40 research outputs found
Development of a Radiometer Front End for Detection of Oil Spill on Sea Surface
Kaikki kappaleet säteilevät sähkömagneettista energiaa. Radiometreillä voidaan mitata tätä säteilyä, ja mittausten avulla voidaan kaukokartoituksessa analysoida monia ilmakehään ja maan pintaan liittyviä ilmiöitä. Säteilyä kerätään useimmiten radiometriin kytketyn antennin avulla. Mikroaaltoalueella säteilystä saatava teho on verrannollinen kohteen kirkkauslämpötilaan. Kirkkauslämpötila on puolestaan verrannollinen kohteen fyysiseen lämpötilaan materiaalin emissiivisyyden kautta. Emissiivisyys voi vaihdella suuresti eri pintojen ja materiaalien välillä. Näin ollen mittaamalla kohteen kirkkauslämpötilaa saadaan tietoa sen ominaisuuksista.
Mikroaaltoradiometrin avulla voidaan havaita öljyläikkä meren pinnalta, sekä määrittää öljyläikän paksuus. Öljy muodostaa ohuen kalvon meren pinnalla. Ohuen kalvon ylä- ja alapinnasta heijastuneet aallot kulkevat eri matkan, jonka seurauksena muodostuu interferenssikuvio. Interferenssi on joko konstruktiivinen tai destruktiivinen riippuen heijastusten vaihe-erosta. Pistetaajuudella tästä seuraa kalvon paksuudesta riippuva sinimuotoinen emissiivisyys. Mallintamalla tämä ilmiö riittävän tarkasti voidaan radiometrin mittaamaa kirkkauslämpötilaa verrata mallinnettuun arvoon ja täten määrittää öljykerroksen paksuus.
Tässä työssä esitetään kahden radiometrin suunnittelu ja testaus. Työ rajoittuu radiometrien alustavaan testausvaiheeseen. Radiometrit on suunniteltu 36,5GHz sekä 89GHz taajuuksille. Työn suurin kontribuutio on radiometrien etupään suunnittelussa ja testauksessa, kalibraatiomenetelmien kehittämisessä sekä kylmäkuorman suunnittelussa. Lisäksi työssä esitetään radiometrien systeemisuunnittelu sekä siihen liittyvät simulaatiot.Siirretty Doriast
Field-effect based chemical and biological sensing : theory and implementation
Electrochemical sensors share many properties of an ideal (bio)chemical sensor. They can be easily miniaturized with high parallel sensing capabilities,with rugged structure and at low cost. The response obtained from thetarget analyte is directly in electrical form allowing convenient data post-processing and simple interfacing to standard electrical components. With field-effect transistor (FET) based sensors, the transducing principle relies on direct detection of interfacial charge allowing detection of various ions and charged macromolecules.
This thesis investigates FET based sensors for biological and chemical sensing. First, an ion-sensitive floating gate FET (ISFGFET) structure is studied and modeled. The proposed model reveals novel abilities of the structure not found in conventional ion-sensitive FETs (ISFETs). With IS-FGFET, we can simultaneously optimize the transistor operating point and modulate the charging of the surface and the ionic screening layer via the field effect. This control is predicted to allow reduced electric double layer screening as well as the possibility to enhance charged molecule attachment to the sensing surface. The model can predict sensor characteristic curves in pH sensing in absolute terms and allows any potential to be computed in the sensor including the electrical part and the electrolyte solution. Furthermore, a compact ISFGFET variant is merged into electric circuit simulator, which allows it to be simulated as a standard electrical component with electrical simulations tools of high computational efficiency, and allows simple modifications such as addition of parasitic elements, temperature effects, or even temporal drifts.
