While interaction with computers used to be dominated by mice and keyboards, new types of sensors now allow users to interact through touch, speech, or using their whole body in 3D space. These new interaction modalities are often referred to as "natural user interfaces" or "NUIs." While 2D NUIs have experienced major success on billions of mobile touch devices sold, 3D NUI systems have so far been unable to deliver a mobile form factor, mainly due to their use of cameras. The fact that cameras require a certain distance from the capture volume has prevented 3D NUI systems from reaching the flat form factor mobile users expect. In this dissertation, we address this issue by sensing 3D input using flat 2D sensors. The systems we present observe the input from 3D objects as 2D imprints upon physical contact. By sampling these imprints at very high resolutions, we obtain the objects' textures. In some cases, a texture uniquely identifies a biometric feature, such as the user's fingerprint. In other cases, an imprint stems from the user's clothing, such as when walking on multitouch floors. By analyzing from which part of the 3D object the 2D imprint results, we reconstruct the object's pose in 3D space. While our main contribution is a general approach to sensing 3D input on 2D sensors upon physical contact, we also demonstrate three applications of our approach. (1) We present high-accuracy touch devices that allow users to reliably touch targets that are a third of the size of those on current touch devices. We show that different users and 3D finger poses systematically affect touch sensing, which current devices perceive as random input noise. We introduce a model for touch that compensates for this systematic effect by deriving the 3D finger pose and the user's identity from each touch imprint. We then investigate this systematic effect in detail and explore how users conceptually touch targets. Our findings indicate that users aim by aligning visual features of their fingers with the target. We present a visual model for touch input that eliminates virtually all systematic effects on touch accuracy. (2) From each touch, we identify users biometrically by analyzing their fingerprints. Our prototype Fiberio integrates fingerprint scanning and a display into the same flat surface, solving a long-standing problem in human-computer interaction: secure authentication on touchscreens. Sensing 3D input and authenticating users upon touch allows Fiberio to implement a variety of applications that traditionally require the bulky setups of current 3D NUI systems. (3) To demonstrate the versatility of 3D reconstruction on larger touch surfaces, we present a high-resolution pressure-sensitive floor that resolves the texture of objects upon touch. Using the same principles as before, our system GravitySpace analyzes all imprints and identifies users based on their shoe soles, detects furniture, and enables accurate touch input using feet. By classifying all imprints, GravitySpace detects the users' body parts that are in contact with the floor and then reconstructs their 3D body poses using inverse kinematics. GravitySpace thus enables a range of applications for future 3D NUI systems based on a flat sensor, such as smart rooms in future homes. We conclude this dissertation by projecting into the future of mobile devices. Focusing on the mobility aspect of our work, we explore how NUI devices may one day augment users directly in the form of implanted devices. / Die Interaktion mit Computern war in den letzten vierzig Jahren stark von Tastatur und Maus geprägt. Neue Arten von Sensoren ermöglichen Computern nun, Eingaben durch Berührungs-, Sprach- oder 3D-Gestensensoren zu erkennen. Solch neuartige Formen der Interaktion werden häufig unter dem Begriff "natürliche Benutzungsschnittstellen" bzw. "NUIs" (englisch natural user interfaces) zusammengefasst. 2D-NUIs ist vor allem auf Mobilgeräten ein Durchbruch gelungen; über eine Milliarde solcher Geräte lassen sich durch Berührungseingaben bedienen. 3D-NUIs haben sich jedoch bisher nicht auf mobilen Plattformen durchsetzen können, da sie Nutzereingaben vorrangig mit Kameras aufzeichnen. Da Kameras Bilder jedoch erst ab einem gewissen Abstand auflösen können, eignen sie sich nicht als Sensor in einer mobilen Plattform. In dieser Arbeit lösen wir dieses Problem mit Hilfe von 2D-Sensoren, von deren Eingaben wir 3D-Informationen rekonstruieren. Unsere Prototypen zeichnen dabei die 2D-Abdrücke der Objekte, die den Sensor berühren, mit hoher Auflösung auf. Aus diesen Abdrücken leiten sie dann die Textur der Objekte ab. Anhand der Stelle der Objektoberfläche, die den Sensor berührt, rekonstruieren unsere Prototypen schließlich die 3D-Ausrichtung des jeweiligen Objektes. Neben unserem Hauptbeitrag der 3D-Rekonstruktion stellen wir drei Anwendungen unserer Methode vor. (1) Wir präsentieren Geräte, die Berührungseingaben dreimal genauer als existierende Geräte messen und damit Nutzern ermöglichen, dreimal kleinere Ziele zuverlässig mit dem Finger auszuwählen. Wir zeigen dabei, dass sowohl die Haltung des Fingers als auch der Benutzer selbst einen systematischen Einfluss auf die vom Sensor gemessene Position ausübt. Da existierende Geräte weder die Haltung des Fingers noch den Benutzer erkennen, nehmen sie solche Variationen als Eingabeungenauigkeit wahr. Wir stellen ein Modell für Berührungseingabe vor, das diese beiden Faktoren integriert, um damit die gemessenen Eingabepositionen zu präzisieren. Anschließend untersuchen wir, welches mentale Modell Nutzer beim Berühren kleiner Ziele mit dem Finger anwenden. Unsere Ergebnisse deuten auf ein visuelles Modell hin, demzufolge Benutzer Merkmale auf der Oberfläche ihres Fingers an einem Ziel ausrichten. Bei der Analyse von Berührungseingaben mit diesem Modell verschwinden nahezu alle zuvor von uns beobachteten systematischen Effekte. (2) Unsere Prototypen identifizieren Nutzer anhand der biometrischen Merkmale von Fingerabdrücken. Unser Prototyp Fiberio integriert dabei einen Fingerabdruckscanner und einen Bildschirm in die selbe Oberfläche und löst somit das seit Langem bestehende Problem der sicheren Authentifizierung auf Berührungsbildschirmen. Gemeinsam mit der 3D-Rekonstruktion von Eingaben ermöglicht diese Fähigkeit Fiberio, eine Reihe von Anwendungen zu implementieren, die bisher den sperrigen Aufbau aktueller 3D-NUI-Systeme voraussetzten. (3) Um die Flexibilität unserer Methode zu zeigen, implementieren wir sie auf einem großen, berührungsempfindlichen Fußboden, der Objekttexturen bei der Eingabe ebenfalls mit hoher Auflösung aufzeichnet. Ähnlich wie zuvor analysiert unser System GravitySpace diese Abdrücke, um Nutzer anhand ihrer Schuhsolen zu identifizieren, Möbelstücke auf dem Boden zu erkennen und Nutzern präzise Eingaben mittels ihrer Schuhe zu ermöglichen. Indem GravitySpace alle Abdrücke klassifiziert, erkennt das System die Körperteile der Benutzer, die sich in Kontakt mit dem Boden befinden. Aus der Anordnung dieser Kontakte schließt GravitySpace dann auf die Körperhaltungen aller Benutzer in 3D. GravitySpace hat daher das Potenzial, Anwendungen für zukünftige 3D-NUI-Systeme auf einer flachen Oberfläche zu implementieren, wie zum Beispiel in zukünftigen intelligenten Wohnungen. Wie schließen diese Arbeit mit einem Ausblick auf zukünftige interaktive Geräte. Dabei konzentrieren wir uns auf den Mobilitätsaspekt aktueller Entwicklungen und beleuchten, wie zukünftige mobile NUI-Geräte Nutzer in Form implantierter Geräte direkt unterstützen können.

