Sensing and Imaging最新文献

Humans are fundamentally interested in monitoring and understanding interactions that occur in and around our bodies. Biological interactions within the body determine our physical condition and can be used to improve medical treatments and develop new drugs. Daily life involves contact with numerous chemicals, ranging from household elements, naturally occurring scents from common plants and animals, and industrial agents. Many chemicals cause adverse health and environmental effects and require regulation to prevent pollution. Chemical detection is critically important for food and environmental quality control efforts, medical diagnostics, and detection of explosives. Thus, sensitive devices are needed for detecting and discriminating chemical and biological samples. Compared to other sensing devices, the Quartz Crystal Microbalance (QCM) is well-established and has been considered and sufficiently sensitive for detecting molecules, chemicals, polymers, and biological assemblies. Due to its simplicity and low cost, the QCM sensor has potential applications in analytical chemistry, surface chemistry, biochemistry, environmental science, and other disciplines. QCM detection measures resonate frequency changes generated by the quartz crystal sensor when covered with a thin film or liquid. The quartz crystal is sandwiched between two metal (typically gold) electrodes. Functionalizing the electrode's surface further enhances frequency change detection through to interactions between the sensor and the targeted material. These sensors are sensitive to high frequencies and can recognize ultrasmall masses. This review will cover advancements in QCM sensor technologies, highlighting in-sensor and real-time analysis. QCM-based sensor function is dictated by the coating material. We present various high-sensitivity coating techniques that use this novel sensor design. Then, we briefly review available measurement parameters and technological interventions that will inform future QCM research. Lastly, we examine QCM's theory and application to enhance our understanding of relevant electrical components and concepts.

Tuberculosis is a very dangerous disease. Therefore, early and quick diagnosis of this disease can increase the chances of overcoming it. Studies show that people with tuberculosis have a lower blood plasma refractive index than healthy people. The performance of the fiber optic sensor based on surface plasmon resonance is investigated for the metal/oxide/graphene structure and for cases where the diameter of the fiber optic core is 300, 600, and 940 µm while blood plasma is considered as the sensing medium. The sensor characteristics such as sensitivity, detection accuracy and figure of merit are simulated for each structure using the theory of matrix method in Wolfram Mathematica software. The simulation results show that the aluminum/lutetium oxide/graphene structure has the highest quality factor when the core diameter of the optical fiber is 940 µm. In continuation of this research, the effects of using alloys with different mixture proportions to improve the quality are investigated. According to results, the structure of aluminum/copper alloy (with a ratio of 30/70)-lutetium oxide graphene is the best choice for improving the quality of the sensor.

Lung cancer is a high-risk disease that affects people all over the world, and lung nodules are the most common sign of early lung cancer. Since early identification of lung cancer can considerably improve a lung scanner patient's chances of survival, an accurate and efficient nodule detection system can be essential. Automatic lung nodule recognition decreases radiologists' effort, as well as the risk of misdiagnosis and missed diagnoses. Hence, this article developed a new lung nodule detection model with four stages like "Image pre-processing, segmentation, feature extraction and classification". In this processes, pre-processing is the first step, in which the input image is subjected to a series of operations. Then, the "Otsu Thresholding model" is used to segment the pre-processed pictures. Then in the third stage, the LBP features are retrieved that is then classified via optimized Convolutional Neural Network (CNN). In this, the activation function and convolutional layer count of CNN is optimally tuned via a proposed algorithm known as Improved Moth Flame Optimization (IMFO). At the end, the betterment of the scheme is validated by carrying out analysis in terms of certain measures. Especially, the accuracy of the proposed work is 6.85%, 2.91%, 1.75%, 0.73%, 1.83%, as well as 4.05% superior to the extant SVM, KNN, CNN, MFO, WTEEB as well as GWO + FRVM methods respectively.