The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
Darrin J Young - One of the best experts on this subject based on the ideXlab platform.
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personal inertial navigation system assisted by mems ground reaction sensor array and interface asic for gps denied environment
IEEE Journal of Solid-state Circuits, 2018Co-Authors: William H Deng, Ozkan Bebek, Cenk M Cavusoglu, C H Mastrangelo, Darrin J YoungAbstract:A personal inertial navigation system (PINS) assisted by a microelectromechanical systems (MEMS)-based $13 \times 26$ ground reaction sensor array (GRSA) and a low-power interface application-specified integrated circuit (ASIC) has been designed and demonstrated for GPS-denied environment. The GRSA operating in a contact mode achieves a sensitivity of approximately 3.7 fF/kPa at each sensor node. An electronic interface system, consisting of a capacitance-to-voltage ( ${C}/{V}$ ) converter followed by a correlated double sampling stage, is designed to convert the GRSA capacitance change to an Analog Output voltage. The Analog Output voltage is then digitized by a 12-bit cyclic Analog-to-digital converter (ADC). Switch capacitance compensation technique is employed to ensure the ADC performance. The ASIC is fabricated in 0.35- $\mu \text{m}$ CMOS process and dissipates a power of 3 mW. The prototype system incorporates a GRSA, an ASIC, and a commercial nine degree-of-freedom (DOF) inertial measurement unit (IMU) in the heel region of a boot. The GRSA can determine an accurate foot-on-ground timing based on the pressure profiles detected during walking, thus enabling an accurate position calculation and a precise zero velocity update. Furthermore, a system calibration procedure measures the IMU inherent directional drift and scaling factor errors, and compensates them for the navigation data to achieve a superior performance. The prototype system demonstrates a position accuracy of approximately 5.5 m over a navigation distance of 3100 m. The prototype system also achieves a consistent performance over different field tests with various distances and random paths. System characterization results further indicate a tradeoff between sensor array size and system resolution for a given navigation performance requirement, thus providing a design guideline for future system optimization.
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personal inertial navigation system assisted by mems ground reaction sensor array and interface asic for gps denied environment
IEEE Journal of Solid-state Circuits, 2018Co-Authors: Qingbo Guo, William H Deng, Ozkan Bebek, Cenk M Cavusoglu, C H Mastrangelo, Darrin J YoungAbstract:A personal inertial navigation system (PINS) assisted by a microelectromechanical systems (MEMS)-based $13 \times 26$ ground reaction sensor array (GRSA) and a low-power interface application-specified integrated circuit (ASIC) has been designed and demonstrated for GPS-denied environment. The GRSA operating in a contact mode achieves a sensitivity of approximately 3.7 fF/kPa at each sensor node. An electronic interface system, consisting of a capacitance-to-voltage ( ${C}/{V}$ ) converter followed by a correlated double sampling stage, is designed to convert the GRSA capacitance change to an Analog Output voltage. The Analog Output voltage is then digitized by a 12-bit cyclic Analog-to-digital converter (ADC). Switch capacitance compensation technique is employed to ensure the ADC performance. The ASIC is fabricated in 0.35- $\mu \text{m}$ CMOS process and dissipates a power of 3 mW. The prototype system incorporates a GRSA, an ASIC, and a commercial nine degree-of-freedom (DOF) inertial measurement unit (IMU) in the heel region of a boot. The GRSA can determine an accurate foot-on-ground timing based on the pressure profiles detected during walking, thus enabling an accurate position calculation and a precise zero velocity update. Furthermore, a system calibration procedure measures the IMU inherent directional drift and scaling factor errors, and compensates them for the navigation data to achieve a superior performance. The prototype system demonstrates a position accuracy of approximately 5.5 m over a navigation distance of 3100 m. The prototype system also achieves a consistent performance over different field tests with various distances and random paths. System characterization results further indicate a tradeoff between sensor array size and system resolution for a given navigation performance requirement, thus providing a design guideline for future system optimization.
William H Deng - One of the best experts on this subject based on the ideXlab platform.
