Multiple-input multiple-output (MIMO) technique in communication system has been widely researched. Compared with single-input single-output (SISO) communication, its properties of higher throughput, more e?cient spectrum and usage make it one of the most significant technology in modern wireless communications. In MIMO system, sphere detection is the fundamental part. The purpose of traditional sphere detection is to achieve the maximum likelihood (ML) demodulation of the MIMO system. However, with the development of advanced forward error correction (FEC) techniques, such as the Convolutional code, Turbo code and LDPC code, the sphere detection algorithms that can provide soft information for the outer decoder attract more interests recently. Considering the computing complexity of generating the soft information, it is important to develop a high-speed VLSI architecture for MIMO detection. The first part of this thesis is about MIMO sphere detection algorithms. Two sphere detection algorithms are introduced. The depth first Schnorr-Euchner (SE) algorithm which generates the ML detection solution and the width first K-BEST algorithm which only generates the nearly-ML detection solution but more efficient in implementation are presented. Based on these algorithms, an improved nearly-ML algorithm with lower complexity and limited performance lose, compared with traditional K-BEST algorithms, is presented. The second part is focused on the hardware design. A 4*4 16-QAM MIMO detection system which can generate both soft information and hard decision solution is designed and implemented in FPGA. With the fully pipelined and parallel structure, it can achieve a throughput of 3.7 Gbps. In this part, the improved nearly-ML algorithm is implmented as a detector to generat both the hard output and candidate list. Then, a soft information calculation block is designed to succeed the detector and produce the log-likelihood ratio (LLR) values for every bit as the soft output.

The mobile wireless communication systems has been growing fast and continuously over the past two decades. Therefore, in order to fulfill the demand for this rapid growth, the standardization bodies along with wireless researchers and mobile operators around the world have been constantly working on new technical specifications.An important problem in modern communication is known as NP complete problem in the Maximum Likelihood (ML) detection of signals transmitting over Multiple Input Multiple Output channel of the OFDM transceiver system. Development of the Sphere Decoder (SD) as a result of the rapid advancement in signal processing techniques provides ML detection for MIMO channels at polynomial time complexity average case. There are weaknesses in the existing SDs. The sphere decoder performance is very sensitive for the most current proposals in order to choose the search radius parameter. At high spectral efficiencies SNR is low or as the problem dimension is high and the complexity coefficient can become very large too. Digital communications of detecting a vector of symbols has importance as, is encountered in several different applications. These symbols are as the finite alphabet and transmitted over a multiple-input multiple-output (MIMO) channel with Gaussian noise. There are no limitation to the detection of symbols spatially multiplexed over a multiple-antenna channel and the multi user detection problem. Efficient algorithms are considered for the detection problems and have recognized well. The algorithm of sphere decoder, orders has optimal performance considering the error probability and this has proved extremely efficient in terms of computational complexity for moderately sized problems in case of signal to noise ratio. At high SNR the algorithm has a polynomial average complexity and it is understood the algorithm has an exponential worst case complexity. The efficiency of the algorithm is ordered the exponential rate derivation of growth. Complexity is positive for the finite SNR and small in the high SNR. To achieve the sphere decoding solution applying Schnorr-Euchner by Maximum likelihood method , Depth-first Stack-based Sequential decoding is used. This thesis focuses on the receiver part of the transceiver system and takes a good look at the near optimal algorithm for sphere detection of a vector of symbols transmitted over MIMO channel. The analysis and algorithms are general in nature. / Cell:+8801553448014

Localization of features and structures in images is an important task in medical image-processing. Characteristic structures and features are used in diagnostics and surgery planning for spatial adjustments of the volumetric data, including image registration or localization of bone-anchors and fiducials. Since this task is highly recurrent, a fast, reliable and automated approach without human interaction and parameter adjustment is of high interest. In this paper we propose and compare four image processing pipelines, including algorithms for automatic detection and localization of spherical features within 3D MRI data. We developed a convolution based method as well as algorithms based on connected-components labeling and analysis and the circular Hough-transform. A blob detection related approach, analyzing the Hessian determinant, was examined. Furthermore, we introduce a novel spherical MRI-marker design. In combination with the proposed algorithms and pipelines, this allows the detection and spatial localization, including the direction, of fiducials and bone-anchors.

In todays society, a growing number of users are demanding more sophisticated services from wireless communication devices. In order to meet these rising demands, it has been proposed to increase the capacity of the wireless channel by using more than one antenna at the transmitter and receiver, thereby creating multiple-input multiple-output (MIMO) channels. Using MIMO communication techniques is a promising way to improve wireless communication technology because in a rich-scattering environment the capacity increases linearly with the number of antennas. However, increasing the number of transmit antennas also increases the complexity of detection at an exponential rate. So while MIMO channels have the potential to greatly increase the capacity of wireless communication systems, they also force a greater computational burden on the receiver. Even suboptimal MIMO detectors that have relatively low complexity, have been shown to achieve unprecedented high spectral efficiency. However, their performance is far inferior to the optimal MIMO detector, meaning they require more transmit power. The fact that the optimal MIMO detector is an impractical solution due to its prohibitive complexity, leaves a performance gap between detectors that require reasonable complexity and the optimal detector. The objective of this research is to bridge this gap and provide new solutions for managing the inherent performance-complexity trade-off in MIMO detection. The optimally-ordered decision-feedback (BODF) detector is a standard low-complexity detector. The contributions of this thesis can be regarded as ways to either improve its performance or reduce its complexity - or both. We propose a novel algorithm to implement the BODF detector based on noise-prediction. This algorithm is more computationally efficient than previously reported implementations of the BODF detector. Another benefit of this algorithm is that it can be used to easily upgrade an existing linear detector into a BODF detector. We propose the partial decision-feedback detector as a strategy to achieve nearly the same performance as the BODF detector, while requiring nearly the same complexity as the linear detector. We propose the family of Chase detectors that allow the receiver to trade performance for reduced complexity. By adapting some simple parameters, a Chase detector may achieve near-ML performance or have near-minimal complexity. We also propose two new detection strategies that belong to the family of Chase detectors called the B-Chase and S-Chase detectors. Both of these detectors can achieve near-optimal performance with less complexity than existing detectors. Finally, we propose the double-sorted lattice-reduction algorithm that achieves near-optimal performance with near-BODF complexity when combined with the decision-feedback detector.

This thesis focuses on robotic 3D vision for application in Bin Picking. The new method based on Conformal Geometric Algebra (CGA) is proposed and tested for identification of spheres in Pointclouds created with 3D scanner. The speed, precision and scalability of this method is compared to traditional descriptors based method. It is proved that CGA maintains the same precision as the traditional method in much shorter time. The CGA based approach seems promising for the use in the future of robotic 3D vision for identification and localization of spheres.