Efficient Detectors for MIMO-OFDM Systems under Spatial Correlation Antenna Arrays

This work analyzes the performance of the implementable detectors for multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) technique under specific and realistic operation system condi- tions, including antenna correlation and array configuration. Time-domain channel model has been used to evaluate the system performance under realistic communication channel and system scenarios, including different channel correlation, modulation order and antenna arrays configurations. A bunch of MIMO-OFDM detectors were analyzed for the purpose of achieve high performance combined with high capacity systems and manageable computational complexity. Numerical Monte-Carlo simulations (MCS) demonstrate the channel selectivity effect, while the impact of the number of antennas, adoption of linear against heuristic-based detection schemes, and the spatial correlation effect under linear and planar antenna arrays are analyzed in the MIMO-OFDM context.Comment: 26 pgs, 16 figures and 5 table

Wavelet-based response spectrum compatible synthesis of accelerograms-Eurocode application (EC8)

An integrated approach for addressing the problem of synthesizing artificial seismic accelerograms compatible with a given displacement design/target spectrum is presented in conjunction with aseismic design applications. Initially, a stochastic dynamics solution is used to obtain a family of simulated non-stationary earthquake records whose response spectrum is on the average in good agreement with the target spectrum. The degree of the agreement depends significantly on the adoption of an appropriate parametric evolutionary power spectral form, which is related to the target spectrum in an approximate manner. The performance of two commonly used spectral forms along with a newly proposed one is assessed with respect to the elastic displacement design spectrum defined by the European code regulations (EC8). Subsequently, the computational versatility of the family of harmonic wavelets is employed to modify iteratively the simulated records to satisfy the compatibility criteria for artificial accelerograms prescribed by EC8. In the process, baseline correction steps, ordinarily taken to ensure that the obtained accelerograms are characterized by physically meaningful velocity and displacement traces, are elucidated. Obviously, the presented approach can be used not only in the case of the EC8, for which extensive numerical results/examples are included, but also for any code provisions mandated by regulatory agencies. In any case, the presented numerical results can be quite useful in any aseismic design process dominated by the EC8 specifications

The Kalai-Smorodinski solution for many-objective Bayesian optimization

An ongoing aim of research in multiobjective Bayesian optimization is to extend its applicability to a large number of objectives. While coping with a limited budget of evaluations, recovering the set of optimal compromise solutions generally requires numerous observations and is less interpretable since this set tends to grow larger with the number of objectives. We thus propose to focus on a specific solution originating from game theory, the Kalai-Smorodinsky solution, which possesses attractive properties. In particular, it ensures equal marginal gains over all objectives. We further make it insensitive to a monotonic transformation of the objectives by considering the objectives in the copula space. A novel tailored algorithm is proposed to search for the solution, in the form of a Bayesian optimization algorithm: sequential sampling decisions are made based on acquisition functions that derive from an instrumental Gaussian process prior. Our approach is tested on four problems with respectively four, six, eight, and nine objectives. The method is available in the Rpackage GPGame available on CRAN at https://cran.r-project.org/package=GPGame

Exact computation of the expectation surfaces for uniform crossover along with bit-flip mutation

Theoretical Computer Science 545, 2014, pp.76-93,Uniform crossover and bit-flip mutation are two popular operators used in genetic algorithms to generate new solutions in an iteration of the algorithm when the solutions are represented by binary strings. We use the Walsh decomposition of pseudo-Boolean functions and properties of Krawtchouk matrices to exactly compute the expected value for the fitness of a child generated by uniform crossover followed by bit-flip mutation from two parent solutions. We prove that this expectation is a polynomial in ρ, the probability of selecting the best-parent bit in the crossover, and μ, the probability of flipping a bit in the mutation. We provide efficient algorithms to compute this polynomial for Onemax and MAX-SAT problems, but the results also hold for other problems such as NK-Landscapes. We also analyze the features of the expectation surfaces.Spanish Ministry of Science and Innovation and FEDER under contract TIN2011-28194 (the roadME project). Air Force Office of Scientific Research, Air Force Materiel Command, USAF, under grant number FA9550-11-1-0088

