The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform

Michael R. James - One of the best experts on this subject based on the ideXlab platform.

  • Multi-Target Track-to-Track Fusion Based on Permutation Matrix Track Association
    2018 IEEE Intelligent Vehicles Symposium (IV), 2018
    Co-Authors: Yusuke Kanzawa, Matthew Derry, Michael R. James
    Abstract:

    This paper proposes the Permutation Matrix Track Association (PMTA) algorithm to support track-to-track, multi-sensor data fusion for multiple targets in an autonomous driving system. In this system, measurement data from different sensor modalities (LIDAR, radar, and vision) is processed by object trackers operating on each sensor modality independently to create the tracks of the objects. The proposed approach fuses the object track lists from each tracker, first by associating the tracks within each track list, followed by a state estimation (filtering) step. The eventual output is the unified tracks of the objects provided for further autonomous driving processing, such as path and motion planning. The Permutation Matrix track association (PMTA) algorithm considers both spatial and temporal information to associate object tracks from different sensor modalities. Experimental results show that the proposed approach improves not only the performance of the multipletarget track-to-track fusion, but also stability and robustness in the resulting speed control and decision making in the autonomous driving system.

  • Intelligent Vehicles Symposium - Multi-Target Track-to-Track Fusion Based on Permutation Matrix Track Association
    2018 IEEE Intelligent Vehicles Symposium (IV), 2018
    Co-Authors: Yusuke Kanzawa, Matthew Derry, Michael R. James
    Abstract:

    This paper proposes the Permutation Matrix Track Association (PMTA) algorithm to support track-to-track, multi-sensor data fusion for multiple targets in an autonomous driving system. In this system, measurement data from different sensor modalities (LIDAR, radar, and vision) is processed by object trackers operating on each sensor modality independently to create the tracks of the objects. The proposed approach fuses the object track lists from each tracker, first by associating the tracks within each track list, followed by a state estimation (filtering) step. The eventual output is the unified tracks of the objects provided for further autonomous driving processing, such as path and motion planning. The Permutation Matrix track association (PMTA) algorithm considers both spatial and temporal information to associate object tracks from different sensor modalities. Experimental results show that the proposed approach improves not only the performance of the multipletarget track-to-track fusion, but also stability and robustness in the resulting speed control and decision making in the autonomous driving system.

Yintang Yang - One of the best experts on this subject based on the ideXlab platform.

  • Permutation Matrix encryption based ultralightweight secure rfid scheme in internet of vehicles
    Sensors, 2019
    Co-Authors: Junbin Kang, Hui Li, Yintang Yang
    Abstract:

    Radio frequency identification (RFID) is a kind of non-contact automatic identification technology. The Internet of Vehicles (IoV) is a derivative of the Internet of Things (IoT), and RFID technology has become one of the key technologies of IoV. Due to the open wireless communication environment in RFID system, the RFID system is easy to be exposed to various malicious attacks, which may result in privacy disclosure. The provision of privacy protection for users is a prerequisite for the wide acceptance of the IoV. In this paper, we discuss the privacy problem of the RFID system in the IoV and present a lightweight RFID authentication scheme based on Permutation Matrix encryption, which can resist some typical attacks and ensure the user’s personal privacy and location privacy. The fast certification speed of the scheme and the low cost of the tag is in line with the high-speed certification requirement in the Internet of vehicles. In this thesis, the specific application scenarios of the proposed RFID authentication scheme in the IoV is also discussed.

Yusuke Kanzawa - One of the best experts on this subject based on the ideXlab platform.

  • Multi-Target Track-to-Track Fusion Based on Permutation Matrix Track Association
    2018 IEEE Intelligent Vehicles Symposium (IV), 2018
    Co-Authors: Yusuke Kanzawa, Matthew Derry, Michael R. James
    Abstract:

    This paper proposes the Permutation Matrix Track Association (PMTA) algorithm to support track-to-track, multi-sensor data fusion for multiple targets in an autonomous driving system. In this system, measurement data from different sensor modalities (LIDAR, radar, and vision) is processed by object trackers operating on each sensor modality independently to create the tracks of the objects. The proposed approach fuses the object track lists from each tracker, first by associating the tracks within each track list, followed by a state estimation (filtering) step. The eventual output is the unified tracks of the objects provided for further autonomous driving processing, such as path and motion planning. The Permutation Matrix track association (PMTA) algorithm considers both spatial and temporal information to associate object tracks from different sensor modalities. Experimental results show that the proposed approach improves not only the performance of the multipletarget track-to-track fusion, but also stability and robustness in the resulting speed control and decision making in the autonomous driving system.

