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The thumb is the first digit of the hand. (Wikipedia.org)






Conferences related to Thumb

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2014 IEEE International Conference on Robotics and Automation (ICRA)

Robotics and Automation


2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014)

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  • 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013)

    Papers are solicited in all related areas in robotics and intelligent systems. Proposals for tutorials and workshops, as well as organized/special sessions are also welcome to address the emerging areas and innovative applications of new technologies.

  • 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012)

    The 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012) will be held in Vilamoura-Algarve, Portugal, during October 7-11, 2012. The theme of the conference will be Robotics for Quality of Life and Sustainable Development. Papers are solicited in all related areas in robotics and intelligent systems. Proposals for tutorials and workshops, as well as organized/special sessions are also welcome to address the emerging areas and innovative applications of new technologies.

  • 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)

    The 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011) will be held in San Francisco, California, USA, during September 25-30, 2011. The theme of the conference will be Human- Centered Robotics, and its format will feature innovations in the form of interactive multimedia presentations and special-topic symposia celebrating 50 years of robotics.

  • 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010)

    Papers are solicited in all related areas in robotics and intelligent systems. Proposals and tutorials and workshops, as well as organized/special sessions are also welcome to address the emerging areas and innovative applications of new technologies.

  • 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009)

    Papers are solicited in all related areas in robotics and intelligent systems. Proposals for tutorials and workshops, as well as organized/special sessions are also welcome to address the emerging areas and innovative applications of new technologies.

  • 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2008)

    IROS 2008 serves as an international forum for robotics researchers to discuss and exchange their ideas on technical problems and their solutions. Conference includes technical presentations, tutorials and workshops, exhibits, posters, competitions, plenary session, and panel discussions.

  • 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007)

  • 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2006)

  • 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2005)


2013 46th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)

Forum for presenting and discussing innovative microarchitecture ideas and techniques foradvanced computing and communication systems, providing a close interaction between academic researchers andindustrial designers and bringing together researchers in fields related to microarchitecture, compilers, chips, andsystems for technical exchange on traditional microarchitecture topics and emerging research areas.

  • 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)

    Forum for presenting and discussing innovative microarchitecture ideas and techniques for advanced computing and communication systems, providing a close interaction between academic researchers and industrial designers and bringing together researchers in fields related to microarchitecture, compilers, chips, and systems for technical exchange on traditional microarchitecture topics and emerging research areas.

  • 2010 43rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)

    MICRO-43 is the premier forum for presenting, discussing and debating new and innovative microarchitecture ideas and techniques for advanced computing and communication systems. The goal of this symposium is to bring together researchers in fields related to processor architecture, compilers, and systems, for technical exchange on traditional MICRO topics as well as new emerging research areas.

  • 2009 42nd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)

    MICRO is the premier forum for presenting, discussing and debating new and innovative microarchitecture ideas and techniques for advanced computing and communication systems. The goal of this symposium is to bring together researchers in fields related to processor architecture, compilers, and systems, for technical exchange on traditional MICRO topics as well as new emerging research areas.

  • 2008 41st IEEE/ACM International Symposium on Microarchitecture (MICRO)

    The 41st International Symposium on Microarchitecture is the premier forum for presenting, discussing, and debating new and innovative microarchitecture ideas and techniques for advanced computing and communication systems. This symposium brings together researchers in fields related to microarchitecture, compilers, and systems for technical exchange on traditional microarchitectural topics and emerging research areas.

  • 2007 40th IEEE/ACM International Symposium on Microarchitecture (MICRO)

    MICRO is the premier forum for presenting, discussing and debating new and innovative microarchitecture ideas and techniques for advanced computing and communication systems. The goal of this symposium is to bring together researchers in fields related to processor architecture, compilers, and systems, for technical exchange on traditional MICRO topics as well as new emerging research areas.

  • 2006 39th IEEE/ACM International Symposium on Microarchitecture (MICRO)

  • 2005 38th IEEE/ACM International Symposium on Microarchitecture (MICRO)


2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR 2013)

The conference presents the latest results from world leading research labs and clinics in the field of rehabilitation robotics. A special focus is on clinical evaluation and promotion of interaction between engineers, clinicians and therapists.


2012 IEEE 19th International Conference on Industrial Engineering and Engineering Management (IE&EM 2012)

The conference's objective is to gather the wisdom of industrial engineering to promote the innovation and development of manufacturing industry while it's themes are desired to stay close to China's economic construction and cutting edge breakthroughs in IE & EM, with a view of seizing the opportunities of economic development in China and the world.

