Research and Development of an Instrument for Detection of Extraterrestrail Life by Optical Rotatory Dispersion Final Technical Report
Polarimeter for extraterrestrial life detection by optical rotatory dispersion
POLARIMETER↗
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Development of an optical fiber interferometer for detection of surface flaws in aluminum
The main objective was to demonstrate the potential of using an optical fiber interferometer (OFI) to detect surface flaws in aluminum samples. Standard ultrasonic excitation was used to generate Rayleigh surface waves. After the waves interacted with a defect, the modified responses were detected using the OFI and the results were analyzed for time-of-flight and frequency content to predict the size and location of the flaws.
Gilbert, John A.↗
A Laboratory Experiment for Demonstrating Post-Coronagraph Wave Front Sensing and Control for Extreme Adaptive Optics
Direct detection of exo-planets from the ground will become a reality with the advent of a new class of extreme-adaptive optics instruments that will come on-line within the next few years. In particular, the Gemini Observatory will be developing the Gemini Planet Imager (GPI) that will be used to make direct observations of young exo-planets. One major technical challenge in reaching the requisite high contrast at small angles is the sensing and control of residual wave front errors after the starlight suppression system. This paper will discuss the nature of this problem, and our approach to the sensing and control task. We will describe a laboratory experiment whose purpose is to provide a means of validating our sensing techniques and control algorithms. The experimental demonstration of sensing and control will be described. Finally, we will comment on the applicability of this technique to other similar high-contrast instruments.
coronography↗
FEASIBILITY OF OPTICAL ROTATORY DISPERSION METHODS APPLIED TO THE DETECTION OF EXTRATERRESTRIAL LIFE.
Optical rotatory dispersion methods applied to the detection of extraterrestrial life
DETECTION↗
Mesoscale observations of lightning from Space Shuttle
Motion pictures have been taken at night by astronauts on the space shuttle showing lightning discharge that spread horizontally at speeds of 100,000/msec for distances over 60 km. Tape recordings have been made of the accompanying optical pulses detected with a photocell optical system. The observations show that lightning is often a mesoscale phenomena that can convey large amounts of electric charge to Earth from an extensive cloud system via a cloud to ground discharge.
Vonnegut, B.↗
Lightning observations from the Space Shuttle
Motion pictures were taken at night from the space shuttle that show lightning discharges spreading horizontally at speeds of .00001 m/sec for distances over 60 km. Tape recordings were made of the accompanying optical pulses detected with a photocell optical system. The observations show that lightning is often a mesoscale phenomenon that conveys large amounts of electric charge and energy derived from an extensive cloud system into a cloud-to-ground discharge. Several video tape recordings of lightning discharges were obtained on shuttle flights since the termination of the NOSL program. The size and location of the lightning illuminated cloud images is now being analyzed, and comparisons are made with meteorological data concerning the cloud system obtained from the McIDAS.
Vonnegut, B.↗
Mesoscale observations of lightning from Space Shuttle
Motion pictures have been taken at night by astronauts on the space shuttle showing lightning discharge that spread horizontally at speeds of 100,000/msec for distances over 60 km. Tape recordings have been made of the accompanying optical pulses detected with a photocell optical system. The observations show that lightning is often a mesoscale phenomena that can convey large amounts of electric charge to earth from an extensive cloud system via a cloud to ground discharge.
Vonnegut, B.↗
Coherent fiber optic sensor for early detection of cataractogenesis in a human eye lens
A lensless backscatter fiber optic probe is used to measure the size distribution of protein molecules inside an excised, but intact, human eye lens. The fiber optic probe, about 5 mm in diameter, can be positioned arbitrarily close to the anterior surface of the eye; it is a trans-receiver, which delivers a Gaussian laser beam into a small region inside the lens and provides a coherent detection of the laser light scattered by the protein molecules in the backward direction. Protein sizes determined from the fast and slow diffusion coefficients show good correlation with the age of the lens and cataractogenesis.
