Identification of the cloud in an image is essential in order to remove the noise in the image and improve the quality of the image. Further the masking of cloud in a satellite image is important in order to obtain a clear image taken from a remote sensing satellite. This thesis presents the identification of cloud in a satellite image, to segregate the cloud part of the image from non-cloud part of the same image like soil, vegetation, water by using certain techniques. Region of interest (ROI) selection technique is used to obtain the desired cloud image from the actual image. To obtain cloud image from non-cloud image masking methods such as automatic masking and manual masking are used in ROI selection technique. A suitable image is obtained from the sensor known as ‘Moderate Resolution Imaging Spectroradiometer’ (MODIS). The manual and automatic masked cloud images are compared and the performance of the cloud masking methods is evaluated. The evaluation of the results indicates that the cloud images are possible to obtained by ROI selection technique through automatic masking and manual cloud masking method.

With a considerable increase in technology and need for security, aninterest has been created in the development of biometric technology.Various personal identification techniques like face recognition, voicerecognition, retinal pattern and fingerprint recognition are in existence.Among all the available techniques, fingerprint recognition isthe best personal identification method, since each person has a uniquefingerprint pattern. Fingerprint image segmentation is a part of preprocessingfor fingerprint image recognition. Segmentation separatesthe foreground part of the fingerprint image from its background part.In this thesis, fingerprint segmentation is implemented using a localradial transformation technique. Here we analyze the data sampled ina circle with a certain radius around each pixel. The circularly sampleddata of image yields a data vector per each image pixel. Fromthis sampled data vector of pixels, the points of interest of the foregroundare obtained. A mask is created by thresholding the points ofinterest we obtained and the fingerprint image is segmented using theobtained mask.This process is carried out on the available databases of fingerprintimages and the obtained results are compared using NIST database.The performance matching is shown using the NIST matching software.

Part I and II of this thesis constitute a theoretical and practical approach on how to open up a laboratory for remote access and enabling students to have access to the equipment 24/7. Part I covers a more general solution for enabling remote access to equipment; the suggested solution can be applied to all types of instruments that can be controlled from a PC based system. Part III and IV of this thesis present an encouragement to collaborate within in the field of remote engineering to utilize the recourses more efficiently. The idea is that universities around the world can share their experiments in a grid laboratory; every university contributes with a small part, but gets access to a wide range of experiments in this grid. Part V of this thesis concerns the modelling and simulation of the remote electronics laboratory with the purpose of estimating the maximum number of simultaneous users without losing the experience of working with a real instrument. The results indicate that one single remote electronics laboratory can handle up to 120 users simultaneously and with 120 users the delay for each user is approximately 2 seconds. / <p>Real physical instruments, Remote handling, Online learning, Engineering education, Remote laboratories, VISIR, Quality of Experience, Remotely controlled laboratories, Remote electronics laboratory, Real experimental objects, Real physical experiments, Remote monitoring, Telemanipulators, Grid laboratory</p>

In this work we study the performance of elevation estimators and lower bounds on the estimation error variance for a low angle target in a smooth sea scenario using an array antenna. The article is structured around some key assumptions on multipath knowledge, signal parameterization and noise covariance, giving the reader a framework in which Maximum Likelihood estimators exploiting different á priori information can be found. The crucial factor that determines the estimator accuracy is the multipath modeling, and there are three alternative levels of knowledge that can be used: 1) two unknown target locations 2) the target and its corresponding sea-reflection are related via simple geometry 3) the sea-reflection coefficient is known as a function of grazing angle. A compact expression for the Cramér–Rao lower bound is derived, including all special cases of the key assumptions. We prove that the Cramér–Rao bound is highly dependent on the multipath model, while it is the same for the different signal parameterizations and that it is independent of the noise covariance. However, the Cramér–Rao bound is sometimes too optimistic and not achievable. The tighter Barankin bound is derived to predict the threshold behavior seen at low SNR. At high SNR the Barankin bound coincides with the Cramér–Rao bound. Simulations show that the Maximum Likelihood methods are statistically efficient and achieve the theoretical lower bound on error variance, in case of high enough SNR. The bounds are also useful tools to design an improved array structure that can give better performance than the standard uniform linear array structure. The influence of the number of sensors and the number of snapshots on the error variance is also studied, showing the rate of improvement with more sensors or snapshots. Finally we discuss the use of multiple frequencies, which is mainly a tool for suppressing ambiguities. We show for which signal models it provides improved performance.

