Audio processing algorithms are increasingly used in cell phones and today’s customers are placing more demands on cell phones. Feature phones, once the advent of mobile phone technology, nowadays do more than just providing the user with MP3 play back or advanced audio effects. These features have become an integral part of medium as well as low-end phones. On the other hand, there is also an endeavor to include as improved quality as possible into products to compete in market and satisfy users’ needs. Tackling the above requirements has been partly satisfied by the advance in hardware design and manufacturing technology. However, as new hardware emerges into market the need for competence to write efficient software and exploit the new features thoroughly and effectively arises. Even though compilers are also keeping up with the new tide space for hand optimized code still exist. Wrapped in the above goal, an effort was made in this thesis to partly cover the competence requirement at Multimedia Section (part of Ericsson Mobile Platforms) to develope optimized code for new processors. Forging persistently ahead with new products, EMP has always incorporated the latest technology into its products among which ARMv6 family of processors has the main central processing role in a number of upcoming products. To fully exploit latest features provided by ARMv6, it was required to probe its new instruction set among which new media processing instructions are of outmost importance. In order to execute DSP-intensive algorithms (e.g. Audio Processing algorithms) efficiently, the implementation should be done in low-level code applying available instruction set. Meanwhile, ARMv6 comes with a number of new features in comparison with its predecessors. SIMD (Single Instruction Multiple Data) and VFP (Vector Floating Point) are the most prominent media processing improvements in ARMv6. Aligned with thesis goals and guidelines, Reverb algorithm which is among one of the most complicated audio features on a hand-held devices was probed. Consequently, its kernel parts were identified and implementation was done both in fixed-point and floating-point using the available resources on hardware. Besides execution time and amount of code memory for each part were measured and provided in tables and charts for comparison purposes. Conclusions were finally drawn based on developed code’s efficiency over ARM compiler’s as well as existing code already developed and tailored to ARMv5 processors. The main criteria for optimization was the execution time. Moreover, quantization effect due to limited precision fixed-point arithmetic was formulated and its effect on quality was elaborated. The outcomes, clearly indicate that hand optimization of kernel parts are superior to Compiler optimized alternative both from the point of code memory as well as execution time. The results also confirmed the presumption that hand optimized code using new instruction set can improve efficiency by an average 25%-50% depending on the algorithm structure and its interaction with other parts of audio effect. Despite its many draw backs, fixed-point implementation remains yet to be the dominant implementation for majority of DSP algorithms on low-power devices.

Propagation measurements at the frequency 3.5 GHz for the WiMAX technology have been conducted. The purpose of these measurements is that a coverage analysis should be accomplished. The mathematical software package MATLAB has been used to analyze the collected data from the measurement campaign. Path loss models have also been used and a comparison between these models and the collected data has been performed. An analysis prediction tool from an application called WRAP has also been used in the comparison with the collected data. In this thesis, diff

This thesis paper proposes a Medium Access Control (MAC) protocol for wireless networks, termed as CD-MMAC that utilizes multiple channels and incorporates opportunistic cooperative diversity dynamically to improve its performance. The IEEE 802.11b standard protocol allows the use of multiple channels available at the physical layer but its MAC protocol is designed only for a single channel. The proposed protocol utilizes multiple channels by using single interface and incorporates opportunistic cooperative diversity by using cross-layer MAC. The new protocol leverages the multi-rate capability of IEEE 802.11b and allows wireless nodes far away from destination node to transmit at a higher rate by using intermediate nodes as a relays. The protocol improves network throughput and packet delivery ratio significantly and reduces packet delay. The performance improvement is further evaluated by simulation and analysis.

Recent advancements in signal and image processing have reduced the time of diagnoses, effort and pressure on the screeners by providing auto diagnostic tools for different diseases. The success rate of these tools greatly depend on the quality of acquired images. Bad image quality can significantly reduce the specificity and the sensitivity which in turn forces screeners back to their tedious job of manual diagnoses. In acquired fundus images, some areas appear to be brighter than the other, that is areas close to the center of the image are always well illuminated, hence appear very bright while areas far from the center are poorly illuminated hence appears to be very dark. Several techniques including the simple thresholding, Naka Rushton (NR) filtering technique and histogram equalization (HE) method have been suggested by various researchers to overcome this problem. However, each of these methods has limitations at their own and hence the need to develop a more robust technique that will provide better performance with greater flexibility. A new method of compensating uneven (irregular) illumination in fundus images termed global-local adaptive histogram equalization using partially-overlapped windows (GLAPOW) is proposed in this paper. The developed algorithm has been tested and the results obtained show superior performance when compared to other known techniques for uneven illumination correction.

