Filters








2,672 Hits in 6.7 sec

Estimation, filtering and fusion for networked systems with network-induced phenomena: New progress and prospects

Jun Hu, Zidong Wang, Dongyan Chen, Fuad E. Alsaadi
2016 Information Fusion  
Firstly, the network-induced phenomena under consideration are briefly recalled including missing/fading measurements, signal quantization, sensor saturations, communication delays, and randomly occurring  ...  Subsequently, some recent results on the estimation, filtering and fusion for systems with the network-induced phenomena are reviewed in great detail.  ...  Very recently, by using the backward Riccati equation method, an effective Hfiltering scheme has been presented in [37] to handle the missing measurements and quantization effects in a same framework  ... 
doi:10.1016/j.inffus.2016.01.001 fatcat:vu75iw6e5nfatku5jrlcbmg5um

Recent Advances on Filtering and Control for Nonlinear Stochastic Complex Systems with Incomplete Information: A Survey

Bo Shen, Zidong Wang, Jinling Liang, Yurong Liu
2012 Mathematical Problems in Engineering  
The incomplete information under consideration mainly includes missing measurements, randomly varying sensor delays, signal quantization, sensor saturations, and signal sampling.  ...  With such incomplete information, the developments on various filtering and control issues are reviewed in great detail.  ...  norm requirement is satisfied. iv In 124 , a new distributed H ∞ -consensus filtering problem over a finite-horizon has been addressed for sensor networks with multiple missing measurements.  ... 
doi:10.1155/2012/530759 fatcat:xw42wa2abzbi5mkrcepzz2d46y

Performance of a Robust Filter-based Approach for Contour Detection in Wireless Sensor Networks

Hadi Alasti, William A. Armstrong, Asis Nasipuri
2007 2007 16th International Conference on Computer Communications and Networks  
The proposed scheme applies multi-level quantization to the sensor signal values to artificially create an edge and then applies spatial filtering for edge detection.  ...  ) in the network.  ...  Hence, appropriate measures must be taken to account for the non-uniform locations of the sampled data while applying filtering in sensor networks.  ... 
doi:10.1109/icccn.2007.4317813 dblp:conf/icccn/AlastiAN07 fatcat:nlddrfk5pfefxmlfww33y7rvz4

Modified Vector Field Histogram with a Neural Network Learning Model for Mobile Robot Path Planning and Obstacle Avoidance

Dr. Bahaa I.Kazem, Ali H. Hamad, Mustafa M. Mozael
2010 International Journal of Advancements in Computing Technology  
In this work, a Modified Vector Field Histogram (MVFH) has been developed to improve path planning and obstacle avoidance for a wheeled driven mobile robot.  ...  Also, digital filter has been utilized for improving the robustness of obstacle avoidance trajectory of mobile robot.  ...  Modified Vector Field Histogram with a Neural Network Learning Model for Mobile Robot Path Planning and Obstacle Avoidance Dr. Bahaa I.Kazem , Ali H. Hamad , Mustafa M.  ... 
doi:10.4156/ijact.vol2.issue5.18 fatcat:6w42xgrtzrftvkh4sarxnokck4

Control strategies for H.264 video decoding under resources constraints

Anne-Marie Alt, Daniel Simon
2010 Proceedings of the Fifth International Workshop on Feedback Control Implementation and Design in Computing Systems and Networks - FeBiD '10  
Automatic control appears to be an enabling technology to handle both the performance dispersion in highly integrated chips and computing power adaptability under varying loads and energy storage constraints  ...  This work in progress paper presents a case study, where a video decoder is controlled via quality loops and frequency scaling, to meet end-users requirements mixing quality and energy consumption related  ...  The quantization step selectively cancels some information from the original video: its effect can be compared to a low-pass filter.  ... 
doi:10.1145/1791204.1791207 fatcat:d2xpcjkizvdylgzd6qpz7vcxoy

Enhancing Localization of Mobile Robots in Distributed Sensor Environments for Reliable Proximity Service Applications

Xingzhen Bai, Zhijing Zhang, Lu Liu, Xiaojun Zhai, John Panneerselvam, Leijiao Ge
2019 IEEE Access  
INDEX TERMS Mobile robot localization, unscented Kalman filter, wireless sensor networks, packet loss, measurements compensation.  ...  Mobile robots can effectively coordinate information among sensor nodes in a distributed physical proximity.  ...  Mobile robots can effectively coordinate for collecting, processing and transmitting information among sensors distributed in physical proximity.  ... 
doi:10.1109/access.2019.2899059 fatcat:uxzcqnqtorebxazhdrmgwpfk7y

Analytical Study of Task Offloading Techniques using Deep Learning

Mr Almelu, Dr. S. Veenadhari, Kamini Maheshwar
2021 SMART MOVES JOURNAL IJOSCIENCE  
Due to excessive latency, bandwidth limitations, and high communication costs, transferring all IoT data to the cloud to solve the missing data problem may have a detrimental impact on network performance  ...  In this work, we show how deep learning may be used to offload tasks in IoT applications.  ...  panel with limited capacity. , facilitated by spatial filtering and uniform scalar quantization.  ... 
doi:10.24113/ijoscience.v7i7.393 fatcat:4ijltsc42fei5lh6cnqjc4iokq

