A high-aperture-efficiency Fabry-Perot (FP) antenna with broadband out-of-band radar cross-section (RCS) reduction enabled by a partially reflective absorptive frequency-selective surface (PRAFSS) is proposed. Unlike conventional absorptive frequency-selective surfaces that emphasize high in-band transmission, the PRAFSS is designed specifically for FP cavity operation. It provides controlled reflection and transmission near 1 GHz to sustain cavity resonance, while providing broadband absorption at higher frequencies for out-of-band scattering suppression. A prototype is designed, fabricated, and measured. The measured -10 dB impedance bandwidth is 965-1009 MHz (4.46%), and the measured gain reaches approximately 13.0 dBi near 985 MHz, with a gain enhancement greater than 2.2 dB relative to the reference antenna. Using the measured gain and the exact 416 mm × 416 mm physical aperture, the measured gain-based aperture efficiency is approximately 85.1%. Full-wave far-field simulations predict a continuous 10 dB monostatic RCS reduction band of 3.05-6.47 GHz. The finite-range measurement shows a consistent reference-normalized backscattering reduction band of 3.15-6.32 GHz. The results demonstrate a cavity-oriented design strategy in which high aperture utilization in the antenna operating band and broadband out-of-band scattering suppression are implemented by different electromagnetic responses of the same PRAFSS.

This study examines interfacial intermetallic compound (IMC) growth in Pb-free/Pb mixed assemblies comprising 450 µm SAC305 solder balls and Cu pads printed with a 0.12 mm layer of Sn63Pb37 solder paste. Samples were reflowed at peak temperatures of 200, 220, 240, and 260 °C, with the time above 217 °C fixed at 40 s. Scanning electron microscopy, energy-dispersive X-ray spectroscopy, and electron backscatter diffraction were used to characterize the interfacial composition, morphology, layer thickness, grain size, and kernel average misorientation (KAM). The IMC layer thickened from 1.5 µm at 200 °C to 6.0 µm at 260 °C, while KAMave increased from 0.36° to 0.59°. At 220 °C, the IMC thickness changed only slightly to 1.6 µm, accompanied by local Pb and Ag enrichment. At 240 °C, the IMC thickness increased sharply to 4.7 µm and KAMave increased to 0.55°, together with larger Cu6Sn5 grains and localized coarsening. At 260 °C, the IMC thickness reached 6.0 µm and KAMave reached 0.59°, while Pb-rich regions became more pronounced and the Ag content decreased markedly, indicating substantial interfacial solute redistribution and increasingly heterogeneous IMC growth. The results link the reflow temperature to changes in interfacial chemistry, and microstructure and can help define a suitable process window for Pb-free/Pb mixed assembly.

Selective laser melting (SLM) produces elemental segregation and nonequilibrium secondary phases in GH4169 superalloys, but the effects of P and C variations on grain-boundary phase evolution and high-temperature tensile behavior remain unclear. Alloys with primarily varied P (0.034-0.058 wt.%) and C (0.009-0.066 wt.%) contents were fabricated and heat treated identically. Microstructures were characterized by scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and transmission electron microscopy (TEM), and tensile properties were evaluated at 650 °C. The as-built alloys exhibited columnar grains, cellular substructures, cell-wall segregation, and Nb-rich Laves-phase particles. After heat treatment, cellular substructures largely disappeared, γ″ precipitates formed in the γ matrix, and fine Ti/Al-rich particles were tentatively attributed to γ'; δ phase, residual Laves phase, and MC-type carbides were present at grain boundaries. Increasing P reduced the δ-phase area fraction from 2.20% to 1.38% and changed its distribution from continuous chains to semi-continuous and discrete arrangements. Increasing C raised the MC-type carbide area fraction from 0.34% to 1.48% while decreasing the δ-phase area fraction from 1.68% to 0.98%, accompanied by carbide coarsening. The 0.050P alloy exhibited the highest yield and ultimate tensile strengths, whereas the 0.009C alloy showed the best strength-ductility balance. These results reveal distinct P- and C-related changes in grain-boundary phase evolution and tensile behavior at 650 °C.