Automatic diagnosis and display of diabetic retinopathy from images of retina using the techniques of digital signal and image processing is presented in this paper. The acquired images undergo pre-processing to equalize uneven illumination associated with the acquired fundus images. This stage also removes noise present in the image. Segmentation stage clusters the image into two distinct classes while the abnormalities detection stage was used to distinguish between candidate lesions and other information. Methods of diagnosis of red spots, bleeding and detection of vein-artery crossover points have also been developed in this work using the color information, shape, size, object length to breadth ration as contained in the acquired digital fundus image. Furthermore, two graphical user interfaces (GUIs) have also been developed during this work; the first is for the collection of lesion data information and was used by the ophthalmologist in marking images for database while the second GUI is for automatic diagnosing and displaying of the result in a user friendly manner. The algorithm was tested with a separate set of 25 fundus images. From this, the result obtained for microaneurysms and haemorrhages diagnosis shows the appropriateness of the method.

Today, active control technology is about to emerge from the research labs into products in various areas. It has become an attractive method where passive techniques have low impact at low frequencies and adding active control to that part is often an attractive solution. The active control technique has been enabled by the rapid development of digital signal processors over the last decades. The focal point in this thesis is active vibration and noise suppression. Two different industrial applications have been subjected to active control to reduce unwanted disturbances. In cutting operations, active vibration suppression has been applied to both external turning and boring operations with successful results. Turning operations, and in particular boring operations, are typical examples of chatter prone machining. In order to implement active vibration control in boring operations a thourough investigation of the boring process has been made in the first two parts in this thesis. The following two parts of the thesis treat active vibration suppression in external turning operations and in boring operations. The second industrial application treats the noise in a fork-lift truck. In the final part of the thesis, active noise suppression has been implemented in the cabin of a fork-lift truck.

The boring operation is a cumbersome manufacturing process plagued by noise and vibration-related problems. A deep internal boring operation in a workpiece is a classic example of chatter-prone machining. The manufacturing industry today is facing tougher tolerances of product surfaces and a desire to process hard-to-cut materials; vibrations must thus be kept to a minimum. An increase in productivity is also interesting from a manufacturing point of view. Penetrating deep and narrow cavities require that the dimensions of the boring bar are long and slender. As a result, the boring bar is inclined to vibrate due to the limited dynamic stiffness. Vibration affects the surface finish, leads to severe noise in the workshop and may also reduce tool life. This report presents an active control solution based on a standard boring bar with an embedded piezo ceramic actuator; this is placed in the area of the peak modal strain energy of the boring bar bending mode to be controlled. An accelerometer is also included in the design; this is mounted as close as possible to the cutting tool. Embedding the electronic parts not only protects them from the harsh environment in a lathe but also enable the design to be used on a general lathe as long as the mounting arrangements are relatively similar. Three different algorithms have been tested in the control system. Since the excitation source of the original vibrations, i.e. the chip formation process cannot be observed directly, the algorithms must be constructed on the basis of a feedback approach. Experimental results from boring operations show that the vibration level can be reduced by 40 dB at the resonance frequency of a fundamental boring bar bending mode; several of its harmonics can also be reduced significantly.

Vibrations in internal turning operations are usually a cumbersome part of the manufacturing process. This article focuses on the boring bar vibrations. Boring bar vibrations in alloyed steel, stainless steel and cast iron have been measured in both the cutting speed direction and the cutting depth direction with the aid of accelerometers. The dynamic response of a boring bar seem to be a time varying process that exhibits non-linear behaviour. The process is influenced by non-stationary parameters that are not under the control of the operator or experimenter. The vibrations are clearly dominated by the first resonance frequency in one of the two directions of the boring bar. The problem with force modulation in rotary machinery, which appears as side band terms in the spectrum, is also addressed. Furthermore, the resonance frequencies of the boring bar are correlated to an Euler-Bernoulli beam model.

The internal turning operation has a history of being a cumbersome metal working process as vibration in boring operations is usually inevitable. In this article, the deflection shapes and/or mode shapes as well as the resonance frequencies of a boring bar have been put under scrutiny. Three methods have been used in order to investigate dynamic properties of a clamped boring bar: a theoretical Euler-Bernoulli beam model, an experimental modal analysis and an operating deflection shape analysis. \\ The results indicate a correlation between the shapes of the deflection shapes and/or mode shapes produced by the three different analysis methods. On the other hand, the orientation of the forced deflection shapes and/or mode shapes and the resonance frequencies demonstrates differences between the three methods. During continuous cutting, it is demonstrated that the bending motion of the first two resonance frequencies is to a large extent in the cutting speed direction.

A classical example of chatter prone machining is the boring operation. Turning under conditions with high vibrations in the cutting tool deteriorates the surface finish and may cause tool breakage. Severe noise is also a consequence of the high vibration levels in the boring bar. Active control is one possible solution to the noise and vibration problem in boring operations. In boring operations the boring bar usually have vibration components in both the cutting speed and the cutting depth direction. The introduction of the control force in different angles in between the cutting speed and the cutting depth directions have been investigated. Furthermore, control path estimates produced when the active boring bar was not in contact with the workpiece and during continuous cutting operation are compared. Experimental results indicate that the control force should be introduced in the cutting speed direction. Although the vibrations are controlled in just the cutting speed direction the vibrations in the cutting depth direction are also reduced significantly.

