Channel Estimation for MIMO MC-CDMA Systems

The concepts of MIMO MC-CDMA are not new but the new technologies to improve their functioning are an emerging area of research. In general, most mobile communication systems transmit bits of information in the radio space to the receiver. The radio channels in mobile radio systems are usually multipath fading channels, which cause inter-symbol interference (ISI) in the received signal. To remove ISI from the signal, there is a need of strong equalizer. In this thesis we have focused on simulating the MIMO MC-CDMA systems in MATLAB and designed the channel estimation for them

Potent Induction of Arabidopsis thaliana Flowering by Elevated Growth Temperature

The transition to flowering is an important event in the plant life cycle and is modulated by several environmental factors including photoperiod, light quality, vernalization, and growth temperature, as well as biotic and abiotic stresses. In contrast to light and vernalization, little is known about the pathways that mediate the responses to other environmental variables. A mild increase in growth temperature, from 23 °C to 27 °C, is equally efficient in inducing flowering of Arabidopsis plants grown in 8-h short days as is transfer to 16-h long days. There is extensive natural variation in this response, and we identify strains with contrasting thermal reaction norms. Exploiting this natural variation, we show that FLOWERING LOCUS C potently suppresses thermal induction, and that the closely related floral repressor FLOWERING LOCUS M is a major-effect quantitative trait locus modulating thermosensitivity. Thermal induction does not require the photoperiod effector CONSTANS, acts upstream of the floral integrator FLOWERING LOCUS T, and depends on the hormone gibberellin. Analysis of mutants defective in salicylic acid biosynthesis suggests that thermal induction is independent of previously identified stress-signaling pathways. Microarray analyses confirm that the genomic responses to floral induction by photoperiod and temperature differ. Furthermore, we report that gene products that participate in RNA splicing are specifically affected by thermal induction. Above a critical threshold, even small changes in temperature can act as cues for the induction of flowering. This response has a genetic basis that is distinct from the known genetic pathways of floral transition, and appears to correlate with changes in RNA processing

Semantic Theory in Ainkurunuru

The Tamil language can be said to be the oldest of the world's languages. Tamil literature deals with various aspects of life. They contain a variety of literature, grammar, stories, essays, poems, proverbs, etc. Moreover, the Sangam literature depicts everyday life. From the Sangam age to the present period, a variety of writers and books have appeared. The Sangam literature consists of a large number of sections viz., Ettutthokai, Patthuppaattu, and Pathinen Kilkanakku books. Though there is Akam and Puram in the Ettutthokai of Sangam literature, in the third of Ainkurunooru ten songs to each of five thinais, that is five hundred songs are found.  All of these songs were sung by different poets at different times and in different contexts. Though all the Pulavars are Tamils, they all belong to different periods. The Tamil language can vary from time to time. Therefore, the vocabulary of each scholar can vary. It was the Ettutthokai that beautifully illustrated the conditions of life of the people of the Sangam age and compiled them as well as illustrated them clearly. Moreover, the Ettutthokai volume of books has shown the true feelings of the people of the Sangam age such as valour, love, justice, benevolence, humanity, pride, and warmth. In Sangam literature, the imaginary lines about love are classified as Akam and the fields of life such as heroism, love, charity, etc., are classified as Puram

Object identification for robotic applications using expert systems

The objective of this project is to develop an intelligent machine vision system for robotic applications to identify engineering tools and components; The imaging system consists of a 2-D digital camera and an ultrasonic range sensor attached to the robot end effector. Images of the target objects are captured by the camera. The images are then processed to remove the signal noise and to extract the object boundary; One major objective is to develop object feature descriptions which are invariant to scaling, translation or orientation. Efficient data reduction to an array of fewer than 25 numbers is achieved by the use of Fourier and regional descriptors. One of the array elements, object thickness, is determined directly from ultrasound range measurement; An expert system was successfully developed to classify the objects based on their descriptors. The knowledge base consists of rules for searching and pattern matching. The sensors were integrated to form a working vision system for the PUMA 500 robot. The performance of the vision system was tested with a set of objects. The expert system was found to be efficient, successful, and reliable in identifying all tested objects even with signal noise being present

Novel statistical approaches for missing values in truncated high-dimensional metabolomics data with a detection threshold.

Despite considerable advances in high throughput technology over the last decade, new challenges have emerged related to the analysis, interpretation, and integration of high-dimensional data. The arrival of omics datasets has contributed to the rapid improvement of systems biology, which seeks the understanding of complex biological systems. Metabolomics is an emerging omics field, where mass spectrometry technologies generate high dimensional datasets. As advances in this area are progressing, the need for better analysis methods to provide correct and adequate results are required. While in other omics sectors such as genomics or proteomics there has and continues to be critical understanding and concern in developing appropriate methods to handle missing values, handling of missing values in metabolomics has been an undervalued step. Missing data are a common issue in all types of medical research and handling missing data has always been a challenge. Since many downstream analyses such as classification methods, clustering methods, and dimension reduction methods require complete datasets, imputation of missing data is a critical and crucial step. The standard approach used is to remove features with one or more missing values or to substitute them with a value such as mean or half minimum substitution. One of the major issues from the missing data in metabolomics is due to a limit of detection, and thus sophisticated methods are needed to incorporate different origins of missingness. This dissertation contributes to the knowledge of missing value imputation methods with three separate but related research projects. The first project consists of a novel missing value imputation method based on a modification of the k nearest neighbor method which accounts for truncation at the minimum value/limit of detection. The approach assumes that the data follows a truncated normal distribution with the truncation point at the detection limit. The aim of the second project arises from the limitation in the first project. While the novel approach is useful, estimation of the truncated mean and standard deviation is problematic in small sample sizes (N \u3c 10). In this project, we develop a Bayesian model for imputing missing values with small sample sizes. The Bayesian paradigm has generally been utilized in the omics field as it exploits the data accessible from related components to acquire data to stabilize parameter estimation. The third project is based on the motivation to determine the impact of missing value imputation on down-stream analyses and whether ranking of imputation methods correlates well with the biological implications of the imputation