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personal inertial navigation system assisted by mems ground reaction sensor array and interface asic for gps denied environment
IEEE Journal of Solid-state Circuits, 2018Co-Authors: William H Deng, Ozkan Bebek, Cenk M Cavusoglu, C H Mastrangelo, Darrin J YoungAbstract:A personal inertial navigation system (PINS) assisted by a microelectromechanical systems (MEMS)-based $13 \times 26$ ground reaction sensor array (GRSA) and a low-power interface application-specified integrated circuit (ASIC) has been designed and demonstrated for GPS-denied environment. The GRSA operating in a contact mode achieves a sensitivity of approximately 3.7 fF/kPa at each sensor node. An electronic interface system, consisting of a capacitance-to-voltage ( ${C}/{V}$ ) converter followed by a correlated double sampling stage, is designed to convert the GRSA capacitance change to an Analog Output voltage. The Analog Output voltage is then digitized by a 12-bit cyclic Analog-to-digital converter (ADC). Switch capacitance compensation technique is employed to ensure the ADC performance. The ASIC is fabricated in 0.35- $\mu \text{m}$ CMOS process and dissipates a power of 3 mW. The prototype system incorporates a GRSA, an ASIC, and a commercial nine degree-of-freedom (DOF) inertial measurement unit (IMU) in the heel region of a boot. The GRSA can determine an accurate foot-on-ground timing based on the pressure profiles detected during walking, thus enabling an accurate position calculation and a precise zero velocity update. Furthermore, a system calibration procedure measures the IMU inherent directional drift and scaling factor errors, and compensates them for the navigation data to achieve a superior performance. The prototype system demonstrates a position accuracy of approximately 5.5 m over a navigation distance of 3100 m. The prototype system also achieves a consistent performance over different field tests with various distances and random paths. System characterization results further indicate a tradeoff between sensor array size and system resolution for a given navigation performance requirement, thus providing a design guideline for future system optimization.
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personal inertial navigation system assisted by mems ground reaction sensor array and interface asic for gps denied environment
IEEE Journal of Solid-state Circuits, 2018Co-Authors: Qingbo Guo, William H Deng, Ozkan Bebek, Cenk M Cavusoglu, C H Mastrangelo, Darrin J YoungAbstract:A personal inertial navigation system (PINS) assisted by a microelectromechanical systems (MEMS)-based $13 \times 26$ ground reaction sensor array (GRSA) and a low-power interface application-specified integrated circuit (ASIC) has been designed and demonstrated for GPS-denied environment. The GRSA operating in a contact mode achieves a sensitivity of approximately 3.7 fF/kPa at each sensor node. An electronic interface system, consisting of a capacitance-to-voltage ( ${C}/{V}$ ) converter followed by a correlated double sampling stage, is designed to convert the GRSA capacitance change to an Analog Output voltage. The Analog Output voltage is then digitized by a 12-bit cyclic Analog-to-digital converter (ADC). Switch capacitance compensation technique is employed to ensure the ADC performance. The ASIC is fabricated in 0.35- $\mu \text{m}$ CMOS process and dissipates a power of 3 mW. The prototype system incorporates a GRSA, an ASIC, and a commercial nine degree-of-freedom (DOF) inertial measurement unit (IMU) in the heel region of a boot. The GRSA can determine an accurate foot-on-ground timing based on the pressure profiles detected during walking, thus enabling an accurate position calculation and a precise zero velocity update. Furthermore, a system calibration procedure measures the IMU inherent directional drift and scaling factor errors, and compensates them for the navigation data to achieve a superior performance. The prototype system demonstrates a position accuracy of approximately 5.5 m over a navigation distance of 3100 m. The prototype system also achieves a consistent performance over different field tests with various distances and random paths. System characterization results further indicate a tradeoff between sensor array size and system resolution for a given navigation performance requirement, thus providing a design guideline for future system optimization.
Terrence J Sejnowski - One of the best experts on this subject based on the ideXlab platform.
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gradient descent for spiking neural networks
Neural Information Processing Systems, 2018Co-Authors: Dongsung Huh, Terrence J SejnowskiAbstract:Most large-scale network models use neurons with static nonlinearities that produce Analog Output, despite the fact that information processing in the brain is predominantly carried out by dynamic neurons that produce discrete pulses called spikes. Research in spike-based computation has been impeded by the lack of efficient supervised learning algorithm for spiking neural networks. Here, we present a gradient descent method for optimizing spiking network models by introducing a differentiable formulation of spiking dynamics and deriving the exact gradient calculation. For demonstration, we trained recurrent spiking networks on two dynamic tasks: one that requires optimizing fast (~ millisecond) spike-based interactions for efficient encoding of information, and a delayed-memory task over extended duration (~ second). The results show that the gradient descent approach indeed optimizes networks dynamics on the time scale of individual spikes as well as on behavioral time scales. In conclusion, our method yields a general purpose supervised learning algorithm for spiking neural networks, which can facilitate further investigations on spike-based computations.