Computer aided synthesis and optimisation of electronic logic circuits

In this thesis, a variety of algorithms for synthesis and optimisation of combinational and sequential logic circuits are developed. These algorithms could be part of new commercial EGAD package for future VLSI digital designs. The results show that considerable saving in components can be achieved resulting in simpler designs that are smaller, cheaper, consume less power and easier to test. The purpose of generating different sets of coefficients related to Reed Muller (RM) is that they contain different number of terms; therefore the minimum one can be selected to design the circuits with reduced gate count. To widen the search space and achieve better synthesis tools, representations of Mixed Polarity Reed Muller (MPRM), Mixed Polarity Dual Reed Muller (MPDRM), and Pseduo Kronecker Reed Muller (PKRO RM) expansions are investigated. Efficient and fast combinatorial techniques and algorithms are developed for the following: â Bidirectional conversion between MPRM/ MPDRM form and Fixed Polarity Reed Muller forms (FPRM)/Fixed Polarity Dual Reed Muller forms (FPDRM) form respectively. The main advantages for these techniques are their simplicity and suitability for single and multi output Boolean functions. â Computing the coefficients of any polarity related to PKRO_RM class starting from FPRM coefficients or Canonical Sum of Products (CSOP). â Computing the coefficients of any polarity related to MPRM/or MPDRM directly from standard form of CSOP/Canonical Product of sums (CPOS) Boolean functions, respectively. The proposed algorithms are efficient in terms of CPU time and can be used for large functions. For optimisation of combinational circuits, new techniques and algorithms based on algebraic techniques are developed which can be used to generate reduced RM expressions to design circuits in RM/DRM domain starting from FPRM/FPDRM, respectively. The outcome for these techniques is expansion in Reed Muller domain with minimal terms. The search space is 3`" Exclusive OR Sum of Product (ESOP)/or Exclusive NOR Product of Sums (ENPOS) expansions. Genetic Algorithms (GAs) are also developed to optimise combinational circuits to find optimal MPRM/MPDRM among 3° different polarities without the need to do exhaustive search. These algorithms are developed for completely and incompletely specified Boolean functions. The experimental results show that GA can find optimum solutions in a short time compared with long time required running exhaustive search in all the benchmarks tested. Multi Objective Genetic Algorithm (MOGA) is developed and implemented to determine the optimal state assignment which results in less area and power dissipation for completely and incompletely specified sequential circuits. The goal is to find the best assignments which reduce the component count and switching activity simultaneously. The experimental results show that saving in components and switching activity are achieved in most of the benchmarks tested compared with recently published research. All algorithms are implemented in C++.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Efficient Transition Probability Computation for Continuous-Time Branching Processes via Compressed Sensing

Branching processes are a class of continuous-time Markov chains (CTMCs) with ubiquitous applications. A general difficulty in statistical inference under partially observed CTMC models arises in computing transition probabilities when the discrete state space is large or uncountable. Classical methods such as matrix exponentiation are infeasible for large or countably infinite state spaces, and sampling-based alternatives are computationally intensive, requiring a large integration step to impute over all possible hidden events. Recent work has successfully applied generating function techniques to computing transition probabilities for linear multitype branching processes. While these techniques often require significantly fewer computations than matrix exponentiation, they also become prohibitive in applications with large populations. We propose a compressed sensing framework that significantly accelerates the generating function method, decreasing computational cost up to a logarithmic factor by only assuming the probability mass of transitions is sparse. We demonstrate accurate and efficient transition probability computations in branching process models for hematopoiesis and transposable element evolution.Comment: 18 pages, 4 figures, 2 table

Computer aided synthesis and optimisation of electronic logic circuits

In this thesis, a variety of algorithms for synthesis and optimisation of combinational and sequential logic circuits are developed. These algorithms could be part of new commercial EGAD package for future VLSI digital designs. The results show that considerable saving in components can be achieved resulting in simpler designs that are smaller, cheaper, consume less power and easier to test.The purpose of generating different sets of coefficients related to Reed Muller (RM) is that they contain different number of terms; therefore the minimum one can be selected to design the circuits with reduced gate count. To widen the search space and achieve better synthesis tools, representations of Mixed Polarity Reed Muller (MPRM), Mixed Polarity Dual Reed Muller (MPDRM), and Pseduo Kronecker Reed Muller (PKRO RM) expansions are investigated. Efficient and fast combinatorial techniques and algorithms are developed for the following:- Bidirectional conversion between MPRM/ MPDRM form and Fixed Polarity Reed Muller forms (FPRM)/Fixed Polarity Dual Reed Muller forms (FPDRM) form respectively. The main advantages for these techniques are their simplicity and suitability for single and multi output Boolean functions.- Computing the coefficients of any polarity related to PKRO_RM class starting from FPRM coefficients or Canonical Sum of Products (CSOP).- Computing the coefficients of any polarity related to MPRM/or MPDRM directly from standard form of CSOP/Canonical Product of sums (CPOS) Boolean functions, respectively. The proposed algorithms are efficient in terms of CPU time and can be used for large functions.For optimisation of combinational circuits, new techniques and algorithms based on algebraic techniques are developed which can be used to generate reduced RM expressions to design circuits in RM/DRM domain starting from FPRM/FPDRM, respectively. The outcome for these techniques is expansion in Reed Muller domain with minimal terms. The search space is 3`" Exclusive OR Sum of Product (ESOP)/or Exclusive NOR Product of Sums (ENPOS) expansions.Genetic Algorithms (GAs) are also developed to optimise combinational circuits to find optimal MPRM/MPDRM among 3° different polarities without the need to do exhaustive search. These algorithms are developed for completely and incompletely specified Boolean functions. The experimental results show that GA can find optimum solutions in a short time compared with long time required running exhaustive search in all the benchmarks tested.Multi Objective Genetic Algorithm (MOGA) is developed and implemented to determine the optimal state assignment which results in less area and power dissipation for completely and incompletely specified sequential circuits. The goal is to find the best assignments which reduce the component count and switching activity simultaneously. The experimental results show that saving in components and switchingactivity are achieved in most of the benchmarks tested compared with recentlypublished research. All algorithms are implemented in C++