Advanced study of video signal processing in low signal to noise environments Quarterly progress report, Oct. 1969 - Jan. 1970
Video signal processing in low signal to noise environments
Carden, F.↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Lock detector for noise-coded signals
Circuit indicates when receiver is locked on pseudorandom-noise-coded signal. Circuit is used for reception of such digitally coded signals as scrambled voice messages or scrambled video. Circuit determines when receiver generated code is correct and synchronized with incoming signal so that receiver can track signal.
Carson, L. M.↗
Determination of in-flight AVIRIS spectral, radiometric, spatial and signal-to-noise characteristics using atmospheric and surface measurements from the vicinity of the rare-earth-bearing carbonatite at Mountain Pass, California
An assessment of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) performance was made for a flight over Mountain Pass, California, July 30, 1987. The flight data were reduced to reflectance using an empirical algorithm which compensates for solar, atmospheric and instrument factors. AVIRIS data in conjunction with surface and atmospheric measurements acquired concurrently were used to develop an improved spectral calibration. An accurate in-flight radiometric calibration was also performed using the LOWTRAN 7 radiative transfer code together with measured surface reflectance and atmospheric optical depths. A direct comparison with coincident Thematic Mapper imagery of Mountain Pass was used to demonstrate the high spatial resolution and good geometric performance of AVIRIS. The in-flight instrument noise was independently determined with two methods which showed good agreement. A signal-to-noise ratio was calculated using data from a uniform playa. This ratio was scaled to the AVIRIS reference radiance model, which provided a basis for comparison with laboratory and other in-flight signal-to-noise determinations.
Green, Robert O.↗
Degradation of signal-to-noise ratio due to amplitude distortion
The effect of filtering on the signal-to-noise ratio (SNR) of a coherently demodulated band-limited signal is determined in the presence of worst-case amplitude ripple. The problem is formulated as an optimizaton in the Hilbert space L2. The form of the worst-case amplitude ripple is specified, and the degradation in the SNR is derived in closed form. It is shown that, when the maximum passband amplitude ripple is 2Delta (peak-to-peak), the SNR is degraded by at most (1-Delta-squared), even when the ripple is unknown or uncompensated. For example, an SNR loss of less than 0.01 dB due to amplitude ripple can be assured by keeping the amplitude ripple under 0.42 dB.
Sadr, Ramin↗
An operational method for estimating signal to noise ratios from data acquired with imaging spectrometers
A method, using the concept of local means and local standard deviations of small imaging blocks and using a box counting procedure, has been developed for unsupervised estimation of the average signal to noise ratios of images in which the noise is additive. The method has been applied to simulated images with Gaussian noise, to images acquired with the Airborne Visible Infrared Imaging Spectrometer and with the Geophysical and Environmental Research Imaging Spectrometer. The method is compared with other techniques for estimating signal to noise ratios from imaging data. It is believed that the method is generally applicable to images having many small homogeneous blocks. Because the method does not take into account interband effects, it is not appropriate to use the method to estimate signal to noise ratios from images having nonnegligible interband radiometric calibration errors at spatial scales less than or equal to the size of the small imaging blocks used in the noise estimation.
Gao, Bo-Cai↗
Direct Signal-to-Noise Quality Comparison between an Electronic and Conventional Stethoscope aboard the International Space Station
Introduction: Evaluation of heart, lung, and bowel sounds is routinely performed with the use of a stethoscope to help detect a broad range of medical conditions. Stethoscope acquired information is even more valuable in a resource limited environments such as the International Space Station (ISS) where additional testing is not available. The high ambient noise level aboard the ISS poses a specific challenge to auscultation by stethoscope. An electronic stethoscope's ambient noise‐reduction, greater sound amplification, recording capabilities, and sound visualization software may be an advantage to a conventional stethoscope in this environment. Methods: A single operator rated signal‐to‐noise quality from a conventional stethoscope (Littman 2218BE) and an electronic stethoscope (Litmann 3200). Borborygmi, pulmonic, and cardiac sound quality was ranked with both stethoscopes. Signal‐to‐noise rankings were preformed on a 1 to 10 subjective scale with 1 being inaudible, 6 the expected quality in an emergency department, 8 the expected quality in a clinic, and 10 the clearest possible quality. Testing took place in the Japanese Pressurized Module (JPM), Unity (Node 2), Destiny (US Lab), Tranquility (Node 3), and the Cupola of the International Space Station. All examinations were conducted at a single point in time. Results: The electronic stethoscope's performance ranked higher than the conventional stethoscope for each body sound in all modules tested. The electronic stethoscope's sound quality was rated between 7 and 10 in all modules tested. In comparison, the traditional stethoscope's sound quality was rated between 4 and 7. The signal to noise ratio of borborygmi showed the biggest difference between stethoscopes. In the modules tested, the auscultation of borborygmi was rated between 5 and 7 by the conventional stethoscope and consistently 10 by the electronic stethoscope. Discussion: This stethoscope comparison was limited to a single operator. However, we believe the results are noteworthy. The electronic stethoscope out preformed the traditional stethoscope in each direct comparison. Consideration should be made to incorporate an electronic stethoscope into current and future space vehicle medical kits.