High comfort for the driver in working vehicles is an important feature as well as a demand from the drivers. Low noise level is an essential factor for the manufacturer to maintain a high standard and comfort of vehicles. In many cases the noise inside the cabin can be related to the engine orders. Hydraulic pumps and fans are also related to the engine but not necessarily integers of the engine order. Passive absorbers are not suitable for the lowest frequencies and one approach is to use an active noise control system to solve the noise problem at low frequencies. In the present experiment loudspeakers were mounted inside the cabin of a fork lift-truck to produce the secondary noise field. To sense the residual noise, microphones were installed close to the driver's head. The aim is to create a zone of reduced noise around the head. Since a large portion of the noise inside the cabin can be related to the engine, an active control system based on a feedforward solution is possible. Experimental results from a feedforward solution of active noise control in a fork-lift truck cabin show that the noise level in the low frequency region can be reduced significantly.

The most common method to attenuate noise in ventilation systems today is passive silencers. For these to efficiently attenuate frequencies below 400 Hz such silencers need to be large and a more neat solution to attenuate low frequencies is to use active noise control (ANC). The usage of ANC in ventilation systems is well known and there are several commercial products available. ANC is not, however, used on a wide basis due to its often high price and poor performance. Since the price is an important factor in ANC systems the expensive laboratory filters and the amplifier that is currently used in the experimental setup at Blekinge Institute of Technology (BTH) need to be replaced with cheaper ones, but without too much performance loss. For easier implementation in ventilation systems the placement of the reference microphone is important, the shorter distance from the anti-noise loud speaker the easier the ANC system is to implement. But if the distance is so small that the ANC system is no longer causal the performance will be decreased and if the reference microphone is close enough to pick up acoustic feedback from the anti-noise loud speaker the performance will also be decreased. In this thesis the expensive laboratory filters will be exchanged to cheaper alternatives, power and total harmonic distortion (THD) measurements will be done on the amplifier that is driving the loud speaker and the reference microphones position will be investigated with measurements on the group delay of the system and the acoustic feedback between the loud speaker and the reference microphone.

High Altitude Platform Stations (HAPs) are communication facilities situated at an altitude of 17 to 30 km and at a specified, nominal, fixed point relative to the Earth. They are mostly solar-powered, unmanned, and remotely-operated. These platforms have the capability of carrying multipurpose communications relay payload, which could be in the form of full base station or, in some cases, a simple transponder as is being used in satellite communication systems. HAPs, when fully deployed will have the capability of providing services and applications ranging from broadband wireless access, navigation and positioning systems, remote-sensing and weather observation/monitoring systems, future generation mobile telephony etc. HAPs are also known to be low cost when it comes to its implementation and are expected to be the next big provider of infrastructure for wireless communications. There have been a lot of ongoing and exciting research works into various aspects of this emergent technology. As radio Engineers, the need to predict the channel quality and analyze the performance evaluation of such stratospheric propagation has generated quite a few models. Although some of the models under consideration are from the existing terrestrial and satellite communications which in some way, have some relationships with this new technology. This thesis work provides some insight into this new aspect of wireless communications in terms of the need for a new system, its benefits, challenges services provided and applications supported. Existing models already researched and developed for HAPS are reviewed; one of them was picked and deeply looked into as regards the propagation and channel efficiency. The analysis of the choice model is presented using one of the performance test for channel models, the bit error rate (BER).

In this thesis work, WiMAX (IEEE 802.16d) PHY layer with underlying OFDM technology and an optional feature called Adaptive Antenna System has been considered. The SUI-3 channel model (Rician fading) is used for creating fading phenomena. An Adaptive Antenna System has been deployed at the receiver module to reduce the fading effects caused by SUI-3 channel model. Adaptive Antenna Systems (AAS) uses different beamforming techniques to focus the wireless beam between the base station and the subscriber station. In this thesis, the transmitter (SS) and receiver (BS) are fixed and AAS installed at the receiver is used to direct the main beam towards the desired LOS signal and nulls to the multipath signals. Pre-FFT beamformer based on Least Mean Square (LMS) algorithm is used. Different aspects of the complete system model were investigated such as adaptive modulation, angle of arrival of the incoming signals and number of array elements. It has been demonstrated through MATLAB simulations that the performance of the system significantly improves by AAS, where beamforming is implemented in the direction of desired user. The performance of the system further increases by increasing the number of antennas at receiver.

