Many emerging applications call for wideband analog-to-digital converters and some require medium-to-high resolution. Incorporating such ADCs allows for shifting as much of the signal processing tasks as possible to the digital domain, where more flexible and programmable circuits are available. However, realizing such ADCs with the existing single stage architectures is very challenging. Therefore, parallel ADC architectures such as time-interleaved structures are used. Unfortunately, such architectures require high-speed high-precision sample-and-hold (S/H) stages that are challenging to implement. In this thesis, a parallel ADC architecture, namely, the frequency-translating hybrid ADC (FTH-ADC) is proposed to increase the conversion speed of the ADCs, which is also suitable for applications requiring medium-to-high resolution ADCs. This architecture addresses the sampling problem by sampling on narrowband baseband subchannels, i.e., sampling is accomplished after splitting the wideband input signals into narrower subbands and frequency-translating them into baseband where identical narrowband baseband S/Hs can be used. Therefore, lower-speed, lower-precision S/Hs are required and single-chip CMOS implementation of the entire ADC is possible. A proof of concept board-level implementation of the FTH-ADC is used to analyze the effects of major analog non-idealities and errors. Error measurement and compensation methods are presented. Using four 8-bit, 100 MHz subband ADCs, four 25 MHz Butterworth filters, two 64-tap FIR reconstruction filters, and four 10-tap FIR compensation filters, a total system with an effective sample rate of 200 MHz is implemented with an effective number of bits of at least 7 bits over the entire 100 MHz input bandwidth. In addition, one path of an 8-GHz, 4-bit, FTH-ADC system, including a highly-linear mixer and a 5th-order, 1 GHz, Butterworth Gm-C filter, is implemented in a 90 nm CMOS technology. Followed by a 4-bit, 4-GHz subband ADC, the blocks consume a total power of 52 mW from a 1.2 V supply, and occupy an area of 0.05 mm2. The mixer-filter has a THD ≤ 5% (26 dB) over its full 1 GHz bandwidth and provides a signal with a voltage swing of 350 mVpp for the subsequent ADC stage.

No description available.

The global Positioning System (GPS) provides location and time information as long as there are unobstructed lines of sight to four or more GPS satellites. However, when this is not the case the signal may be inaccurate or sometimes even completely blocked. In these situations the Inertial Navigation System (INS) is an appropriate choice for positioning.  An INS has already been proposed in a previous thesis by Erik Andersson and the objective of this thesis is to fuse the GPS with the INS in a proper way. A part of this project is to decide the reliability of the GPS.Three methods for GPS reliability detection have been proposed. One method based on the statistical properties of each of the separate systems, and two methods based on the statistical properties of the residuals between the GPS and INS. Two methods for GPS/INS integration have been proposed. One method based on a bank of parallel running Kalman filters and one method based on an adaptive observer.The method based on Kalman filter diverged. By adding a state that was suppose to represent the bias of the noise an attempt was to fix this problem made. The filter still diverged and was not examined any further. Among the other two algorithms did the one that uses both magnetometer and gyroscope presents a better result than the one that uses only gyroscope. However, the result differences between the two algorithms were not big and the result may change if a better INS is used.

Face detection is a preprocessing step for face recognition algorithms. It is the localization of face/faces in an image or image sequence. Once the face(s) are localized, other computer vision algorithms such as face recognition, image compression, camera auto focusing etc are applied. Because of the multiple usage areas, there are many research efforts in face processing. Face detection is a challenging computer vision problem because of lighting conditions, a high degree of variability in size, shape, background, color, etc. To build fully automated systems, robust and efficient face detection algorithms are required. Numerous techniques have been developed to detect faces in a single image; in this project we have used a classification-based face detection method using Gabor filter features. We have designed five frequencies corresponding to eight orientations channels for extracting facial features from local images. The feature vector based on Gabor filter is used as the input of the face/non-face classifier, which is a Support Vector Machine (SVM) on a reduced feature subspace extracted by using principal component analysis (PCA). Experimental results show promising performance especially on single face images where 78% accuracy is achieved with 0 false acceptances.

Indoor navigation could be used in many applications to enhance performance in its specific area. Anything from serious life critical tasks like aiding firefighters or coordinating military attacks to more simple every day use like finding a desired shop in a large supermarket could be considered. Smartphones of today introduce an interesting platform with capabilities like existing, more clumsy, indoor navigation systems. The iPhone 3GS is a powerful smartphone that lets the programmer use its hardware in an efficient and easy way. The iPhone 3GS has a 3-axis accelerometer, a 3-axis magnetometer and hardware accelerated image rendering which is used in this thesis to track the user on an indoor map. A particle filter is used to track the position of the user. The implementation shows how many particles the iPhone will be able to handle and update in real time without lag in the application.

