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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Potential Adoption and Benefits of Co-Optimized Multimode Engines and Fuels for U.S. Light-Duty Vehicles

Exploring a diverse portfolio of technologies for decarbonization is crucial to understanding the potential impacts of different technological solutions and their associated environmental implications. Using high-octane, high-sensitivity biofuel blends in co-optimized multimode engines can increase engine efficiency and reduce vehicle emissions. Here, the multimode engine research focuses on the benefits of light-duty vehicle engines, which can operate in multiple modes depending on the vehicle's load. Low-temperature combustion can improve efficiency and reduce emissions (such as those from oxides of nitrogen and particulate matter) during low-load operation, while spark ignition performance is maintained in high-load operation. These advanced engines can be optimized to run on blends of biobased fuels. This analysis models scenarios for potential market adoption of co-optimized multimode vehicles fueled by three different bioblendstocks: ethanol, isopropanol, and isobutanol. An integrated modeling approach is used to forecast the energy and environmental impacts of the deployment of co-optimized multimode vehicles and fuels in the light-duty sector over the 2020-to-2050 time horizon. The multidisciplinary approach combines vehicle sales modeling, system dynamics modeling of the biorefining industry, and life cycle assessment to estimate the emissions and energy benefits. The models consider market forces such as consumer preferences for vehicle attributes, biofuel supply and demand dynamics subject to biorefinery capacity build-out and bioresource constraints, and forecasted changes to the U.S. bulk energy system over time. Market adoption of co-optimized vehicles is evaluated across a wide parameter space for incremental vehicle cost and engine efficiency improvement. This analysis reveals that the deployment of co-optimized multimode fuels and vehicles results in up to a 5% reduction in annual sector-wide life cycle greenhouse gas (GHG) emissions by 2050, relative to a business-as-usual scenario, but is also indicates environmental trade-offs, such as higher life cycle water-use. Emission benefits could potentially increase beyond 2050, as the new technologies penetrate the market and gain a foothold. Results also show that, under certain circumstances, vehicles with engines co-optimized for use with high-octane, high-sensitivity biofuel blends can be cost-competitive with conventional gasoline, while reducing GHG emissions. Our modeling results indicate that co-optimized multimode fuels and engines can be strategically leveraged in tandem with electrification to decarbonize the light-duty sector. Co-optimized vehicles could play a role in the early years of the time horizon, while electric vehicles (EVs) could become more competitive in the later years, highlighting the complementary benefits of these technologies for GHG reductions.

Oke, Doris↗
DOE OSTI2026

MOSAIC-CONUS: A Multimodal, Multi-Temporally Paired Dataset for Earth Sciences

Earth embeddings—vector representations of geographic locations indexed in space and time—are emerging as a unifying interface for geospatial AI. However, their quality depends not only on model design, but on how multimodal Earth observation (EO) data are spatially indexed, temporally aligned, and cross-modally associated during pretraining. We introduce MOSAIC-CONUS (Multimodal Observations with Spatially Aligned Imagery, Urban Points of Interest, In-Situ Measurements and Text Captions), a large-scale EO dataset over the contiguous United States, organized around 250,000 stratified point indices that serve as stable spatial keys across seven modalities: active radar, passive optical imagery, lidar-derived elevation, land cover, functional context, hydrometeorological measurements, and textual summaries. Unlike existing EO datasets, MOSAIC-CONUS introduces four contributions not jointly addressed in prior work: 1. an open-source, large-scale multimodal EO corpus structured around point-indexed data designed to support Earth embedding learning; 2. explicit radar-optical pairing tables spanning twelve temporal alignment regimes, formalizing cross-sensor alignment as a controllable variable for analyzing how temporal mismatch across modalities influences learned embeddings quality; 3. a benchmark suite spanning cross-modal retrieval, annual nightlights regression, and basin-held-out streamflow prediction, positioning MOSAIC-CONUS as a benchmark-ready resource for multimodal AI systems; and 4. a language-based embedding layer through co-registered textual summaries, enabling Earth embeddings to function as a queryable interface for agentic AI systems. The dataset and pairing protocols are publicly released.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

