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NASA NTRS2001

Orientation processing mechanisms revealed by the plaid tilt illusion

The tilt after-effect (TAE) and tilt illusion (TI) have revealed a great deal about the nature of orientation coding of 1-dimensional (1D) lines and gratings. Comparatively little research however has addressed the mechanisms responsible for encoding the orientation of 2-dimensional (2D) plaid stimuli. A multi-stage model of edge detection has recently been proposed [Georgeson, M. A. (1998) Image & Vision Computing, 16(6-7), 389-405] to account for the perceived structure of a plaid stimulus that incorporates extraction of the zero-crossings (ZCs) of the plaid. Data is presented showing that the ZCs of a plaid inducing stimulus can interact with vertical grating test stimulus to induce a standard tilt illusion. However, by considering the second-order structure of a plaid rather than ZCs, it was shown that the perceived orientation of the vertical test grating results from the combination of orientation illusions due to the first- and second-order components of an inducing plaid. The data suggest that the mechanisms encoding the orientation of second-order contours are similar to, and interact directly with, those that encode first-order contours.

Orientation/physiology↗

NASA NTRS1996

Characterization of the early pulmonary inflammatory response associated with PTFE fume exposure

Heating of polytetrafluoroethylene (PTFE) has been described to release fumes containing ultrafine particles (approximately 18 nm diam). These fumes can be highly toxic in the respiratory tract inducing extensive pulmonary edema with hemorrhagic inflammation. Fischer-344 rats were exposed to PTFE fumes generated by temperatures ranging from 450 to 460 degrees C for 15 min at an exposure concentration of 5 x 10(5) particles/cm3, equivalent to approximately 50 micrograms/m3. Responses were examined 4 hr post-treatment when these rats demonstrated 60-85% neutrophils (PMNs) in their lung lavage. Increases in abundance for messages encoding the antioxidants manganese superoxide dismutase and metallothionein (MT) increased 15- and 40-fold, respectively. For messages encoding the pro- and anti-inflammatory cytokines: inducible nitric oxide synthase, interleukin 1 alpha, 1 beta, and 6 (IL-1 alpha, IL-1 beta, and IL-6), macrophage inflammatory protein-2, and tumor necrosis factor-alpha (TNF alpha) increases of 5-, 5-, 10-, 40-, 40-, and 15-fold were present. Vascular endothelial growth factor, which may play a role in the integrity of the endothelial barrier, was decreased to 20% of controls. In situ sections were hybridized with 33P cRNA probes encoding IL-6, MT, surfactant protein C, and TNF alpha. Increased mRNA abundance for MT and IL-6 was expressed around all airways and interstitial regions with MT and IL-6 demonstrating similar spatial distribution. Large numbers of activated PMNs expressed IL-6, MT, and TNF alpha. Additionally, pulmonary macrophages and epithelial cells were actively involved. These observations support the notion that PTFE fumes containing ultrafine particles initiate a severe inflammatory response at low inhaled particle mass concentrations, which is suggestive of an oxidative injury. Furthermore, PMNs may actively regulate the inflammatory process through cytokine and antioxidant expression.

Non-NASA Center↗

NASA NTRS1994

Continuous in vitro evolution of bacteriophage RNA polymerase promoters

Rapid in vitro evolution of bacteriophage T7, T3, and SP6 RNA polymerase promoters was achieved by a method that allows continuous enrichment of DNAs that contain functional promoter elements. This method exploits the ability of a special class of nucleic acid molecules to replicate continuously in the presence of both a reverse transcriptase and a DNA-dependent RNA polymerase. Replication involves the synthesis of both RNA and cDNA intermediates. The cDNA strand contains an embedded promoter sequence, which becomes converted to a functional double-stranded promoter element, leading to the production of RNA transcripts. Synthetic cDNAs, including those that contain randomized promoter sequences, can be used to initiate the amplification cycle. However, only those cDNAs that contain functional promoter sequences are able to produce RNA transcripts. Furthermore, each RNA transcript encodes the RNA polymerase promoter sequence that was responsible for initiation of its own transcription. Thus, the population of amplifying molecules quickly becomes enriched for those templates that encode functional promoters. Optimal promoter sequences for phage T7, T3, and SP6 RNA polymerase were identified after a 2-h amplification reaction, initiated in each case with a pool of synthetic cDNAs encoding greater than 10(10) promoter sequence variants.