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gradient descent for spiking neural networks
arXiv: Neurons and Cognition, 2017Co-Authors: Dongsung Huh, Terrence J SejnowskiAbstract:Much of studies on neural computation are based on network models of static neurons that produce Analog Output, despite the fact that information processing in the brain is predominantly carried out by dynamic neurons that produce discrete pulses called spikes. Research in spike-based computation has been impeded by the lack of efficient supervised learning algorithm for spiking networks. Here, we present a gradient descent method for optimizing spiking network models by introducing a differentiable formulation of spiking networks and deriving the exact gradient calculation. For demonstration, we trained recurrent spiking networks on two dynamic tasks: one that requires optimizing fast (~millisecond) spike-based interactions for efficient encoding of information, and a delayed memory XOR task over extended duration (~second). The results show that our method indeed optimizes the spiking network dynamics on the time scale of individual spikes as well as behavioral time scales. In conclusion, our result offers a general purpose supervised learning algorithm for spiking neural networks, thus advancing further investigations on spike-based computation.
Ozkan Bebek - One of the best experts on this subject based on the ideXlab platform.
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personal inertial navigation system assisted by mems ground reaction sensor array and interface asic for gps denied environment
IEEE Journal of Solid-state Circuits, 2018Co-Authors: William H Deng, Ozkan Bebek, Cenk M Cavusoglu, C H Mastrangelo, Darrin J YoungAbstract:A personal inertial navigation system (PINS) assisted by a microelectromechanical systems (MEMS)-based $13 \times 26$ ground reaction sensor array (GRSA) and a low-power interface application-specified integrated circuit (ASIC) has been designed and demonstrated for GPS-denied environment. The GRSA operating in a contact mode achieves a sensitivity of approximately 3.7 fF/kPa at each sensor node. An electronic interface system, consisting of a capacitance-to-voltage ( ${C}/{V}$ ) converter followed by a correlated double sampling stage, is designed to convert the GRSA capacitance change to an Analog Output voltage. The Analog Output voltage is then digitized by a 12-bit cyclic Analog-to-digital converter (ADC). Switch capacitance compensation technique is employed to ensure the ADC performance. The ASIC is fabricated in 0.35- $\mu \text{m}$ CMOS process and dissipates a power of 3 mW. The prototype system incorporates a GRSA, an ASIC, and a commercial nine degree-of-freedom (DOF) inertial measurement unit (IMU) in the heel region of a boot. The GRSA can determine an accurate foot-on-ground timing based on the pressure profiles detected during walking, thus enabling an accurate position calculation and a precise zero velocity update. Furthermore, a system calibration procedure measures the IMU inherent directional drift and scaling factor errors, and compensates them for the navigation data to achieve a superior performance. The prototype system demonstrates a position accuracy of approximately 5.5 m over a navigation distance of 3100 m. The prototype system also achieves a consistent performance over different field tests with various distances and random paths. System characterization results further indicate a tradeoff between sensor array size and system resolution for a given navigation performance requirement, thus providing a design guideline for future system optimization.
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personal inertial navigation system assisted by mems ground reaction sensor array and interface asic for gps denied environment
IEEE Journal of Solid-state Circuits, 2018Co-Authors: Qingbo Guo, William H Deng, Ozkan Bebek, Cenk M Cavusoglu, C H Mastrangelo, Darrin J YoungAbstract:A personal inertial navigation system (PINS) assisted by a microelectromechanical systems (MEMS)-based $13 \times 26$ ground reaction sensor array (GRSA) and a low-power interface application-specified integrated circuit (ASIC) has been designed and demonstrated for GPS-denied environment. The GRSA operating in a contact mode achieves a sensitivity of approximately 3.7 fF/kPa at each sensor node. An electronic interface system, consisting of a capacitance-to-voltage ( ${C}/{V}$ ) converter followed by a correlated double sampling stage, is designed to convert the GRSA capacitance change to an Analog Output voltage. The Analog Output voltage is then digitized by a 12-bit cyclic Analog-to-digital converter (ADC). Switch capacitance compensation technique is employed to ensure the ADC performance. The ASIC is fabricated in 0.35- $\mu \text{m}$ CMOS process and dissipates a power of 3 mW. The prototype system incorporates a GRSA, an ASIC, and a commercial nine degree-of-freedom (DOF) inertial measurement unit (IMU) in the heel region of a boot. The GRSA can determine an accurate foot-on-ground timing based on the pressure profiles detected during walking, thus enabling an accurate position calculation and a precise zero velocity update. Furthermore, a system calibration procedure measures the IMU inherent directional drift and scaling factor errors, and compensates them for the navigation data to achieve a superior performance. The prototype system demonstrates a position accuracy of approximately 5.5 m over a navigation distance of 3100 m. The prototype system also achieves a consistent performance over different field tests with various distances and random paths. System characterization results further indicate a tradeoff between sensor array size and system resolution for a given navigation performance requirement, thus providing a design guideline for future system optimization.
