On the DMT of TDD-SIMO Systems with Channel-Dependent Reverse Channel Training

This paper investigates the Diversity-Multiplexing gain Trade-off (DMT) of a training based reciprocal Single Input Multiple Output (SIMO) system, with (i) perfect Channel State Information (CSI) at the Receiver (CSIR) and noisy CSI at the Transmitter (CSIT), and (ii) noisy CSIR and noisy CSIT. In both the cases, the CSIT is acquired through Reverse Channel Training (RCT), i.e., by sending a training sequence from the receiver to the transmitter. A channel-dependent fixed-power training scheme is proposed for acquiring CSIT, along with a forward-link data transmit power control scheme. With perfect CSIR, the proposed scheme is shown to achieve a diversity order that is quadratically increasing with the number of receive antennas. This is in contrast with conventional orthogonal RCT schemes, where the diversity order is known to saturate as the number of receive antennas is increased, for a given channel coherence time. Moreover, the proposed scheme can achieve a larger DMT compared to the orthogonal training scheme. With noisy CSIR and noisy CSIT, a three-way training scheme is proposed and its DMT performance is analyzed. It is shown that nearly the same diversity order is achievable as in the perfect CSIR case. The time-overhead in the training schemes is explicitly accounted for in this work, and the results show that the proposed channel-dependent RCT and data power control schemes offer a significant improvement in terms of the DMT, compared to channel-agnostic orthogonal RCT schemes. The outage performance of the proposed scheme is illustrated through Monte-Carlo simulations.Comment: Accepted for publication in IEEE Transactions on Communication

Regionalization Of Hydrometeorological Variables In India Using Cluster Analysis

Regionalization of hydrometeorological variables such as rainfall and temperature is necessary for various applications related to water resources planning and management. Sampling variability and randomness associated with the variables, as well as non-availability and paucity of data pose a challenge in modelling the variables. This challenge can be addressed by using stochastic models that utilize information from hydrometeorologically similar locations for modelling the variables. A set of locations that are hydrometeorologically similar are referred to as homogeneous region or pooling group and the process of identifying a homogeneous region is referred to as regionalization. The thesis concerns development of new approaches to regionalization of (i) extreme rainfall,(ii) maximum and minimum temperatures, and (iii) rainfall together with maximum and minimum temperatures. Regionalization of extreme rainfall and frequency analysis based on resulting regions yields quantile estimates that find use in design of water control (e.g., barrages, dams, levees) and conveyance structures (e.g., culverts, storm sewers, spillways) to mitigate damages that are likely due to floods triggered by extreme rainfall, and land-use planning and management. Regionalization based on both rainfall and temperature yield regions that could be used to address a wide spectrum of problems such as meteorological drought analysis, agricultural planning to cope with water shortages during droughts, downscaling of precipitation and temperature. Conventional approaches to regionalization of extreme rainfall are based extensively on statistics derived from extreme rainfall. Therefore delineated regions are susceptible to sampling variability and randomness associated with extreme rainfall records, which is undesirable. To address this, the idea of forming regions by considering attributes for regionalization as seasonality measure and site location indicators (which could be determined even for ungauged locations) is explored. For regionalization, Global Fuzzy c-means (GFCM) cluster analysis based methodology is developed in L-moment framework. The methodology is used to arrive at a set of 25 homogeneous extreme rainfall regions over India considering gridded rainfall records at daily scale, as there is dearth of regionalization studies on extreme rainfall in India Results are compared with those based on commonly used region of influence (ROI) approach that forms site-specific regions for quantile estimation, but lacks ability to delineate a geographical area into a reasonable number of homogeneous regions. Gridded data constitute spatially averaged rainfall that might originate from a different process (more synoptic) than point rainfall (more convective). Therefore to investigate utility of the developed GFCM methodology in arriving at meaningful regions when applied to point rainfall data, the methodology is applied to daily rainfall records available for 1032 gauges in Karnataka state of India. The application yielded 22 homogeneous extreme rainfall regions. Experiments carried out to examine utility of GFCM and ROI based regions in arriving at quantile estimates for ungauged sites in the study area reveal that performance of GFCM methodology is fairly close to that of ROI approach. Errors were marginally lower in the case of GFCM approach in analysis with observed point rainfall data over Karnataka, while its converse was noted in the case of analysis with gridded rainfall data over India. Neither of the approaches (CA, ROI) was found to be consistent in yielding least error in quantile estimates over all the sites. The existing approaches to regionalization of temperature are based on temperature time series or their related statistics, rather than attributes effecting temperature in the study area. Therefore independent validation of the delineated regions for homogeneity in temperature is not possible. Another drawback of the existing approaches is that they require adequate number of sites with contemporaneous temperature records for regionalization, because the delineated regions are susceptible to sampling variability and randomness associated with the temperature records that are often (i) short in length, (ii) limited over contemporaneous time period and (iii) spatially sparse. To address these issues, a two-stage clustering approach is developed to arrive at regions that are homogeneous in terms of both monthly maximum and minimum temperatures ( and ). First-stage of the approach involves (i) identifying a common set of possible predictors (LSAVs) influencing and over the entire study area, and (ii) using correlations of those predictors with and along with location indicators (latitude, longitude and altitude) as the basis to delineate sites in the study area into hard clusters through global k-means clustering algorithm. The second stage involves (i) identifying appropriate LSAVs corresponding to each of the first-stage clusters, which could be considered as potential predictors, and (ii) using the potential predictors along with location indicators (latitude, longitude and altitude) as the basis to partition each of the first-stage clusters into homogeneous temperature regions through global fuzzy c-means clustering algorithm. A set of 28 homogeneous temperature regions was delineated over India using the proposed approach. Those regions are shown to be effective when compared to an existing set of 6 temperature regions over India for which inter-site cross-correlations were found to be weak and negative for several months, which is undesirable. Effectiveness of the newly formed regions is demonstrated. Utility of the proposed maxTminT homogeneous temperature regions in arriving at PET estimates for ungauged locations within the study area was demonstrated. The estimates were found to be better when compared to those based on the existing regions. The existing approaches to regionalization of hydrometeorological variables are based on principal components (PCs)/ statistics/indices determined from time-series of those variables at monthly and seasonal scale. An issue with use of PCs for regionalization is that they have to be extracted from contemporaneous records of hydrometeorological variables. Therefore delineated regions may not be effective when the available records are limited over contemporaneous time period. A drawback associated with the use of statistics/indices is that they (i) may not be meaningful when data exhibit nonstationarity and (ii) do not encompass complete information in the original time series. Consequently the resulting regions may not be effective for the desired purpose. To address these issues, a new approach is proposed. It considers information extracted from wavelet transformations of the observed multivariate hydrometeorological time series as the basis for regionalization by global fuzzy c-means clustering procedure. The approach can account for dynamic variability in the time series and its nonstationarity (if any). Effectiveness of the proposed approach in forming homogeneous hydrometeorological regions is demonstrated by application to India, as there are no prior attempts to form such regions over the country. The investigations resulted in identification of 29 regions over India, which are found to be effective and meaningful. Drought Severity-Area-Frequency (SAF) curves are developed for each of the newly formed regions considering the drought index to be Standardized Precipitation Evapotranspiration Index (SPEI)