Dhadwal, Harbans S.↗
Coherent Detection of High-Rate Optical PPM Signals
A method of coherent detection of high-rate pulse-position modulation (PPM) on a received laser beam has been conceived as a means of reducing the deleterious effects of noise and atmospheric turbulence in free-space optical communication using focal-plane detector array technologies. In comparison with a receiver based on direct detection of the intensity modulation of a PPM signal, a receiver based on the present method of coherent detection performs well at much higher background levels. In principle, the coherent-detection receiver can exhibit quantum-limited performance despite atmospheric turbulence. The key components of such a receiver include standard receiver optics, a laser that serves as a local oscillator, a focal-plane array of photodetectors, and a signal-processing and data-acquisition assembly needed to sample the focal-plane fields and reconstruct the pulsed signal prior to detection. The received PPM-modulated laser beam and the local-oscillator beam are focused onto the photodetector array, where they are mixed in the detection process. The two lasers are of the same or nearly the same frequency. If the two lasers are of different frequencies, then the coherent detection process is characterized as heterodyne and, using traditional heterodyne-detection terminology, the difference between the two laser frequencies is denoted the intermediate frequency (IF). If the two laser beams are of the same frequency and remain aligned in phase, then the coherent detection process is characterized as homodyne (essentially, heterodyne detection at zero IF). As a result of the inherent squaring operation of each photodetector, the output current includes an IF component that contains the signal modulation. The amplitude of the IF component is proportional to the product of the local-oscillator signal amplitude and the PPM signal amplitude. Hence, by using a sufficiently strong local-oscillator signal, one can make the PPM-modulated IF signal strong enough to overcome thermal noise in the receiver circuits: this is what makes it possible to achieve near-quantum-limited detection in the presence of strong background. Following quantum-limited coherent detection, the outputs of the individual photodetectors are automatically aligned in phase by use of one or more adaptive array compensation algorithms [e.g., the least-mean-square (LMS) algorithm]. Then the outputs are combined and the resulting signal is processed to extract the high-rate information, as though the PPM signal were received by a single photodetector. In a continuing series of experiments to test this method (see Fig. 1), the local oscillator has a wavelength of 1,064 nm, and another laser is used as a signal transmitter at a slightly different wavelength to establish an IF of about 6 MHz. There are 16 photodetectors in a 4 4 focal-plane array; the detector outputs are digitized at a sampling rate of 25 MHz, and the signals in digital form are combined by use of the LMS algorithm. Convergence of the adaptive combining algorithm in the presence of simulated atmospheric turbulence for optical PPM signals has already been demonstrated in the laboratory; the combined output is shown in Fig. 2(a), and Fig. 2(b) shows the behavior of the phase of the combining weights as a function of time (or samples). We observe that the phase of the weights has a sawtooth shape due to the continuously changing phase in the down-converted output, which is not exactly at zero frequency. Detailed performance analysis of this coherent free-space optical communication system in the presence of simulated atmospheric turbulence is currently under way.
Vilnrotter, Victor↗
Optical temperature sensor utilizing birefringent crystals
A temperature sensor comprising an optical transducer member having an array of birefringent crystals. The length and, accordingly, the sensitivity to temperature change of successive birefringent crystals varies according to a particular relationship. The transducer is interconnected with a fiber optic transmission and detecting system. Respective optical output signals that are transmitted from the birefringent crystals via the fiber optic transmission system are detected and decoded so as to correspond to digits of a numbering system, whereby an accurate digital representation of temperature can ultimately be provided.
Quick, William H.↗
Window flaw detection by backscatter lighting
Portable fiber-optic probe detects tiny flaws in transparent materials. Probe transmits light through surface to illuminate interior of material by backscattering off its edges. Light-sensitive contact paper records scratch pattern. Technique can be used for rapid visual checks. Flexible fiber optics are safely used in explosive or flammable areas; they present no hazard of breakage or contamination in controlled environments.
Crockett, L. K.↗
Water Across Synthetic Aperture Radar Data (WASARD): SAR Water Body Classification for the Open Data Cube
The detection of inland water bodies from Synthetic Aperture Radar (SAR) data provides a great advantage over water detection with optical data, since SAR imaging is not impeded by cloud cover. Traditional methods of detecting water from SAR data involves using thresholding methods that can be labor intensive and imprecise. This paper describes Water Across Synthetic Aperture Radar Data (WASARD): a method of water detection from SAR data which automates and simplifies the thresholding process using machine learning on training data created from Geoscience Australia’s WOFS algorithm. Of the machine learning models tested, the Linear Support Vector Machine was determined to be optimal, with the option of training using solely the VH polarization or a combination of the VH and VV polarizations. WASARD was able to identify water in the target area with a correlation of 97% with WOFS. Sentinel-1, Open Data Cube, Earth Observations, Machine Learning, Water Detection 1. INTRODUCTION Water classification is an important function of Earth imaging satellites, as accurate remote classification of land and water can assist in land use analysis, flood prediction, climate change research, as well as a variety of agricultural applications [2]. The ability to