Search Results (4)

Accurately obtaining a patient’s respiratory rate is crucial for promptly identifying any sudden changes in their condition during emergencies. Typically, the respiratory rate is assessed through a combination of impedance change measurements and electrocardiography (ECG). However, impedance measurements are prone to interference from body movements. Conversely, a capnometer coupled with a ventilator offers a method of measuring the respiratory rate that is unaffected by body movements. However, capnometers are mainly used to evaluate respiration when using a ventilator or an Ambu bag by measuring the CO₂ concentration at the breathing circuit, and they are not used only to measure the respiratory rate. Furthermore, capnometers are not suitable as wearable devices because they require intubation or a mask that covers the nose and mouth to prevent air leaks during the measurement. In this study, we developed a reliable system for measuring the respiratory rate utilizing a small wearable MOx sensor that is unaffected by body movements and not connected to the breathing circuit. Subsequently, we conducted experimental assessments to gauge the accuracy of the rate estimation achieved by the system. In order to avoid the effects of abnormal states on the estimation accuracy, we also evaluated the classification performance for distinguishing between normal and abnormal respiration using a one-class SVM-based approach. The developed system achieved 80% for both true positive and true negative rates. Our experimental findings reveal that the respiratory rate can be precisely determined without being influenced by body movements. Full article

Retinal blood vessels possess a complex structure in the retina and are considered an important biomarker for several retinal diseases. Ophthalmic diseases result in specific changes in the retinal vasculature; for example, diabetic retinopathy causes the retinal vessels to swell, and depending upon disease severity, fluid or blood can leak. Similarly, hypertensive retinopathy causes a change in the retinal vasculature due to the thinning of these vessels. Central retinal vein occlusion (CRVO) is a phenomenon in which the main vein causes drainage of the blood from the retina and this main vein can close completely or partially with symptoms of blurred vision and similar eye problems. Considering the importance of the retinal vasculature as an ophthalmic disease biomarker, ophthalmologists manually analyze retinal vascular changes. Manual analysis is a tedious task that requires constant observation to detect changes. The deep learning-based methods can ease the problem by learning from the annotations provided by an expert ophthalmologist. However, current deep learning-based methods are relatively inaccurate, computationally expensive, complex, and require image preprocessing for final detection. Moreover, existing methods are unable to provide a better true positive rate (sensitivity), which shows that the model can predict most of the vessel pixels. Therefore, this study presents the so-called vessel segmentation ultra-lite network (VSUL-Net) to accurately extract the retinal vasculature from the background. The proposed VSUL-Net comprises only 0.37 million trainable parameters and uses an original image as input without preprocessing. The VSUL-Net uses a retention block that specifically maintains the larger feature map size and low-level spatial information transfer. This retention block results in better sensitivity of the proposed VSUL-Net without using expensive preprocessing schemes. The proposed method was tested on three publicly available datasets: digital retinal images for vessel extraction (DRIVE), structured analysis of retina (STARE), and children’s heart health study in England database (CHASE-DB1) for retinal vasculature segmentation. The experimental results demonstrated that VSUL-Net provides robust segmentation of retinal vasculature with sensitivity (Sen), specificity (Spe), accuracy (Acc), and area under the curve (AUC) values of 83.80%, 98.21%, 96.95%, and 98.54%, respectively, for DRIVE, 81.73%, 98.35%, 97.17%, and 98.69%, respectively, for CHASE-DB1, and 86.64%, 98.13%, 97.27%, and 99.01%, respectively, for STARE datasets. The proposed method provides an accurate segmentation mask for deep ophthalmic analysis. Full article

A 2.45 GHz microwave-plasma chemical-vapor deposition (MPCVD) reactor was designed and built in-house by collaborating with Guangdong TrueOne Semiconductor Technology Co., Ltd. A cylindrical cavity was designed as the deposition chamber and a circumferential coaxial-mode transformer located at the top of the cavity was adopted as the antenna. Two quartz-ring windows that were placed far away from the plasma and cooled by water-cooling cavity walls were used to affix the antenna to the cavity and act as a vacuum seal for the reactor, respectively. This design improved the sealing and protected the quartz windows. In addition, a numerical simulation was proposed to predict the electric-field and plasma-density distributions in the cavity. Based on the simulation results, a microwave-plasma reactor with TM₀₂₁ mode was built. The leak rate of this new reactor was tested to be as low as 1 × 10⁻⁸ Pa·m³·s⁻¹, and the maximal microwave power was as high as 10 kW. Then, single-crystal diamond films were grown with the morphology and crystalline quality characterized by an optical microscope, atomic force microscope (AFM), Raman spectrometer, photoluminescence (PL) spectrometer, and high-resolution X-ray diffractometer. It was shown that the newly developed MPCVD reactor can produce diamond films with high quality and purity. Full article

: Gas transport mechanisms that characterize the hermetic behavior of MEMS packages are fundamentally different depending upon which sealing materials are used in the packages. In metallic seals, gas transport occurs through a few nanoscale leak channels (gas conduction) that are produced randomly during the solder reflow process, while gas transport in polymeric seals occurs through the bulk material (gas diffusion). In this review article, the techniques to measure true leak rates of MEMS packages with the two sealing materials are described and discussed: a Helium mass spectrometer based technique for metallic sealing and a gas diffusion based model for polymeric sealing. Full article