IEEE Organizations related to Sensor Signal Processing And Array Sensor Fusion

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Conferences related to Sensor Signal Processing And Array Sensor Fusion

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2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and severalco-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students, academics and industry researchers.

  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conferenceand 27co-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students,academics and industry.

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    computer, vision, pattern, cvpr, machine, learning

  • 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. Main conference plus 50 workshop only attendees and approximately 50 exhibitors and volunteers.

  • 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Topics of interest include all aspects of computer vision and pattern recognition including motion and tracking,stereo, object recognition, object detection, color detection plus many more

  • 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Sensors Early and Biologically-Biologically-inspired Vision, Color and Texture, Segmentation and Grouping, Computational Photography and Video

  • 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics, motion analysis and physics-based vision.

  • 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics,motion analysis and physics-based vision.

  • 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2006 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)


2020 IEEE International Conference on Robotics and Automation (ICRA)

The International Conference on Robotics and Automation (ICRA) is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work.


2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

The Conference focuses on all aspects of instrumentation and measurement science andtechnology research development and applications. The list of program topics includes but isnot limited to: Measurement Science & Education, Measurement Systems, Measurement DataAcquisition, Measurements of Physical Quantities, and Measurement Applications.


GLOBECOM 2020 - 2020 IEEE Global Communications Conference

IEEE Global Communications Conference (GLOBECOM) is one of the IEEE Communications Society’s two flagship conferences dedicated to driving innovation in nearly every aspect of communications. Each year, more than 2,900 scientific researchers and their management submit proposals for program sessions to be held at the annual conference. After extensive peer review, the best of the proposals are selected for the conference program, which includes technical papers, tutorials, workshops and industry sessions designed specifically to advance technologies, systems and infrastructure that are continuing to reshape the world and provide all users with access to an unprecedented spectrum of high-speed, seamless and cost-effective global telecommunications services.


ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.


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Periodicals related to Sensor Signal Processing And Array Sensor Fusion

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Aerospace and Electronic Systems Magazine, IEEE

The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.


Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


Circuits and Systems I: Regular Papers, IEEE Transactions on

Part I will now contain regular papers focusing on all matters related to fundamental theory, applications, analog and digital signal processing. Part II will report on the latest significant results across all of these topic areas.


Communications Letters, IEEE

Covers topics in the scope of IEEE Transactions on Communications but in the form of very brief publication (maximum of 6column lengths, including all diagrams and tables.)


Communications Magazine, IEEE

IEEE Communications Magazine was the number three most-cited journal in telecommunications and the number eighteen cited journal in electrical and electronics engineering in 2004, according to the annual Journal Citation Report (2004 edition) published by the Institute for Scientific Information. Read more at http://www.ieee.org/products/citations.html. This magazine covers all areas of communications such as lightwave telecommunications, high-speed data communications, personal communications ...


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Most published Xplore authors for Sensor Signal Processing And Array Sensor Fusion

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Xplore Articles related to Sensor Signal Processing And Array Sensor Fusion

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A Processing Method of Pressure Sensor Array Signal and Data

2010 International Conference on Electrical and Control Engineering, 2010

Pressure sensor output performance susceptible to environmental temperature, voltage fluctuation, etc. parameters of non-target effects, and sensor arrays of sensors exists between the cross-talk, thus greatly reducing the measurement accuracy. In response to these issues the sensor signal processing procedures, the use of multi-sensor information fusion of data from different sensors for effective integration. Mathematical statistics-based approach to the confidence ...


Collaborative classification applications in sensor networks

Sensor Array and Multichannel Signal Processing Workshop Proceedings, 2002, 2002

Distributed sensor networks are a significant technology nowadays. Inexpensive, smart devices with multiple sensors provide opportunities for instrumenting, monitoring and controlling targeting systems. Such sensor nodes have capability for acquiring and embedded-processing of variety of data forms. Collaborative signal processing and fusion algorithms are needed to aggregate the distributed data from among the nodes in the network, including possibly multiple ...


