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In many industrial processes, an apparent lack of data limits the development of data-driven soft sensors. There are, however, often opportunities to learn stronger models by being more data-efficient. To achieve this, one can leverage…
Learning to automatically perceive smell is becoming increasingly important with applications in monitoring the quality of food and drinks for healthy living. In todays age of proliferation of internet of things devices, the deployment of…
We use active microrheology to determine the frequency dependent moduli of a linear viscoelastic fluid in terms of the polymer time constant ($\lambda$), and the polymer ($\mu_p$) and solvent viscosity ($\mu_s$), respectively. We measure…
The unique properties and atomic thickness of two-dimensional (2D) materials enable smaller and better nanoelectromechanical sensors with novel functionalities. During the last decade, many studies have successfully shown the feasibility of…
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A model is presented for predicting the capture of magnetic micro/nano-particles in a bioseparation microsystem. This bioseparator consists of an array of conductive elements embedded beneath a rectangular microfluidic channel. The magnetic…
Perception in robot manipulation has been actively explored with the goal of advancing and integrating vision and touch for global and local feature extraction. However, it is difficult to perceive certain object internal states, and the…
A self-consistent mode-coupling theory is presented for the viscosity of solutions of charged rod-like polymers. The static structure factor used in the theory is obtained from polymer integral equation theory; the Debye-H\"{u}ckel…
In this work, we show that a recently proposed method for experimental nonlinear modal analysis based on the extended periodic motion concept is well suited to extract modal properties for strongly nonlinear systems (i.e. in the presence of…
The validity of the dissipative quantum model of olfaction has not been examined yet and therefore the model suffers from the lack of experimental support. Here, we generalize the model and propose a numerical analysis of the dissipative…
Analytical low-current and numerical high--current models for the impedance of a hydrogen--fed anode of an anode--supported button SOFC are developed. The models use the dusty gas transport model for the binary H$_2$--H$_2$O mixture. We…
Modal decomposition techniques are important tools for the analysis of unsteady flows and, in order to provide meaningful insights with respect to coherent structures and their characteristic frequencies, the modes must possess a robust…
Nanomechanical systems offer a versatile platform for both fundamental science and industrial applications. Resonating vibration has been demonstrated to enable an ultrasensitive detection of various physical quantities, with emerging…
Opto-mechano-fluidic resonators (OMFRs) are a unique optofluidics platform that can measure the acoustic properties of fluids and bioanalytes in a fully-contained microfluidic system. By confining light in ultra-high-Q whispering gallery…
The bulk acoustic modes of liquid droplets, well understood from a theoretical perspective, have rarely been observed experimentally. Here, we report the direct observation of acoustic vibrational modes in a picoliter-scale droplet,…
A cantilevered piezoelectric smart composite beam, consisting of perfectly bonded elastic, viscoelastic and piezoelectric layers, is considered. The piezoelectric layer is actuated by a voltage source. Both fully dynamic and electrostatic…
This paper proposes a strategy for the detection and triangulation of structural anomalies in solid media. The method revolves around the construction of sparse representations of the medium's dynamic response, obtained by learning…
Atmospheric modeling has recently experienced a surge with the advent of deep learning. Most of these models, however, predict concentrations of pollutants following a data-driven approach in which the physical laws that govern their…
Multimodal sentiment analysis is an important area for understanding the user's internal states. Deep learning methods were effective, but the problem of poor interpretability has gradually gained attention. Previous works have attempted to…