  • 2011 IEEE 18th International Conference on Industrial Engineering and Engineering Management (IE&EM 2011)

    Advanced Decision Analysis and Methods ,Knowledge Management,Engineering Economy and Cost Analysis,Global celebaration and Communication,Global Information System Integration and Interaction,Global Manufacturing and Management,Information and Product Lifecycle Management,Intelligent Systems,Manufacturing Systems,Operations Research,Production Planning and Control,Quality Control and Management,Reliability and Maintenance Engineering,Safety, Security and Risk Management,Service Management,Systems Modeli

  • 2010 IEEE 17th International Conference on Industrial Engineering and Engineering Management (IE&EM 2010)

    Advanced Decision Analysis and Methods ,Knowledge Management,Engineering Economy and Cost Analysis,Global celebaration and Communication,Global Information System Integration and Interaction,Global Manufacturing and Management,Information and Product Lifecycle Management,Intelligent Systems,Manufacturing Systems,Operations Research,Production Planning and Control,Quality Control and Management,Reliability and Maintenance Engineering,Safety, Security and Risk Management,Service Management,Systems Modeling/S

  • 2009 IEEE 16th International Conference on Industrial Engineering and Engineering Management (IE&EM 2009)

    The 16th conference of IE&EM will promote development of methods and applications in all fields of industrial engineering and engineering management, and provide an excellent opportunity for researchers to discuss modern approaches and techniques for IE systems and their applications, as an academic platform of the experience and outcome exchange.


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Kinematics analysis and Simulation of the laryngeal minimally invasive operation continuum mechanism

Ying Yu; Guizhen Liu; Jianjun Wu; Zhenkun Zhu; Jiongjie Zhao 2015 IEEE International Conference on Information and Automation, 2015

In view of the influence of factors such as the small throat operation space and influence factors such as fibrillation in the process of operation, this paper put forward the design scheme of the continuum mechanism which is divided into two modules as the body part and end actuator mechanism, presenting the design method of continuum body part and the ...


Beyond Pinch and Flick: Enriching Mobile Gesture Interaction

Yang Li Computer, 2009

An open source toolkit lets developers easily create mobile gesture applications.


Classification of flexible three-fingered hand grasping pattern based on BP neural network

Zhen Qian; Fang Xu; Guanjun Bao; Sheng Xu; Shibo Cai; Jianchao Zhang; Qinghua Yang 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014), 2014

In robotic application, flexible actuator is the end terminal parts. Rigid actuators are accurate but have poor security and practicability. This paper designed a new type of pneumatic dexterous hand - flexible three-fingered hand. The flexible three-fingered hand grasping pattern can be divided into griping, grasping and holding. The pattern classification of flexible three- fingered hand is designed based on ...


Evolving aggregation behaviors for swarm robotic systems: a systematic case study

E. Bahgeci; E. Sahin Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005., 2005

When one attempts to use artificial evolution to develop behaviors for a swarm robotic system, he is faced with decisions to be made regarding the parameters of the evolution. In this paper, aggregation behavior is chosen as a case, where performance and scalability of aggregation behaviors of perceptron controllers that are evolved for a simulated swarm robotic system are systematically ...


A survey on hand gesture recognition for simple mouse control

R. Suriya; V. Vijayachamundeeswari International Conference on Information Communication and Embedded Systems (ICICES2014), 2014

Hand Gesture recognition is the upcoming technology for Human-computer interaction and many research works are carried out in this direction to facilitate and improvise "Interaction with Computer". It enables human being to interact in a natural way with ease and convenience without wearing any additional device. It can be applied for various applications like sign language recognition, robot control, virtual ...


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eLearning

Kinematics analysis and Simulation of the laryngeal minimally invasive operation continuum mechanism

Ying Yu; Guizhen Liu; Jianjun Wu; Zhenkun Zhu; Jiongjie Zhao 2015 IEEE International Conference on Information and Automation, 2015

In view of the influence of factors such as the small throat operation space and influence factors such as fibrillation in the process of operation, this paper put forward the design scheme of the continuum mechanism which is divided into two modules as the body part and end actuator mechanism, presenting the design method of continuum body part and the ...


Beyond Pinch and Flick: Enriching Mobile Gesture Interaction

Yang Li Computer, 2009

An open source toolkit lets developers easily create mobile gesture applications.


Classification of flexible three-fingered hand grasping pattern based on BP neural network

Zhen Qian; Fang Xu; Guanjun Bao; Sheng Xu; Shibo Cai; Jianchao Zhang; Qinghua Yang 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014), 2014

In robotic application, flexible actuator is the end terminal parts. Rigid actuators are accurate but have poor security and practicability. This paper designed a new type of pneumatic dexterous hand - flexible three-fingered hand. The flexible three-fingered hand grasping pattern can be divided into griping, grasping and holding. The pattern classification of flexible three- fingered hand is designed based on ...


Evolving aggregation behaviors for swarm robotic systems: a systematic case study

E. Bahgeci; E. Sahin Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005., 2005

When one attempts to use artificial evolution to develop behaviors for a swarm robotic system, he is faced with decisions to be made regarding the parameters of the evolution. In this paper, aggregation behavior is chosen as a case, where performance and scalability of aggregation behaviors of perceptron controllers that are evolved for a simulated swarm robotic system are systematically ...


