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- ItemNoise Sources and Requirements for Confocal Raman Spectrometers in Biosensor Applications(Basel : MDPI, 2021) Jahn, Izabella J.; Grjasnow, Alexej; John, Henry; Weber, Karina; Popp, Jürgen; Hauswald, WalterRaman spectroscopy probes the biochemical composition of samples in a non-destructive, non-invasive and label-free fashion yielding specific information on a molecular level. Nevertheless, the Raman effect is very weak. The detection of all inelastically scattered photons with highest efficiency is therefore crucial as well as the identification of all noise sources present in the system. Here we provide a study for performance comparison and assessment of different spectrometers for confocal Raman spectroscopy in biosensor applications. A low-cost, home-built Raman spectrometer with a complementary metal-oxide-semiconductor (CMOS) camera, a middle price-class mini charge-coupled device (CCD) Raman spectrometer and a laboratory grade confocal Raman system with a deeply cooled CCD detector are compared. It is often overlooked that the sample itself is the most important “optical” component in a Raman spectrometer and its properties contribute most significantly to the signal-to-noise ratio. For this purpose, different representative samples: a crystalline silicon wafer, a polypropylene sample and E. coli bacteria were measured under similar conditions using the three confocal Raman spectrometers. We show that biosensor applications do not in every case profit from the most expensive equipment. Finally, a small Raman database of three different bacteria species is set up with the middle price-class mini CCD Raman spectrometer in order to demonstrate the potential of a compact setup for pathogen discrimination.
- ItemOperational Parameters for Sub-Nano Tesla Field Resolution of PHMR Sensors in Harsh Environments(Basel : MDPI, 2021) Jeon, Taehyeong; Das, Proloy Taran; Kim, Mijin; Jeon, Changyeop; Lim, Byeonghwa; Soldatov, Ivan; Kim, CheolGiThe resolution of planar-Hall magnetoresistive (PHMR) sensors was investigated in the frequency range from 0.5 Hz to 200 Hz in terms of its sensitivity, average noise level, and detectivity. Analysis of the sensor sensitivity and voltage noise response was performed by varying operational parameters such as sensor geometrical architectures, sensor configurations, sensing currents, and temperature. All the measurements of PHMR sensors were carried out under both constant current (CC) and constant voltage (CV) modes. In the present study, Barkhausen noise was revealed in 1/f noise component and found less significant in the PHMR sensor configuration. Under measured noise spectral density at optimized conditions, the best magnetic field detectivity was achieved better than 550 pT/√Hz at 100 Hz and close to 1.1 nT/√Hz at 10 Hz for a tri-layer multi-ring PHMR sensor in an unshielded environment. Furthermore, the promising feasibility and possible routes for further improvement of the sensor resolution are discussed.
- ItemMeasurement of Water Vapor Condensation on Apple Surfaces during Controlled Atmosphere Storage(Basel : MDPI, 2023) Linke, Manfred; Praeger, Ulrike; Neuwald, Daniel A.; Geyer, MartinApples are stored at temperatures close to 0 °C and high relative humidity (up to 95%) under controlled atmosphere conditions. Under these conditions, the cyclic operation of the refrigeration machine and the associated temperature fluctuations can lead to localized undershoots of the dew point on fruit surfaces. The primary question for the present study was to prove that such condensation processes can be measured under practical conditions during apple storage. Using the example of a measuring point in the upper apple layer of a large bin in the supply air area, this evidence was provided. Using two independent measuring methods, a wetness sensor attached to the apple surface and determination of climatic conditions near the fruit, the phases of condensation, namely active condensation and evaporation, were measured over three weeks as a function of the operating time of the cooling system components (refrigeration machine, fans, defrosting regime). The system for measurement and continuous data acquisition in the case of an airtight CA-storage room is presented and the influence of the operation of the cooling system components in relation to condensation phenomena was evaluated. Depending on the set point specifications for ventilation and defrost control, condensed water was present on the apple surface between 33.4% and 100% of the duration of the varying cooling/re-warming cycles.