Advanced Multimodal Solutions for Information Presentation

High-workload, fast-paced, and degraded sensory environments are the likeliest candidates to benefit from multimodal information presentation. For example, during EVA (Extra-Vehicular Activity) and telerobotic operations, the sensory restrictions associated with a space environment provide a major challenge to maintaining the situation awareness (SA) required for safe operations. Multimodal displays hold promise to enhance situation awareness and task performance by utilizing different sensory modalities and maximizing their effectiveness based on appropriate interaction between modalities. During EVA, the visual and auditory channels are likely to be the most utilized with tasks such as monitoring the visual environment, attending visual and auditory displays, and maintaining multichannel auditory communications. Previous studies have shown that compared to unimodal displays (spatial auditory or 2D visual), bimodal presentation of information can improve operator performance during simulated extravehicular activity on planetary surfaces for tasks as diverse as orientation, localization or docking, particularly when the visual environment is degraded or workload is increased. Tactile displays offer a third sensory channel that may both offload information processing effort and provide a means to capture attention when urgently required. For example, recent studies suggest that including tactile cues may result in increased orientation and alerting accuracy, improved task response time and decreased workload, as well as provide self-orientation cues in microgravity on the ISS (International Space Station). An important overall issue is that context-dependent factors like task complexity, sensory degradation, peripersonal vs. extrapersonal space operations, workload, experience level, and operator fatigue tend to vary greatly in complex real-world environments and it will be difficult to design a multimodal interface that performs well under all conditions. As a possible solution, adaptive systems have been proposed in which the information presented to the user changes as a function of taskcontext-dependent factors. However, this presupposes that adequate methods for detecting andor predicting such factors are developed. Further, research in adaptive systems for aviation suggests that they can sometimes serve to increase workload and reduce situational awareness. It will be critical to develop multimodal display guidelines that include consideration of smart systems that can select the best display method for a particular contextsituation.The scope of the current work is an analysis of potential multimodal display technologies for long duration missions and, in particular, will focus on their potential role in EVA activities. The review will address multimodal (combined visual, auditory andor tactile) displays investigated by NASA, industry, and DoD (Dept. of Defense). It also considers the need for adaptive information systems to accommodate a variety of operational contexts such as crew status (e.g., fatigue, workload level) and task environment (e.g., EVA, habitat, rover, spacecraft). Current approaches to guidelines and best practices for combining modalities for the most effective information displays are also reviewed. Potential issues in developing interface guidelines for the Exploration Information System (EIS) are briefly considered.

multimodal display↗
NASA NTRS2021

Advanced Multimodal Solutions for Information Presentation

High-workload, fast-paced, and degraded sensory environments are the likeliest candidates to benefit from multimodal information presentation. For example, during extra-vehicular activity (EVA) and telerobotic operations, the sensory restrictions associated with such a hostile environment provide a major challenge to maintaining the situation awareness (SA) required for safe operations. In particular, orientation, navigation, and collision avoidance are critical aspects of EVA tasks that need to be addressed to ensure the safety of the crew and the success of the mission. Multimodal displays hold promise to enhance situation awareness and task performance by utilizing different sensory modalities and maximizing their effectiveness based on appropriate interaction between modalities. Multimodal displays will also play an important role for long-duration information systems and will likely begin to be developed in the early phases of cislunar Gateway, and later lunar or Mars transit missions. Information systems are envisioned for LDEMs that require spacecraft with greater crew autonomy and increased dependence on computer-provided information needed to perform routine tasks, as well as time- and safety critical tasks. Such a system will require a single, common interface that is easy to learn and use and accesses key information from all relevant vehicle/habitat systems to enable task performance in both nominal and emergency conditions. Understanding of multimodal display technologies and their interactions will help to inform interface guidelines for LDEMs. The scope of the current report is an analysis of potential multimodal display technologies for long duration missions and, in particular, will focus on their potential role in EVA activities. The review will address multimodal (combined visual, auditory and/or tactile) displays investigated by the National Aeronautics and Space Administration (NASA), industry, and Department of Defense (DoD). It also considers the need for adaptive information systems to accommodate a variety of operational contexts such as crew status (e.g., fatigue, workload level) and task environment (e.g., EVA, habitat, rover, spacecraft). Current approaches to guidelines and best practices for combining modalities for the most effective information displays are also reviewed. Potential issues in developing interface guidelines for LDEMs are briefly considered.

multimodal displays↗
DOE OSTI2026

Fast Sideband Control of a Multimode Cavity Memory with Weak Dispersive Coupling to a Transmon