In modern systems for air surveillance, it is important to have a high quality situationassessment. SAAB has a system for air surveillance, and in this thesis possibleimprovements of the tracking performance of this system are explored. The focushas been on improving the tracking of highly maneuverable targets observed withlow sampling rate. To evaluate improvements of the tracking performance, a componentthat is similar to the one used in SAAB’s present tracker was implementedin an Interacting Multiple Model (IMM) structure. The use of an Auxiliary ParticleFilter for improving the tracking performance is explored, and a way to fita particle filter into SAAB’s existing IMM framework is proposed. The differentfilters were implemented in Matlab, and evaluation was done by the meansof Monte Carlo simulations. The results from Monte Carlo simulations show significantimprovement when tracking in two dimensions. However, the results inthree dimensions do not display any substantial overall improvement when usingthe particle filter compared to using SAAB’s present filter. It is therefore notworthwhile to switch the filter used in SAAB’s present tracker for a particle filter,at least not under the high SNR circumstances presented in this thesis. However,further studies within this area are recommended before any final decisions aremade.

Renewable power sources with a relatively uneven or constant DC power production require synchronization methods to work with the current utility power grid. The solution to this synchronization problem has been solved with semiconductor based converters and advanced switching algorithms. To enable switching algorithms that work well with the grids amplitude, phase-shift and frequency, the current waveform has to be measured and estimated.    There are many sources of noise that will add distortion of the current waveform, making its appearance less similar to the grids. The distorted measurement affects the accuracy of the converters negatively. Therefore, using a filter algorithm to attenuate the grid noise is required.  This project uses a Kalman filter with the aim to decrease the noise and estimate the current phase shift for a three phase power-grid.  To achieve reliable and fast calculation, implementing the Kalman filter within a FPGA were done.The project contains results from both simulated MATLAB data and the FPGAs real time data. The method was able to estimate the grid within a few Hz frequency deviation and enable some noise reduction. For larger degree of harmonic distortion during steady state operation, the Kalman filter could remove more of the harmonic distortion. Limits and differences with MATLAB are discussed for the FPGA implemented Kalman filter.

The aim of this thesis work is to investigate the algorithms of speech recognition. The author programmed and simulated the designed systems for algorithms of speech recognition in MATLAB. There are two systems designed in this thesis. One is based on the shape information of the cross-correlation plotting. The other one is to use the Wiener Filter to realize the speech recognition. The simulations of the programmed systems in MATLAB are accomplished by using the microphone to record the speaking words. After running the program in MATLAB, MATLAB will ask people to record the words three times. The first and second recorded words are different words which will be used as the reference signals in the designed systems. The third recorded word is the same word as the one of the first two recorded words. After recording words, the words will become the signals’ information which will be sampled and stored in MATLAB. Then MATLAB should be able to give the judgment that which word is recorded at the third time compared with the first two reference words according to the algorithms programmed in MATLAB. The author invited different people from different countries to test the designed systems. The results of simulations for both designed systems show that the designed systems both work well when the first two reference recordings and the third time recording are recorded from the same person. But the designed systems all have the defects when the first two reference recordings and the third time recording are recorded from the different people. However, if the testing environment is quiet enough and the speaker is the same person for three time recordings, the successful probability of the speech recognition is approach to 100%. Thus, the designed systems actually work well for the basical speech recognition.

The aim of platooning is to enable several Heavy Duty Vehicles (HDVs) to drive in a convoy and act as one unit to decrease the fuel consumption. By introducing wireless communication and tight control, the distance between the HDVs can be decreased significantly. This implies a reduction of the air drag and consequently the fuel consumption for all the HDVs in the platoon. The challenge in platooning is to keep the HDVs as close as possible to each other without endangering safety. Therefore, sensor fusion is necessary to get an accurate estimate of the relative distance and velocity, which is a pre-requisite for the controller. This master thesis aims at developing a sensor fusion framework from on-board sensor information as well as other vehicles’ sensor information communicated over a WiFi link. The most important sensors are GPS, that gives a rough position of each HDV, and radar that provides relative distance for each pair of HDV’s in the platoon. A distributed solution is developed, where an Extended Kalman Filter (EKF) estimates the state of the whole platoon. The state vector includes position, velocity and length of each HDV, which is used in a Model Predictive Control (MPC). Furthermore, a method is discussed on how to handle vehicles outside the platoon and how various road surfaces can be managed. This master thesis is a part of a project consisting of three parallel master’s theses. The other two master’s theses investigate and implement rough pre-processing of data, time synchronization and MPC associated with platooning. It was found that the three implemented systems could reduce the average fuel consumption by 11.1 %.

Simulation is the process of computing a behavior determined by a given model of a system of interest.  Modeling is the process of creating a model that formally describes a given class of system.  Modeling and simulation can be used to quickly and cheaply study and understand new technologies.  Today, a wide range of systems are simulated using different tools.  However, converting models into simulation codes can still be difficult and time consuming. In this thesis, we study how a new modeling and simulation language called Acumen can be used to model basic physical links.  This language is aimed at bridging the gap between modeling and simulation.  We focus on basic physical links as an interesting type of system to model and simulate. We also focus on comparing Acumen to MATLAB and Simulink.  The types of links we consider include models of an RC low-pass filter, Amplitude Modulation, Frequency Modulation, Amplitude Shift Keying, Phase Shift Keying and Frequency Shift Keying systems. Each of these examples is modeled in Acumen, MATLAB and Simulink. We find that, for the most part, Acumen allows us to naturally express a wide range of modulation techniques mentioned above. When compared to MATLAB ad Simulink, we find that Acumen is simple language to understand. Acumen codes are described in a more natural way. Simplicity is the biggest advantage of Acumen.