Samuel L. Shue
- Courses3
- Reviews5
- School: University of North Carolina
- Campus: Charlotte
- Department: Electrical Engineering
- Email address: Join to see
- Phone: Join to see
-
Location:
9201 University City Blvd
Charlotte, NC - 28223 - Dates at University of North Carolina: December 2017 - April 2019
- Office Hours: Join to see
Biography
University of North Carolina Charlotte - Electrical Engineering
Experience
University of North Carolina at Charlotte
Adjunct Professor
Samuel worked at University of North Carolina at Charlotte as a Adjunct Professor
Evatran
Consultant
Samuel worked at Evatran as a Consultant
IAM Robotics
Software Engineer
Samuel worked at IAM Robotics as a Software Engineer
NLA Diagnostics LLC
Embedded Systems Engineer
Samuel worked at NLA Diagnostics LLC as a Embedded Systems Engineer
The University of North Carolina at Charlotte
Research Assistant
Samuel worked at The University of North Carolina at Charlotte as a Research Assistant
Education
University of North Carolina at Charlotte
Master's degree
Electrical and Electronics Engineering
Embedded Systems, Mobile Robotics, Wireless Sensor Networks, Machine Vision, Artificial Intelligence, Control Systems, Signal ProcessingUniversity of North Carolina at Charlotte
Doctor of Philosophy (Ph.D.)
Electrical and Electronics Engineering
University of North Carolina at Charlotte
Bachelor of Science (BS)
Computer Engineering
Computer EngineeringUniversity of North Carolina at Charlotte
Adjunct Professor
Publications
Utilization of wireless signal strength for mobile robot localization in indoor environments
The University of North Carolina at Charlotte
Localization is a key component of any mobile robot application. For any task a mobile robot might need to perform, precise knowledge of its pose within its environment is critical. Mobile robots employ a multitude of sensors to estimate position, orientation, and mapping of its environment. Distance to wireless beacons through signal strength decay can be integrated into a simultaneous localization and mapping (SLAM) algorithm of a mobile robot equipped with a wireless transceiver, with an emphasis on indoor environments. However, radio signal strength does not predictably attenuate indoors as it does in open environments due to signal interference, absorption, and reflection from objects within the environment, incicting unexpected amplification or decay at the receiver known as multipath interference. This causes erroneous distance estimations due to the unexpected changes in signal strength attenuation. In this research, models of radio propagation as it relates to the received signal strength indicator (RSSI) are explored along with localization techniques which utilize these models. For development and testing of RSSI-based localization techniques a simulation method has been described which utilizes a Markov chain to provide realistic multipath interference on simulated RSSI data. Using this simulation technique, a multipath mitigation method is proposed and applied to a range-only SLAM algorithm.
Utilization of wireless signal strength for mobile robot localization in indoor environments
The University of North Carolina at Charlotte
Localization is a key component of any mobile robot application. For any task a mobile robot might need to perform, precise knowledge of its pose within its environment is critical. Mobile robots employ a multitude of sensors to estimate position, orientation, and mapping of its environment. Distance to wireless beacons through signal strength decay can be integrated into a simultaneous localization and mapping (SLAM) algorithm of a mobile robot equipped with a wireless transceiver, with an emphasis on indoor environments. However, radio signal strength does not predictably attenuate indoors as it does in open environments due to signal interference, absorption, and reflection from objects within the environment, incicting unexpected amplification or decay at the receiver known as multipath interference. This causes erroneous distance estimations due to the unexpected changes in signal strength attenuation. In this research, models of radio propagation as it relates to the received signal strength indicator (RSSI) are explored along with localization techniques which utilize these models. For development and testing of RSSI-based localization techniques a simulation method has been described which utilizes a Markov chain to provide realistic multipath interference on simulated RSSI data. Using this simulation technique, a multipath mitigation method is proposed and applied to a range-only SLAM algorithm.
HubVis: Software for gravitational lens estimation and visualization from Hubble data
IEEE SoutheastCon
The Hubble Ultra-Deep Field (HUDF) image spans less than one millionth of the sky, but it contains as many as 10,000 galaxies at distances on the order of 13 billion light years. However, Hubble images are difficult to process and require merging of multiple images from different optical wavelengths. In addition, the interpretation of such images can be complicated by intervening distortion caused by gravitational lensing. To address these issues, a NetBeans visualization tool called HubVis is being developed that includes the capability to: 1) simulate simple gravitational lensing and 2) search for gravitational lenses in Hubble data. The search method utilizes a novel algorithm to estimate gravitational lenses in Hubble data. Results are presented illustrating the current capabilities of the tool.
Utilization of wireless signal strength for mobile robot localization in indoor environments
The University of North Carolina at Charlotte
Localization is a key component of any mobile robot application. For any task a mobile robot might need to perform, precise knowledge of its pose within its environment is critical. Mobile robots employ a multitude of sensors to estimate position, orientation, and mapping of its environment. Distance to wireless beacons through signal strength decay can be integrated into a simultaneous localization and mapping (SLAM) algorithm of a mobile robot equipped with a wireless transceiver, with an emphasis on indoor environments. However, radio signal strength does not predictably attenuate indoors as it does in open environments due to signal interference, absorption, and reflection from objects within the environment, incicting unexpected amplification or decay at the receiver known as multipath interference. This causes erroneous distance estimations due to the unexpected changes in signal strength attenuation. In this research, models of radio propagation as it relates to the received signal strength indicator (RSSI) are explored along with localization techniques which utilize these models. For development and testing of RSSI-based localization techniques a simulation method has been described which utilizes a Markov chain to provide realistic multipath interference on simulated RSSI data. Using this simulation technique, a multipath mitigation method is proposed and applied to a range-only SLAM algorithm.
