[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

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Biomechanics And Motor Control Of Human Movement

A method of providing RF energy to a wireless transponder device includes outputting primary RF energy in a form wherein the primary RF energy comprises a plurality of RF bursts, with each consecutive pair of the RF bursts being separated by an associated time interval, and outputting secondary continuous wave RF energy in between each of the RF bursts of the primary RF energy during at least a portion of each of the associated time intervals. Also, a reader device implementing this method.

Powering and reading implanted devices

Clinical experience has shown that heart sounds can be an effective tool to noninvasively diagnose some forms of heart disease. In this paper, an algorithm based on time-frequency analysis is used for the decomposition of the heart sounds. The decomposition algorithm is based on the S-method. The S-method is a time-frequency representation that can produce a distribution equal or close to the sum of the Wigner distributions of individual signal components. The decomposition algorithm is used for segmentation of the heart sound recordings that contain either of the two sounds: opening snap or third heart sound, which indicate distinct heart diseases. The results show that the algorithm effectively decomposes each heart beat into the corresponding components. Hence, they may be used in conjunction with a classification algorithm, allowing automatic decomposition and classification of the heart diseases associated with the opening snap and the third heart sound.

An algorithm for decomposition of heart sounds based on S-method

Dysphagia management, from screening procedures to diagnostic methods and therapeutic approaches, is about to change dramatically. This change is prompted not solely by great discoveries in medicine or physiology, but by advances in electronics and data science and close collaboration and cross-pollination between these two disciplines. In this editorial, we will provide a brief overview of the role of artificial intelligence in dysphagia management.

/pdf/artificial-intelligence-and-dysphagia-novel-solutions-to-old-4iqq5df0q2.pdf

Artificial intelligence and dysphagia: novel solutions to old problems.

/pdf/education-gear-students-up-for-big-medical-data-2ivjtovj72.pdf

Education: Gear students up for big medical data

Graphs are irregular structures which naturally account for data integrity, however, traditional approaches have been established outside Signal Processing, and largely focus on analyzing the underlying graphs rather than signals on graphs. Given the rapidly increasing availability of multisensor and multinode measurements, likely recorded on irregular or ad-hoc grids, it would be extremely advantageous to analyze such structured data as graph signals and thus benefit from the ability of graphs to incorporate spatial awareness of the sensing locations, sensor importance, and local versus global sensor association. The aim of this lecture note is therefore to establish a common language between graph signals, defined on irregular signal domains, and some of the most fundamental paradigms in DSP, such as spectral analysis of multichannel signals, system transfer function, digital filter design, parameter estimation, and optimal filter design. This is achieved through a physically meaningful and intuitive real-world example of geographically distributed multisensor temperature estimation. A similar spatial multisensor arrangement is already widely used in Signal Processing curricula to introduce minimum variance estimators and Kalman filters \cite{HM}, and by adopting this framework we facilitate a seamless integration of graph theory into the curriculum of existing DSP courses. By bridging the gap between standard approaches and graph signal processing, we also show that standard methods can be thought of as special cases of their graph counterparts, evaluated on linear graphs. It is hoped that our approach would not only help to demystify graph theoretic approaches in education and research but it would also empower practitioners to explore a whole host of otherwise prohibitive modern applications.

Ervin Sejdic

Papers

Powering and reading implanted devices

An algorithm for decomposition of heart sounds based on S-method

Artificial intelligence and dysphagia: novel solutions to old problems.

Education: Gear students up for big medical data

An Example-Driven Introduction to Data Analytics on Graphs