Asim Bhatti

Anodization parameters influencing the morphology and electrical properties of TiO2 nanotubes for living cell interfacing and investigations

Nanotube structures have attracted tremendous attention in recent years in many applications. Among such nanotube structures, titania nanotubes (TiO2) have received paramount attention in the medical domain due to their unique properties, represented by high corrosion resistance, good mechanical properties, high specific surface area, as well as great cell proliferation, adhesion and mineralization. Although lot of research has been reported in developing optimized titanium nanotube structures for different medical applications, however there is a lack of unified literature source that could provide information about the key parameters and experimental conditions required to develop such optimized structure. This paper addresses this gap, by focussing on the fabrication of TiO2 nanotubes through anodization process on both pure titanium and titanium alloys substrates to exploit the biocompatibility and electrical conductivity aspects, critical factors for many medical applications from implants to in-vivo and in-vitro living cell studies. It is shown that the morphology of TiO2 directly impacts the biocompatibility aspects of the titanium in terms of cell proliferation, adhesion and mineralization. Similarly, TiO2 nanotube wall thickness of 30-40nm has shown to exhibit improved electrical behaviour, a critical factor in brain mapping and behaviour investigations if such nanotubes are employed as micro-nano-electrodes.

The impact of self-efficacy and perceived system efficacy on effectiveness of virtual training systems

This study developed and tested a research model which examined the impact of user perceptions of self-efficacy (SE) and virtual environment (VE) efficacy on the effectiveness of VE training systems. The model distinguishes between the perceptions of ones own capability to perform trained tasks effectively and the perceptions of system performance, regarding the established parameters from literature. Specifically, the model posits that user perceptions will have positive effects on task performance and memory. Seventy-six adults participated in a VE in a controlled experiment, designed to empirically test the model. Each participant performed a series of object assembly tasks. The task involved selecting, rotating, releasing, inserting and manipulating 3D objects. Initially, the results of factor analysis demonstrated dimensionality of two user perception measures and produced a set of empirical validated factors underlining the VE efficacy. The results of regression analysis revealed that SE had a significant positive effect on perceived VE efficacy. No significant effects were found of perceptions on performance and memory. Furthermore, the study provided insights into the relationships between the perception measures and performance measures for assessing the efficacy of VE training systems. The study also addressed how well users learn, perform, adapt to and perceive the VE training, which provides valuable insight into the system efficacy. Research and practical implications are presented at the end of the paper.

Towards autonomous image fusion

Mobile robots are providing great assistance operating in hazardous environments such as nuclear cores, battlefields, natural disasters, and even at the nano-level of human cells. These robots are usually equipped with a wide variety of sensors in order to collect data and guide their navigation. Whether a single robot operating all sensors or a swarm of cooperating robots operating their special sensors, the captured data can be too large to be transferred across limited resources (e.g. bandwidth, battery, processing, and response time) in hazardous environments. Therefore, local computations have to be carried out on board the swarming robots to assess the worthiness of captured data and the capacity of fused information in a certain spatial dimension as well as selection of proper combination of fusion algorithms and metrics. This paper introduces to the concepts of Type-I and Type-II fusion errors, fusion capacity, and fusion worthiness. These concepts together form the ladder leading to autonomous fusion systems.

Design and evaluation of a haptically enable virtual environment for object assembly training

Virtual training systems are attracting paramount attention from the manufacturing industries due to their potential advantages over the conventional training practices. Significant cost savings can be realized due to the shorter times for the development of different training-scenarios as well as reuse of existing designed engineering (math) models. This paper presents a newly developed virtual environment (VE) for training of procedure tasks i.e. object assembly. Unlike existing VE systems, the presented idea tries to imitate real physical training scenarios by providing comprehensive user interaction, constrained within the physical limitations of the real world. These physical constrains are imposed by the haptics devices in the virtual environment. As a result, in contrast to the existing VE systems that are capable of providing knowledge generally about assembly sequences only, the proposed system helps in cognitive learning and procedural skill development due to its high physically interactive nature. In addition a novel evaluation framework has also been proposed to evaluate system efficacy through a large scale of user-testing, which is often been neglected by design experts in the field of VEs. Results confirm the practical significance of evaluating a VE design by involving sample of real and representative users through the effective discovery of critical usability problems and system deficiencies. Results also indicate benefits of collecting multimodal information for accurate and comprehensive assessment of system efficacy. Evaluation results and improvement of existing design are also presented.

Multi-wavelets from B-spline super-functions with approximation order

Approximation order is an important feature of all wavelets. It implies that polynomials up to degree p - 1 are in the space spanned by the scaling function(s). In the scalar case, the scalar sum rules determine the approximation order or the left eigenvectors of the infinite down-sampled convolution matrix H determine the combinations of scaling functions required to produce the desired polynomial. For multi-wavelets the condition for approximation order is similar to the conditions in the scalar case. Generalized left eigenvectors of the matrix Hf; a finite portion of H determines the combinations of scaling functions that produce the desired superfunction from which polynomials of desired degree can be reproduced. The superfunctions in this work are taken to be B-splines. However, any refinable function can serve as the superfunction. The condition of approximation order is derived and new, symmetric, compactly supported and orthogonal multi-wavelets with approximation orders one, two, three and four are constructed.