The area of teleconferencing has long yielded great interest in the signal processing community. The main reasons for this are probably the huge interest from the industry and the challenging problems of the topic. The problems of teleconferencing are relevant for several different disciplines in signal processing. Three of these are Acoustic Echo Cancellation, System Identification and Sensor Array Signal Processing. In this thesis some problems related to these disciplines are studied. The thesis is divided into 6 parts, one for each paper included. In the first part a new adaptive algorithm is applied to the acoustic echo cancellation problem. It is shown to perform much better than the Normalized Least Mean Squares (NLMS) algorithm and while it performs worse than the standard Recursive Least Squares (RLS) algorithm it is shown to be computationally simpler than this. In the second part the hierarchical RLS algorithm is analyzed. The extraordinary results presented for this algorithm in previous papers are discussed and explained. In the third part a new initialization method for RLS is presented that yields the exact Least Squares estimates while not being computationally more demanding than RLS. This is an important contribution since the standard initialization of the RLS algorithm is somewhat arbitrary. In the fourth part a method is presented that deals with the problem of estimating the common factors out of an arbitrary number of polynomials. Two problems of array processing and system identification are stated as problems for common factor estimation and the presented method is applied to these. For these two problems the method is shown to perform better than existing methods. In the fifth part a method for beamforming using few sensors is presented. Data-dependent beamformers usually perform badly when there are few sensors in the array, particularly when the beamformer constraints are numerous. The method presented deals with this problem by approximately fulfilling the beamformer constraints and hence getting extra degrees of freedom for suppressing interferences. In the sixth part the previously unsolved problem of array processing of non-zero mean signals is solved for the colored noise case. Methods are presented both for the estimation problem and the detection problem and are shown to perform well in numerical examples.

Before using a parametric model one has to be sure that it offers a reasonable description of the system to be modeled. If a bad model structure is employed, the obtained model will also be bad, no matter how good is the parameter estimation method. There exist many possible ways of validating candidate models. This thesis focuses on one of the most common ways, i.e., the use of information criteria. First, some common information criteria are presented, and in the later chapters, various extentions and implementations are shown. An important extention, which is advocated in the thesis, is the multi-model (or model averaging) approach to model selection. This multi-model approach consists of forming a weighted sum of several candidate models, which then can be used for inference.

No description available.

This thesis focuses on the possibility of using ultrasonic measurement techniques for energy gas characterization. The idea is to combine both on-line flow measurements with non-invasive fluid characterization in the same measurement setup using the same sensor(s). The long-term goal of the project is to develop measurement methods based on ultrasonic techniques that can measure; the flow rate, the energy content, detect impurities, and estimate the composition. In this thesis different problems concerning gas characterization and modeling are addressed. The information obtained from ultrasonic measurements are limited to spectral variations in the attenuation and phase velocity. Hence, part of the research is focused on estimating these quantities accurately with low uncertainty. Another area is parametric modeling and identification of the bulk modulus, where a new model structure for gas mixtures with complex dynamic behavior and/or unknown components is presented, capable of handling the combined effect of the absorption mechanisms. Finally, the problem of estimating the composition of a gas mixture is considered. The results show that it is possible to estimate the composition of processed and upgraded biogas, with high accuracy and precision, by combining the developed estimation techniques with multiple linear regression methods. The thesis consists of two parts. The first part includes an introduction to the research area together with a short summary of the contributions. The second part contains a collection of four papers describing the research. / <p>Godkänd; 2006; 20061115 (ysko)</p>