Non-NASA Center↗

NASA NTRS2005

An XML Representation for Crew Procedures

NASA ensures safe operation of complex systems through the use of formally-documented procedures, which encode the operational knowledge of the system as derived from system experts. Crew members use procedure documentation on the ground for training purposes and on-board space shuttle and space station to guide their activities. Investigators at JSC are developing a new representation for procedures that is content-based (as opposed to display-based). Instead of specifying how a procedure should look on the printed page, the content-based representation will identify the components of a procedure and (more importantly) how the components are related (e.g., how the activities within a procedure are sequenced; what resources need to be available for each activity). This approach will allow different sets of rules to be created for displaying procedures on a computer screen, on a hand-held personal digital assistant (PDA), verbally, or on a printed page, and will also allow intelligent reasoning processes to automatically interpret and use procedure definitions. During his NASA fellowship, Dr. Simpson examined how various industries represent procedures (also called business processes or workflows), in areas such as manufacturing, accounting, shipping, or customer service. A useful method for designing and evaluating workflow representation languages is by determining their ability to encode various workflow patterns, which depict abstract relationships between the components of a procedure removed from the context of a specific procedure or industry. Investigators have used this type of analysis to evaluate how well-suited existing workflow representation languages are for various industries based on the workflow patterns that commonly arise across industry-specific procedures. Based on this type of analysis, it is already clear that existing workflow representations capture discrete flow of control (i.e., when one activity should start and stop based on when other activities start and stop), but do not capture the flow of data, materials, resources or priorities. Existing workflow representation languages are also limited to representing sequences of discrete activities, and cannot encode procedures involving continuous flow of information or materials between activities.

Simpson, Richard C.↗

NASA NTRS2005

Multiple Layer Identification Label Using Stacked Identification Symbols

An automatic identification system and method are provided which employ a machine readable multiple layer label. The label has a plurality of machine readable marking layers stacked one upon another. Each of the marking layers encodes an identification symbol detectable using one or more sensing technologies. The various marking layers may comprise the same marking material or each marking layer may comprise a different medium having characteristics detectable by a different sensing technology. These sensing technologies include x-ray, radar, capacitance, thermal, magnetic and ultrasonic. A complete symbol may be encoded within each marking layer or a symbol may be segmented into fragments which are then divided within a single marking layer or encoded across multiple marking layers.

Harry F Schramm↗

NASA NTRS2006

Generative Representations for Computer-Automated Evolutionary Design

With the increasing computational power of computers, software design systems are progressing from being tools for architects and designers to express their ideas to tools capable of creating designs under human guidance. One of the main limitations for these computer-automated design systems is the representation with which they encode designs. If the representation cannot encode a certain design, then the design system cannot produce it. To be able to produce new types of designs, and not just optimize pre-defined parameterizations, evolutionary design systems must use generative representations. Generative representations are assembly procedures, or algorithms, for constructing a design thereby allowing for truly novel design solutions to be encoded. In addition, by enabling modularity, regularity and hierarchy, the level of sophistication that can be evolved is increased. We demonstrate the advantages of generative representations on two different design domains: the evolution of spacecraft antennas and the evolution of 3D objects.

Hornby, Gregory S.↗

NASA NTRS2005

Multiple layer identification label using stacked identification symbols

Schramm, Harry F.↗

NASA NTRS2004

Transgenic plants with increased calcium stores

The present invention provides transgenic plants over-expressing a transgene encoding a calcium-binding protein or peptide (CaBP). Preferably, the CaBP is a calcium storage protein and over-expression thereof does not have undue adverse effects on calcium homeostasis or biochemical pathways that are regulated by calcium. In preferred embodiments, the CaBP is calreticulin (CRT) or calsequestrin. In more preferred embodiments, the CaBP is the C-domain of CRT, a fragment of the C-domain, or multimers of the foregoing. In other preferred embodiments, the CaBP is localized to the endoplasmic reticulum by operatively associating the transgene encoding the CaBP with an endoplasmic reticulum localization peptide. Alternatively, the CaBP is targeted to any other sub-cellular compartment that permits the calcium to be stored in a form that is biologically available to the plant. Also provided are methods of producing plants with desirable phenotypic traits by transformation of the plant with a transgene encoding a CaBP. Such phenotypic traits include increased calcium storage, enhanced resistance to calcium-limiting conditions, enhanced growth and viability, increased disease and stress resistance, enhanced flower and fruit production, reduced senescence, and a decreased need for fertilizer production. Further provided are plants with enhanced nutritional value as human food or animal feed.

