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Equivalent time sampling of a signal may be performed by using an embedded system to create trigger pulses for data acquisition (DAQ) system to record the response. The trigger pulse to the DAQ system may be propagated through digital delay chips controlled by the embedded system. The exemplary embodiments allow the DAQ system to be triggered from locally generated trigger pulses or from external or remote sources.
We propose a self-powered remote multi-sensing system for traffic sensing which is powered by the collective energy harvested from the mechanical vibration of the road caused by the passing vehicles and from the temperature gradient between the asphalt of the road and the soil underneath. A stacked piezoelectric transducer converts mechanical vibrations into electrical energy and a thermoelectric generator harvests the thermal energy from the thermal gradient. Electrical energy signals from the stacked piezoelectric transducer and the thermoelectric generators are converted into usable DC power to recharge the battery using AC-DC and DC-DC converters working simultaneously. The multi-sensing system comprises an embedded system with a microcontroller that acquires data from the sensors and sends the sensory data to an IoT transceiver which transmits the data as RF packets to an ethernet gateway. The gateway converts the RF packets into Internet Protocol (IP) packets and sends them to a remote server. Laboratory and road-testing results showed over 98% sensory data accuracy with the system functioning solely powered by the energy harvested from the alternative energy sources. The successful maximum transmission distance obtained between the IoT, and the gateway was approximately 1 mile, which is a considerable transmission distance achieved in an urban environment. Successful operation of the self-powered multi-sensing system under both laboratory and road conditions contributes considerably to the fields of energy harvesting and self-powered remote sensing systems. The energy flow chart and efficiency for the steps in the system were found to be mechanical power from vehicles to the energy harvester of 0.25%, stacked PZT transducer efficiency was found to be 37%, and for the TEGs the efficiency is 11%. AC-to-DC and DC-to-DC converters’ efficiencies were found to be 90% and 11%. The wireless communication RF transceiver efficiency was found to be 62.5%.
Utilities across the world are wrestling with evolving market dynamics, population growth and climate change. Distributed Energy Resources (DERs) such as solar photovoltaics, distributed generators and energy storage systems are becoming important parts of the U.S. energy mix. These factors are driving an increasing need for low-cost grid asset monitoring. Aside from being costly, traditional utility monitoring systems are not sufficiently robust and do not provide real-time visibility into the condition of grid assets such as transformers. Lack of accurate real-time measurements on performance has resulted in the use of lagging indicators, such as oil sample analysis. To address this need, an innovative embedded optical sensing technology, Transformer Real-time Assessment Intelligent System (TRANSENSOR) was developed, validated and demonstrated in this project. To date, TRANSENSOR has focused on transformers but is extendable to other grid assets. In addition to the technology being embedded into new transformers during manufacturing, a retrofit configuration that can be installed on existing transformers in the field was also developed. It is anticipated that the ability to retrofit existing transformers will help accelerate adoption of the underlying TRANSENSOR technology. Phase 1 of the project focused on laboratory development and qualification of the technology. Following iterations and exploration of relevant optical sensing modalities and multiplexed configurations of interest for the transformer environment, an effective candidate configuration was agreed upon, down-selected and custom-designed for embedding into General Electric (GE) network transformers. Two new GE 500 kilovolt ampere (kVA), 27 kilovolt (kV) distribution network transformers were built with embedded fiber-optic (FO) sensors and successfully qualified per industry standards at GE’s Shreveport, Louisiana facility during Phase 1 of the project. Following the successful completion of Phase 1, the team proceeded to Phase 2, which focused on a field demonstration of the technology. Over Phase 2, the first new GE transformer built with embedded fiber-optic sensors was installed in an above-ground cage and connected to the grid at Con Edison’s Astoria facility. A second transformer equipped with TRANSENSOR was installed in an underground vault. Additionally, an older (1982 year model) GE transformer in an above-ground cage was retrofitted with fiber- optic sensors and reconnected to the grid at ConEd’s facility. Analysis of data acquired from the sensors showed interesting correlations with transformer loading. Additionally, key events such as transformer low-voltage network connection, primary-side energizing, and a pressure loss event from an oil sampling were detected by the TRANSENSOR system. Online data processing/feature extraction algorithms for the second transformer in the underground vault detected key features and event alerts that were transmitted through a wireless 4G connection. The remote deployment concept showed promising results with data collection running for a total of 8 months across the three (3) GE transformers instrumented for the Phase 2 field demonstration. This sets the stage well for further development and commercialization.