Automatic spike sorting by unsupervised clustering with diffusion maps and silhouettes

Abstract Knowledge of the activity of single neurons is crucial for understanding neural functions. Therefore the process of attributing every single spike to a particular neuron, called spike sorting, is particularly important in electrophysiological data analysis. This task however is greatly complicated because of numerous factors. Bursts or fast changes in ion channel activation or deactivation can cause a large variability of spike waveforms. Another considerable source of uncertainties results from noise caused by firing of nearby neurons. Movement of electrodes and external electrical noise from the environment also hamper the spike sorting. This paper introduces an integrated approach of diffusion maps (DM), silhouette statistics, and k-means clustering methods for spike sorting. DM is employed to extract spike features that are highly capable of discriminating different spike shapes. The combination of k-means and silhouette statistics provides an automatic unsupervised clustering, which takes features extracted by DM as inputs. Experimental results demonstrate the noticeable superiority of the features extracted by DM compared to those selected by wavelet transformation (WT). Accordingly, the proposed integrated method significantly dominates the popular existing combination of WT and superparamagnetic clustering regarding spike sorting accuracy.

Performance Evaluation of Multi-Frame Super-Resolution Algorithms

Multi-frame super-resolution algorithms aim to increase spatial resolution by fusing information from several low-resolution perspectives of a scene. While a wide array of super-resolution algorithms now exist, the comparative capability of these techniques in practical scenarios has not been adequately explored. In addition, a standard quantitative method for assessing the relative merit of super-resolution algorithms is required. This paper presents a comprehensive practical comparison of existing super-resolution techniques using a shared platform and 4 common greyscale reference images. In total, 13 different super-resolution algorithms are evaluated, and as accurate alignment is critical to the super-resolution process, 6 registration algorithms are also included in the analysis. Pixel-based visual information fidelity (VIFP) is selected from the 12 image quality metrics reviewed as the measure most suited to the appraisal of super-resolved images. Experimental results show that Bayesian super-resolution methods utilizing the simultaneous autoregressive (SAR) prior produce the highest quality images when combined with generalized stochastic Lucas-Kanade optical flow registration.

Synthesis and Growth Mechanism of Thin-Film TiO2 Nanotube Arrays on Focused-Ion-Beam Micropatterned 3D Isolated Regions of Titanium on Silicon

In this paper, the fabrication and growth mechanism of net-shaped micropatterned self-organized thin-film TiO2 nanotube (TFTN) arrays on a silicon substrate are reported. Electrochemical anodization is used to grow the nanotubes from thin-film titanium sputtered on a silicon substrate with an average diameter of ~30 nm and a length of ~1.5 μm using aqueous and organic-based types of electrolytes. The fabrication and growth mechanism of TFTN arrays from micropatterned three-dimensional isolated islands of sputtered titanium on a silicon substrate is demonstrated for the first time using focused-ion-beam (FIB) technique. This work demonstrates the use of the FIB technique as a simple, high-resolution, and maskless method for high-aspect-ratio etching for the creation of isolated islands and shows great promise toward the use of the proposed approach for the development of metal oxide nanostructured devices and their integration with micro- and nanosystems within silicon-based integrated-circuit devices.

DEPTH ESTIMATION USING MULTIWAVELET ANALYSIS BASED STEREO VISION APPROACH

The problem of dimensional defects in aluminum die- casting is widespread throughout the foundry industry and their detection is of paramount importance in maintaining product quality. Due to the unpredictable factory environment and metallic, with highly reflective, nature of aluminum die-castings, it is extremely hard to estimate true dimensionality of the die-casting, autonomously. In this work, we propose a novel robust 3D reconstruction algorithm capable of reconstructing dimensionally accurate 3D depth models of the aluminum die-castings. The developed system is very simple and cost effective as it consists of only a stereo cameras pair and a simple fluorescent light. The developed system is capable of estimating surface depths within the tolerance of 1.5 mm. Moreover, the system is invariant to illuminative variations and orientation of the objects in the input image space, which makes the developed system highly robust. Due to its hardware simplicity and robustness, it can be implemented in different factory environments without a significant change in the setup.

M-band multi-wavelets from spline super functions with approximation order

A simple method for constructing M-band multi-wavelets using the super function idea is presented. The method is based on formulating the approximation order requirement in terms of a matrix equation and recognizing the generalized left eigenvectors of the matrix Lf a finite portion of the down sampled convolution matrix L as the coefficients that linearly combine to form the desired M-band spline super function with the desired approximation order. Several 3-band multi-wavelets with approximation orders two and three with multiplicity two and three are constructed.