Mitigating ancilla-mediated error channels is a critical challenge in controlling high-quality superconducting cavities using circuit quantum electrodynamics (cQED). We address this by weakening the dispersive coupling while demonstrating fast, high-fidelity multimode control through transmon-mediated sideband interactions. We implement transmon-cavity SWAP gates with speeds up to 30 times larger than the bare dispersive coupling. Combined with transmon rotations, this enables universal state preparation in a single mode, though achieving unitary gates and extending control to multiple modes remains a challenge. In this work, we overcome this limitation by introducing two control strategies: (i) a shelving technique that stores populations in sideband-transparent states, and (ii) a method that exploits the dispersive shift to implement photon-number-selective transmon-cavity SWAP gates. We use these protocols to prepare Fock and binomial code states across any of the ten modes of a multimode cavity with millisecond coherence times—serving as a multimode quantum memory. We demonstrate unitaries that encode and decode an arbitrary qubit state from the transmon into corresponding vacuum and Fock state superpositions, as well as entangled NOON states of cavity mode pairs—a scheme extendable to arbitrary multimode Fock encodings in the cavity modes. Furthermore, we implement a new binomial encoding gate that converts arbitrary transmon superpositions into binomial code states in any cavity mode at a rate exceeding the dispersive shifts in our system, achieving an average post-selected state fidelity of 96.3% in a 4 μ⁢s gate time. By using precalibrated transmon and sideband pulses, our work demonstrates multimode control with significantly reduced calibration overhead, enabling efficient unitary operations using sideband interactions in multimode cQED systems.

Huang, Jordan [Rutgers Univ., Piscataway, NJ (Unit↗
DOE OSTI2026

Data Fusion for the Development of a Multimodal Freight Transload Facilities Dataset in the U.S.

To withstand the growing demand of commodity volume and its strain on the transportation infrastructure, it is necessary to identify the flow of commodities by route and mode. However, a national multimodal freight routing model does not exist for the U.S. The development of such model requires multiple building blocks, such as virtual representations of roadway, railway, and waterway networks, transload facilities (TFs), and access/egress links. Most of these blocks have a robust database in the U.S., except for the TFs. Here, this paper presents the fusion of dispersed and heterogeneous representations of multimodal TFs into a single, comprehensive, geospatial freight TF dataset. The TF dataset is derived from several sources, including the U.S. Army Corps of Engineers Master Docks Plus, the National Transportation Atlas Database, the Intermodal Association of North America, industry publications, and other public information. First, individual datasets were queried and reconciled. A geocoding/reverse geocoding process was applied to get the best street address and latitude/longitude location for each terminal. Then, duplicate terminals were identified by a fuzzy match algorithm based on terminal name and location, and removed. Validation was performed by visual inspection of random facilities. The main contributions of this work are: a publicly available version of the TF dataset, including facility location and multimodal transfer capability of 9,003 facilities, and an enterprise-version with the same facilities but including commodity handling capabilities. The main purpose of developing the TF dataset is to inform multimodal routing algorithms. The proposed TF dataset allows for credibly modeling the multimodal transfer of commodities within shipment routes.

Commodity Routing↗
NASA NTRS1992

Investigation of the bandwidth of multimode optical fibers used with 1550-nm LED and laser sources

Multimode optical fibers are not intended to be used with 1550-nm sources; however, it is desirable to utilize 1300/1550-nm wavelength division multiplexing (WDM) on some multimode fibers at Kennedy Space Center (KSC). No information from fiber vendors nor from the literature is available to support this use. Preliminary studies at KSC have suggested that these fibers might be usable at 1550-nm if the fibers possessed enough bandwidth when sourced by LEDs. Detailed bandwidth studies were made on 12 multimode fibers using 1300- and 1550-nm lasers and LEDs. The results showed that the modal bandwidth at 1550-nm was about 50 percent of the 1300-nm value and that the chromatic dispersion could be predicted by extrapolating the vendor's specifications for wavelengths outside the 1550-nm region. Utilizing these data, predictions of the fiber's optical bandwidth were accurately made. Problems with launch conditions and possible differential attenuation at connectors was noted at 1300-nm but was less significant at 1550-nm. It appears that the multimode fibers studied will offer adequate performance in the 1550-nm region for a number of current KSC needs. Studies of additional fibers are encouraged to gain more confidence and better understanding of the 1550-nm bandwidth of KSC's multimode optical fibers before committing to 1300/1550-nm WDM.

