Engineering the Hardware/Software Interface for Robotic Platforms - A Comparison of Applied Model Checking with Prolog and Alloy

Robotic platforms serve different use cases ranging from experiments for prototyping assistive applications up to embedded systems for realizing cyber-physical systems in various domains. We are using 1:10 scale miniature vehicles as a robotic platform to conduct research in the domain of self-driving cars and collaborative vehicle fleets. Thus, experiments with different sensors like e.g.~ultra-sonic, infrared, and rotary encoders need to be prepared and realized using our vehicle platform. For each setup, we need to configure the hardware/software interface board to handle all sensors and actors. Therefore, we need to find a specific configuration setting for each pin of the interface board that can handle our current hardware setup but which is also flexible enough to support further sensors or actors for future use cases. In this paper, we show how to model the domain of the configuration space for a hardware/software interface board to enable model checking for solving the tasks of finding any, all, and the best possible pin configuration. We present results from a formal experiment applying the declarative languages Alloy and Prolog to guide the process of engineering the hardware/software interface for robotic platforms on the example of a configuration complexity up to ten pins resulting in a configuration space greater than 14.5 million possibilities. Our results show that our domain model in Alloy performs better compared to Prolog to find feasible solutions for larger configurations with an average time of 0.58s. To find the best solution, our model for Prolog performs better taking only 1.38s for the largest desired configuration; however, this important use case is currently not covered by the existing tools for the hardware used as an example in this article.Comment: Presented at DSLRob 2013 (arXiv:cs/1312.5952

COHERENT/INCOHERENT MAGNETIZATION DYNAMICS OF NANOMAGNETIC DEVICES FOR ULTRA-LOW ENERGY COMPUTING

Nanomagnetic computing devices are inherently nonvolatile and show unique transfer characteristics while their switching energy requirements are on par, if not better than state of the art CMOS based devices. These characteristics make them very attractive for both Boolean and non-Boolean computing applications. Among different strategies employed to switch nanomagnetic computing devices e.g. magnetic field, spin transfer torque, spin orbit torque etc., strain induced switching has been shown to be among the most energy efficient. Strain switched nanomagnetic devices are also amenable for non-Boolean computing applications. Such strain mediated magnetization switching, termed here as “Straintronics”, is implemented by switching the magnetization of the magnetic layer of a magnetostrictive-piezoelectric nanoscale heterostructure by applying an electric field in the underlying piezoelectric layer. The modes of “straintronic” switching: coherent vs. incoherent switching of spins can affect device performance such as speed, energy dissipation and switching error in such devices. There was relatively little research performed on understanding the switching mechanism (coherent vs. incoherent) in xiv straintronic devices and their adaptation for non-Boolean computing, both of which have been studied in this thesis. Detailed studies of the effects of nanomagnet geometry and size on the coherence of the switching process and ultimately device performance of such strain switched nanomagnetic devices have been performed. These studies also contributed in optimizing designs for low energy, low dynamic error operation of straintronic logic devices and identified avenues for further research. A Novel non-Boolean “straintronic” computing device (Ternary Content Addressable Memory, abbreviated as TCAM) has been proposed and evaluated through numerical simulations. This device showed significant improvement over existing CMOS device based TCAM implementation in terms of scaling, energy-delay product, operational simplicity etc. The experimental part of this thesis answered a very fundamental question in strain induced magnetization rotation. Specifically, this experiment studied the variation in magnetization orientation for strain induced magnetization rotation along the thickness of a magnetostrictive thin film using polarized neutron reflectometry and demonstrated non-uniform magnetization rotation along the thickness of the sample. Additional experimental work was performed to lay the groundwork for ultra-low voltage straintronic switching demonstration. Preliminary sample fabrication and characterization that can potentially lead to low voltage (~10-100 mV) operation and local clocking of such devices has been performed

Study of Multiferroic Properties of Ferroelectric- Ferromagnetic Heterostructures BZT-BCT/LSMO

Currently, there has been a flurry of research focused on multiferroic materials due to their potential applications. Lead (Pb)-based ferroelectric and multiferroic materials (PZT, PMN-PT, PZN-PT etc.) have been widely used for sensors, actuators, and electro-mechanical applications due to their excellent dielectric and piezoelectric properties. However, these materials are facing global restriction due to the toxicity of Pb. In this thesis, multiferroic properties of ferroelectric-ferromagnetic heterostructures consist of Pb-free perovskite oxides 0.5Ba(Zr0.2Ti0.8)O3-0.5 (Ba0.7 Ca0.3)TiO3 (BZT-BCT) and La0.7Sr0.3MnO3 (LSMO) have been studied. The heterostructures BZT-BCT/LSMO were fabricated on LaAlO3 (LAO) and Pt substrates by pulsed laser deposition. Structural and crystalline qualities of the films have been investigated through theta-2theta scan, rocking curve, and phi-scan of X-Ray diffraction (XRD) and Raman spectroscopy. Ferroelectric and ferromagnetic properties have been characterized using the Sawyer-Tower method, a SQUID magnetometer, and Ferromagnetic resonance (FMR) spectroscopy. A well-behaved magnetization-magnetic field (M-H) hysteresis has been observed in LSMO as well as heterostructures, indicating ferromagnetism in the films. FMR spectroscopy data support the static magnetization data obtained using SQUID. These results may guide the development of next-generation lead-free ferroelectric-ferromagnetic heterostructures for magnetoelectric device applications

