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A superresolution imaging approach that localizes very small targets, such as red blood cells or droplets of injected photoacoustic dye, has significantly improved spatial resolution in various biological and medical imaging modalities. However, this superior spatial resolution is achieved by sacrificing temporal resolution because many raw image frames, each containing the localization target, must be superimposed to form a sufficiently sampled high-density superresolution image. Here, we demonstrate a computational strategy based on deep neural networks (DNNs) to reconstruct high-density superresolution images from far fewer raw image frames. The localization strategy can be applied for both 3D label-free localization optical-resolution photoacoustic microscopy (OR-PAM) and 2D labeled localization photoacoustic computed tomography (PACT). For the former, the required number of raw volumetric frames is reduced from tens to fewer than ten. For the latter, the required number of raw 2D frames is reduced by 12 fold. Therefore, our proposed method has simultaneously improved temporal (via the DNN) and spatial (via the localization method) resolutions in both label-free microscopy and labeled tomography. Deep-learning powered localization PA imaging can potentially provide a practical tool in preclinical and clinical studies requiring fast temporal and fine spatial resolutions.Computational strategy based on deep neural networks enables to reconstruct high-density superresolution localization photoacoustic images from far fewer raw image frames.

Tactile sensors for robotic applications enhance the performance of robotic end-effectors as they ca n provide tactile information to operate various tasks. In particular, tactile sensors can measure multi-axial force and detect slip can aid the end-effectors in grasping diverse objects in an unstructured environment. We propose BaroTac, which measures three-axial forces and detects slip with a barometric pressure sensor chip (BPSC) for robotic applications. A BPSC is an off-the-shelf commercial sensor that is inexpensive, easy to customize, robust, and simple to use. While a single BPSC-based tactile sensor can measure pressure, an array of BPSC-based tactile sensors can measure multi-axial force through the reactivity of each sensor and detect slip by observing high frequency due to slip vibration. We first experiment with defining the fundamental characteristics of a single-cell BPSC-based sensor to set the design parameters of our proposed sensor. Thereafter, we suggest the sensing method of BaroTac: calibration matrix for three-axis force measurement and discrete wavelet transform (DWT) for slip detection. Subsequently, we validate the three-axis force measuring ability and slip detectability of the fabricated multi-cell BPSC-based tactile sensor. The sensor measures three-axis force with low error (0.14, 0.18, and 0.3% in the X-, Y- and Z-axis, respectively) and discriminates slip in the high-frequency range (75–150 Hz). We finally show the practical applicability of BaroTac by installing them on the commercial robotic gripper and controlling the gripper to grasp common objects based on our sensor feedback.

Label-free optical coherence tomography angiography (OCTA) has become a premium imaging tool in clinics to obtain structural and functional information of microvasculatures. One primary technical drawback for OCTA, however, is its imaging speed. The current protocols require high sampling density and multiple acquisitions of cross-sectional B-scans to form one image frame, resulting in low acquisition speed. Recently, deep learning (DL)-based methods have gained attention in accelerating the OCTA acquisition process. They achieve faster acquisition using two independent reconstructing approaches: high-quality angiograms from a few repeated B-scans and high-resolution angiograms from undersampled data. While these approaches have shown promising results, they provide limited solutions that only partially account for the OCTA scanning mechanism. Herein, we propose an integrated DL method to simultaneously tackle both factors and further enhance the reconstruction performance in speed and quality. We designed an end-to-end deep neural network (DNN) framework with a two-staged adversarial training scheme to reconstruct fully-sampled, high-quality (8 repeated B-scans) angiograms from their corresponding undersampled, low-quality (2 repeated B-scans) counterparts by successively enhancing the pixel resolution and the image quality. Using an in-vivo mouse brain vasculature dataset, we evaluate our proposed framework through quantitative and qualitative assessments and demonstrate that our method can achieve superior reconstruction performance compared to the conventional means. Our DL-based framework can accelerate the OCTA imaging speed from 16 to 256 × while preserving the image quality, thus enabling a convenient software-only solution to enhance preclinical and clinical studies.