Breathing Signal Fusion in Pressure Sensor Arrays

2008 IEEE International Workshop on Medical Measurements and Applications, 2008

Pressure sensors can be used to unobtrusively obtain breathing signals from a person in bed. Obtaining a single representation of the breathing signal from an array of such sensors requires data-level fusion. We propose a decision directed adaptive linear estimator to perform this fusion online. The proposed method was compared with three other online fusion methods and two offline methods ...


Transient feature extraction based on phase space fusion by partial-least-square regression analysis of sensor array signals

2011 International Conference on Emerging Trends in Electrical and Computer Technology, 2011

Pattern classification based on transient signal analysis provides an effective method for identification of dynamical systems. The partial-least- square regression (PLSR) is most commonly used to generate parametric representation of phase space defined by measured signals and their time derivatives. The PLS component scores are interpreted as object features for pattern identification. In this paper, we consider sensor array transients, ...


Processing-aware compression for sensor networks

Sensor Array and Multichannel Signal Processing Workshop Proceedings, 2002, 2002

In sensor networks, where power and bandwidth are at a premium, there is a clear need to use compression to limit the amount of information exchanged by the sensors. We study the signal compression problem in situations where signals are being processed for the purpose of source localization. In these scenarios, compression should be optimized for the accuracy of source ...


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Educational Resources on Sensor Signal Processing And Array Sensor Fusion

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IEEE-USA E-Books

  • A Processing Method of Pressure Sensor Array Signal and Data

    Pressure sensor output performance susceptible to environmental temperature, voltage fluctuation, etc. parameters of non-target effects, and sensor arrays of sensors exists between the cross-talk, thus greatly reducing the measurement accuracy. In response to these issues the sensor signal processing procedures, the use of multi-sensor information fusion of data from different sensors for effective integration. Mathematical statistics-based approach to the confidence distance matrix and relation matrix of useful data fusion processing, the final maximum probability method and maximum likelihood method for effective integration. The experimental results show that the method eliminates the effects of temperature on the pressure sensors, enabling a single sensor error of the uncertainty of the impact of reduced pressure on the distribution of measurement data to improve the stability and accuracy, making the pressure improved the accuracy of image reconstruction.

  • Collaborative classification applications in sensor networks

    Distributed sensor networks are a significant technology nowadays. Inexpensive, smart devices with multiple sensors provide opportunities for instrumenting, monitoring and controlling targeting systems. Such sensor nodes have capability for acquiring and embedded-processing of variety of data forms. Collaborative signal processing and fusion algorithms are needed to aggregate the distributed data from among the nodes in the network, including possibly multiple modalities of data within a sensor node, to make decisions in a reliable and efficient manner. One of the important sensor network applications is target classification in battlefields. This paper presents improved moving vehicle target classification performance using data obtained from sensor networks with collaboration both across nodes and within a node in terms of multimodal fusion. Results show that a 50% relative improvement in classification error can be obtained using collaboration both in the case of single vehicle target and those involving multi-vehicle convoys.

  • Breathing Signal Fusion in Pressure Sensor Arrays

    Pressure sensors can be used to unobtrusively obtain breathing signals from a person in bed. Obtaining a single representation of the breathing signal from an array of such sensors requires data-level fusion. We propose a decision directed adaptive linear estimator to perform this fusion online. The proposed method was compared with three other online fusion methods and two offline methods using one hundred data records collected from five healthy participants. The decision directed adaptive linear estimator had signal to noise ratios comparable to the offline correlation method that it was adapted from and better mutual information results. In the presence of movement noise and for low amplitude signals, the proposed method also provides good fusion performance.

