MRI image (Magnetic Resonance Imaging) as a universal body checkup method in modern medicine. It can help doctors to analyze the condition of patients as soon as possible. As the medical images, the MRI images have high quality and a large amount of data, which requires more transmission time and larger storage capacity. To reduce transmission time and storage capacity, the compression and decompression technology is applied. Now most MRI images are colour, but most theses still use gray MRI images to research. Compressed color MRI images is a new research area. In this thesis, some basic theories of the compression technoloy and medical technology were firstly introduced, then basic strcture and kernel algorithm of Huffman coding were explained in detail. Finally, Huffman coding was implemented in MATLAB to compress and decompress the colour MRI images.The result of the experiment shows that the Huffman coding in colour MRI image compression can get high compression ratio and coding efficient.

Transmission and reception of signals in wireless communication systems is affected by additive interference corrupting the signal. Traditionally, the interference is assumed to be AWGN and the system designs are usually based on that assumption. Modern military platforms consists of many electrical components and systems and as such the noise affecting the signals is often a product of interference between the components and systems. This type of noise tend to be very impulsive in nature. The standard AWGN model is not suited for impulsive noise which leaves an opportunity to investigate the performance of a demodulation scheme adapted to the current interference environment in order to increase the performance gain. To properly analyze the performance of an interference-adapted demodulator, knowledge about the characteristic parameters of the chosen noise model is required to perform the necessary calculations.  This project combines the aspect of adaptive demodulation with parameter estimation evaluation. Four different parameter estimation techniques specifically customized for Symmetric alpha-Stable distributed noise were implemented and examined. The four methods were the Empirical Characteristic Function (ECF) method, Fractional Lower-Order Moments (FLOM) method, Extreme-Order Statistics (EOS) method as well as the Quantiles method. The effectiveness and performance of the methods were investigated in two Symmetric alpha-Stable processes of varying level of impulsiveness as well as two Class A processes in order to monitor the performance in noise not specifically distributed according to the intended model, functioning as an arbitrary representation of non-Gaussian interference. The results were evaluated using the measure of Kullback-Leibler Divergence. The demodulator was designed for Symmetric alpha-Stable distributed noise and implemented using an LLR-algorithm. The simulations were performed using an LDPC-coding protocol and the experiment was conducted in both Class A and Symmetric alpha-Stable distributed noise. The modulation schemes were 4-QAM and BPSK. The simulations showed that ECF was the most consistent parameter estimation method overall, regardless of distribution model or number of available samples. FLOM performed well in alpha-Stable noise but struggled in Class A processes. EOS and Quantiles shared the struggles of fewer available samples. The experiments show that an alpha-Stable adapted demodulator coupled with a parameter estimation technique based on the empirical characteristic function (ECF) is a very competitive and viable option in impulsive interference environments regardless of the origin of the noise distribution. The performance gain vis-a-vis demodulation using the standard AWGN option exceeded thresholds of upwards 25 dB for impulsive noise processes.

This report is the result of a Master Thesis project which is a part of the Master programme in Electrical Engineering at Uppsala universitet. The purpose of this Master Thesis project is to develop an embedded platform for the design and implementation of optimal digital filters, in particular, the Kalman and the Wiener filter. In this project these filters are used for noise reduction on noisy signals. The project is a further development of a previous Master Thesis project where a Universal Filter Bank was developed. The Filter Bankis used for designing and implementing various linear digital filters such as lowpass, highpass, bandpass and bandstop. The Filter Bank is a hand held box with two input and two output connections and a human-device interface (HDI) including a Liquid Crystal Display (LCD) and a keypad. It contains anti-aliasing and reconstruction (analog) filters and an ARM 32-bit Microcontroller Unit (MCU) which is programmed in the C programming language. The HDI lets the user specify a desired digital filter. In this project the Kalman and Wiener filtering algorithms were first developed in MATLAB and tested with simulated autoregressive–movingaverage (ARMA) processes (signals) in additive white noise. Aftershown to work, they were implemented on the ARM 32-bit MCU development kit, and finally ported to the Filter Bank. A user interface specially for the specifications of the filters has been created. The Kalman and Wiener filtering algorithms have been tested using the same noisy ARMA processes and assessed in terms of the Normalized Mean Square Error (NMSE). The results have shown that both the Wiener and Kalman filters running on the development kit and the Filter Bank are successful in reducing noise. The Kalman filter is shown to perform better than the Wiener filter, which can be due to the extra information about the signal used in the Kalman filter. The performance of both algorithms are heavily dependent on the pre knowledge about the desired signal.

