<p>In recent years, the use of alcohol and psychoactive drugs in combination withdriving has recieved increased attention. The lack of in-vehicle devices capable ofdetecting recent drug consumption and the difficulties associated with the breathbasedalcolocks in use today makes it interesting to investigate methods that areable to non-invasivelly measure analytes directly in the blood.</p><p>The assignment of this project, commissioned by Volvo Technology Corporationand Volvo Car Corporation, is to map substances that constitute a possible threatto traffic safety, identify suitable detection markers as a proof of administrationof these substances, and study possible non-invasive techniques to detect thesemarkers. The objective is to present for Volvo if and how to continue evaluatingand developing a non-invasive detection device.</p><p>The project has been carried out by performing an extensive literature study and averification experiment. From the literature review, a number of substances affectingdriving performance could be identified, and a metabolic study was performedfor each drug to map suitable biomarkers. Furthermore, two potential techniquesfor non-invasive detection, near-infrared Raman spectroscopy and near-infraredspectroscopy, were found and evaluated. The experiment was conducted usingnear-infrared Raman spectroscopy, with the aim of investigating the sensitivityand linearity of the method for ethanol detection.</p><p>Based on the theoretical evaluation, both near-infrared Raman spectroscopy andnear-infrared spectroscopy are expected to have potential for non-invasive detectionof ethanol. The experiment further proved the theoretical conclusionsmade for near-infrared Raman spectroscopy. However, neither of the techniquesis thought to have potential for drug detection.Altogether, we believe that non-invasive ethanol detection is possible, but suggestfurther experiments in order to determine which technique to be preferred.</p>

In recent years, the use of alcohol and psychoactive drugs in combination withdriving has recieved increased attention. The lack of in-vehicle devices capable ofdetecting recent drug consumption and the difficulties associated with the breathbasedalcolocks in use today makes it interesting to investigate methods that areable to non-invasivelly measure analytes directly in the blood. The assignment of this project, commissioned by Volvo Technology Corporationand Volvo Car Corporation, is to map substances that constitute a possible threatto traffic safety, identify suitable detection markers as a proof of administrationof these substances, and study possible non-invasive techniques to detect thesemarkers. The objective is to present for Volvo if and how to continue evaluatingand developing a non-invasive detection device. The project has been carried out by performing an extensive literature study and averification experiment. From the literature review, a number of substances affectingdriving performance could be identified, and a metabolic study was performedfor each drug to map suitable biomarkers. Furthermore, two potential techniquesfor non-invasive detection, near-infrared Raman spectroscopy and near-infraredspectroscopy, were found and evaluated. The experiment was conducted usingnear-infrared Raman spectroscopy, with the aim of investigating the sensitivityand linearity of the method for ethanol detection. Based on the theoretical evaluation, both near-infrared Raman spectroscopy andnear-infrared spectroscopy are expected to have potential for non-invasive detectionof ethanol. The experiment further proved the theoretical conclusionsmade for near-infrared Raman spectroscopy. However, neither of the techniquesis thought to have potential for drug detection.Altogether, we believe that non-invasive ethanol detection is possible, but suggestfurther experiments in order to determine which technique to be preferred.

Conventional fish monitoring requires considerable investments of equipment and labour, and often harmful and potentially fatal techniques. Emerging methods allow detection of aquatic animals by collecting water and extracting DNA that has been shed to the environment (eDNA). Present knowledge gaps in the field include minimum densities necessary for consistent detection, and persistence of eDNA after a target species has left a site. I conducted three experiments at a salmon hatchery in British Columbia to address these knowledge gaps. Water samples were taken from flow-through tanks with juvenile Coho Salmon densities ranging from 38.0g/1000L to 0.6g/1000L. To simulate field surveys in recently abandoned habitats, I sampled water from tanks after removing fish, at flow-through volumes ranging from 20,000L to 1,000,000L. Post removal sampling occurred starting at one hour and ending after just over four days of flow-through time. Water samples from tanks containing one or more fish tested positive for Coho DNA at least 70% of the time, increasing at higher densities. Samples taken after removing the fish had detection probability of 75% at flow-through volume of 40,000L. Detection failed at flow-through volumes greater than 80,000L. In stream samples, all sites with Coho or salmonid presence confirmed by conventional trapping also tested positive for target species’ eDNA. Two sites tested positive for Coho eDNA where conventional methods failed, indicating a possible higher sensitivity of eDNA sampling. I also mapped the distribution of juvenile Coho Salmon through multiple tributaries of a productive salmon system with conventional and eDNA detections. This study improves on an emerging method with a new species by addressing existing uncertainties regarding eDNA detection threshold, and signal persistence through dilution in a simulated stream pool habitat. It also demonstrates that eDNA methods can be used to assess coastal streams for presence of juvenile and resident salmonid fishes. / Graduate