White, Preston A., III↗
DOE OSTI2026

Generalist multimodal AI: A review of architectures, challenges and opportunities

Multimodal models are expected to be a critical component to future advances in artificial intelligence. Here, this field is starting to grow rapidly with a surge of new design elements motivated by the success of foundation models in natural language processing (NLP) and vision. It is widely hoped that further extending the foundation models to multiple modalities (e.g., text, image, video, sensor, time series, graph, etc.) will ultimately lead to generalist multimodal models, i.e. one model across different data modalities and tasks. However, there is little research that systematically analyzes recent multimodal models (particularly the ones that work beyond text and vision) with respect to the underling architecture proposed. Therefore, this work provides a fresh perspective on generalist multimodal models (GMMs) via a novel architecture and training configuration specific taxonomy. This includes factors such as Unifiability, Modularity, and Adaptability that are pertinent and essential to the wide adoption and application of GMMs. The review further highlights key challenges and prospects for the field and guide the researchers into the new advancements.

Artificial intelligence (AI)↗
DOE OSTI2026

Joint Optimization of Multimodal Transit Frequency and Shared Autonomous Vehicle Fleet Size with Hybrid Metaheuristic and Nonlinear Programming

Shared autonomous vehicles (SAVs) bring competition to traditional transit services but redesigning multimodal transit network can utilize SAVs as feeders to enhance service efficiency and coverage. This paper presents an optimization framework for the joint multimodal transit frequency and SAV fleet size problem, a variant of the transit network frequency setting problem. The objective is to maximize total transit ridership (including SAV-fed trips and subtracting boarding rejections) across multiple time periods under budget constraints, considering endogenous mode choice (transit, point-to-point SAVs, driving) and route selection, while allowing for strategic route removal by setting frequencies to zero. Due to the problem’s non-linear, non-convex nature and the computational challenges of large-scale networks, we develop a hybrid solution approach that combines a metaheuristic approach (particle swarm optimization) with nonlinear programming for local solution refinement. To ensure computational tractability, the framework integrates analytical approximation models for SAV waiting times based on fleet utilization, multimodal network assignment for route choice, and multinomial logit mode choice behavior, bypassing the need for computationally intensive simulations within the main optimization loop. Applied to the Chicago metropolitan area’s multimodal network, our method illustrates a 33.3% increase in transit ridership through optimized transit route frequencies and SAV integration, particularly enhancing off-peak service accessibility and strategically reallocating resources.

Ng, Max↗
NASA NTRS2003

Parallel Information Processing (Image Transmission Via Fiber Bundle and Multimode Fiber

Growing demand for visual, user-friendly representation of information inspires search for the new methods of image transmission. Currently used in-series (sequential) methods of information processing are inherently slow and are designed mainly for transmission of one or two dimensional arrays of data. Conventional transmission of data by fibers requires many fibers with array of laser diodes and photodetectors. In practice, fiber bundles are also used for transmission of images. Image is formed on the fiber-optic bundle entrance surface and each fiber transmits the incident image to the exit surface. Since the fibers do not preserve phase, only 2D intensity distribution can be transmitted in this way. Each single mode fiber transmit only one pixel of an image. Multimode fibers may be also used, so that each mode represent different pixel element. Direct transmission of image through multimode fiber is hindered by the mode scrambling and phase randomization. To overcome these obstacles wavelength and time-division multiplexing have been used, with each pixel transmitted on a separate wavelength or time interval. Phase-conjugate techniques also was tested in, but only in the unpractical scheme when reconstructed image return back to the fiber input end. Another method of three-dimensional imaging over single mode fibers was demonstrated in, using laser light of reduced spatial coherence. Coherence encoding, needed for a transmission of images by this methods, was realized with grating interferometer or with the help of an acousto-optic deflector. We suggest simple practical holographic method of image transmission over single multimode fiber or over fiber bundle with coherent light using filtering by holographic optical elements. Originally this method was successfully tested for the single multimode fiber. In this research we have modified holographic method for transmission of laser illuminated images over commercially available fiber bundle (fiber endoscope, or fiberscope).

Kukhtarev, Nicholai↗
NASA NTRS2014

Angular and Polarization Response of Multimode Sensors with Resistive-Grid Absorbers