HubVis: Software for gravitational lens estimation and visualization from Hubble data
IEEE SoutheastCon
The Hubble Ultra-Deep Field (HUDF) image spans less than one millionth of the sky, but it contains as many as 10,000 galaxies at distances on the order of 13 billion light years. However, Hubble images are difficult to process and require merging of multiple images from different optical wavelengths. In addition, the interpretation of such images can be complicated by intervening distortion caused by gravitational lensing. To address these issues, a NetBeans visualization tool called HubVis is being developed that includes the capability to: 1) simulate simple gravitational lensing and 2) search for gravitational lenses in Hubble data. The search method utilizes a novel algorithm to estimate gravitational lenses in Hubble data. Results are presented illustrating the current capabilities of the tool.
A Survey of Robotic Applications in Wireless Sensor Networks
IEEE SoutheastCon
Robotics can have many applications in wireless sensor networks. Robotics can be used to help solve many problems in wireless sensor networks, such as localizing nodes, acting as data mules, repositioning nodes, detecting and reacting to sensor failure, aggregate sensor data, and even sometimes provide mobile battery chargers for the nodes. Inversely, wireless sensor networks can help solve many problems in robotics, such as localization of the robot, path planning, mapping, and sensing.
Utilization of wireless signal strength for mobile robot localization in indoor environments
The University of North Carolina at Charlotte
Localization is a key component of any mobile robot application. For any task a mobile robot might need to perform, precise knowledge of its pose within its environment is critical. Mobile robots employ a multitude of sensors to estimate position, orientation, and mapping of its environment. Distance to wireless beacons through signal strength decay can be integrated into a simultaneous localization and mapping (SLAM) algorithm of a mobile robot equipped with a wireless transceiver, with an emphasis on indoor environments. However, radio signal strength does not predictably attenuate indoors as it does in open environments due to signal interference, absorption, and reflection from objects within the environment, incicting unexpected amplification or decay at the receiver known as multipath interference. This causes erroneous distance estimations due to the unexpected changes in signal strength attenuation. In this research, models of radio propagation as it relates to the received signal strength indicator (RSSI) are explored along with localization techniques which utilize these models. For development and testing of RSSI-based localization techniques a simulation method has been described which utilizes a Markov chain to provide realistic multipath interference on simulated RSSI data. Using this simulation technique, a multipath mitigation method is proposed and applied to a range-only SLAM algorithm.
HubVis: Software for gravitational lens estimation and visualization from Hubble data
IEEE SoutheastCon
The Hubble Ultra-Deep Field (HUDF) image spans less than one millionth of the sky, but it contains as many as 10,000 galaxies at distances on the order of 13 billion light years. However, Hubble images are difficult to process and require merging of multiple images from different optical wavelengths. In addition, the interpretation of such images can be complicated by intervening distortion caused by gravitational lensing. To address these issues, a NetBeans visualization tool called HubVis is being developed that includes the capability to: 1) simulate simple gravitational lensing and 2) search for gravitational lenses in Hubble data. The search method utilizes a novel algorithm to estimate gravitational lenses in Hubble data. Results are presented illustrating the current capabilities of the tool.
A Survey of Robotic Applications in Wireless Sensor Networks
IEEE SoutheastCon
Robotics can have many applications in wireless sensor networks. Robotics can be used to help solve many problems in wireless sensor networks, such as localizing nodes, acting as data mules, repositioning nodes, detecting and reacting to sensor failure, aggregate sensor data, and even sometimes provide mobile battery chargers for the nodes. Inversely, wireless sensor networks can help solve many problems in robotics, such as localization of the robot, path planning, mapping, and sensing.
Reducing the effect of signal multipath fading in RSSI-distance estimation using Kalman filters
Proceedings of the 19th Communications & Networking Symposium
Received Signal Strength Indication (RSSI) has often been used in location estimation and tracking applications. Signal strength between wireless transceivers degrades according to how much distance is between them, allowing the distance to be estimated. With this estimated distance between wireless devices, location estimation techniques can be used. However, one of the largest hindrances in using RSSI for distance estimation for indoor applications is reflections from multipath effects from the wireless signal. This will generate large, unexpected amplifications or attenuations in the signal data which deviate significantly from the variance of the signal's noise. There are RSSI-Distance estimation models which account for multipath fading, but they do not perform well at short distances. Here we propose an RSSI-distance estimation technique for indoor mobile applications using a Kalman filter. The Kalman filter is a recursive Bayesian filter, which models the noise of each input as a Gaussian distribution. Using the estimated motion and distance within a distributed, wireless network, the effects of multipath fading can be reduced for distance estimation with Kalman filtering.
Utilization of wireless signal strength for mobile robot localization in indoor environments
The University of North Carolina at Charlotte
Localization is a key component of any mobile robot application. For any task a mobile robot might need to perform, precise knowledge of its pose within its environment is critical. Mobile robots employ a multitude of sensors to estimate position, orientation, and mapping of its environment. Distance to wireless beacons through signal strength decay can be integrated into a simultaneous localization and mapping (SLAM) algorithm of a mobile robot equipped with a wireless transceiver, with an emphasis on indoor environments. However, radio signal strength does not predictably attenuate indoors as it does in open environments due to signal interference, absorption, and reflection from objects within the environment, incicting unexpected amplification or decay at the receiver known as multipath interference. This causes erroneous distance estimations due to the unexpected changes in signal strength attenuation. In this research, models of radio propagation as it relates to the received signal strength indicator (RSSI) are explored along with localization techniques which utilize these models. For development and testing of RSSI-based localization techniques a simulation method has been described which utilizes a Markov chain to provide realistic multipath interference on simulated RSSI data. Using this simulation technique, a multipath mitigation method is proposed and applied to a range-only SLAM algorithm.
HubVis: Software for gravitational lens estimation and visualization from Hubble data
IEEE SoutheastCon
The Hubble Ultra-Deep Field (HUDF) image spans less than one millionth of the sky, but it contains as many as 10,000 galaxies at distances on the order of 13 billion light years. However, Hubble images are difficult to process and require merging of multiple images from different optical wavelengths. In addition, the interpretation of such images can be complicated by intervening distortion caused by gravitational lensing. To address these issues, a NetBeans visualization tool called HubVis is being developed that includes the capability to: 1) simulate simple gravitational lensing and 2) search for gravitational lenses in Hubble data. The search method utilizes a novel algorithm to estimate gravitational lenses in Hubble data. Results are presented illustrating the current capabilities of the tool.
