On the Application of the Baum-Welch Algorithm for Modeling the Land Mobile Satellite Channel

Accurate channel models are of high importance for the design of upcoming mobile satellite systems. Nowadays most of the models for the LMSC are based on Markov chains and rely on measurement data, rather than on pure theoretical considerations. A key problem lies in the determination of the model parameters out of the observed data. In this work we face the issue of state identification of the underlying Markov model whose model parameters are a priori unknown. This can be seen as a HMM problem. For finding the ML estimates of such model parameters the BW algorithm is adapted to the context of channel modeling. Numerical results on test data sequences reveal the capabilities of the proposed algorithm. Results on real measurement data are finally presented.Comment: IEEE Globecom 201

A Mobile Wireless Channel State Recognition Algorihm: Introduction, Definition, and Verification - Sensing for Cognitive Environmental Awareness

This research includes mobile wireless systems limited by time and frequency dispersive channels. A blind mobile wireless channel (MWC) state recognition (CSR) algorithm that detects hidden coherent nonselective and noncoherent selective processes is verified. Because the algorithm is blind, it releases capacity based on current channel state that traditionally is fixed and reserved for channel gain estimation and distortion mitigation. The CSR algorithm enables cognitive communication system control including signal processing, resource allocation/deallocation, or distortion mitigation selections based on channel coherence states. MWC coherent and noncoherent states, ergodicity, stationarity, uncorrelated scattering, and Markov processes are assumed for each time block. Furthermore, a hidden Markov model (HMM) is utilized to represent the statistical relationships between hidden dispersive processes and observed receive waveform processes. First-order and second-order statistical extracted features support state hard decisions which are combined in order to increase the accuracy of channel state estimates. This research effort has architected, designed, and verified a blind statistical feature recognition algorithm capable of detecting coherent nonselective, single time selective, single frequency selective, or dual selective noncoherent states. A MWC coherence state model (CSM) was designed to represent these hidden dispersive processes. Extracted statistical features are input into a parallel set of trained HMMs that compute state sequence conditional likelihoods. Hard state decisions are combined to produce a single most likely channel state estimate for each time block. To verify the CSR algorithm performance, combinations of hidden state sequences are applied to the CSR algorithm and verified against input hidden state sequences. State sequence recognition accuracy sensitivity was found to be above 99% while specificity was determined to be above 98% averaged across all features, states, and sequences. While these results establish the feasibility of a MWC blind CSR algorithm, optimal configuration requires future research to further improve performance including: 1) characterizing the range of input signal configurations, 2) waveform feature block size reduction, 3) HMM parameter tracking, 4) HMM computational complexity and latency reduction, 5) feature soft decision combining, 6) recursive implementation, 7) interfacing with state based mobile wireless communication control processes, and 8) extension to wired or wireless waveform recognition

Noise modeling for standard CENELEC A-band power line communication channel

Power line communications (PLC) usage of low-voltage electrical power supply network as a medium of communication provides an alternative for the telecommunication access and in-house communication. Historically, power lines were majorly used for controlling appliances, however, with recent technology advancements power lines are now able to compete favorably and successfully with other relatively stable home automation and networking technologies like fixed line and wireless. Regardless of the advantages PLC has to offer, like every other communication technology, it has its own technical challenges it must overcome to be fully deployed and maximize its full potential. Such challenges includes noise, which can originate from appliances connected across the network or can be coupled unto the network. Harmful interference to other wireless spectrum users such as broadcast stations, and signal attenuation are other challenges faced by usage of the power line as a communication medium. PLC suffers the risk of not living up to its full development as a reliable means of communication if proper understanding of the channel potential and characteristic is not known. Therefore, understanding of the channel potential and characteristics can be obtained through measurement and modeling of the PLC channel. This model and measurements of the channel characteristics can then be utilized in designing a good PLC system which is able to withstand and mitigate the effect of the different kind of noise and disturbance present on the PLC network. This research therefore aims at formulizing and modeling the error pattern/behavior of noise and disturbances of an in-house CENELEC A-band based on experimental measurements. This is achieved by carrying out a real time experimental measurement of noise over a complete day to show the noise behavior. Error sequences are then generated from the measurement for the different classes of noise present on the CENELEC A-band and the use of Fritchman model, a Markovian chain model, is then employed to model the CENELEC A-band channel. This involves the use of Baum-Welch algorithm (an iterative algorithm) to estimate the model parameters of the three-state Markovian Fritchman model assumed. This precise channel model can then be used to design a good PLC system and facilitate the design of efficient coding and/or modulation schemes to enhance reliable communication on the PLC network. Therefore, answering the question of “how to formulize and model the error pattern/behavior of noise and disturbances of an in-house CENELEC A-band based on experimental measurements”

Efficient Channel Modeling Methods for Mobile Communication Systems

Siirretty Doriast

Global Maximum Likelihood Decoding with Hidden Markov Models

This thesis presents a summary of research in areas related to speech communications on degraded channels using very low data rate (VLR) digital voice coders. Background is presented on the nature of voice encoding, problems encountered with real world communications channels and some traditional solutions to these problems. Recent developments which use the Hidden Markov Model (HMM) and Vector Quantization (VQ) to enhance performance are reviewed. A proposal for a new channel decoding technique is then presented. This proposed technique uses the Hidden Markov Model in conjunction with a VLR voice encoder using Vector Quantization. It performs globally maximum likelihood estimates of received vectors over the joint region of received channel signals and possible vector decisions. Finally experimental results which are based on a simulation of the concept are presented.Electrical Engineerin

