13,939 research outputs found

    Graduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    Forschungsbericht / Hochschule Mittweida

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    Monitoring Cardiovascular Physiology using Bio-compatible AlN Piezoelectric Skin Sensors

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    Arterial pulse waves contain a wealth of parameters indicative of cardiovascular disease. As such, monitoring them continuously and unobtrusively can provide health professionals with a steady stream of cardiovascular health indices, allowing for the development of efficient, individualized treatments and early cardiovascular disease diagnosis solutions. Blood pulsations in superficial arteries cause skin surface deformations, typically undetectable to the human eye; therefore, Microelectromechanical systems (MEMS) can be used to measure these deformations and thus create unobtrusive pulse wave monitoring devices. Miniaturized ultrathin and flexible Aluminium Nitride (AlN) piezoelectric MEMS are highly sensitive to minute mechanical deformations, making them suitable for detecting the skin deformations caused by cardiac events and consequently providing multiple biomarkers useful for monitoring cardiovascular health and assessing cardiovascular disease risk. Conventional wearable continuous pulse wave monitoring solutions are typically large and based on technologies limiting their versatility. Therefore, we propose the adoption of 29.5 μm-thick biocompatible, skin-conforming devices on piezoelectric AlN to create versatile, multipurpose arterial pulse wave monitoring devices. In our initial trials, the devices are placed over arteries along the wrist (radial artery), neck (carotid artery), and suprasternal notch (on the chest wall and close to the ascending aorta). We also leverage the mechano-acoustic properties of the device to detect heart muscle vibrations corresponding to heart sounds S1 and S2 from the suprasternal notch measurement site. Finally, we characterize the piezoelectric device outputs observed with the cardiac cycle events using synchronized electrocardiogram (ECG) reference signals and provide information on heart rate, breathing rate, and heart sounds. The extracted parameters strongly agree with reference values as illustrated by minimum Pearson correlation coefficients (r) of 0.81 for pulse rate and 0.95 for breathing rate

    Testing pALPIDE sensors for particle detection and Characterization of a Laser beam using a webcam CMOS sensor

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    The upgrade program of the Large Hadron Collider (LHC) was implemented during the second Long Shutdown program (2019/2020). For this program, the ALICE Collaboration (A Large Ion Collider Experiment) proposed, among others, a new detector called Muon Forward Tracker (MFT). The primary goal of the MFT detector, installed on December 2021 and located between the Inner Tracker System (ITS) and the Muon Spectrometer, is to improve the capability of vertex reconstruction. The MFT is equipped with the same pixel sensors used for the ITS upgrade. These sensors are the ALICE Pixel Detectors (ALPIDE), a kind of monolithic active pixel sensor. The MFT is composed of five arrays of pixel sensors which are configured as parallel discs covering −3.6 < η < −2.45. Some prototypes were designed in order to achieve the final version of the ALPIDE, such as the pALPIDE family, which was divided into three versions (i.e., pALPIDE-1,2,3). The ALICE upgrade also included a new system for the data taking and simulation called Online-offline (O2) to replace AliRoot. We designed the geometry of two non-active parts of the MFT and included them in the O2 system. The first goal of this thesis is focused on the characterization of the pALPIDE-2. This sensor is segmented into four groups corresponding to four types of pixels. This characterization includes the test of analogue and digital. According to these tests, we identified a group of pixels that do not work correctly. The threshold scan tests showed the threshold level in each pixel is influenced by the input capacitance according to its n-well size and the surrounding area. Also, we studied the response of the pALPIDE-2 when it was exposed to a soft x-ray source, varying the distance between them. This test showed that the hit count changed according to the inverse square of the distance. iv The second goal of this thesis was to implement a low-cost tool based on a CMOS sensor to characterize laser beams. This tool comprises a Raspberry, a Pi Camera with a pitch size of 1.4 µm, and an optical system. To test the accuracy of the results of this tool, we made similar measurements with other sensors. A photodiode and a light-dependent resistor performed these measurements, which showed the spot radius size compatibility. However, the CMOS sensor expressed the highest precision and is a more affordable tool than commercial devices

    Crystal Structures of Metal Complexes

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    This reprint contains 11 papers published in a Special Issue of Molecules entitled "Crystal Structures of Metal Complexes". I will be very happy if readers will be interested in the crystal structures of metal complexes

    Inverse Design of Metamaterials for Tailored Linear and Nonlinear Optical Responses Using Deep Learning