A survey on hand gesture recognition for simple mouse control

R. Suriya; V. Vijayachamundeeswari International Conference on Information Communication and Embedded Systems (ICICES2014), 2014

Hand Gesture recognition is the upcoming technology for Human-computer interaction and many research works are carried out in this direction to facilitate and improvise "Interaction with Computer". It enables human being to interact in a natural way with ease and convenience without wearing any additional device. It can be applied for various applications like sign language recognition, robot control, virtual ...


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IEEE-USA E-Books

  • Optimal Strategies and Heuristics for Ecological Search Problems

    All animals, including humans, search for a variety of different things in their natural environment, from food to mates to a suitable place to live. Most types of search can be represented as stopping problems of varying complexity, in which the animal has to decide when to stop searching and accept the current option. All forms of search take time, and in solving a stopping problem the animal has to trade off this time cost against the expected benefits of continuing to search. This chapter discusses two main approaches to predicting search behavior: the optimality approach and the heuristics approach. The optimality approach identifies the best possible solution to a search problem and thereby sets an upper bound to what natural selection can achieve. The heuristics approach considers simple decision algorithms, or "rules of thumb," which animals may use to implement efficient search behavior. Although few studies have tried to integrate these functional and mechanistic perspectives, they are likely to provide complementary insights. Often, the form of an optimal strategy suggests which kinds of heuristics might be expected to evolve. Stopping problems may be simple, repeated, or embedded in other stopping problems. For example, if searchers assess the value of each encountered option by examining a series of cues, the assessment process can be considered as another stopping problem. When the searcher is uncertain about the environment it is in, its previous experiences during search can strongly influence the optimal behavior. Where a limited number of items can be accepted, as in mate search, a key constraint is whether the searcher can return to previously encountered items. Some search problems are complicated by the fact that the encountered items are themselves searching. The chapter concludes with a discussion of some open questions for future research.

  • Oracle Indexing

    This chapter contains sections titled: Rules of Thumb on Indexing Creating and Using Ubiquitous b-Tree Indexes Advanced Indexing Scheme I: Covering Indexes versus Index-Organized Tables Advanced Indexing Scheme II: Function-Based Indexes (FBIs) Unusual Indexing Scheme I: BITMAP Indexes Unusual Indexing Scheme II: Reverse Key Indexes Unusual Indexing Scheme III: Compressed Composite Indexes How To Create Oracle Indexes Summary Recommended Reading Exercises

  • Ballistic Missile Defense Systems Analysis

    This chapter contains sections titled: 10.1 Becoming a Systems Analyst, 10.2 Introduction, 10.3 Coverage Analysis, 10.4 Leakage Analysis, 10.5 Balancing Leakage Sources, 10.6 Philosophy and Rules of Thumb, Appendices, About the Author

  • Bibliography

    Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical.This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.

  • Core Analysis

    Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical.This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.

  • Algorithmic Extensions

    Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical.This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.

  • Quantized FIR Filter Design Using Compensating Zeros

    This chapter contains sections titled: Quantized Filter Design Figures of Merit Filter Structures Example 1: A Windowed FIR Filter Simple Quantization Compensating Zeros Quantization Example 2: A Biorthogonal FIR Filter Design Rules-of-Thumb Conclusions References

  • Power and speed

    This chapter contains sections titled: Introduction, Air resistance, Conclusions on air resistance, Slope and rolling resistance, Steady-speed power equation, Rules of thumb, Acceleration, Measurement of on-road power, Discussion of insights regarding power and drag, Bicycles versus other vehicles, Human versus animal muscle power, Bicycling versus other human- powered locomotion, Notes, References

  • Index of Algorithms, Figures, and Tables

    Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical.This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.

  • No title

    This book introduces basic supervised learning algorithms applicable to natural language processing (NLP) and shows how the performance of these algorithms can often be improved by exploiting the marginal distribution of large amounts of unlabeled data. One reason for that is data sparsity, i.e., the limited amounts of data we have available in NLP. However, in most real- world NLP applications our labeled data is also heavily biased. This book introduces extensions of supervised learning algorithms to cope with data sparsity and different kinds of sampling bias. This book is intended to be both readable by first-year students and interesting to the expert audience. My intention was to introduce what is necessary to appreciate the major challenges we face in contemporary NLP related to data sparsity and sampling bias, without wasting too much time on details about supervised learning algorithms or particular NLP applications. I use text classification, part-of- speech tagging, and depen ency parsing as running examples, and limit myself to a small set of cardinal learning algorithms. I have worried less about theoretical guarantees ("this algorithm never does too badly") than about useful rules of thumb ("in this case this algorithm may perform really well"). In NLP, data is so noisy, biased, and non-stationary that few theoretical guarantees can be established and we are typically left with our gut feelings and a catalogue of crazy ideas. I hope this book will provide its readers with both. Throughout the book we include snippets of Python code and empirical evaluations, when relevant.



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