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In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and other neuroimaging methodologies. In a similar time frame, “deep learning” (a term for the use of artificial neural networks with convolutional, recurrent, or similarly sophisticated architectures) has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on linear calculations; a number of studies have applied deep learning techniques to neuroimaging data, but we believe that those have barely scratched the surface of the potential deep learning holds for the field. In this paper, we provide a brief introduction to deep learning for those new to the technique, explore the logistical pros and cons of using deep learning to analyze neuroimaging data – which we term “deep MVPA,” or dMVPA – and introduce a new software toolbox (the “Deep Learning In Neuroimaging: Exploration, Analysis, Tools, and Education” package, DeLINEATE for short) intended to facilitate dMVPA for neuroscientists (and indeed, scientists more broadly) everywhere. 

Abstract Aerosol jet printing (AJP) is a direct-write additive manufacturing (AM) method, emerging as the process of choice for the fabrication of a broad spectrum of electronics, such as sensors, transistors, and optoelectronic devices. However, AJP is a highly complex process, prone to intrinsic gradual drifts. Consequently, real-time process monitoring and control in AJP is a bourgeoning need. The goal of this work is to establish an integrated, smart platform for in situ and real-time monitoring of the functional properties of AJ-printed electronics. In pursuit of this goal, the objective is to forward a multiple-input, single-output (MISO) intelligent learning model—based on sparse representation classification (SRC)—to estimate the functional properties (e.g., resistance) in situ as well as in real-time. The aim is to classify the resistance of printed electronic traces (lines) as a function of AJP process parameters and the trace morphology characteristics (e.g., line width, thickness, and cross-sectional area (CSA)). To realize this objective, line morphology is captured using a series of images, acquired: (i) in situ via an integrated high-resolution imaging system and (ii) in real-time via the AJP standard process monitor camera. Utilizing image processing algorithms developed in-house, a wide range of 2D and 3D morphology features are extracted, constituting the primary source of data for the training, validation, and testing of the SRC model. The four-point probe method (also known as Kelvin sensing) is used to measure the resistance of the deposited traces and as a result, to define a priori class labels. The results of this study exhibited that using the presented approach, the resistance (and potentially, other functional properties) of printed electronics can be estimated both in situ and in real-time with an accuracy of ≥ 90%. 

Aerosol jet printing (AJP) is a complex process for additive electronics that is often unstable. To overcome this instability, observation while printing and control of the printing process using image-based monitoring is demonstrated. This monitoring is validated against images taken after the print and shown highly correlated and useful for the determination of printed linewidth. These images and the observed linewidth are used as input for closed-loop control of the printing process, with print speed changed in response to changes in the observed linewidth. Regression is used to relate these quantities and forms the basis of proportional and proportional integral control. Electrical test structures were printed with controlled and uncontrolled printing, and it was found that the control influenced their linewidth and electrical properties, giving improved uniformity in both size and electrical performance.