A Survey of Robotic Applications in Wireless Sensor Networks
IEEE SoutheastCon
Robotics can have many applications in wireless sensor networks. Robotics can be used to help solve many problems in wireless sensor networks, such as localizing nodes, acting as data mules, repositioning nodes, detecting and reacting to sensor failure, aggregate sensor data, and even sometimes provide mobile battery chargers for the nodes. Inversely, wireless sensor networks can help solve many problems in robotics, such as localization of the robot, path planning, mapping, and sensing.
Reducing the effect of signal multipath fading in RSSI-distance estimation using Kalman filters
Proceedings of the 19th Communications & Networking Symposium
Received Signal Strength Indication (RSSI) has often been used in location estimation and tracking applications. Signal strength between wireless transceivers degrades according to how much distance is between them, allowing the distance to be estimated. With this estimated distance between wireless devices, location estimation techniques can be used. However, one of the largest hindrances in using RSSI for distance estimation for indoor applications is reflections from multipath effects from the wireless signal. This will generate large, unexpected amplifications or attenuations in the signal data which deviate significantly from the variance of the signal's noise. There are RSSI-Distance estimation models which account for multipath fading, but they do not perform well at short distances. Here we propose an RSSI-distance estimation technique for indoor mobile applications using a Kalman filter. The Kalman filter is a recursive Bayesian filter, which models the noise of each input as a Gaussian distribution. Using the estimated motion and distance within a distributed, wireless network, the effects of multipath fading can be reduced for distance estimation with Kalman filtering.
Design and implementation of an open-source Wireless Sensor Network development platform
IEEE SoutheastCon
Wireless Sensor Networks is a growing research field and often researchers use a development platform to implement their experimental ideas and protocols. Wireless sensor network development boards provide interfaces to hardware, radios, real time operating systems, and routing protocols, allowing quick implementation of ideas. However, the selection of WSN development boards available is limited, and sometimes, depending on the popularity of the platform, support and resources are scarce. In this paper, a development platform design is presented, which employs commonly used and widely supported open source hardware and software. The board is also designed to use standard hardware interfaces to make adding additional sensors an easy task.
Utilization of wireless signal strength for mobile robot localization in indoor environments
The University of North Carolina at Charlotte
Localization is a key component of any mobile robot application. For any task a mobile robot might need to perform, precise knowledge of its pose within its environment is critical. Mobile robots employ a multitude of sensors to estimate position, orientation, and mapping of its environment. Distance to wireless beacons through signal strength decay can be integrated into a simultaneous localization and mapping (SLAM) algorithm of a mobile robot equipped with a wireless transceiver, with an emphasis on indoor environments. However, radio signal strength does not predictably attenuate indoors as it does in open environments due to signal interference, absorption, and reflection from objects within the environment, incicting unexpected amplification or decay at the receiver known as multipath interference. This causes erroneous distance estimations due to the unexpected changes in signal strength attenuation. In this research, models of radio propagation as it relates to the received signal strength indicator (RSSI) are explored along with localization techniques which utilize these models. For development and testing of RSSI-based localization techniques a simulation method has been described which utilizes a Markov chain to provide realistic multipath interference on simulated RSSI data. Using this simulation technique, a multipath mitigation method is proposed and applied to a range-only SLAM algorithm.
HubVis: Software for gravitational lens estimation and visualization from Hubble data
IEEE SoutheastCon
The Hubble Ultra-Deep Field (HUDF) image spans less than one millionth of the sky, but it contains as many as 10,000 galaxies at distances on the order of 13 billion light years. However, Hubble images are difficult to process and require merging of multiple images from different optical wavelengths. In addition, the interpretation of such images can be complicated by intervening distortion caused by gravitational lensing. To address these issues, a NetBeans visualization tool called HubVis is being developed that includes the capability to: 1) simulate simple gravitational lensing and 2) search for gravitational lenses in Hubble data. The search method utilizes a novel algorithm to estimate gravitational lenses in Hubble data. Results are presented illustrating the current capabilities of the tool.
A Survey of Robotic Applications in Wireless Sensor Networks
IEEE SoutheastCon
Robotics can have many applications in wireless sensor networks. Robotics can be used to help solve many problems in wireless sensor networks, such as localizing nodes, acting as data mules, repositioning nodes, detecting and reacting to sensor failure, aggregate sensor data, and even sometimes provide mobile battery chargers for the nodes. Inversely, wireless sensor networks can help solve many problems in robotics, such as localization of the robot, path planning, mapping, and sensing.
Reducing the effect of signal multipath fading in RSSI-distance estimation using Kalman filters
Proceedings of the 19th Communications & Networking Symposium
Received Signal Strength Indication (RSSI) has often been used in location estimation and tracking applications. Signal strength between wireless transceivers degrades according to how much distance is between them, allowing the distance to be estimated. With this estimated distance between wireless devices, location estimation techniques can be used. However, one of the largest hindrances in using RSSI for distance estimation for indoor applications is reflections from multipath effects from the wireless signal. This will generate large, unexpected amplifications or attenuations in the signal data which deviate significantly from the variance of the signal's noise. There are RSSI-Distance estimation models which account for multipath fading, but they do not perform well at short distances. Here we propose an RSSI-distance estimation technique for indoor mobile applications using a Kalman filter. The Kalman filter is a recursive Bayesian filter, which models the noise of each input as a Gaussian distribution. Using the estimated motion and distance within a distributed, wireless network, the effects of multipath fading can be reduced for distance estimation with Kalman filtering.
