In this thesis, two signal-to-noise-ratio (SNR) estimators are analyzed for orthogonal frequency division multiplexing (OFDM) systems with imperfect synchronization. The two SNR estimators under investigation are the maximum likelihood estimator (MLE) and the second- and fourth-order moments estimator (MME). Mathematical analysis shows that the performance of MME is not affected by the advanced timing offset while a fixed bias of MLE is induced. Mathematical analysis also shows that the same frequency offset induces different bias for the two estimators under investigation. Finally, a novel carrier frequency offset and timing offset tracking scheme based on SNR estimation is proposed in this thesis.

Syftet med denna uppsats är att ge en ökad förstårelse för hur lärare inom det obligatoriska skolväsendet undervisar om kost inom ramen för Idrott och hälsa. Kost innefattas i det bredare hälsobegreppet, ett begrepp som det inte finns en entydlig definition av och som många lärare upplever som otydligt. Kost förekommer bara en gång i kursplanen för Idrott och hälsa vilket kan ha att göra med vår målstyrda skola där innehållsval lämnas till enskilda lärare.Vi har därför valt att undersöka hur lärare i Idrott och hälsa undervisar om kost, vilka metodval de gör, hur undervisning tar sig uttryck, vad de rekommenderar eleverna att äta samt hur undervisningen förhåller sig till Livsmedelsverkets rekommendationer. För att ge uppsatsen en djupare dimension har vi valt att knyta an en genusteori och undersöka om det förekommer skillnader mellan genus i ovan ställda frågeställningar.   Vi genomförde en kvantitativ enkätstudie över internet där vi valde att skicka ut enkäter till fler än 500 lärare på högstadiet. Vi fick svar från över 120 stycken och resultatet visat att i snitt 9 av 10 lärare undervisar om kost och att de väljer att göra det i anknytning till praktiska och teoretiska lektioner. När det sker under teoripass så är det genom traditionell förmedlingspedagog. Två av tre lärare väljer att lägga ner mellan 1-6 timmar på kostundervisningen under ett läsår och vanligast är det att använda traditionella läromedel.   Kostundervisningen knöts vanligtvis an till kostrekommendationer eller kost i samband med träning. De flesta rekommenderade eleverna att äta enligt Tallriksmodellen, en modell som bygger på att man ska äta allsidigt i rätt mängd. Allsidighet och variation var också ledordet när informanterna beskrev mer specifikt hur de rekommenderade eleverna att äta. Undervisningen förhöll sig därför inte helt efter vad Livsmedelsverket rekommenderar för bra kost, men mycket finns ändå med indirekt i Tallriksmodellen som informanterna anammat.   Det finns skillnader mellan genus, men det är svårt att dra några större slutsatser om skillnader då vi har haft ett begränsat urval av informanter. Sammanfattningsvis tyder resultatet på att fler kvinnor än män väljer att inte undervisa om kost, men att de kvinnor som undervisar om kost gör det i större utsträckning per läsår.

Signal-to-noise ratio (SNR) estimation is one of the important research topics in wireless communications. In the receiver, many algorithms require SNR information to achieve optimal performance. In this thesis, an autocorrelation based SNR estimator is proposed. The proposed method utilizes the correlation properties of symbol sequence and the uncorrelated properties of noise sequence to distinguish the signal power from the received signal. Curve fitting method is used for SNR estimator to predict the signal power. Mean and variance performance of the proposed SNR estimator is compared with that of the conventional SNR estimator by computer simulations. These simulations consider additive white Gaussian noise and multipath Rayleigh fading channel with BPSK, 8PSK, 16QAM and 64QAM modulation schemes. According to the simulation results, the proposed method can provide better performance than conventional methods in both mean and mean-square-error.

