Shoeprints are often recovered at crime scenes and are the most abundant form of evidence at a crime scene, and in some cases, it is proved to be as accurate as fingerprints. The basis for shoeprint impression evidence is determining the source of a shoeprint impression recovered from a crime scene. This shoeprint evidence collected are often noisy and unclear. To obtain a clear image, the shoeprint evidence should be enhanced by de-noising and improving the quality of the picture. In the thesis, we introduced a novel shoeprint enhancement algorithm based on sparse representation for obtaining the complete dictionary from a set of shoeprint patches which allows us to represent them as a sparse linear combination of dictionary atoms. In the proposed algorithm, we first pre-process the image by SMQT method, and then Daubechies first level DWT is applied. The SVD of the image is computed, and Inverse Discrete Wavelet Transform(IDWT) is applied. To the singular value decomposed image, l1-norm minimization sparse representation employed by the K-SVD algorithm is computed where the image is divided into predefined shoeprint image patches of size 8 by 8. Shoeprint images of three different databases with different image quality are tested. The performance of the algorithm is assessed by comparing the original shoeprint image and the image obtained after proposed algorithm based on objective and subjective parameters like PSNR, MSE, and MOS. The results show the proposed method gives better performance in terms of contrast (Variance) and brightness (Mean). Finally, as a conclusion, we state that the proposed algorithm enhances the image better than the existing method DWT-SVD.

No description available.

This study is concerned with the processing of signals received by an array of sensor elements which may range from acoustic transducers in a sonar system to microwave horns in a radar system. The main aim of the work is to devise techniques for resolving the signals arriving from closely spaced sources in order to determine the presence and direction of these sources.

This report aims to evaluate the performance gains that can be obtained by introducing cooperative localization in an indoor firefighter localization system, through the use of scenario-based simulations. Robust and accurate indoor localization for firefighters is a problem that is not yet resolved. Harsh environmental conditions and stringent size, weight, power and cost (SWaP-C) requirements are obstacles that have to be considered. Foot-mounted inertial navigation systems (INS) are being evaluated for first responder localization, but they have an accumulating position error that grows over time. By using ultrawideband (UWB) ranging between the firefighters and combining range measurements with position and uncertainty estimates from the foot-mounted INS via a cooperative localization approach it is possible to reduce the position error significantly. An error model for the position estimates received from single and dual foot-mounted INS is proposed based on experimental results, and it contains a scaling error which depends on the distance travelled and a heading error which grows linearly over time. The position error for these dead-reckoning systems depend upon the type of movement. Hence, the error model allows for varying position errors in order to mimic different movements that occur in typical firefighter operations. Similarly, an error model for the UWB range measurements was designed where the range measurements experience a bias and variance, which is determined by the number of walls between the transmitter and the receiver. By implementing these error models in a scenario-based simulation environment it is possible to evaluate the performance gain of different cooperative localization algorithms. The scenarios are designed to provide realistic movements of smoke divers in a search and rescue operation. A centralized extended Kalman Filter (EKF) algorithm has been implemented, and the position accuracy and heading improvements obtained through cooperative localization are evaluated over a smoke diving operation scenario. Using the proposed cooperative localization scheme it was possible to reduce the position errors by up to 70% in a designed scenario, where a three-person smoke diver team performs a search and rescue operation in two small apartments, where varying sight- and heat conditions sometimes forces the firefighters to search close to the floor by crawling.

No description available.

Sensor networks consist of sensors (e.g., radar and cameras) and processing units (e.g., estimators), where in the former information extraction occurs and in the latter estimates are formed. In decentralized estimation information extracted by sensors has been pre-processed at an intermediate processing unit prior to arriving at an estimator. Pre-processing of information allows for the complexity of large systems and systems-of-systems to be significantly reduced, and also makes the sensor network robust and flexible. One of the main disadvantages of pre-processing information is that information becomes correlated. These correlations, if not handled carefully, potentially lead to underestimated uncertainties about the calculated estimates.  In conservative estimation the unknown correlations are handled by ensuring that the uncertainty about an estimate is not underestimated. If this is ensured the estimate is said to be conservative. Neglecting correlations means information is double counted which in worst case implies diverging estimates with fatal consequences. While ensuring conservative estimates is the main goal, it is desirable for a conservative estimator, as for any estimator, to provide an error covariance which is as small as possible. Application areas where conservative estimation is relevant are setups where multiple agents cooperate to accomplish a common objective, e.g., target tracking, surveillance and air policing.  The first part of this thesis deals with theoretical matters where the conservative linear unbiased estimation problem is formalized. This part proposes an extension of classical linear estimation theory to the conservative estimation problem. The conservative linear unbiased estimator (CLUE) is suggested as a robust and practical alternative for estimation problems where the correlations are unknown. Optimality criteria for the CLUE are provided and further investigated. It is shown that finding an optimal CLUE is more complicated than finding an optimal linear unbiased estimator in the classical version of the problem. To simplify the problem, a CLUE that is optimal under certain restrictions will also be investigated. The latter is named restricted best CLUE. An important result is a theorem that gives a closed form solution to a restricted best CLUE. Furthermore, several conservative estimation methods are described followed by an analysis of their properties. The methods are shown to be conservative and optimal under different assumptions about the underlying correlations.  The second part of the thesis focuses on practical aspects of the conservative approach to decentralized estimation in configurations where the communication channel is constrained. The diagonal covariance approximation is proposed as a data reduction technique that complies with the communication constraints and if handled correctly can be shown to preserve conservative estimates. Several information selection methods are derived that can reduce the amount of data being transmitted in the communication channel. Using the information selection methods it is possible to decide what information other actors of the sensor network find useful.