This thesis descries the evolution and testing of a fully portable, inductive loop vehicle counter system. As a component of the NFS Embedded Distributed Simulation for Transportation System Management project, the system's cellular modem transmits real-time data to servers at Georgia Institute of Technology. From there, the data can be fed into simulations predicting travel behavior. Researchers revised both the detector circuit, and the temporary, reusable loop pad several times over multiple rounds of field testing. The final tested version of this system demonstrates the efficacy of uncommonly small inductive loops. When paired with a reliable data transmission channel, the system was shown to capture nearly 96% of actual through traffic.

The waste of building energy consumption is a major challenge in the world. And the real-time detection of human thermal comfort is an effective way to meet this issue. As mentioned in name, it means to detect the human’s comfort level in real-time and non-invasively. However, due to the various factors such as individual difference of thermal comfort, elements related to climatic (temperature, humidity, illumination, etc.) and so on, there is still a long way to implement this strategy in real life. From another perspective, the current HVAC (heating, ventilating and air-conditioning) systems cannot provide flexible interaction channels to adjust atmosphere, and naturally fails to satisfy requirements of users. All of them indicate the necessity to develop a detection method for human thermal comfort. In this paper, a non-invasion detection method toward human thermal comfort is proposed from two perspectives: macro human postures and skin textures. In posture part, OpenPose is used for analyzing the position coordinates of human body key points’ in images, for example, elbow, knee, and hipbone, etc. And the results of analyzing would be interpreted from the term of thermal comfort. In skin textures, deep neural network is used to predict the temperature of human skins via images. Based on Fanger’s theory of thermal comfort, the results of both parts are satisfying: subjects’ postures can be captured and interpreted into different thermal comfort level: hot, cold and comfort. And the absolute error of prediction from neurons network is less than 0.125 degrees centigrade which is the equipment error of thermometer used in data acquisition. With the solution proposed by this paper, it is promising to non-invasively detect the thermal comfort level of users from postures and skin textures. Finally, theconclusion and future work are discussed in final chapter. / Slöseriet med att bygga energiförbrukningen är en stor utmaning i världen. Ochdetektering av mänsklig termisk komfort i realtid är ett effektivt sätt att lösaproblemet. Som nämns i namn betyder det att detektera människans komfortnivå i realtid och icke-invasivt. På grund av de olika faktorerna som individuell skillnad i termisk komfort, är emellertid faktorer som är relaterade till klimat (temperatur, luftfuktighet, belysning etc.) det fortfarande en lång väg att implementera denna strategi i verkligheten. Från ett annat perspektiv kan nuvarande system för uppvärmning, ventilation och luftkonditionering inte tillhandahålla flexibla interaktionskanaler för att anpassa atmosfären och naturligtvis misslyckas till nöjda krav från användarna. Alla indikerar nödvändigheten av att utveckla en detekteringsmetod för mänsklig termisk komfort. I detta dokument föreslås en ickeinvasion detekteringsmetod mot mänsklig termisk komfort från två perspektiv: makro mänskliga hållningar och hudtexturer. I hållningspartiet används OpenPose för att analysera positionskoordinaterna för kroppens huvudpunkter i bilder, till exempel armbåge, knä och höftben osv. Och resultaten av analysen skulle tolkas från termen av termisk komfort. I hudtexturer används djupt neuralt nätverk för att förutse temperaturen på mänskliga skinn via bilder. Baserat på Fangers teorin om värmekomfort är resultaten av båda delarna tillfredsställande: subjektens hållningar kan fångas och tolkas till olika värmekomfortnivåer: varm, kall och komfort. Och det absoluta felet av prediktering från neuronnätverket är mindre än 0,125 grader Celsius, vilket är utrustningsfelet hos termometern som används vid datainsamling. Med lösningar i detta papper är det lovande att detektera användarens värmekomfortnivå fritt från invändningar och hudtexturer. Slutligen diskuteras slutsatserna och detframtida arbetet i sista kapitlet.

