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Adaptive Filtering 2015 |
As the power of digital signal processors has increased, adaptive filters are now routinely used in many devices as varied as mobile phones, printers, cameras, power systems, GPS devices, and medical monitoring equipment. An adaptive filter uses an optimization algorithm in a system with a linear filter to adjust parameters that have a transfer function controlled by variable parameters. Because of the complexity of the optimization algorithms, most of these adaptive filters are digital filters. They are required for some applications because some parameters of the desired processing operation are not known in advance or are changing. The works cited here are articles about adaptive filtering as it relates to the Science of Security. Articles were published in 2015.
Ozdil, O.; Gunes, A., “Unsupervised Hyperspectral Image Segmentation Using Adaptive Bilateral Filtering,” in Signal Processing and Communications Applications Conference (SIU), 2015 23th, vol., no., pp. 1010–1013, 16–19 May 2015. doi:10.1109/SIU.2015.7130003
Abstract: This paper proposes the use of adaptive bilateral filter for the segmentation of hyperspectral images. First, the spectral bands are selected according to the information contained in each band. Then on each band, adaptive bilateral filter is applied in order to increase the spatial correlation of each pixel. The results are evaluated based on the successful segmentation percentage. It is shown that the segmentation accuracy of k-means clustering algorithm is increased.
Keywords: adaptive filters; correlation methods; hyperspectral imaging; image segmentation; adaptive bilateral filtering; k-means clustering; segmentation accuracy; spatial correlation; spectral bands; unsupervised hyperspectral image segmentation; Biomedical imaging; Correlation; Histograms; Hyperspectral imaging; Image segmentation; Hyperspectral image processing; bilateral filter; k-means algorithm; segmentation (ID#: 15-7160)
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7130003&isnumber=7129794
Yixian Zhu; Xianghong Cheng; Lei Wang; Ling Zhou, “An Intelligent Fault-Tolerant Strategy for AUV Integrated Navigation Systems,” in Advanced Intelligent Mechatronics (AIM), 2015 IEEE International Conference on, vol., no., pp. 269–274, 7–11 July 2015. doi:10.1109/AIM.2015.7222543
Abstract: To ensure the security and reliability of the autonomous underwater vehicle (AUV), an intelligent fault-tolerant strategy for integrated navigation systems is presented in this paper. The improved federated Kalman filter (FKF) is designed to fuse the multiple subsystems, including strapdown inertial navigation system (SINS), magnetic compass (MCP), Doppler velocity log (DVL) and terrain aided navigation (TAN). The intelligent fault-tolerant structure of SINS/MCP/DVL/TAN integrated navigation system is first established, which includes adaptive local filters and fault isolation decision (FID) modules. Fuzzy logic is introduced to adaptively adjust the measurement covariance matrixes of local filters online. FID module is implemented based on fuzzy reasoning. The simulation results show that, the proposed fault-tolerant strategy detects and insulates the faults effectively, which can greatly improve the reliability and guarantee the safety of AUVs in complex underwater environments.
Keywords: Kalman filters; autonomous underwater vehicles; covariance matrices; fault tolerant control; inertial navigation; AUV; AUV integrated navigation systems; DVL; Doppler velocity log; FID modules; MCP; SINS; TAN; adaptive local filters; autonomous underwater vehicle; fault isolation decision module; fuzzy logic; fuzzy reasoning; improved federated Kalman filter; integrated navigation systems; intelligent fault-tolerant strategy; magnetic compass; measurement covariance matrixes; strapdown inertial navigation system; terrain aided navigation; Adaptive filters; Covariance matrices; Fault tolerance; Fault tolerant systems; Navigation; Noise measurement; Sensors (ID#: 15-7161)
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7222543&isnumber=7222494
Nivedhitha, R.; Abirami, S.; Krishnan, R.B.; Raajan, N.R., “Proficient Toning Mechanism for Firewall Policy Assessment,” in Circuit, Power and Computing Technologies (ICCPCT), 2015 International Conference on, vol., no., pp. 1–8, 19–20 March 2015. doi:10.1109/ICCPCT.2015.7159306
Abstract: The tool firewall is the software or hardware procedure that facilitates to guard data and it filter the entire traffic voyage the network boundary. It might be configured to restrict or to permit certain devices or applications to access our data sources available in our network. Packet matching over the firewall tool can be treated as a taper setting trouble: All network data packet consist of its own addressing fields, which must be examined beside every firewall policies to locate the earliest identical rule. Surviving Firewall applications such as CISCO PIX Firewalls and Checkpoint FireWall-1 provide various built-in software tools that permit firewalls as Bundle or Sorted and these tacked Firewalls will partake their charges. The main accusatives of these surviving mechanisms are focusing only to mend the Performance, Exploitation of resources and protection. But still these mechanisms not succeed to attain superior execution while focusing on usage of resources. To handle this difficulty, the projected study is applied in Java software as a Firewall tool which holds an Adaptive Firewall Policies filtering procedure using “Arithmetic Proficient Toning” mechanism, which upgrades the performance of the firewalls over the network in conditions of resources exploitation, services delay and throughput. This anticipated work brought out an adaptative Firewall Policies Diminution Procedure along with an efficient packet filtering mechanism, which dilutes firewall rules execution without compromising the System Security. From the results of our anticipated research, it is founded that this projected practice is a proficient and practical algorithm for firewall policy toning and it dilutes the overall servicing cost, which helps to attain concert at a more prominent grade.