Design and implementation of an open-source Wireless Sensor Network development platform
IEEE SoutheastCon
Wireless Sensor Networks is a growing research field and often researchers use a development platform to implement their experimental ideas and protocols. Wireless sensor network development boards provide interfaces to hardware, radios, real time operating systems, and routing protocols, allowing quick implementation of ideas. However, the selection of WSN development boards available is limited, and sometimes, depending on the popularity of the platform, support and resources are scarce. In this paper, a development platform design is presented, which employs commonly used and widely supported open source hardware and software. The board is also designed to use standard hardware interfaces to make adding additional sensors an easy task.
Implementation of Dynamic Source Routing using 802.15. 4 on XBee Series 1 Modules
IEEE SoutheastCon
An implementation of Dynamic Source Routing on 802.15.4 using XBee Series 1 modules is presented. This implementation demonstrates the use of Dynamic Source Routing to determine the route from initiator (source) node to target (destination) node and used it to deliver message packets within an intra-network of wireless motes. The wireless motes compromise of Atmega 328P based microcontroller board (Red Board) interfaced with XBee Series 1. The algorithm itself searches for the desired route based on first come first serve basis and uses it to forward the message packet to the target node. Due to the dynamic nature of the protocol, the network has self-healing ability. The software library developed in the course of this implementation provides the user an interface to implement customized multi-hopping on XBee Series1 due to absence of any underlying operating system.
Utilization of wireless signal strength for mobile robot localization in indoor environments
The University of North Carolina at Charlotte
Localization is a key component of any mobile robot application. For any task a mobile robot might need to perform, precise knowledge of its pose within its environment is critical. Mobile robots employ a multitude of sensors to estimate position, orientation, and mapping of its environment. Distance to wireless beacons through signal strength decay can be integrated into a simultaneous localization and mapping (SLAM) algorithm of a mobile robot equipped with a wireless transceiver, with an emphasis on indoor environments. However, radio signal strength does not predictably attenuate indoors as it does in open environments due to signal interference, absorption, and reflection from objects within the environment, incicting unexpected amplification or decay at the receiver known as multipath interference. This causes erroneous distance estimations due to the unexpected changes in signal strength attenuation. In this research, models of radio propagation as it relates to the received signal strength indicator (RSSI) are explored along with localization techniques which utilize these models. For development and testing of RSSI-based localization techniques a simulation method has been described which utilizes a Markov chain to provide realistic multipath interference on simulated RSSI data. Using this simulation technique, a multipath mitigation method is proposed and applied to a range-only SLAM algorithm.
HubVis: Software for gravitational lens estimation and visualization from Hubble data
IEEE SoutheastCon
The Hubble Ultra-Deep Field (HUDF) image spans less than one millionth of the sky, but it contains as many as 10,000 galaxies at distances on the order of 13 billion light years. However, Hubble images are difficult to process and require merging of multiple images from different optical wavelengths. In addition, the interpretation of such images can be complicated by intervening distortion caused by gravitational lensing. To address these issues, a NetBeans visualization tool called HubVis is being developed that includes the capability to: 1) simulate simple gravitational lensing and 2) search for gravitational lenses in Hubble data. The search method utilizes a novel algorithm to estimate gravitational lenses in Hubble data. Results are presented illustrating the current capabilities of the tool.
A Survey of Robotic Applications in Wireless Sensor Networks
IEEE SoutheastCon
Robotics can have many applications in wireless sensor networks. Robotics can be used to help solve many problems in wireless sensor networks, such as localizing nodes, acting as data mules, repositioning nodes, detecting and reacting to sensor failure, aggregate sensor data, and even sometimes provide mobile battery chargers for the nodes. Inversely, wireless sensor networks can help solve many problems in robotics, such as localization of the robot, path planning, mapping, and sensing.
Reducing the effect of signal multipath fading in RSSI-distance estimation using Kalman filters
Proceedings of the 19th Communications & Networking Symposium
Received Signal Strength Indication (RSSI) has often been used in location estimation and tracking applications. Signal strength between wireless transceivers degrades according to how much distance is between them, allowing the distance to be estimated. With this estimated distance between wireless devices, location estimation techniques can be used. However, one of the largest hindrances in using RSSI for distance estimation for indoor applications is reflections from multipath effects from the wireless signal. This will generate large, unexpected amplifications or attenuations in the signal data which deviate significantly from the variance of the signal's noise. There are RSSI-Distance estimation models which account for multipath fading, but they do not perform well at short distances. Here we propose an RSSI-distance estimation technique for indoor mobile applications using a Kalman filter. The Kalman filter is a recursive Bayesian filter, which models the noise of each input as a Gaussian distribution. Using the estimated motion and distance within a distributed, wireless network, the effects of multipath fading can be reduced for distance estimation with Kalman filtering.
Design and implementation of an open-source Wireless Sensor Network development platform
IEEE SoutheastCon
Wireless Sensor Networks is a growing research field and often researchers use a development platform to implement their experimental ideas and protocols. Wireless sensor network development boards provide interfaces to hardware, radios, real time operating systems, and routing protocols, allowing quick implementation of ideas. However, the selection of WSN development boards available is limited, and sometimes, depending on the popularity of the platform, support and resources are scarce. In this paper, a development platform design is presented, which employs commonly used and widely supported open source hardware and software. The board is also designed to use standard hardware interfaces to make adding additional sensors an easy task.
Implementation of Dynamic Source Routing using 802.15. 4 on XBee Series 1 Modules
IEEE SoutheastCon
An implementation of Dynamic Source Routing on 802.15.4 using XBee Series 1 modules is presented. This implementation demonstrates the use of Dynamic Source Routing to determine the route from initiator (source) node to target (destination) node and used it to deliver message packets within an intra-network of wireless motes. The wireless motes compromise of Atmega 328P based microcontroller board (Red Board) interfaced with XBee Series 1. The algorithm itself searches for the desired route based on first come first serve basis and uses it to forward the message packet to the target node. Due to the dynamic nature of the protocol, the network has self-healing ability. The software library developed in the course of this implementation provides the user an interface to implement customized multi-hopping on XBee Series1 due to absence of any underlying operating system.
