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A key challenge for addressing micro- and nanoplastics (MNPs) in the environment is being able to characterize their chemical properties, morphologies, and quantities in complex matrices. Current techniques, such as Fourier transform infrared spectroscopy, provide these broad characterizations but are unsuitable for studying MNPs in spectrally congested or complex chemical environments. Here, we introduce a new, super-resolution infrared absorption technique to characterize MNPs, called infrared photothermal heterodyne imaging (IR-PHI). IR-PHI has a spatial resolution of ∼300 nm and can determine the chemical identity, morphology, and quantity of MNPs in a single analysis with high sensitivity. Specimens are supported on CaF2 coverslips under ambient conditions from where we (1) quantify MNPs from nylon tea bags after steeping in ultrapure water at 25 and 95 °C, (2) identify MNP chemical or morphological changes after steeping at 95 °C, and (3) chemically identify MNPs in sieved road dust. In all cases, no special sample preparation was required. MNPs released from nylon tea bags at 25 °C were fiber-like and had characteristic IR frequencies corresponding to thermally extruded nylon. At 95 °C, degradation of the nylon chemical structure was observed via the disappearance of amide group IR frequencies, indicating chain scission of the nylon backbone. This degradation was also observed through morphological changes, where MNPs altered shape from fiber-like to quasi-spherical. In road dust, IR-PHI analysis reveals the presence of numerous aggregate and single-particle (

Source separation is an important step to study signals that are not easy or possible to record individually. Common methods such as deep clustering, however, cannot be applied to signals of an unknown number of sources and/or signals that overlap in time and/or frequency-a common problem in bioacoustic recordings. This work presents an approach, using a supervised learning framework, to parse individual sources from a spectrogram of a mixture that contains a variable number of overlapping sources. This method isolates individual sources in the time-frequency domain using only one function but in two separate steps, one for the detection of the number of sources and corresponding bounding boxes, and a second step for the segmentation in which masks of individual sounds are extracted. This approach handles the full separation of overlapping sources in both time and frequency using deep neural networks in an applicable manner to other tasks such as bird audio detection. This paper presents method and reports on its performance to parse individual bat signals from recordings containing hundreds of overlapping bat echolocation signals. This method can be extended to other bioacoustic recordings with a variable number of sources and signals that overlap in time and/or frequency.

Accurate and robust feature matching is a critical issue in the preprocessing of multispectral image data sets. Higher order features, such as lines can provide useful matching information but are heavily affected by the unreliable detection of lines. Existing methods typically make the unrealistic assumption that end points of lines can be accurately detected across the reference and test images. To address the unreliable detection of line end points, this paper proposes mapping line intersections and then employing tentatively mapped intersections as “anchor” points to compute line descriptors. The computed line descriptors are utilized to determine whether the two lines forming an intersection are matched with the two lines forming its mapped intersection. This eliminates the reliance on the accurate detection of line end points and results in improved matching accuracy. The proposed method is tested on a large number of multispectral images containing various scenes. Experimental results show that it can effectively deal with the detection inaccuracy of end points for line matching.

Infrared photothermal heterodyne imaging (IR-PHI) is an all-optical table top approach that enables super-resolution mid-infrared microscopy and spectroscopy. The underlying principle behind IR-PHI is the detection of photothermal changes to specimens induced by their absorption of infrared radiation. Because detection of resulting refractive index and scattering cross section changes is done using a visible (probe) laser, IR-PHI exhibits a spatial resolution of ∼300 nm. This is significantly below the mid-infrared diffraction limit and is unlike conventional infrared absorption microscopy where spatial resolution is of order ∼5μm. Despite having achieved mid-infrared super-resolution, IR-PHI’s spatial resolution is ultimately limited by the visible probe laser’s diffraction limit. This hinders immediate application to studying samples residing in spatially congested environments. To circumvent this, we demonstrate further enhancements to IR-PHI’s spatial resolution using a deep learning network that addresses the Abbe diffraction limit as well as background artifacts, introduced by experimental raster scanning. What results is a twofold improvement in feature resolution from 300 to ∼150 nm.

The weakly supervised sound event detection (WSSED) problem is the task of predicting the presence of sound events and their corresponding starting and ending points in a weakly labeled dataset. A weak dataset associates each training sample (a short recording) to one or more present sources. Networks that solely rely on convolutional and recurrent layers cannot directly relate multiple frames in a recording. Motivated by attention and graph neural networks, we introduce the concept of an affinity mixup (AM) to incorporate time-level similarities and make a connection between frames. This regularization technique mixes up features in different layers using an adaptive affinity matrix. Our proposed affinity mixup network (AMN) improves over state-of-the-art techniques event-F1 scores by 8.2%.