  • Transient feature extraction based on phase space fusion by partial-least-square regression analysis of sensor array signals

    Pattern classification based on transient signal analysis provides an effective method for identification of dynamical systems. The partial-least- square regression (PLSR) is most commonly used to generate parametric representation of phase space defined by measured signals and their time derivatives. The PLS component scores are interpreted as object features for pattern identification. In this paper, we consider sensor array transients, and propose PLSR based fusion of phase spaces of individual sensors into a single virtual phase space. Motivation for this approach comes from realizing that (i) multiplicity of array sensors encodes information about object diversity, and (ii) PLSR models object diversities in terms of small number of latent variables. The approach is validated through a case study of vapor identification by electronic nose based on surface-acoustic-wave (SAW) chemical sensor array. A comparison of results with and without fusion shows substantial improvement in vapor class separability after phase space fusion.

  • Processing-aware compression for sensor networks

    In sensor networks, where power and bandwidth are at a premium, there is a clear need to use compression to limit the amount of information exchanged by the sensors. We study the signal compression problem in situations where signals are being processed for the purpose of source localization. In these scenarios, compression should be optimized for the accuracy of source localization, rather than to provide a reproduction of the signals with some desired fidelity. We show how this leads to novel design techniques that have clear advantages over standard quantizer design approaches.

  • Monolithic CMOS multi-transducer gas sensor microsystem

    This paper presents a monolithically integrated multi-transducer microsystem to detect organic and inorganic gases. The system comprises two polymer-based sensor arrays, a metal-oxide-based sensor array, driving and signal processing electronics and a digital communication interface. The chip is fabricated in industrial 0.8 /spl mu/m CMOS-technology with subsequent post-CMOS micromachining and operates at a supply voltage of 5 volt. The monolithic integration of different transducer types with associated driving and read-out circuitry significantly improves the discrimination capability of the system and reduces the packaging efforts.

  • Distributed Signal Processing in a Bandwidth Constrained Sensor Network

    This paper presents a powerpoint presentation that focuses on the distributed signal processing capability of bandwidth constrained sensor networks. A simple (isotropic) universal decentralised estimation scheme (DES) with known PDF are presented for the ad hoc sensor network along with sensor networks with a fusion center. The paper also examines the trade-off of network size and MSE under the bandwidth constraint

  • Data fusion in wireless sensor array networks with signal and noise correlation mismatch

    Data fusion from closely spaced nonuniformly distributed sensor arrays with large sensor count requires a smart mechanism for minimizing information redundancy while maintaining high signal fidelity. In this work, we investigate the detection of multiple signal sources impinging on a nonuniformly spaced array of sensors when the additive noise has local spatial correlation characteristics. In particular, we show that if the signal spatial correlation length does not match the noise correlation length, significant performance loss occurs because of improper subarray selection. We propose to formulate the log-likelihood function (LLF) in the multiresolution domain by spatially whitening the wavelet-transformed observations followed by local universal thresholding. The LLF obtained is independent of the noise covariance term, thereby yielding significant improvement over classical time domain LLF tests. Performance comparison with data averaging and decision fusion detectors is carried out to illustrate the potential advantages of the proposed method.

  • A nested sensor array focusing on near field targets

    A nested virtual array subband beamforming system is proposed for applications where broadband signal targets are located within the near field of the array. Subband multirate processing and near field beamforming techniques are used jointly for the nested array to improve the performances and reduce the computational complexity. A new noise model, namely the broadband near field spherically isotropic noise model, is also proposed for the optimization design of near field beamformers. It is shown that near field beamforming is essential for better distance discrimination of near field targets, reduced beampattern variations for broadband signals, and stronger reverberation suppression.

  • Distributed source localization algorithms for acoustic ad-hoc sensor networks

    In this paper, we develop and evaluate distributed implementations of source localization estimators from energy-based measurements obtained via an ad-hoc network of acoustic sensors. The distributed locally constructed algorithms that we present produce at each node a sequence of estimates approximating a desired source localization algorithm. As our investigation reveals, the localization performance of these distributed algorithms depends on the type of desired localization algorithm, the network topology and the number of communication and fusion steps employed in these approximations.



Standards related to Sensor Signal Processing And Array Sensor Fusion

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Jobs related to Sensor Signal Processing And Array Sensor Fusion

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