Next, another transistor based configuration, the extended-gate ISFET is studied. The simplicity of the proposed configuration allows a universal potentiometric approach where a wide variety of chemical and biological sensors can be constructed. The design philosophy for this sensing structure is to use the shelf electric components and standard electric manufacturing processes. Such an extended-gate structure is beneficial since the dry electronics can be completely separated from the wet sensing environment. The extended-gate allows simple functionalization towards chemical and biological sensing. A proof-of-concept of this structure was verified through organo modified gold platforms with ion-selective membranes. A comparison with standard open-circuit potentiometry reveals that the sensing elements in a disposable sensing platform arrays provide comparable performance to traditional electrodes. Finally, a universal battery operated hand-held electrical readout device is designed for multiplexed detection of the disposable sensors with wireless smartphone data plotting, control, and storage.
Organic polymers play an important role in the interfacial properties of sensors studied in this thesis. The polymer coating is attractive in chemical sensing because of its redox sensitivity, bio-immobilization capability, ion-to-electron transducing capability, and applicability, for example via a simple low-cost drop-casting. This structure simplifies the design of the sensor substantially and the coating increases the amount of possible target applications.Siirretty Doriast
Detection principles of biological and chemical FET sensors
The seminal importance of detecting ions and molecules for point-of-care tests has driven the search for more sensitive, specific, and robust sensors. Electronic detection holds promise for future miniaturized in-situ applications and can be integrated into existing electronic manufacturing processes and technology. The resulting small devices will be inherently well suited for multiplexed and parallel detection. In this review, different field-effect transistor (FET) structures and detection principles are discussed, including label-free and indirect detection mechanisms. The fundamental detection principle governing every potentiometric sensor is introduced, and different state-of-the-art FET sensor structures are reviewed. This is followed by an analysis of electrolyte interfaces and their influence on sensor operation. Finally, the fundamentals of different detection mechanisms are reviewed and some detection schemes are discussed. In the conclusion, current commercial efforts are briefly considered
Personalization of Affective Models to Enable Neuropsychiatric Digital Precision Health Interventions: A Feasibility Study
Mobile digital therapeutics for autism spectrum disorder (ASD) often target
emotion recognition and evocation, which is a challenge for children with ASD.
While such mobile applications often use computer vision machine learning (ML)
models to guide the adaptive nature of the digital intervention, a single model
is usually deployed and applied to all children. Here, we explore the potential
of model personalization, or training a single emotion recognition model per
person, to improve the performance of these underlying emotion recognition
models used to guide digital health therapies for children with ASD. We
conducted experiments on the Emognition dataset, a video dataset of human
subjects evoking a series of emotions. For a subset of 10 individuals in the
dataset with a sufficient representation of at least two ground truth emotion
labels, we trained a personalized version of three classical ML models on a set
of 51 features extracted from each video frame. We measured the importance of
each facial feature for all personalized models and observed differing ranked
lists of top features across subjects, motivating the need for model
personalization. We then compared the personalized models against a generalized
model trained using data from all 10 participants. The mean F1-scores achieved
by the personalized models were 90.48%, 92.66%, and 86.40%, respectively. By
contrast, the mean F1-scores reached by non-personalized models trained on
different human subjects and evaluated using the same test set were 88.55%,
91.78%, and 80.42%, respectively. The personalized models outperformed the
generalized models for 7 out of 10 participants. PCA analyses on the remaining
3 participants revealed relatively facial configuration differences between
emotion labels within each subject, suggesting that personalized ML will fail
when the variation among data points within a subjects data is too low
Uncertainty of Radiometer Calibration Loads and Its Impact on Radiometric Measurements
We present an uncertainty analysis of radiometer calibration.