In the bus industry, it is well-known that bus doors often cause buses to stand at a stop after either a door could not be opened or closed. This causes huge costs for the bus owner. Therefore, it is desirable to be able to predict whether a door's behaviour begins to become too close to causing such an event. Based on this background, this study has been completed at Scania CV AB in Södertälje in order to find out which signals from a door system that can be used to determine if a door is at risk of failing. The main problems that this study is based on are: - Which existing sensors can be used to measure the most common errors before causing a bus that cannot drive? - Is it possible to find a model that can indicate how to adjust speed and attenuation to get a "perfect" adjusted door? These questions were studied by making measurements on a Scania City Wide bus with double inswing door leafs where measurements were made only on the rear door leaf and for its closing behaviour. To make measurements on the door an oscilloscope was used which measured the voltage across a potentiometer with a voltage that varied depending on how open the door was. Another measurement was made on the embedded control system in the bus body. The noise-corrupted measurement data was filtered with a simple type of filter, called moving average filter. In order to find different methods that predict if there were any errors on the door's settings or components, curves were examined over the doors' position during a closing event. One thing that was studied was which position value that was present when the door couldn’t get closed more. Another thing was the curve's slope in different segments of the closing. The slope of the curve was calculated by using the least squares method, which is a mathematical method in linear algebra. Based on the slope in some curve segments of a closing event, a model was created that could guess the door closing time as well as the damping. This model functioned in a convincing way for measurements from the test bus that was used when developing the model. Other models that the study resulted in was one that could warn if the potentiometer values began to approach a limit when the door could no longer be closed, as well as a model that calculated the closing and opening times of the door. Through these models, the idea was that a warning signal could be sent to a system that warned that the door should be adjusted. When some measurements for the guess model were made on another bus, it gave indications that the model did not work as well as the test bus. But one conclusion that could be drawn was that there was an indication that the door was misaligned, even though the model guessed wrong for the present settings. Another conclusion was that it is likely possible to proceed further with some research on this model to find a new model that is somewhat more general for all individual bus doors. / Inom bussbranschen är det välkänt att bussdörrarna alltför ofta orsakar att bussar blir stående vid en hållplats efter att en dörr antingen inte kunde öppnas eller stängas. Detta orsakar stora kostnader för bussägaren. Därför är det önskvärt att kunna förutsäga om en dörrs beteende börjar bli farligt nära att orsaka en sådan händelse. Utifrån denna bakgrund har denna studie utförts vid Scania CV AB i Södertälje för att försöka hitta vilka signaler från ett dörrsystem som kan användas för att kunna avgöra om en dörr riskerar att sluta fungera. De huvudsakliga frågeställningarna som studien utgått ifrån är:  ▪ Vilka befintliga sensorer kan användas för att mäta de vanligaste felen innan de orsakar en buss som inte kan köra vidare?  ▪ Är det möjligt att hitta en modell som kan indikera hur man bör justera hastighet och dämpning för att få en "perfekt" justerad dörr?  Dessa frågor studerades genom att göra mätningar på en Scania City Wide buss med dubbla inåtgående dörrblad där mätningar endast gjordes på det bakre dörrbladet och för dess stängningsbeteende. För att göra mätningarna på dörren användes ett oscilloskop som mätte spänningen över en potentiometer som varierade en spänningen beroende på hur öppen dörren var. Ytterligare en mätning gjordes på det inbyggda styrsystemet i busskarossen. Mätdata filtrerades med enkel typ av filter, glidande medelvärdsfilter, som tog bort brus som smutsat ner mätningarna. För att komma fram till olika metoder att förutsäga om det var något fel på dörrens inställningar eller komponenter så studerades kurvor över dörrarnas position vid en stängning. Det som studerades var för vilka positionsvärden dörren inte kunde stängas mer samt kurvans lutning i olika segment av stängningen. Lutningen på kurvan togs fram genom användning av minsta kvadratmetoden som är en matematisk metod inom linjär algebra. Utifrån lutningen inom vissa kurvsegment av en stängning skapades en modell som kunde gissa en dörrs stängningstid samt dämpningsinställning. Denna modell fungerade på ett övertygande sätt för mätningar på den testbuss som användes vid framtagningen av modellen. Andra modeller som studien mynnade ut i var en som kunde förvarna om potentiometervärdena började närma sig en gräns då dörren inte längre gick att stänga samt en modell som beräknade stängnings- och öppningstiderna för dörren. Genom dessa modeller var tanken att en varningssignal skulle kunna skickas till berörd och varna att dörren bör justeras. När några mätningar för gissningsmodellen gjordes på en annan buss så gav det indikationer på att modellen inte fungerade lika väl som för testbussen. Men en slutsats som man kunde dra var att man fick en indikation att dörren var feljusterad även om modellen gissade fel på hur inställningarna var. Ytterligare en slutsats var att det troligtvis går att utifrån denna modell forska vidare för att finna en ny modell som är något mer generell för alla bussdörrsindivider.

Finite-length impulse response (FIR) filters are one of the most commonly used digital signal processing algorithms used nowadays where a FPGA is the device used to implement it. The continued development of the FPGA device through the insertion of dedicated blocks raised the need to study the advantages offered by different FPGA families. The work presented in this thesis study the special features offered by FPGAs for FIR filters and introduce a cost model of resource utilization. The used method consist of several stages including reading, classification of features and generating coefficients. The results show that FPGAs have common features but also specific differences in features as well as resource utilization. It has been shown that there is misconception when dealing with FPGAs when it comes to FIR filter as compared to ASICs.

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.