The Vehicular Ad-hoc Network (VANET) has been studied in many fields since it has the ability to provide a variety of services, such as detecting oncoming collisions and providing warning signals to alert the driver. The services provided by VANET are often based on collaboration among vehicles that are equipped with relatively simple motion sensors and GPS units. Awareness of its precise location is vital to every vehicle in VANET so that it can provide accurate data to its peers. Currently, typical localization techniques integrate GPS receiver data and measurements of the vehicle’s motion. However, when the vehicle passes through an environment that creates a multipath effect, these techniques fail to produce the high localization accuracy that they attain in open environments. Unfortunately, vehicles often travel in environments that cause a multipath effect, such as areas with high buildings, trees, or tunnels. The goal of this research is to minimize the multipath effect with respect to the localization accuracy of vehicles in VANET. The proposed technique first detects whether there is a noise in the vehicle location estimate that is caused by the multipath effect using neural network technique. It next takes advantage of the communications among the VANET vehicles in order to obtain more information from the vehicle’s neighbours, such as distances from target vehicle and their location estimates. The proposed technique integrates all these pieces of information with the vehicle’s own data and applies optimization techniques in order to minimize the location estimate error. The new techniques presented in this thesis decrease the error in the location estimate by 53% in the best cases, and in the worst case produce almost the same error in the location estimate as the traditional technique. Moreover, the simulation results show that 60% of the vehicles in VANET decrease the error in their location estimates by more than 13.8%.