Control strategies for H.264 video decoding under resources constraints

Anne-Marie Alt, Daniel Simon
2010 ACM SIGOPS Operating Systems Review  
Automatic control appears to be an enabling technology to handle both the performance dispersion in highly integrated chips and computing power adaptability under varying loads and energy storage constraints  ...  This work in progress paper presents a case study, where a video decoder is controlled via quality loops and frequency scaling, to meet end-users requirements mixing quality and energy consumption related  ...  The quantization step selectively cancels some information from the original video: its effect can be compared to a low-pass filter.  ... 
doi:10.1145/1842733.1842743 fatcat:mn2lbiyrgbbbbjdqvxm62usrb4

Particle filtering with applications in networked systems: a survey

Wenshuo Li, Zidong Wang, Yuan Yuan, Lei Guo
2016 Complex & Intelligent Systems  
In this survey paper, we aim to review the particle filtering method and its applications in networked systems.  ...  We first provide an overview of the particle filtering methods as well as networked systems, and then investigate the recent progress in B Zidong Wang  ...  Yuan and L. Guo  ... 
doi:10.1007/s40747-016-0028-2 fatcat:fj6xjuaenfa3riys43mgvdgiye

Wormhole detection using channel characteristics

Shalabh Jain, Tuan Ta, John S. Baras
2012 2012 IEEE International Conference on Communications (ICC)  
The potential applications and pervasive nature of mobile ad-hoc networks (MANETs) has made them an attractive target for attackers.  ...  We demonstrate the loss of this symmetry in case of a wormhole attack and propose a method to detect and flag the adversary.  ...  We find that these channel assumptions hold reasonably well in case of static sensor networks, or mobile nodes with slow movements.  ... 
doi:10.1109/icc.2012.6364768 dblp:conf/icc/JainTB12 fatcat:iizobezsb5btznl3axrn6p7c5u

Multi-target tracking using proximity sensors

Ting He, Chatschik Bisdikian, Lance Kaplan, Wei Wei, Don Towsley
2010 2010 - MILCOM 2010 MILITARY COMMUNICATIONS CONFERENCE  
the problem into discrete space by quantizing the target mobility model and the relaxed likelihood function.  ...  In contrast to classic multi-target tracking which assumes 1-1 association between measurements and targets, we show that proximity measurements do not have such association and thus require a different  ...  INTRODUCTION Target tracking is a critical functionality of sensor networks with broad applications in surveillance and military operations.  ... 
doi:10.1109/milcom.2010.5680242 fatcat:lag2oirzpbaf3evlihqbuq7jmq

2013 Index IEEE Transactions on Automatic Control Vol. 58

2013 IEEE Transactions on Automatic Control  
., TAC March 2013 1696-1706 Hammouri, H., see Nadri, M., TAC March 2013 757-762 Hammouri, H., see Benachour, M. S., TAC Dec. 2013 3011-3023 Han, Q.  ...  -L., see Yue, D., TAC Feb. 2013 475-481 Han, S., see Wang, X., TAC April 2013 975-988 Hariharan, S., Zheng, Z., and Shroff, N. B  ...  ., +, TAC Aug. 2013 2071-2076 Finite-Horizon H Filtering With Missing Measurements and Quantization Effects.  ... 
doi:10.1109/tac.2013.2295962 fatcat:3zpqog4r4nhoxgo4vodx4sj3l4

Table of Contents

2021 IEEE Transactions on Signal Processing  
Palomar Measurement Bounds for Compressed Sensing in Sensor Networks With Missing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Chi Centralized Cooperative Sensor Fusion for Dynamic Sensor Network With Limited Field-of-View via Labeled Multi-Bernoulli Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tsp.2021.3136798 fatcat:kzkdhzcz3fgx3jv6gfjofooseq

LoRa-LiSK: A Lightweight Shared Secret Key Generation Scheme for LoRa Networks

Aisha Kanwal Junejo, Fatma Benkhelifa, Boon Wong, Julie A. McCann
2021 IEEE Internet of Things Journal  
Our proposed LoRa-LiSK scheme consists of several preprocessing techniques (timestamp matching, two sample Kolmogorov Smirnov tests, and a Savitzky-Golay filter), multi-level quantization, information  ...  The real-world wireless sensor networks (WSNs) and LoRa use cases might not meet both requirements, thus making the current PLS schemes inappropriate for these systems.  ...  ACKNOWLEDGMENT This work is partially supported by two EPSRC grants, Science of Sensor Systems (S4) programme (EP/N007565/1), and PETRAS Logistics 4.0 project (EP/S035362/1).  ... 
doi:10.1109/jiot.2021.3103009 fatcat:n7dvu32mc5eulfwcljt5dc3m5u

Table of Contents

2021 IEEE Transactions on Signal Processing  
Sensing in Sensor Networks With Missing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Huang Multi-Sensor Filtering Fusion With Parametric Uncertainties and Measurement Censoring: Monotonicity and Boundedness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tsp.2021.3136800 fatcat:zhf46mb3rbdlnnh3u2xizgxof4
« Previous Showing results 1 — 15 out of 2,672 results