Cenk M Cavusoglu - One of the best experts on this subject based on the ideXlab platform.
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personal inertial navigation system assisted by mems ground reaction sensor array and interface asic for gps denied environment
IEEE Journal of Solid-state Circuits, 2018Co-Authors: William H Deng, Ozkan Bebek, Cenk M Cavusoglu, C H Mastrangelo, Darrin J YoungAbstract:A personal inertial navigation system (PINS) assisted by a microelectromechanical systems (MEMS)-based $13 \times 26$ ground reaction sensor array (GRSA) and a low-power interface application-specified integrated circuit (ASIC) has been designed and demonstrated for GPS-denied environment. The GRSA operating in a contact mode achieves a sensitivity of approximately 3.7 fF/kPa at each sensor node. An electronic interface system, consisting of a capacitance-to-voltage ( ${C}/{V}$ ) converter followed by a correlated double sampling stage, is designed to convert the GRSA capacitance change to an Analog Output voltage. The Analog Output voltage is then digitized by a 12-bit cyclic Analog-to-digital converter (ADC). Switch capacitance compensation technique is employed to ensure the ADC performance. The ASIC is fabricated in 0.35- $\mu \text{m}$ CMOS process and dissipates a power of 3 mW. The prototype system incorporates a GRSA, an ASIC, and a commercial nine degree-of-freedom (DOF) inertial measurement unit (IMU) in the heel region of a boot. The GRSA can determine an accurate foot-on-ground timing based on the pressure profiles detected during walking, thus enabling an accurate position calculation and a precise zero velocity update. Furthermore, a system calibration procedure measures the IMU inherent directional drift and scaling factor errors, and compensates them for the navigation data to achieve a superior performance. The prototype system demonstrates a position accuracy of approximately 5.5 m over a navigation distance of 3100 m. The prototype system also achieves a consistent performance over different field tests with various distances and random paths. System characterization results further indicate a tradeoff between sensor array size and system resolution for a given navigation performance requirement, thus providing a design guideline for future system optimization.
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personal inertial navigation system assisted by mems ground reaction sensor array and interface asic for gps denied environment
IEEE Journal of Solid-state Circuits, 2018Co-Authors: Qingbo Guo, William H Deng, Ozkan Bebek, Cenk M Cavusoglu, C H Mastrangelo, Darrin J YoungAbstract:A personal inertial navigation system (PINS) assisted by a microelectromechanical systems (MEMS)-based $13 \times 26$ ground reaction sensor array (GRSA) and a low-power interface application-specified integrated circuit (ASIC) has been designed and demonstrated for GPS-denied environment. The GRSA operating in a contact mode achieves a sensitivity of approximately 3.7 fF/kPa at each sensor node. An electronic interface system, consisting of a capacitance-to-voltage ( ${C}/{V}$ ) converter followed by a correlated double sampling stage, is designed to convert the GRSA capacitance change to an Analog Output voltage. The Analog Output voltage is then digitized by a 12-bit cyclic Analog-to-digital converter (ADC). Switch capacitance compensation technique is employed to ensure the ADC performance. The ASIC is fabricated in 0.35- $\mu \text{m}$ CMOS process and dissipates a power of 3 mW. The prototype system incorporates a GRSA, an ASIC, and a commercial nine degree-of-freedom (DOF) inertial measurement unit (IMU) in the heel region of a boot. The GRSA can determine an accurate foot-on-ground timing based on the pressure profiles detected during walking, thus enabling an accurate position calculation and a precise zero velocity update. Furthermore, a system calibration procedure measures the IMU inherent directional drift and scaling factor errors, and compensates them for the navigation data to achieve a superior performance. The prototype system demonstrates a position accuracy of approximately 5.5 m over a navigation distance of 3100 m. The prototype system also achieves a consistent performance over different field tests with various distances and random paths. System characterization results further indicate a tradeoff between sensor array size and system resolution for a given navigation performance requirement, thus providing a design guideline for future system optimization.