This thesis investigates the developpment of gender agreement in determiners and adjectives in the spontaneous speech of L2 French by five groups of Swedish learners: beginners at the university, secondary school students, university students, teacher candidates and PhD students. Different types of determiners are examined, such as definite and the indefinite articles. Adjectival agreement is studied in different positions in relation to the noun, such as the attributive anteposition, the attributive postposition and the predicative position. The aim is to identify the developmental sequence of gender agreement through a longitudinal study of learners at different levels of acquisition. The analysis is based on spoken language, i.e. 81 interviews belonging to the InterFra-corpus, Stockholm University. Our data also includes 8 oral productions from a control group of native speakers. The study is in three parts: one for the agreement between determiners and nouns, another for the agreement between adjectives and nouns and, finally, a study considering agreement between all three items within the noun phrase, i.e. determiner, noun and adjective (Det-N-Adj). A sequence of acquisition for gender agreement on determiners and adjectives is proposed based on the productions of four learner groups and compared to the six developmental stages suggested by Bartning and Schlyter (2004). Results have showed that there is an acquisition order of gender agreement in different parts of the nominal phrase, according to the type of determiner and the positions of the adjective. A qualitative analysis has shown a random use of gender agreement on determiners and some nouns are used with both genders on the determiner. Also, the type-token ratio is very low at the beginning of the acquisition, which partly explains the high accuracy rate (100 %). The study considering agreement between all three constituents within the noun phrase revealed that advanced learners have higher accuracy rate for gender agreement on adjectives within the noun phrase with the presence of a determiner that marks gender distinction (i.e. a non-elided, singular determiner). Results also showed that the feminine form of the adjectives remains difficult at higher acquisitional levels.

The Vehicular Ad-hoc Network (VANET) has been studied in many fields since it has the ability to provide a variety of services, such as detecting oncoming collisions and providing warning signals to alert the driver. The services provided by VANET are often based on collaboration among vehicles that are equipped with relatively simple motion sensors and GPS units. Awareness of its precise location is vital to every vehicle in VANET so that it can provide accurate data to its peers. Currently, typical localization techniques integrate GPS receiver data and measurements of the vehicle’s motion. However, when the vehicle passes through an environment that creates a multipath effect, these techniques fail to produce the high localization accuracy that they attain in open environments. Unfortunately, vehicles often travel in environments that cause a multipath effect, such as areas with high buildings, trees, or tunnels. The goal of this research is to minimize the multipath effect with respect to the localization accuracy of vehicles in VANET. The proposed technique first detects whether there is a noise in the vehicle location estimate that is caused by the multipath effect using neural network technique. It next takes advantage of the communications among the VANET vehicles in order to obtain more information from the vehicle’s neighbours, such as distances from target vehicle and their location estimates. The proposed technique integrates all these pieces of information with the vehicle’s own data and applies optimization techniques in order to minimize the location estimate error. The new techniques presented in this thesis decrease the error in the location estimate by 53% in the best cases, and in the worst case produce almost the same error in the location estimate as the traditional technique. Moreover, the simulation results show that 60% of the vehicles in VANET decrease the error in their location estimates by more than 13.8%.

