Research on performance enhancement for electromagnetic analysis and power analysis in cryptographic LSI

制度:新 ; 報告番号:甲3785号 ; 学位の種類:博士(工学) ; 授与年月日:2012/11/19 ; 早大学位記番号:新6161Waseda Universit

Learning Algorithm Design for Human-Robot Skill Transfer

In this research, we develop an intelligent learning scheme for performing human-robot skills transfer. Techniques adopted in the scheme include the Dynamic Movement Prim- itive (DMP) method with Dynamic Time Warping (DTW), Gaussian Mixture Model (G- MM) with Gaussian Mixture Regression (GMR) and the Radical Basis Function Neural Networks (RBFNNs). A series of experiments are conducted on a Baxter robot, a NAO robot and a KUKA iiwa robot to verify the effectiveness of the proposed design.During the design of the intelligent learning scheme, an online tracking system is de- veloped to control the arm and head movement of the NAO robot using a Kinect sensor. The NAO robot is a humanoid robot with 5 degrees of freedom (DOF) for each arm. The joint motions of the operator’s head and arm are captured by a Kinect V2 sensor, and this information is then transferred into the workspace via the forward and inverse kinematics. In addition, to improve the tracking performance, a Kalman filter is further employed to fuse motion signals from the operator sensed by the Kinect V2 sensor and a pair of MYO armbands, so as to teleoperate the Baxter robot. In this regard, a new strategy is developed using the vector approach to accomplish a specific motion capture task. For instance, the arm motion of the operator is captured by a Kinect sensor and programmed through a processing software. Two MYO armbands with embedded inertial measurement units are worn by the operator to aid the robots in detecting and replicating the operator’s arm movements. For this purpose, the armbands help to recognize and calculate the precise velocity of motion of the operator’s arm. Additionally, a neural network based adaptive controller is designed and implemented on the Baxter robot to illustrate the validation forthe teleoperation of the Baxter robot.Subsequently, an enhanced teaching interface has been developed for the robot using DMP and GMR. Motion signals are collected from a human demonstrator via the Kinect v2 sensor, and the data is sent to a remote PC for teleoperating the Baxter robot. At this stage, the DMP is utilized to model and generalize the movements. In order to learn from multiple demonstrations, DTW is used for the preprocessing of the data recorded on the robot platform, and GMM is employed for the evaluation of DMP to generate multiple patterns after the completion of the teaching process. Next, we apply the GMR algorithm to generate a synthesized trajectory to minimize position errors in the three dimensional (3D) space. This approach has been tested by performing tasks on a KUKA iiwa and a Baxter robot, respectively.Finally, an optimized DMP is added to the teaching interface. A character recombination technology based on DMP segmentation that uses verbal command has also been developed and incorporated in a Baxter robot platform. To imitate the recorded motion signals produced by the demonstrator, the operator trains the Baxter robot by physically guiding it to complete the given task. This is repeated five times, and the generated training data set is utilized via the playback system. Subsequently, the DTW is employed to preprocess the experimental data. For modelling and overall movement control, DMP is chosen. The GMM is used to generate multiple patterns after implementing the teaching process. Next, we employ the GMR algorithm to reduce position errors in the 3D space after a synthesized trajectory has been generated. The Baxter robot, remotely controlled by the user datagram protocol (UDP) in a PC, records and reproduces every trajectory. Additionally, Dragon Natural Speaking software is adopted to transcribe the voice data. This proposed approach has been verified by enabling the Baxter robot to perform a writing task of drawing robot has been taught to write only one character

Empirical Foundations of Socio-Indexical Structure: Inquiries in Corpus Sociophonetics and Perceptual Learning

Speech is highly variable and systematic, governed by the internal linguistic system and socio-indexical factors. The systematic relationship of socio-indexical factors and variable phonetic forms, referred to here as socio-indexical structure, has been the cornerstone of sociophonetic research over the last several decades. Research has provided mounting evidence that listeners track and exploit cross-talker variability during speech processing tasks. As one such example, recent work has demonstrated listeners’ sensitivity to talker characteristics via retuning phonetic categories (i.e., perceptual learning) in response to talker-specific patterns. Drawing on Bayesian models, researchers have argued that listeners’ perceptual learning is influenced by listeners’ prior experience with socio-indexical factors conditioning segmental variation. From experience listeners form beliefs about the underlying cause of variation to determine when to adapt to talker-specific forms and generalize to other similar talkers. However, theoretical work has over-simplified descriptions of socio-indexical structure, leaving open questions about the nature and range of phonetic variation that listeners track and exploit.This dissertation seeks to provide both theoretical and empirical foundations of socio-indexical structure at the intersection of individual talkers and geographic dialects drawing on mixed methods. Using large-scale datasets of American English vowel measurements, the corpus analyses probe different quantitative descriptions of socio-indexical structure under various scopes of socio-indexical granularity and internal organizations across the vowel space. The corpus analyses reveal an asymmetry in socio-indexical conditioning of the joint cue distributions (i.e., F1xF2) across several simulations whereby some categories (e.g., /eɪ/) are conditioned by dialect, while others are conditioned by individual talker identity alone (e.g., /ʊ/; Chapter 4). Additionally, analyses show that individual talkers diverge from their dialect areas less for dialect conditioned vowels compared to talker conditioned vowels, confirming talkers’ distributional patterns generally align with their communities. Additional analyses highlight how internal principles provide specificity to socio-indexical conditioning of variability, focusing on the acoustic overlap of vowel pairs and individual cue dimensions (Chapter 5). Such descriptions suggest acoustic overlap across some vowel pairs may be attenuated by socio-indexical information while other vowel pairs generally demonstrate stability across talkers and dialects (e.g., /æ/ and /a/). Finally, descriptions of individual cue dimensions demonstrate multimodal distributions both across and within talkers for some categories conditioned by dialects (e.g., /ɔ/; Chapter 5). Following from Bayesian models of speech processing and causal inference, this dissertation tests whether a priori links to socio-indexical structure influence perceptual learning (Chapter 6). A lexically guided perceptual learning experiment tests whether the asymmetry of socio-indexical conditioning (dialect vs. talker) observed in the corpus analyses correlates with listeners’ learning and generalization behavior after exposure to novel shifts in one of two vowels (/eɪ/ and /ʊ/) in a female speaker’s voice. The results demonstrate learning a novel shift in /ʊ/ but not in /eɪ/, with generalization of post-test categorization to a novel male talker but not a novel female talker. These results suggest that the asymmetry of social conditioning alone may guide listeners’ behavior for these vowels and challenge our current understanding of listeners’ adaptation to vocalic variability and the role of socio-indexical structure in perceptual learning. Overall, this dissertation advances our understanding of socially conditioned variation across speech production and perception