Computational fluid dynamics modeling and in situ physics-based monitoring of aerosol jet printing toward functional assurance of additively-manufactured, flexible and hybrid electronics

Aerosol jet printing (AJP)—a direct-write, additive manufacturing technique—has emerged as the process of choice particularly for the fabrication of flexible and hybrid electronics. AJP has paved the way for high-resolution device fabrication with high placement accuracy, edge definition, and adhesion. In addition, AJP accommodates a broad range of ink viscosity, and allows for printing on non-planer surfaces. Despite the unique advantages and host of strategic applications, AJP is a highly unstable and complex process, prone to gradual drifts in machine behavior and deposited material. Hence, real-time monitoring and control of AJP process is a burgeoning need. In pursuit of this goal, the objectives of the work are, as follows: (i) In situ image acquisition from the traces/lines of printed electronic devices right after deposition. To realize this objective, the AJP experimental setup was instrumented with a high-resolution charge-coupled device (CCD) camera, mounted on a variable-magnification lens (in addition to the standard imaging system, already installed on the AJ printer). (ii) In situ image processing and quantification of the trace morphology. In this regard, several customized image processing algorithms were devised to quantify/extract various aspects of the trace morphology from online images. In addition, based on the concept of shape-from-shading (SfS), several other algorithms were introduced, allowing for not only reconstruction of the 3D profile of the AJ-printed electronic traces, but also quantification of 3D morphology traits, such as thickness, cross-sectional area, and surface roughness, among others. (iii) Development of a supervised multiple-input, single-output (MISO) machine learning model—based on sparse representation for classification (SRC)—with the aim to estimate the device functional properties (e.g., resistance) in near real-time with an accuracy of ≥ 90%. (iv) Forwarding a computational fluid dynamics (CFD) model to explain the underlying aerodynamic phenomena behind aerosol transport and deposition in AJP process, observed experimentally. Overall, this doctoral dissertation paves the way for: (i) implementation of physics-based real-time monitoring and control of AJP process toward conformal material deposition and device fabrication; and (ii) optimal design of direct-write components, such as nozzles, deposition heads, virtual impactors, atomizers, etc

The Impact of Television and Short Message Service Advertising on Customer Behaviour and Brand Attitude

Marketing, advertising, and communications processes have changed to strategically capitalize on an increasingly digitally transformed, technologically empowered, globally interconnected consumer, or what service-dominant logic refers to as actors that are resource integrators. Customers are co-creators of value in the collaborative or sharing economy, and seek to actively reap the benefits of new knowledge growing at an exponential rate. However, developing models of customer behavior, especially the influence of a new kind of advertising based on the integrated use of television, web, and social networks, is a challenge. Our study starts from a preliminary empirical observation of the impact of television cooking shows on the variations of potential demand (queries on Google) and the purchase of branded/unbranded culinary products used on the show. Neural networks were used to determine significant correlations, which resulted in an operative Marketing 3.0 model. This model clearly explicates this impact factor on the consumer-purchasing process generated by a new mode of creating information and communications technology–based communication. Keywords: Customer behavior, knowledge management, online advertising, smart consumer, value co-creatio

Features extraction using random matrix theory.

Representing the complex data in a concise and accurate way is a special stage in data mining methodology. Redundant and noisy data affects generalization power of any classification algorithm, undermines the results of any clustering algorithm and finally encumbers the monitoring of large dynamic systems. This work provides several efficient approaches to all aforementioned sides of the analysis. We established, that notable difference can be made, if the results from the theory of ensembles of random matrices are employed. Particularly important result of our study is a discovered family of methods based on projecting the data set on different subsets of the correlation spectrum. Generally, we start with traditional correlation matrix of a given data set. We perform singular value decomposition, and establish boundaries between essential and unimportant eigen-components of the spectrum. Then, depending on the nature of the problem at hand we either use former or later part for the projection purpose. Projecting the spectrum of interest is a common technique in linear and non-linear spectral methods such as Principal Component Analysis, Independent Component Analysis and Kernel Principal Component Analysis. Usually the part of the spectrum to project is defined by the amount of variance of overall data or feature space in non-linear case. The applicability of these spectral methods is limited by the assumption that larger variance has important dynamics, i.e. if the data has a high signal-to-noise ratio. If it is true, projection of principal components targets two problems in data mining, reduction in the number of features and selection of more important features. Our methodology does not make an assumption of high signal-to-noise ratio, instead, using the rigorous instruments of Random Matrix Theory (RNIT) it identifies the presence of noise and establishes its boundaries. The knowledge of the structure of the spectrum gives us possibility to make more insightful projections. For instance, in the application to router network traffic, the reconstruction error procedure for anomaly detection is based on the projection of noisy part of the spectrum. Whereas, in bioinformatics application of clustering the different types of leukemia, implicit denoising of the correlation matrix is achieved by decomposing the spectrum to random and non-random parts. For temporal high dimensional data, spectrum and eigenvectors of its correlation matrix is another representation of the data. Thus, eigenvalues, components of the eigenvectors, inverse participation ratio of eigenvector components and other operators of eigen analysis are spectral features of dynamic system. In our work we proposed to extract spectral features using the RMT. We demonstrated that with extracted spectral features we can monitor the changing dynamics of network traffic. Experimenting with the delayed correlation matrices of network traffic and extracting its spectral features, we visualized the delayed processes in the system. We demonstrated in our work that broad range of applications in feature extraction can benefit from the novel RMT based approach to the spectral representation of the data

NASA Earth Resources Survey Symposium. Volume 2-A: Special session presentations. Plenary summaries

Practical application of earth resources survey data is considered. The utilization and results of data from NASA programs involving LANDSAT, the Skylab Earth Resources Experiment Package, and aircraft, as well as other data acquisition programs are included. User services and requirements and applications in land use, agriculture, coastal zone management, and geology are among the topics covered. For Vol. 1A, see N76-17469

Computer Aided Verification

This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018. The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and techniques, from algorithmic and logical foundations of verification to practical applications in distributed, networked, cyber-physical, and autonomous systems. They are organized in topical sections on model checking, program analysis using polyhedra, synthesis, learning, runtime verification, hybrid and timed systems, tools, probabilistic systems, static analysis, theory and security, SAT, SMT and decisions procedures, concurrency, and CPS, hardware, industrial applications

Intelligent Systems

This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier

Real-time spatio-temporal coherence estimation for autonomous mode identification and invariance tracking

A general method of anomaly detection from time-correlated sensor data is disclosed. Multiple time-correlated signals are received. Their cross-signal behavior is compared against a fixed library of invariants. The library is constructed during a training process, which is itself data-driven using the same time-correlated signals. The method is applicable to a broad class of problems and is designed to respond to any departure from normal operation, including faults or events that lie outside the training envelope