Compact linear tapered slot antenna for UWB applications

A novel ultra-wideband (UWB) antenna consisting of a linear tapered slot in the ground plane and a microstrip to slotline transition is investigated. The antenna possesses a wide bandwidth from 2.95?14 GHz for |S₁₁| < - dB and shows stable radiation patterns with an average gain of 3 dBi throughout the band. Measured group delay and transmission characteristics indicate that the antenna has good pulse handling capabilities.     

Cortical neural prosthesis performance improves when eye position is monitored.

Neural prostheses that extract signals directly from cortical neurons have recently become feasible as assistive technologies for tetraplegic individuals. Significant effort toward improving the performance of these systems is now warranted. A simple technique that can improve prosthesis performance is to account for the direction of gaze in the operation of the prosthesis. This proposal stems from recent discoveries that the direction of gaze influences neural activity in several areas that are commonly targeted for electrode implantation in neural prosthetics. Here, we first demonstrate that neural prosthesis performance does improve when eye position is taken into account. We then show that eye position can be estimated directly from neural activity, and thus performance gains can be realized even without a device that tracks eye position.     

Fenton-Like Oxidation of Polycyclic Aromatic Hydrocarbons in Soils Using Electrokinetics

An integrated electrochemical oxidation process that utilizes electrokinetics (EK) to deliver the oxidant (5–10% hydrogen peroxide, H2O2) and chelant [40 mM of ethylenediaminetetraacetic acid (EDTA) or diethylenetriaminepentaacetic acid (DTPA)] or iron chelate (1.4 mM Fe-EDTA or Fe-DTPA) to oxidize polycyclic aromatic hydrocarbons (PAHs) in soils was investigated. Batch and bench-scale EK experiments were conducted using: (a) kaolin, a low permeability clayey soil, spiked with phenanthrene at 500 mg/kg, and (b) former manufactured gas plant (MGP) soil, a high buffering silty soil, contaminated by a variety of PAHs (1493 mg/kg). Batch experiments showed that chelant solutions dissolve native iron minerals to form soluble Fe-chelates that remain available even at higher pH conditions of soil for the Fenton-like oxidation of the PAHs. In EK experiments, a 5–10% H2O2 solution was delivered from the anode and a chelant solution or iron-chelate was delivered from the cathode. Preflushing of soil with 5% ethanol and ferrous sulfate (1.4 mM) prior to oxidant delivery was also investigated. An electric potential of 2 VDC/cm was applied in all tests to induce electroosmotic flow for 5–8 days for kaolin and 25 days for the MGP field soil. In the absence of any chelating agent, phenanthrene oxidation was catalyzed by native iron present in kaolin soil, and 49.8–82.3% of phenanthrene was oxidized by increasing H2O2 concentration from 5–10%. At 5% H2O2 concentration, phenanthrene oxidation was not increased by using 40 mM EDTA, 40 mM DTPA or 1.4 mM Fe-DTPA, but it increased to 70% using 1.4 mM Fe-EDTA. Maximum phenanthrene oxidation (90.5%) was observed by 5% ethanol preflushing and then treating with 5% H2O2 at the anode and 1.4 mM Fe-EDTA at the cathode. However, preflushing with 1.4 mM ferrous sulfate did not improve phenanthrene oxidation. The results with the MGP field soil indicated that delivery of 5% H2O2 alone resulted in oxidation of 39.8% of total PAHs (especially 2- and 3-ring PAHs). The use of EDTA and Fe-EDTA did not increase PAHs oxidation in this soil. Overall, the results reveal that an optimized in situ combined technology of EK and Fenton-like process has the potential to oxidize PAHs in low permeability and/or high buffering soils.     

