A generalized least-squares framework for rare-variant analysis in family data.

Rare variants may, in part, explain some of the hereditability missing in current genome-wide association studies. Many gene-based rare-variant analysis approaches proposed in recent years are aimed at population-based samples, although analysis strategies for family-based samples are clearly warranted since the family-based design has the potential to enhance our ability to enrich for rare causal variants. We have recently developed the generalized least squares, sequence kernel association test, or GLS-SKAT, approach for the rare-variant analyses in family samples, in which the kinship matrix that was computed from the high dimension genetic data was used to decorrelate the family structure. We then applied the SKAT-O approach for gene-/region-based inference in the decorrelated data. In this study, we applied this GLS-SKAT method to the systolic blood pressure data in the simulated family sample distributed by the Genetic Analysis Workshop 18. We compared the GLS-SKAT approach to the rare-variant analysis approach implemented in family-based association test-v1 and demonstrated that the GLS-SKAT approach provides superior power and good control of type I error rate

Millimeter wave MIMO communications : high-resolution angle acquisition and low-resolution time-frequency synchronization

Knowledge of the propagation channel is critical to exploit the full benefit of multiple-input multiple-output (MIMO) techniques in millimeter wave (mmWave) cellular systems. Obtaining accurate channel state information in mmWave systems, however, is challenging due to high estimation overhead, high computational complexity and on-grid setting. It is also desirable to reduce the analog-to-digital converters (ADCs) resolution at mmWave frequencies to reduce power consumption and implementation costs. The use of low-precision ADCs, though, brings new design challenges to practical cellular networks. In the first part of this dissertation, we develop several new methods to estimate and track the mmWave channel's angle-of-departure and angle-of-arrival with high accuracy and low overhead. The key ingredient of the proposed strategies is custom designed beam pairs, from which there exists an invertible function of the angle to be estimated. We further extend the proposed algorithms to dual-polarized MIMO in wideband channels, and angle tracking design for fast-varying environments. We derive analytical angle estimation error performance of the proposed methods in single-path channels. We also use numerical examples to characterize the robustness of the proposed approaches to various transceiver settings and channel conditions. In the second part of this dissertation, we focus on improving the low-resolution time-frequency synchronization performance for mmWave cellular systems. In our system model, the base station uses analog beams to send the synchronization signal with infinite-resolution digital-to-analog converters (DACs). The user equipment employs a fully digital front end to detect the synchronization signal with low-resolution ADCs. For low-resolution timing synchronization, we propose a new multi-beam probing based strategy, targeting at maximizing the minimum received synchronization signal-to-quantization-plus-noise ratio among all serving users. Regarding low-resolution frequency synchronization, we construct new sequences for carrier frequency offset (CFO) estimation and compensation. We use both analytical and numerical examples to show that the proposed sequences and the corresponding metrics used for retrieving the CFOs are robust to the quantization distortion.Electrical and Computer Engineerin

Investigation and improvement of zinc electrodes for electrochemical cells quarterly report no. 2, oct. - dec. 1964

Influence of separator and surfactant on growth rate of zinc deposits in electrochemical cell

Classication of semantic memories using multitaper spectral estimation

The research on classication of semantic memories is still very young. Several methods have been tested ranging from magnetic resonance imaging (MRI) to electrocorticog- raphy (ECoG). This report describes an alternative way of classifying signals collected from an electroencephalogram (EEG) into categories using the Thomson multitaper method of spectral estimation, as well as a logistic regression model. The aim for this report is to expand the research eld with an approach that complements the current options of classication. Data was distributed from the department of Psychology at Lund University, and the experimental paradigm was to classify three types of semantic memories (faces, landmarks and objects) based on their neural patterns. Based on the cross-validation from the mentioned methods, a classier could successfully be trained for the "faces" and "landmarks" categories with an average success rate of 55% and 51% respectively. The classier accurately responded to the onset of the stimuli (p < 0:001 for faces, p = 0:015 for landmarks). No classier for the "objects" category could be trained using this method. These results indicate that the multitaper method of spec- tral estimation can be useful in detecting neural patterns. Several ways to rene these methods are discussed

Classification of musical genres using hidden Markov models

The music content online is expanding fast, and music streaming services are in need for algorithms that sort new music. Sorting music by their characteristics often comes down to considering the genre of the music. Numerous studies have been made on automatic classification of audio files using spectral analysis and machine learning methods. However, many of the completed studies have been unrealistic in terms of usefulness in real settings, choosing genres that are very dissimilar. The aim of this master’s thesis is to try a more realistic scenario, with genres of which the border between them is uncertain, such as Pop and R&B. Mel-frequency cepstral coefficients (MFCCs) were extracted from audio files and used as a multidimensional Gaussian input to a hidden Markov model (HMM) to classify the four genres Pop, Jazz, Classical and R&B. An alternative method is tested, using a more theoretical approach of music characteristics to improve classification. The maximum total accuracy obtained when tested on an external test set was 0.742 for audio data, and 0.540 for theoretical data, implying that a combination of the two methods will not result in an increase of accuracy. Different methods of evaluation and possible alternative approaches are discussed

Distributionally Robust Semi-Supervised Learning for People-Centric Sensing

Semi-supervised learning is crucial for alleviating labelling burdens in people-centric sensing. However, human-generated data inherently suffer from distribution shift in semi-supervised learning due to the diverse biological conditions and behavior patterns of humans. To address this problem, we propose a generic distributionally robust model for semi-supervised learning on distributionally shifted data. Considering both the discrepancy and the consistency between the labeled data and the unlabeled data, we learn the latent features that reduce person-specific discrepancy and preserve task-specific consistency. We evaluate our model in a variety of people-centric recognition tasks on real-world datasets, including intention recognition, activity recognition, muscular movement recognition and gesture recognition. The experiment results demonstrate that the proposed model outperforms the state-of-the-art methods.Comment: 8 pages, accepted by AAAI201