Ultrasonic vibration-assisted machining (UVAM) can improve the machinability of difficult-to-machine titanium alloys; however, the relationship between machining-load fluctuations, subsurface microstructure, and electrochemical response remains insufficiently established. In this study, a wireless in-situ monitoring system was integrated with electron backscatter diffraction (EBSD), electrochemical impedance spectroscopy (EIS), potentiodynamic polarization, and scanning electrochemical microscopy (SECM) to compare conventional milling (CM), low-excitation UVAM (L-UVAM), and high-excitation UVAM (H-UVAM) of Ti-6Al-4V. Under the investigated conditions, H-UVAM reduced the RMS value of the measured axial load signal by 43.3% compared with CM and decreased the variation in the resultant bending-moment signal. The EBSD results showed a reduction in the mean grain size from 11.67 μm for CM to 10.07 μm for H-UVAM, together with an increase in the measured high-angle grain-boundary fraction from 48.27% to 59.39%. Electrochemical measurements further indicated a lower corrosion current density and a higher fitted barrier resistance for the H-UVAM surface. SECM mapping showed a narrower local current distribution under H-UVAM than under CM. These results demonstrate a consistent association between reduced machining-load fluctuations, modified subsurface crystallographic features, and improved electrochemical response. Because surface roughness, residual stress, tool wear, and passive-film chemistry were not independently quantified, the present work does not attribute the corrosion response exclusively to microstructural changes. Instead, it provides a cross-scale experimental framework for correlating machining dynamics with surface integrity and corrosion-related performance in machined titanium alloys.

We present kikuchipy, an open-source toolbox for analysis of electron backscatter diffraction patterns, written in Python. The software is capable of both Hough and dictionary indexing and orientation and/or projection center refinement of patterns stored in file formats from all major vendors. Indexing results can be validated using maps independent of indexing and by visually comparing experimental and simulated patterns. By leveraging scientific packages in the Python ecosystem, emphasis is put on making the indexing workflow flexible and improve results through fast iteration. The software's capabilities are demonstrated on three application examples: analysis of orientation relationships in a super duplex stainless steel, phase differentiation of aluminum and silicon in a cast modified Al-Si alloy, and phase differentiation of particles in an Al-Mn alloy as Al6Mn or α-AlMnSi. The diffraction patterns and analysis workflows are made publicly available. kikuchipy was created and is developed as a resource for the electron microscopy community, allowing anyone to improve the software or include it into their own analysis workflows or softwares.

4D-STEM-based orientation and phase mapping has enabled rapid microstructure quantification that can be directly combined with standard TEM- and STEM-based imaging modes. Typically, orientation mapping is coupled with beam precession (i.e., precession electron diffraction) to achieve high indexing rates, adding to the cost and often decreasing the spatial resolution of the approach. This paper introduces a new postprocessing approach modeled after the nonlocal pattern averaging and reindexing algorithm developed for the electron backscatter diffraction community, wherein postcollection patterns are averaged using a distance similarity parameter. Results from Ni and Au thin films show that indexing rates can be significantly improved using this postprocessing technique due to improved signal-to-noise ratios in the diffraction patterns. Interestingly, the highest indexing rates are achieved in samples heavily damaged via ion irradiation, suggesting that averaging over curved lattices further improves indexing rates. The source code is made freely available at https://github.com/USNavalResearchLaboratory/PyEBSDIndex.

The goal of this research was to quantify the variability of seafloor backscattering strength occurring on time scales of weeks to months. Acoustic data were acquired using a 2-m-tall stationary mooring placed on the seafloor in a shallow water, sandy site located at the mouth of the Piscataqua River, New Hampshire, USA. Seafloor backscattering was measured at three frequencies (70, 200 and 333 kHz) and at low grazing angles (11° to 20°). Results show that scattering strength at fixed grazing angles varied weekly up to 10 dB and up to 18.8 dB over the 8-month deployment.