Bioremediation of contaminated soil beds and groundwater — A simulation study

Pollution has reached levels which demand immediate attention and scientific and technological solutions are required on an urgent basis. We are concerned in this paper with bioremediation of soil and groundwater, i.e. the use of indigenous micro-organisms to clean up soil beds and groundwater contaminated with organic pollutants. To achieve managedin situ bioremediation in practice, treated water is recycled with added nutrients into the ground so that oxygen and nitrogen are carried with the water to the subsurface regions. Sorption, convective-dispersive flow and chemical and biological transformations are the chief processes involved that have to be modelled. Here we discuss a simulation model developed to aid in designing an efficient system that maximizes the rate of biodegradation. Simulation models are a must in this case since laboratory experiments take time periods of the order of months. An unusual feature of this simulation model is that it is governed by coupled partial and ordinary differential equations. Partial differential equations (PDEs) model the diffusion and biodegradation processes occurring in the micropores of soil aggregates while ordinary differential equations (ODEs) describe the bioremediation in the interstitial spaces between soil aggregates, both partial and ordinary differential equations being nonlinear. The model is applied to the case of high initial contaminant concentrations. This work is part of a joint project with Regional Research Laboratory, Jorhat and has been carried out in close cooperation with N N Dutta. Discussions with K S Yajnik have been very useful.     

Constructing Hydraulic Robot Models Using Memory-Based Learning

Hydraulic machines used in mining and excavation applications are nonlinear systems. Apart from the nonlinearity due to the dynamic coupling between the different links there are significant actuator nonlinearities due to the inherent properties of the hydraulic system. Optimal motion planning for these machines, i.e., planning motions that optimize a user-selectable combination of criteria such as time, energy, etc., would help the designers of such machines, besides aiding the development of more productive robotic machines. Optimal motion planning in turn requires fast (computationally efficient) machine models in order to be practically usable. This work proposes a method for constructing hydraulic machine models using memory-based learning. We demonstrate the approach by constructing a machine model of a 25-ton hydraulic excavator with a 10 m maximum reach. The learning method is used to construct the hydraulic actuator model and is used in conjunction with a linkage dynamic model to construct a complete excavator model that is much faster than an analytical model. Our test results show an average bucket tip position prediction error of 1 m over 50 sec of machine operation. This is better than any comparable speed model reported in the literature. The results also show that the approach effectively captures the interactions between the different hydraulic actuators. The excavator model is used in a time-optimal motion planning scheme. We demonstrate the optimization results on a real excavator testbed to underscore the effectiveness of the model for optimal motion computation.     

A high precision coupled bending–extension triangular finite element for laminated plates

It is well recognized that the estimation of interlaminar stresses and strain energy release rates is important in designing laminated composite panels. Generally coupled bending–extension finite elements are necessary to study laminates to include the effects of coupling and/or combined transverse and extensional loads. Such elements are normally formulated adapting the classical theory of bending and extension. While the classical laminated plate theory of bending has provision to obtain interlaminar stresses due to transverse loading, it is necessary to include certain higher order terms in the extensional theory in order to obtain the interlaminar stresses due to inplane loads. A high precision triangular element based on a theory which includes both the bending and extension with necessary higher order terms is presented in this paper. The performance of this element is validated with the aid of examples. Numerical results for displacements in symmetric and unsymmetric laminates under bending loads have been given. Numerical results for interlaminar stresses in symmetric and unsymmetric laminates have been given for the well-known benchmark problem of a coupon with free edges. Strain energy release rate components at the delamination tip in coupons with unsymmetric sublaminates have been given. The effects of delamination length and location on the components of the strain energy release rate have been studied. Results indicated that with the use of this element, the interlaminar stresses can be estimated reasonably accurately, over a major part of the laminate except in a small local region close to the free edge. Global–local analysis with three-dimensional elements in the local region, is suggested to obtain local stresses more accurately. Interlaminar stresses at the boundary of a hole in a perforated plate under extension have been obtained to illustrate the use of the present element in a global–local analysis strategy.     