Mobile Banking is a New Dimension in Banking System of Bangladesh: A Case Study on DBBL and bKash

This study is an earnest effort to find out the potentiality of mobile banking to provide basic banking services to the vast majority of unbanked people. This study is an exploratory research based on primary dat from field as well as secondary data from various publications, adopted with descriptive in nature. Research was gone through over 120 respondents focusing the point of using the mobile in banking, its safety, speedyness, cost, service nature, and of user class. Of two Mobile banking Bank, DBBL and BRAC’s subsidiary bKash, 120  respondents were selected for information acquiring.  61 % respondents think it saves time than traditional banking, the highest number of respondents use mobile banking for ‘fund transfer ’ service, that is 22%,. Out of 120 respondents 56%  replied it is less costlier than traditional banking, 100% respondents did agree that it is speedy, and 38% respondents are of upper class. Although this concept is new in Bangladesh but its potentiality is high due to handset availability and convenience. From this research, other researchers and policy makers will get an insight about the problems and prospects of mobile banking in Bangladesh.

A Machine Learning Framework for Identifying Molecular Biomarkers from Transcriptomic Cancer Data

Cancer is a complex molecular process due to abnormal changes in the genome, such as mutation and copy number variation, and epigenetic aberrations such as dysregulations of long non-coding RNA (lncRNA). These abnormal changes are reflected in transcriptome by turning oncogenes on and tumor suppressor genes off, which are considered cancer biomarkers. However, transcriptomic data is high dimensional, and finding the best subset of genes (features) related to causing cancer is computationally challenging and expensive. Thus, developing a feature selection framework to discover molecular biomarkers for cancer is critical. Traditional approaches for biomarker discovery calculate the fold change for each gene, comparing expression profiles between tumor and healthy samples, thus failing to capture the combined effect of the whole gene set. Also, these approaches do not always investigate cancer-type prediction capabilities using discovered biomarkers. In this work, we proposed a machine learning-based framework to address all of the above challenges in discovering lncRNA biomarkers. First, we developed a machine learning pipeline that takes lncRNA expression profiles of cancer samples as input and outputs a small set of key lncRNAs that can accurately predict multiple cancer types. A significant innovation of our work is its ability to identify biomarkers without using healthy samples. However, this initial framework cannot identify cancer-specific lncRNAs. Second, we extended our framework to identify cancer type and subtype-specific lncRNAs. Third, we proposed to use a state-of-the-art deep learning algorithm concrete autoencoder (CAE) in an unsupervised setting, which efficiently identifies a subset of the most informative features. However, CAE does not identify reproducible features in different runs due to its stochastic nature. Thus, we proposed a multi-run CAE (mrCAE) to identify a stable set of features to address this issue. Our deep learning-based pipeline significantly extended the previous state-of-the-art feature selection techniques. Finally, we showed that discovered biomarkers are biologically relevant using literature review and prognostically significant using survival analyses. The discovered novel biomarkers could be used as a screening tool for different cancer diagnoses and as therapeutic targets

Thin Film Studies Toward Improving the Performance of Accelerator Electron Sources

Future electron accelerators require DC high voltage photoguns to operate beyond the present state of the art to conduct new experiments that require ultra-bright electron beams with high average current and higher bunch charge. To meet these demands, the accelerators must demonstrate improvements in a number of photogun areas including vacuum, field emission elimination in high voltage electrodes, and photocathodes. This dissertation illustrates how these improvements can be achieved by the application of suitable thin-films to the photogun structure for producing ultra-bright electron beams. This work is composed of three complementary studies. First, the outgassing rates of three nominally identical 304L stainless steel vacuum chambers were studied to determine the effects of chamber coatings (silicon and titanium nitride) and heat treatments. For an uncoated stainless steel chamber, the diffusion limited outgassing was taken over by the recombination limited process as soon as a low outgassing rate of ~1.79(+/-0.05) x 10—13 Torr L s—1 cm—2 was achieved. An amorphous silicon coating on the stainless steel chambers exhibited recombination limited behavior and any heat treatment became ineffective in reducing the outgassing rate. A TiN coated chamber yielded the smallest apparent outgassing rate of all the chambers: 6.44(+/-0.05) x 10—13 Torr L s—1 cm—2 following an initial 90 °C bake and 2(+/-20) x 10—16 Torr L s—1 cm—2 following the final bake in the series. This perceived low outgassing rate was attributed to the small pumping nature of TiN coating itself. Second, the high voltage performance of three TiN-coated aluminum electrodes, before and after gas conditioning with helium, were compared to that of bare aluminum electrodes and electrodes manufactured from titanium alloy (Ti-6Al-4V). This study suggests that aluminum electrodes, coated with TiN, could simplify the task of implementing photocathode cooling, which is required for future high current electron beam applications. The best performing TiN-coated aluminum electrode demonstrated less than 15 pA of field emission current at —175 kV for a 10 mm cathode/anode gap, which corresponds to a field strength of 22.5 MV/m. Third, the effect of antimony thickness on the performance of bialkali-antimonide photocathodes was studied. The high-capacity effusion source enabled us to successfully manufacture photocathodes having a maximum QE around 10% and extended low voltage 1/e lifetime (\u3e 90 days) at 532 nm via the co-deposition method, with relatively thick layers of antimony (≥ 300 nm). We speculate that alkali co-deposition provides optimized stoichiometry for photocathodes manufactured using thick Sb layers, which could serve as a reservoir for the alkali. In summary, this research examined the effectiveness of thin films applied on photogun chamber components to achieve an extremely high vacuum, to eliminate high voltage induced field emission from electrodes, and to generate photocurrent with high quantum yield with an extended operational lifetime. Simultaneous implementation of these findings can meet the challenges of future ultra-bright photoguns

