The major challenges facing resource constraint wireless devices are error resilience, security and speed. Three joint schemes are presented in this research which could be broadly divided into error correction based and cipher based. The error correction based ciphers take advantage of the properties of LDPC codes and Nordstrom Robinson code. A cipher-based cryptosystem is also presented in this research. The complexity of this scheme is reduced compared to conventional schemes. The securities of the ciphers are analyzed against known-plaintext and chosen-plaintext attacks and are found to be secure. Randomization test was also conducted on these schemes and the results are presented. For the proof of concept, the schemes were implemented in software and hardware and these shows a reduction in hardware usage compared to conventional schemes. As a result, joint schemes for error correction and security provide security to the physical layer of wireless communication systems, a layer in the protocol stack where currently little or no security is implemented. In this physical layer security approach, the properties of powerful error correcting codes are exploited to deliver reliability to the intended parties, high security against eavesdroppers and efficiency in communication system. The notion of a highly secure and reliable physical layer has the potential to significantly change how communication system designers and users think of the physical layer since the error control codes employed in this work will have the dual roles of both reliability and security.

This research discusses how the design of a tornado forward error correcting channel code (FEC) sends digital data stream profiles to the receiver. The complete design was based on the Tornado channel code, binary phase shift keying (BPSK) modulation on a Gaussian channel (AWGN). The communication link was simulated by using Matlab, which shows the theoretical systems efficiency. Then the data stream was input as data to be simulated communication systems using Matlab. The purpose of this paper is to introduce the audience to a simulation technique that has been successfully used to determine how well a FEC expected to work when transferring digital data streams. The goal is to use this data to show how FEC optimizes a digital data stream to gain a better digital communications systems. The results conclude by making comparisons of different possible styles for the Tornado FEC code.

No description available.

Single cell RNA sequencing allows expression profiles for individual cells to be obtained thus offering unprecedented insight into the behavior of individual cells. Insight gained from exploration of individual cells has implications in both cancer and developmental biology. Much of the power of these models is derived from the shear amount and granularity of the data that can be collected; however, with this power comes the deleterious introduction of batch effects. Samples sequenced on different days, by different technicians can show variance that cannot be attributed to biological condition, but rather is only due to the batch in which it was sequenced. These batch effects can cause alterations to the perceived relationships between the main effect and the outcome of interest, for instance cancer status, the main effect of cancer status may be hidden by the unwanted and unmodeled variance. Two known methods for the correction of batch effects in bulk RNA sequencing data are ComBat-Seq and Surrogate Variable Analysis; in this work, we demonstrate that when cell-type is known, inclusion of that covariate in the ComBat-Seq results in an appropriate correction of the batch effect. We also demonstrate that when cell-type is not known, SVA can be used to infer cell-type information form the latent structure of the count matrix with some loss of accuracy compared to the correction with cell type. This cell type information can be used in place of the actual cell-type covariate information to correct single cell RNA sequencing data with ComBat-Seq; inclusion of surrogate variables helps the accuracy of the correction in certain scenarios. Additionally, in the case where cell-type is not known, and the cell proportions are balanced between batches we demonstrate that ComBat-Seq can be used naive to cell-type information. The efficacy of this procedure is demonstrated with two simulated datasets and a dataset containing Jurkat and t293 cells. These results are then compared to Harmony, a recently reported batch correction algorithm. The procedure, herein reported, has benefits over harmony in certain situations such as when a counts matrix is needed for further analysis or when there is thought to be substantial intra-cell-type variability across different batches.

Syftet med denna litteraturstudie är att ta reda på vad den senaste forskningen säger om lärares korrigering av uttal hos elever i engelskundervisningen. Detta är viktigt då eleverna behöver ett funktionellt tal för att göra sig förstådda på målspråket och vissa uttalsfel kan försvåra begripligheten. Studien är baserad på sju artiklar som valts ut genom sökning i databaser. Artiklarna har systematiskt analyserats genom att ett kategoriseringsschema skapats med olika begrepp för att kunna identifiera mönster. Förr i tiden skulle alla fel alltid korrigeras eftersom målet var att uppnå ett modersmålsliknande uttal. På grund av alla olika modersmål och dialekter är detta i stort sett omöjligt och därför har man kommit fram till att det viktigaste är att eleverna förvärvar ett begripligt, funktionellt tal. Det ställs höga krav på lärare då de ofta ställs inför svåra dilemman. Lärare måste många gånger fatta snabba beslut angående vilken typ av feedback de ska ge sina elever vid uttalsfel i engelska. Det kan vara svårt att veta hur mycket och när lärare ska korrigera elevernas fel och vad är det egentligen som ska korrigeras. Resultatet påvisade många strategier för korrigering men att alla inte är så effektiva. Det bästa för eleverna är en trygg lärmiljö och att försöka framkalla självkorrigering genom att läraren ställer frågor och uppmanar eleverna till att tänka själva och komma fram till rätt svar. Detta kräver att eleverna har goda kunskaper om språkregler vilket även ställer krav på lärare att introducera dessa i de tidiga åldrarna. Lärare måste också analysera vilka fel eleverna gör för att kunna korrigera dem så effektivt som möjligt. Eleverna kan göra fel men också misstag. Fel ska hanteras direkt när de uppstår, det krävs även då en förklaring för att eleverna ska förstå språkreglerna. Misstag kan ignoreras då det oftast handlar om felsägningar. Det framkom även i studien att många lärare känner en osäkerhet om intonation och betoning men som är mycket viktigt för att ord ska få rätt betydelse. Att ge feedback och korrigera elevernas uttal är inte enkelt och kan vara väldigt känsligt. Därför är det viktigt att lärare är medvetna om hur effektiva deras korrigeringstekniker är och hur dessa tas emot av eleverna.