Genetic K-means algorithm

In this paper, we propose a novel hybrid genetic algorithm (GA) that finds a globally optimal partition of a given data into a specified number of clusters. GA's used earlier in clustering employ either an expensive crossover operator to generate valid child chromosomes from parent chromosomes or a costly fitness function or both. To circumvent these expensive operations, we hybridize GA with a classical gradient descent algorithm used in clustering, viz. K-means algorithm. Hence, the name genetic K-means algorithm (GKA). We define K-means operator, one-step of K-means algorithm, and use it in GKA as a search operator instead of crossover. We also define a biased mutation operator specific to clustering called distance-based-mutation. Using finite Markov chain theory, we prove that the GKA converges to the global optimum. It is observed in the simulations that GKA converges to the best known optimum corresponding to the given data in concurrence with the convergence result. It is also observed that GKA searches faster than some of the other evolutionary algorithms used for clustering.     

Simulating the Rise Characteristics of Gas Bubbles in Liquids Using CFD

With the advent of modern tools of computational fluid dynamics (CFD), it is now possible to obtain detailed information on the bubble shape, morphology and rise characteristics from knowledge of system properties and geometry. In this paper we try to give a flavor of the power of CFD tools and summarize recent advances.     

Transient model of oxygen-starved proton exchange membrane fuel cell for predicting voltages and hydrogen emissions

Transfer (crossover) leaks initiated by the chemical deterioration of the PEM and the resulting performance degradation has been previously identified as one the primary life-limiting factors in fuel cells. The leaks result in reduced oxygen levels in affected cells, where a secondary factor intimately related to this is high hydrogen emissions in the cathode exhaust when some cells operate in fully-oxygen-starved conditions. This paper builds on previous work that developed a unified fuel cell model that predicts cell voltage behavior under driving (normal) and driven (oxygen-starved) conditions, where this latest analysis now explicitly includes hydrogen pumping and emissions release when operating under oxygen-depleted conditions. In addition to considering diffusion effects and electrochemical effects, the model tracks the evolution of hydrogen in the cell cathode when no oxygen remains to generate water. The voltage response of the model under normal (non-starved) conditions is first validated for steady-state and transient (current step-change) conditions against previously published experiments, and then the model is used to simulate the cell voltage and stack hydrogen emissions behavior measured from three different commercially available fuel cell stacks. In the first fuel cell stack, a 9-cell commercial short stack, only one cell was fully oxygen-starved. Excellent agreement is seen between the measured and simulated hydrogen release concentrations (where air injection was used downstream of the stack to ensure adequate oxygen levels for measurement with a catalytic hydrogen sensor and to condense water vapor in the exhaust), where the role of hydrogen pumping is seen to contribute significantly to the release behavior. The first fuel cell stack is then used transiently in comparison with testing performed where the hydrogen injection level in the cell is changed quickly, where the model gives good agreement with the measured emission response and cell voltage behavior. Further comparisons with test data from a second and third 10-cell commercial short stack models operated with stack inlet hydrogen injection show good agreement with measured emissions onset versus current, where the observed threshold of starvation and emissions occurs a few percent sooner in the third model than the simulation, but the overall behavior is well predicted.     

Ni nanoparticles intercalated LTA-type nanozeolite (KZ) supported on reduced graphene oxide for 4-nitrophenol reduction

We present a novel nanocomposite catalyst, Ni nanoparticles (NPs) intercalated LTA-type nanozeolite (KZ) on reduced graphene oxide (RGO), abbreviated as KZ-Ni/RGO for the reduction of environmental pollutant 4-nitrophenol (4-NP) to 4-aminophenol (4-AP). The structure, composition and morphology of the catalyst were characterized by using the X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) techniques. The presence of Ni inside the nanocomposite was confirmed by the elemental mapping analysis. 4-NP reduction reaction shows the high catalytic activity (30 min) to KZ-Ni/RGO nanocomposite compared to the KZ-Ni (75 min) with excellent stability up to 5 cycles.