As a part of the Proton Improvement Plan – II (PIP-II) at Fermilab, instrumentation systems are being modernized to take advantage of the higher speeds and ease of use offered by standardized embedded systems like MicroTCA. A rear-transition module (RTM) is being designed to interface with said embedded systems. In each of the four identical channels on the RTM, the differential signal from an alternating-current current transformer (ACCT) transimpedance amplifier will again be amplified by a differential operation-amplifier, then filtered by a low-pass topology. The conditioned signal is then digitized at a maximum of 10MS/s by an analog to digital converter (ADC) integrated circuit. After digitization, the ADC passes the data to an off the shelf AdvancedMC (AMC) Xilinx FPGA module using low voltage differential signals. This paper will describe the simulation of analog circuitry for signal conditioning, simulation of digital signal integrity based on physical design as well as verification of design characteristics critical to signal integrity. This work aims to create a methodology that can be applied to future RTMs requiring application of high-speed digital design principles.
As a part of the Proton Improvement Plan II (PIP-II) at Fermilab, instrumentation systems are being modernized to take advantage of the higher speeds and ease of use offered by standardized embedded systems like MicroTCA. A rear-transition module (RTM) is being designed to interface with said embedded systems. In each of the four identical channels on the RTM, the differential signal from an alternating-current current transformer (ACCT) transimpedance amplifier will again be amplified by a differential operation-amplifier, then filtered by a low-pass topology. The conditioned signal is then digitized at a maximum of 10MS/s by an analog to digital converter (ADC) integrated circuit. After digitization, the ADC passes the data to an off the shelf AdvancedMC (AMC) Xilinx FPGA module using low voltage differential signals. This paper will describe the simulation of analog circuitry for signal conditioning, simulation of digital signal integrity based on physical design as well as verification of design characteristics critical to signal integrity. This work aims to create a methodology that can be applied to future RTMs requiring application of high-speed digital design principles.
Data acquisition from multiple beam current monitors is required for electron accelerator production of Mo-99. A two channel system capable of recording data from two beam current monitors has been developed, is currently in use, and is discussed below. The development of a cost-effective method of extending this system to more than two channels and integrating of these measurements into an accelerator control system is the main focus of this report. Data from these current monitors is digitized, processed, and stored by a digital data acquisition system. Limitations and drawbacks with the currently deployed digital data acquisition system have been identified as have been potential solutions, or at least improvements, to these problems. This report will discuss and document the efforts we've made in improving the flexibility and lowering the cost of the data acquisition system while maintaining the minimum requirements.
We discuss reduced-scaling strategies employing recently introduced sub-system embedding sub-algebras coupled-cluster formalism (SES-CC) to describe many-body systems. These strategies utilize properties of the SES-CC formulations where the equations describing certain classes of sub- systems can be integrated into a computational flows composed coupled eigenvalue problems of reduced dimensionality. Additionally, these flows can be defined at the level of the CC Ansatz defined by selected classes of cluster amplitudes, which define the wave function ”memory” of possible partitionings of the many-body system into constituent sub-systems. One of the possible ways of solving these coupled problems is through implementing procedures, where the information is passed between the sub-systems in a self-consistent manner. As a special case, we consider local flow formulations where the so-called local character of correlation effects can be closely related to properties of sub-system embedding sub-algebras employing localized molecular basis. We also generalize flow equations to the time domain and to downfolding methods utilizing double exponential unitary CC Ansatz (DUCC), where reduced dimensionality of constituent sub-problems offer a possibility of efficient utilization of limited quantum resources in modeling realistic systems.
Rapid advances in embedded system complexity and capability provides exciting opportunities for transportation security deployment. Manufacturers and developers of these embedded systems continue to provide lower cost and more powerful solutions that can be leveraged by researchers and engineers. Furthermore, deploying these devices at the “edge” of the Internet-of-Things (IoT) infrastructure provides opportunities for highly capable applications in transport security. In an edge computation architecture, the device is co-located at the source of the data in the larger IoT structure – this provides computational capability at the location directly where the data is collected. For shipment transport security, this provides a direct compute node for digestion of data and mitigation actions in real-time. In our application, the vehicle provides a significant amount of this data that can be processed in real-time via the Controller Area Network Transport Security Tracking and Reporting (C-STAR) edge device. Utilization of a computational node located on the vehicle, such as the C-STAR, capitalizes on previously discussed opportunities of edge architectures. In this paper, we will discuss this security solution’s usability, current deployments, and scalability to further applications in transport security. First, we will cover the supported vehicle platforms that can leverage the C-STAR technology. This will be particularly relevant to medium- and heavy-duty vehicles transporting high-risk shipments. Second, we will speak to current deployments of the C-STAR that are ongoing. Finally, we will discuss additional areas for expansion such as maturing the onboard algorithms through continuing collaborations.