Flora of Sacred Groves at Sriharikota Island, Andhra Pradesh, India

Sriharikota is botanically interesting place in Andhra Pradesh by virtue of being an island in Nellore District harbouring a rich vegetation and a popular place also because of establishment of Rocket Launching Station. The anecdote behind the same Sriharikota is that there are half a million of Siva Lingams present in the island. The legend derived its strength from the words ‘arc’ (half) and cotti (crore), ‘Sri’ being a qualifying term. However, the fact is that there are a good number of dilapidated temples around which note- worthy vegetation, worth a critical study. It is said that a number of idols also were found during excavation operations while construction programme of SHAR establishment was carried out. One such idol is presently installed at newly constructed temple in the area. Hence a study of flora of sacred groves is undertaken. A good number of medicinal plants are recorded around the sacred groves. However 18 plants only of high importance are reported here, such as Albizzia amara, Lannea coromandelica, Loesneriella obtusifolia, Strychnos nux-vomica and Strychnos potatorum etc

Biodiversity Tools For Boosting Immune System Of Homosapiens: An In Vitro Study Of Abutilon Indicium Leaves

Climate change is attributed directly or indirectly to human activity that alters the compositions of the global atmosphere. Human beings are both agents and victims of environmental change. Therefore, climate change is the main reason for the environmental challenge that the world faces today. To overcome these negative impact on human health, biodiversity has given powerful tools and healing powers in the form of plants and herbs for boosting human body’s immune system which keeps homosapiens finally strong, hale and healthy. Many complex diseases including heart problems require long and expensive treatment which common man in developing countries cannot afford. India has a long history for the treatment of various diseases using traditional medicinal plants. In contrast to synthetic compounds, herbal products are safer with minimum side effects and preferred largely for the treatment of various ailments. Thromboembolism involving the arterial or venous circulation or arising from the heart is a common cause of morbidity and mortality. India with its numerous plants variety offers costless method and inexpensive treatment to a number of disorders such as thromboembolism. The present study explores how to find out the in vitro anticoagulant activities of abutilon indicium leaves extracts, in addition to comparing and contrasting the findings with othersimilarstudiesauthoredbyanumberof medical practitioner

A User-Centric Continuous Authentication Modality Evaluation And Selection Scheme

One of one-time authentication’s most prominent vulnerabilities is the possibility of lunchtime attacks. In such scenarios, an adversary could exploit an unattended device with an active session and no measures are taken to prevent them from committing malicious acts. To address this issue, continuous authentication is utilized by continuously verifying whether an individual is a device’s rightful owner through various modalities, with data sourced from sensors. In the current body of research within this rising domain, various single-modal and multi-modal continuous authentication systems exist, that focus on employing unique combinations of modalities and improving existing supervised learning models used to solve this classification problem. However, no solutions allow prospective continuous authentication users to obtain the most suitable combination of modalities given their unique circumstances. Therefore, in this thesis, we design a user-centric continuous authentication modality evaluation and selection scheme. The scheme employs a multi-criteria decision analysis model, which involves compiling a list of continuous authentication systems, modalities, and associated sensors. As part of this scheme, we design security, privacy, and usability frameworks to conduct systematic analyses of the list of sensors and modalities gathered, while considering the system’s performance. The proposed scheme can be utilized to generate a ranked list of combinations of modalities appropriate for the user

