Numerous techniques for 3D face reconstruction from a single image exist, making use of large facial databases. However, they commonly encounter quality issues due to the absence of information from alternate perspectives. For example, 3D reconstruction with a single front view input data has limited realism, particularly for profile views. We have observed that multiple-view 3D face reconstruction yields higher-quality models compared to single-view reconstruction. Based on this observation, we propose a novel pipeline that combines several deep-learning methods to enhance the quality of reconstruction from a single frontal view. Our method requires only a single image (front view) as input, yet it generates multiple realistic facial viewpoints using various deep-learning networks. These viewpoints are utilized to create a 3D facial model, significantly enhancing the 3D face quality. Traditional image-space editing has limitations in manipulating content and styles while preserving high quality. However, editing in the latent space, which is the space after encoding or before decoding in a neural network, offers greater capabilities for manipulating a given photo. Motivated by the ability of neural networks to generate 2D images from an extensive database and recognizing that multi-view 3D face reconstruction outperforms single-view approaches, we propose a new pipeline. This pipeline involves latent space manipulation by first finding a latent vector corresponding to a given image using the Generative Adversarial Network (GAN) inversion method. We then search for nearby latent vectors to synthesize multiple pose images from the provided input image, aiming to enhance 3D face reconstruction. The generated images are then fed into Diffusion models, another image synthesis network, to generate their respective profile views. The Diffusion model is known for producing more realistic large-angle variations of a given object than GAN models do. Subsequently, all these images (multi-view images) are fed into an Autoencoder, a neural network designed for 3D face model predictions, to derive the 3D structure of the face. Finally, the texture of the 3D face model is combined to enhance its realism, and certain areas of the 3D shape are refined to correct any unrealistic aspects. Our experimental results validate the effectiveness and efficiency of our method in reconstructing highly accurate 3D models of human faces from a single input (front view input) image. The reconstructed models retain high visual fidelity to the original image, even without the need for a 3D database.

RF and acoustic communications are widely used in terrestrial and underwater environments, respectively. This thesis examines the use of ultrasonic communication alternately in terrestrial applications. We first investigate the ultrasonic channel in order to observe whether reliable communication is possible among the ultrasonic nodes as an alternative to RF-based communications. Some key characteristics of the single-input-single-output (SISO) and single-inputmultiple- output (SIMO) ultrasonic channel are inspected with extensive experiments utilizing ultrasonic transmitters and receivers. Well known receiver diversity techniques are employed to combine the observations of multiple receiving ultrasonic transducers in a SIMO scheme and receiver diversity gain is attained. The thesis also covers the implementation of a receiver node by using a low-cost microcontroller.

As resources are limited, radio spectrum becomes congested due to the growth of wireless applications. However, measurements address the fact that most of the licensed spectrums experience low utilization even in intensively teeming areas. In the exertion to improve the utilization of the limited spectrum resources, cognitive radio networks have emerged as a powerful technique to resolve this problem. There are two types of user in cognitive radio networks (CRNs) named as primary user (PU) and secondary user (SU). Therein, the CRN enables the SU to utilize the unused licensed frequency of the PU if it possibly finds the vacant spectrum or white space (known as opportunistic spectrum access). Alternatively, SU can transmit simultaneously with the PU provided that transmission power of SU does not cause any harmful interference to the PU (known as spectrum sharing systems). In this thesis work, we study fundamental knowledge of the CRNs and focus on the performance analysis of the single input multiple output (SIMO) system for spectrum sharing approach. We assume that a secondary transmitter (SU-Tx) has full channel state information (CSI). The SU-Tx can adjust its transmit power not to cause harmful interference to the PU and obtain an optimal transmit rate. In particular, we derive the closed-form expressions for the cumulative distribution function (CDF), outage probability and an analytical expression for symbol error probability (SEP). / As resources are limited, radio spectrum becomes congested due to the growth of wireless applications. However, measurements address the fact that most of the licensed spectrums experience low utilization even in intensively teeming areas. In the exertion to improve the utilization of the limited spectrum resources, cognitive radio networks have emerged as a powerful technique to resolve this problem. There are two types of user in cognitive radio networks (CRNs) named as primary user (PU) and secondary user (SU). Therein, the CRN enables the SU to utilize the unused licensed frequency of the PU if it possibly finds the vacant spectrum or white space (known as opportunistic spectrum access). Alternatively, SU can transmit simultaneously with the PU provided that transmission power of SU does not cause any harmful interference to the PU (known as spectrum sharing systems). In this thesis work, we study fundamental knowledge of the CRNs and focus on the performance analysis of the single input multiple output (SIMO) system for spectrum sharing approach. We assume that a secondary transmitter (SU-Tx) has full channel state information (CSI). The SU-Tx can adjust its transmit power not to cause harmful interference to the PU and obtain an optimal transmit rate. In particular, we derive the closed-form expressions for the cumulative distribution function (CDF), outage probability and an analytical expression for symbol error probability (SEP). / Iqbal Hasan Haider, cell: +46704571807 MD. Fazla Rabby, cell: +46734965477

This thesis presents rules that characterize the root locus for polynomials that are nonlinear in the root-locus parameter k. Classical root locus applies to polynomials that are affine in k. In contrast, this thesis considers polynomials that are quadratic or cubic in k. In particular, we focus on constructing the root locus for linear feedback control systems, where the closed-loop denominator polynomial is quadratic or cubic in k. First, we present quadratic root-locus rules for a controller class that yields a closed-loop denominator polynomial that is quadratic in k. Next, we develop cubic root-locus rules for a controller class that yields a closed-loop denominator polynomial that is cubic in k. Finally, we extend the quadratic root-locus rules to accommodate a larger class of controllers. We also provide controller design examples to demonstrate the quadratic and cubic root locus. For example, we show that the triple integrator can be high-gain stabilized using a controller that yields a closed-loop denominator polynomial that is quadratic in k. Similarly, we show that the quadruple integrator can be high-gain stabilized using a controller that yields a closed-loop denominator polynomial that is cubic in k.

No description available.

No description available.

Spectrum, environment and location awareness are key characteristics of cognitive radio (CR). Knowledge of a user’s location as well as the surrounding environment type may enhance various CR tasks, such as spectrum sensing, dynamic channel allocation and interference management. This dissertation deals with the optimisation of adaptive localisation techniques for CR. The first part entails the development and evaluation of an efficient bandwidth determination (BD) model, which is a key component of the cognitive positioning system. This bandwidth efficiency is achieved using the Cramer-Rao lower bound derivations for a single-input-multiple-output (SIMO) antenna scheme. The performances of the single-input-single-output (SISO) and SIMO BD models are compared using three different generalised environmental models, viz. rural, urban and suburban areas. In the case of all three scenarios, the results reveal a marked improvement in the bandwidth efficiency for a SIMO antenna positioning scheme, especially for the 1×3 urban case, where a 62% root mean square error (RMSE) improvement over the SISO system is observed. The second part of the dissertation involves the presentation of a multiband time-of arrival (TOA) positioning technique for CR. The RMSE positional accuracy is evaluated using a fixed and dynamic bandwidth availability model. In the case of the fixed bandwidth availability model, the multiband TOA positioning model is initially evaluated using the two-step maximum-likelihood (TSML) location estimation algorithm for a scenario where line-of-sight represents the dominant signal path. Thereafter, a more realistic dynamic bandwidth availability model has been proposed, which is based on data obtained from an ultra-high frequency spectrum occupancy measurement campaign. The RMSE performance is then verified using the non-linear least squares, linear least squares and TSML location estimation techniques, using five different bandwidths. The proposed multiband positioning model performs well in poor signal-to-noise ratio conditions (-10 dB to 0 dB) when compared to a single band TOA system. These results indicate the advantage of opportunistic TOA location estimation in a CR environment. / Dissertation (MEng)--University of Pretoria, 2012. / Electrical, Electronic and Computer Engineering / unrestricted

Multiple-Input Multiple-Output (MIMO) communication using multiple antennas has received significant attention in recent years, both in the academia and industry, as they offer additional spatial dimensions for high-rate and reliable communication, without expending valuable bandwidth. However, exploiting these promised benefits of MIMO systems critically depends on fast and accurate acquisition of Channel State Information (CSI) at the Receiver (CSIR) and the Transmitter (CSIT). In Time Division Duplex (TDD) MIMO systems, where the forward channel and the reverse channel are the same, it is possible to exploit this reciprocity to reduce the overhead involved in acquiring CSI, both in terms of training duration and power. Further, many popular and efficient transmission schemes such as beam forming, spatial multiplexing over dominant channel modes, etc. do not require full CSI at the transmitter. In such cases, it is possible to reduce the Reverse Channel Training (RCT) overhead by only learning the part of the channel that is required for data transmission at the transmitter. In this thesis, we propose and analyze several novel channel-dependent RCT schemes for MIMO systems and analyze their performance in terms of (a) the mean-square error in the channel estimate, (b) lower bounds on the capacity, and (c) the diversity-multiplexing gain tradeoff. We show that the proposed training schemes offer significant performance improvement relative to conventional channel-agnostic RCT schemes. The main take-home messages from this thesis are as follows: • Exploiting CSI while designing the RCT sequence improves the performance. • The training sequence should be designed so as to convey only the part of the CSI required for data transmission by the transmitter. • Power-controlled RCT, when feasible, significantly outperforms fixed power RCT.

We consider the problem of Adaptive Control of finite-dimensional, stable, Linear Time Invariant (LTI) plants. Amongst such plants, the subclass regarding which an upper bound on the order is not known or which are known to be nonminimum phase (zeros in the unstable region) pose formidable problems in their own right. On one hand, if an upper bound on the order of the plant is not known, adaptive control usually involves some form of order estimation. On the other hand, when the plant is allowed to be either minimum phase or nonminimum phase, the adaptive control problem, as is well-known, becomes considerably-less tractable. In this study, the class of unknown plants considered is such that no information is available on the upper bound of the plant order and, further, the plant may be either minimum phase or nonminimum phase. Albeit known to be stable, such plants throw myriads of challenges in the context of adaptive control. Adaptive control involving such plants has been addressed [79] in a Model Reference Adaptive Control (MRAC) framework. There, the inputs and outputs of the unknown plant are the only quantities available by measurement in terms of which any form of modeling of the unknown plant may be made. Inputs to the reference model have been taken from certain restricted classes of bounded signals. In particular, the three classes of inputs considered are piecewise continuous bounded functions which asymptotically approach • a nonzero constant, • a sinusoid, and • a sinusoid with a nonzero shift. Moreover, the control law is such that adaptation is carried out at specific instants separated by progressively larger intervals of time. The schemes there have been proved to be e-optimal in the sense of a suitably formulated optimality criterion. If, however, the reference model inputs be extended to the class of piecewise continuous bounded functions, that would compound the complexity of the adaptive control problem. Only one attempt [78] in adaptive control in such a setting has come to our notice. The problem there has been tackled by an application of the theory of Pade Approximations to time moments of an LTI system. Based on a time moments estimation procedure, a simple adaptive scheme for Single-Input Single-Output (SISO) systems with only a cascade compensator has been reported. The first chapter is essentially meant to ensure that the problem we seek to address in the field of adaptive control indeed has scope for research. Having defined Adaptive Control, we selectively scan through the literature on LTI systems, with focus on MRAC. We look out in particular for studies involving plants of which not much is known regarding their order and systems which are possibly nonminimum phase. We found no evidence to assert that the problem of adaptive control of stable LTI systems, not necessarily minimum phase and of unknown upper bound on the order, was explored enough, save two attempts involving SISO systems. Taking absence of evidence (of in-depth study) for evidence of absence, we make a case for the problem and formally state it. We preview the thesis. We set two targets before us in Chapter 2. The first is to review one of the existing procedures attacking the problem we intend to address. Since the approach is based on the notion of time moments of an LTI system, and as we are to employ Pade Approximations as a tool, we uncover these concepts to the limited extent of our requirement. The adaptive procedure, Plant Command Modifier Scheme (PCMS) [78], for SISO plants is reported in some detail. It stands supported on an algorithm specially designed to estimate the time moments of an LTI system given no more than its input and output. Model following there has been sought to be achieved by matching the first few time moments of the reference model by the corresponding ones of the overall compensated plant. The plant time moment estimates have been taken to represent the unknown plant. The second of the goals is to analyze PCMS critically so that it may serve as a forerunner to our work. We conclude the chapter after accomplishing these goals. In Chapter 3, we devise a time moment estimator for SISO systems from a perspective which is conceptually equivalent to, yet functionally different from, that appropriated in [78]. It is a recipe to obtain estimates of time moments of a system by computing time moment estimates of system input and output signals measured up to current time. Pade approximations come by handy for this purpose. The lacunae exposed by a critical examination of PCMS in Chapter 2 guide us to progressively refine the estimator. Infirmities in the control part of PCMS too have come to light on our probing into it. A few of these will be fixed by way of fabricating two exclusively cascade compensators. We encounter some more issues, traceable to the estimator, which need redressal. Instead of directly fine-tuning the estimator itself, as is the norm, we propose the idea of 'estimating' the lopsidedness of the estimator by using it on the fully known reference model. This will enable us to effect corrections and obtain admissible estimates. Next, we explore the possibility of incorporating feedback compensation in addition to the existing cascade compensation. With output error minimization in mind, we come up with three schemes in this category. In the process, we anticipate the risk of instability due to feedback and handle it by means of an instability preventer with an inbuilt instability detector. Extensive simulations with minimum and rionminimum phase unknown plants employing the various schemes proposed are presented. A systematic study of simulation results reveals a dyad of hierarchies of progressively enhanced overall performance. One is in the sequence of the proposed schemes and the other in going for matching more and more moments. Based on our experiments we pick one of the feedback schemes as the best. Chapter 4 is conceived of as a bridge between SISO and multivariable systems. A transition from SISO to Multi-Input Multi-Output (MIMO) adaptive control is not a proposition confined to the mathematics of dimension-enhancement. A descent from the MIMO to the SISO case is expected to be relatively simple, though. So to transit as smoothly and gracefully as possible, some issues have to be placed in perspective before exploring multivariable systems. We succinctly debate on the efforts in pursuit of the exact vis-a-vis the accurate, and their implications. We then set some notations and formulate certain results which serve to unify and simplify the development in the subsequent three chapters. We list a few standard results from matrix theory which are to be of frequent use in handling multivariable systems. We derive control laws for Single-Input Multi-Output (SIMO) systems in Chapter 5. Expectedly, SIMO systems display traits of observability and uncontrollability. Results of illustrative simulations are furnished. In Chapter 6, we formulate control laws for Multi-Input Single-Output (MISO) systems. Characteristics of unobservability and controllability stand out there. We present case studies. Before actually setting foot onto MIMO systems, we venture to conjecture on what to expect there. We work out all the cascade and feedback adaptive schemes for square and nonsquare MIMO systems in Chapter 7. We show that MIMO laws when projected to MISO, SIMO and SISO cases agree with the corresponding laws in the respective cases. Thus the generality of our treatment of MIMO systems over other multivariable and scalar systems is established. We report simulations of instances depicting satisfactory performance and highlight the limitations of the schemes in tackling the family of plants of unknown upper bound on the order and possibly nonminimum phase. This forms the culmination of our exercise which took off from the reported work involving SISO systems [78]. Up to the end of the 7th chapter, we are in pursuit of solutions for the problem as general as in §1.4. For SISO systems, with input restrictions, the problem has been addressed in [79]. The laws proposed there carry out adaptation only at certain discrete instants; with respect to a suitably chosen cost, the final laws are proved to be e>optimal. In Chapter 8, aided by initial suboptimal control laws, we finally devise two algorithms with continuous-time adaptation and prove their optimality. Simulations with minimum and nonminimum phase plants reveal the effectiveness of the various laws, besides throwing light on the bootstrapping and auto-rectifying features of the algorithms. In the tail-piece, we summarize the work and wind up matters reserved for later deliberation. As we critically review the present work, we decant the take-home message. A short note on applications followed by some loud thinking as a spin-off of this report will take us to finis.

The statistics of the amplitude, time and angle of arrival of multipaths in an indoor environment are all necessary components of multipath models used to simulate the performance of spatial diversity in receive antenna configurations. The model presented by Saleh and Valenzuela, was added to by Spencer et. al., and included all three of these parameters for a 7 GHz channel. A system was built to measure these multipath parameters at 2.4 GHz for multiple locations in an indoor environment. Another system was built to measure the angle of transmission for a 6 GHz channel. The addition of this parameter allows spatial diversity at the transmitter along with the receiver to be simulated. The process of going from raw measurement data to discrete arrivals and then to clustered arrivals is analyzed. Many possible errors associated with discrete arrival processing are discussed along with possible solutions. Four clustering methods are compared and their relative strengths and weaknesses are pointed out. The effects that errors in the clustering process have on parameter estimation and model performance are also simulated.