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The primary thrust of the SETI (Search for Extraterrestrial Intelligence) Program is to search the microwave region of the spectrum for signals of extraterrestrial intelligent origin. The SETI program plan and current SETI breadboard instrument development activities are described.
This document describes the test-bed designed for the investigation of passive direction finding, recognition, and classification of speech and sound sources using sensor arrays. The test-bed forms the experimental basis of the Intelligent Small-Scale Spatial Direction Finder (ISS-SDF) project, aimed at furthering digital signal processing and intelligent sensor capabilities of sensor array technology in applications such as rocket engine diagnostics, sensor health prognostics, and structural anomaly detection. This form of intelligent sensor technology has potential for significant impact on NASA exploration, earth science and propulsion test capabilities. The test-bed consists of microphone arrays, power and signal distribution modules, web-based data acquisition, wireless Ethernet, modeling, simulation and visualization software tools. The Acoustic Sensor Array Modeler I (ASAM I) is used for studying steering capabilities of acoustic arrays and testing DSP techniques. Spatial sound distribution visualization is modeled using the Acoustic Sphere Analysis and Visualization (ASAV-I) tool.
It is believed that the Galaxy might contain ten billion potential life sites. In view of the physical inaccessibility of extraterrestrial life on account of the vast distances involved, a logical first step in a search for extraterrestrial intelligence (SETI) appears to be an attempt to detect signals already being radiated. The characteristics of the signals to be expected are discussed together with the search strategy of a NASA program. It is pointed out that all presently planned searches will use existing radio-astronomy antennas. If no extraterrestrial intelligence signals are discovered, society will have to decide whether SETI justifies a dedicated facility of much greater collecting area. Attention is given to a multichannel spectrum analyzer, CW signal detection, pulse detection, the pattern detector, and details of SETI system operation.
The SETI (Search for Extraterrestrial Intelligence) Project's primary thrust is to search the microwave region of the spectrum for signals of extraterrestrial intelligent origin. The project will search a well defined volume of search parameter space using existing antennae and a sophisticated data acquisition and analysis system. Two major components are included, the target survey, which will observe at very high sensitivity all attractive stellar candidates within 75 light years of the Sun, and the sky survey, which will observe the entire celestial sphere at a lower sensitivity.
Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.
It is pointed out that an effective search for extraterrestrial intelligence (SETI) presents a signal processing task with unusual requirements. In the first place, algorithms with a very low false alarm probability are needed. This implies, that the interpretation of noise as a signal must be rare. Secondly in a SETI, attention must be given to signals having narrow-band or pulsed features. Theoretical considerations are discussed along with aspects of pulse detector motivation, independent pulse trains and the false alarm rate, and software considerations.
Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.
The Sky Survey observing program is one of two complementary strategies that NASA plans to use in its microwave Search for Extraterrestrial Intelligence (SETI). The primary objective of the Sky Survey is to search the entire sky over the frequency range 1000-10,000 MHz for evidence of narrow band signals of extraterrestrial, intelligent origin. Spectrum analyzers with upwards of 10 million channels and data rates in excess of 10 gigabits per second are required to complete the survey in less than 7 years. To lay the foundation for the operational SETI Sky Survey, a prototype system has been built to test and refine real time signal detection algorithms, to test scan strategies and observatory control functions, and to test algorithms designed to reject radio frequency interference. This paper presents a high level description of the prototype hardware and reports on the preparations to deploy the system to the 34-m antenna at the research and development station of NASA's Deep Space Communication Complex, Goldstone, California.
The SETI Sky Survey Observing Program is one of two complimentary strategies that NASA plans to use in its microwave Search for Extraterrestrial Intelligence (SETI). The primary objective of the sky survey is to search the entire sky over the frequency range of 1.0 to 10.0 GHz for evidence of narrow band signals of extraterrestrial intelligent origin. Frequency resolutions of 30 Hz or narrower will be used across the entire band. Spectrum analyzers with upwards of ten million channels are required to keep the survey time approximately 6 years. Data rates in excess of 10 megabits per second will be generated in the data taking process. Sophisticated data processing techniques will be required to determine the ever changing receiver baselines, and to detect and archive potential SETI signals. Existing radio telescopes, including several of NASA's Deep Space Network (DSN) 34 meter antennas located at Goldstone, CA and Tidbinbilla, Australia will be used for the observations. The JPL has the primary responsibility to develop and carry out the sky survey. In order to lay the foundation for the full scale SETI Sky Survey, a prototype system is being developed at the JPL. The system will be installed at the new 34-m high efficiency antenna at the Deep Space Station (DSS) 13 research and development station, Goldstone, CA, where it will be used to initiate the observational phase of the NASA SETI Sky Survey. It is anticipated that the early observations will be useful to test signal detection algorithms, scan strategies, and radio frequency interference rejection schemes. The SETI specific elements of the prototype system are: (1) the Wide Band Spectrum Analyzer (WBSA); a 2-million channel fast Fourier transformation (FFT) spectrum analyzer which covers an instantaneous bandpass of 40 MHz; (2) the signal detection processor; and (3) the SETI Sky Survey Manager, a network-based C-language environment that provides observatory control, performs data acquisition and analysis algorithms. A high level description of the prototype hardware and software systems will be given and the current status of the system development will be reported.
A targeted high-sensitivity search for narrow-band signals near a wavelength of 18 cm has been conducted using the 91-m radiotelescope of the National Radio Astronomy Observatory. The search included 201 nearby solar-type stars and achieved a frequency resolution of 5.5 Hz over a 1.4-MHz bandwidth. This high spectral resolution was obtained through a non-real-time reduction procedure using a Mark I VLBI recording terminal in conjunction with the CDC 7600 computational facility at the NASA-Ames Research Center. This is the first high-resolution search for narrow-band signals in this wavelength regime. To date it is the most sensitive search per unit observing time of any search strategy which does not postulate a unique magic frequency. Data show no evidence for narrow-band signals due to extraterrestrial intelligence at a 12-standard-deviation upper limit on signal strength of 1.1 x 10 to the -23rd W/sq m.