Utilization of XBee ZigBee modules and MATLAB for RSSI localization applications
IEEE SoutheastCon 2017
Localization is an important attribute for wireless sensor networks. Received signal strength indicator (RSSI) can be used to estimate distance between transceivers. Using these estimated distances, location of nodes within a network can be determined using various localization algorithms, such as trilateration. For implementation and testing of localization techniques, utilizing a sensor network development platform reduces time and difficulty during the process. Here we present a platform based on XBee ZigBee wireless modules, Arduino, and MATLAB for algorithm testing and debugging. For validation of this platform, a trilateration localization method is implemented.
Utilization of wireless signal strength for mobile robot localization in indoor environments
The University of North Carolina at Charlotte
Localization is a key component of any mobile robot application. For any task a mobile robot might need to perform, precise knowledge of its pose within its environment is critical. Mobile robots employ a multitude of sensors to estimate position, orientation, and mapping of its environment. Distance to wireless beacons through signal strength decay can be integrated into a simultaneous localization and mapping (SLAM) algorithm of a mobile robot equipped with a wireless transceiver, with an emphasis on indoor environments. However, radio signal strength does not predictably attenuate indoors as it does in open environments due to signal interference, absorption, and reflection from objects within the environment, incicting unexpected amplification or decay at the receiver known as multipath interference. This causes erroneous distance estimations due to the unexpected changes in signal strength attenuation. In this research, models of radio propagation as it relates to the received signal strength indicator (RSSI) are explored along with localization techniques which utilize these models. For development and testing of RSSI-based localization techniques a simulation method has been described which utilizes a Markov chain to provide realistic multipath interference on simulated RSSI data. Using this simulation technique, a multipath mitigation method is proposed and applied to a range-only SLAM algorithm.
HubVis: Software for gravitational lens estimation and visualization from Hubble data
IEEE SoutheastCon
The Hubble Ultra-Deep Field (HUDF) image spans less than one millionth of the sky, but it contains as many as 10,000 galaxies at distances on the order of 13 billion light years. However, Hubble images are difficult to process and require merging of multiple images from different optical wavelengths. In addition, the interpretation of such images can be complicated by intervening distortion caused by gravitational lensing. To address these issues, a NetBeans visualization tool called HubVis is being developed that includes the capability to: 1) simulate simple gravitational lensing and 2) search for gravitational lenses in Hubble data. The search method utilizes a novel algorithm to estimate gravitational lenses in Hubble data. Results are presented illustrating the current capabilities of the tool.
A Survey of Robotic Applications in Wireless Sensor Networks
IEEE SoutheastCon
Robotics can have many applications in wireless sensor networks. Robotics can be used to help solve many problems in wireless sensor networks, such as localizing nodes, acting as data mules, repositioning nodes, detecting and reacting to sensor failure, aggregate sensor data, and even sometimes provide mobile battery chargers for the nodes. Inversely, wireless sensor networks can help solve many problems in robotics, such as localization of the robot, path planning, mapping, and sensing.
Reducing the effect of signal multipath fading in RSSI-distance estimation using Kalman filters
Proceedings of the 19th Communications & Networking Symposium
Received Signal Strength Indication (RSSI) has often been used in location estimation and tracking applications. Signal strength between wireless transceivers degrades according to how much distance is between them, allowing the distance to be estimated. With this estimated distance between wireless devices, location estimation techniques can be used. However, one of the largest hindrances in using RSSI for distance estimation for indoor applications is reflections from multipath effects from the wireless signal. This will generate large, unexpected amplifications or attenuations in the signal data which deviate significantly from the variance of the signal's noise. There are RSSI-Distance estimation models which account for multipath fading, but they do not perform well at short distances. Here we propose an RSSI-distance estimation technique for indoor mobile applications using a Kalman filter. The Kalman filter is a recursive Bayesian filter, which models the noise of each input as a Gaussian distribution. Using the estimated motion and distance within a distributed, wireless network, the effects of multipath fading can be reduced for distance estimation with Kalman filtering.
Design and implementation of an open-source Wireless Sensor Network development platform
IEEE SoutheastCon
Wireless Sensor Networks is a growing research field and often researchers use a development platform to implement their experimental ideas and protocols. Wireless sensor network development boards provide interfaces to hardware, radios, real time operating systems, and routing protocols, allowing quick implementation of ideas. However, the selection of WSN development boards available is limited, and sometimes, depending on the popularity of the platform, support and resources are scarce. In this paper, a development platform design is presented, which employs commonly used and widely supported open source hardware and software. The board is also designed to use standard hardware interfaces to make adding additional sensors an easy task.
Implementation of Dynamic Source Routing using 802.15. 4 on XBee Series 1 Modules
IEEE SoutheastCon
An implementation of Dynamic Source Routing on 802.15.4 using XBee Series 1 modules is presented. This implementation demonstrates the use of Dynamic Source Routing to determine the route from initiator (source) node to target (destination) node and used it to deliver message packets within an intra-network of wireless motes. The wireless motes compromise of Atmega 328P based microcontroller board (Red Board) interfaced with XBee Series 1. The algorithm itself searches for the desired route based on first come first serve basis and uses it to forward the message packet to the target node. Due to the dynamic nature of the protocol, the network has self-healing ability. The software library developed in the course of this implementation provides the user an interface to implement customized multi-hopping on XBee Series1 due to absence of any underlying operating system.
Utilization of XBee ZigBee modules and MATLAB for RSSI localization applications
IEEE SoutheastCon 2017
Localization is an important attribute for wireless sensor networks. Received signal strength indicator (RSSI) can be used to estimate distance between transceivers. Using these estimated distances, location of nodes within a network can be determined using various localization algorithms, such as trilateration. For implementation and testing of localization techniques, utilizing a sensor network development platform reduces time and difficulty during the process. Here we present a platform based on XBee ZigBee wireless modules, Arduino, and MATLAB for algorithm testing and debugging. For validation of this platform, a trilateration localization method is implemented.