- ItemPassive Detection and Imaging of Human Body Radiation Using an Uncooled Field-Effect Transistor-Based THz Detector(Basel : MDPI, 2020) Čibiraitė-Lukenskienė, Dovilė; Ikamas, Kęstutis; Lisauskas, Tautvydas; Krozer, Viktor; Roskos, Hartmut G.; Lisauskas, AlvydasThis work presents, to our knowledge, the first completely passive imaging with human-body-emitted radiation in the lower THz frequency range using a broadband uncooled detector. The sensor consists of a Si CMOS field-effect transistor with an integrated log-spiral THz antenna. This THz sensor was measured to exhibit a rather flat responsivity over the 0.1–1.5-THz frequency range, with values√ of the optical responsivity and noise-equivalent power of around 40 mA/W and 42 pW/ Hz, respectively. These values are in good agreement with simulations which suggest an even broader flat responsivity range exceeding 2.0 THz. The successful imaging demonstrates the impressive thermal sensitivity which can be achieved with such a sensor. Recording of a 2.3 × 7.5-cm2-sized image of the fingers of a hand with a pixel size of 1 mm2 at a scanning speed of 1 mm/s leads to a signal-to-noise ratio of 2 and a noise-equivalent temperature difference of 4.4 K. This approach shows a new sensing approach with field-effect transistors as THz detectors which are usually used for active THz detection. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- ItemIoT-Based Sensor Data Fusion for Determining Optimality Degrees of Microclimate Parameters in Commercial Greenhouse Production of Tomato(Basel : MDPI, 2020) Rezvani, Sayed Moin-eddin; Abyaneh, Hamid Zare; Shamshiri, Redmond R.; Balasundram, Siva K.; Dworak, Volker; Goodarzi, Mohsen; Sultan, Muhammad; Mahns, BenjaminOptimum microclimate parameters, including air temperature (T), relative humidity (RH) and vapor pressure deficit (VPD) that are uniformly distributed inside greenhouse crop production systems are essential to prevent yield loss and fruit quality. The objective of this research was to determine the spatial and temporal variations in the microclimate data of a commercial greenhouse with tomato plants located in the mid-west of Iran. For this purpose, wireless sensor data fusion was incorporated with a membership function model called Optimality Degree (OptDeg) for real-time monitoring and dynamic assessment of T, RH and VPD in different light conditions and growth stages of tomato. This approach allows growers to have a simultaneous projection of raw data into a normalized index between 0 and 1. Custom-built hardware and software based on the concept of the Internet-of-Things, including Low-Power Wide-Area Network (LoRaWAN) transmitter nodes, a multi-channel LoRaWAN gateway and a web-based data monitoring dashboard were used for data collection, data processing and monitoring. The experimental approach consisted of the collection of meteorological data from the external environment by means of a weather station and via a grid of 20 wireless sensor nodes distributed in two horizontal planes at two different heights inside the greenhouse. Offline data processing for sensors calibration and model validation was carried in multiple MATLAB Simulink blocks. Preliminary results revealed a significant deviation of the microclimate parameters from optimal growth conditions for tomato cultivation due to the inaccurate timer-based heating and cooling control systems used in the greenhouse. The mean OptDeg of T, RH and VPD were 0.67, 0.94, 0.94 in January, 0.45, 0.36, 0.42 in June and 0.44, 0.0, 0.12 in July, respectively. An in-depth analysis of data revealed that averaged OptDeg values, as well as their spatial variations in the horizontal profile were closer to the plants’ comfort zone in the cold season as compared with those in the warm season. This was attributed to the use of heating systems in the cold season and the lack of automated cooling devices in the warm season. This study confirmed the applicability of using IoT sensors for real-time model-based assessment of greenhouse microclimate on a commercial scale. The presented IoT sensor node and the Simulink model provide growers with a better insight into interpreting crop growth environment. The outcome of this research contributes to the improvement of closed-field cultivation of tomato by providing an integrated decision-making framework that explores microclimate variation at different growth stages in the production season.
- ItemPrediction of Pest Insect Appearance Using Sensors and Machine Learning(Basel : MDPI, 2021) Marković, Dušan; Vujičić, Dejan; Tanasković, Snežana; Đorđević, Borislav; Ranđić, Siniša; Stamenković, ZoranThe appearance of pest insects can lead to a loss in yield if farmers do not respond in a timely manner to suppress their spread. Occurrences and numbers of insects can be monitored through insect traps, which include their permanent touring and checking of their condition. Another more efficient way is to set up sensor devices with a camera at the traps that will photograph the traps and forward the images to the Internet, where the pest insect’s appearance will be predicted by image analysis. Weather conditions, temperature and relative humidity are the parameters that affect the appearance of some pests, such as Helicoverpa armigera. This paper presents a model of machine learning that can predict the appearance of insects during a season on a daily basis, taking into account the air temperature and relative humidity. Several machine learning algorithms for classification were applied and their accuracy for the prediction of insect occurrence was presented (up to 76.5%). Since the data used for testing were given in chronological order according to the days when the measurement was performed, the existing model was expanded to take into account the periods of three and five days. The extended method showed better accuracy of prediction and a lower percentage of false detections. In the case of a period of five days, the accuracy of the affected detections was 86.3%, while the percentage of false detections was 11%. The proposed model of machine learning can help farmers to detect the occurrence of pests and save the time and resources needed to check the fields.