Buildings are responsible for 40% of US energy consumption, and sensing and control technologies are an important element in creating a truly sustainable built environment. Motion-based occupancy sensors are often part of these control systems, but are usually altered or disabled in response to occupants' complaints, at the expense of energy savings. Can we leverage commodity hardware developed for other sectors and embedded software to produce more capable sensors for robust building controls? The National Renewable Energy Laboratory's (NREL) 'Image Processing Occupancy Sensor (IPOS)' is one example of leveraging embedded systems to create smarter, more reliable, multi-function sensors that open the door to new control strategies for building heating, cooling, ventilation, and lighting control. In this keynote, we will discuss how cost-effective embedded systems are changing the state-of-the-art of building sensing and control.
An emerging use-case of machine learning (ML) is to train a model on a high-performance system and deploy the trained model on energy-constrained embedded systems. Neuromorphic hardware platforms, which operate on principles of the biological brain, can significantly lower the energy overhead of a machine learning inference task, making these platforms an attractive solution for embedded ML systems. In this paper, we present a design-technology tradeoff analysis to implement such inference tasks on the processing elements (PEs) of a Non-Volatile Memory (NVM)-based neuromorphic hardware. Through detailed circuit-level simulations at scaled process technology nodes, we show the negative impact of technology scaling on the information-processing latency, which impacts the quality-of-service (QoS) of an embedded ML system. At a finer granularity, the latency inside a PE depends on 1) the delay introduced by parasitic components on its current paths, and 2) the varying delay to sense different resistance states of its NVM cells. Based on these two observations, we make the following three contributions. First, on the technology front, we propose an optimization scheme where the NVM resistance state that takes the longest time to sense is set on current paths having the least delay, and vice versa, reducing the average PE latency, which improves the QoS. Second, on the architecture front, we introduce isolation transistors within each PE to partition it into regions that can be individually power-gated, reducing both latency and energy. Finally, on the system-software front, we propose a mechanism to leverage the proposed technological and architectural enhancements when implementing a machine-learning inference task on neuromorphic PEs of the hardware. Evaluations with a recent neuromorphic hardware architecture show that our proposed design-technology co-optimization approach improves both performance and energy efficiency of machine-learning inference tasks without incurring high cost-per-bit.
As applications of Artificial Intelligence (AI) continue to expand, there are increasing opportunities to leverage applied AI methodologies with mobile transportation focused embedded systems. Current applications of AI in transportation focus on a variety of areas, including fuel efficiency, safety, security, and other broad fields of optimization or detection. To leverage these AI workflows and methodologies in the field, teams must utilize complex embedded systems capable of implementing these AI-enabled algorithms in real-time. In this paper, we will investigate how these algorithms can be integrated into existing technologies leveraging vehicle data - such as the Controller Area Network Transport Security Tracking and Reporting Unit (C-STAR). The C-STAR technology is an embedded platform with onboard computation capable of running next generation algorithms in vehicle systems AI, such as preventative maintenance, driver authentication, and transport security. As deployed in the field, the C-STAR has a limited AI functionality –this paper will directly discuss how a device like C-STAR can be utilized and the advantages of integrating these new technologies. We will open with relevant background information and transportation projects that leverage AI, focusing specifically on those around transport security such as vehicle identification, anomaly detection, and deterrence. We will then extend this into potential opportunities and scaling for AI methodologies using platforms like the C-STAR. Finally, we will speak directly to the challenges of deploying AI-powered workflows, such as computing power needs, bandwidth, hallucinations, and other regulatory considerations.
An embedded system is a small footprint computing unit that typically serves a specific purpose closely associated with measurements and control of hardware devices. These units are designed for reasonable durability and operations in a wide range of operating conditions. Some embedded systems support real-time operations and can demonstrate high levels of reliability. Many have failsafe mechanisms built to handle graceful shutdown of the device in exception conditions. The available memory, processing power, and network connectivity of these devices are limited due to the nature of their specific-purpose design and intended application. Industry practice is to carefully design the software for the available hardware capability to suit desired deployment needs. Volttron is an open source agent development and deployment platform designed to enable researchers to interact with devices and appliances without having to write drivers themselves. Hosting Volttron on small footprint embeddable devices enables its demonstration for embedded use. This report details the steps required and the experience in setting up and running Volttron applications on three small footprint devices: the Intel Next Unit of Computing (NUC), the Raspberry Pi 2, and the BeagleBone Black. In addition, the report also details preliminary investigation of the execution performance of Volttron on these devices.
