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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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A High-Throughput Oversampled Polyphase Filter Bank Using Vivado HLS and PYNQ on a RFSoC
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Intercell Transformer (ICT) Design Optimization and Interphase Crosstalk Mitigation of a 100-kW SiC Filter-Less Grid-Connected PV String Inverter
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Kalman Filter-Based Estimation of Surface Conductivity and Surface Variations in Scanning Tunneling Microscopy
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A Multilevel Active CM Noise Power Filter for Multilevel Inverters
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Bio-Radar Cardiac Signal Model used for HRV Assessment and Evaluation Using Adaptive Filtering
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Development of ML FPGA Filter for Particle Identification and Tracking in Real Time
Real-time data processing is a frontier field in experimental particle physics. Machine Learning methods are widely used and have proven to be very powerful in particle physics. The growing computational power of modern FPGA boards allows us to add more sophisticated algorithms for real time data processing. Many tasks could be solved using modern Machine Learning (ML) algorithms which are naturally suited for FPGA architectures. The FPGA-based machine learning algorithm provides an extremely low, sub-microsecond, latency decision and makes information-rich data sets for event selection. We report work has started to evaluate an FPGA based Machine Learning (ML) algorithm for a real-time particle identification and tracking with Transition Radiation Detector (TRD) and e/m calorimeter. The first target is the GlueX experiment, with a plan to build a TRD based on GEM technology. GlueX trigger latency is 3.3 μs.
Spoofing Resilient State Estimation for the Power Grid Using an Extended Kalman Filter
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FunM2C: A Filter for Uncertainty Visualization of Multivariate Data on Multi-Core Devices
Uncertainty visualization is an emerging research topic in data visualization because neglecting uncertainty in visualization can lead to inaccurate assessments. In this paper, we study the propagation of multivariate data uncertainty in visualization. Although there have been a few advancements in probabilistic uncertainty visualization of multivariate data, three critical challenges remain to be addressed. First, the state-of-the-art probabilistic uncertainty visualization framework is limited to bivariate data (two variables). Second, existing uncertainty visualization algorithms use computationally intensive techniques and lack support for cross-platform portability. Third, as a consequence of the computational expense, integration into production visualization tools is impractical. In this work, we address all three issues and make a threefold contribution. First, we take a step to generalize the state-of-the-art probabilistic framework for bivariate data to multivariate data with an arbitrary number of variables. Second, through utilization of VTK-m’s shared-memory parallelism and cross-platform compatibility features, we demonstrate acceleration of multivariate uncertainty visualization on different many-core architectures, including OpenMP and AMD GPUs. Third, we demonstrate the integration of our algorithms with the ParaView software. We demonstrate the utility of our algorithms through experiments on multivariate simulation data with three and four variables.
Enhancing the hunt for new phenomena in dijet final states using anomaly detection filters at the high-luminosity large Hadron Collider
In the realm of dijet searches in high-energy physics, a significant challenge has emerged: with experiments producing more and more data, the traditional methods of using analytic functions to describe dijet mass spectra start to fail. Here, to address this, we suggest the application of an anomaly detection approach to eliminate less interesting background events based on event final states. This method not only bypasses the limitations of conventional background models but also significantly enhances our ability to detect potential signals of new physics. Through simulations that mimic the conditions of the upcoming high-luminosity large Hadron collider, we demonstrate the strength and efficiency of this approach in dealing with large data volumes. The integration of unsupervised machine learning into our experimental framework paves the way for a promising avenue to unveil hidden physics discoveries within the overwhelming influx of data.
Data to accompany the publication "Observations of a directional acoustic low pass filter imposed by strong tropospheric winds"
Infrasound data and radiosonde profiles. These data were collected during the AtmoSense campaign.
An FPGA-based hardware accelerator supporting sensitive sequence homology filtering with profile hidden Markov models
Abstract Background Sequence alignment lies at the heart of genome sequence annotation. While the BLAST suite of alignment tools has long held an important role in alignment-based sequence database search, greater sensitivity is achieved through the use of profile hidden Markov models (pHMMs). Here, we describe an FPGA hardware accelerator, called HAVAC, that targets a key bottleneck step (SSV) in the analysis pipeline of the popular pHMM alignment tool, HMMER. Results The HAVAC kernel calculates the SSV matrix at 1739 GCUPS on a $$\sim$$ ∼ $3000 Xilinx Alveo U50 FPGA accelerator card, $$\sim$$ ∼ 227× faster than the optimized SSV implementation in nhmmer . Accounting for PCI-e data transfer data processing, HAVAC is 65× faster than nhmmer’s SSV with one thread and 35× faster than nhmmer with four threads, and uses $$\sim$$ ∼ 31% the energy of a traditional high end Intel CPU. Conclusions HAVAC demonstrates the potential offered by FPGA hardware accelerators to produce dramatic speed gains in sequence annotation and related bioinformatics applications. Because these computations are performed on a co-processor, the host CPU remains free to simultaneously compute other aspects of the analysis pipeline.
Adaptive Noise Filtering Techniques for Radiation Portal Monitors
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Narrow-bandwidth tunable optical filter stabilized by Newton’s rings fringe analysis
The resolution of grating spectrometers is insufficient to resolve many features present in the emission spectra of solid-state quantum emitters. Spectral resolution may be improved by inserting a Fabry–Perot interferometer (FPI) whose length is tuned by a piezoelectric actuator. Yet piezo creep and thermal fluctuations cause instability that makes this solution unsuitable for measurement times longer than tens of seconds. To overcome this challenge, we employ active feedback derived from the Newton’s rings interference pattern formed by the reflection of a single-frequency laser from the FPI cavity. The resulting FPI transmission frequency is stable to within 50 MHz over several hours.