In this paper we exploit the amplitude diversity provided by two sensors to achieve blind separation of two speech sources. We propose a simple and highly computationally efficient method for separating sources that are W-disjoint orthogonal (W-DO), that are sources whose time-frequency representations are disjoint sets. The Degenerate Unmixing and Estimation Technique (DUET), a powerful and efficient method that exploits the W-disjoint orthogonality property, requires extensive computations for maximum likehood parameter learning. Our proposed method avoids all the computations required for parameters estimation by assuming that the sources are "cross high-low diverse (CH-LD)", an assumption that is explained later and that can be satisfied exploiting the sensors settings/directions. With this assumption and the W-disjoint orthogonality property, two binary time-frequency masks that can extract the original sources from one of the two mixtures, can be constructed directly from the amplitude ratios of the time-frequency points of the two mixtures. The method works very well when tested with both artificial and real mixtures. Its performance is comparable to DUET, and it requires only 2% of the computations required by the DUET method. Moreover, it is free of convergence problems that lead to poor SIR ratios in the first parts of the signals. As with all binary masking approaches, the method suffers from artifacts that appear in the output signals.

This master degree project is how to implement a speech recognition system on a DSK – ADSP-BF533 EZ-KIT LITE REV 1.5 based on the theory of the Hidden Markov Model (HMM). The implementation is based on the theory in the master degree project Speech Recognition using Hidden Markov Model by Mikael Nilsson and Marcus Ejnarsson, MEE-01-27. The work accomplished in the project is by reference to the theory, implementing a MFCC, Mel Frequency Cepstrum Coefficient function, a training function, which creates Hidden Markov Models of specific utterances and a testing function, testing utterances on the models created by the training-function. These functions where first created in MatLab. Then the test-function where implemented on the DSK. An evaluation of the implementation is performed.

Human fingerprints are rich in details called minutiae, which can be used as identification marks for fingerprint verification. The goal of this thesis is to develop a complete system for fingerprint verification through extracting and matching minutiae. A neural network is trained using the back-propagation algorithm and will work as a classifier to locate various minutiae. To achieve good minutiae extraction in fingerprints with varying quality, preprocessing in form of binarization and skeletonization is first applied on fingerprints before they are evaluated by the neural network. Extracted minutiae are then considered as a 2D point pattern problem and an algorithm is used to determine the number of matching points between two point patterns. Performance of the developed system is evaluated on a database with fingerprints from different people and experimental results are presented.

This paper presents a new approach for fingerprint enhancement by using directional filters and binarization. A straightforward method for automatically tuning the size of local area is obtained by analyzing entire fingerprint image in the frequency domain. Hence, the algorithm will adjust adaptively to the local area of the fingerprint image, independent on the characteristics of the fingerprint sensor or the physical appearance of the fingerprints. Frequency analysis is carried out in the local areas to design directional filters. Experimental results are presented.

In this paper improvements to a previous work are presented. Removing the redundant artifacts in the fingerprint mask is introduced enhancing the final result. The proposed method is entirely adaptive process adjusting to each fingerprint without any further supervision of the user. Hence, the algorithm is insensitive to the characteristics of the fingerprint sensor and the various physical appearances of the fingerprints. Further, a detailed description of fingerprint mask generation not fully described in the previous work is presented. The improved experimental results are presented.

Human fingerprints are rich in details denoted minutiae. In this paper a method of minutiae extraction from fingerprint skeletons is described. To identify the different shapes and types of minutiae a neural network is trained to work as a classifier. The proposed neural network is applied throughout the fingerprint skeleton to locate various minutiae. A scheme to speed up the process is also presented. Extracted minutiae can then be used as identification marks for automatic fingerprint matching.

In this thesis we introduce and investigate a method combining Principle Component Analysis (PCA) and Independent Component Analysis (ICA) for Blind Source Separation (BSS). A recursive method for the PCA is applied to meet the demands of a real-time application, and for the ICA algorithm, the Information maximization principle is used. In an effort to address convolutive BSS, the separation is performed in the frequency domain. By doing so, the problem reduces to the simple stantaneous case, and existing instantaneous BSS model can be used. However, frequency domain BSS is subject to both permutation and scaling ambiguities. This thesis examines several methods to solve these problems, like Direction Of Arrival (DOA) and the Kurtosis. Furthermore, results are presented based on Matlab simulations as well as from a real-time implementation. Evaluations show that the combined PCA-ICA algorithm outperforms both the PCA and ICA alone. The algorithm was also successfully implemented in real-time with comparable noise suppression capability compared to Matlab implementation. Future work which include ways to improve efficiency of the algorithms is also discussed.

In this paper, we introduce and investigate a method combining principle component analysis (PCA) and independent component analysis (ICA) for blind source separation (BSS). A recursive method for the PCA is applied to meet the demands of a real-time application, and likewise a modified on-line version of the information maximization principle is used. The combined PCA-ICA algorithm, first reduces the dimension of the problem and then separates the signals. Evaluation of the proposed algorithm in a real room shows superior noise suppression capabilities compared to the use of PCA or ICA individually. The proposed algorithm achieves an impressive noise suppression/separation of up to 14 dB with only two microphones. Most importantly this is achieved with negligible distortion of the recovered signal. © 2005 IEEE.

