The Experts below are selected from a list of 12381 Experts worldwide ranked by ideXlab platform
Mark E Walton - One of the best experts on this subject based on the ideXlab platform.
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pyphotometry open source python based hardware and software for fiber photometry Data Acquisition
Scientific Reports, 2019Co-Authors: Thomas Akam, Mark E WaltonAbstract:Fiber photometry is the process of recording bulk neural activity by measuring fluorescence changes in activity sensitive indicators such as GCaMP through an optical fiber. We present a system of open source hardware and software for fiber photometry Data Acquisition consisting of a compact, low cost, Data Acquisition Board built around the Micropython microcontroller, and a cross platform graphical user interface (GUI) for controlling Acquisition and visualising signals. The system can acquire two analog and two digital signals, and control two external LEDs via built in LED drivers. Time-division multiplexed illumination allows independent readout of fluorescence evoked by different excitation wavelengths from a single photoreceiver signal. Validation experiments indicate this approach offers better signal to noise for a given average excitation light intensity than sinusoidally-modulated illumination. pyPhotometry is substantially cheaper than commercial hardware filling the same role, and we anticipate, as an open source and comparatively simple tool, it will be easily adaptable and therefore of broad interest to a wide range of users.
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pyphotometry open source python based hardware and software for fiber photometry Data Acquisition
bioRxiv, 2018Co-Authors: Thomas Akam, Mark E WaltonAbstract:Fiber photometry is the process of recording bulk neural activity by measuring fluorescence changes in activity sensitive indicators (e.g. GCaMP) through an optical fiber. We present a system of open source hardware and software for fiber photometry Data Acquisition consisting of a compact, low cost, Data Acquisition Board built around the Micropython microcontroller, and a cross platform graphical user interface (GUI) for controlling Acquisition and visualising signals. The system can acquire two analog and two digital signals, and control two external LEDs via built in LED drivers. Time-division multiplexed illumination allows independent readout of fluorescence evoked by different excitation wavelengths from a single photoreceiver signal. Validation experiments indicate this approach offers better signal to noise for a given average excitation light intensity than sinusoidally-modulated illumination. pyPhotometry is substantially cheaper than commercial hardware filling the same role, and we anticipate, as an open source and comparatively simple tool, it will be easily adaptable and therefore of broad interest to a wide range of users.
Xing Heng - One of the best experts on this subject based on the ideXlab platform.
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development of a distributed hybrid seismic electrical Data Acquisition system based on the narrowband internet of things nb iot technology
Geoscientific Instrumentation Methods and Data Systems, 2019Co-Authors: Qisheng Zhang, Feng Guo, Shuaiqing Qiao, Qimao Zhang, Shiyang Liu, Yueyun Luo, Yuefeng Niu, Xing HengAbstract:Abstract. The ambiguity of geophysical inversions, which is based on a single geophysical method, is a long-standing problem in geophysical exploration. Therefore, multi-method geophysical prospecting has become a popular topic. In multi-method geophysical prospecting, the joint inversion of seismic and electric Data has been extensively researched for decades. However, the methods used for hybrid seismic–electric Data Acquisition that form the base for multi-method geophysical prospecting techniques have not yet been explored in detail. In this work, we developed a distributed, high-precision, hybrid seismic–electrical Data Acquisition system using advanced Narrowband Internet of Things (NB-IoT) technology. The system was equipped with a hybrid Data Acquisition Board, a high-performance embedded motherBoard based on field-programmable gate array, an advanced RISC machine, and host software. The Data Acquisition Board used an ADS1278 24 bit analog-to-digital converter and FPGA-based digital filtering techniques to perform high-precision Data Acquisition. The equivalent input noise of the Data Acquisition Board was only 0.5 µ V with a sampling rate of 1000 samples per second and front-end gain of 40 dB. The multiple Data Acquisition stations of our system were synchronized using oven-controlled crystal oscillators and global positioning system technologies. Consequently, the clock frequency error of the system was less than 10 −9 Hz at 1 Hz after calibration, and the synchronization accuracy of the Data Acquisition stations was ±200 ns. The use of sophisticated NB-IoT technologies allowed the long-distance wireless communication between the control center and the Data Acquisition stations. In validation experiments, it was found that our system was operationally stable and reliable, produced highly accurate Data, and it was functionally flexible and convenient. Furthermore, using this system, it is also possible to monitor the real-time quality of Data Acquisition processes. We believe that the results obtained in this study will drive the advancement of prospective integrated seismic–electrical technologies and promote the use of IoT technologies in geophysical instrumentation.