identify bodies of water remotely via satellite is immensely cheaper than contracting surveys of the areas in question, meaning that an application that can accurately use satellite data towards this function can make valuable information available to nations which would not be able to afford it otherwise. Highly reliable applications for the remote detection of water currently exist for use with optical satellite data such as that provided by LANDSAT. One such application, Geoscience Australia’s Water Observations from Space (WOFS) has already been ported for use with the Open Data Cube [6]. However, water detection using optical data from Landsat is constrained by its relatively long revisit cycle of 16 days [5], and water detection using any optical data is constrained in that it lacks the ability to make accurate classifications through cloud cover [2]. The alternative solution which solves these problems is water detection using SAR data, which images the Earth using cloud-penetrating microwaves. Because of its advantages over optical data, much research has been done into water detection using SAR data. Traditionally, this has been done using the thresholding method, which involves picking a polarization band and labeling all pixels for which this band’s value is below a certain threshold as containing water. The thresholding method works since water tends to return a much lower backscatter value to the satellite than land [1]. However, this method can be flawed since estimating the proper threshold is often imprecise, complicated, and labor intensive for the end user. Thresholding also tends to use data from only one SAR polarization, when a combination of polarizations can provide insight into whether water is present. [2] In order to alleviate these problems, this paper presents an application for the Open Data Cube to detect water from SAR data using support vector machine (SVM) classification. 2. PLATFORM WASARD is an application for the Open Data Cube, a mechanism which provides a simple yet efficient means of ingesting, storing, and retrieving remote sensing data. Data can be ingested and made analysis ready according to whatever specifications the researcher chooses, and easily resampled to artificially alter a scene’s resolution. Currently WASARD supports water detection on scenes from ESA’s Sentinel-1 and JAXA’s ALOS. When testing WASARD, Sentinel-1 was most commonly used due to its relatively high spatial resolution and its rapid 6 day revisit cycle [5]. With minor alterations to the application's code, however, it could support data from other satellites. 3. METHODOLOGY Using supervised classification, WASARD compares SAR data to a dataset pre-classified by WOFS in order to train an SVM classifier. This classifier is then used to detect water in other SAR scenes outside the training set. Accuracy was measured according to the following metrics: Precision: a measure of what percentage of the points WASARD labels as water are truly water Recall: a measure of what percentage of the total water cover WASARD was able to identify. F1 Score: a harmonic average of the precision and recall scores Both precision and recall are calculated at the end of the training phase, when the trained classifier is compared to a testing dataset. Because the WOFS algorithm’s classifications are used as the truth values when training a WASARD classifier, when precision and recall are mentioned in this paper, they are always with respect to the values produced by WOFS on a similar scene of Landsat data, which themselves have a classification accuracy of 97% [6]. Visual representations of water identified by WASARD in this paper were produced using the function wasard_plot(), which is included in WASARD. 3.1 Algorithm Selection The machine learning model used by WASARD is the Linear Support Vector Machine (SVM). This model uses a supervised learning algorithm to develop a classifier, meaning it creates a vector which can be multiplied by the vector formed by the relevant data bands to determine whether a pixel in a SAR scene contains water. This classifier is trained by comparing data points from selected bands in a SAR scene to their respective labels, which in this case are “water” or “not water” as given by the WOFS algorithm. The SVM was selected over the Random Forest model, which outperformed the SVM in training speed, but had a greater classification time and lower accuracy, and the Multilayer Perceptron Artificial Neural Network, which had a slightly higher average accuracy than the SVM, but much greater training and classification times. Figure 1: Visual representation of the SVM Classifier. Each white point represents a pixel in a SAR scene. In Figure 1, the diagonal line separating pixels determined to be water from those determined not to be water represents the actual classification vector produced by the SVM. It is worth noting that once the model has been trained, classification of pixels is done in a similar manner as in the thresholding method. This is especially true if only one band was used to train the model. 3.1 Feature Selection Sentinel-1 collects data from two bands: the Vertical/Vertical polarization (VV) and the Vertical/Horizontal polarization (VH). When 100 SVM classifiers were created for each polarization individually, and for the combination of the two, the following results were achieved: Figure 2: Accuracy of classifiers trained using different polarization bands. Precision and Recall were measured with respect to the values produced by WOFS. Figure 2 demonstrates that using both the VV and VH bands trades slightly lower recall for significantly greater precision when compared with the VH band alone, and that using the VV band alone is inferior in both metrics. WASARD therefore defaults to using both the VV and VH bands, and includes the option to use solely the VH band. The VV polarization’s lower precision compared to the VH polarization is in contrast to results from previous research and may merit further analysis [4]. 