In child day-care centers the noise level can rise to high levels and in some cases become so high that the people present risk hearing damage. The purpose of this investigation was to study how the noise level depends on the different activities during the day. The study was performed at a child day-care center and 6 children and 5 adult female teachers participated. The participants had a microphone attached next to the ear connected to a wearable digital recorder. A total of 32.5 hours of data was recorded. By listening tests the recorded data could be sorted by activity and by number of people present in the same room as the test subject. Activities were classified as belonging to one of the following: outdoor activity, indoor play, singing, storytelling and gathering. Further, by listening, the data was classified in small group/large group (3 or less/more than 3). The results show that the average noise level (LAeq) for outdoor activity was the highest and was measured to 88.1 dBA (average over 7h52min). Singing was 81.5 dBA (1h26min), indoor play 81.3 dBA (19h21min), storytelling 76.6 dBA (1h09min) and gathering 75.0 dBA (2h44min). The noise level difference between all activities except between singing and indoor play and gathering and storytelling could be verified using t-test (p<0.001). Further, the results showed that the average noise level was 86.6 dBA (14h11min) for the large group and 79.6 dBA (18h21min) for the small group. This difference, of 7.0 dB was statistically validated (p<0.001) using t-test.

This master’s thesis work was performed at Optimum Biometric Labs, OBL, located in Karlskrona, Sweden. Optimum Biometric Labs perform independent scenario evaluations to companies who develop biometric devices. The company has a product Optimum preConTM which is surveillance and diagnosis tool for biometric systems. This thesis work’s objective was to develop a conceptual model and implement it as an additional layer above the biometric layer with intelligence about the biometric users. The layer is influenced by the general procedure of biometrics in a multimodal behavioural way. It is working in an unsupervised way and performs in an unsupervised manner. While biometric systems are increasingly adopted the technologies have some inherent problems such as false match and false non-match. In practice, a rejected user can not be interpreted as an impostor since the user simply might have problems using his/her biometric feature. The proposed methods in this project are dealing with these problems when analysing biometric usage in runtime. Another fact which may give rise to false rejections is template aging; a phenomenon where the enrolled user’s template is too old compared towards the user’s current biometric feature. A theoretical approach of template aging was known; however since the analysis of template aging detection was correlated with potential system flaws such as device defects or human generated risks such as impostor attacks this task would become difficult to solve in an unsupervised system but when ignoring the definition of template aging, the detection of similar effects was possible. One of the objectives of this project was to detect template aging in a predictive sense; this task failed to be carried out because the absence of basis performing this kind of tasks. The developed program performs abnormality detection at each incoming event from a biometric system. Each verification attempt is assumed to be from a genuine user unless any deviation according to the user's history is found, an abnormality. The possibility of an impostor attack depends on the degree of the abnormality. The application makes relative decisions between fraud possibilities or if genuine user was the source of what caused the deviations. This is presented as an alarm with the degree of impostor possibility. This intelligent layer has increased Optimum preCon´s capacity as a surveillance tool for biometrics. This product is an efficient complement to biometric systems in a steady up-going worldwide market.

This paper presents a personal voice analyzer and trainer that allow the user to perform four daily exercises to improve the voice capacity. The system grades how well the user is performing the exercises by analyzing the duration, the intensity and the pitch of the user’s voice.

Noise dosimeters are valuable tools in assessing the individual noise dose in the workplace. At non-industrial work places with a high degree of communication, such measurements would include the wearer’s own voice which would be registered as noise. This may not always be desirable. The purpose of this investigation was to study the effect of the wearers own voice in noise dosimeter measurements, and especially the difference between children and adults as test subjects. The study took place at a day-care center and sixteen children and thirteen adult female preschool teachers participated. The participants wore a digital recorder during the day, which recorded the sound signal and vibrations originating from an accelerometer attached to the neck of the test subjects, for distinguishing of whether the subject was speaking or not. Thus, average A-weighted noise levels with and without the influence of the subjects own voice could be obtained. The Leq for the measurements with and without the own voice was 84.6 dBA and 72.2 dBA for the children, respectively, and 79.3 dBA and 70.0 dBA for adults. Student’s t-test showed a significant (p<0.01) difference of 12.4 dBA for children and 9.3 dBA for adults when comparing measurements including and excluding the own voice and also that the difference was significantly larger for children. Thus, the study conclude that the influence from the own voice implied an augmentation of the Leq value and that there is a significant difference between children and adults in how large this augmentation is.

This paper presents an asynchronous resampling implementation on a low-power fixed-point DSP, which uses around 47% less computational resources compared to the solution provided by the DSP manufacturer, without compromising audio quality.