As touch screen technologies advanced, a digital stylus has become one of the essential accessories for a smart device. However, most of the digital styluses so far provide limited tactile feedback to a user. Therefore we focused on the limitation and noted the potential that a digital stylus may offer the sensation of realistic interaction with virtual environments on a touch screen using a 2.5D haptic system. Thus, we developed a haptic stylus with SMA (Shape Memory Alloy) and a 2.5D haptic rendering algorithm to provide lateral skin-stretch feedback to mimic the interaction force between fingertip and a stylus probing over a bumpy surface. We conducted two psychophysical experiments to evaluate the effect of 2.5D haptic feedback on the perception of virtual object geometry. Experiment 1 investigated the human perception of virtual bump size felt via the proposed lateral skin-stretch stylus and a vibrotactile stylus as reference. Experiment 2 tested the participants’ ability to count the number of virtual bumps rendered via the two types of haptic styluses. The results of Experiment 1 indicate that the participants felt the size of virtual bumps rendered with lateral skin-stretch stylus significantly sensitively than the vibrotactile stylus. Similarly, the participants counted the number of virtual bumps rendered with the lateral skin-stretch stylus significantly better than with the vibrotactile stylus. A common result of the two experiments is a significantly longer mean trial time for the skin-stretch stylus than the vibrotactile stylus.

Mesenchymal stem cells (MSCs) are increasingly used as regenerative therapies for patients in the preclinical and clinical phases of various diseases. However, the main limitations of such therapies include functional heterogeneity and the lack of appropriate quality control (QC) methods for functional screening of MSC lines; thus, clinical outcomes are inconsistent. Recently, machine learning (ML)-based methods, in conjunction with single-cell morphological profiling, have been proposed as alternatives to conventional in vitro/vivo assays that evaluate MSC functions. Such methods perform in silico analyses of MSC functions by training ML algorithms to find highly nonlinear connections between MSC functions and morphology. Although such approaches are promising, they are limited in that extensive, high-content single-cell imaging is required; moreover, manually identified morphological features cannot be generalized to other experimental settings. To address these limitations, we propose an end-to-end deep learning (DL) framework for functional screening of MSC lines using live-cell microscopic images of MSC populations. We quantitatively evaluate various convolutional neural network (CNN) models and demonstrate that our method accurately classifies in vitro MSC lines to high/low multilineage differentiating stress-enduring (MUSE) cells markers from multiple donors. A total of 6,120 cell images were obtained from 8 MSC lines, and they were classified into two groups according to MUSE cell markers analyzed by immunofluorescence staining and FACS. The optimized DenseNet121 model showed area under the curve (AUC) 0.975, accuracy 0.922, F1 0.922, sensitivity 0.905, specificity 0.942, positive predictive value 0.940, and negative predictive value 0.908. Therefore, our DL-based framework is a convenient high-throughput method that could serve as an effective QC strategy in future clinical biomanufacturing processes.

Switched reluctance motors (SRMs) have gained widespread attention across various industries due to their inherent advantages, including simple construction, high efficiency, and the absence of permanent magnets. However, a critical aspect of SRM operation is the need for position sensors. Consequently, extensive research has been conducted on position-sensorless control techniques for SRMs. However, unexpected faults are critical for achieving position-sensorless control performance. This paper proposes a fault-tolerant position-sensorless control method for SRMs based on an adaptive sliding mode observer (ASMO) that specifically addresses the issue of converter faults. The proposed method estimates the faulty phase voltage in real-time, enabling the design of a dynamic model of the SRM that accurately reflects the fault conditions. The paper presents a theoretical analysis of the convergence condition of the estimation error. As a result, the proposed method can accurately estimate the faulty phase voltage and the rotor position using only phase current measurements, without the need for a dedicated position sensor. Simulation results are provided to demonstrate the performance of the proposed control method and its superiority compared to the existing sliding mode observer-based position-sensorless control method.