High sensitivity receiver systems with near ideal polarization sensitivity are highly desirable for development of millimeter and sub-millimeter radio astronomy. Multimoded bolometers provide a unique solution to achieve such sensitivity, for which hundreds of single-mode sensors would otherwise be required. The primary concern in employing such multimoded sensors for polarimetery is the control of the polarization systematics. In this paper, we examine the angular- and polarization- dependent absorption pattern of a thin resistive grid or membrane, which models an absorber used for a multimoded bolometer. The result shows that a freestanding thin resistive absorber with a surface resistivity of eta/2, where eta is the impedance of free space, attains a beam pattern with equal E- and H-plane responses, leading to zero cross polarization. For a resistive-grid absorber, the condition is met when a pair of grids is positioned orthogonal to each other and both have a resistivity of eta/2. When a reflective backshort termination is employed to improve absorption efficiency, the cross-polar level can be suppressed below −30 dB if acceptance angle of the sensor is limited to < or approx. 60deg. The small cross-polar systematics have even-parity patterns and do not contaminate the measurements of odd-parity polarization patterns, for which many of recent instruments for cosmic microwave background are designed. Underlying symmetry that suppresses these cross-polar systematics is discussed in detail. The estimates and formalism provided in this paper offer key tools in the design consideration of the instruments using the multimoded polarimeters.

High sensitivity receiver systems↗
DOE OSTI2026

FIRM: federated image reconstruction using multimodal tomographic data

Here, we propose a federated algorithm for reconstructing images using multimodal tomographic data sourced from dispersed locations, addressing the challenges of traditional unimodal approaches that are prone to noise and reduced image quality, as well as the limitations of centralized multimodal approaches that require extensive data transfer, leading to significant communication overhead, storage demands, and potential data privacy concerns. Our approach formulates a joint inverse optimization problem incorporating multimodality constraints and solves it in a federated framework through local gradient computations complemented by lightweight central operations, thereby ensuring data decentralization. Leveraging the connection between our federated algorithm and the quadratic penalty method, we introduce an adaptive step-size rule with guaranteed sublinear convergence. Numerical results demonstrate superior computational efficiency and improved image reconstruction quality compared to existing approaches.

federated algorithm↗
DOE OSTI2024

Unsupervised multimodal fusion of in-process sensor data for advanced manufacturing process monitoring

Effective monitoring of manufacturing processes is crucial for maintaining product quality and operational efficiency. Modern manufacturing environments often generate vast amounts of complementary multimodal data, including visual imagery from various perspectives and resolutions, hyperspectral data, and machine health monitoring information such as actuator positions, accelerometer readings, and temperature measurements. However, fusing and interpreting this complex, high-dimensional data presents significant challenges, particularly when labeled datasets are unavailable or impractical to obtain. This paper presents a novel approach to multimodal sensor data fusion in manufacturing processes, inspired by the Contrastive Language-Image Pre-training (CLIP) model. We leverage contrastive learning techniques to correlate different data modalities without the need for labeled data, overcoming limitations of traditional supervised machine learning methods in manufacturing contexts. Our proposed method demonstrates the ability to handle and learn encoders for five distinct modalities: visual imagery, audio signals, laser position (x and y coordinates), and laser power measurements. By compressing these high-dimensional datasets into low-dimensional representational spaces, our approach facilitates downstream tasks such as process control, anomaly detection, and quality assurance. The unsupervised nature of our method makes it broadly applicable across various manufacturing domains, where large volumes of unlabeled sensor data are common. We evaluate the effectiveness of our approach through a series of experiments, demonstrating its potential to enhance process monitoring capabilities in advanced manufacturing systems. This research contributes to the field of smart manufacturing by providing a flexible, scalable framework for multimodal data fusion that can adapt to diverse manufacturing environments and sensor configurations. The proposed method paves the way for more robust, data-driven decision-making in complex manufacturing processes.

Contrastive Learning↗
DOE OSTI2025

A Representation Fusion Framework for Decoupling Diagnostic Information in Multimodal Learning

Modern medicine increasingly relies on multimodal data, ranging from clinical notes to imaging and genomics, to guide diagnosis and treatment. However, integrating these heterogeneous data sources in a principled and interpretable manner remains a major challenge. We present MODES (Multi-mOdal Disentangled Embedding Space), a representation fusion framework that explicitly separates shared and modality-specific factors of variation, offering a structured latent space for multimodal information that improves both prediction and interpretability. By leveraging pre-trained unimodal foundation models, MODES mitigates the dependency on extensive paired datasets, crucial in data-scarce clinical settings. We introduce a masking strategy that optimizes representation dimensionality by eliminating low-information dimensions, to achieve compact, information-rich representations. Our framework demonstrates superior performance in predicting diagnoses and phenotypes compared to unimodal and conventional fusion models. MODES also enables robust diagnostic inference in missing data scenarios, offering an opportunity toward interpretable and efficient multimodal diagnostics in personalized healthcare.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Multimodality in the Search for New Physics in Pulsar Timing Data and the Case of Kination-amplified Gravitational-wave Background from Inflation

