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This introductory article surveys new techniques, supported by automated tools, for the analysis of software to ensure reliability and safety. Special focus is on model checking techniques. The article also introduces the five papers that are enclosed in this special journal volume.
This presentation briefly discusses a research effort on mitigation techniques of pulsed radio frequency interference (RFI) on a Low-Density-Parity-Check (LDPC) code. This problem is of considerable interest in the context of providing reliable communications to the space vehicle which might suffer severe degradation due to pulsed RFI sources such as large radars. The LDPC code is one of modern forward-error-correction (FEC) codes which have the decoding performance to approach the Shannon Limit. The LDPC code studied here is the AR4JA (2048, 1024) code recommended by the Consultative Committee for Space Data Systems (CCSDS) and it has been chosen for some spacecraft design. Even though this code is designed as a powerful FEC code in the additive white Gaussian noise channel, simulation data and test results show that the performance of this LDPC decoder is severely degraded when exposed to the pulsed RFI specified in the spacecraft s transponder specifications. An analysis work (through modeling and simulation) has been conducted to evaluate the impact of the pulsed RFI and a few implemental techniques have been investigated to mitigate the pulsed RFI impact by reshuffling the soft-decision-data available at the input of the LDPC decoder. The simulation results show that the LDPC decoding performance of codeword error rate (CWER) under pulsed RFI can be improved up to four orders of magnitude through a simple soft-decision-data reshuffle scheme. This study reveals that an error floor of LDPC decoding performance appears around CWER=1E-4 when the proposed technique is applied to mitigate the pulsed RFI impact. The mechanism causing this error floor remains unknown, further investigation is necessary.
Formal behavioral specifications written early in the system-design process and communicated across all design phases have been shown to increase the efficiency, consistency, and quality of the system under development. To prevent introducing design or verification errors, it is crucial to test specifications for satisfiability. Our focus here is on specifications expressed in linear temporal logic (LTL). We introduce a novel encoding of symbolic transition-based Buchi automata and a novel, "sloppy," transition encoding, both of which result in improved scalability. We also define novel BDD variable orders based on tree decomposition of formula parse trees. We describe and extensively test a new multi-encoding approach utilizing these novel encoding techniques to create 30 encoding variations. We show that our novel encodings translate to significant, sometimes exponential, improvement over the current standard encoding for symbolic LTL satisfiability checking.
HiVy is a software tool set that enables verification through model checking of designs represented as finite-state machines or statecharts. HiVy provides automated translation of (1) statecharts created by use of the MathWorks Stateflow program to (2) Promela, the input language of the Spin model checker, which can then be used to verify, or trace logical errors in, distributed software systems. HiVy can operate directly on Stateflow models, or its abstract syntax of hierarchical sequential automata (HSA) can be used independently as an intermediate format for translation to Promela. In a typical design application, HiVy parses and reformats Stateflow model file data using the programs SfParse and sf2hsa, respectively. If the parsing effort is successful, an abstract syntax tree is delivered into a file named with the extension .hsa. If the design comprises several model files, they may be merged into one .hsa file before translation into Promela. Stateflow scope is preserved, and name clashes are avoided in the merge process. The HiVy program hsa2pr translates the model from the intermediate HSA format into Promela. Additionally, HiVy provides through translation a list of all statechart model propositions that are the means for formalizing linear temporal logic (LTL) properties about the model for Spin verification.
The Low Density Parity Check (LDPC) Code decoding algorithm make use of a scaled receive signal derived from maximizing the log-likelihood ratio of the received signal. The scaling factor (often called the combining ratio) in an AWGN channel is a ratio between signal amplitude and noise variance. Accurately estimating this ratio has shown as much as 0.6 dB decoding performance gain. This presentation briefly describes three methods for estimating the combining ratio: a Pilot-Guided estimation method, a Blind estimation method, and a Simulation-Based Look-Up table. The Pilot Guided Estimation method has shown that the maximum likelihood estimates of signal amplitude is the mean inner product of the received sequence and the known sequence, the attached synchronization marker (ASM) , and signal variance is the difference of the mean of the squared received sequence and the square of the signal amplitude. This method has the advantage of simplicity at the expense of latency since several frames worth of ASMs. The Blind estimation method s maximum likelihood estimator is the average of the product of the received signal with the hyperbolic tangent of the product combining ratio and the received signal. The root of this equation can be determined by an iterative binary search between 0 and 1 after normalizing the received sequence. This method has the benefit of requiring one frame of data to estimate the combining ratio which is good for faster changing channels compared to the previous method, however it is computationally expensive. The final method uses a look-up table based on prior simulated results to determine signal amplitude and noise variance. In this method the received mean signal strength is controlled to a constant soft decision value. The magnitude of the deviation is averaged over a predetermined number of samples. This value is referenced in a look up table to determine the combining ratio that prior simulation associated with the average magnitude of the deviation. This method is more complicated than the Pilot-Guided Method due to the gain control circuitry, but does not have the real-time computation complexity of the Blind Estimation method. Each of these methods can be used to provide an accurate estimation of the combining ratio, and the final selection of the estimation method depends on other design constraints.