The digitized format of microstructures, or digital microstructures, plays a crucial role in modern-day materials research. Unfortunately, the acquisition of digital microstructures through experimental means can be unsuccessful in delivering sufficient resolution that is necessary to capture all relevant geometric features of the microstructures. The resolution-sensitive microstructural features overlooked due to insufficient resolution may limit one’s ability to conduct a thorough microstructure characterization and material behavior analysis such as mechanical analysis based on numerical modeling. Here, a highly efficient super-resolution imaging based on deep learning is developed using a deep super-resolution residual network to super-resolved low-resolution (LR) microstructure data for microstructure characterization and finite element (FE) mechanical analysis. Microstructure characterization and FE model based mechanical analysis using the super-resolved microstructure data not only proved to be as accurate as those based on high-resolution (HR) data but also provided insights on local microstructural features such as grain boundary normal and local stress distribution, which can be only partially considered or entirely disregarded in LR data-based analysis.

Increased ammonia (NH3) emissions from intensive agriculture negatively affect environmental and ecosystem health, contributing to formation of particulate matter (PM) and the potent greenhouse gas, N2O. Better understanding NH3 emissions from the manure composting process and their behavior as a constituent of the atmospheric aerosol load is a crucial element in creating better farm management systems, improving public health outcomes, and mitigating the broader environmental and climatic impacts of agriculture. Retarded generation of PM with a major constituent source of NH3 is a primary mechanism for evaluating the effects of agricultural contribution to PM. This study aimed to quantify NH3 emissions, examine the influence of environmental factors, and investigate the relationship between precursor gases (SO2, NOx, NH3) and PM2.5 at a modern manure composting facility in Paju, South Korea. Over 35 days, average internal concentrations of NH3, SO2, and NOx were significantly higher than external levels. NH3 concentrations reached 3.64 ± 0.06 mg m−3 at 3 m height and 2.43 ± 0.16 mg m−3 at ground level, while the total NH3 flux from the facility was 24.47 ± 1.39 NH3-N kg d−1. Internal PM2.5 concentrations (36.9 ± 2.6 µg m−3) were about 50% higher than external levels (23.7 ± 2 µg m−3), with a moderate correlation (r = 0.341) suggesting some contribution of external PM2.5 to internal levels. Despite large quantities of internal emissions, the facility’s sealed design with a negative pressure ventilation system effectively minimized external emissions. These results suggest that while manure composting facilities are significant sources of NH3 and PM2.5, advanced systems like high-volume ventilation and scrubbing technologies can effectively reduce their impact on regional air pollution, contributing to better environmental management in agriculture.

Increased ammonia (NH[sub.3]) emissions from intensive agriculture negatively affect environmental and ecosystem health, contributing to formation of particulate matter (PM) and the potent greenhouse gas, N[sub.2]O. Better understanding NH[sub.3] emissions from the manure composting process and their behavior as a constituent of the atmospheric aerosol load is a crucial element in creating better farm management systems, improving public health outcomes, and mitigating the broader environmental and climatic impacts of agriculture. Retarded generation of PM with a major constituent source of NH[sub.3] is a primary mechanism for evaluating the effects of agricultural contribution to PM. This study aimed to quantify NH[sub.3] emissions, examine the influence of environmental factors, and investigate the relationship between precursor gases (SO[sub.2], NO[sub.x], NH[sub.3]) and PM[sub.2.5] at a modern manure composting facility in Paju, South Korea. Over 35 days, average internal concentrations of NH[sub.3], SO[sub.2], and NO[sub.x] were significantly higher than external levels. NH[sub.3] concentrations reached 3.64 ± 0.06 mg m[sup.−3] at 3 m height and 2.43 ± 0.16 mg m[sup.−3] at ground level, while the total NH[sub.3] flux from the facility was 24.47 ± 1.39 NH[sub.3]-N kg d[sup.−1]. Internal PM[sub.2.5] concentrations (36.9 ± 2.6 µg m[sup.−3]) were about 50% higher than external levels (23.7 ± 2 µg m[sup.−3]), with a moderate correlation (r = 0.341) suggesting some contribution of external PM[sub.2.5] to internal levels. Despite large quantities of internal emissions, the facility’s sealed design with a negative pressure ventilation system effectively minimized external emissions. These results suggest that while manure composting facilities are significant sources of NH[sub.3] and PM[sub.2.5], advanced systems like high-volume ventilation and scrubbing technologies can effectively reduce their impact on regional air pollution, contributing to better environmental management in agriculture.