Approaches to Quantifying EEG Features for Design Protocol Analysis

Recently, physiological signals such as eye-tracking and gesture analysis, galvanic skin response (GSR), electrocardiograms (ECG) and electroencephalograms (EEG) have been used by design researchers to extract significant information to describe the conceptual design process. We study a set of video-based design protocols recorded on subjects performing design tasks on a sketchpad while having their EEG monitored. The conceptual design process is rich with information on how designer’s do design. Many methods exist to analyze the conceptual design process, the most popular one being concurrent verbal protocols. A recurring problem in design protocol analysis is to segment and code protocol data into logical and semantic units. This is usually a manual step and little work has been done on fully automated segmentation techniques. Also, verbal protocols are known to fail in some circumstances such as when dealing with creativity, insight (e.g. Aha! experience, gestalt), concurrent, nonverbalizable (e.g. facial recognition) and nonconscious processes. We propose different approaches to study the conceptual design process using electroencephalograms (EEG). More specifically, we use spatio-temporal and frequency domain features. Our research is based on machine learning techniques used on EEG signals (functional microstate analysis), source localization (LORETA) and on a novel method of segmentation for design protocols based on EEG features. Using these techniques, we measure mental effort, fatigue and concentration in the conceptual design process, in addition to creativity and insight/nonverbalizable processing. We discuss the strengths and weaknesses of such approaches

Fast and scalable similarity and correlation queries on time series data

Time series are ubiquitous in many fields ranging from financial applications such as the stock market to scientific applications and sensor data. Hence, there has been an increasing interest in time series indexing over the past years because there has been an increasing need for fast methods for analyzing and querying these datasets that are often too big for practical brute force analysis. We start with the main contributions to the field over the past decade and a half. We will then proceed by describing new solutions to correlation analysis on time series datasets using an existing index called the Compact Multi-Resolution Index (CMRI). We describe new algorithms for indexed correlation analysis using Pearson's product moment coefficient and using the multidimensional correlation coefficient and introduce a new measure called Dynamic Time Warping Correlation (DTWC) based on Dynamic Time Warping (DTW). In addition to these linear correlation algorithms, we propose an algorithm called rank order correlation on a non-linear/monotonic measure. To support these algorithms, we revised the Compact Multi-Resolution Index (CMRI) and propose a new index for time series datasets which improves over the sizes, speed and precision of CMRI. We call this index the reduced Compact Multi-Resolution Index (rCMRI). We evaluate the performance of rCMRI compared to CMRI for range queries and range query based queries

cicutaria

Phacelia cicutaria E. GreeneCaterpillar phaceliaWatts Val Rd. 2.7 m E of jct Pittman Hill Rd., sharp left turn over culvert.N. facing slope with pocket, about 50 feet high, below with water, rocky, much oak humus, fairly dry. Limited Ft. H. WdldQuercus wislizenii, Aesculus, Ceanothus cuneatus, Rhus diversiloba, Lupinus albifrons, Amsinckia, Brodiaea pulchella, B. volublilis, B. ixioides, B. laxafarily abundantTotal population in bloo

Benaki museum athens : Early islamic ceramics ninth to late twelfth centuries

xviii, 323 hlm. : il.; 32 cm

Religious and royal architecture of the early Bahmani period (748/1347-825/1422)

EThOS - Electronic Theses Online ServiceGBUnited Kingdo