Marshburn, Thomas↗
Signal-to-noise enhancement in ground-based intensity observations of solar p modes
Intensity observations of solar p modes are needed to form a complete picture of wave propagation in the photosphere. Ground-based intensity observations are severely hampered by terrestrial atmospheric noise. Partial cancellation of the noise power can be achieved if two spectra having disparate signal-to- noise ratios, and based on time series acquired simultaneously at the same site, are combined. A method of combining the spectra is suggested in which one amplitude is scaled and subtracted from the other. The result is squared yielding a positive-definite power density. To test the method, the intensity of light scattered by the Earth's atmnosphere was recorded at fifteen- second intervals in two narrow bands centered on 0.5 microns and 1.6 microns. When the two resulting spectra were combined, the noise power was attenuated by a factor of 2.7. The scale factor was varied about its optimum value, revealing that noise peaks have a different siganture than signal peaks, and opening up the possibility of a new tool in discrimination against noise peaks. Maxima at symmetry-allowed frequencies and minima at symmetry- forbidden frequencies indicate that the possibility that these results are obtained by chance is only 6.1 x 10(exp -4). The positions of these maxima and minima also support the solar-cycle dependent frequency shifts found by Palle, Regulo, and Roca Cortes.
Germain, Marvin E.↗
Signal-to-noise enhancement using a superconducting time-variant filter
RF communication signal to noise improvement by time variant tracking filter with high Q superconductive resonant cavity
Arndt, G. D.↗
Heterodyne signal-to-noise ratios in acoustic mode scattering experiments
The relation between the signal to noise ratio (SNR) obtained in heterodyne detection of radiation scattered from acoustic modes in crystalline solids and the scattered spectral density function is studied. It is shown that in addition to the information provided by the measured frequency shifts and line widths, measurement of the SNR provides a determination of the absolute elasto-optical (Pockel's) constants. Examples are given for cubic crystals, and acceptable SNR values are obtained for scattering from thermally excited phonons at 10.6 microns, with no external perturbation of the sample necessary. The results indicate the special advantages of the method for the study of semiconductors.
Cochran, W. R.↗
Machine Learning-Enabled Wearable Piezoelectric Acoustic Sensor for Real-Time Breast Abnormality Detection
In contemporary society, breast health has become a significant public health concern, particularly among women. According to statistics from the World Health Organization, both the incidence and mortality rates of breast tumors have steadily increased in recent years. Therefore, effective early-stage screening and postoperative monitoring are essential for maintaining breast health. However, conventional clinical diagnostic modalities are typically bulky, operationally complex, and unsuitable for continuous real-time monitoring, which limits their use in portable and everyday health management applications. To address these limitations, this study proposes a machine learning-integrated wearable piezoelectric sensing platform as an auxiliary tool for breast health assessment. The device consists of a PDMS matching layer embedded with flexible silver nanowires, a P(VDF-TrFE) piezoelectric layer, and a multi-channel low-noise signal acquisition circuit. It is capable of acquiring acoustic echo signals from tissue-mimicking environments and automatically evaluating signal validity using a convolutional neural network (CNN). By integrating piezoelectric sensing with deep learning-based signal analysis, the proposed system achieves a signal-to-noise ratio exceeding 70 dB and a real-time classification accuracy above 96% under controlled conditions. These results demonstrate that the platform provides a compact, portable, and intelligent approach for wearable sensing of mechanical heterogeneity and highlight its potential for future development in continuous biomedical monitoring technologies.
He, Shuaitong↗
Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers
Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.
Machine Learning↗
Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers
Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.
Machine Learning↗
Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers
Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.
Machine Learning↗
On signal and noise level estimation in a coherent PCM channel
Joint maximum likelihood estimates for signal amplitude and noise power density in coherent pulse code modulation channel with white Gaussian noise and correlation receiver
SIGNAL TO NOISE RATIO↗
Advanced study of video signal processing in low signal to noise environments Semiannual progress report, 1967-1968
Mathematical model for Apollo video signal processing in low signal to noise ratio environments
Carden, F.↗
Signal-to-noise ratio analysis and evaluation of the Hadamard imaging technique
The signal-to-noise ratio performance of the Hadamard imaging technique is analyzed and an experimental evaluation of a laboratory Hadamard imager is presented. A comparison between the performances of Hadamard and conventional imaging techniques shows that the Hadamard technique is superior only when the imaging objective lens is required to have an effective F (focus) number of about 2 or slower.
Jobson, D. J.↗
Development of a P-I-N HgCdTe photomixer for laser heterodyne spectrometry
An improved HgCdTe photomixer technology was demonstrated employing a p-i-n photodiode structure. The i-region was near intrinsic n-type HgCdTe; the n-region was formed by B+ ion implantation; and the p-region was formed either by a shallow Au diffusion or by a Pt Schottky barrier. Experimental devices in a back-side illuminated mesa diode configuration were fabricated, tested, and delivered. The best photomixer was packaged in a 24-hour LN2 dewar along with a cooled GaAs FET preamplifier. Testing was performed by mixing black-body radiation with a CO2 laser beam and measuring the IF signal, noise, and signal-to-noise ratio in the GHz frequency range. Signal bandwidth for this photomixer was 1.3 GHz. The heterodyne NEP was 4.4 x 10 to the -20 W/Hz out to 1 GHz increasing to 8.6 x 10 to the -10 W/Hz at 2 GHz. Other photomixers delivered on this program had heterodyne NEPs at 1 GHz ranging from 8 x 10 to the -20 to 4.4 x 10 to the -19 W/Hz and NEP bandwidths from 2 to 4 GHz.
Bratt, Peter R.↗
IKONOS Signal-to-Noise Ratio Estimation
This viewgraph presentation focuses on the differences in Signal-to-Noise Ratio (SNR) between IKONOS imagery with and without Modulation Transfer Function Correction (MTFC). The researchers used a simulated scene to evaluate the effects of MTFC on SNR. They also used four very uniform IKONOS scenes, two of Antarctica, one of Ivanpah, CA, and one of Mali to estimate SNR.
Zanoni, Vicki↗