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The observations of Posner (1983) that on a wideband Gaussian broadcast channel ordinary time-shared coding performs almost as well as broadcast coding are investigated. A quantitative version of Posner's results is derived. A numerical example comparing the performance of broadcast coding and time-shared coding for a Gaussian broadcast channel model is presented.
Many redundancy removal algorithms employ some sort of run length code. Blocks of timing words are coded with synchronization words inserted between blocks. The probability of incorrectly reconstructing a sample because of a channel error in the timing data is a monotonically nondecreasing function of time since the last synchronization word. In this paper we compute the 'probability that the accumulated magnitude of timing errors equal zero' as a function of time since the last synchronization word for a zero-order predictor (ZOP). The result is valid for any data source that can be modeled by a first-order Markov chain and any digital channel that can be modeled by a channel transition matrix. An example is presented.
A continuous wave Light Detection and Ranging (CW LiDAR) system utilizes two or more laser frequencies and time or range shifted pseudorandom noise (PN) codes to discriminate between the laser frequencies. The performance of these codes can be improved by subtracting out the bias before processing. The CW LiDAR system may be mounted to an artificial satellite orbiting the earth, and the relative strength of the return signal for each frequency can be utilized to determine the concentration of selected gases or other substances in the atmosphere.
Time synchronization system for synchronizing clocks at remote locations with master clock using moon reflected coded signals
In a time-varying signal-to-noise ration (SNR) environment, symbol rate is often changed to maximize data return. However, the symbol-rate change has some undesirable effects, such as changing the transmission bandwidth and perhaps causing the receiver symbol loop to lose lock temporarily, thus losing some data. In this article, we are proposing an alternate way of varying the data rate without changing the symbol rate and, therefore, the transmission bandwidth. The data rate change is achieved in a seamless fashion by puncturing the convolutionally encoded symbol stream to adapt to the changing SNR environment. We have also derived an exact expression to enumerate the number of distinct puncturing patterns. To demonstrate this seamless rate change capability, we searched for good puncturing patterns for the Galileo (14,1/4) convolutional code and changed the data rates by using the punctured codes to match the Galileo SNR profile of November 9, 1997. We show that this scheme reduces the symbol-rate changes from nine to two and provides a comparable data return in a day and a higher symbol SNR during most of the day.
In a time-varying signal-to-noise (SNR) environment, symbol rate is often changed to maximize ata return. However, the symbol-rate change has some undesirable effects such as changing the ransmission bandwidth and perhaps causing the receiver symbol loop to lose lock temporarily, thus osing some data. In this article, we are proposing an alternate way of varying the data rate without hanging the symbol rate and therefore the transmission bandwidth. The data rate change is achieved n a seamless fashion by puncturing the convolutionally encoded symbol stream to adapt to the hanging SNR environment. We have also derived an exact expression to enumerate the number of nique puncturing patterns. To demonstrate this seamless rate-change capability, we searched for good uncturing patterns for the Galileo (14, 1/4) convolutional code and changed the data rates by using the unctured codes to match the Galileo SNR profile of November 9, 1997.
We describe the accomplishments jointly achieved by Kitware and Sandia over the fiscal years 2016 through 2020 to benefit the Advanced Scientific Computed (ASC) Advanced Technology Development and Mitigation (ATDM) project. As a result of our collaboration, we have improved the Trilinos and ATDM application developer experience by decreasing the time to build, making it easier to identify and resolve build and test defects, and addressing other issues . We have also reduced the turnaround time for continuous integration (CI) results. For example, the combined improvements likely cut the wall clock time to run automated builds of Trilinos posting to CDash by approximately 6x or more in many cases. We primarily achieved these benefits by contributing changes to the Kitware CMake/CTest/CDash suite of open source software development support tools. As a result, ASC developers can now spend more time improving code and less time chasing bugs. And, without this work, one can argue that the stabilization of Trilinos for the ATDM platforms would not have been feasible which would have had a large negative impact on an important internal FY20 L1 milestone.