  • Intelligent Vehicles Symposium - Multi-Target Track-to-Track Fusion Based on Permutation Matrix Track Association
    2018 IEEE Intelligent Vehicles Symposium (IV), 2018
    Co-Authors: Yusuke Kanzawa, Matthew Derry, Michael R. James
    Abstract:

    This paper proposes the Permutation Matrix Track Association (PMTA) algorithm to support track-to-track, multi-sensor data fusion for multiple targets in an autonomous driving system. In this system, measurement data from different sensor modalities (LIDAR, radar, and vision) is processed by object trackers operating on each sensor modality independently to create the tracks of the objects. The proposed approach fuses the object track lists from each tracker, first by associating the tracks within each track list, followed by a state estimation (filtering) step. The eventual output is the unified tracks of the objects provided for further autonomous driving processing, such as path and motion planning. The Permutation Matrix track association (PMTA) algorithm considers both spatial and temporal information to associate object tracks from different sensor modalities. Experimental results show that the proposed approach improves not only the performance of the multipletarget track-to-track fusion, but also stability and robustness in the resulting speed control and decision making in the autonomous driving system.

Hal Caswell - One of the best experts on this subject based on the ideXlab platform.

  • Matrix models and sensitivity analysis of populations classified by age and stage a vec Permutation Matrix approach
    Theoretical Ecology, 2012
    Co-Authors: Hal Caswell
    Abstract:

    Matrix population models in which individuals are classified by both age and stage can be constructed using the vec-Permutation Matrix. The resulting age-stage models can be used to derive the age-specific consequences of a stage-specific life history or to describe populations in which the vital rates respond to both age and stage. I derive a general formula for the sensitivity of any output (scalar, vector, or Matrix-valued) of the model, to any vector of parameters, using Matrix calculus. The matrices describing age-stage dynamics are almost always reducible; I present results giving conditions under which population growth is ergodic from any initial condition. As an example, I analyze a published stage-specific model of Scotch broom (Cytisus scoparius), an invasive perennial shrub. Sensitivity analysis of the population growth rate finds that the selection gradients on adult survival do not always decrease with age but may increase over a range of ages. This may have implications for the evolution of senescence in stage-classified populations. I also derive and analyze the joint distribution of age and stage at death and present a sensitivity analysis of this distribution and of the marginal distribution of age at death.

  • the use of the vec Permutation Matrix in spatial Matrix population models
    Ecological Modelling, 2005
    Co-Authors: Christine M Hunter, Hal Caswell
    Abstract:

    Matrix models for a metapopulation can be formulated in two ways: in terms of the stage distribution within each spatial patch, or in terms of the spatial distribution within each stage. In either case, the entries of the projection Matrix combine demographic and dispersal information in potentially complicated ways. We show how to construct such models from a simple block-diagonal formulation of the demographic and dispersal processes, using a special Permutation Matrix called the vec-Permutation Matrix. This formulation makes it easy to calculate and interpret the sensitivity and elasticity of λ to changes in stage- and patch-specific demographic and dispersal parameters.

Huijiao Wang - One of the best experts on this subject based on the ideXlab platform.

  • CyberC - Ultralightweight RFID Authentication Protocol Based on Permutation Matrix Encryption
    2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2019
    Co-Authors: Tinghui Huang, Yong Ding, Zhen Wang, Huijiao Wang
    Abstract:

    Radio frequency identification (RFID) is a kind of non-contact automatic identification system. Because the communication between the RFID tag and the reader is vulnerable, and the hardware resources of the low-cost RFID tag are very limited, this paper proposes an ultralightweight RFID authentication protocol based on Permutation Matrix encryption (UPBPM). This scheme assigns two different Permutation matrices to each tag, so that each tag has unique encryption and decryption process, which effectively enhances the security of the protocol; and the scheme saves two rounds of authentication information to resist desynchronization attacks; and the dynamic identifiers are used to ensure the anonymity of the tag. Security analysis shows that UPBPM can resist all the possible attacks. Performance evaluation shows that UPBPM uses fewer computing resources on tag without increasing communication cost.

  • Ultralightweight RFID Authentication Protocol Based on Permutation Matrix Encryption
    2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2019
    Co-Authors: Tinghui Huang, Yong Ding, Zhen Wang, Huijiao Wang
    Abstract:

    Radio frequency identification (RFID) is a kind of non-contact automatic identification system. Because the communication between the RFID tag and the reader is vulnerable, and the hardware resources of the low-cost RFID tag are very limited, this paper proposes an ultralightweight RFID authentication protocol based on Permutation Matrix encryption (UPBPM). This scheme assigns two different Permutation matrices to each tag, so that each tag has unique encryption and decryption process, which effectively enhances the security of the protocol; and the scheme saves two rounds of authentication information to resist desynchronization attacks; and the dynamic identifiers are used to ensure the anonymity of the tag. Security analysis shows that UPBPM can resist all the possible attacks. Performance evaluation shows that UPBPM uses fewer computing resources on tag without increasing communication cost.