Computer-Assisted Algorithms for Ultrasound Imaging Systems

Ultrasound imaging works on the principle of transmitting ultrasound waves into the body and reconstructs the images of internal organs based on the strength of the echoes. Ultrasound imaging is considered to be safer, economical and can image the organs in real-time, which makes it widely used diagnostic imaging modality in health-care. Ultrasound imaging covers the broad spectrum of medical diagnostics; these include diagnosis of kidney, liver, pancreas, fetal monitoring, etc. Currently, the diagnosis through ultrasound scanning is clinic-centered, and the patients who are in need of ultrasound scanning has to visit the hospitals for getting the diagnosis. The services of an ultrasound system are constrained to hospitals and did not translate to its potential in remote health-care and point-of-care diagnostics due to its high form factor, shortage of sonographers, low signal to noise ratio, high diagnostic subjectivity, etc. In this thesis, we address these issues with an objective of making ultrasound imaging more reliable to use in point-of-care and remote health-care applications. To achieve the goal, we propose (i) computer-assisted algorithms to improve diagnostic accuracy and assist semi-skilled persons in scanning, (ii) speckle suppression algorithms to improve the diagnostic quality of ultrasound image, (iii) a reliable telesonography framework to address the shortage of sonographers, and (iv) a programmable portable ultrasound scanner to operate in point-of-care and remote health-care applications

Fast Region of Interest detection for fetal genital organs in B-mode ultrasound images

Genital organ detection of fetus in B-mode ultrasound images has a considerable significance. It is useful to know any malformations present in the genital organs and also to determine the sex of the fetus. In this paper we propose a Feature from Accelerated Segment Test (FAST) technique for approximate detection of fetal genitals in ultrasound images. FAST algorithm is capable of producing the corner points at a higher speed which falls on the fetal genital organs. A window of size 60×60 pixels being corner point as a center is considered as Region of Interest (ROI), where genital organ of fetus is anticipated. The efficiency of the algorithm is calculated as the ratio of number of images where corner points are placed on the fetus genital organ to the total number of images tested. FAST algorithm is robust to speckles present in the image, machine independent, fast and also computationally less intensive to implement in real time with an efficiency of 96.7%

Automatic organ validation of b-mode ultrasound images for transmission to cloud

Miniaturization in size of Medical ultrasound scanning machine made it to use in point of care applications. Lack of sonographers and their unwillingness to work in rural areas limit the benefits of ultrasound system in rural healthcare. Diagnosis of patients through ultrasound is done by visualizing the ultrasound scanned images of organs. Diagnosis through telemedicine involves transmitting of ultrasound images from rural locations to cloud, where sonographer can remotely access the ultrasound data from cloud and generate the report, thus reducing the geographical separation between patients and doctors. Due to lack of adequate sonographers, ultrasound scanning in remote areas is operated by semi-skilled clinicians. Most of the images generated by semi-skilled clinicians are not useful for diagnosis. Transmitting all these images increases the data in cloud, drains the battery of portable ultrasound machine and increases latency in medication. This paper provides automatic B-mode ultrasound image validation based on organ information present in the image for diagnosis, thus avoiding transmission of invalid images to cloud. Linear kernel SVM classifier trained with first order statistic features of image with/without organs is used to classify the images into valid and invalid for diagnosis. The algorithm resulted with a recognition efficiency of 94.2% in classifying the ultrasound images

Work from home:Benefits and Pitfalls among software employees

Introduction: Work from home is referred to as employees need not go to corporate companies, but instead perform their work at home using an internet connection, by satellite connection. The employees are happier working from home than in the office. Benefits of working from home is the employees can spend time with family, releasing travelling stress. Therefore, the aim of this study is to find work from home : benefits and pitfalls among the software employees Materials and methods: In this study sample size was 100 software employees between the age group of 20-50 years from software companies in Chennai, Tamil Nadu, India. The questionnaire comprising 21 questions were created in google forms and sent to 100 software employees through whats app. And the data is collected and analysed by spss version 23. Descriptive statistics was expressed by means of number and frequency and percentage and the chi square test was used to find out associated between variables. Levels of statistical significance will be P<0.05 Results: Out of the total 101 responses, 67% were males and 33% were females. 40.59% males said yes that their home is office silent and 26.73% said no. And females said 11.88% said yes that their home is office silent and 20.79%said no. 17.82% female said work from home time saving 2.97% said it is stressful. 0.99% males said time saving and 3.96% said stressful Conclusion: Based on the results of the present study, it can be concluded that work from home is found to be beneficial among the study participants