Evaluation of the Visible Infrared Imaging Radiometer Suite (VIIRS) Cloud Base Height (CBH) Pixel-level Retrieval Algorithm for Single-layer Water Clouds

Evaluation of the Visible Infrared Imaging Radiometer Suite (VIIRS) Cloud Base Height (CBH) product was accomplished. CBH is an important factor for aviation, but a lack of coverage for ground-based retrieval is a significant limitation. Space-based retrieval is essential; therefore, the VIIRS CBH pixel-level retrieval algorithm was assessed for single-layer water clouds. Accurate (truth) measurements were needed not only for the CBH product, but also for VIIRS cloud optical thickness (COT), effective particle size (EPS), and cloud top height (CTH). Data from Atmospheric Radiation Measurement (ARM) sites were used, with VIIRS-ARM matchups created from June 2013 through October 2015 for four locations. After initial CBH results were large and highly variable, the VIIRS CTH product was replaced with the ARM (truth) CTH product. This substantially reduced variability and errors in the VIIRS CBH products, demonstrating that the CBH algorithm is fundamentally sound. Thus, future research is needed to reduce errors in the VIIRS CTH products in order to ensure the CBH products are suitable for aviation support

The FIRE Cirrus Science Results 1993

FIRE (First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment) is a U.S. cloud-radiation research program that seeks to improve our basic understanding and parameterizations of cirrus and marine stratocumulus cloud systems and ISCCP data products. The FIRE Cirrus Science Conference was held in Breckenridge, CO, 14-17 Jun. 1993, to present results of cirrus research for the second phase of FIRE (1989-present) and to refine cirrus research goals and priorities for the next phase of FIRE (1994-future). This Conference Publication contains the text of short papers presented at the conference. The papers describe research analyses of data collected at the Cirrus Intensive Field Observations-2 field experiment conducted in Kansas, 13 Nov. - 7 Dec. 1991

Statistical techniques for improving prediction in crop progress stages with meteorological and satellite data

Οι εκθέσεις προόδου της καλλιέργειας (CPR) του USDA παρουσιάζουν την εβδομαδιαία πρόοδο που σημειώθηκε στα διάφορα φαινολογικά στάδια των επιλεγμένων καλλιεργειών και ιδιαίτερα του καλαμποκιού. Σε αυτή την διπλωματική, ο στόχος μας ήταν να προβλέψουμε τα CPR ενός ολόκληρου έτους λαμβάνοντας υπόψη διαθέσιμα δεδομένα από συναφή χαρακτηριστικά με τρόπο που να μπορούμε να νικήσουμε τις προβλέψεις βάσει εμπειρικών μέσων από ιστορικά δεδομένα. Για το λόγο αυτό, χρησιμοποιήσαμε δύο χαρακτηριστικά, τον δείκτη κανονικοποιημένης βλάστησης (NDVI) και τις συγκεντρωτικές ημέρες καλλιέργειας (AGDDs). Προκειμένου να επιτευχθεί ο στόχος μας, εφαρμόσαμε αρκετές προσεγγίσεις μοντελοποίησης, συμπεριλαμβανομένων μοντέλων ανεξάρτητων μήξεων και κρυμμένα μοντέλα HMMs και συγκρίναμε διαφορετικούς τύπους εκτιμητών και προγνωστικών λαμβάνοντας υπόψη και τα δύο χαρακτηριστικά ή τη χωριστή επεξεργασία τους ή πραγματοποιώντας μετασχηματισμούς δεδομένων, όπως διαφορές. Τα αποτελέσματα έδειξαν ότι τα προαναφερθέντα μοντέλα δεν μπορούν να προβλέψουν καλύτερα από τα ιστορικά δεδομένα. Τέλος, κατορθώσαμε να λάβουμε καλύτερες προβλέψεις χρησιμοποιώντας απλή γραμμική παλινδρόμηση. Αυτή η μελέτη μπορεί να επεκταθεί σε διάφορες κατευθύνσεις για μελλοντικές εργασίες.Crop Progress Reports (CPRs) of the USDA are listing the weekly progress made in the different phenological stages of selected crops and in particular of corn. In this thesis, our goal was to predict the CPRs of a full year by taking into account available data from related features in a way that we can beat the predictions based on empirical means from historical data. For this reason, we used two features, the mean Normalized Difference Vegetation Index (NDVI) and the Accumulated Growing Degree Days (AGDDs). In order to achieve our target we implemented several modeling approaches, including Independent Mixture Models and Hidden Markov Models HMMs and we compared different type of estimators and predictors by taking into account both features or treating them separately, or making data transformations, such as differences. The results showed that the aforementioned models cannot predict better than the historical data. Finally, we managed to obtain better predictions by using Simple Linear Regression. This study can be extended in several directions for future work

Earth System Science Research Using Datra and Products from Terra, Aqua, and ACRIM Satellites

The report describes the research conducted at CSR to extend MODIS data and products to the applications required by users in the State of Texas. This research presented in this report was completed during the timeframe of August 2004 - December 31, 2007. However, since annual reports were filed in December 2005 and 2006, results obtained during calendar year 2007 are emphasized in the report. The stated goals of the project were to complete the fundamental research needed to create two types of new, Level 3 products for the air quality community in Texas from data collected by NASA s EOS Terra and Aqua missions

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas