Adaptive multilevel quadrature amplitude radio implementation in programmable logic

Emerging broadband wireless packet data networks are increasingly employing spectrally efficient modulation methods like Quadrature Amplitude Modulation (QAM) to increase the channel efficiency and maximize data throughput. Unfortunately, the performance of high level QAM modulations in the wireless channel is sensitive to channel imperfections and throughput is degraded significantly at low signal-to-noise ratios due to bit errors and packet retransmission. To obtain a more “robust” physical layer, broadband systems are employing multilevel QAM (M-QAM) to mitigate this reduction in throughput by adapting the QAM modulation level to maintain acceptable packet error rate (PER) performance in changing channel conditions. This thesis presents an adaptive M-QAM modem hardware architecture, suitable for use as a modem core for programmable software defined radios (SDRs) and broadband wireless applications. The modem operates in “burst” mode, and can reliably synchronize to different QAM constellations “burst-by-burst”. Two main improvements exploit commonality in the M-QAM constellations to minimize the redundant hardware required. First, the burst synchronization functions (carrier, clock, amplitude, and modulation level) operate reliably without prior knowledge of the QAM modulation level used in the burst. Second, a unique bit stuffing and shifting technique is employed which supports variable bit rate operation, while reducing the core signal processing functions to common hardware for all constellations. These features make this architecture especially attractive for implementation with Field Programmable Gate Arrays (FPGAs) and Application-Specific Integrated Circuits (ASICs); both of which are becoming popular for highly integrated, cost-effective wireless transceivers

Exploring the Internal Statistics: Single Image Super-Resolution, Completion and Captioning

Image enhancement has drawn increasingly attention in improving image quality or interpretability. It aims to modify images to achieve a better perception for human visual system or a more suitable representation for further analysis in a variety of applications such as medical imaging, remote sensing, and video surveillance. Based on different attributes of the given input images, enhancement tasks vary, e.g., noise removal, deblurring, resolution enhancement, prediction of missing pixels, etc. The latter two are usually referred to as image super-resolution and image inpainting (or completion). Image super-resolution and completion are numerically ill-posed problems. Multi-frame-based approaches make use of the presence of aliasing in multiple frames of the same scene. For cases where only one input image is available, it is extremely challenging to estimate the unknown pixel values. In this dissertation, we target at single image super-resolution and completion by exploring the internal statistics within the input image and across scales. An internal gradient similarity-based single image super-resolution algorithm is first presented. Then we demonstrate that the proposed framework could be naturally extended to accomplish super-resolution and completion simultaneously. Afterwards, a hybrid learning-based single image super-resolution approach is proposed to benefit from both external and internal statistics. This framework hinges on image-level hallucination from externally learned regression models as well as gradient level pyramid self-awareness for edges and textures refinement. The framework is then employed to break the resolution limitation of the passive microwave imagery and to boost the tracking accuracy of the sea ice movements. To extend our research to the quality enhancement of the depth maps, a novel system is presented to handle circumstances where only one pair of registered low-resolution intensity and depth images are available. High quality RGB and depth images are generated after the system. Extensive experimental results have demonstrated the effectiveness of all the proposed frameworks both quantitatively and qualitatively. Different from image super-resolution and completion which belong to low-level vision research, image captioning is a high-level vision task related to the semantic understanding of an input image. It is a natural task for human beings. However, image captioning remains challenging from a computer vision point of view especially due to the fact that the task itself is ambiguous. In principle, descriptions of an image can talk about any visual aspects in it varying from object attributes to scene features, or even refer to objects that are not depicted and the hidden interaction or connection that requires common sense knowledge to analyze. Therefore, learning-based image captioning is in general a data-driven task, which relies on the training dataset. Descriptions in the majority of the existing image-sentence datasets are generated by humans under specific instructions. Real-world sentence data is rarely directly utilized for training since it is sometimes noisy and unbalanced, which makes it ‘imperfect’ for the training of the image captioning task. In this dissertation, we present a novel image captioning framework to deal with the uncontrolled image-sentence dataset where descriptions could be strongly or weakly correlated to the image content and in arbitrary lengths. A self-guiding learning process is proposed to fully reveal the internal statistics of the training dataset and to look into the learning process in a global way and generate descriptions that are syntactically correct and semantically sound

Dual-mode photoacoustic and ultrasound imaging system based on a Fabry-Pérot scanner