Wyatt, Sarah↗

NASA NTRS1984

Optical analog-to-digital converter

A method and apparatus for converting the intensity of an unknown optical signal (B) into an electrical signal in digital form utilizes two elongated optical attenuators (11, 13), one for the unknown optical signal from a source (10) and one for a known optical signal (A) from a variable source (12), a plurality of photodetectors (e.g., 17, 18) along each attenuator for detecting the intensity of the optical signals, and a plurality of comparators (e.g., 21) connected to the photodetectors in pairs to determine at what points being compared the attenuated known signal equals the attenuated unknown signal. The intensity of the unknown relative to the known is thus determined by the output of a particular comparator. That output is automatically encoded to a relative intensity value in digital form through a balancing feedback control (24) and encoder (23). The digital value may be converted to analog form in a D-to-A converter (27) and used to vary the source of the known signal so that the attenuated intensity of the known signal at a predetermined point (comparator 16) equals the attenuated intensity of the unknown signal at the predetermined point of comparison. If the known signal is then equal to the unknown, there is verification of the analog-to-digital conversion being complete. Otherwise the output of the comparator indicating equality at some other point along the attenuators will provide an output which is encoded and added, through an accumulator comprised of a register (25) and an adder (26), to a previous relative intensity value thereby to further vary the intensity of the known signal source. The steps are repeated until full conversion is verified.

Evanchuk, Vincent L.↗

NASA NTRS2009

Improving 3D Wavelet-Based Compression of Hyperspectral Images

Two methods of increasing the effectiveness of three-dimensional (3D) wavelet-based compression of hyperspectral images have been developed. (As used here, images signifies both images and digital data representing images.) The methods are oriented toward reducing or eliminating detrimental effects of a phenomenon, referred to as spectral ringing, that is described below. In 3D wavelet-based compression, an image is represented by a multiresolution wavelet decomposition consisting of several subbands obtained by applying wavelet transforms in the two spatial dimensions corresponding to the two spatial coordinate axes of the image plane, and by applying wavelet transforms in the spectral dimension. Spectral ringing is named after the more familiar spatial ringing (spurious spatial oscillations) that can be seen parallel to and near edges in ordinary images reconstructed from compressed data. These ringing phenomena are attributable to effects of quantization. In hyperspectral data, the individual spectral bands play the role of edges, causing spurious oscillations to occur in the spectral dimension. In the absence of such corrective measures as the present two methods, spectral ringing can manifest itself as systematic biases in some reconstructed spectral bands and can reduce the effectiveness of compression of spatially-low-pass subbands. One of the two methods is denoted mean subtraction. The basic idea of this method is to subtract mean values from spatial planes of spatially low-pass subbands prior to encoding, because (a) such spatial planes often have mean values that are far from zero and (b) zero-mean data are better suited for compression by methods that are effective for subbands of two-dimensional (2D) images. In this method, after the 3D wavelet decomposition is performed, mean values are computed for and subtracted from each spatial plane of each spatially-low-pass subband. The resulting data are converted to sign-magnitude form and compressed in a manner similar to that of a baseline hyperspectral- image-compression method. The mean values are encoded in the compressed bit stream and added back to the data at the appropriate decompression step. The overhead incurred by encoding the mean values only a few bits per spectral band is negligible with respect to the huge size of a typical hyperspectral data set. The other method is denoted modified decomposition. This method is so named because it involves a modified version of a commonly used multiresolution wavelet decomposition, known in the art as the 3D Mallat decomposition, in which (a) the first of multiple stages of a 3D wavelet transform is applied to the entire dataset and (b) subsequent stages are applied only to the horizontally-, vertically-, and spectrally-low-pass subband from the preceding stage. In the modified decomposition, in stages after the first, not only is the spatially-low-pass, spectrally-low-pass subband further decomposed, but also spatially-low-pass, spectrally-high-pass subbands are further decomposed spatially. Either method can be used alone to improve the quality of a reconstructed image (see figure). Alternatively, the two methods can be combined by first performing modified decomposition, then subtracting the mean values from spatial planes of spatially-low-pass subbands.

