition, MWNT-CS-NW/GOx biosensor possesses better reproducibility, storage stability and there is negligible interference from other electroactive components.     

Activity-based divergent supply chain planning for competitive advantage in the risky global environment: A DEMATEL-ANP fuzzy goal programming approach

Supply chain management allows modern enterprises to relax their own capacities and produce in a more flexible manner for diversified consumer demands. However, for an enterprise with divergent supply chain (DSC) and multiple product lines, to plan the production allocation for higher competitive advantage in the risky global market is a challenging problem. The existing literature still has not address this problem very well. This paper is aimed to treat this problem by using an integrated approach of activity based costing (ABC) and management, five forces analysis, risk and value-at-risk analysis, decision making trial and evaluation laboratory (DEMATEL), analytic network process (ANP), and fuzzy goal programming (FGP). The proposed model can effectively incorporate the key factors of precise costing, managerial constraints, competitive advantage analysis, and risk management into DSC forecasting and multi-objective production planning. A case study of a consumer-oriented cell phone DSC is also presented. The sensitivity analysis shows that identifying and relaxing crucial constraints can play an important role in DSC planning for higher competitive advantage and lower risk.     

An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine Classifier

In this paper, an automatic diagnosis system for diabetes on Linear Discriminant Analysis (LDA) and Morlet Wavelet Support Vector Machine Classifier: LDA–MWSVM is introduced. The structure of this automatic system based on LDA-MWSVM for the diagnosis of diabetes is composed of three stages: The feature extraction and feature reduction stage by using the Linear Discriminant Analysis (LDA) method and the classification stage by using Morlet Wavelet Support Vector Machine (MWSVM) classifier stage. The Linear Discriminant Analysis (LDA) is used to separate features variables between healthy and patient (diabetes) data in the first stage. The healthy and patient (diabetes) features obtained in the first stage are given to inputs of the MWSVM classifier in the second stage. Finally, in the third stage, the correct diagnosis performance of this automatic system based on LDA–MWSVM for the diagnosis of diabetes is calculated by using sensitivity and specificity analysis, classification accuracy, and confusion matrix, respectively. The classification accuracy of this system was obtained at about 89.74%.