The planar Fabry-Pérot (FP) scanner is an ultrasound detector that simultaneously provides high sensitivity, a high density of small (sub-100 μm) acoustic elements, and a broad bandwidth (> 30 MHz). These features enable the FP scanner to acquire high-resolution 3D in vivo photoacoustic images of biological tissues up to depths of approximately 10 mm. The aim was to add complementary morphological ultrasound contrast to photoacoustic images to extend their clinical applicability. This was achieved by developing a dual-mode photoacoustic and ultrasound imaging system based on the FP scanner, which was modified to transmit optically generated ultrasound. The FP sensor head was coated with an optically absorbing polydimethylsiloxane(PDMS) composite layer, which was excited with nanosecond laser pulses to generate broadband planar ultrasound waves for pulse-echo imaging. First, an all-optical ultrasound system was developed using a highly absorbing carbon nanotube-PDMS composite coating. The system was characterised with a series of experiments, and its imaging performance was tested on tissue mimicking phantoms and ex vivo tissue samples. Second, the effect of the frequency content of the detected signals and the effect of spatial aliasing on the image quality were investigated in simulation. A broadband system was found to reduce the effect of spatial undersampling of high frequencies which results in a reduction of contrast due to the formation of grating lobe artefacts. Third, to improve the image quality, frequency and angle compounding were explored in simulations and experimentally. Coherent and incoherent compounding were considered, as well as the effect of the filter bandwidth on frequency compounded images, and the influence of the number and spread of angles used in angle compounded images. Finally, a dual- mode photoacoustic and ultrasound imaging system was demonstrated with a gold nanoparticle-PDMS composite which enabled wavelength-selective absorption of light. The system was shown to obtain high-resolution 3D dual-mode images providing complementary contrast from optically absorbing and acoustically scattering structures

Comparison of super-resolution algorithms applied to retinal images

A critical challenge in biomedical imaging is to optimally balance the trade-off among image resolution, signal-to-noise ratio, and acquisition time. Acquiring a high-resolution image is possible; however, it is either expensive or time consuming or both. Resolution is also limited by the physical properties of the imaging device, such as the nature and size of the input source radiation and the optics of the device. Super-resolution (SR), which is an off-line approach for improving the resolution of an image, is free of these trade-offs. Several methodologies, such as interpolation, frequency domain, regularization, and learning-based approaches, have been developed over the past several years for SR of natural images. We review some of these methods and demonstrate the positive impact expected from SR of retinal images and investigate the performance of various SR techniques. We use a fundus image as an example for simulations

Signal processing for high-definition television

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1995.Includes bibliographical references (p. 60-62).by Peter Monta.Ph.D

Efficient Algorithms for Large-Scale Image Analysis

This work develops highly efficient algorithms for analyzing large images. Applications include object-based change detection and screening. The algorithms are 10-100 times as fast as existing software, sometimes even outperforming FGPA/GPU hardware, because they are designed to suit the computer architecture. This thesis describes the implementation details and the underlying algorithm engineering methodology, so that both may also be applied to other applications

Design and FPGA implementation of a SISO and a MIMO wireless system for software defined radio

MIMO (Multiple-input Multiple-output) technology combined with space time coding techniques provides significant increase in performance and capacity over an equivalent SISO (Single-input Single-output) system while maintaining the same bandwidth and transmission power. MIMO has emerged as the major breakthrough in recent communication technologies. To migrate from SISO to MIMO system, multiple RF (Radio Frequency) front ends and additional signal processing are required. Software defined radio (SDR) allows MIMO and other evolving techniques to be added to current systems through software update instead of hardware replacement. SDR provides a flexible and economic solution to the system upgrade and migration. In this thesis, an SDR based SISO system using QPSK modulation scheme is implemented on FPGA. The system produces signal with an intermediate frequency of 25 MHz and throughput of 12.5 Mbps. One carrier recovery and two symbol timing recovery algorithms (Gardner and Maximum Likelihood) are investigated and implemented. A 2x1 MIMO system using Alamouti scheme and CORDIC based carrier recovery is designed as well. The SDR based SISO system can be easily incorporated to the MIMO design. Throughout this thesis, detailed design information is presented along with both computer simulation results and real hardware performance. The comparisons of different algorithms and component structures are also provided. Based on these comparisons, the suitable algorithm or structure according to specific implementation considerations and system requirement can be selected. The design and implementation are processed based on a system-level design flow. System modeling and simulation are performed using Xilinx's System Generator for DSP and Simulink. After it is mapped to HDL (Hardware Description Language) netlist, the design is synthesized and implemented by Xilinx's ISE tool. The generated bit-stream is then downloaded to target FPGA to program the device. The hardware performance is measured by BER (Bit Error Rate) tester, oscilloscope and spectrum analyzer. This thesis is an initial project for future work of Wireless Design Laboratory at Concordia University. The system realized in this project can be viewed as a base of future MIMO implementation with different number of antennas and advanced signal processing techniques