Convolutional Neural Networks (CNNs) allow fast and precise image recognition. Nowadays this capability is highly requested in the embedded system domain for video processing applications such as video surveillance and homeland security. Moreover, with the increasing requirement of portable and ubiquitous processing, power consumption is a key issue to be accounted for.In this paper, we present an FPGA implementation of CNN designed for addressing portability and power efficiency. Performance characterization results show that the proposed implementation is as efficient as a general purpose 16-core CPU, and almost 15 times faster than a SoC GPU for mobile application. Moreover, external memory footprint is reduced by 84% with respect to a standard CNN software application.
The purpose of this study is to investigate the effectiveness of combining two newly developed web-based tools for the foreign language DDL classroom. One is a KWIC concordance tool, WebParaNews, and the other is a lexical profiling tool, the LagoWordProfiler. Both are freeware and are based on the same parallel corpus, ParaNews, which consists of newspaper texts in English along with their aligned translations in Japanese. Using the same syllabus to teach various types of noun phrases for ten weeks, only one tool was used with the 2013 group, and both of the two tools were used in combination with the 2014 group. In order to reconfirm the effectiveness of combining two tools, both of the two tools were also used in 2015 group. In each year the teaching effect was measured using a pre-and post-test, and students' feedback was collected using a 31-item questionnaire. Groups using both tools performed better than the single tool group on the gain between the pre-and post-test and gave more positive student feedback. This combined-resource approach using different types of information from two corpus tools may be more helpful for understanding the targeted grammar items than a more traditional single tool approach.
Our goal is to create advanced engineering systems such as a soft human interactive robot. The robot developed here is named RI-MAN. RI-MAN exhibits the skill and ability to realize human care and welfare tasks. RI-MAN can search out a specific person in real time by fuing audio and visual information, and understand human speech based on a sound recognition function. In addition, RI-MAN's body is coverd with soft touch sensors, and RI-MAN can react to the amplitude and location of external forces. Using all these sensor functions, RI-MAN can successfully follow human commands and hold up a dummy of the same size as an adult human. RI-MAN will become an invaluable partner robot. AbstractThis video is concerned with a scenario for developing speech-based Human-Robot Interaction (sHRI) components for Ubiquitous Robot Companion (URC) Intelligent Service Robots as one of the next-generation growth engine industries in Korea. Here the URC means that it will provide the necessary services at any time and place to meet the user' s requirements. Thus, it combines the network function with the current concept of a robot in order to enhance mobility and human interface. The main characteristics of this video are to combine of text-independent speaker recognition and Korean-based spontaneous speech recognition from two speakers in scenario. Especially each speaker communicates with service robot through spontaneous speech recognition with continuous words to provide useful information such as daily life schedule and TV program suitable to the speaker recognized. On the basis of these components, consumers will be able to utilize various speech-based services of the robot. AbstractIn this video, we present a scenario for developing vision-based Human-Robot Interaction (vHRI) components for Ubiquitous Robot Companion (URC) Intelligent Service Robots, which exploit strong Information Technology (IT) infrastructure. Here the URC means that it will provide the necessary services at any time and place to meet the user' s requirements. Thus, it combines the network function with the current concept of a robot in order to enhance mobility and human interface. The vHRI components used in this video consist of caller identification, face recognition/verification, and gesture recognition. Firstly we perform face verification and recognition for the imposter and a member of family from face images of two users, respectively. After that, we use gesture recognition to provide the selection of TV channels under noise or long-distance environments. Finally, we demonstrate the usefulness through vision-based scenario shown from video. The robot platform used in this video is WEVER, which is a URC intelligent service robot developed
This paper discusses the design of a decentralized capturing behavior by multiple mobile robots. The design is based on a gradient descent method using local information. The task of capturing a target is divided into two subtasks; the enclosing subtask and the grasping subtask. An analysis of the convergence of the local control policy in the enclosing subtask is provided, while ensuring that the neighborhood relation of the robot system is preserved. In the grasping subtask, the force-closure condition in decentralized form is used to design a local objective function. A local estimation of the shape of the object is introduced so that each robot can decide how to move on the basis of only the available local information. The proposed local control policies were evaluated using simulations and the flexibility of the system was verified owing to the decentralized nature of the system. The enclosing subtask was implemented using multiple mobile robots with local observation from omnidirectional CCD cameras.
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