A novel technique for fabrication of metallic structures on polyimide by selective electroless copper plating using ion implantation

The successful use of palladium ion implantation into polyimide to seed an electroless plated film of copper on the polyimide surface is reported. Polyimide (Hitachi PIX 3400) was implanted with palladium ions to doses of 1.5 × 10¹⁵ − 1.2 × 10¹⁷ ions cm⁻² using a MEVVA ion implanter. The implanted ions acted as sites for nucleation of copper film. A copper film was then deposited on implanted polyimide using a commercial electroless plating solution. The ion energy was kept low enough to facilitate a low critical ‘seed’ threshold dose that was measured to be 3.6× 10¹⁶ Pd ions cm⁻². Test patterns were made using polyimide to study the adaptability of this technique to form thick structures. Plated films were studied with optical microscopy, Rutherford Backscattering Spectrometry (RBS) and Profilometry. The adhesion of films was qualitatively assessed by a ‘scotch tape test’. The film growth (thickness) was observed to be linear with plating time. A higher implantation dose led to greater plating rates. The adhesion was found to improve with increasing dose.     

A micromachined electrochemical sensor for free chlorine monitoring in drinking water.

In this work, we designed, fabricated and tested a disposable, flow-through amperometric sensor for free chlorine determination in water. The sensor is based on the principle of an electrochemical cell. The substrate, as well as the top microfluidic layer, is made up of a polymer material. The advantages include; (a) disposability from low cost; (b) stable operation range from three-electrode design; (c) fluidic interconnections that provide on line testing capabilities; and (d) transparent substrate which provides for future integration of on-chip optics. The sensor showed a good response and linearity in the chlorine concentration ranging from 0.3 to 1.6 ppm, which applies to common chlorination process for drinking water purification.     

ESTIMATION OF THE PREDICTION ERROR VARIANCE AND AN R2 MEASURE BY AUTOREGRESSIVE MODEL FITTING

Abstract. For predicting the future values of a stationary process { x_t } ( t = 0, pL 1, pL 2,…) on the basis of its past, two key parameters are the variance V ( h ), h ≥ 1, of the h -step prediction error and Z ( h ) ={ R (0) - V ( h )}/ R (0), the corresponding measure, in an R ² sense, of the predictability of the process from its past, where R (0) denotes the process variance. The estimation of V ( h ) and Z ( h ) from a realization of T consecutive observations of { x_t } is considered, without requiring that the process follows a finite parameter model. Three different autoregressive estimators are examined and are shown to be asymptotically equivalent in the sense that as T ∝ they have the same asymptotic normal distribution. The question of bias in estimating these parameters is also examined and a bias correction is proposed. Finite sample behaviour is investigated by a simulation study.     

HSIC-based affinity measure for learning on graphs

Pattern Analysis and Applications - The performance of graph-based learning techniques largely relies on the edges defined between the vertices of the graph. These edges that represent the affinity...     

CP2EH: a comprehensive privacy-preserving e-health scheme over cloud

The Journal of Supercomputing - In the Attribute-Based Encryption (ABE) scheme, patients encrypt their electronic health record (EHR), attach the appropriate attributes with it, and outsource them...     

Soft computing-based approaches to predict energy consumption and stability margin of six-legged robots moving on gradient terrains

Soft computing-based approaches have been developed to predict specific energy consumption and stability margin of a six-legged robot ascending and descending some gradient terrains. Three different neuro-fuzzy and one neural network-based approaches have been developed. The performances of these approaches are compared among themselves, through computer simulations. Genetic algorithm-tuned multiple adaptive neuro-fuzzy inference system is found to perform better than other three approaches for predicting both the outputs. This could be due to a more exhaustive search carried out by the genetic algorithm in comparison with back-propagation algorithm and the use of two separate adaptive neuro-fuzzy inference systems for two different outputs. A designer may use the developed soft computing-based approaches in order to predict specific energy consumption and stability margin of the robot for a set of input parameters, beforehand.     

Effects of turning gait parameters on energy consumption and stability of a six-legged walking robot

Minimization of energy consumption plays a key role in the locomotion of a multi-legged robot used for various purposes. Turning gaits are the most general and important factors for omni-directional walking of a six-legged robot. This paper presents an analysis on energy consumption of a six-legged robot during its turning motion over a flat terrain. An energy consumption model is developed for statically stable wave gaits in order to minimize dissipating energy for optimal feet forces distributions. The effects of gait parameters, namely angular velocity, angular stroke and duty factors are studied on energy consumption, as the six-legged robot walks along a circular path of constant radius with wave gait. The variations of average power consumption and energy consumption per unit weight per unit traveled length with the angular velocity and angular stroke are compared for the turning gaits of a robot with four different duty factors. Computer simulations show that wave gait with a low duty factor is more energy-efficient compared to that with a high duty factor at the highest possible angular velocity. A stability analysis based on normalized energy stability margin is performed for turning motion of the robot with four duty factors for different angular strokes.