Researchers in neurophysiology in Lund have undertaken measurements of the spinal cord in rats where the movement of the spinal cord due to blood flow and breathing has proved to be a major obstacle in their research. Except from manufacturing electrodes specialized for the task, an idea has been to reduce the effect of the motion by moving a table on which the object is placed, in a compensating manner. To further explore this idea, cooperation with an engineer at the department of Neurophysiology, Lund University, has been the subject of this thesis. A table movable in three directions was constructed. DC-motors controlling an eccentric were chosen as actuators for the motion control. A circuit for direct analogue control of DC-motors implementing proportional, integrating and differentiating stages aimed for standard PID control was built. The circuit allows current control to avoid overload of the motor as well as balancing of DC-noise from the amplifier stages. Recordings of the spinal cord motion as well as heart and breathing signals was done with a laser-vibrometer. Signal analysis was performed to investigate the suitability of reference-based feed forward control with the filtered-x LMS algorithm. The analysis showed poor result for this method.

Cognitive radio is an intelligent technology that helps in resolving the issue of spectrum scarcity. In a spectrum sharing network, where secondary user can communicate simultaneously along with the primary user in the same frequency band, one of the challenges is to obtain balance between two conflicting goals that are to minimize the interference to the primary users and to improve the performance of the secondary user. In our paper we have considered a primary link and a secondary link (cognitive link) in a fading channel. To improve the performance of the secondary user by maintaining the Quality of Service (Qos) to the primary user, we considered varying the transmit power of the cognitive user. For this we proposed ANFIS based opportunistic power control strategy with primary user's SNR and primary user's interference channel gain as inputs. By using fuzzy inference system, Qos of primary user is adhered and there is no need of complex feedback channel from primary receiver. The simulation results of the proposed strategy shows better performance than the one without power control.

In our aging society, the design and implementation of a high-performance autonomous distributed vision information system for autonomous physical services become ever more important. In line with this development, the proposed Intelligent Vision Agent System, IVAS, is able to automatically detect and identify a target for a specific task by surveying a human activities space. The main subject of this thesis is the optimal configuration of a sensor system meant to capture the target objects and their environment within certain required specifications. The thesis thus discusses how a discrete sensor causes a depth spatial quantisation uncertainty, which significantly contributes to the 3D depth reconstruction accuracy. For a sensor stereo pair, the quantisation uncertainty is represented by the intervals between the iso-disparity surfaces. A mathematical geometry model is then proposed to analyse the iso-disparity surfaces and optimise the sensors’ configurations according to the required constrains. The thesis also introduces the dithering algorithm which significantly reduces the depth reconstruction uncertainty. This algorithm assures high depth reconstruction accuracy from a few images captured by low-resolution sensors. To ensure the visibility needed for surveillance, tracking, and 3D reconstruction, the thesis introduces constraints of the target space, the stereo pair characteristics, and the depth reconstruction accuracy. The target space, the space in which human activity takes place, is modelled as a tetrahedron, and a field of view in spherical coordinates is proposed. The minimum number of stereo pairs necessary to cover the entire target space and the arrangement of the stereo pairs’ movement is optimised through integer linear programming. In order to better understand human behaviour and perception, the proposed adaptive measurement method makes use of a fuzzily defined variable, FDV. The FDV approach enables an estimation of a quality index based on qualitative and quantitative factors. The suggested method uses a neural network as a tool that contains a learning function that allows the integration of the human factor into a quantitative quality index. The thesis consists of two parts, where Part I gives a brief overview of the applied theory and research methods used, and Part II contains the five papers included in the thesis.

The Intelligent Vision Agent System, IVAS, is a system for automatic target detection, identification and information processing for use in human activities surveillance. This system consists of multiple sensors, and with control of their deployment and autonomous servo. Finding the optimal configuration for these sensors in order to capture the target objects and their environment to a required specification is a crucial problem. With a stereo pair of sensors, the 3D space can be discretized by an iso-disparity surface, and the depth reconstruction accuracy of the space is closely related to the iso-disparity curve positions. This paper presents a method to enable planning the position of these multiple stereo sensors in indoor environments. The proposed method is a mathematical geometry model, used to analyze the iso-disparity surface. We will show that the distribution of the iso-disparity surface and the depth reconstruction accuracy are controllable by the parameters of such model. This model can be used to dynamically adjust the positions, poses and baselines lengths of multiple stereo pairs of cameras in 3D space in order to get sufficient visibility and accuracy for surveillance tracking and 3D reconstruction. We implement the model and present uncertainty maps of depth reconstruction calculated while varying the baseline length, focal length, stereo convergence angle and sensor pixel length. The results of these experiments show how the depth reconstruction uncertainty depends on stereo pair’s baseline length, zooming and sensor physical properties.