Hardware specialization has become the silver bullet to achieve efficient high performance, from Systems-on-Chip systems, where hardware specialization can be ``extreme'', to large-scale HPC systems. As the complexity of the systems increases, so does the complexity of programming such architectures in a portable way. This work introduces the Minos Computing Library (MCL), as system software, programming model, and programming model runtime that facilitate programming extremely heterogeneous systems. MCL supports the execution of several multi-threaded applications within the same compute node, performs asynchronous execution of application tasks, efficiently balances computation across hardware resources, and provides performance portability. We show that code developed on a personal desktop automatically scales up to fully utilize powerful workstations with 8 GPUs and down to power-efficient embedded systems. MCL provides up to 17.5x speedup over OpenCL on NVIDIA DGX-1 systems and up to 1.88x speedup on single-GPU systems. In multi-application workloads, MCL dynamically resource allocation provides up to 2.43x performance improvement over manual, static allocation of computing resources.
In this work, we present an accurate and efficient finite-difference formulation and parallel implementation of Kohn-Sham Density (Operator) Functional Theory (DFT) for non periodic systems embedded in a bulk environment. Specifically, employing non-local pseudopotentials, local reformulation of electrostatics, and truncation of the spatial Kohn-Sham Hamiltonian, and the Linear Scaling Spectral Quadrature method to solve for the pointwise electronic fields in real-space and the non-local component of the atomic force, we develop a parallel finite difference framework suitable for distributed memory computing architectures to simulate non-periodic systems embedded in a bulk environment. Choosing examples from magnesium-aluminum alloys, we first demonstrate the convergence of energies and forces with respect to spectral quadrature polynomial order, and the width of the spatially truncated Hamiltonian. Next, we demonstrate the parallel scaling of our framework, and show that the computation time and memory scale linearly with respect to the number of atoms. Next, we use the developed framework to simulate isolated point defects and their interactions in magnesium-aluminum alloys. Our findings conclude that the binding energies of divacancies, Al solute-vacancy and two Al solute atoms are anisotropic and are dependent on cell size. Furthermore, the binding is favorable in all three cases.
Mesoscale organization of convection is typically not represented in global circulation models, and hence its influence on the global circulation is not accounted for. The heating component of a parameterization that represents the dynamical and physical effects of circulations associated with organized convection, referred to as the multiscale coherent structure parameterization (MCSP), is implemented in the Energy Exascale Earth System Model version 1 (E3SMv1). Numerical simulations are conducted to assess its impact on the simulated climate. Besides E3SMv1 simulations, we performed high-resolution (1 km) simulations using the Weather Research and Forecasting (WRF) Model to determine the temperature tendencies induced by mesoscale convective systems embedded in deep convection. We tuned the free parameters of the MCSP based on the WRF simulations. MCSP heating enhances Kevin wave spectra in E3SMv1, improves the representation of the Madden-Julian Oscillation, and impacts on mean precipitation that vary with locations and seasons, including a reduction of biases over the tropical Pacific.
We report the design, the fabrication, and the magneto-transport study of an electron bilayer system embedded in an undoped Si/SiGe double-quantum-well heterostructure. Additionally, the combined Hall densities (n Hall ) ranging from 2.6 × 10 10 cm -2 to 2.7 × 10 11 cm -2 were achieved, yielding a maximal combined Hall mobility (μ Hall ) of 7.7 × 10 5 cm 2 /(V • s) at the highest density. Simultaneous electron population of both quantum wells is clearly observed through a Hall mobility drop as the Hall density is increased to n Hall > 3.3 × 10 10 cm -2 , consistent with Schrödinger-Poisson simulations. Furthermore, the integer and fractional quantum Hall effects are observed in the device, and single-layer behavior is observed when both layers have comparable densities, either due to spontaneous interlayer coherence or to the symmetric-antisymmetric gap.
The JSNS^2-II (the second phase of JSNS^2, J-PARC Sterile Neutrino Search at J-PARC Spallation Neutron Source) is an experiment aimed at searching for sterile neutrinos. Here, this experiment has entered its second phase, employing two liquid scintillator detectors located at near and far positions from the neutrino source. Recently, the far detector of the experiment has been completed and is currently in the calibration phase. The following properties of all functioning Photo-Multiplier-Tubes (PMTs) to detect the neutrino events in the far detector have been calibrated: PMT gain, its dependence of supplied High Voltage (HV), Peak-to-Valley (PV), and signal timing. This paper presents a detailed description of the calibration process utilizing the LED system. The LED system of the far detector uses two Ultra-Violet (UV) LEDs, which are effective in calibrating all of the PMTs at once. The UV light is converted into the visible light wavelengths inside liquid scintillator via the wavelength shifters, providing pseudo-isotropic light. To achieve a good energy resolution for physics events, a relative gain adjustment of up to 10% is required for all functioning PMTs. This will be achieved using the HV curves measured and the results of the LED calibration. The Peak-to-Valley (PV) ratio values, which distinguish the single photo-electron signal from the pedestal, are similar to those from the production company. Additionally, the precision of the PMT signal timing is measured to be 2.1 ns. This meets the event reconstruction requirement of 10 ns.
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