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development of a distributed hybrid seismic electrical Data Acquisition system based on nb iot technology
Geoscientific Instrumentation Methods and Data Systems Discussions, 2019Co-Authors: Qisheng Zhang, Feng Guo, Shuaiqing Qiao, Qimao Zhang, Shiyang Liu, Yueyun Luo, Yuefeng Niu, Xing HengAbstract:Abstract. The non-uniqueness of geophysical inversions, which is based on a single geophysical method, is a long-standing problem in geophysical exploration. Therefore, multi-method geophysical prospecting has become a popular topic. In multi-method geophysical prospecting, the joint inversion of seismic and electric Data has been extensively researched for decades. However, the methods used for hybrid seismic-electric Data Acquisition that form the base for multi-method geophysical prospecting techniques, have not yet been explored in detail. In this work, we developed a distributed, high-precision, and hybrid seismic-electrical Data Acquisition system using advanced Narrow Band-Internet of Things (NB-IoT) technology. The system was equipped with hybrid Data Acquisition Board, a high-performance embedded motherBoard based on field-programmable gate array and advanced RISC machine, and host software. The Data Acquisition Board used an ADS1278 24-bit analog-to-digital converter and FPGA-based digital filtering techniques to perform high-precision Data Acquisition. The equivalent input noise of the Data Acquisition Board was only 0.5 µV with a sampling rate of 1000 samples-per-second and front-end gain of 40 dB. The multiple Data Acquisition stations of our system were synchronized using oven-controlled crystal oscillators and global positioning system technologies. Consequently, the clock frequency error of the system was less than 10−9 Hz @ 1 Hz after calibration, and the synchronization accuracy of the Data Acquisition stations was ±200 ns. The use of sophisticated NB-IoT technologies allowed the long-distance wireless communication between control center and Data Acquisition stations. In validation experiments, it was found that our system was operationally stable and reliable, produced highly accurate Data, and functionally flexible and convenient. Furthermore, using this system, it is also possible to monitor the real-time quality of Data Acquisition processes. We believe that the results obtained in this study will drive the advancement of prospective integrated seismic-electrical technologies and promote the use of IoT technologies in geophysical instrumentation.
Thomas Akam - One of the best experts on this subject based on the ideXlab platform.
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pyphotometry open source python based hardware and software for fiber photometry Data Acquisition
Scientific Reports, 2019Co-Authors: Thomas Akam, Mark E WaltonAbstract:Fiber photometry is the process of recording bulk neural activity by measuring fluorescence changes in activity sensitive indicators such as GCaMP through an optical fiber. We present a system of open source hardware and software for fiber photometry Data Acquisition consisting of a compact, low cost, Data Acquisition Board built around the Micropython microcontroller, and a cross platform graphical user interface (GUI) for controlling Acquisition and visualising signals. The system can acquire two analog and two digital signals, and control two external LEDs via built in LED drivers. Time-division multiplexed illumination allows independent readout of fluorescence evoked by different excitation wavelengths from a single photoreceiver signal. Validation experiments indicate this approach offers better signal to noise for a given average excitation light intensity than sinusoidally-modulated illumination. pyPhotometry is substantially cheaper than commercial hardware filling the same role, and we anticipate, as an open source and comparatively simple tool, it will be easily adaptable and therefore of broad interest to a wide range of users.
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pyphotometry open source python based hardware and software for fiber photometry Data Acquisition
bioRxiv, 2018Co-Authors: Thomas Akam, Mark E WaltonAbstract:Fiber photometry is the process of recording bulk neural activity by measuring fluorescence changes in activity sensitive indicators (e.g. GCaMP) through an optical fiber. We present a system of open source hardware and software for fiber photometry Data Acquisition consisting of a compact, low cost, Data Acquisition Board built around the Micropython microcontroller, and a cross platform graphical user interface (GUI) for controlling Acquisition and visualising signals. The system can acquire two analog and two digital signals, and control two external LEDs via built in LED drivers. Time-division multiplexed illumination allows independent readout of fluorescence evoked by different excitation wavelengths from a single photoreceiver signal. Validation experiments indicate this approach offers better signal to noise for a given average excitation light intensity than sinusoidally-modulated illumination. pyPhotometry is substantially cheaper than commercial hardware filling the same role, and we anticipate, as an open source and comparatively simple tool, it will be easily adaptable and therefore of broad interest to a wide range of users.