Large quantum computers have the potential to vastly outperform any classical computer. The biggest obstacle to building quantum computers of such size is noise. For example, state of the art superconducting quantum computers have average decoherence (loss of information) times of just microseconds. Thus, the field of quantum error correction is especially crucial to progress in the development of quantum technologies. In this research, we study quantum error correction for general noise, which is given by a linear Hermitian map. In standard quantum error correction, the usual assumption is to constrain the errors to completely positive maps, which is a special case of linear Hermitian maps. We establish constraints and sufficient conditions for the possible error correcting codes that can be used for linear Hermitian maps. Afterwards, we expand these sufficient conditions to cover a large class of general errors. These conditions lead to currently known conditions in the limit that the error map becomes completely positive. The later chapters give general results for quantum evolution maps: a set of weak repeated projective measurements that never break entanglement and the asymmetric depolarizing map composed with a not completely positive map that gives a completely positive composition. Finally, we give examples.

Indiana University-Purdue University Indianapolis (IUPUI) / The advances in health care technology help physicians make more accurate diagnoses about the health conditions of their patients. Positron Emission Tomography/Computed Tomography (PET/CT) is one of the many tools currently used to diagnose health and disease in patients. PET/CT explorations are typically used to detect: cancer, heart diseases, disorders in the central nervous system. Since PET/CT studies can take up to 60 minutes or more, it is impossible for patients to remain motionless throughout the scanning process. This movements create motion-related artifacts which alter the quantitative and qualitative results produced by the scanning process. The patient's motion results in image blurring, reduction in the image signal to noise ratio, and reduced image contrast, which could lead to misdiagnoses. In the literature, software and hardware-based techniques have been studied to implement motion correction over medical files. Techniques based on the use of an external motion tracking system are preferred by researchers because they present a better accuracy. This thesis proposes a motion correction system that uses 3D affine registrations using particle swarm optimization and an off-the-shelf Microsoft Kinect camera to eliminate or reduce errors caused by the patient's motion during a medical imaging study.