A fast-running unsteady aerodynamics code, LINFLUX, was previously developed for predicting turbomachinery flutter. This linearized code, based on a frequency domain method, models the effects of steady blade loading through a nonlinear steady flow field. The LINFLUX code, which is 6 to 7 times faster than the corresponding nonlinear time domain code, is suitable for use in the initial design phase. Earlier, this code was verified through application to a research fan, and it was shown that the predictions of work per cycle and flutter compared well with those from a nonlinear time-marching aeroelastic code, TURBO-AE. Now, the LINFLUX code has been applied to real configurations: fans developed under the Energy Efficient Engine (E-cubed) Program and the Quiet Aircraft Technology (QAT) project. The LINFLUX code starts with a steady nonlinear aerodynamic flow field and solves the unsteady linearized Euler equations to calculate the unsteady aerodynamic forces on the turbomachinery blades. First, a steady aerodynamic solution is computed for given operating conditions using the nonlinear unsteady aerodynamic code TURBO-AE. A blade vibration analysis is done to determine the frequencies and mode shapes of the vibrating blades, and an interface code is used to convert the steady aerodynamic solution to a form required by LINFLUX. A preprocessor is used to interpolate the mode shapes from the structural dynamics mesh onto the computational fluid dynamics mesh. Then, LINFLUX is used to calculate the unsteady aerodynamic pressure distribution for a given vibration mode, frequency, and interblade phase angle. Finally, a post-processor uses the unsteady pressures to calculate the generalized aerodynamic forces, eigenvalues, an esponse amplitudes. The eigenvalues determine the flutter frequency and damping. Results of flutter calculations from the LINFLUX code are presented for (1) the E-cubed fan developed under the E-cubed program and (2) the Quiet High Speed Fan (QHSF) developed under the Quiet Aircraft Technology project. The results are compared with those obtained from the TURBO-AE code. A graph of the work done per vibration cycle for the first vibration mode of the E-cubed fan is shown. It can be seen that the LINFLUX results show a very good comparison with TURBO-AE results over the entire range of interblade phase angle. The work done per vibration cycle for the first vibration mode of the QHSF fan is shown. Once again, the LINFLUX results compare very well with the results from the TURBOAE code.
A major objective of the Hypercube Matrix Computation effort at the Jet Propulsion Laboratory (JPL) is to investigate the applicability of a parallel computing architecture to the solution of large-scale electromagnetic scattering problems. Three scattering analysis codes are being implemented and assessed on a JPL/California Institute of Technology (Caltech) Mark 3 Hypercube. The codes, which utilize different underlying algorithms, give a means of evaluating the general applicability of this parallel architecture. The three analysis codes being implemented are a frequency domain method of moments code, a time domain finite difference code, and a frequency domain finite elements code. These analysis capabilities are being integrated into an electromagnetics interactive analysis workstation which can serve as a design tool for the construction of antennas and other radiating or scattering structures. The first two years of work on the Hypercube Matrix Computation effort is summarized. It includes both new developments and results as well as work previously reported in the Hypercube Matrix Computation Task: Final Report for 1986 to 1987 (JPL Publication 87-18).
With the development of new advanced instruments for remote sensing applications, sensor data will be generated at a rate that not only requires increased onboard processing and storage capability, but imposes demands on the space to ground communication link and ground data management-communication system. Data compression and error control codes provide viable means to alleviate these demands. Two types of data compression have been studied by many researchers in the area of information theory: a lossless technique that guarantees full reconstruction of the data, and a lossy technique which generally gives higher data compaction ratio but incurs some distortion in the reconstructed data. To satisfy the many science disciplines which NASA supports, lossless data compression becomes a primary focus for the technology development. While transmitting the data obtained by any lossless data compression, it is very important to use some error-control code. For a long time, convolutional codes have been widely used in satellite telecommunications. To more efficiently transform the data obtained by the Rice algorithm, it is required to meet the a posteriori probability (APP) for each decoded bit. A relevant algorithm for this purpose has been proposed which minimizes the bit error probability in the decoding linear block and convolutional codes and meets the APP for each decoded bit. However, recent results on iterative decoding of 'Turbo codes', turn conventional wisdom on its head and suggest fundamentally new techniques. During the past several months of this research, the following approaches have been developed: (1) a new lossless data compression algorithm, which is much better than the extended Rice algorithm for various types of sensor data, (2) a new approach to determine the generalized Hamming weights of the algebraic-geometric codes defined by a large class of curves in high-dimensional spaces, (3) some efficient improved geometric Goppa codes for disk memory systems and high-speed mass memory systems, and (4) a tree based approach for data compression using dynamic programming.
A research effort was conducted with the goal of reducing computer time of a Navier Stokes Computer Code for prediction of viscous flow fields about lifting bodies. A two-dimensional, time-dependent, laminar, transonic computer code (STOKES) was modified to incorporate a non-uniform timestep procedure. The non-uniform time-step requires updating of a zone only as often as required by its own stability criteria or that of its immediate neighbors. In the uniform timestep scheme each zone is updated as often as required by the least stable zone of the finite difference mesh. Because of less frequent update of program variables it was expected that the nonuniform timestep would result in a reduction of execution time by a factor of five to ten. Available funding was exhausted prior to successful demonstration of the benefits to be derived from the non-uniform time-step method.