Radio frequency (RF) signal monitoring generally emphasizes intentionally generated signals, such as WiFi, Bluetooth, or cellular transmissions. However, electronic devices also produce unintended radiated emissions (UREs), which could also be useful in RF spectrum analysis. In either case, deriving intelligence from RF signals is typically a human-intensive process requiring significant domain knowledge. In the Augmented Human Analysis (AHA) project, we investigate the utility of dimensionally aligned signal projection (DASP) and machine learning (ML) algorithms for accelerating RF analysis workflows. We find that while DASP algorithms can indeed highlight signal characteristics relevant for classification tasks, the choice of algorithmic hyperparameters greatly affects performance. To address this challenge, we evaluate the quality of DASP outputs using the silhouette score, which measures how well data points cluster; high silhouette scores indicate good clustering, and thus good hyperparameter values. This approach is critical for machine learning pipelines as the DASP parameters cannot be directly optimized during model training. By identifying good DASP parameters, and thus good DASP outputs, as a preprocessing step, we can decrease the amount of effort required for downstream ML model training. We demonstrate our workflow using a dataset of UREs from common household devices, showing that even without the aid of ML, proper selection of DASP parameters enables clustering by device type.
This report presents a uniform compilation of works dealing with the search for extraterrestrial intelligence. Entries are by first author, with cross-reference by topic index and by periodical index. This bibliography updates earlier bibliographies on this general topic while concentrating on research related to listening for signals from extraterrestrial intelligence.
The problem of receiving and identifying a single microwave signal transmitted by extraterrestrial intelligent beings is analyzed in the cases where the signal is designed to catch our attention and the signal is designed for internal purposes of another civilization. Six variables which yield uncertainty as to the exact signal which should be searched for are described: polarization, modulation, flux level, direction, frequency (including bandwidth and drift rate), and time. It is shown that if all reasonable variations of these parameters are to be examined sequentially for 1000 seconds, the search would take over a million times longer than the age of the Universe. Ways to simplify the search are considered, including widening the frequency bin, selecting specific targets, cutting the observation time, using a Fourier transform device for data processing, and building larger antennas as well as better low-noise receivers.
An 8 million channel spectrum analyzer (MCSA) was designed the meet to meet the needs of a SETI program. The MCSA puts out a very large data base at very high rates. The development of a device which follows the MCSA, is presented.
An evaluation is made of microwave receiving systems designed to search for signals from extraterrestrial intelligence. Specific design concepts are analyzed parametrically to determine whether the optimum antenna system location is on earth, in space, or on the moon. Parameters considered include the hypothesized number of transmitting civilizations, the number of stars that must be searched to give any desired probability of receiving a signal, the antenna collecting area, the search time, the search range, and the cost. This analysis suggests that (1) search systems based on the moon are not cost-competitive, (2) if the search is extended only a few hundred light years from the earth, a Cyclops-type array on earth may be the most cost-effective system, (3) for a search extending to 500 light years or more, a substantial cost and search-time advantage can be achieved with a large spherical reflector in space with multiple feeds, (4) radio frequency interference shields can be provided for space systems, and (5) cost can range from a few hundred million to tens of billions of dollars, depending on the parameter values assumed.
The search for radio signals from extraterrestrial intelligent beings (SETI) requires the use of large instantaneous bandwidth (500 MHz) and high resolution (20 Hz) spectrometers. Digital systems with a high degree of modularity can be used to provide this capability, and this method has been widely discussed. Another technique for meeting the SETI requirement is to use a crossed Bragg cell spectrometer as described by Psaltis and Casasent. This technique makes use of the Folded Spectrum concept, introduced by Thomas. The Folded Spectrum is a 2-D Fourier Transform of a raster scanned 1-D signal. It is directly related to the long 1-D spectrum of the original signal and is ideally suited for optical signal processing. The folded spectrum technique has received little attention to date, primarily because early systems made use of photographic film which are unsuitable for the real time data analysis and voluminous data requirements of SETI. An analysis of the crossed Bragg cell spectrometer is presented as a method to achieve the spectral processing requirements for SETI. Systematic noise contributions unique to the Bragg cell system will be discussed.
The search for radio signals from extraterrestrial intelligent (SETI) beings requires the use of large instantaneous bandwidth (500 MHz) and high resolution (20 Hz) spectrometers. Digital systems with a high degree of modularity can be used to provide this capability, and this method has been widely discussed. Another technique for meeting the SETI requirement is to use a crossed Bragg-cell spectrometer as described by Psaltis and Casasent (1979). This technique makes use of the Folded Spectrum concept, introduced by Thomas (1966). The Folded Spectrum is a two-dimensional Fourier Transform of a raster scanned one-dimensional signal. It is directly related to the long one-dimensional spectrum of the original signal and is ideally suited for optical signal processing.
The ability of an intelligent agent to process complex signals such as those found in audio or video depends heavily on the nature of the internal representation of the relevant information. Our report explores the mechanisms underlying this process by investigating theories inspired by the function of the neocortex. Specifically, we focus on the phenomenon of polychronization, which describes the self-organization in a spiking neural network resulting from the interplay between network structure, driven spiking activity, and synaptic plasticity. What emerges are groups of neurons that exhibit reproducible, time-locked patterns of spiking activity. We propose that this representation is well suited to spatio-temporal signal processing, as it naturally resembles patterns found in real-world signals. We explore the computational properties of this method and demonstrate the ability of a simple polychronizing network to learn different spatio-temporal signals.