Measuring bite force is essential for assessing the masticatory system and diagnosing oral disease. Existing measurement devices have low spatial resolution and susceptibility to electromagnetic interference. This paper presents a machine learning (ML)-assisted distributed fiber optic sensing system based on Scattering Level Multiplexing (SLMux) for high-resolution bite force analysis. Enhanced backscattered data were acquired through optical backscattered reflectometry from 88 sensing points along the dental arch. Measured data were reconstructed into a two-dimensional map of bite force and analyzed through an ML pipeline. Sector classification across 4 regions and weight prediction were processed by an end-to-end fine-tuned ResNet-18 Convolutional Neural Network (CNN) and classical ML approaches. ResNet-18 is compared with Logistic Regression, Support Vector Machine (SVM), Random Forest, XGBoost (Extreme Gradient Boosting), Extra Trees, and k-Nearest Neighbors (kNN) trained on handcrafted features. On sector classification, Logistic Regression achieved the best performance (98.71% accuracy). On the weight prediction task, formulated as a 12-class problem, the end-to-end ResNet-18 CNN substantially outperformed all classical models, reaching 51.28% accuracy and a mean absolute error of 78 g, versus 124 g for the best classical model. A regression-based ResNet-18 variant was also trained on the wavelength-shift and weight data, resulting in a mean absolute error of 62.6 g. The results indicate that integrating ML with distributed fiber-optic sensing has the potential to enhance dental diagnostics and treatment planning.

Photodynamic therapy (PDT) with chlorin-based photosensitizers requires real-time monitoring of photosensitizer distribution and photobleaching to guide treatment, yet existing endoscopic fluorescence systems rely on short-wavelength (375-450 nm) excitation that is spectrally incompatible with 660 nm chlorin e6 (Ce6)-PDT, forcing interruption of therapy for diagnostic imaging. We developed VENERA-red-660, a dual-channel endoscopic system using a single 660 nm laser for simultaneous white-light and Ce6 fluorescence imaging (>670 nm detection) without interrupting irradiation. System sensitivity was validated on optical phantoms (Ce6 0.1-5.0 mg/L), and phototheranostics was performed in patients with basal cell carcinoma (n = 5), vulvar intraepithelial neoplasia grade III (n = 3), and peritoneal carcinomatosis (n = 10) using intravenous chlorin-based photosensitizer. Phantom testing showed a linear fluorescence response (R[2] = 0.998) down to the lowest tested concentration of 0.1 mg/L. Pre-PDT fluorescence contrast indices confirmed photosensitizer selectivity in all three tumor types, decreasing by 57%, 66%, and 60% after treatment, consistent with photobleaching; continuous intra-PDT monitoring confirmed progressive photobleaching kinetics, and confocal microscopy verified cellular-level Ce6 selectivity. Increased backscattered laser signal after PDT was consistent with treatment-induced changes in tissue optical properties. These pilot findings demonstrate that single-source phototheranostic monitoring is feasible across external and laparoscopic access routes, supporting further evaluation of VENERA-red-660 as a tool for intraoperative PDT dosimetry.

Biological tissues are composed of discrete compartments of biochemical media that often exhibit subtle differences in refractive index. Light propagating through these compartments partially diffracts in a forward direction with a π / 2 phase shift. We introduce a microscopy technique to image this scattering signal in thick tissues, called back-illumination interference tomography (BIT). An incoherent source is demagnified and imaged past the focal plane of a high-numerical aperture objective lens, producing a small, semicoherent source of backscattered light. This backscattered light undergoes a phase inversion over the narrow depth of field of the microscope, providing interference contrast to weakly scattering objects at the focal plane. BIT offers a different source of contrast to conventional illumination and oblique back-illumination microscopy. Compared to these techniques, we show that BIT improves contrast to blood cells in vitro in microfluidic chambers and in vivo in a human capillary. Finally, we apply BIT to unstained, unlabeled bulk human tissue ex vivo and compare side-by-side to adjacent frozen sections stained with hematoxylin and eosin. These results demonstrate the potential of BIT to provide high-resolution, high-speed, 3D imaging of unprocessed biological tissues.

Rice (Oryza sativa L.) is Bangladesh's staple crop, cultivated across three distinct seasons: Rabi (Boro), Kharif-1 (Aus) and Kharif-2 (Aman). This study develops an integrated, cloud-independent framework for mapping Aus and Aman rice areas using freely available Sentinel-1 C-band Synthetic Aperture Radar (SAR) imagery; to accomplish this, supervised machine-learning (ML) algorithms are used in the Google Earth Engine (GEE) platform. Time-series dual-polarization (VV and VH) backscatter data were analyzed for June-November 2021 (Aman) and March-August 2022 (Aus). Four ML models such as Classification and Regression Tree (CART), Random Forest (RF), k-Nearest Neighbour (k-NN), and Support Vector Machine (SVM) were evaluated to identify the most accurate mapping approach. Among all ML models, k-NN achieved the highest accuracy of 94.5% and 99.8% for Aman and Aus, respectively. Despite the higher accuracy, RF for Aman (92.7% accuracy) and CART for Aus (99.6%) were finally selected based on their lower deviation from area estimates reported by Bangladesh Bureau of Statistics (BBS). The estimated Aman area (5.72 Mha) was closely matched with the official figure from the Bangladesh Bureau of Statistics (BBS), while the Aus estimate (0.76 Mha) showed a 29% underestimation compared to BBS data (1.06 Mha). However, the results demonstrate that Sentinel-1 SAR combined with ML classifiers provides a reliable, scalable, and weather-resilient approach for multi-season rice mapping. The proposed methodology establishes a strong foundation for timely production forecasting and climate-resilient agricultural planning, particularly in cloud-prone tropical regions.