We investigate the kination-amplified inflationary gravitational-wave background (GWB) interpretation of the signal recently reported by various pulsar timing array (PTA) experiments. Kination is a post-inflationary phase in the expansion history dominated by the kinetic energy of some scalar field, characterized by a stiff equation of state w = 1. Within the inflationary GWB model, we identify two modes that can fit the current data sets (NANOGrav and EPTA) with equal likelihood: the kination-amplification (KA) mode and the ordinary, no-kination-amplification (no-KA) mode. The multimodality of the likelihood motivates a Bayesian analysis with nested sampling. We analyze the free spectra of current PTA data and mock free spectra constructed with higher signal-to-noise ratios using nested sampling. The analysis of the mock spectrum designed to be consistent with the best fit to the NANOGrav 15 yr (NG15) data successfully reveals the expected bimodal posterior for the first time while excluding the reheating mode that appears in the fit to the current NG15 data, making a case for our correct and comprehensive treatment of potential multimodal posteriors arising from future PTA data sets. The resultant Bayes factor is $\mathcal{B}$ $\equiv$ Z no–KA /Z KA = 2.9 ± 1.9, indicating comparable statistical significance between the two modes. Given the theoretical model-building challenges of producing highly blue-tilted primordial tensor spectra, the KA mode has the advantage of requiring less blue primordial spectra, compared with the no-KA mode. The synergy between future cosmic microwave background polarization, pulsar timing, and laser interferometer measurements of gravitational waves will help resolve the ambiguity implied by the multimodal posterior in PTA-only searches.

Cosmology↗
NASA NTRS1977

Far-field multimodal acoustic radiation directivity

A comparison is made between approximate equations for far-field acoustic radiation patterns and exact equations for single and multimodal excitations in order to determine the validity range for the approximate approach. It is found that for single-mode cases: (1) the gross behavior of the primary lobes is adequately described by the approximate equations, (2) some error is found for lower and zero radial order modes, and (3) some agreement is yielded between exact and approximate sidelobes, although the approximate equation was not intended to simulate sidelobes. Multimodal approximate equations are compared to exact equations for various distributions of modal power; for all cases excellent agreement is found. For multimodal patterns it is noted that many modes influence the final level and shape of the directivity curve, although the major contributions are from the higher radial order modes.

Saule, A. V.↗
NASA NTRS1990

Study of wavelength division multiplexing as a means of increasing the number of channels in multimode fiber optic communication links

A number of optical communication lines are now in use at the Kennedy Space Center (KSC) for the transmission of voice, computer data, and video signals. Presently, all of these channels utilize a single carrier wavelength centered near 1300 nm. The theoretical bandwidth of the fiber far exceeds the utilized capacity. Yet, practical considerations limit the usable bandwidth. The fibers have the capability of transmitting a multiplicity of signals simultaneously in each of two separate bands (1300 and 1550 nm). Thus, in principle, the number of transmission channels can be increased without installing new cable if some means of wavelength division multiplexing (WDM) can be utilized. The main goal of these experiments was to demonstrate that a factor of 2 increase in bandwidth utilization can share the same fiber in both a unidirectional configuration and a bidirectional mode of operation. Both signal and multimode fiber are installed at KSC. The great majority is multimode; therefore, this effort concentrated on multimode systems.

Bates, Harry↗
NASA NTRS1991

Reflection Effects in Multimode Fiber Systems Utilizing Laser Transmitters

A number of optical communication lines are now in use at NASA-Kennedy for the transmission of voice, computer data, and video signals. Now, all of these channels use a single carrier wavelength centered near 1300 or 1550 nm. Engineering tests in the past have given indications of the growth of systematic and random noise in the RF spectrum of a fiber network as the number of connector pairs is increased. This noise seems to occur when a laser transmitter is used instead of a LED. It has been suggested that the noise is caused by back reflections created at connector fiber interfaces. Experiments were performed to explore the effect of reflection on the transmitting laser under conditions of reflective feedback. This effort included computer integration of some of the instrumentation in the fiber optic lab using the Lab View software recently acquired by the lab group. The main goal was to interface the Anritsu Optical and RF spectrum analyzers to the MacIntosh II computer so that laser spectra and network RF spectra could be simultaneously and rapidly acquired in a form convenient for analysis. Both single and multimode fiber is installed at Kennedy. Since most are multimode, this effort concentrated on multimode systems.

Bates, Harry E.↗