This report presents the mechanical verification of a self-stabilizing distributed clock synchronization protocol for arbitrary digraphs in the absence of faults. This protocol does not rely on assumptions about the initial state of the system, other than the presence of at least one node, and no central clock or a centrally generated signal, pulse, or message is used. The system under study is an arbitrary, non-partitioned digraph ranging from fully connected to 1-connected networks of nodes while allowing for differences in the network elements. Nodes are anonymous, i.e., they do not have unique identities. There is no theoretical limit on the maximum number of participating nodes. The only constraint on the behavior of the node is that the interactions with other nodes are restricted to defined links and interfaces. This protocol deterministically converges within a time bound that is a linear function of the self-stabilization period. A bounded model of the protocol is verified using the Symbolic Model Verifier (SMV) for a subset of digraphs. Modeling challenges of the protocol and the system are addressed. The model checking effort is focused on verifying correctness of the bounded model of the protocol as well as confirmation of claims of determinism and linear convergence with respect to the self-stabilization period.
A method for statically checking an object-oriented computer program module includes the step of identifying objects within a computer program module, at least one of the objects having a plurality of references thereto, possibly from multiple clients. A discipline of permissions is imposed on the objects identified within the computer program module. The permissions enable tracking, from among a discrete set of changeable states, a subset of states each object might be in. A determination is made regarding whether the imposed permissions are violated by a potential reference to any of the identified objects. The results of the determination are output to a user.
When NASA was created in 1958 one of the elements incorporated into this new agency was the Army Ballistic Missile Agency (ABMA) in Huntsville, AL and its subordinate Missile Firing Laboratory (MFL) in Cape Canaveral. Under NASA, the MFL became the Launch Operations Directorate of the George C. Marshall Space Flight Center in Huntsville, but expanding operations in the build up to Apollo dictated that it be given the status of a full fledged Center in July, 1 962[ 1]. The next year it was renamed the John F. Kennedy Space Center (KS C) after the president whose vision transformed its first decade of operation. The ABMA was under the technical leadership of Dr. Werner Von Braun. The MEL was run by his deputy Dr. Kurt Debus, an electrical engineer whose experience in the field began in the early days of V-2 testing in war time Germany. In 1952 a group led by Debus arrived in Cape Canaveral to begin test launches of the new Redstone missile [2]. During the 50's, The MFL built several launch complexes and tested the Redstone, Jupiter and Jupiter C missiles. This small experienced team of engineers and technicians formed the seed from which has grown the KSC team of today. This article briefly reviews the evolution of the KSC electronic technologies for integration, check-out and launch of space vehicles and payloads during NASA's first 50 years.
This presentation will discuss methods to which ModelCheck can be implemented to not only improve model quality, but also satisfy both employees and management through different sets of quality checks. This approach allows a standard set of modeling practices to be upheld throughout a company, with minimal interaction required by the end user. The presenter will demonstrate how to create multiple ModelCheck standards, preventing users from evading the system, and how it can improve the quality of drawings and models.
The rise of innovative unmanned aeronautical systems and the emergence of commercial space activities have resulted in a number of relatively new aerospace organizations that are designing innovative systems and solutions. These organizations use a variety of commercial off-the-shelf and in-house-developed simulation and analysis tools including 6-degree-of-freedom (6-DOF) flight simulation tools. The increased affordability of computing capability has made highfidelity flight simulation practical for all participants. Verification of the tools' equations-of-motion and environment models (e.g., atmosphere, gravitation, and geodesy) is desirable to assure accuracy of results. However, aside from simple textbook examples, minimal verification data exists in open literature for 6-DOF flight simulation problems. This assessment compared multiple solution trajectories to a set of verification check-cases that covered atmospheric and exo-atmospheric (i.e., orbital) flight. Each scenario consisted of predefined flight vehicles, initial conditions, and maneuvers. These scenarios were implemented and executed in a variety of analytical and real-time simulation tools. This tool-set included simulation tools in a variety of programming languages based on modified flat-Earth, round- Earth, and rotating oblate spheroidal Earth geodesy and gravitation models, and independently derived equations-of-motion and propagation techniques. The resulting simulated parameter trajectories were compared by over-plotting and difference-plotting to yield a family of solutions. In total, seven simulation tools were exercised.