Klimesh, Matthew↗

NASA NTRS2009

Hardware Implementation of Serially Concatenated PPM Decoder

A prototype decoder for a serially concatenated pulse position modulation (SCPPM) code has been implemented in a field-programmable gate array (FPGA). At the time of this reporting, this is the first known hardware SCPPM decoder. The SCPPM coding scheme, conceived for free-space optical communications with both deep-space and terrestrial applications in mind, is an improvement of several dB over the conventional Reed-Solomon PPM scheme. The design of the FPGA SCPPM decoder is based on a turbo decoding algorithm that requires relatively low computational complexity while delivering error-rate performance within approximately 1 dB of channel capacity. The SCPPM encoder consists of an outer convolutional encoder, an interleaver, an accumulator, and an inner modulation encoder (more precisely, a mapping of bits to PPM symbols). Each code is describable by a trellis (a finite directed graph). The SCPPM decoder consists of an inner soft-in-soft-out (SISO) module, a de-interleaver, an outer SISO module, and an interleaver connected in a loop (see figure). Each SISO module applies the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm to compute a-posteriori bit log-likelihood ratios (LLRs) from apriori LLRs by traversing the code trellis in forward and backward directions. The SISO modules iteratively refine the LLRs by passing the estimates between one another much like the working of a turbine engine. Extrinsic information (the difference between the a-posteriori and a-priori LLRs) is exchanged rather than the a-posteriori LLRs to minimize undesired feedback. All computations are performed in the logarithmic domain, wherein multiplications are translated into additions, thereby reducing complexity and sensitivity to fixed-point implementation roundoff errors. To lower the required memory for storing channel likelihood data and the amounts of data transfer between the decoder and the receiver, one can discard the majority of channel likelihoods, using only the remainder in operation of the decoder. This is accomplished in the receiver by transmitting only a subset consisting of the likelihoods that correspond to time slots containing the largest numbers of observed photons during each PPM symbol period. The assumed number of observed photons in the remaining time slots is set to the mean of a noise slot. In low background noise, the selection of a small subset in this manner results in only negligible loss. Other features of the decoder design to reduce complexity and increase speed include (1) quantization of metrics in an efficient procedure chosen to incur no more than a small performance loss and (2) the use of the max-star function that allows sum of exponentials to be computed by simple operations that involve only an addition, a subtraction, and a table lookup. Another prominent feature of the design is a provision for access to interleaver and de-interleaver memory in a single clock cycle, eliminating the multiple clock-cycle latency characteristic of prior interleaver and de-interleaver designs.

Moision, Bruce↗

NASA NTRS2008

Spiking Neurons for Analysis of Patterns

Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological neurons). These features enable the neurons to adapt their responses to high-rate inputs from sensors, and to adapt their firing thresholds to mitigate noise or effects of potential sensor failure. The mathematical derivation of the SVM starts from a prior model, known in the art as the point soma model, which captures all of the salient properties of neuronal response while keeping the computational cost low. The point-soma latency time is modified to be an exponentially decaying function of the strength of the applied potential. Choosing computational efficiency over biological fidelity, the dendrites surrounding a neuron are represented by simplified compartmental submodels and there are no dendritic spines. Updates to the dendritic potential, calcium-ion concentrations and conductances, and potassium-ion conductances are done by use of equations similar to those of the point soma. Diffusion processes in dendrites are modeled by averaging among nearest-neighbor compartments. Inputs to each of the dendritic compartments come from sensors. Alternatively or in addition, when an affected neuron is part of a pool, inputs can come from other spiking neurons. At present, SVM neural networks are implemented by computational simulation, using algorithms that encode the SVM and its submodels. However, it should be possible to implement these neural networks in hardware: The differential equations for the dendritic and cellular processes in the SVM model of spiking neurons map to equivalent circuits that can be implemented directly in analog very-large-scale integrated (VLSI) circuits.

