Essays about: "Matrix Inversion Method"

Showing result 1 - 5 of 7 essays containing the words Matrix Inversion Method.

  1. 1. A Conjugate Residual Solver with Kernel Fusion for massive MIMO Detection

    University essay from Högskolan i Halmstad/Centrum för forskning om tillämpade intelligenta system (CAISR)

    Author : Ioannis Broumas; [2023]
    Keywords : MIMO; massive MIMO; GPU; CUDA; Software Defined Radio; SDR; MMSE; ZF; zero-forcing; parallel detection; iterative methods; conjugate residual; parallel computing; kernel fusion;

    Abstract : This thesis presents a comparison of a GPU implementation of the Conjugate Residual method as a sequence of generic library kernels against implementations ofthe method with custom kernels to expose the performance gains of a keyoptimization strategy, kernel fusion, for memory-bound operations which is to makeefficient reuse of the processed data. For massive MIMO the iterative solver is to be employed at the linear detection stageto overcome the computational bottleneck of the matrix inversion required in theequalization process, which is 𝒪(𝑛3) for direct solvers. READ MORE

  2. 2. Machine Unlearning and hyperparameters optimization in Gaussian Process regression

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Matthis Manthe; [2021]
    Keywords : GDPR; Machine Unlearning; Data removal; Gaussian Process Regression; Product-of-Experts.; RGPD; Désapprentissage; Suppression de données; Gaussian Process regression; Product-of-Experts.; DSF; avlärningen; dataraderingen; Gaussian Process regression; Produkt-av-experter.;

    Abstract : The establishment of the General Data Protection Regulation (GDPR) in Europe in 2018, including the "Right to be Forgotten" poses important questions about the necessity of efficient data deletion techniques for trained Machine Learning models to completely enforce this right, since retraining from scratch such models whenever a data point must be deleted seems impractical. We tackle such a problem for Gaussian Process Regression and define in this paper an efficient exact unlearning technique for Gaussian Process Regression which completely include the optimization of the hyperparameters of the kernel function. READ MORE

  3. 3. Computation of posterior covariances of object points in bundle adjustment

    University essay from Umeå universitet/Institutionen för datavetenskap

    Author : Niklas Kallin; [2019]
    Keywords : ;

    Abstract : Bundle adjustment (BA) is a photogrammetric method for optimal estimation of parameters from image measurements. The parameters include 3D coordinates of objects points (OP). The result of the bundle adjustment process is a vector of estimates and its covariance matrix, C. The elements of this matrix contain quality indicators of the estimation. READ MORE

  4. 4. Transfer Path Analysis of Wind Noise on a Passenger Car

    University essay from KTH/Farkost och flyg

    Author : Ren Huawei; [2019]
    Keywords : Transfer Path Analysis TPA ; Wind Noise; Principle Com-ponent Analysis PCA ; Matrix Inversion Method; Transfer Path Analysis TPA ; Wind Noise; Principle Com-ponent Analysis PCA ; Matrix Inversion Method;

    Abstract : Over the last years, due to the development of quieter engines and drivetrains, the importance of addressing the vehicle wind noise problem has significantly increased.In this thesis work, several existing Transfer Path Analysis methods have been applied on an experimental database acquired during a wind tunnel test on a passenger car with the objective of analyzing the distribution of the wind noise sources and their contribution to the target microphones located inside the vehicle. READ MORE

  5. 5. Inversion of 2D Magnetotelluric and Radiomagnetotelluric data with Non-Linear Conjugate Gradient techniques

    University essay from Uppsala universitet/Geofysik

    Author : Dominik Zbinden; [2015]
    Keywords : Numerical solutions; inverse theory; non-linear conjugate gradients; preconditioning; electromagnetic theory; magnetotellurics;

    Abstract : I implemented and tested the method of Non-Linear Conjugate Gradients (NLCG) to invert magnetotelluric (MT) and radiomagnetotelluric (RMT) data in two dimensions. The forward problem and the objective function gradients were computed using finite-difference methods. READ MORE