A task learning mechanism for the telerobots

Telerobotic systems have attracted growing attention because of their superiority in the dangerous or unknown interaction tasks. It is very challengeable to exploit such systems to implement complex tasks in an autonomous way. In this paper, we propose a task learning framework to represent the manipulation skill demonstrated by a remotely controlled robot.Gaussian mixture model is utilized to encode and parametrize the smooth task trajectory according to the observations from the demonstrations. After encoding the demonstrated trajectory, a new task trajectory is generated based on the variability information of the learned model. Experimental results have demonstrated the feasibility of the proposed method

Design of Voice-Controlled Intelligent Robot

This project aims at developing a robot on a field programmable gate array, which can perform tasks by taking the user’s voice commands. The robot has two wheels for locomotion and IR sensors. It can also autonomously direct itself according to the obstacles coming in its path with the help of IR sensors. The objective is to make the robot to recognize a limited set of commands for giving directions (typically 4), and move accordingly. Mel-frequency cepstral coefficients are used as the features that represent the input voice-commands and dynamic time warping algorithm is implemented for the recognition of the commands. The entire voice-command recognition module has both hardware and software components. The software runs on MicroBlaze, a 32-bit soft-core processor from Xilinx. A hardware-software co-design of a voice-command feature extraction circuit is implemented and validated on Spartan-6 FPGA. It is also compared against a complete softwarebased implementation and a complete hardware-based design

Integrated input modeling and memory management for image processing applications

Image processing applications often demand powerful calculations and real-time performance with low power and energy consumption. Programmable hardware provides inherent parallelism and flexibility making it a good implementation choice for this application domain. In this work we introduce a new modeling technique combining Cyclo-Static Dataflow (CSDF) base model semantics and Homogeneous Parameterized Dataflow (HPDF) meta-modeling framework, which exposes more levels of parallelism than previous models and can be used to reduce buffer sizes. We model two different applications and show how we can achieve efficient scheduling and memory organization, which is crucial for this application domain, since large amounts of data are processed, and storing intermediate results usually requires the use of off-chip resources, causing slower data access and higher power consumption. We also designed a reusable wishbone compliant memory controller module that can be used to access the Xilinx Multimedia Board’s memory chips using single accesses or burst mode

System-on-Chip Design for Audio Processing

Nowadays System-on-Chip (SoC) is present in every electronic system. SoC popularity is based on higher performance, reduced size, less power consumption, and alleviation of time to market by design reuse. Device scaling enabled SoC to integrate more functionality into a single chip and hence system complexity, like Audio Processing system, is no more barriers for the SoC designer. Speaker recognition/verification is one of the applications in biometrics for preventing identity fraud. It is suitable for real time scenarios and remote recognition over phone. In this project, I have designed a SoC system for Audio Processing on Altera DE2 board, FPGA platform, which automatically verify or recognize the speaker Identity. Mel Frequency Capestral Coefficient (MFCC) is used for feature extraction of the voice signal. Large samples of extracted feature are used to train the system by using Backpropagation Neural Network. After training, speaker verification done in real time by first extracting speaker voice feature, applying trained network on extracted feature, and comparing it with the stored database. Experimental result shows that the designed system is able to verify person’s identity

Distributed Support Vector Machine With Graphics Processing Units

Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) optimization problem. Sequential Minimal Optimization (SMO) is a decomposition-based algorithm which breaks this large QP problem into a series of smallest possible QP problems. However, it still costs O(n2) computation time. In our SVM implementation, we can do training with huge data sets in a distributed manner (by breaking the dataset into chunks, then using Message Passing Interface (MPI) to distribute each chunk to a different machine and processing SVM training within each chunk). In addition, we moved the kernel calculation part in SVM classification to a graphics processing unit (GPU) which has zero scheduling overhead to create concurrent threads. In this thesis, we will take advantage of this GPU architecture to improve the classification performance of SVM

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Development of a cognitive robotic system for simple surgical tasks

The introduction of robotic surgery within the operating rooms has significantly improved the quality of many surgical procedures. Recently, the research on medical robotic systems focused on increasing the level of autonomy in order to give them the possibility to carry out simple surgical actions autonomously. This paper reports on the development of technologies for introducing automation within the surgical workflow. The results have been obtained during the ongoing FP7 European funded project Intelligent Surgical Robotics (I-SUR). The main goal of the project is to demonstrate that autonomous robotic surgical systems can carry out simple surgical tasks effectively and without major intervention by surgeons. To fulfil this goal, we have developed innovative solutions (both in terms of technologies and algorithms) for the following aspects: fabrication of soft organ models starting from CT images, surgical planning and execution of movement of robot arms in contact with a deformable environment, designing a surgical interface minimizing the cognitive load of the surgeon supervising the actions, intra-operative sensing and reasoning to detect normal transitions and unexpected events. All these technologies have been integrated using a component-based software architecture to control a novel robot designed to perform the surgical actions under study. In this work we provide an overview of our system and report on preliminary results of the automatic execution of needle insertion for the cryoablation of kidney tumours

Sociometric badges : wearable technology for measuring human behavior

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.Includes bibliographical references (p. 137-144).We present the design, implementation and deployment of a wearable computing research platform for measuring and analyzing human behavior in a variety of settings and applications. We propose the use of wearable sociometric badges capable of automatically measuring the amount of face-to-face interaction, conversational time, physical proximity to other people, and physical activity levels using social signals derived from vocal features, body motion, and relative location to capture individual and collective patterns of behavior. Our goal is to be able to understand how patterns of behavior shape individuals and organizations. We attempt to use on-body sensors in large groups of people for extended periods of time in naturalistic settings for the purpose of identifying, measuring, and quantifying social interactions, information flow, and organizational dynamics. We deployed this research platform in a group of 22 employees working in a real organization over a period of one month. Using these automatic measurements we were able to predict employees' self-assessment of productivity, job satisfaction, and their own perception of group interaction quality. An initial exploratory data analysis indicates that it is possible to automatically capture patterns of behavior using this wearable platform.by Daniel Olguín Olguín.S.M

HUMAN-ROBOT COLLABORATION IN ROBOTIC-ASSISTED SURGICAL TRAINING

Ph.DDOCTOR OF PHILOSOPH