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    The conventional process for developing an optimal design for nonlinear optical responses is based on a trial-and-error approach that is largely inefficient and does not necessarily lead to an ideal result. Deep learning can automate this process and widen the realm of nonlinear geometries and devices. This research illustrates a deep learning framework used to create an optimal plasmonic design for metamaterials with specific desired optical responses, both linear and nonlinear. The algorithm can produce plasmonic patterns that can maximize second-harmonic nonlinear effects of a nonlinear metamaterial. A nanolaminate metamaterial is used as a nonlinear material, and a plasmonic patterns are fabricated on the prepared nanolaminate to demonstrate the validity and efficacy of the deep learning algorithm for second-harmonic generation. Photonic upconversion from the infrared regime to the visible spectrum can occur through sum-frequency generation. The deep learning algorithm was improved to optimize a nonlinear plasmonic metamaterial for sum-frequency generation. The framework was then further expanded using transfer learning to lessen computation resources required to optimize metamaterials for new design parameters. The deep learning architecture applied in this research can be expanded to other optical responses and drive the innovation of novel optical applications.Ph.D

    Smart Gas Sensors: Materials, Technologies, Practical ‎Applications, and Use of Machine Learning – A Review

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    The electronic nose, popularly known as the E-nose, that combines gas sensor arrays (GSAs) with machine learning has gained a strong foothold in gas sensing technology. The E-nose designed to mimic the human olfactory system, is used for the detection and identification of various volatile compounds. The GSAs develop a unique signal fingerprint for each volatile compound to enable pattern recognition using machine learning algorithms. The inexpensive, portable and non-invasive characteristics of the E-nose system have rendered it indispensable within the gas-sensing arena. As a result, E-noses have been widely employed in several applications in the areas of the food industry, health management, disease diagnosis, water and air quality control, and toxic gas leakage detection. This paper reviews the various sensor fabrication technologies of GSAs and highlights the main operational framework of the E-nose system. The paper details vital signal pre-processing techniques of feature extraction, feature selection, in addition to machine learning algorithms such as SVM, kNN, ANN, and Random Forests for determining the type of gas and estimating its concentration in a competitive environment. The paper further explores the potential applications of E-noses for diagnosing diseases, monitoring air quality, assessing the quality of food samples and estimating concentrations of volatile organic compounds (VOCs) in air and in food samples. The review concludes with some challenges faced by E-nose, alternative ways to tackle them and proposes some recommendations as potential future work for further development and design enhancement of E-noses

    Scanning cavity microscopy of a single-crystal diamond membrane

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    Spin-bearing color centers in the solid state are promising candidates for the realization of quantum networks and distributed quantum computing. A remaining key challenge is their efficient and reliable interfacing to photons. Incorporating minimally processed membranes into open-access microcavities represents a promising route for Purcellenhanced spin-photon interfaces: it enables significant emission enhancement and efficient photon collection, minimizes deteriorating influence on the quantum emitter, and allows for full spatial and spectral tunability, key for controllably addressing suitable emitters with desired optical and spin properties. Here, we study the properties of a high-finesse fiber Fabry-P\'erot microcavity with integrated single-crystal diamond membranes by scanning cavity microscopy. We observe spatially resolved the effects of the diamond-air interface on the cavity mode structure: a strong correlation of the cavity finesse and mode structure with the diamond thickness and surface topography, significant transverse-mode mixing under diamond-like conditions, and mode-character-dependent polarization-mode splitting. Our results reveal the influence of the diamond surface on the achievable Purcell enhancement, which helps to clarify the route towards optimized spin-photon interfaces

    Selected Advances of Quantum Biophotonics – a Short Review

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    This article discusses four fields of study with the potential to revolutionize our understanding and interaction with biological systems: quantum biophotonics, molecular and supramolecular bioelectronics, quantum-based approaches in gaming, and nano-biophotonics. Quantum biophotonics uses photonics, biochemistry, biophysics, and quantum information technologies to study biological systems at the sub-nanoscale level. Molecular and supramolecular bioelectronics aim to develop biosensors for medical diagnosis, environmental monitoring, and food safety by designing materials and devices that interface with biological systems at the molecular level. Quantum-based approaches in gaming improve modeling of complex systems, while nanomedicine enhances disease diagnosis, treatment, and prevention using nanoscale devices and sensors developed with quantum biophotonics. Lastly, nano-biophotonics studies cellular structures and functions with unprecedented resolution
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