In particle image velocimetry (PIV) the measurement signal is contained in the recorded intensity of the particle image pattern superimposed on a variety of noise sources. The inherent amount of signal mutual information between consecutive images governs the strength of the resulting PIV cross correlation and ultimately the accuracy and uncertainty of the produced PIV measurements. Hence we posit that the correlation signal-to-noise-ratio (SNR) metrics calculated from the correlation plane can be used to quantify the quality of the correlation and the resulting uncertainty of an individual measurement. A new SNR metric termed "mutual information" (MI) which quantifies the amount of common information (particle pattern) between two consecutive images is also introduced and investigated. This measure provides a direct estimation of the apparent NIFIFO parameter of an image pair providing an alternative approach towards uncertainty estimation but also connecting the current development to one of the most fundamental principles of PIV and the previous established theory. We extend the original work by Charonko and Vlachos and present a framework for evaluating the correlation strength using a set of different metrics, which in turn are used to develop models for uncertainty estimation. Several corrections have been applied in this work. The metrics and corresponding models presented herein are expanded to be applicable to both standard and filtered correlations by applying a subtraction of the minimum correlation value to remove the effect of the background image noise. In addition, the notion of a "valid" measurement is redefined with respect to the correlation peak width in order to be consistent with uncertainty quantification principles and distinct from an "outlier" measurement. Finally the type and significance of the error distribution function is investigated. These advancements lead to robust uncertainty estimation models, which are tested against both synthetic benchmark data as well as actual experimental measurements. In this work, U68.5 uncertainties are estimated at the 68.5% confidence level while U95 uncertainties are estimated at 95% confidence level. For all cases the resulting calculated coverage factors approximate the expected theoretical confidence intervals thus demonstrating the applicability of these new models for estimation of uncertainty for individual PIV measurements. / Master of Science

International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada / The advent of a new generation of analog to digital converters (ADC’s) provides the aerospace signal-conditioning engineer with many design advantages, trade-offs and challenges for their next generation of signal conditioning systems. These advantages include increased range, resolution, accuracy, channel-count and sampling rate. However, in order to capitalize on these advantages, it is important to understand the trade-offs involved and to specify these systems correctly. Trade-offs include: • Analog vs. Digital signal conditioning • Implementation issues such as 12-bits vs. 16-bits (or even 24-bits) • Topology issues such as multiplexers vs. multiple ADC’s • Filter-type selection • Sigma-Delta vs. Successive Approximation ADC’s. Specification challenges include: • Total DC error vs. gain and offset (and drift, excitation, DNL, crosstalk, etc.) • ENOB vs. SINAD (or THD, SNR or Noise) • Coherency issues such as filter phase distortion vs. delay This paper will discuss some of these aspects and attempts to produce a succinct specification for the next generation of airborne signal conditioning, while also outlining some of the lessons learned in developing the same.

No description available.

In this work, first the Cramer-Rao lower bound (CRLB) of the signal-to-noise ratio (SNR) estimate for binary phase shift keying (BPSK) modulated signals in additive white Gaussian noise (AWGN) channels is derived. All the steps and results of this CRLB derivation are shown in a detailed manner. Two major estimation scenarios are considered herein: the non-data-aided (NDA) and data-aided (DA) frameworks, respectively. The non-data-aided scenario does not assume the periodic transmission of known data symbols (pilots) to limit the system throughput, while the data-aided scenario assumes the transmission of known transmit data symbols or training sequences to estimate the channel parameters. The Cramer-Rao lower bounds for the non-data-aided and data-aided scenarios are derived. In addition, the modified Cramer-Rao lower bound (MCRLB) is also calculated and compared to the true CRLBs. It is shown that in the low SNR regime the true CRLB is tighter than the MCRLB in the non-data-aided estimation scenario. Second, the Bayesian Cramer-Rao lower bound (BCRLB) for SNR estimate is considered for BPSK modulated signals in the presence of time-selective fading channels. Only the data-aided scenario is considered, and the time-selective fading channel is modeled by means of a polynomial function. A BCRLB on the variance of the SNR estimate is found and the simulation results are presented.

No description available.

The main objective of this thesis is to evaluate accuracy and precision of the machine vision system used to calibrate a prepared environment foroptical navigation. Rotationally independent optimized colour reference labels (symbols) creates an environment. Any number of symbols can be used. A symbol carries 8–bit (0 to 255) information, which can be designed for different values by using Matlab algorithms. An optical navigation system enters into the environment and captures thesymbols. The symbols are then decoded to determine the geographical positions of the symbols from reference position of the system by using Matlab algorithms. Then, the system is moved to a known position and the same set of symbols are captured, decoded and located. The process is repeated for several positions of the system to find precision and accuracy. Finally, the results are analysed.

In speaker verification, the mismatches between the training speech and the testing speech can greatly affect the robustness of classification algorithms, and the mismatches are mainly caused by the changes in the noise types and the signal to noise ratios. This thesis aims at finding the most robust classification methods under multi-noise and multiple signal to noise ratio conditions. Comparison of several well-known state of the art classification algorithms and features in speaker verification are made through examining the performance of small-set speaker verification system (e.g. voice lock for a family). The effect of the testing speech length is also examined. The i-vector/Probabilistic Linear Discriminant Analysis method with compensation strategies is shown to provide a stable performance for both previously seen and previously unseen noise scenarios, and a C++ implementation with online processing and multi-threading is developed for this approach.