Fluorescence microscopy is a cornerstone of biological research. However, fluorescent labeling is challenging in live cells and is constrained by photobleaching and phototoxicity. Label-free methods allow cells to be studied in their native state, but most techniques have poor contrast, lack 3D capability, rely on complex optics, or fail to provide structural information. We present here broadband backscattering confocal microscopy (BBCM), which employs a broadband supercontinuum laser and collects backscattered light in a confocal geometry using a photomultiplier tube. Broadband illumination averages out size-dependent oscillations that confound monochromatic backscattering. This eliminates blind spots and intensity ambiguities, allowing all scatterers to be visible, with the signal increasing approximately linearly with scatterer size. BBCM is easy to retrofit to standard confocal microscopes, requires no specialized optics, and is straightforward for nonspecialists. It enables high-contrast, label-free 3D imaging of live cells with size sensitivity to subcellular structures without employing custom optics or complex data processing.

Honey bees play a vital role in global agriculture. Understanding the movement of a queen bee within a hive box is essential for advancing both biological research and practical apiculture. In this paper, we present SOPAR, a novel Radio Frequency (RF) Sub-harmonic Oscillating Parametric Resonator designed for non-invasive localization and tracking of a queen bee inside a full-size hive box. The core component of SOPAR is a lightweight RF backscatter tag that can be attached to a bee's thorax without affecting its normal behavior. The tag comprises two passive resonators: (i) an inner spiral inductor bridged by a varactor diode and (ii) an outer circular inductor with a gap bridged by a chip capacitor. The outer inductor harvests energy from the external excitation signal, driving the oscillation of the inner spiral resonator to produce sub-harmonic backscattered signals at half the excitation frequency. The frequency separation between excitation and backscatter signals eliminates self-interference at the RF reader, significantly improving signal-to-noise ratio (SNR) and detection range. The layout of the two resonators is meticulously optimized to maximize magnetic coupling, thereby minimizing the overall tag size. Building on this dual-resonator tag, we design an RF reader with a Bayesian estimation algorithm that localizes the tagged bee by exploiting the spatio-temporal characteristics of the sub-harmonic backscattered signals. We have built a prototype of SOPAR, featuring a tag with a diameter of only 3.7 mm and a weight of less than 10 mg. Extensive experiments demonstrate that SOPAR achieves a median localization error of 3.7 cm when tracking a queen bee in a full-size hive box. Moreover, the results confirm that SOPAR remains robust under diverse environmental conditions.

Wireless microphones are essential tools in business, education, entertainment, and other domains. However, most existing designs rely on batteries, leading to the inconvenience of frequent recharging and the risk of unexpected power failure during use. In this paper, we present TagMic, a battery-free wireless microphone enabled by a novel radio frequency (RF) backscatter technology. TagMic is built on two key innovations. (i) Parametric backscatter tag design: This design enables the RF tag to operate at separate excitation and reflection frequencies, fundamentally mitigating the self-interference problem inherent in conventional RFID systems. Unlike harmonic backscatter approaches, it also requires a significantly lower activation voltage, resulting in a longer communication range. (ii) Voice modulation via RF coupling: A passive piezoelectric sensor is integrated with the RF tag through RF coupling to enable analog-domain frequency modulation (FM), directly encoding voice signals onto the backscattered signal. This eliminates the need for digital signal processing, allowing for truly continuous voice streaming. We have built a prototype of TagMic and evaluated it under realistic conditions. Extensive experiments demonstrate its effectiveness in achieving battery-free, continuous, and seamless wireless voice streaming in realistic applications.