Huntsberger, Terrance↗

NASA NTRS2009

Linear Actuator Has Long Stroke and High Resolution

The term precision linear actuator, direct drive ( PLADD ) refers to a robust linear actuator designed to be capable of repeatedly performing, over a lifetime of the order of 5 to 10 years, positioning maneuvers that include, variously, submicron increments or slews of the order of a centimeter. The PLADD is capable of both long stroke (120 mm) and high resolution (repeatable increments of 20 nm). Unlike precise linear actuators of prior design, the PLADD contains no gears, levers, or hydraulic converters. The PLADD, now at the prototype stage of development, is intended for original use as a coarse-positioning actuator in a spaceborne interferometer. The PLADD could also be adapted to terrestrial applications in which there are requirements for long stroke and high resolution: potential applications include medical imaging and fabrication of semiconductor devices. The PLADD (see figure) includes a commercially available ball-screw actuator driven directly by a commercially available three-phase brushless DC motor. The ball-screw actuator comprises a spring-preloaded ball nut on a ball screw that is restrained against rotation as described below. The motor is coupled directly (that is, without an intervening gear train) to a drive link that, in turn, is coupled to the ball nut. By eliminating the gear train, the direct-drive design eliminates the complexity, backlash, and potential for misalignment associated with a gear train. To prevent inadvertent movement, there is a brake that includes flexured levers compressed against the drive link by preload springs. This is a power-off brake: There are also piezoelectric stacks that can be activated to oppose the springs and push the levers away from the drive link. Hence, power must be applied to the piezoelectric stacks to release the drive link from braking. To help ensure long operational life, all of the mechanical drive components are immersed in an oil bath within hermetically sealed bellows. The outer end of the bellows holds the outer end of the ball screw, thereby preventing rotation of the ball screw. Positioning is controlled by an electronic control system that includes digital and analog subsystems that interact with the motor and brake and with two sensor/encoder units: a Hall-effect-sensor rotation encoder and a linear glass-scale encoder. This system implements a proportional + integral + derivative control algorithm that results in variation of voltage commands to each of the three pairs of windings of the brushless DC motor. In one of two alternative control modes, the voltages are applied to the windings in a trapezoidal commutation scheme on the basis of timing signals obtained from the Hall-effect sensors; this scheme yields relatively coarse positioning - 24 steps per motor revolution. The second control mode involves a sinusoidal commutation scheme in which the output of the linear glass-scale encoder is transposed to rotational increments to yield much finer position feedback - more than 400,000 steps per revolution.

Cook, Brant T.↗

NASA NTRS2009

Number Codes Readable by Magnetic-Field-Response Recorders

A method of encoding and reading numbers incorporates some of the features of conventional optical bar coding and radio-frequency identification (RFID) tagging, but overcomes some of the disadvantages of both: (1) Unlike in conventional optical bar coding, numbers can be read without having a line of sight to a tag; and (2) the tag circuitry is simpler than the circuitry used in conventional RFID. The method is based largely on the principles described in Magnetic-Field-Response Measurement-Acquisition System (LAR-16908), NASA Tech Briefs, Vol. 30, No. 6 (June 2006) page 28. To recapitulate: A noncontact system includes a monitoring unit that acquires measurements from sensors at distances of the order of several meters. Each sensor is a passive radio-frequency (RF) resonant circuit in the form of one or more inductor(s) and capacitor(s). The monitoring unit a handheld unit denoted a magnetic field response recorder (MFRR) generates an RF magnetic field that excites oscillations in the resonant circuits resulting in the sensors responding with their own radiated magnetic field. The resonance frequency of each sensor is made to differ significantly from that of the other sensors to facilitate distinction among the responses of different sensors. The MFRR measures selected aspects of the sensor responses: in a typical application, the sensors are designed so that their resonance frequencies vary somewhat with the sensed physical quantities and, accordingly, the MFRR measures the resonance frequencies and variations thereof as indications of those quantities. In the present method, the resonance circuits are not used as sensors. Instead, the circuits are made to resonate at fixed frequencies that correspond to digits to be encoded. The number-encoding scheme is best explained by means of examples in which each resonant circuit consists of a spiral trace inductor electrically connected to a set of parallel-connected capacitors in the form of interdigitated electrode pairs (see figure). The inductor and capacitor(s) in each resonant circuit can be fabricated as a patterned thin metal film by means of established metal-deposition and -patterning techniques. The capacitance and, hence, the resonance frequency, depends on the number of interdigitated electrodes connected to the inductor. In a similar manner, sets of electrodes could be used.