Soil moisture is an essential climate variable and provides critical state information for hydrological applications. The state of soil moisture influences the exchange of water and energy between the earth surface and the atmosphere, partitions infiltration and runoff, can limit the net primary productivity of a region and govern the dynamics of geochemical processes. Satellite observations can be used to provide information about this important variable but are often available at a scale that is far greater than most hydrological processes. The scope of the research presented in this dissertation was to identify practical methods to facilitate the use of coarse scale satellite soil moisture information in higher resolution hydrological and land-surface modelling applications. Research was primarily conducted in the Hamilton-Halton watershed of Southern Ontario, Canada, although other watersheds and datasets were periodically used in some chapters. A comprehensive review was conducted on the use of high resolution soil moisture information for hydrological applications, and data assimilation was identified as the most common method for integrating soil moisture information into a hydrological model. It was also identified that most watersheds displayed the property of temporal persistence and that root-zone soil moisture was of greater importance than surface soil moisture (Appendix B). In light of this information, the focus of this research was the downscaling of soil moisture and brightness temperature (TB) observations from the Soil Moisture and Ocean Salinity (SMOS) passive microwave satellite. Satellite observations are sensitive to surface soil moisture, while rootzone soil moisture provides the greatest benefit to hydrological and land surface applications. To overcome this discrepancy, artificial neural networks (ANN) were evaluated as a method to estimate rootzone soil moisture from surface observations that accounted for the known non-linearities of soil moisture processes. The ANN model was trained with a numerical soil moisture physics model and validated using in situ observations from the McMaster Mesonet and USDA SCAN sites. The ANN was capable of accurately depicting the rootzone soil moisture based on its training data at multiple sites, but was limited when the temporal distribution of soil moisture at a particular site was considerably different than the training data. Therefore, with the appropriate training data, ANNs are a viable method for predicting rootzone soil moisture from surface observations such as those available from satellites. To provide high resolution soil moisture information from coarse resolution satellite data, bias correction was proposed and evaluated as a downscaling method for both soil moisture and TB. Using in situ data from two well instrumented USDA watersheds and a hydrological land-surface scheme (HLSS), it was found that temporal evolution of both soil moisture and TB at fine scale (~1 km) could be well characterized by the temporal evolution of the coarse scale (~20 km) soil moisture and TB. The fine scale spatial distribution of soil moisture could be predicted with a high degree of skill by correcting the bias between the coarse and fine scale soil moisture/TB. In studying the correction of biases, it was found that naïve application of bias correction methods could result in the introduction of multiplicative biases in the bias corrected dataset. The theoretical implications of this for a data assimilation system were discussed although not yet evaluated. A bootstrap resampling approach was evaluated as a solution to this problem and it was found that resampled data could result in a robust bias correction that eliminated additive bias in most instances while limiting the induction of multiplicative bias. This new method was found to significantly outperform the standard bias correction techniques. / Thesis / Doctor of Philosophy (PhD) / Soil moisture is an important hydrological variable. The state of soil moisture controls the partition between the runoff and infiltration as well as the exchange of heat from the surface to the atmosphere. Therefore, an accurate depiction of the state of soil moisture is important for producing accurate flood and drought forecasts, numerical weather prediction and agricultural forecasts. The state of soil moisture can be observed from space using microwave remote sensing measurements. However, the resolution of most passive microwave observations, such as those from the European Space Agency Soil Moisture and Ocean Salinity (SMOS) satellite are at a resolution of approximately 40 km which is far more coarse than the approximately 1 km resolution of most hydrological processes. The work in this thesis presented bias correction methods as a mean to match the spatial scale of the satellite observations to high resolution hydrological and land surface models. These data were generated and compared using an advanced land surface hydrological scheme under development at Environment Canada. It was found that simple bias correction methods were capable of effectively downscaling SMOS observations to the a scale of 1 km without the loss of information from the satellite. A new bias correction method was also presented that was found to significantly outperform standard techniques.

This study investigated the efficacy of dynamic written corrective feedback (DWCF) on intermediate-high students' writing accuracy when compared to a traditional grammar instruction approach. DWCF is an innovative written corrective feedback method that requires a multifaceted process and interaction between the teacher and the students in order to help the students improve their writing accuracy. The central principle of DWCF is that feedback should be manageable, meaningful, timely, and constant. The research question was raised based on the positive effects of DWCF found in advanced-low and advanced-mid proficiency level students (Evans et al., in press; Evans, Hartshorn, & Strong-Krause, 2009; Hartshorn, 2008; Hartshorn et al., in press). Similar to previous studies, this study attempted to examine the effectiveness of DWCF in terms of proficiency level. It further explored students' perspectives and attitudes towards DWCF. Two groups of ESL students participated in this study: a control group (n=18) that was taught using a traditional grammar instruction method, and a treatment group (n=35) that was taught using a DWCF approach. The findings in this study revealed that both methods improved the intermediate-high students' linguistic accuracy in writing. However, the findings of this study suggest that the instruction utilizing DWCF is preferable to traditional grammar instruction when it comes to improving intermediate-high students' writing accuracy for two reasons: first, DWCF was slightly more effective than the traditional grammar instruction used, and second, students strongly preferred the instruction using DWCF to traditional grammar instruction. The findings of this study further validate other work suggesting the positive effects found in advanced proficiency levels. This study indicates that ESL learners benefit from manageable, meaningful, timely, and constant error feedback in improving their linguistic accuracy in writing. Furthermore, this study suggests the desirability of applying DWCF to other contexts.

No / Wavelet based multicarrier systems have attracted the attention of the researchers over the past few years to replace the conventional OFDM systems in the next generation communication systems. In this paper we have investigated the performance of such wavelet based systems using forward error correction with covolutional coding and interleaving in a Wavelet-SISO system and then in a Wavelet multicarrier modulation (WMCM) multiple input multiple output (MIMO) system using Convolutional coding and beamforming to reduce the source bit rate and overall system error and increase the data rate. Results show outstanding Bit Error Rate vs. Signal to Noise Ratio Performance. Other than better performance the proposed systems keep the computational burden off the receiver that has more cost and power constraints.