Simultaneous tracking & activity recognition (star) using many anonymous

A variable displacement pump control having, in addition to a pressure compensator (with or without an auxiliary modulator), a multi-purpose valve which is open at pump startup to purge air therefrom, which is automatically closed after purging of the air, and which is opened in the manner of a relief valve responsive to a sudden rise in pump discharge pressure caused as by rapid closing of a directional control valve. The control is further characterized in that the pressure compensating pilot valve also constitutes a pilot valve for the multi-purpose valve to permit opening of a relief passage by the latter when pump discharge pressure suddenly rises.

[1]  Donald Reid The application of multiple target tracking theory to ocean surveillance , 1979, 1979 18th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

[2]  Y. Bar-Shalom Tracking and data association , 1988 .

[3]  G. C. Wei,et al.  A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms , 1990 .

[4]  Mitchell P. Marcus,et al.  Parsing a Natural Language Using Mutual Information Statistics , 1990, AAAI.

[5]  L. Burgio,et al.  Studying disruptive vocalization and contextual factors in the nursing home using computer-assisted real-time observation. , 1994, Journal of gerontology.

[6]  Thad Starner,et al.  Visual Recognition of American Sign Language Using Hidden Markov Models. , 1995 .

[7]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[8]  Katia P. Sycara,et al.  Distributed Intelligent Agents , 1996, IEEE Expert.

[9]  Bradley J. Rhodes,et al.  The wearable remembrance agent: A system for augmented memory , 1997, Digest of Papers. First International Symposium on Wearable Computers.

[10]  K. Buckwalter,et al.  Measuring problem behaviors in dementia: developing a methodological agenda. , 1997, ANS. Advances in nursing science.

[11]  M. Lawton,et al.  Methodological aspects of the study of streams of behavior in elders with dementing illness. , 1997, Alzheimer disease and associated disorders.

[12]  Stuart J. Russell,et al.  Object identification in a Bayesian context , 1997, IJCAI 1997.

[13]  J. Mcneil Disabilities affect one-fifth of all Americans: Proportion could increase in coming decades , 1997 .

[14]  Ingrid Zukerman,et al.  Towards a Bayesian Model for Keyhole Plan Recognition in Large Domains , 1997 .

[15]  Andrew McCallum,et al.  A comparison of event models for naive bayes text classification , 1998, AAAI 1998.

[16]  Jeff A. Bilmes,et al.  A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models , 1998 .

[17]  J. Langford,et al.  Monte Carlo Hidden Markov Models , 1998 .

[18]  Alex Pentland,et al.  Auditory Context Awareness via Wearable Computing , 1998 .

[19]  Michael C. Mozer,et al.  The Neural Network House: An Environment that Adapts to its Inhabitants , 1998 .

[20]  Shlomo Argamon,et al.  Committee-Based Sample Selection for Probabilistic Classifiers , 1999, J. Artif. Intell. Res..

[21]  Aaron F. Bobick,et al.  A Framework for Recognizing Multi-Agent Action from Visual Evidence , 1999, AAAI/IAAI.

[22]  Takeo Kanade,et al.  Advances in Cooperative Multi-Sensor Video Surveillance , 1999 .

[23]  Yaacov Ritov,et al.  Tracking Many Objects with Many Sensors , 1999, IJCAI.

[24]  Irfan A. Essa,et al.  Exploiting human actions and object context for recognition tasks , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[25]  Arnaud Doucet,et al.  Markov chain Monte Carlo data association for target tracking , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[26]  Barry Brumitt,et al.  EasyLiving: Technologies for Intelligent Environments , 2000, HUC.

[27]  Chad Burkey Environmental interfaces: HomeLab , 2000, CHI Extended Abstracts.

[28]  Gregory D. Abowd,et al.  Charting past, present, and future research in ubiquitous computing , 2000, TCHI.

[29]  Gregory D. Abowd,et al.  Living laboratories: the future computing environments group at the Georgia Institute of Technology , 2000, CHI Extended Abstracts.

[30]  Patrick Pérez,et al.  The (MR)MTPF: particle filters to track multiple targets using multiple receivers , 2001 .

[31]  Wolfram Burgard,et al.  Particle Filters for Mobile Robot Localization , 2001, Sequential Monte Carlo Methods in Practice.

[32]  Michael J. Black,et al.  Learning image statistics for Bayesian tracking , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[33]  Nando de Freitas,et al.  Sequential Monte Carlo in Practice , 2001 .

