154 research outputs found

    Electronic drive and acquisition system for mass spectrometry

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    The present invention discloses a mixed signal RF drive electronics board that offers small, low power, reliable, and customizable method for driving and generating mass spectra from a mass spectrometer, and for control of other functions such as electron ionizer, ion focusing, single-ion detection, multi-channel data accumulation and, if desired, front-end interfaces such as pumps, valves, heaters, and columns

    Theory, design and implementation of an IF cancellation module for use in a stepped frequency continuous wave ground penetrating radar

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    Bibliography: leaves 64-68.A device has been designed that cancels the leakage signal between the transmit and receive antenna in a Stepped Frequency Continuous Wave Ground Penetrating Radar. The front end of the radar operates at high signal levels and, as a result, a large signal is coupled directly from the transmit to the receive antenna. This signal uses a signiï¬ cant part of the dynamic range of the data-capturing device, an analogue-to-digital converter (ADC). The objective of this cancellation is thus to increase the effective instantaneous dynamic range of the radar system. Simulations show that 10-bit amplitude and phase resolution in the digital cancellation circuit would achieve maximum cancellation in the presence of phase noise and other sources of error. This result is conï¬ rmed when the hardware is tested. The device was constructed and operates as intended. Tests show that cancellation exceeding 53dBm is possible through careful calibration. It was concluded that the device could successfully be integrated into the SFCW GPR and that it would achieve an increase in the instantaneous dynamic range

    Timing Signals and Radio Frequency Distribution Using Ethernet Networks for High Energy Physics Applications

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    Timing networks are used around the world in various applications from telecommunications systems to industrial processes, and from radio astronomy to high energy physics. Most timing networks are implemented using proprietary technologies at high operation and maintenance costs. This thesis presents a novel timing network capable of distributed timing with subnanosecond accuracy. The network, developed at CERN and codenamed “White- Rabbit”, uses a non-dedicated Ethernet link to distribute timing and data packets without infringing the sub-nanosecond timing accuracy required for high energy physics applications. The first part of this thesis proposes a new digital circuit capable of measuring time differences between two digital clock signals with sub-picosecond time resolution. The proposed digital circuit measures and compensates for the phase variations between the transmitted and received network clocks required to achieve the sub-nanosecond timing accuracy. Circuit design, implementation and performance verification are reported. The second part of this thesis investigates and proposes a new method to distribute radio frequency (RF) signals over Ethernet networks. The main goal of existing distributed RF schemes, such as Radio-Over-Fibre or Digitised Radio-Over-Fibre, is to increase the bandwidth capacity taking advantage of the higher performance of digital optical links. These schemes tend to employ dedicated and costly technologies, deemed unnecessary for applications with lower bandwidth requirements. This work proposes the distribution of RF signals over the “White-Rabbit” network, to convey phase and frequency information from a reference base node to a large numbers of remote nodes, thus achieving high performance and cost reduction of the timing network. Hence, this thesis reports the design and implementation of a new distributed RF system architecture; analysed and tested using a purpose-built simulation environment, with results used to optimise a new bespoke FPGA implementation. The performance is evaluated through phase-noise spectra, the Allan-Variance, and signalto- noise ratio measurements of the distributed signals

    Real-time DSP-enabled digital subcarrier cross-connect (DSXC) for optical communication networks

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    Elastic optical networking (EON) is intended to offer flexible channel wavelength granularity to meet the requirement of high spectral efficiency (SE) in today’s optical networks. However, optical cross-connects (OXC) and switches based on optical wavelength division multiplexing (WDM) are not flexible enough due to the coarse bandwidth granularity imposed by optical filtering. Thus, OXC may not meet the requirements of many applications which require finer bandwidth granularities than that carried by an entire wavelength channel. In order to achieve highly flexible and fine enough bandwidth granularities, electrical digital subcarrier cross-connect (DSXC) can be utilized in EON. As presented in this dissertation, my research work focuses on the investigation and implementation of real-time digital signal processing (DSP) enabled DSXC which can dynamically assign both bandwidth and power to each individual sub-wavelength channel, known as subcarrier. This DSXC is based on digital subcarrier multiplexing (DSCM), which is a frequency division multiplexing (FDM) technique that multiplexes a large number of digitally created subcarriers on each optical wavelength. Compared with OXC based on optical WDM, DSXC based on DSCM has much finer bandwidth granularities and flexibilities for dynamic bandwidth allocation. Based on a field programmable gate array (FPGA) hardware platform, we have designed and implemented a real-time DSP-enabled DSXC which uses Nyquist FDM as the multiplexing scheme. For the first time, we demonstrated real-time DSP enabled real-time DSXC which uses resampling filters for channel selection and frequency translation. This circuit-based DSXC supports flexible and fine data-rate subcarrier channel granularities, offering a low latency data plane, transparency to modulation formats, and the capability of compensating transmission impairments in the digital domain. The experimentally demonstrated 8×8 DSXC makes use of a Virtex-7 FPGA platform, which supports any-to-any switching of eight subcarrier channels with mixed modulation formats and data rates. Digital resampling filters, which enable frequency selections and translations of multiple subcarrier channels, have much lower DSP complexity and reduced FPGA resources requirements (DSP slices used in FPGA) in comparison to the traditional technique based on I/Q mixing and filtering. We have also investigated the feasibility of using distributed arithmetic (DA) for real-time DSXC to completely eliminate the usage of DSP slices in FPGA implementation. For the first time, we experimentally demonstrated the implementation of real-time frequency translation and channel selection based on the DA architecture in the same FPGA platform. Compared with resampling filters that leverage multipliers, the DA-based approach eliminates the need of DSP slices in the FPGA implementation and significantly reduces the hardware cost. In addition, with a processing latency that equals to a few clock cycles, a DA-based resampling filter is significantly faster when compared to a conventional direct-structured FIR filter whose overall latency is proportional to the filter order. The DA-based DSXC is, therefore, able to achieve not only the improved spectral efficiency, programmability of multiple orthogonal subcarrier channels, and low hardware resources requirements, but also much reduced cross-connect switching latency when implemented in a real-time DSP hardware platform. This reduced latency can be critically important for time-sensitive applications such as 5G mobile fronthaul, cloud radio access network (C-RAN), cloud-based robot control, tele-surgery and network gaming

