fieldprogrammable Gate Array (FPGA) technology has been used extensively in space applications where the natural radiation environment presents major challenges to electronic parts. Commercial FPGA technology is tren...
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ISBN:
(纸本)9798350341430
fieldprogrammable Gate Array (FPGA) technology has been used extensively in space applications where the natural radiation environment presents major challenges to electronic parts. Commercial FPGA technology is trending to deep nano-meter silicon processes, which impacts the availability of radiation resilience FPGA chips. Space systems require long timeframes for development and launch, and often the electronics and code may become obsolete or require updating before the system can be launched. FPGA logic/fabric-size continues to grow dramatically which allows and practically requires more and more IP cores to be integrated within a chip. New IP cores and tools will be needed to enable space designs with commercial FPGA technology to withstand radiation. this paper discusses the challenges in designing FPGA-based space systems and potential open-source and commercial technologies that will be useful to space application developers. It also references an ongoing FPGA based space telescope spectrometer design to discuss different aspects of complex FPGA design with mixed analog and digital circuits.
Over the last decade, field-programmable Gate Array (FPGA) technology has made significant strides, notably with Advanced Micro Devices Inc. (AMD) pioneering Multi-processor Systems-on-Chip (MPSoC). the addition of RF...
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ISBN:
(纸本)9798350385939;9798350385922
Over the last decade, field-programmable Gate Array (FPGA) technology has made significant strides, notably with Advanced Micro Devices Inc. (AMD) pioneering Multi-processor Systems-on-Chip (MPSoC). the addition of RF-class analog technology has further advanced MPSoCs, resulting in Radio Frequency System-on-a-Chip (RFSoC). RFSoCs combine data converters withprogrammablelogic, dedicated processors, and high-speed transceivers, making them ideal for large-N applications in radio astronomy/cosmology experiments. the Raman Research Institute has embarked upon building next-generation digital correlation spectrometers using RFSoC evaluation boards like AMD's ZCU111 and ZCU216, advancing towards a compact, custom-designed third generation RFSoC-based platform named "Cosmology and radio astronomy experiments with RFSoC Integrated Signal Processing" (CRISP). these technological advancements are pivotal for high precision cosmology experiments, detecting subtle signals from recombination and reionization epochs. the paper describes the digital correlation receivers based on evaluation platforms, signal processing firmware, and the architecture of CRISP leveraging Size, Weight, and Power (SWaP) attributes.
Internet of things (IoT) enters every aspect of life, image processing has advanced to a new level. Numerous applications in the field of image processing, such as image filtering and processing, are being produced us...
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this paper proposes a study on the use of a fuzzy-logic scheme to control LED lighting systems in buildings to achieve energy efficiency while maintaining illumination levels in line withthe EN12462-1 standard protoc...
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During major natural disasters like extreme weather events and wildfires, it is very important to notify citizens when the danger level exceeds certain thresholds. However, defining these thresholds in an entirely dis...
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In this paper, a real-time, multi-player Android application is proposed, specifically a word game. the suggested application enables users to compete with other players in coming up with words using the same letters ...
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this study suggests an efficient adaptive online learning system that makes use of a triangular membership function to display the student's conceptual understanding in a selected topic area. the level of understa...
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Magnetic Particle Inspection (MPI) is a non-destructive testing (NDT) technique widely used in industry to detect surface and sub-surface flaws in ferromagnetic materials. MPI is a simple, fast, and cost-effective tec...
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ISBN:
(纸本)9798350380132;9798350380125
Magnetic Particle Inspection (MPI) is a non-destructive testing (NDT) technique widely used in industry to detect surface and sub-surface flaws in ferromagnetic materials. MPI is a simple, fast, and cost-effective technique, particularly useful for evaluating the integrity of welds and critical parts. Detecting surface and sub-surface defects in ferromagnetic materials is essential for ensuring product quality, safety, performance, and regulatory compliance across various industries. therefore, in this study, the magnetic field and its gradient for rectangular prism-shaped defects in ferromagnetic materials were analyzed. the magnetic fields were calculated using analytical equations. the magnetic field of a 2x7x19 mm sized permanent magnet was measured using a gaussmeter to verify theoretical results. Subsequently, these analyses were used to interpret the forces acting on non-interacting micro to nano-sized particles and magnetic fluid on the crack region. the EFH-1 light hydrocarbon-based magnetic fluid, with an average particle size of about 11.6 nm, was used to experimentally study a 1x1.5x62.5 mm crack on a solid magnetic material. the distribution of the magnetic fluid on the crack was studied for different heights and magnetic field values. For a 2 ml layer of EFH-1 at a height of 2-3.5 mm from the magnetized material, a nearly Gaussian-shaped fluid distribution with a width of around 3.7-5.0 mm was observed. the experimental results are presented and compared withthe theoretical analysis.
Withthe new advancements in cloud computing and internet of things (IoT), mobile applications are transforming the healthcare sector offering solutions for both users and researchers. the massive amounts of data gene...
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Recent progressions in federated learning (FL) have facilitated the development of decentralized collaborative Internet-of-things (IoT) applications. However, data-driven FL algorithms face the challenge of heterogene...
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ISBN:
(纸本)9798400700361
Recent progressions in federated learning (FL) have facilitated the development of decentralized collaborative Internet-of-things (IoT) applications. However, data-driven FL algorithms face the challenge of heterogeneity in participating IoT devices, including their deployment environment and calibration settings. Fail to follow these device-specific properties can degenerate the model performance. To address this issue, we present FedSTL in this poster abstract, which is a two-staged personalized FL framework with clustering for sequential prediction tasks in IoT. FedSTL first identifies client properties as Signal Temporal logic (STL) specifications. then, a partitioning component of FedSTL associates each client to an aggregation center, while the framework continues to infer properties for the cluster. At the training stage, both cluster and client models are encouraged to follow customized properties to achieve a hierarchical property enhancing strategy. Further, we show preliminary results of FedSTL in this poster abstract under a synthetic multitask IoT environment and a real-world traffic prediction scenario.
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