[34]  George Casella,et al.  Implementations of the Monte Carlo EM Algorithm , 2001 .

[35]  Gaetano Borriello,et al.  Location Systems for Ubiquitous Computing , 2001, Computer.

[36]  Michael K. Pitt,et al.  Auxiliary Variable Based Particle Filters , 2001, Sequential Monte Carlo Methods in Practice.

[37]  Fredrik Gustafsson,et al.  Monte Carlo data association for multiple target tracking , 2001 .

[38]  L. Burgio,et al.  Temporal patterns of disruptive vocalization in elderly nursing home residents , 2001, International journal of geriatric psychiatry.

[39]  Andy Hopper,et al.  Implementing a Sentient Computing System , 2001, Computer.

[40]  Eric Horvitz,et al.  Layered representations for human activity recognition , 2002, Proceedings. Fourth IEEE International Conference on Multimodal Interfaces.

[41]  Martha E. Pollack,et al.  Execution monitoring with quantitative temporal Bayesian networks , 2002 .

[42]  Sakuko Otake,et al.  Long-term remote behavioral monitoring of the elderly using sensors installed in domestic houses , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[43]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[44]  Kenji Mase,et al.  Activity and Location Recognition Using Wearable Sensors , 2002, IEEE Pervasive Comput..

[45]  Wolfram Burgard,et al.  Learning motion patterns of persons for mobile service robots , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[46]  Paul R. Cohen,et al.  An Unsupervised Algorithm for Segmenting Categorical Timeseries into Episodes , 2002, Pattern Detection and Discovery.

[47]  Dieter Fox,et al.  Bayesian Filtering for Location Estimation , 2003, IEEE Pervasive Comput..

[48]  Frank Dellaert,et al.  Efficient particle filter-based tracking of multiple interacting targets using an MRF-based motion model , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[49]  T. Togawa,et al.  The concept of the home health monitoring , 2003, Proceedings 5th International Workshop on Enterprise Networking and Computing in Healthcare Industry (HealthCom).

[50]  Allison Woodruff,et al.  The mad hatter's cocktail party: a social mobile audio space supporting multiple simultaneous conversations , 2003, CHI '03.

[51]  Alex Pentland,et al.  Sensing and modeling human networks using the sociometer , 2003, Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings..

[52]  Shuichi Yoshino,et al.  A new in-door location detection method adopting learning algorithms , 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[53]  C. Atkeson,et al.  Toward the Automatic Assessment of Behavioral Distrubances of Dementia , 2003 .

[54]  Telecommunications Board,et al.  IT Roadmap to a Geospatial Future , 2003 .

[55]  William C. Mann,et al.  Enabling location-aware pervasive computing applications for the elderly , 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[56]  D. Fox,et al.  Bayesian Techniques for Location Estimation , 2003 .

[57]  Christopher G. Atkeson,et al.  The Narrator : A Daily Activity Summarizer Using Simple Sensors in an Instrumented Environment , 2003 .

[58]  Henry A. Kautz,et al.  Inferring High-Level Behavior from Low-Level Sensors , 2003, UbiComp.

[59]  Henry A. Kautz,et al.  Guide: Towards Understanding Daily Life via Auto- Identification and Statistical Analysis , 2003 .

[60]  Stanislav Kovacic,et al.  Trajectory Based Assessment of Coordinated Human Activity , 2003, ICVS.

[61]  Kent Larson,et al.  Tools for Studying Behavior and Technology in Natural Settings , 2003, UbiComp.

[62]  Christopher G. Atkeson,et al.  Predicting human interruptibility with sensors: a Wizard of Oz feasibility study , 2003, CHI '03.

[63]  Paul R. Cohen,et al.  Bayesian Clustering by Dynamics Contents 1 Introduction 1 2 Clustering Markov Chains 2 , 2022 .

[64]  Frank Dellaert,et al.  EM, MCMC, and Chain Flipping for Structure from Motion with Unknown Correspondence , 2004, Machine Learning.

[65]  Lars Erik Holmquist,et al.  Supporting group collaboration with interpersonal awareness devices , 1999, Personal Technologies.

[66]  Donald E. Brown,et al.  Health-status monitoring through analysis of behavioral patterns , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[67]  J. Movellan Tutorial on Hidden Markov Models , 2006 .