The procedure can be used to determine the uncertainty
in the nominal brightness temperature of the unknown scene. A
total power radiometer requires frequent calibration with known
reference loads that are connected to the radiometer. Our analysis
includes uncertainties from the radiometer calibration loads and
from the connecting network (CN) that is required to multiplex
calibration loads and scene to the radiometer input. We show the
design and analysis of three calibration loads and how their uncertainties
propagate from load terminals to the radiometer calibration
plane and to the scene. All three loads, including a cryogenic
load, are simple, inexpensive, and show great stability and
accuracy. We give an uncertainty calculation example for our three
calibration loads and for the CN. We validate our model and the
long-term stability of the loads through measurements. The analysis
is done at 52 GHz, but the analysis and the construction of the
loads are generic and easily scalable to other frequencies.</div
Radiometric Resolution Analysis and a Simulation Model
Total power radiometer has a simple configuration and the best theoretical resolution. Gain fluctuations and calibration errors, however, can induce severe errors in the solved scene brightness temperature. To estimate the overall radiometer performance we present a numerical simulation tool that can be used to determine the radiometric resolution. Our model considers three main components that degrade the radiometric resolution: thermal noise, 1/f noise and calibration errors. These error sources have long been known to exist, but comprehensive models able to account all these effects quantitatively and accurately in a practical manner have been missing. We have developed a radiometer simulation model that is able to produce radiometer signals that incorporate realistic radiometer effects that show up as noise and other errors in the radiometer video signal. Our simulation tool integrates the fundamental radiometer theories numerically and allows the investigation of different calibration schemes and receiver topologies. The model can be used as a guide for design and optimization as well as for verification of the radiometer performance. Moreover, it can be extended to a much larger and more complex radiometer systems allowing better system level performance estimation and optimization with minimal bread-board implementations. The model mimics real radiometer video data and thus the complete data analysis pipeline can be developed and verified before the real video data is available. In this paper, the model has been applied to a total power radiometer operating in the 52 GHz frequency range.</p
Advances in non-invasive blood pressure measurement techniques
Hypertension, or elevated blood pressure (BP), is a marker for many cardiovascular diseases and can lead to life threatening conditions such as heart failure, coronary artery disease and stroke. Several techniques have recently been proposed and investigated for non-invasive BP monitoring. The increasing desire for telemonitoring solutions that allow patients to manage their own conditions from home has accelerated the development of new BP monitoring techniques. In this review, we present the recent progress in non-invasive blood pressure monitoring solutions emphasizing clinical validation and trade-offs between available techniques. We introduce the current BP measurement techniques with their underlying operating principles. New promising proof-of-concept studies are presented and recent modeling and machine learning approaches for improved BP estimation are summarized. This aids discussions on how new BP monitors should evaluated in order to bring forth new home monitoring solutions in wearable form factor. Finally, we discuss on unresolved challenges in making convenient, reliable and validated BP monitoring solutions.</p
Cardiac monitoring of dogs via smartphone mechanocardiography : a feasibility study
Abstract
Background
In the context of monitoring dogs, usually, accelerometers have been used to measure the dog’s movement activity. Here, we study another application of the accelerometers (and gyroscopes)—seismocardiography (SCG) and gyrocardiography (GCG)—to monitor the dog’s heart. Together, 3-axis SCG and 3-axis GCG constitute of 6-axis mechanocardiography (MCG), which is inbuilt to most modern smartphones. Thus, the objective of this study is to assess the feasibility of using a smartphone-only solution to studying dog’s heart.
Methods
A clinical trial (CT) was conducted at the University Small Animal Hospital, University of Helsinki, Finland. 14 dogs (3 breeds) including 18 measurements (about one half of all) where the dog’s status was such that it was still and not panting were further selected for the heart rate (HR) analysis (each signal with a duration of 1 min). The measurement device in the CT was a custom Holter monitor including synchronized 6-axis MCG and ECG. In addition, 16 dogs (9 breeds, one mixed-breed) were measured at home settings by the dog owners themselves using Sony Xperia Android smartphone sensor to further validate the applicability of the method.
Results
The developed algorithm was able to select 10 good-quality signals from the 18 CT measurements, and for 7 of these, the automated algorithm was able to detect HR with deviation below or equal to 5 bpm (compared to ECG). Further visual analysis verified that, for approximately half of the dogs, the signal quality at home environment was sufficient for HR extraction at least in some signal locations, while the motion artifacts due to dog’s movements are the main challenges of the method.
Conclusion
With improved data analysis techniques for managing noisy measurements, the proposed approach could be useful in home use. The advantage of the method is that it can operate as a stand-alone application without requiring any extra equipment (such as smart collar or ECG patch)