A recently established method for multi-target tracking which both estimates the time-varying number of targets and their states from a sequence of observation sets in the presence of data association uncertainty, detection uncertainty, noise and false alarms is the probability hypothesis density (PHD) recursion. The approach involves modeling the respective collections of targets and measurements as random finite sets and to propagate the posterior intensity, which is a first order statistic of the random finite set of targets, in time. A closed form solution to the PHD filter recursion for multi-target tracking is provided by the Gaussian Mixture Probability Hypothesis Density filter (GM-PHD filter), whose posterior intensity function is estimated by a sum of weighted Gaussian components, including means, weights and covariances that can be propagated analytically in time. Besides the GM-PHD filter algorithm implementation, choose the probability density function for representing target births in GM-PHD recursion and true target trajectory generation to get best tracking performance is a challenge and is the purpose of this thesis work. One reference to judge the performance of the algorithm is the target detection time, as given in this thesis.

The problem of controlling the noise level in the environment has been the focus of a tremendous amount of research over the years. Active Noise Cancellation (ANC) is one such approach that has been proposed for reduction of steady state noise. ANC refers to an electromechanical or electro acoustic technique of canceling an acoustic disturbance to yield a quieter environment. The basic principle of ANC is to introduce a canceling “anti-noise” signal that has the same amplitude but the exact opposite phase, thus resulting in an attenuated residual noise signal. Wideband ANC systems often involve adaptive filter lengths, with hundreds of taps. Using sub band processing can considerably reduce the length of the adaptive filter. This thesis presents Filtered-X Least Mean Squares (FXLMS) algorithm to implement it on a fixed point digital signal processor (DSP), ADUC7026 micro controller from Analog devices. Results show that the implementation in fixed point matches the performance of a floating point implementation.

The increased mobility in society has led to a need for convenient mobile communication in many different type of environments. Environments such as a motorcycle helmet, engine rooms and most industrial sites share a common challenge in that they often offer significant acoustic background noise. Noise reduces the speech intelligibility and consequently limits the potential of mobile speech communications. Existing single channel solutions for speech enhancement may perform adequately when the level of noise is moderate. When the noise level becomes significant, additional use of the spatial domain in order to successfully perform speech enhancement is a potential solution. This is achieved by including several microphones in an array placed in the vicinity of the person speaking. A beamforming algorithm is hereby used to combine the microphones such that the desired speech signal is enhanced. The interest in using microphone arrays for broadband speech and audio processing has increased in recent years. There have been a considerable amount of interesting applications published using beamforming techniques for hands-free voice communication in cars, hearing-aids, teleconferencing and multimedia applications. Most of proposed solutions deal exclusively with environments where the noise is moderate. This thesis is a study of noise reduction in a helmet communication system on a moving motorcycle. The environment is analyzed under different driving conditions and a speech enhancement solution is proposed that operates successfully in all driving conditions. The motorcycle environment can exhibit extremely high noise levels, when driving at high speed, while it can produce a low noise levels at moderate speeds. This fact implies that different solutions are required. It is demonstrated in the thesis that a cascaded combination of a calibrated subband beamforming technique, together with a single channel solution provides good results at all noise levels. The proposed solution operates in the frequency domain, where all microphone signals are decomposed by a subband filter bank prior to the speech enhancement processing. Since the subband transformation is an important component of the overall system performance, a method for filter bank design is also provided in the thesis. The design is such that the aliasing effects in the transformations are minimized while a small delay of the total system is maintained.

In this report a real case study of the sound environment within a helmet while driving motorcycle is investigated. A solution to perform speech enhancement is proposed for the purpose of mobile speech communication. A microphone array, mounted onto the face shield in front of the user's mouth, is used to capture the spatio-temporal properties of the acoustic wave ¯eld inside the helmet. The power of the spatially spread noise within the helmet is small when standing still while it may heavily exceed the power of the speech when driving at high speeds. This will result in dramatically reduced speech intelligibility in the communication channel. The highly dynamic noise level imposes a challenge for existing speech enhancement solutions. We propose a subband adaptive system for speech enhancement which consists of a soft constrained beamformer in cascade with a signal-to-noise ratio dependent single microphone solution. The beamformer make use of a calibration signal gathered in the actual environment from the speaker's position. This calibration procedure e±ciently captures the acoustical properties in the environment. Evaluation of the beamformer and the single microphone algorithm, both as either parts by them selves and as a cascaded structure, together with the optimal subband Wiener solution is presented. It is shown that a cascaded combination of the calibrated subband beamforming technique together with the single channel solution outperforms either one by it self, and provides near optimal results at all noise levels.

This paper discusses speech enhancement in an enclosed environment such as communication in a motorcycle helmet. A new constrained subband adaptive beamformer is proposed, which uses the concept of an earlier proposed calibrated beamformer mainly developed for a hands-free in-car environment. The highly non-stationary nature of the disturbing sound field encountered in an motorcycle helmet and the fact that the source is situated in the extreme nearfield of the array, causes the beamformer to produce an unwanted fluctuation in the output level. The spatially constrained beamformer proposed in this paper makes sure that the output maintains a constant gain, as long as the corresponding source originates from the desired location.