- ItemLow Complexity Radar Gesture Recognition Using Synthetic Training Data(Basel : MDPI, 2022) Zhao, Yanhua; Sark, Vladica; Krstic, Milos; Grass, EckhardDevelopments in radio detection and ranging (radar) technology have made hand gesture recognition feasible. In heat map-based gesture recognition, feature images have a large size and require complex neural networks to extract information. Machine learning methods typically require large amounts of data and collecting hand gestures with radar is time- and energy-consuming. Therefore, a low computational complexity algorithm for hand gesture recognition based on a frequency-modulated continuous-wave (FMCW) radar and a synthetic hand gesture feature generator are proposed. In the low computational complexity algorithm, two-dimensional Fast Fourier Transform is implemented on the radar raw data to generate a range-Doppler matrix. After that, background modelling is applied to separate the dynamic object and the static background. Then a bin with the highest magnitude in the range-Doppler matrix is selected to locate the target and obtain its range and velocity. The bins at this location along the dimension of the antenna can be utilised to calculate the angle of the target using Fourier beam steering. In the synthetic generator, the Blender software is used to generate different hand gestures and trajectories and then the range, velocity and angle of targets are extracted directly from the trajectory. The experimental results demonstrate that the average recognition accuracy of the model on the test set can reach 89.13% when the synthetic data are used as the training set and the real data are used as the test set. This indicates that the generation of synthetic data can make a meaningful contribution in the pre-training phase.
- ItemWide Field Spectral Imaging with Shifted Excitation Raman Difference Spectroscopy Using the Nod and Shuffle Technique(Basel : MDPI, 2020) Korinth, Florian; Schmälzlin, Elmar; Stiebing, Clara; Urrutia, Tanya; Micheva, Genoveva; Sandin, Christer; Müller, André; Maiwald, Martin; Sumpf, Bernd; Krafft, Christoph; Tränkle, Günther; Roth, Martin M; Popp, JürgenWide field Raman imaging using the integral field spectroscopy approach was used as a fast, one shot imaging method for the simultaneous collection of all spectra composing a Raman image. For the suppression of autofluorescence and background signals such as room light, shifted excitation Raman difference spectroscopy (SERDS) was applied to remove background artifacts in Raman spectra. To reduce acquisition times in wide field SERDS imaging, we adapted the nod and shuffle technique from astrophysics and implemented it into a wide field SERDS imaging setup. In our adapted version, the nod corresponds to the change in excitation wavelength, whereas the shuffle corresponds to the shifting of charges up and down on a Charge-Coupled Device (CCD) chip synchronous to the change in excitation wavelength. We coupled this improved wide field SERDS imaging setup to diode lasers with 784.4/785.5 and 457.7/458.9 nm excitation and applied it to samples such as paracetamol and aspirin tablets, polystyrene and polymethyl methacrylate beads, as well as pork meat using multiple accumulations with acquisition times in the range of 50 to 200 ms. The results tackle two main challenges of SERDS imaging: gradual photobleaching changes the autofluorescence background, and multiple readouts of CCD detector prolong the acquisition time.
- ItemRaman Signal Enhancement Tunable by Gold-Covered Porous Silicon Films with Different Morphology(Basel : MDPI, 2020) Agafilushkina, Svetlana N.; Žukovskaja, Olga; Dyakov, Sergey A.; Weber, Karina; Sivakov, Vladimir; Popp, Jürgen; Cialla-May, Dana; Osminkina, Liubov A.The ease of fabrication, large surface area, tunable pore size and morphology as well surface modification capabilities of a porous silicon (PSi) layer make it widely used for sensoric applications. The pore size of a PSi layer can be an important parameter when used as a matrix for creating surface-enhanced Raman scattering (SERS) surfaces. Here, we evaluated the SERS activity of PSi with pores ranging in size from meso to macro, the surface of which was coated with gold nanoparticles (Au NPs). We found that different pore diameters in the PSi layers provide different morphology of the gold coating, from an almost monolayer to 50 nm distance between nanoparticles. Methylene blue (MB) and 4-mercaptopyridine (4-MPy) were used to describe the SERS activity of obtained Au/PSi surfaces. The best Raman signal enhancement was shown when the internal diameter of torus-shaped Au NPs is around 35 nm. To understand the role of plasmonic resonances in the observed SERS spectrum, we performed electromagnetic simulations of Raman scattering intensity as a function of the internal diameter. The results of these simulations are consistent with the obtained experimental data
- ItemSemiconductor Gas Sensors: Materials, Technology, Design, and Application(Basel : MDPI, 2020) Nikolic, Maria Vesna; Milovanovic, Vladimir; Vasiljevic, Zorka Z.; Stamenkovic, ZoranThis paper presents an overview of semiconductor materials used in gas sensors, their technology, design, and application. Semiconductor materials include metal oxides, conducting polymers, carbon nanotubes, and 2D materials. Metal oxides are most often the first choice due to their ease of fabrication, low cost, high sensitivity, and stability. Some of their disadvantages are low selectivity and high operating temperature. Conducting polymers have the advantage of a low operating temperature and can detect many organic vapors. They are flexible but affected by humidity. Carbon nanotubes are chemically and mechanically stable and are sensitive towards NO and NH3, but need dopants or modifications to sense other gases. Graphene, transition metal chalcogenides, boron nitride, transition metal carbides/nitrides, metal organic frameworks, and metal oxide nanosheets as 2D materials represent gas-sensing materials of the future, especially in medical devices, such as breath sensing. This overview covers the most used semiconducting materials in gas sensing, their synthesis methods and morphology, especially oxide nanostructures, heterostructures, and 2D materials, as well as sensor technology and design, application in advance electronic circuits and systems, and research challenges from the perspective of emerging technologies. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.