Part of the US Department of Transportation investigation of Toyota sudden unintended acceleration (SUA) involved analysis of the throttle control software, JPL Laboratory for Reliable Software applied several techniques including static analysis and logic model checking, to the software; A handful of logic models were build, Some weaknesses were identified; however, no cause for SUA was found; The full NASA report includes numerous other analyses
In this work, we study the performance of structured Low-Density Parity-Check (LDPC) Codes together with bandwidth efficient modulations. We consider protograph-based LDPC codes that facilitate high-speed hardware implementations and have minimum distances that grow linearly with block sizes. We cover various higher- order modulations such as 8-PSK, 16-APSK, and 16-QAM. During demodulation, a demapper transforms the received in-phase and quadrature samples into reliability information that feeds the binary LDPC decoder. We will compare various low-complexity demappers and provide simulation results for assorted coded-modulation combinations on the additive white Gaussian noise and independent Rayleigh fading channels.
This paper reports the simulated performance of each of the nine accumulate-repeat-4-jagged-accumulate (AR4JA) low-density parity-check (LDPC) codes [3] when used in conjunction with binary phase-shift-keying (BPSK), quadrature PSK (QPSK), 8-PSK, 16-ary amplitude PSK (16- APSK), and 32-APSK.We also report the performance under various mappings of bits to modulation symbols, 16-APSK and 32-APSK ring scalings, log-likelihood ratio (LLR) approximations, and decoder variations. One of the simple and well-performing LLR approximations can be expressed in a general equation that applies to all of the modulation types.
A dual check valve includes, a housing having a cavity fluidically connecting three ports, a movable member movably engaged within the cavity from at least a first position occluding a first port of the three ports, a second position occluding a second port of the three ports, and a third position allowing flow between both the first port, the second port and a third port of the three ports.
The new check standard model of the NASA Ames 11-ft Transonic Wind Tunnel was chosen for a future validation of the facility's wall interference correction system. The chosen validation approach takes advantage of the fact that test conditions experienced by a large model in the slotted part of the tunnel's test section will change significantly if a subset of the slots is temporarily sealed. Therefore, the model's aerodynamic coefficients have to be recorded, corrected, and compared for two different test section configurations in order to perform the validation. Test section configurations with highly accurate Mach number and dynamic pressure calibrations were selected for the validation. First, the model is tested with all test section slots in open configuration while keeping the model's center of rotation on the tunnel centerline. In the next step, slots on the test section floor are sealed and the model is moved to a new center of rotation that is 33 inches below the tunnel centerline. Then, the original angle of attack sweeps are repeated. Afterwards, wall interference corrections are applied to both test data sets and response surface models of the resulting aerodynamic coefficients in interference-free flow are generated. Finally, the response surface models are used to predict the aerodynamic coefficients for a family of angles of attack while keeping dynamic pressure, Mach number, and Reynolds number constant. The validation is considered successful if the corrected aerodynamic coefficients obtained from the related response surface model pair show good agreement. Residual differences between the corrected coefficient sets will be analyzed as well because they are an indicator of the overall accuracy of the facility's wall interference correction process.
Radiances from the Atmospheric Infrared Sounder (AIRS) show excellent stability and are traceable to SI standards through the On-Board Calibrator (OBC) blackbody. The OBC can be used to check the nonlinearity by turning off the heater and letting the target float from 308K to roughly 261K while acquiring data. The OBC Float test was performed once prior to launch, and again shortly after launch. Until now, the OBC Float test did not produce accurate nonlinearity results.
This paper is part of an e ort to assess the current capability of Computational Fluid Dynamics (CFD) to accurately predict the experimental forces and base pressure on the Check Standard Model (CSM). The wind tunnel data for this model is gathered during annual monitoring of test-to-test repeatability for the Langley Unitary Plan Wind Tunnel (LUPWT). A blind database designed to help quantify the uncertainty of simulating LUPWT for the CSM repeatability tests was run using the Launch, Ascent and Vehicle Aerodynamics (LAVA) CFD code. The Reynolds-averaged Navier-Stokes equations were used for the predictions presented in the paper. Prior to running the database, best practices were established both for simulating and characterizing the ow of the high-speed legof the LUPWT where the test took place, as well the CSM when inserted within its test section.
Belief propagation (BP) on LDPC codes is an iterative decoding algorithm that performs information transfer on the Tanner graph, which represents the code. In each iteration, the algorithm exchanges information (LLR) between variable nodes and check nodes through the edges of the graph. LLR values represent the probability that a given bit in a transmitted codeword equals 0 or 1, given a received word. In the hardware part, recent advancements in intelligent technologies, such as artificial intelligence, big data analytics, autonomous vehicles, and speech/image recognition, have heightened the demand for faster calculations and reduced energy consumption.