Qisheng Zhang - One of the best experts on this subject based on the ideXlab platform.
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development of a distributed hybrid seismic electrical Data Acquisition system based on the narrowband internet of things nb iot technology
Geoscientific Instrumentation Methods and Data Systems, 2019Co-Authors: Qisheng Zhang, Feng Guo, Shuaiqing Qiao, Qimao Zhang, Shiyang Liu, Yueyun Luo, Yuefeng Niu, Xing HengAbstract:Abstract. The ambiguity of geophysical inversions, which is based on a single geophysical method, is a long-standing problem in geophysical exploration. Therefore, multi-method geophysical prospecting has become a popular topic. In multi-method geophysical prospecting, the joint inversion of seismic and electric Data has been extensively researched for decades. However, the methods used for hybrid seismic–electric Data Acquisition that form the base for multi-method geophysical prospecting techniques have not yet been explored in detail. In this work, we developed a distributed, high-precision, hybrid seismic–electrical Data Acquisition system using advanced Narrowband Internet of Things (NB-IoT) technology. The system was equipped with a hybrid Data Acquisition Board, a high-performance embedded motherBoard based on field-programmable gate array, an advanced RISC machine, and host software. The Data Acquisition Board used an ADS1278 24 bit analog-to-digital converter and FPGA-based digital filtering techniques to perform high-precision Data Acquisition. The equivalent input noise of the Data Acquisition Board was only 0.5 µ V with a sampling rate of 1000 samples per second and front-end gain of 40 dB. The multiple Data Acquisition stations of our system were synchronized using oven-controlled crystal oscillators and global positioning system technologies. Consequently, the clock frequency error of the system was less than 10 −9 Hz at 1 Hz after calibration, and the synchronization accuracy of the Data Acquisition stations was ±200 ns. The use of sophisticated NB-IoT technologies allowed the long-distance wireless communication between the control center and the Data Acquisition stations. In validation experiments, it was found that our system was operationally stable and reliable, produced highly accurate Data, and it was functionally flexible and convenient. Furthermore, using this system, it is also possible to monitor the real-time quality of Data Acquisition processes. We believe that the results obtained in this study will drive the advancement of prospective integrated seismic–electrical technologies and promote the use of IoT technologies in geophysical instrumentation.
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development of a distributed hybrid seismic electrical Data Acquisition system based on nb iot technology
Geoscientific Instrumentation Methods and Data Systems Discussions, 2019Co-Authors: Qisheng Zhang, Feng Guo, Shuaiqing Qiao, Qimao Zhang, Shiyang Liu, Yueyun Luo, Yuefeng Niu, Xing HengAbstract:Abstract. The non-uniqueness of geophysical inversions, which is based on a single geophysical method, is a long-standing problem in geophysical exploration. Therefore, multi-method geophysical prospecting has become a popular topic. In multi-method geophysical prospecting, the joint inversion of seismic and electric Data has been extensively researched for decades. However, the methods used for hybrid seismic-electric Data Acquisition that form the base for multi-method geophysical prospecting techniques, have not yet been explored in detail. In this work, we developed a distributed, high-precision, and hybrid seismic-electrical Data Acquisition system using advanced Narrow Band-Internet of Things (NB-IoT) technology. The system was equipped with hybrid Data Acquisition Board, a high-performance embedded motherBoard based on field-programmable gate array and advanced RISC machine, and host software. The Data Acquisition Board used an ADS1278 24-bit analog-to-digital converter and FPGA-based digital filtering techniques to perform high-precision Data Acquisition. The equivalent input noise of the Data Acquisition Board was only 0.5 µV with a sampling rate of 1000 samples-per-second and front-end gain of 40 dB. The multiple Data Acquisition stations of our system were synchronized using oven-controlled crystal oscillators and global positioning system technologies. Consequently, the clock frequency error of the system was less than 10−9 Hz @ 1 Hz after calibration, and the synchronization accuracy of the Data Acquisition stations was ±200 ns. The use of sophisticated NB-IoT technologies allowed the long-distance wireless communication between control center and Data Acquisition stations. In validation experiments, it was found that our system was operationally stable and reliable, produced highly accurate Data, and functionally flexible and convenient. Furthermore, using this system, it is also possible to monitor the real-time quality of Data Acquisition processes. We believe that the results obtained in this study will drive the advancement of prospective integrated seismic-electrical technologies and promote the use of IoT technologies in geophysical instrumentation.