Procedurally Generated Environments for Simulating RSSI-Localization Applications
CNS 2017
Received signal strength indicator (RSSI) is often used in wireless localization applications as it attenuates as the signal propagates through the environment. Signal strength attenuation models allow distance to be estimated between transceivers and utilized in positioning techniques, such as trilateration and simultaneous localization and mapping (SLAM) algorithms. To evaluate these methods, it is costly and time consuming to construct hardware to physically test each implementation of an algorithm. However, simulating RSSI can prove difficult due the many environmental factors that impact attenuation, such as multipath interference. Simulating multipath effects require detailed information about the operating environment’s properties (geometry, materials), and can be computationally expensive. This work describes a simulation method which procedurally generates RSSI values at given distances for wireless nodes utilizing collected data from a given environment type and a Markov chain. To demonstrate the effectiveness of this method, a range-only SLAM algorithm is simulated utilizing this environment.
Utilization of wireless signal strength for mobile robot localization in indoor environments
The University of North Carolina at Charlotte
Localization is a key component of any mobile robot application. For any task a mobile robot might need to perform, precise knowledge of its pose within its environment is critical. Mobile robots employ a multitude of sensors to estimate position, orientation, and mapping of its environment. Distance to wireless beacons through signal strength decay can be integrated into a simultaneous localization and mapping (SLAM) algorithm of a mobile robot equipped with a wireless transceiver, with an emphasis on indoor environments. However, radio signal strength does not predictably attenuate indoors as it does in open environments due to signal interference, absorption, and reflection from objects within the environment, incicting unexpected amplification or decay at the receiver known as multipath interference. This causes erroneous distance estimations due to the unexpected changes in signal strength attenuation. In this research, models of radio propagation as it relates to the received signal strength indicator (RSSI) are explored along with localization techniques which utilize these models. For development and testing of RSSI-based localization techniques a simulation method has been described which utilizes a Markov chain to provide realistic multipath interference on simulated RSSI data. Using this simulation technique, a multipath mitigation method is proposed and applied to a range-only SLAM algorithm.
HubVis: Software for gravitational lens estimation and visualization from Hubble data
IEEE SoutheastCon
The Hubble Ultra-Deep Field (HUDF) image spans less than one millionth of the sky, but it contains as many as 10,000 galaxies at distances on the order of 13 billion light years. However, Hubble images are difficult to process and require merging of multiple images from different optical wavelengths. In addition, the interpretation of such images can be complicated by intervening distortion caused by gravitational lensing. To address these issues, a NetBeans visualization tool called HubVis is being developed that includes the capability to: 1) simulate simple gravitational lensing and 2) search for gravitational lenses in Hubble data. The search method utilizes a novel algorithm to estimate gravitational lenses in Hubble data. Results are presented illustrating the current capabilities of the tool.
A Survey of Robotic Applications in Wireless Sensor Networks
IEEE SoutheastCon
Robotics can have many applications in wireless sensor networks. Robotics can be used to help solve many problems in wireless sensor networks, such as localizing nodes, acting as data mules, repositioning nodes, detecting and reacting to sensor failure, aggregate sensor data, and even sometimes provide mobile battery chargers for the nodes. Inversely, wireless sensor networks can help solve many problems in robotics, such as localization of the robot, path planning, mapping, and sensing.
Reducing the effect of signal multipath fading in RSSI-distance estimation using Kalman filters
Proceedings of the 19th Communications & Networking Symposium
Received Signal Strength Indication (RSSI) has often been used in location estimation and tracking applications. Signal strength between wireless transceivers degrades according to how much distance is between them, allowing the distance to be estimated. With this estimated distance between wireless devices, location estimation techniques can be used. However, one of the largest hindrances in using RSSI for distance estimation for indoor applications is reflections from multipath effects from the wireless signal. This will generate large, unexpected amplifications or attenuations in the signal data which deviate significantly from the variance of the signal's noise. There are RSSI-Distance estimation models which account for multipath fading, but they do not perform well at short distances. Here we propose an RSSI-distance estimation technique for indoor mobile applications using a Kalman filter. The Kalman filter is a recursive Bayesian filter, which models the noise of each input as a Gaussian distribution. Using the estimated motion and distance within a distributed, wireless network, the effects of multipath fading can be reduced for distance estimation with Kalman filtering.
Design and implementation of an open-source Wireless Sensor Network development platform
IEEE SoutheastCon
Wireless Sensor Networks is a growing research field and often researchers use a development platform to implement their experimental ideas and protocols. Wireless sensor network development boards provide interfaces to hardware, radios, real time operating systems, and routing protocols, allowing quick implementation of ideas. However, the selection of WSN development boards available is limited, and sometimes, depending on the popularity of the platform, support and resources are scarce. In this paper, a development platform design is presented, which employs commonly used and widely supported open source hardware and software. The board is also designed to use standard hardware interfaces to make adding additional sensors an easy task.
Implementation of Dynamic Source Routing using 802.15. 4 on XBee Series 1 Modules
IEEE SoutheastCon
An implementation of Dynamic Source Routing on 802.15.4 using XBee Series 1 modules is presented. This implementation demonstrates the use of Dynamic Source Routing to determine the route from initiator (source) node to target (destination) node and used it to deliver message packets within an intra-network of wireless motes. The wireless motes compromise of Atmega 328P based microcontroller board (Red Board) interfaced with XBee Series 1. The algorithm itself searches for the desired route based on first come first serve basis and uses it to forward the message packet to the target node. Due to the dynamic nature of the protocol, the network has self-healing ability. The software library developed in the course of this implementation provides the user an interface to implement customized multi-hopping on XBee Series1 due to absence of any underlying operating system.