Due to the growing use of large language models (LLMs) by developers and researchers, it has become essential to reliably evaluate their ability to generate code that uses specialized libraries. We explore the use of compile-time and run-time evaluation of LLM-generated Kokkos code through extending the methods used by OpenAI with the HumanEval dataset. Our evaluation framework is based on the first 40 prompts from the Kokkos138 dataset. We start by discussing two different forms of LLM prompting, using entirely plain English or providing pseudocode for added context. These two methods are used to generate Kokkos code with the Llama-3.1-8B-Instruct and CodeQwen1.5-7B-Chat models. We found that both forms of prompting led to high failure rates and difficulties with reliably parsing LLM-generated code, while prompts with pseudocode for context generally led to improved results on more complicated tests.
Digital measurement of differential time delay of pseudorandom coded signals
Integrated distributed energy resources (DER) in a distribution system need to follow grid codes to avoid violations that result in DER/circuit segment disconnection. To comply with grid code requirements at the grid edge level, network constrained grid edge energy management system (EMS) can be deployed. The objective of grid edge EMS is to provide economic solution for active and reactive power DER setpoints at each dispatch interval and ensure voltage regulation to support secure interconnection of the grid edge segment to the distribution system with multiple inverter based DER units. In this work, real-time simulation of grid code compliant grid edge EMS is deployed in a realistic feeder circuit segment. For real-time simulation, communication between the grid edge EMS and DERs is done exploiting IEC 61850-7-420. It enables interoperability among different DERs and grid edge EMS. No prior art has deployed IEC 61850-7-420 GOOSE communication protocol for grid edge EMS. Conversion of IEC 61850 GOOSE messages to Modbus communication protocol is also performed to communicate with grid edge EMS in commodity-off the shelf embedded boards in this work. The real-time simulation in OPAL-RT real-time digital simulator shows the out-performance of grid edge EMS by reducing the voltage violation in the distribution circuit.
A program which was written to simulate Real Time Minimal-Byte-Error Probability (RTMBEP) decoding of full unit-memory (FUM) convolutional codes on a 3-bit quantized AWGN channel is described. This program was used to compute the symbol-error probability of FUM codes and to determine the signal to noise (SNR) required to achieve a bit error rate (BER) of 10 to the minus 6th power for corresponding concatenated systems. A (6,6/30) FUM code, 6-bit Reed-Solomon code combination was found to achieve the required BER at a SNR of 1.886 dB. The RTMBEP algorithm was then modified for decoding partial unit-memory (PUM) convolutional codes. A simulation program was also written to simulate the symbol-error probability of these codes.
Coded representation of time signals--day, hour, minute, second--is changed from binary-coded decimal (BCD) to IRIG standard time-code format B by circuit that uses nine integrated circuits. Input to code-converter circuit is parallel BCD pulses on bus output is serial pulses of IRIG-B on single line.
We plan to perform the following sets of computations on unadapted (fixed) grids: 1) Structured RANS set 1 (Code: CFL3D, Grid: Str-OnetoOne-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Spalart-Allmaras), 2) Structured RANS set 2 (Code: CFL3D, Grid: Str-OnetoOne-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Menter SST), 3) Structured RANS set 3 (time permitting) (Code: CFL3D, Grid: Str-OnetoOne-B-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Menter SST), 4) Unstructured RANS set 1 (Code: FUN3D, Grid: Unst-Mixed-FromTet-Nodecentered-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Spalart-Allmaras), and 5) Unstructured RANS set 2 (time permitting) (Code: FUN3D, Grid: Unst-Hex-FromOnetoOne-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Spalart-Allmaras),. Optional case 3 is not being computed. CFL3D is a structured upwind-biased cell-centered RANS code,1 and FUN3D is an unstructured upwind-biased node-centered RANS code
We plan to perform the following sets of computations on unadapted (fixed) grids: 1) Structured RANS set 1 (Code: CFL3D, Grid: Str-OnetoOne-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Spalart-Allmaras), 2) Structured RANS set 2 (Code: CFL3D, Grid: Str-OnetoOne-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Menter SST), 3) Structured RANS set 3 (time permitting) (Code: CFL3D, Grid: Str-OnetoOne-B-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Menter SST), 4) Unstructured RANS set 1 (Code: FUN3D, Grid: Unst-Mixed-FromTet-Nodecentered-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Spalart-Allmaras), and 5) Unstructured RANS set 2 (time permitting) (Code: FUN3D, Grid: Unst-Hex-FromOnetoOne-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Spalart-Allmaras),. Optional case 3 is not being computed. CFL3D is a structured upwind-biased cell-centered RANS code,1 and FUN3D is an unstructured upwind-biased node-centered RANS code