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Since their initial discovery, many theories have been put forward to explain why RRATs show different emission behavior from other pulsars. These include radio emission being disrupted by fallback of supernova material (Li 2006), trapped plasma being released from radiation belts (Luo & Melrose 2007), and circumstellar material affecting the charge density in the magnetosphere (Cordes & Shannon 2008). Alternatively, RRATs may be just one part of the neutron star intermittency spectrum, which sits as the extension of nulling pulsars with extremely high nulling fractions (Burke-Spolaor 2013). In order to better understand their relation to other pulsars and the nature of the emission, we require the discovery of additional RRATs and, most importantly, long-term monitoring and timing observations.
In this paper, we introduce our observations and data analysis methods for eight RRATs, followed by their timing solutions and the results from other studies probing other emission parameters. We also conduct a study of the RRAT population based on these and other timing solutions in order to find the similarities and differences in the spin-down properties of RRATs and normal pulsars.
We performed several analyses varying from preparing the raw observation data to the analysis of pulse properties, to measuring phase-connected timing parameters. Here we describe those steps in detail.
Prior to getting a timing solution, we must first calculate the spin period. We do this by measuring the differences between pulse arrival times and calculating the greatest common denominator of these differences. With a small number of detected pulses in an observation, there is a probability that this will be an integer multiple of the actual spin period. In order to find the probability of measuring the true period given some number of randomly distributed pulses, we created a large number of simulated RRAT-like timeseries with given sample time, period and pulse number, and calculate the greatest common denominator of the differences. The result shows that the number of pulses largely determines the probability of calculating an incorrect period (which is an integer multiple of the true period). The relationship between this probability and the number of pulses detected is shown in Figure 2 (also see McLaughlin et al. 2006). If eight or more pulses are detected, the probability of this method determining the correct period is greater than 99%. Note that this calculation assumes that all of the pulses used for the calculation are actually from the source; if RFI pulses are mistakenly included in the calculation, the period will most likely be incorrect regardless of the number of pulses. Fortunately, the accuracy of the period can be later confirmed through the timing process.
Many RRATs cannot be detected by summing all the rotations over an observation. Therefore, to create integrated pulse profiles, the most straightforward way is adding all detectable single pulses after phase corrections from the timing model. This provides us with relatively stable pulse profiles. For some RRATs that are less "transient-like," we can create integrated pulse profiles by folding the "on"-phase data for each observation, or even folding the entire observation as for other pulsars.
During the S/N calculation process, we were careful to convert between the different definitions of S/N used by the search algorithm and for creating our pulse amplitude plots (which we denote as "search S/N" and "profile S/N"). This difference is due to the different time resolutions of these two algorithms (the width of the boxcar smoothing function in the search algorithm versus the bin width of our single-pulse profiles used for the pulse amplitude plots). The relationship between S/N and time resolution is given in Keane & Petroff (2015).
However, to better understand their mechanism and evolution, we still need to extend our database of timing solutions and emission test results for more RRATs, in both quantity and quality. Further surveys for new RRATs, accompanied by sensitive timing observations and the development of new techniques, will help us achieve our goals.
We report on a long-term monitoring campaign of 1E 1841-045, the 12 s anomalous X-ray pulsar and magnetar candidate at the center of the supernova remnant Kes 73. We have obtained approximately monthly observations of the pulsar with the Rossi X-Ray Timing Explorer (RXTE) spanning over 2 years, during which time 1E 1841-045 is found to be rotating with sufficient stability to derive a phase-connected timing solution. A linear ephemeris is consistent with measurements of the pulse period made over the last 15 years with the Ginga, ASCA, RXTE, and BeppoSAX observatories. Phase residuals suggest the presence of ``timing noise,'' as is typically observed from young radio pulsars. These results confirm a rapid, constant spin-down for the pulsar, which continues to maintain a steady flux; this is inconsistent with most accretion scenarios.
However, the chip must be supported by the appropriate surrounding components (power, timing, memory, input/output, antenna, etc.) to work. The most important part is ensuring the timing solution is running at optimal levels. The timing solution is just as important as the chip portion.
There are numerous consequences that can happen if you decide to implement your timing solution last. The most common one is that engineers who have saved the timing solution for last end up with not a lot of board to work with and have few or no options to get power to it. Additionally, both crystals and oscillators should be as close to the chip as possible for optimal performance (minimizing noise and signal degradation).
The result could lead to an entire reconfiguration. That means lost parts, lost labor, and an entire redesign of the board to appropriately accommodate this vital passive component. Let that be a lesson to always consider your timing solution early in the process.
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SiTime released the Elite RF precision timing platform, intended to enhance the timing architecture in wireless infrastructures. Like all members of the new class of MEMS-based temperature-compensated crystal oscillators (TCXOs), its Elite RF Super-TCXO is purpose-built to withstand extreme environments in which 5G radios are deployed, while delivering the phase noise, accuracy, and resilience demanded by the application.
The single, highly integrated device also meets the performance requirements specified by the IEEE 1588v2 timing synchronization protocol. Offering a higher reliability than legacy mini oven-controlled crystal oscillators (OCXOs), the Elite RF Super-TCXO provides a reliable timing platform, consuming less power and board space, without additional components like jitter cleaners and VCXOs for generating RF-capable clocks.
The SiT5376 and the SiT5377 are 100-ppb precision MEMS Super-TCXOs with an output that can be digitally pulled by up to 400 ppm with a resolution of 0.05 ppt. Both can be factory-programmed to any combination of frequency, voltage, and pull range; are compliant to the GR-1244 Stratum 3 oscillator specifications; and use the company's DualMEMS and TurboCompensation temperature-sensing technology for stable timing in the presence of environmental stressors, including (EMI).
By following a V-model process, our engineers investigate multicore systems and produce evidence about multicore timing behavior. Our industry-leading tooling, including our unique RapiDaemon technology (which generates interference during tests), reduces analysis effort through automation.
When developing safety-critical applications to DO-178C, AMC 20-193 and CAST-32A guidelines or ISO 26262 standards, there are special requirements for using multicore processors. Evidence must be produced to demonstrate that software operates within its timing deadlines.
The goal of multicore timing analysis is to produce execution time evidence for these complex systems. In multicore processors, multiple cores compete for the same shared resources, resulting in potential interference channels that can affect execution time. MACH178 and Rapita's Multicore Timing Solution account for interference to produce robust execution time evidence in multicore systems.
RapiTime, part of the Rapita Verification Suite (RVS), is the timing analysis component of our Multicore Timing Solution. Our customers have qualified RapiTime on several DO178C DAL A projects where it has been successfully used to generate certification evidence by some of the most well-known aerospace companies in the world. See our Case Studies.
It is possible for companies to perform multicore timing analysis internally, but it is a highly complex undertaking which is very costly in terms of budget and effort. Anecdotally, one of our customers reported that it took them five years and a budget in the millions of dollars to analyze one specific platform.
MACH178 and our Multicore Timing Solution are typically delivered as a turn-key solution, from initial system analysis and configuration all the way through to providing evidence for certification.
Usually, it is highly desirable to have RTOS/HV support for enabling user-level access to hardware event monitors. Context switch information is also very valuable when performing multicore timing analysis.
Analyzing interference effects is a difficult challenge that cannot be automatically solved through a software-only solution. Using approaches developed for timing analysis of single-core systems would result in a high level of pessimism, as it would assume that the highest level of interference possible is feasible, while this is almost never the case.
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