International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / In 1998, AlliedSignal Technical Services (ATSC) installed three fully autonomous 13-meter satellite tracking systems for the Integrated Program Office of the National Oceanic and Atmospheric Administration (NOAA) at the Command and Data Acquisition Station near Fairbanks, Alaska. These systems track and command NOAA Polar Orbiting Weather Satellites and Defense Meteorological Satellites. Each tracking system operates for extended periods of time with little intervention other than periodic scheduling contacts. Schedule execution initiates equipment configuration, including establishing the RF communications link to the satellite. Station autonomy is achieved through use of a robust scheduler that permits remote users and the System Administrator to request pass activities for any of the supported missions. Spacecraft in the mission set are scheduled for normal operations according to the priority they have been assigned. Once the scheduler resolves conflicts, it builds a human-readable control script that executes all required support activities. Pass adds or deletes generate new schedule scripts and can be performed in seconds. The systems can be configured to support CCSDS and TDM telemetry processing, but the units installed at Fairbanks required only telemetry and command through-put capabilities. Received telemetry data is buffered on disk-storage for immediate, post-pass playback, and also on tape for long-term archiving purposes. The system can autonomously support up to 20 spacecraft with 5 different configuration setups each. L-Band, S-Band and X-Band frequencies are supported.

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Most contemporary modems provide a bit-oriented service at the physical layer of the OSI reference model. They do not provide any specific support for packet data transmission. A novel Hop-Aligned Slow Frequency Hopping (HA-SFH) concept is proposed to provide a packet traffic service in a Partial Band Jamming (PBJ) environment. Frequently, SFH is used together with deep interleaving and powerful Forward Error Correction (FEC) to combat PBJ. The proposed HA-SFH exploits the error control mechanism of the link layer packet protocol and reduces the data redundancy by using a low gain FEC, it results in a more bandwidth efficient system. The HA-SFH and deep interleaving SFH (DI-SFH) performance were analysed and compared by using a series of simulations and experiments employing a network simulator and hardware such as Viterbi decoders and a channel noise simulator, etc. The results are presented in terms of throughput and average frame delay, which are more meaningful qualities than BER for packet traffic. It shows concrete results that HA-SFH performs 2 times better than DI-SFH in terms of throughput under PBJ. This thesis also discusses a wide range of technical issues involving the implementation of HA-SFH such as the coding level required, protocol enhancement, networking traffic, adaptive FEC, channel state estimation, etc. It also explores an adaptive code rate system called the Smart Codec (SmCodec), which can be integrated into HA-SFH. The further development of the SmCodec also makes it a suitable system for use in commercial PSK modems to combat the effect of channel fading (eg. from rain). A new channel state estimation technique is also proposed. It estimates the channel state from the block error rate and the required estimation time is as low as 2 seconds for the most required range of E_b/N_o at a 64 kbps link (the estimation time decreases when the link speed increases).

Doctor of Philosophy / Department of Agronomy / Ignacio Ciampitti / The adoption of Remote Sensing (RS) in agriculture have been mainly utilized to inference about biological processes in a scalable manner over space and time. In this context, this work first explores two non-traditional approaches for rapid derivation of plant performance under field conditions. Both approaches focus on plant metrics extraction exploiting high spatial resolution from Unmanned Aerial Systems (UAS). Second, we investigate the spatial-temporal dynamics of corn (Zea mays L.) phenology and yield in the corn belt region utilizing high temporal resolution from satellite. To evaluate the impact of the adoption of RS for deriving plant/crop performance the following objectives were established: i) investigate the implementation of digital aerial photogrammetry to derive plant metrics (plant height and biomass) in corn; ii) implement and test a methodology for detecting and counting corn plants via very high spatial resolution imagery in the context of precision agriculture; iii) derive key phenological metrics of corn via high temporal resolution satellite imagery and identify links between the derived metrics and yield trends over the last 14 years for corn within the corn belt region. For the first objective, main findings indicate that digital aerial photogrammetry can be utilized to derive plant height and assist in plant biomass estimation. Results also suggest that plant biomass predictability significantly increases when integrating the aerial plant height estimate and ground stem diameter. For the second objective, the workflow implemented demostrates adequate performance to detect and count corn plants in the image. Its robustness highly dependends on the spatial resolution of the image, limitations and future research paths are further discussed. Lastly, for the third objective, outcomes evidenced that for a long-term perspective (14 years), an extended reproductive stage significantly correlates with high yield for corn. When considering a shorter-term period (last 4 years) mainly characterized by optimal growth conditions, early season green-up rate and late season senescence rate positively describe yield trend in the region. The significance of the variables changed according to the time-span considered. It is noticed that when optimal growth conditions are met, modern-hybrids can capitalize by increasing yield, due to primarily a faster (green-up) rate before flowering and on senescence rate better describes yield under these conditions. The entire research project investigates opportunities and needs for integrating remote sensing into the agronomic-based inference process.