Analysis of Longitudinal Data and Model Selection

An important issue in regression analysis of longitudinal data is model parsimony, that is, finding a model with as few regression variables as possible while retaining good properties of the parameter estimates. In this vein, joint modelling of mean and variance taking into account the intra subject correlation has been standard in recent literature (Pourahmadi, 1999, 2000; Ye and Pan, 2006; and Leng, Zhang, and Pan, 2010). Zhang, Leng, and Tang (2015) propose joint parametric modelling of the means, variances and correlations by decomposing the correlation matrix via hyperspherical co-ordinates and show that this results in unconstrained parameterization, fast computation, easy interpretation of the parameters, and model parsimony. We investigate the properties of the estimates of the regression parameters through semiparametric modelling of the means and variances and study the impact of this to model parsimony. An extensive simulation study is conducted. Three datasets, namely, a biomedical dataset, an environmental dataset and a cattle dataset are analysed. In longitudinal studies, researchers frequently encounter covariates that are varying over time (see for example Huang, Wu, and Zhou, 2002). We consider a generalized partially linear varying coefficient model for such data and propose a regression spline based approach to estimate the mean and covariance parameters jointly where the correlation matrix is decomposed via hyperspherical co-ordinates. A simulation study is conducted to investigate the properties of the estimates of the regression parameters in terms of bias and standard error and to analyse a real data set taken from a multi-center AIDS cohort study. The problem of model selection in regression analysis through the use of forward selection, backward elimination and stepwise selection has been well developed in the literature. The main assumption in this, of course, is that the data are normally distributed and the main tool used here is either a t test or an F test. However, properties of these model selection procedures in the framework of generalized linear models are not well-known. We study here the properties of these procedures in generalized linear models, of which the normal linear regression model is a special case. The main tools that is being used are the score test, the F-test, other large sample tests, such as, the likelihood ratio test and the Wald test; the AIC and the BIC are included in the comparison. A systematic study, through simulations, of the properties of this procedure is conducted, in terms of level and power, for normal, Poisson and binomial regression models. Extensions for over-dispersed Poisson and over-dispersed binomial regression models are also given and evaluated. The methods are applied to analyse three data sets. In practice, it often occurs that an abundance of zero counts arise in data where a discrete generalized linear model may fail to fit but a zero-inflated generalized linear model can be the ideal choice. Researchers often encounter a large number of covariates in such model and need to decide which are potentially important. To find a parsimonious model we develop a model selection procedure using the score test, the Wald test and the likelihood ratio test; also the AIC and the BIC are included in the comparison. Simulation studies are carried out to investigate the performance of these procedures, in terms of level and power, for zero-inflated Poisson and zero-inflated binomial regression models. The methodology is illustrated through two real examples

Design and development of a powder mixing device used in the deposition of high velocity oxy-fuel (HVOF) thermal spray functionally graded coatings; Kabir Al Mamun

The application of Functionally Graded Materials (FGMs) is quite difficult, but thermal spray processes like Plasma spray have demonstrated their unique potential in producing graded deposits, where researchers have used twin powder feed systems to mix different proportions of powders. However the HVOF (High Velocity Oxy-Fuel) process does not possess this feature. FGMs vary in composition and/or microstructure from one boundary (substrate) to another (top service surface), and innovative characteristics result from the gradient from metals to ceramics or non-metallic to metals. The present study investigates an innovative modification of a HVOF thermal spray process to produce f~~nctionallgyr aded thick coatings. In order to deposit thick coatings, certain problems have to be overcome. Graded coatings enable gradual variation of the coating composition and/or microstructure, which offers the possibility of reducing residual stress bui Id-up with in coatings. In order to spray such a coating, modification to a commercial powder feed hopper was required to enable it to deposit two powders simultaneously which allows deposition of different layers of coating with changing chemical compositions, without interruption to the spraying process. Various concepts for this modification were identified and one design was selected, having been validated through use of a process model, developed using ANSYS Flotran Finite Element Analysis. Post nod el ling the design was manuFactured and tested experimentally for functionality. In the current research the mixing of different proportions of powders was controlled by a computer using Lab VlEW software and hardware, which allowed the control and repeatability of the microstructure when producing functionally graded coatings