Woodard, Stanley E.↗

NASA NTRS2007

Generative Representations for Automated Design of Robots

A method of automated design of complex, modular robots involves an evolutionary process in which generative representations of designs are used. The term generative representations as used here signifies, loosely, representations that consist of or include algorithms, computer programs, and the like, wherein encoded designs can reuse elements of their encoding and thereby evolve toward greater complexity. Automated design of robots through synthetic evolutionary processes has already been demonstrated, but it is not clear whether genetically inspired search algorithms can yield designs that are sufficiently complex for practical engineering. The ultimate success of such algorithms as tools for automation of design depends on the scaling properties of representations of designs. A nongenerative representation (one in which each element of the encoded design is used at most once in translating to the design) scales linearly with the number of elements. Search algorithms that use nongenerative representations quickly become intractable (search times vary approximately exponentially with numbers of design elements), and thus are not amenable to scaling to complex designs. Generative representations are compact representations and were devised as means to circumvent the above-mentioned fundamental restriction on scalability. In the present method, a robot is defined by a compact programmatic form (its generative representation) and the evolutionary variation takes place on this form. The evolutionary process is an iterative one, wherein each cycle consists of the following steps: 1. Generative representations are generated in an evolutionary subprocess. 2. Each generative representation is a program that, when compiled, produces an assembly procedure. 3. In a computational simulation, a constructor executes an assembly procedure to generate a robot. 4. A physical-simulation program tests the performance of a simulated constructed robot, evaluating the performance according to a fitness criterion to yield a figure of merit that is fed back into the evolutionary subprocess of the next iteration. In comparison with prior approaches to automated evolutionary design of robots, the use of generative representations offers two advantages: First, a generative representation enables the reuse of components in regular and hierarchical ways and thereby serves a systematic means of creating more complex modules out of simpler ones. Second, the evolved generative representation may capture intrinsic properties of the design problem, so that variations in the representations move through the design space more effectively than do equivalent variations in a nongenerative representation. This method has been demonstrated by using it to design some robots that move, variously, by walking, rolling, or sliding. Some of the robots were built (see figure). Although these robots are very simple, in comparison with robots designed by humans, their structures are more regular, modular, hierarchical, and complex than are those of evolved designs of comparable functionality synthesized by use of nongenerative representations.

Homby, Gregory S.↗

NASA NTRS2010

Performance Bounds on Two Concatenated, Interleaved Codes

A method has been developed of computing bounds on the performance of a code comprised of two linear binary codes generated by two encoders serially concatenated through an interleaver. Originally intended for use in evaluating the performances of some codes proposed for deep-space communication links, the method can also be used in evaluating the performances of short-block-length codes in other applications. The method applies, more specifically, to a communication system in which following processes take place: At the transmitter, the original binary information that one seeks to transmit is first processed by an encoder into an outer code (Co) characterized by, among other things, a pair of numbers (n,k), where n (n > k)is the total number of code bits associated with k information bits and n k bits are used for correcting or at least detecting errors. Next, the outer code is processed through either a block or a convolutional interleaver. In the block interleaver, the words of the outer code are processed in blocks of I words. In the convolutional interleaver, the interleaving operation is performed bit-wise in N rows with delays that are multiples of B bits. The output of the interleaver is processed through a second encoder to obtain an inner code (Ci) characterized by (ni,ki). The output of the inner code is transmitted over an additive-white-Gaussian- noise channel characterized by a symbol signal-to-noise ratio (SNR) Es/No and a bit SNR Eb/No. At the receiver, an inner decoder generates estimates of bits. Depending on whether a block or a convolutional interleaver is used at the transmitter, the sequence of estimated bits is processed through a block or a convolutional de-interleaver, respectively, to obtain estimates of code words. Then the estimates of the code words are processed through an outer decoder, which generates estimates of the original information along with flags indicating which estimates are presumed to be correct and which are found to be erroneous. From the perspective of the present method, the topic of major interest is the performance of the communication system as quantified in the word-error rate and the undetected-error rate as functions of the SNRs and the total latency of the interleaver and inner code. The method is embodied in equations that describe bounds on these functions. Throughout the derivation of the equations that embody the method, it is assumed that the decoder for the outer code corrects any error pattern of t or fewer errors, detects any error pattern of s or fewer errors, may detect some error patterns of more than s errors, and does not correct any patterns of more than t errors. Because a mathematically complete description of the equations that embody the method and of the derivation of the equations would greatly exceed the space available for this article, it must suffice to summarize by reporting that the derivation includes consideration of several complex issues, including relationships between latency and memory requirements for block and convolutional codes, burst error statistics, enumeration of error-event intersections, and effects of different interleaving depths. In a demonstration, the method was used to calculate bounds on the performances of several communication systems, each based on serial concatenation of a (63,56) expurgated Hamming code with a convolutional inner code through a convolutional interleaver. The bounds calculated by use of the method were compared with results of numerical simulations of performances of the systems to show the regions where the bounds are tight (see figure).