    Can my chip behave like my brain?

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    Many decades ago, Carver Mead established the foundations of neuromorphic systems. Neuromorphic systems are analog circuits that emulate biology. These circuits utilize subthreshold dynamics of CMOS transistors to mimic the behavior of neurons. The objective is to not only simulate the human brain, but also to build useful applications using these bio-inspired circuits for ultra low power speech processing, image processing, and robotics. This can be achieved using reconfigurable hardware, like field programmable analog arrays (FPAAs), which enable configuring different applications on a cross platform system. As digital systems saturate in terms of power efficiency, this alternate approach has the potential to improve computational efficiency by approximately eight orders of magnitude. These systems, which include analog, digital, and neuromorphic elements combine to result in a very powerful reconfigurable processing machine.Ph.D

    A compact high-energy particle detector for low-cost deep space missions

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    Over the last few decades particle physics has led to many new discoveries, laying the foundation for modern science. However, there are still many unanswered questions which the next generation of particle detectors could address, potentially expanding our knowledge and understanding of the Universe. Owing to recent technological advancements, electronic sensors are now able to acquire measurements previously unobtainable, creating opportunities for new deep-space high-energy particle missions. Consequently, a new compact instrument was developed capable of detecting gamma rays, neutrons and charged particles. This instrument combines the latest in FPGA System-on-Chip technology as the central processor and a 3x3 array of silicon photomultipliers coupled with an organic plastic scintillator as the detector. Using modern digital pulse shape discrimination and signal processing techniques, the scintillator and photomultiplier combination has been shown to accurately discriminate between the di_erent particle types and provide information such as total energy and incident direction. The instrument demonstrated the ability to capture 30,000 particle events per second across 9 channels - around 15 times that of the U.S. based CLAS detector. Furthermore, the input signals are simultaneously sampled at a maximum rate of 5 GSPS across all channels with 14-bit resolution. Future developments will include FPGA-implemented digital signal processing as well as hardware design for small satellite based deep-space missions that can overcome radiation vulnerability

    Study and design of an interface for remote audio processing

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    This project focused on the study and design of an interface for remote audio processing, with the objective of acquiring by filtering, biasing, and amplifying an analog signal before digitizing it by means of two MCP3208 ADCs to achieve a 24-bit resolution signal. The resulting digital signal was then transmitted to a Raspberry Pi using SPI protocol, where it was processed by a Flask server that could be accessed from both local and remote networks. The design of the PCB was a critical component of the project, as it had to accommodate various components and ensure accurate signal acquisition and transmission. The PCB design was created using KiCad software, which allowed for the precise placement and routing of all components. A major challenge in the design of the interface was to ensure that the analog signal was not distorted during acquisition and amplification. This was achieved through careful selection of amplifier components and using high-pass and low-pass filters to remove any unwanted noise. Once the analog signal was acquired and digitized, the resulting digital signal was transmitted to the Raspberry Pi using SPI protocol. The Raspberry Pi acted as the host for a Flask server, which could be accessed from local and remote networks using a web browser. The Flask server allowed for the processing of the digital signal and provided a user interface for controlling the gain and filtering parameters of the analog signal. This enabled the user to adjust the signal parameters to suit their specific requirements, making the interface highly flexible and adaptable to a variety of audio processing applications. The final interface was capable of remote audio processing, making it highly useful in scenarios where the audio signal needed to be acquired and processed in a location separate from the user. For example, it could be used in a recording studio, where the audio signal from the microphone could be remotely processed using the interface. The gain and filtering parameters could be adjusted in real-time, allowing the sound engineer to fine-tune the audio signal to produce the desired recording. In conclusion, the project demonstrated the feasibility and potential benefits of using a remote audio processing system for various applications. The design of the PCB, selection of components, and use of the Flask server enabled the creation of an interface that was highly flexible, accurate, and adaptable to a variety of audio processing requirements. Overall, the project represents a significant step forward in the field of remote audio processing, with the potential to benefit many different applications in the future
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