Essays about: "Noise Filtering"

Showing result 1 - 5 of 127 essays containing the words Noise Filtering.

  1. 1. Robustness Analysis of Perfusion Parameter Calculations

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

    Author : Alicia Palmér; [2024]
    Keywords : Perfusion; Medical image analysis; Dynamic Contrast Enhanced Magnetic Resonance Imaging; Tofts model; Functional imaging; Optimization; T1 map; Perfusion; Medicinsk bildanalys; Dynamisk kontrastförstärkt magnetisk resonanstomografibildtagning; Tofts-modell; Funktionell bildbehandling; Optimering; T1 karta;

    Abstract : Cancer is one of the most common causes of death worldwide. When given optimal treatment, however, the risk of severe illness may greatly be reduced. Determining optimal treatment in turn requires evaluation of disease progression and response to potential, previous treatment. READ MORE

  3. 2. Robust Portfolio Optimization with Correlation Penalties

    University essay from KTH/Matematisk statistik

    Author : Pelle Nydahl; [2023]
    Keywords : Portfolio Optimization; Portfolio Allocation; Robust Optimization; Correlation; Risk Factor Model; EMA Filtering; Weighted Linear Regression; Portföljoptimering; Portföljallokering; Robust optimering; Korrelation; Riskfaktor-modell; EMA-filtrering; Viktad linjär regression;

    Abstract : Robust portfolio optimization models attempt to address the standard optimization method's high sensitivity to noise in the parameter estimates, by taking an investor's uncertainty about the estimates into account when finding an optimal portfolio. In this thesis, we study robust variations of an extension of the mean-variance problem, where an additional term penalizing the portfolio's correlation with an exogenous return sequence is included in the objective. READ MORE

  4. 3. Digital Front End Algorithms for Sub-Band Full Duplex

    University essay from Lunds universitet/Institutionen för elektro- och informationsteknik

    Author : Midhat Rizvi; Khaled Al-Khateeb; [2023]
    Keywords : Adjacent Channel Leakage Ratio; Bit Error Rate; Clipping and Filtering; Crest Factor Reduction; Digital front end; Digital Pre-Distortion Error Vector Magnitude; Frequency Division Duplex; Power Amplifier; Peak to Average Power Ratio; Peak Cancellation Crest Factor Reduction; Sub Band Full Duplex; Self-Interference Cancellation; Signal-to-Interference Noise Ratio; Signal-to-Noise Ratio; Turbo Clipping; Time Division Duplex; Technology and Engineering;

    Abstract : Sub-band full duplex is a new communication scheme technology, where a single frequency band is partitioned into sub-bands for downlink (DL) and up-link(UL) transmissions, and both can take place simultaneously. The idea behind the sub-band full duplex development is to improve the throughput, and coverage and reduce the latency of the UL communication by allowing the UL reception during the DL transmission. READ MORE

  5. 4. Forecasting Swedish FCR-D Prices using Penalized Multivariate Time Series Techniques

    University essay from Lunds universitet/Nationalekonomiska institutionen; Lunds universitet/Statistiska institutionen

    Author : Franz Lennart Wunderlich; Sebastian Brugger; [2023]
    Keywords : Swedish Energy Market; Multivariate Time-Series; Lasso; Forecasting; Noise Filtering; Business and Economics;

    Abstract : The Swedish energy market is becoming more and more sustainable, with an increasing volume and number of diversified energy sources being continuously added to the mix. To stabilize the grid frequency, auctions are held to offer energy providers incentives to produce or consume energy on short notice. READ MORE

  6. 5. Effects of visualization using different convolution kernels in Julia

    University essay from KTH/Skolan för teknikvetenskap (SCI)

    Author : Nils Forsberg; Axel Nilsson; [2023]
    Keywords : Convolution; Julia; visualization; streamlines; vorticity; interpolation; filter;

    Abstract : Many real-world engineering problems require large amounts of data in order to accurately model and predict outcomes. However, this data is often noisy, sampled and discontinuous, making the data difficult to process and giving rise to incorrect models. READ MORE