Keywords: Java; firewalls; CISCO PIX firewalls; Java software; adaptative firewall policies diminution procedure; adaptive firewall policies filtering procedure; arithmetic proficient toning mechanism; checkpoint firewall-1; firewall policy assessment; firewall rule execution; packet filtering mechanism; packet matching; resources exploitation; software tools; surviving mechanisms; traffic voyage filtering; Data structures; Databases; Filtering; Firewalls (computing); Protocols; Software; Adaptative Filter; Firewall Policies Assessment; Proficient Toning; Resource Exploitation (ID#: 15-7162)
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7159306&isnumber=7159156
Rathgeb, C.; Gomez-Barrero, M.; Busch, C.; Galbally, J.; Fierrez, J., “Towards Cancelable Multi-Biometrics Based on Bloom Filters: A Case Study on Feature Level Fusion of Face and Iris,” in Biometrics and Forensics (IWBF), 2015 International Workshop on, vol., no., pp. 1–6, 3–4 March 2015. doi:10.1109/IWBF.2015.7110225
Abstract: In this work we propose a generic framework for generating an irreversible representation of multiple biometric templates based on adaptive Bloom filters. The presented technique enables a feature level fusion of different biometrics (face and iris) to a single protected template, improving privacy protection compared to the corresponding systems based on a single biometric trait. At the same time, a significant gain in biometric performance is achieved, confirming the soundness of the proposed technique.
Keywords: data privacy; data structures; face recognition; image fusion; iris recognition; adaptive bloom filter; biometric performance; biometric trait; face feature level fusion; iris feature level fusion; multibiometrics; multiple biometric template; privacy protection; Face; Feature extraction; Iris recognition; Privacy; Transforms; Bloom filter; Template protection; biometric fusion; face; iris (ID#: 15-7163)
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7110225&isnumber=7110217
More, V.; Kumar, H.; Kaingade, S.; Gaidhani, P.; Gupta, N., “Visual Odometry Using Optic Flow for Unmanned Aerial Vehicles,” in Cognitive Computing and Information Processing (CCIP), 2015 International Conference on, vol., no., pp. 1–6, 3–4 March 2015. doi:10.1109/CCIP.2015.7100731
Abstract: Use of computer vision on Unmanned Aerial Vehicles (UAV) has been a promising area of research given its potential applications in exploration, surveillance and security. Localization in indoor, unknown environments can become increasingly difficult due to irregularities or complete absence of GPS. Advent of small, light and high performance cameras and computing hardware has enabled design of autonomous systems. In this paper, the optic flow principle is employed for estimating two dimensional motion of the UAV using a downward facing monocular camera. Combining it with an ultrasonic sensor, UAV’s three dimensional position is estimated. Position estimation and trajectory tracking have been performed and verified in a laboratory setup. All computations are carried out onboard the UAV using a miniature single board computer.
Keywords: autonomous aerial vehicles; cameras; distance measurement; image sequences; motion estimation; robot vision; ultrasonic transducers; GPS; UAV three dimensional position estimation; autonomous systems; computer vision; downward facing monocular camera; high performance cameras; miniature single board computer; optic flow; trajectory tracking; two dimensional motion estimation; ultrasonic sensor; unmanned aerial vehicles; visual odometry; Adaptive optics; Cameras; Computer vision; Image motion analysis; Optical filters; Optical imaging; Optical sensors; UAV; navigation; odometry; optic flow (ID#: 15-7164)
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7100731&isnumber=7100673
Vandana, M.; Manmadhan, S., “Self Learning Network Traffic Classification,” in Innovations in Information, Embedded and Communication Systems (ICIIECS), 2015 International Conference on, vol., no., pp. 1–5, 19–20 March 2015. doi:10.1109/ICIIECS.2015.7193038
Abstract: Network management is part of traffic engineering and security. The current solutions - Deep Packet Inspection (DPI) and statistical classification, rely on the availability of a training set. In case of these there is a cumbersome need to regularly update the signatures. Further their visibility is limited to classes the classifier has been trained for. Unsupervised algorithms have been envisioned as a an alternative to automatically identify classes of traffic. To address these issues Self Learning Network Traffic Classification is proposed. It uses unsupervised algorithms along with an adaptive seeding approach to automatically lets classes of traffic to emerge, making them identified and labelled. Unlike traditional classifiers, there is no need of a-priori knowledge of signatures nor a training set to extract the signatures. Instead, Self Learning Network Traffic Classification automatically groups flows into pure (or homogeneous) clusters using simple statistical features. This label assignment (which is still based on some manual intervention) ensures that class labels can be easily discovered. Furthermore, Self Learning Network Traffic Classification uses an iterative seeding approach which will boost its ability to cope with new protocols and applications. Unlike state-of-art classifiers, the biggest advantage of Self Learning Network Traffic Classification is its ability to discover new protocols and applications in an almost automated fashion.
Keywords: pattern classification; statistical analysis; traffic engineering computing; unsupervised learning; DPI; adaptive seeding approach; deep packet inspection; network management; protocols; self learning network traffic classification; statistical classification; traffic engineering; unsupervised machine learning; Classification algorithms; Clustering algorithms; Filtering; IP networks; Ports (Computers); Protocols; Telecommunication traffic; Traffic classification; clustering; self-seeding; unsupervised machine learning (ID#: 15-7165)
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7193038&isnumber=7192777
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Articles listed on these pages have been found on publicly available Internet pages and are cited with links to those pages. Some of the information included herein has been reprinted with permission from the authors or data repositories. Direct any requests via Email to news@scienceofsecurity.net for removal of the links or modifications to specific citations. Please include the ID# of the specific citation in your correspondence.