Fusarium-Infektionen sind ein ernstes Problem im Weizenanbau, da die ausgeschiedenen Mykotoxine gesundheitsschädlich sind. Die Krankheitssymptome sind durch bildanalytische Methoden im Vorfeld der Ernte detektierbar, so dass befallene Partien getrennt geerntet werden könnten. Künstlich mit Fusarium culmorum infizierte Weizenpflanzen wurden mittels Chlorophyllfluoreszenz- und hyperspektraler Bildanalyse in Zeitreihenversuchen analysiert. Bei den Chlorophyllfluoreszenz-Bildanalysen wurde die photosynthetische Effizienz (Fv/Fm) und deren räumliche Ungleichverteilung genutzt, um den Effekt des Krankheitsgrades auf die photosynthetische Aktivität zu ermitteln. Mit dieser Methode ist eine sehr frühzeitige Erkennung möglich, da eine Verringerung der photosynthetischen Effizienz bei den kranken Ähren zwischen dem 6. und 11. Tag nach Inokulation festgestellt wurde. Der Befallsgrad korreliert mit der photosynthetischen Effizienz. Die Berechnung des kumulativen Fv/Fm bei 0,3 führte zu einer sehr effektiven und genauen Erkennung der Partiellen Taubährigkeit ab einem Befallsgrad von mindestens 5% und einer Differenzierungsgenauigkeit von 10%. Die Chlorophyllfluoreszenz-Bildanalyse war unter Freilandbedingungen bei Einhaltung der Rahmenbedingungen zur Fusarium-Erkennung ebenfalls möglich, wenn auch mit schlechteren Erkennungsraten. Die Aufnahmen im Labor mit einem hyperspektralen Bildanalysesystem im Spektralbereich von 400-1000 nm zeigten Unterschiede in distinkten Wellenlängenbereichen und lassen die Erkennung kranker Ähren in einem Zeitfenster von BBCH 71 bis BBCH 85 zu. Die bildanalytische Klassifizierung mittels des „Spectral Angle Mapper“ liefert gute Ergebnisse, ist aber sehr zeitaufwändig. Alternativ dazu nutzt der neu erstellte head blight-Index (HBI) die spektralen Unterschiede im Bereich 665-675 nm und 550-560 nm und kann eine feldtaugliche Klassifizierungsmöglichkeit zur Erkennung von Partieller Taubährigkeit sein. / Fusarium infections are serious problems in wheat production. Mycotoxins, synthesised by the fungi, are toxic and harmful in both human and animal nutrition. The symptoms of this so-called head blight disease caused by Fusarium spp. are recognizable by various image analysis methods prior to harvest. Using this information, farmer could differently utilize affected cereals, if necessary. Healthy and artificially Fusarium culmorum-infected wheat plants were analyzed with a chlorophyll fluorescence and hyper-spectral imaging system in time series. With chlorophyll fluorescence imaging (CFA), the potential maximum photochemical efficiency (Fv/Fm) and its spatially variable distribution was analyzed to determine the interactions between the degree of disease and the photosynthetic activity. By means of this method, early recognition was reliably achieved by a decreased photochemical efficiency in diseased ears between 6th and 11th day after inoculation. The degree of disease correlated with photochemical efficiency. At a degree of Fusarium infection of 5% and higher, calculation of the cumulative Fv/Fm at 0.3 enabled a very effective and precise determination of Fusarium head blight with a differentiation accuracy of at least 10%. Though less effective, CFA successfully detected head blight under outdoor conditions, if some boundary conditions (e.g. no direct solar irradiation) were observed. In the laboratory, a hyperspectral imaging system (wave length range 400 to 1000 nm) indicated specific spectral differences between affected and unaffected wheat ears. These differences allowed head blight recognition during BBCH 71 to BBCH 85. Imaging classification with the “Spectral Angle Mapper” method achieved good results; it is, however, very time-consuming. Alternatively, the newly derived head blight index (HBI), using spectral differences in the wave length ranges of 665 to 675 nm and 550 to 560 nm, can be a suitable outdoor classification method to evaluate head blight.