Accurate forecasting is a real problem at many companies and that includes Alfa Laval in Lund. Alfa Laval experiences problems forecasting for future raw material demand. Management is aware that the forecasting methods used today can be improved or replaced by others. A change could lead to better forecasting accuracy and lower errors which means less inventory, shorter cycle times and better customer service at lower costs. The purpose of this study is to analyze Alfa Laval’s current forecasting models for demand of raw material used for pressed plates, and then determine if other models are better suited for taking into consideration trends and seasonal variation.

Video image detection system (VDS) equipment provides real-time traffic data for monitored highways directly to the traffic management center (TMC) of the Georgia Department of Transportation. However, at any given time, approximately 30 to 35% of the 1,600 camera stations (STNs) fail to work properly. The main reasons for malfunctions in the VDS system include long term road construction activity and operational limitations. Thus, providing alternative data sources for offline VDS stations and developing tools that can help detect problems with VDS stations can facilitate the successful operation of the TMC. To estimate the travel speed of non-working STNs, this research examined global positioning system (GPS) data from vehicles using the ATMS-monitored freeway system as a potential alternative measure to VDS. The goal of this study is to compare VDS speed data for the estimation of the travel speed on freeways with GPS-equipped vehicle trip data, and to assess the differences between these measurements as a potential function of traffic and roadway conditions, environmental, conditions, and driver/vehicle characteristics. The difference between GPS and VDS speeds is affected by various factors such as congestion level (expressed as level of service), onroad truck percentage, facility design (number of lanes and freeway sub-type), posted speed limit, weather, daylight, and time of day. The relationship between monitored speed difference and congestion level was particularly large and was observed to interact with most other factors. Classification and regression tree (CART) analysis results indicated that driver age was the most relevant variable in explaining variation for the southbound of freeway dataset and freeway sub-type, speed limit, driver age, and number of lane were the most influential variables for the northbound of freeway dataset. The combination of several variables had significant contribution in the reduction of the deviation for both the northbound and the southbound dataset. Although this study identifies potential relationships between speed difference and various factors, the results of the CART analysis should be considered with the driver sample size to yield statistically significant results. Expanded sampling with larger number of drivers would enrich this study results.

The purpose of this thesis was to determine whether practice-induced adjustments and retention of a goal directed isometric motor accuracy task were similar between ipsilateral upper and lower limb and whether there is an ipsilateral transfer between upper and lower limbs. In addition, this thesis project aimed to determine whether motor output variability and the activity of the involved agonist and antagonist muscles could predict any of the above stated changes. Sixteen young adults (8 men, 8 women; 22.1 or - 2.1 years) performed 80 trials of goal directed isometric contractions that involved accurately matching a target force of 25% MVC in 200 ms, either with the upper limb or the lower limb followed by the other limb. After an interval of 48 hours, 10 trials similar to the practice trials were performed to examine retention. Feedback of performance was provided in the form of a force-time trajectory along with numerical error values for force and time on each trial. End-point error was quantified as the absolute deviation from the targeted force and time. Motor output variability was quantified as the SD of force, SD of time to peak force and SD of force trajectory. The practice-induced adjustments for force and time endpoint accuracy were similar for the two limbs, however, two days later, retention of the force accuracy was better with the upper limb compared with the lower limb. Practice-induced reduction and practice-to-retention increase in force and time endpoint error were predicted by respective changes in peak force and time to peak force trial-to-trial variability for both limbs. In addition, the changes in accuracy were predicted by the changes in the activity of the involved agonist and antagonist muscles. Nonetheless, the changes in muscle activity differed between the two limbs. The adjustments in muscle activity were also different during the practice session despite the fact that the rate of improvement was similar for the two limbs. Finally, there was an asymmetric transfer of force accuracy from the lower limb to the ipsilateral upper limb, which was associated with the changes in motor output variability. The upper limb, which is inherently less variable as compared to the lower limb, may have retained the task better due to the formation of a stronger muscle synergy (or stronger internal model) to perform the contractions with accuracy. The lower limb, on the other hand may have formed a weaker internal model due to the greater interference from amplified signal-dependent noise (motor output variability) or an alternative motor plan, which may have been concerned primarily with the minimization of motor output variability instead of formation of a muscle synergy to perform the contractions accurately.