Utilization of XBee ZigBee modules and MATLAB for RSSI localization applications
IEEE SoutheastCon 2017
Localization is an important attribute for wireless sensor networks. Received signal strength indicator (RSSI) can be used to estimate distance between transceivers. Using these estimated distances, location of nodes within a network can be determined using various localization algorithms, such as trilateration. For implementation and testing of localization techniques, utilizing a sensor network development platform reduces time and difficulty during the process. Here we present a platform based on XBee ZigBee wireless modules, Arduino, and MATLAB for algorithm testing and debugging. For validation of this platform, a trilateration localization method is implemented.
Procedurally Generated Environments for Simulating RSSI-Localization Applications
CNS 2017
Received signal strength indicator (RSSI) is often used in wireless localization applications as it attenuates as the signal propagates through the environment. Signal strength attenuation models allow distance to be estimated between transceivers and utilized in positioning techniques, such as trilateration and simultaneous localization and mapping (SLAM) algorithms. To evaluate these methods, it is costly and time consuming to construct hardware to physically test each implementation of an algorithm. However, simulating RSSI can prove difficult due the many environmental factors that impact attenuation, such as multipath interference. Simulating multipath effects require detailed information about the operating environment’s properties (geometry, materials), and can be computationally expensive. This work describes a simulation method which procedurally generates RSSI values at given distances for wireless nodes utilizing collected data from a given environment type and a Markov chain. To demonstrate the effectiveness of this method, a range-only SLAM algorithm is simulated utilizing this environment.
Development of a portable XBee C library and RSSI triangulation localization framework
HONET
Wireless Sensor Networks is a growing research field and researchers use a variety of wireless devices to perform radio communications. The XBee is a common wireless module that has become very popular amongst hobbyists and researchers alike due to its simple and efficient design. However, to use the XBee in complex networks, the user must construct specific packets to communicate within an XBee network. Here we will introduce an XBee C library which provides a modular, hardware-independent C library for XBee. Also using this library, we will explore establishing a framework for performing RSSI triangulation localization without the use of anchor nodes.
Positions
IEEE UNCC Student Branch
SoutheastCon Coordinator
I organized planning and trip details for getting undergraduate and graduate students to SoutheastCon 2013, held in Jacksonville, FL.
IEEE UNCC Student Branch
SoutheastCon Coordinator
I organized planning and trip details for getting undergraduate and graduate students to SoutheastCon 2013, held in Jacksonville, FL.
Charlotte Area Robotics
President, Vice President, Secretary, Project Mentor
I began serving as Secretary for CAR in 2011, preparing meeting slides and topics for the president. After becoming president in 2012, I began organizing teams for the IEEE SoutheastCon Hardware competition, which has become an annual activity for the club. I also developed a new club structure, separating club functions into general meetings, workshops, and competition/project team meetings. As vice president during 2014, I assisted the president with any duties he was unable to fulfill. In 2015, I became a project mentor, directing our hardware team by suggesting approaches, hardware/software designs, and assisting in troubleshooting.
IEEE UNCC Student Branch
SoutheastCon Coordinator
I organized planning and trip details for getting undergraduate and graduate students to SoutheastCon 2013, held in Jacksonville, FL.
Charlotte Area Robotics
President, Vice President, Secretary, Project Mentor
I began serving as Secretary for CAR in 2011, preparing meeting slides and topics for the president. After becoming president in 2012, I began organizing teams for the IEEE SoutheastCon Hardware competition, which has become an annual activity for the club. I also developed a new club structure, separating club functions into general meetings, workshops, and competition/project team meetings. As vice president during 2014, I assisted the president with any duties he was unable to fulfill. In 2015, I became a project mentor, directing our hardware team by suggesting approaches, hardware/software designs, and assisting in troubleshooting.
IEEE UNCC Student Branch
SoutheastCon Coordinator
I organized planning and trip details for getting undergraduate and graduate students to SoutheastCon 2013, held in Jacksonville, FL.
Charlotte Area Robotics
President, Vice President, Secretary, Project Mentor
I began serving as Secretary for CAR in 2011, preparing meeting slides and topics for the president. After becoming president in 2012, I began organizing teams for the IEEE SoutheastCon Hardware competition, which has become an annual activity for the club. I also developed a new club structure, separating club functions into general meetings, workshops, and competition/project team meetings. As vice president during 2014, I assisted the president with any duties he was unable to fulfill. In 2015, I became a project mentor, directing our hardware team by suggesting approaches, hardware/software designs, and assisting in troubleshooting.
IEEE UNCC Student Branch
SoutheastCon Coordinator
I organized planning and trip details for getting undergraduate and graduate students to SoutheastCon 2013, held in Jacksonville, FL.
Charlotte Area Robotics
President, Vice President, Secretary, Project Mentor
I began serving as Secretary for CAR in 2011, preparing meeting slides and topics for the president. After becoming president in 2012, I began organizing teams for the IEEE SoutheastCon Hardware competition, which has become an annual activity for the club. I also developed a new club structure, separating club functions into general meetings, workshops, and competition/project team meetings. As vice president during 2014, I assisted the president with any duties he was unable to fulfill. In 2015, I became a project mentor, directing our hardware team by suggesting approaches, hardware/software designs, and assisting in troubleshooting.
IEEE UNCC Student Branch
SoutheastCon Coordinator
I organized planning and trip details for getting undergraduate and graduate students to SoutheastCon 2013, held in Jacksonville, FL.
Charlotte Area Robotics
President, Vice President, Secretary, Project Mentor
I began serving as Secretary for CAR in 2011, preparing meeting slides and topics for the president. After becoming president in 2012, I began organizing teams for the IEEE SoutheastCon Hardware competition, which has become an annual activity for the club. I also developed a new club structure, separating club functions into general meetings, workshops, and competition/project team meetings. As vice president during 2014, I assisted the president with any duties he was unable to fulfill. In 2015, I became a project mentor, directing our hardware team by suggesting approaches, hardware/software designs, and assisting in troubleshooting.