Moision, Bruce↗

NASA NTRS2010

AutoGen Version 5.0

Version 5.0 of the AutoGen software has been released. Previous versions, variously denoted Autogen and autogen, were reported in two articles: Automated Sequence Generation Process and Software (NPO-30746), Software Tech Briefs (Special Supplement to NASA Tech Briefs), September 2007, page 30, and Autogen Version 2.0 (NPO- 41501), NASA Tech Briefs, Vol. 31, No. 10 (October 2007), page 58. To recapitulate: AutoGen (now signifying automatic sequence generation ) automates the generation of sequences of commands in a standard format for uplink to spacecraft. AutoGen requires fewer workers than are needed for older manual sequence-generation processes, and greatly reduces sequence-generation times. The sequences are embodied in spacecraft activity sequence files (SASFs). AutoGen automates generation of SASFs by use of another previously reported program called APGEN. AutoGen encodes knowledge of different mission phases and of how the resultant commands must differ among the phases. AutoGen also provides means for customizing sequences through use of configuration files. The approach followed in developing AutoGen has involved encoding the behaviors of a system into a model and encoding algorithms for context-sensitive customizations of the modeled behaviors. This version of AutoGen addressed the MRO (Mars Reconnaissance Orbiter) primary science phase (PSP) mission phase. On previous Mars missions this phase has more commonly been referred to as mapping phase. This version addressed the unique aspects of sequencing orbital operations and specifically the mission specific adaptation of orbital operations for MRO. This version also includes capabilities for MRO s role in Mars relay support for UHF relay communications with the MER rovers and the Phoenix lander.

Gladden, Roy E.↗

NASA NTRS2011

Single-Frame Terrain Mapping Software for Robotic Vehicles

This software is a component in an unmanned ground vehicle (UGV) perception system that builds compact, single-frame terrain maps for distribution to other systems, such as a world model or an operator control unit, over a local area network (LAN). Each cell in the map encodes an elevation value, terrain classification, object classification, terrain traversability, terrain roughness, and a confidence value into four bytes of memory. The input to this software component is a range image (from a lidar or stereo vision system), and optionally a terrain classification image and an object classification image, both registered to the range image. The single-frame terrain map generates estimates of the support surface elevation, ground cover elevation, and minimum canopy elevation; generates terrain traversability cost; detects low overhangs and high-density obstacles; and can perform geometry-based terrain classification (ground, ground cover, unknown). A new origin is automatically selected for each single-frame terrain map in global coordinates such that it coincides with the corner of a world map cell. That way, single-frame terrain maps correctly line up with the world map, facilitating the merging of map data into the world map. Instead of using 32 bits to store the floating-point elevation for a map cell, the vehicle elevation is assigned to the map origin elevation and reports the change in elevation (from the origin elevation) in terms of the number of discrete steps. The single-frame terrain map elevation resolution is 2 cm. At that resolution, terrain elevation from 20.5 to 20.5 m (with respect to the vehicle's elevation) is encoded into 11 bits. For each four-byte map cell, bits are assigned to encode elevation, terrain roughness, terrain classification, object classification, terrain traversability cost, and a confidence value. The vehicle s current position and orientation, the map origin, and the map cell resolution are all included in a header for each map. The map is compressed into a vector prior to delivery to another system.

Rankin, Arturo L.↗