Feng Guo - One of the best experts on this subject based on the ideXlab platform.
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development of a distributed hybrid seismic electrical Data Acquisition system based on the narrowband internet of things nb iot technology
Geoscientific Instrumentation Methods and Data Systems, 2019Co-Authors: Qisheng Zhang, Feng Guo, Shuaiqing Qiao, Qimao Zhang, Shiyang Liu, Yueyun Luo, Yuefeng Niu, Xing HengAbstract:Abstract. The ambiguity of geophysical inversions, which is based on a single geophysical method, is a long-standing problem in geophysical exploration. Therefore, multi-method geophysical prospecting has become a popular topic. In multi-method geophysical prospecting, the joint inversion of seismic and electric Data has been extensively researched for decades. However, the methods used for hybrid seismic–electric Data Acquisition that form the base for multi-method geophysical prospecting techniques have not yet been explored in detail. In this work, we developed a distributed, high-precision, hybrid seismic–electrical Data Acquisition system using advanced Narrowband Internet of Things (NB-IoT) technology. The system was equipped with a hybrid Data Acquisition Board, a high-performance embedded motherBoard based on field-programmable gate array, an advanced RISC machine, and host software. The Data Acquisition Board used an ADS1278 24 bit analog-to-digital converter and FPGA-based digital filtering techniques to perform high-precision Data Acquisition. The equivalent input noise of the Data Acquisition Board was only 0.5 µ V with a sampling rate of 1000 samples per second and front-end gain of 40 dB. The multiple Data Acquisition stations of our system were synchronized using oven-controlled crystal oscillators and global positioning system technologies. Consequently, the clock frequency error of the system was less than 10 −9 Hz at 1 Hz after calibration, and the synchronization accuracy of the Data Acquisition stations was ±200 ns. The use of sophisticated NB-IoT technologies allowed the long-distance wireless communication between the control center and the Data Acquisition stations. In validation experiments, it was found that our system was operationally stable and reliable, produced highly accurate Data, and it was functionally flexible and convenient. Furthermore, using this system, it is also possible to monitor the real-time quality of Data Acquisition processes. We believe that the results obtained in this study will drive the advancement of prospective integrated seismic–electrical technologies and promote the use of IoT technologies in geophysical instrumentation.
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development of a distributed hybrid seismic electrical Data Acquisition system based on nb iot technology
Geoscientific Instrumentation Methods and Data Systems Discussions, 2019Co-Authors: Qisheng Zhang, Feng Guo, Shuaiqing Qiao, Qimao Zhang, Shiyang Liu, Yueyun Luo, Yuefeng Niu, Xing HengAbstract:Abstract. The non-uniqueness of geophysical inversions, which is based on a single geophysical method, is a long-standing problem in geophysical exploration. Therefore, multi-method geophysical prospecting has become a popular topic. In multi-method geophysical prospecting, the joint inversion of seismic and electric Data has been extensively researched for decades. However, the methods used for hybrid seismic-electric Data Acquisition that form the base for multi-method geophysical prospecting techniques, have not yet been explored in detail. In this work, we developed a distributed, high-precision, and hybrid seismic-electrical Data Acquisition system using advanced Narrow Band-Internet of Things (NB-IoT) technology. The system was equipped with hybrid Data Acquisition Board, a high-performance embedded motherBoard based on field-programmable gate array and advanced RISC machine, and host software. The Data Acquisition Board used an ADS1278 24-bit analog-to-digital converter and FPGA-based digital filtering techniques to perform high-precision Data Acquisition. The equivalent input noise of the Data Acquisition Board was only 0.5 µV with a sampling rate of 1000 samples-per-second and front-end gain of 40 dB. The multiple Data Acquisition stations of our system were synchronized using oven-controlled crystal oscillators and global positioning system technologies. Consequently, the clock frequency error of the system was less than 10−9 Hz @ 1 Hz after calibration, and the synchronization accuracy of the Data Acquisition stations was ±200 ns. The use of sophisticated NB-IoT technologies allowed the long-distance wireless communication between control center and Data Acquisition stations. In validation experiments, it was found that our system was operationally stable and reliable, produced highly accurate Data, and functionally flexible and convenient. Furthermore, using this system, it is also possible to monitor the real-time quality of Data Acquisition processes. We believe that the results obtained in this study will drive the advancement of prospective integrated seismic-electrical technologies and promote the use of IoT technologies in geophysical instrumentation.