Math skills are essential to daily life, impacting a person?s ability to function at home, work, and in the community. Although reading has been the focus in recent years, many students struggle in math. The inability to master math calculation and problem solving has contributed to the rising incidence of student failure, referrals for special education evaluations, and dropout rates. Studies have shown that curriculum based measurement (CBM) is a well-established tool for formative assessment, and could potentially be used for other purposes such as a prediction of state standards test scores, however to date there are limited validity studies between mathematics CBM and standard-based assessment. This research examined a brief assessment that reported to be aligned to national curriculum standards in order to predict student performance on state standards-based mathematics curriculum, identify students at-risk of failure, and plan instruction. Evidence was gathered on the System to Enhance Educational Performance Grade 3 Focal Mathematics Assessment Instrument (STEEP3M) as a formative, universal screener. Using a sample of 337 students and 22 instructional staff, four qualities of the STEEP3M were examined: a) internal consistency and criterion related validity (concurrent); b) screening students for a multi-tiered decision-making process; c) utility for instructional planning and intervention recommendations; and d) efficiency of administration, scoring, and reporting results which were the basis of the four research questions for this study. Several optimized solutions were generated from Receiver Operator Curve (ROC) statistical analysis; however none demonstrated that the STEEP3M maximized either sensitivity or specificity. In semi-structured interviews teachers reported that they would consider using the STEEP3M, however only as a part of a decision-making rubric along with other measures. Further, teachers indicated that lessons are developed before the school year starts, more in response to the sequence of the state standards than to students? needs. While the STEEP3M was sufficiently long enough for high-stakes or criterion-referenced decisions, this study found that the test does not provide sufficient diagnostic information for multi-tiered decision-making for intervention or instructional planning. Although practical and efficient to administer, the conclusions of this study show the test does not provide sufficient information on the content domain and does not accurately classify students in need of assistance.

Precision control of translational motion is vital for many CNC machine tools as the motion of the machinery affects the dimensional tolerance of the manufactured goods. However, the direct measurement along with the accurate motion control of machine usually requires relatively expensive sensors i.e. potentiometers, linear scales, laser interferometers. Hence, this study attempts to develop reference models utilizing low-cost sensors (i.e. rotary encoders) for accurate position estimation. First, an indirect measurement performance is investigated on a Timing Belt driven carriage by a DC Motor with a backlash included Gearbox head. An advanced interpolated technique is proposed to compensate the position errors while using indirect measurement to reduce the total cost. Then, a similar study was realized with a ball screw driven system. Next, a cable drum driven measurement technique is proposed to the machines which have long travel distance like plasma cutters. A test setup is proposed and manufactured to investigate the capstan drive systems. Finally, characteristics of Optical Mouse Sensors are investigated from different point of views and a test setup is proposed and manufactured to evaluate their performances in long terms. Beside all of these parts, motion control algorithms and motion control integrated circuits are designed and manufactured to realize experimental studies in a detailed manner.

Among the various remote sensing methods that can be used to map forest areas, the K Nearest Neighbor (KNN) supervised classification method is becoming increasingly popular for creating forest inventories in some countries. In this study, the utility of the KNN algorithm is evaluated for forest/non-forest/water stratification. Antalya is selected as the study area. The data used are composed of Landsat TM and Landsat ETM satellite images, acquired in 1987 and 2002, respectively, SRTM 90 meters digital elevation model (DEM) and land use data from the year 2003. The accuracies of different modifications of the KNN algorithm are evaluated using Leave One Out, which is a special case of K-fold cross-validation, and traditional accuracy assessment using error matrices. The best parameters are found to be Euclidean distance metric, inverse distance weighting, and k equal to 14, while using bands 4, 3 and 2. With these parameters, the cross-validation error is 0.009174, and the overall accuracy is around 86%. The results are compared with those from the Maximum Likelihood algorithm. KNN results are found to be accurate enough for practical applicability of this method for mapping forest areas.