The ongoing development of Advanced Driver Assistance Systems (ADAS) and autonomous vehicles pushes the requirements of accurate measurements from several on-board sensors, one of which being the Light Detection And Ranging (LiDAR). The performance of the LiDAR measurements relies partially on the reflectivity of the target, the level of background noise and the angle of incidence to the measurement scene. This thesis investigates if the temporal pulse shape due to non-perpendicular measurement scenes can be used to construct matched filters and how these filters affects the performance of the direct-detection LiDAR measurements. The temporal pulse shape was obtained by creating a simulation environment which replicates different measurement scenes by simulating the propagation of a laser pulse. The geometry of the measurement scene was derived from the radiometric concept of foreshortened area and the stochastic nature of the laser pulse was implemented using a Poisson process. Different matched filters were constructed by averaging the detected temporal pulse shape for different angles. The validity of the simulated temporal pulse shape was evaluated against real LiDAR measurements. The performance of the filters was evaluated both in terms of signal-to-noise ratio (SNR) and the receiver operating characteristics (ROC). It was found that the general behavior of the temporal pulse shape were similar for the simulated and the real LiDAR data. Some improvements and a more accurate model of the detector is probably necessary in order to improve the accuracy of the simulation environment. It was also found that all of the created matched filters increased both the SNR and the ROC in relation to not filtering at all. Further, it was found that for angles > 87° the correctly matched filter gave the best improvement.

Loudspeakers transform electrical energy into acoustic waves but most of the electrical energy is lost as heat. It is possible to increase the electro-acoustic efficiency by changing the electrical, mechanical or acoustical design of the loudspeaker but with these changes come unwanted side effects. An efficient loudspeaker exhibits strongly nonlinear behavior, but the nonlinear effects can be reduced by the means of implementing an algorithm on a digital signal processor. This is of great interest for Axis Communications since it makes it possible to increase the efficiency in their products without loss of sound quality. The purpose of this thesis was to investigate nonlinear distortion in high efficient electrodynamic loudspeakers using a simulation software, COMSOL Multiphysics, and to model the nonlinear response of the loudspeaker behavior. The simulations were conducted in order to get an understanding of the nonlinear behavior but also to estimate data for the theoretical model. Further, a nonlinear model describing the voice coil excursion was investigated with the purpose of constructing a nonlinear filter able to reduce nonlinear distortion. The result of the simulation showed it was possible to estimate the force factor as a function of voice coil displacement and that it also should be possible, according to the magnetic fields and streamlines, to obtain the function that describes the voice coil inductance from the same simulation of the loudspeaker. The numerical implementation of the voice coil excursion showed the voice coil was operating in the wrong domain when it was driven with a real input signal. Hence, the Volterra series need further work before a nonlinear filter can be derived.

5G brings a few key technological improvements compared to previous generations in telecommunications. These include, but are not limited to, greater speeds, increased capacity and lower latency. These improvements are in part due to using high band frequencies, where increased capacity is found. By advancements in various technologies, mobile broadband traffic has become increasingly chatty, i.e. more small packets are being sent. From a capacity standpoint this characteristic poses a challenge for early 5G millimeter-wave advanced antenna systems. This thesis investigates if network performance of 5G millimetre-wave systems can be improved by increasing the utilisation of the bandwidth by using adaptive beamforming. Two adaptive codebook approaches are proposed; a single- beam and a multi-beam approach. The simulations are performed in an outdoor urban macro scenario. The results show that for a small packet scenario with good coverage the ability to frequency multiplex users is important for good network performance.

This thesis deals with two different topics: High speed communication on twisted pair wires (digital subscriber lines) and low complexity multiuser detectors. The major part of this thesis concerns a technique for high speed communication over the telephone network called Very high bit rate Digital Subscriber Line VDSL). VDSL is not standardized yet but it is intended to offer bit rates up to 52 Mbit per second on twisted-pair wires. An important problem in VDSL is crosstalk between wire pairs, especially Near End Cross Talk (NEXT). A novel duplex method, called Zipper, that mitigates NEXT is presented herein. Zipper is a flexible duplex method that has high duplex efficiency and offers compatibility with existing services. It is based on Discrete Multi Tone (DMT) modulation, and uses different subcarriers in the two different transmission directions. The method relies on an additional cyclic extension to ensure orthogonality between the transmission directions. Zipper achieves best performance when all transmitters in the access network are synchronized, but it can also operate in an asynchronous mode with only a small loss in performance. Another important issue for VDSL is the problem with Radio Frequency Interference (RFI). The copper wires can act as large antennas and hence can transmit and receive radio signals. Herein the problem of radio frequency signals interfering with VDSL systems (called RFI-ingress) is addressed. The proposed method for suppressing the RFI works in the frequency domain of the DMT-receiver and can be used by any DMT-based VDSL system. By modeling the RFI and measuring the disturbance on some unmodulated subcarriers we can extrapolate and subtract the disturbance on all the other subcarriers. For a typical scenario with an average Signal-to-Noise Ratio (SNR) of 30 dB without RFI, about 20 dB can be lost due to RFI, but with the presented RFI-canceller this SNR-loss is reduced to less than 1 dB. The last part of this thesis deals with low complexity multiuser detection in a direct sequence code division multiple access system. The Maximum Likelihood Sequence Detector (MLSD) gives very good performance but is known to be very computationally complex. The detector presented herein is a simple threshold detector that makes MLSD-decisions on some, but not necessarily all, bits. A pipelined structure of the detector is presented which is attractive from an implementation point of view, since it allows parallel processing of the data. Using a single-user matched filter detector as post processor, taking care of the previously undetected bits, a complete multiuser detector with very low complexity is achieved. This detector gives better performance than the decorrelator receiver for a limited number of simultaneous users, e.g. up to 25 users with a spreading factor of 127. / <p>Godkänd; 1998; 20070404 (ysko)</p>