3.2 Training a Classifier The steps in training a classifier with WASARD are 1. Selecting two scenes (one SAR, one optical) with the same spatial extents, and acquired close to each other in time, with a preference that the scenes are taken on the same day. 2. Using the WOFS algorithm to produce an array of the detected water in the scene of optical data, to be used as the labels during supervised learning 3. Data points from the selected bands from the SAR acquisition are bundled together into an array with the corresponding labels gathered from WOFS. A random sample with an equal number of points labeled “Water” and “Not Water” is selected to be partitioned into a training and a testing dataset 4. Using Scikit-Learn’s LinearSVC object, the training dataset is used to produce a classifier, which is then tested against the testing dataset to determine its precision and recall The result is a wasard_classifier object, which has the following attributes: 1. f1, recall, and precision: 3 metrics used to determine the classifier’s accuracy 2. Coefficient: Vector which the SVM uses to make its predictions. The classifier detects water when the dot product of the coefficient and the vector formed by the SAR bands is positive 3. Save(): allows a user to save a classifier to the disk in order to use it without retraining 4. wasard_classify(): Classifies an entire xarray of SAR data using the SVM classifier All of the above steps are performed automatically when the user creates a wasard_classifier object. 3.3 Classifying a Dataset Once the classifier has been created, it can be used to detect water in an xarray of SAR data using wasard_classify(). By taking the dot product of the classifier’s coefficients and the vector formed by the selected bands of SAR data, an array of predictions is constructed. A classifier can effectively be used on the same spatial extents as the ones where it was trained, or on any area with a similar landscape. While
Kreiser, Zachary↗
Design and Application of a Photomultiplier Detection System for Artificial Meteors
Electro-optical photomultiplier detection system for artificial meteors
Ayers, W. G.↗
Simultaneous detection and intensity estimation of an optical image
A statistical model for simultaneous detection and single parameter estimation of a stochastic signal against background noise is obtained. Two strategies, Bayes and maximum likelihood, are discussed. The detection of an optical point source imaged on a photosensitive surface and the estimation of its intensity based on such strategies are examined. An almost optimum estimate is also proposed and its statistical properties are studied.
Wang, L.↗
The Future of Satellite-based Lightning Detection
The future of satellite-based optical lightning detection, beyond the end of the current TRMM mission, is discussed. Opportunities for new low-earth orbit missions are reviewed. The potential for geostationary observations is significant; such observations provide order-of-magnitude gains in sampling and data efficiency over existing satellite convective observations. The feasibility and performance (resolution, sensitivity) of geostationary measurements using current technology is discussed. In addition to direct and continuous hemispheric observation of lighting, geostationary measurements have the potential (through data assimilation) to dramatically improve short and medium range forecasts, offering benefits to prediction of NOx productions and/or vertical transport.
Bocippio, Dennis J.↗
Optical test unit design for the direct detection laser tranceiver program
The Optical Test Unit (OTU) for the NASA Direct Detection Laser Tranceiver (DDLT) Experiment was designed to perform a wide variety of electrical and optical tests with minimum cost and risk. Choosing instruments that could do their own processing and carefully examining each step of the integration and test sequence helped reduce the cost and complexity of the system, allowing us to meet a very ambitious schedule. For some tests, less elegant schemes can be used than the traditional system test methods, while still maintaining the integrity of the test system. This paper explores the problems encountered and options that were available in designing the system test equipment for this optical communication system.
Reed, Patricia A.↗
Remote sensing of atmospheric winds using a coherent, CW lidar and speckle-turbulence interaction
Speckle turbulence interaction has the potential for allowing single ended remote sensing of the path averaged vector crosswind in a plane perpendicular to the line of sight to a target. If a laser transmitter is used to illuminate a target, the resultant speckle field generated by the target is randomly perturbed by the atmospheric turbulence as it propagates back to the location of the transmitter-receiver. When a cross wind is present, this scintillation pattern will move with time across the receiver. A continuous wave (cw) laser transmitter of modest power level in conjunction with optical heterodyne detection was used to exploit the speckel turbulence interaction and measure the crosswind. The use of a cw transmitter at 10.6 microns and optical heterodyne detection has many advantages over direct detection and a double pulsed source in the visible or near infrared. These advantages include the availability of compact, reliable and inexpensive transmitters, better penetration of smoke, dust and fog; stable output power; low beam pointing jitter; and considerably reduced complexity in the receiver electronics.
Holmes, J. F.↗
GRO J2058+42 Observations with Chandra and Detection of a Likely Optical Counterpart
GRO J2058+42 is a 195-s transient X-ray pulsar discovered in 1995 with BATSE. In 1996, RXTE located GRO J2058+42 to a 90% confidence error circle with a 4-arcminute radius. On 20 February 2004, the region including the error circle was observed with Chandra ACIS-I. No X-ray sources were detected within the error circle, however, 2 faint sources were detected in the ACIS-I field-of-view. We obtained follow-up optical observations of the brightest object, that had about 64 X-ray counts and was just 0.3 arcmin outside the error circle. The optical spectrum contained a strong H-alpha line and corresponds to an infrared object in the 2MASS catalog, indicating a Be/X-ray system. Although X-ray pulsations were undetectable due to the low count rate, this object is most likely the optical counterpart to GRO J2058+42. We present results from the Chandra observation and from the optical observations.
Wilson, Colleen A.↗