With an increased demand on productivity and safety in industry, new issues in terms of automated material handling arise. This results in industries not having a homogenous fleet of trucks and driven and driverless trucks are mixed in a dynamic environment. Driven trucks are more flexible than driverless trucks, but are also involved in more accidents. A transition from driven to driverless trucks can increase safety, but also productivity in terms of fewer accidents and more accurate delivery. Hence, reliable and standardized solutions that avoid accidents are important to achieve high productivity and safety. There are two different safety standards for driverless trucks for Europe (EN1525) and U.S. (B56.5–2012) and they have developed differently. In terms of obstacles, they both consider contact with humans. However, a machinery-shaped object has recently been added to the U.S. standard (B56.5–2012). The U.S. standard also considers different materials for different sensors and non-contact sensors. For obstacle detection, the historical contact-sensitive mechanical bumpers as well as the traditional laser scanner used today both have limitations – they do not detect hanging objects. In this work we have identified several thin objects that are of interest in an industrial environment. A test apparatus with a thin structure is introduced for a more uniform way to evaluate sensors. To detect thin obstacles, we used a standard setup of a stereo system and developed this further to a trinocular system (a stereo system with three cameras). We also propose a method to evaluate 3D sensors based on the information from a 2D range sensor. The 3D model is created by measuring the position of a reflector with known position to an object with a known size. The trinocular system, a 3D TOF camera and a Kinect sensor are evaluated with this method. The results showed that the method can be used to evaluate sensors. It also showed that 3D sensor systems have potential to be used on driverless trucks to detect obstacles, initially as a complement to existing safety classed sensors. To improve safety and productivity, there is a need for harmonization of the European and the U.S. safety standards. Furthermore, parallel development of sensor systems and standards is needed to make use of state-of-the-art technology for sensors.

Aspects of modern communication systems is the overall theme of this thesis with emphasis placed on multicarrier modulation. The work considers four facets of such systems; namely duplexing design, interference mitigation, channel estimation and multiuser detection. The first area deals with duplexing design for very high bit rate digital subscriber lines (VDSL) using discrete multitone modulation (DMT). We present a novel method based on DMT - the Zipper duplex method. Zipper is proposed for VDSL in different standardization bodies worldwide - International (ITU), North America (ANSI) and in Europe (ETSI) where it also has been accepted as a part of the VDSL standard. Zipper has superior flexibility and spectrum efficiency. This is obtained by freely assigning different subcarriers for the up- and downstream direction. In one design Zipper operates fully network synchronized by using a masterclock. In an asynchronous design Zipper operates without any reference to a masterclock which is a requirement for unbundled networks but reduces some of the flexibility. To obtain highest flexibility in unbundled networks, without using a masterclock, an algorithm is derived that self-synchronizes all Zipper modems. Another area deals with interference- and distortion mitigation. Narrowband interference (NBI) in orthogonal frequency division multiplexing- (OFDM) and DMT-based systems is considered. NBI can be very harmful for both radio- and wireline systems. We introduce two efficient NBI cancellers for OFDM and DMT. One canceller is based on a deterministic polynomial model of the NBI. The other canceller models it as a narrowband stochastic process and use the linear minimum mean square error (LMMSE) criterion for the cancellation. We lower its complexity by using the theory of optimal rank reduction. Impulse noise is a different type of harmful interference that can be encountered in VDSL. In this thesis we study the effects of impulse noise in DMT-based VDSL systems and present a robust generalized likelihood ratio test for detecting impulse noise. It is used for obtaining reliable erasures in a Reed-Solomon decoding scheme which reduces the probability of symbol errors significantly. Pilot symbol assisted modulation (PSAM) can be used in OFDM for tracking the distortion variations in a fading radio channel. We analyze the pilot symbol spacing in PSAM as a trade-off between high effective SNR and good channel tracking capabilities for two channel estimators with different complexities. Code division multiple access (CDMA) is part of the standard for the third generation of mobile phones. In this thesis we present a low complexity multiuser detector for a wireless DS-CDMA system. With a pipelined structure it can produce maximum likelihood sequence detector (MLSD) decisions on many of the received bits by only performing additions after the front end matched filters. / Godkänd; 2001; 20061113 (haneit)