IEEE UNCC Student Branch
SoutheastCon Coordinator
I organized planning and trip details for getting undergraduate and graduate students to SoutheastCon 2013, held in Jacksonville, FL.
Charlotte Area Robotics
President, Vice President, Secretary, Project Mentor
I began serving as Secretary for CAR in 2011, preparing meeting slides and topics for the president. After becoming president in 2012, I began organizing teams for the IEEE SoutheastCon Hardware competition, which has become an annual activity for the club. I also developed a new club structure, separating club functions into general meetings, workshops, and competition/project team meetings. As vice president during 2014, I assisted the president with any duties he was unable to fulfill. In 2015, I became a project mentor, directing our hardware team by suggesting approaches, hardware/software designs, and assisting in troubleshooting.
IEEE UNCC Student Branch
SoutheastCon Coordinator
I organized planning and trip details for getting undergraduate and graduate students to SoutheastCon 2013, held in Jacksonville, FL.
Charlotte Area Robotics
President, Vice President, Secretary, Project Mentor
I began serving as Secretary for CAR in 2011, preparing meeting slides and topics for the president. After becoming president in 2012, I began organizing teams for the IEEE SoutheastCon Hardware competition, which has become an annual activity for the club. I also developed a new club structure, separating club functions into general meetings, workshops, and competition/project team meetings. As vice president during 2014, I assisted the president with any duties he was unable to fulfill. In 2015, I became a project mentor, directing our hardware team by suggesting approaches, hardware/software designs, and assisting in troubleshooting.
IEEE UNCC Student Branch
SoutheastCon Coordinator
I organized planning and trip details for getting undergraduate and graduate students to SoutheastCon 2013, held in Jacksonville, FL.
Charlotte Area Robotics
President, Vice President, Secretary, Project Mentor
I began serving as Secretary for CAR in 2011, preparing meeting slides and topics for the president. After becoming president in 2012, I began organizing teams for the IEEE SoutheastCon Hardware competition, which has become an annual activity for the club. I also developed a new club structure, separating club functions into general meetings, workshops, and competition/project team meetings. As vice president during 2014, I assisted the president with any duties he was unable to fulfill. In 2015, I became a project mentor, directing our hardware team by suggesting approaches, hardware/software designs, and assisting in troubleshooting.
IEEE UNCC Student Branch
SoutheastCon Coordinator
I organized planning and trip details for getting undergraduate and graduate students to SoutheastCon 2013, held in Jacksonville, FL.
Charlotte Area Robotics
President, Vice President, Secretary, Project Mentor
I began serving as Secretary for CAR in 2011, preparing meeting slides and topics for the president. After becoming president in 2012, I began organizing teams for the IEEE SoutheastCon Hardware competition, which has become an annual activity for the club. I also developed a new club structure, separating club functions into general meetings, workshops, and competition/project team meetings. As vice president during 2014, I assisted the president with any duties he was unable to fulfill. In 2015, I became a project mentor, directing our hardware team by suggesting approaches, hardware/software designs, and assisting in troubleshooting.
IEEE UNCC Student Branch
SoutheastCon Coordinator
I organized planning and trip details for getting undergraduate and graduate students to SoutheastCon 2013, held in Jacksonville, FL.
Charlotte Area Robotics
President, Vice President, Secretary, Project Mentor
I began serving as Secretary for CAR in 2011, preparing meeting slides and topics for the president. After becoming president in 2012, I began organizing teams for the IEEE SoutheastCon Hardware competition, which has become an annual activity for the club. I also developed a new club structure, separating club functions into general meetings, workshops, and competition/project team meetings. As vice president during 2014, I assisted the president with any duties he was unable to fulfill. In 2015, I became a project mentor, directing our hardware team by suggesting approaches, hardware/software designs, and assisting in troubleshooting.
IEEE UNCC Student Branch
SoutheastCon Coordinator
I organized planning and trip details for getting undergraduate and graduate students to SoutheastCon 2013, held in Jacksonville, FL.
Charlotte Area Robotics
President, Vice President, Secretary, Project Mentor
I began serving as Secretary for CAR in 2011, preparing meeting slides and topics for the president. After becoming president in 2012, I began organizing teams for the IEEE SoutheastCon Hardware competition, which has become an annual activity for the club. I also developed a new club structure, separating club functions into general meetings, workshops, and competition/project team meetings. As vice president during 2014, I assisted the president with any duties he was unable to fulfill. In 2015, I became a project mentor, directing our hardware team by suggesting approaches, hardware/software designs, and assisting in troubleshooting.
IEEE UNCC Student Branch
SoutheastCon Coordinator
I organized planning and trip details for getting undergraduate and graduate students to SoutheastCon 2013, held in Jacksonville, FL.
Charlotte Area Robotics
President, Vice President, Secretary, Project Mentor
I began serving as Secretary for CAR in 2011, preparing meeting slides and topics for the president. After becoming president in 2012, I began organizing teams for the IEEE SoutheastCon Hardware competition, which has become an annual activity for the club. I also developed a new club structure, separating club functions into general meetings, workshops, and competition/project team meetings. As vice president during 2014, I assisted the president with any duties he was unable to fulfill. In 2015, I became a project mentor, directing our hardware team by suggesting approaches, hardware/software designs, and assisting in troubleshooting.
IEEE UNCC Student Branch
SoutheastCon Coordinator
I organized planning and trip details for getting undergraduate and graduate students to SoutheastCon 2013, held in Jacksonville, FL.
Charlotte Area Robotics
President, Vice President, Secretary, Project Mentor
I began serving as Secretary for CAR in 2011, preparing meeting slides and topics for the president. After becoming president in 2012, I began organizing teams for the IEEE SoutheastCon Hardware competition, which has become an annual activity for the club. I also developed a new club structure, separating club functions into general meetings, workshops, and competition/project team meetings. As vice president during 2014, I assisted the president with any duties he was unable to fulfill. In 2015, I became a project mentor, directing our hardware team by suggesting approaches, hardware/software designs, and assisting in troubleshooting.