In this paper, we consider the design of a class of unified complex Hadamard transform (UCHT) sequences. An effi- cient modification is imposed to those sequences to better suit applications in asynchronous code-division multiple- access (CDMA) systems. These modified UCHT sequences preserve the orthogonality of the original UCHT sequences and offer increased design options due to an increased number of parameters. The design of UCHT, modified UCHT, and Oppermann sequences is then formulated with reference to optimizing the maximum nontrivial aperiodic correlation value. These optimization problems can then be solved efficiently using a genetic algorithm with the maximum nontrivial aperiodic correlation value serving in the definition of a fitness function. Numerical examples illustrate the benefits of modified UCHT sequences over the original UCHT sequences.

The number of spreading sequences required for Direct Sequence Code Division Multiple Access (DS-CDMA) systems depends on the number of simultaneous users in the system. Often a sequence family provides more sequences than are required; in many cases the selection of the employed sequences is a computationally intensive task. This selection is a key consideration, as the properties of the sequences assigned affect the error performance in the system. In this paper, a branch and bound algorithm is presented to perform this selection based on two different cost functions. Numerical results are presented to demonstrate the improved performance of this algorithm over previous work.

The number of spreading sequences required for Direct Sequence Code Division Multiple Access (DS-CDMA) systems depends on the number of simultaneous users on the channel. The correlation properties of the sequences used affect the bit error rate of the system. Often a sequence family provides more sequences than are required and in many cases the selection of the employed sequences is a computationally intensive task. In this paper, a branch and bound algorithm is presented to optimise the subset of available sequences given the required subset size. In contrast to previous approaches, the resulting subset is guaranteed to be optimal. Numerical results are presented to demonstrate the improved performance of this algorithm over previous work.

Large Area Synchronous (LAS) sequences are a class of ternary interference free window spreading sequences. One of their advantages is the ability to construct permutation LAS families in order to reduce adjacent cell interference (ACI) in cellular systems. There has been little previous numerical work to examine the effect of using permutation families compared to simply reusing the same LAS family across different cells. The bit error rates resulting from two cell systems employing both permutation families and sequence reuse are considered here by simulation.

Numerous spread spectrum applications require families of long spreading sequences, often used at very high chip rates. In this paper, an algebraically simple way of generating long sequences by combining shorter polyphase sequences is presented, aimed at asynchronous spread spectrum systems. The approach is motivated by the fact that polyphase sequences offer increased design options, in terms of the supported range of correlation characteristics, while combination sequences allow for simpler generation of long sequences. It leads to the definition of combination Oppermann sequences, the properties of which are investigated in this paper. Numerical results indicate that the families of these proposed combination sequences provide favourable aperiodic correlations. The presented family of combination Oppermann sequences is therefore suitable for applications that rely on rapid synchronisation and are required to provide multiple access to the system.

Large Area (LA) sequences form a class of ternary spreading sequences which exhibit an interference free window. In addition, these sequences have low correlation properties outside this window. Work to date has concentrated on examining the parameters and performance of LA sequences with reference to only a small number of example families. In this paper we develop general conditions which an LA family must satisfy. The development of these conditions allows for the production of computationally efficient tests to determine whether a given family is an LA family. In particular, these tests can form the basis for algorithms to construct LA families, allowing for a larger number of families with the potential for higher energy efficiency than those of previous work.

In this paper we introduce periodic Oppermann sequences, which constitute a special class of polyphase sequences. The properties of these sequences are presented, and indicate that periodic Oppermann sequences are suitable for combination to generate families of longer sequences. Numerical examples show that periodic Oppermann sequences can be designed for ranging or synchronisation applications or for supporting multiple access spread spectrum communication systems.

This paper presents a new practical approach to complex Chebyshev approximation by semi-infinite linear programming. By the new front-end technique, the associated semi-infinite linear programming problem is solved exploiting the finiteness of the related Lagrange multipliers by adapting finite-dimensional linear programming to the dual semi-infinite problem, and thereby taking advantage of the numerical siability and efficiency of conventional linear programming software packages. Furthermore, the optimization procedure is simple to describe theoretically and straightforward to implement in computer coding. The new design technique is therefore. highly accessible. The algorithm is formally introduced as the linear Dual Nested Complex Approximation (DNCA) algorithm. The DNCA algorithm is versatile and can be applied to a variety of applications such as narrow-band as well as broad-band beamformers with any geometry, conventional Finite Impulse Response (FIR) filters, analog and digital Laguerre networks. and digital FIR equalizers. The proposed optimization technique is applied to several numerical examples dealing with the design of a narrow-band base-station antenna array for mobile communication.