Ray tracing for constructive solid modeling

This thesis describes a system for the creation and realistic depiction of non-geometric, complex, three dimensional solid models by utilizing a ray tracing algorithm and a graphics relational database. Geometric primitives such as a sphere, cylinder, block, and cone are combined together by using the Boolean set operations of union (+), intersection (&), and difference (-). The three dimensional solid models are built based on the concept of constructive solid geometric modeling. The database provides functions for the creation, transformation, and deletion of the primitives and models. A model may be displayed as a wireframe for a fast display or as a shaded solid for a realistic display

Correlation and path analysis between yield and different morphological characters in Kinnow Mandarin (C. Noballis × C. Deliciosa)

A study of interrelationship and cause-effect analysis of yield of Kinnow Mandarin (C. Noballis × C. deliciosa) and its component traits for 104 and 96 trees from Indora (Location L-1) and Indpur (Location L-2) blocks of Kangra district respectively had been done in 2014-15. F-test suggested that there was significant (1.47) variation among yield characteristics between these two locations except for fruit weight (gm) and LD ratio. Results obtained from path coefficient analysis, showed that for location L-1, number of fruits per branch (0.229), plant height (0.215) and tree girth (0.212) had highest and direct effect on yield per tree whereas for location L-2, fruit weight (0.38), number of flowers per branch (0.176) and plant girth (0.161) had highest direct effect on yield per plant. Thus, number of fruits per branch, number of flowers per branch and tree girth were the most important yield components of kinnow crop which should be exploited through a breeding programme for improving its yield potential

Machine learning Based Bearing Fault Classification Using Higher Order Spectral Analysis

In the defense sector, where mission success often hinges on the reliability of complex mechanical systems, the health of bearings within aircraft, naval vessels, ground vehicles, missile systems, drones, and robotic platforms is paramount. Different signal processing techniques along with Higher Order Spectral Analysis (HOSA) have been used in literature for the fault diagnosis of bearings. Bispectral analysis offers a valuable means of finding higher-order statistical associations within signals, thus proving to detect the nonlinearities among Gaussian and non-Gaussian data. Their resilience to noise and capacity to unveil concealed information render them advantageous across a range of applications. Therefore, this research proposesa novel approach of utilizing the features extracted directly from the Bispectrum for classifying the bearing faults, departing from the common practice in other literature where the Bispectrum is treated as an image for fault classification. In this work vibration signalsare used to detect the bearing faults. The features from the non-redundant region and diagonal slice of the Bispectrum are used to capture the statistical and higher-order spectral characteristics of the vibration signal. A set of sixteen machine learning models, viz., Decision Trees, K-Nearest Neighbors, Naive Bayes, and Support Vector Machine, is employed to classify the bearing faults. The evaluation process involves a robust 10-fold cross-validation technique. The results reveal that the Decision Tree algorithm outperformed all others, achieving a remarkable accuracy rate of 100 %. The naive Bayes algorithm also demonstrated the least performance, with an accuracy score of 99.68 %. The results obtained from these algorithms have been compared with those achieved using Convolutional Neural Network (CNN), revealing that the training time of these algorithms is significantly shorter in comparison to CNN

Energy Aware Resource Allocation for Clouds Using Two Level Ant Colony Optimization

In cloud environment resources are dynamically allocated, adjusted, and deallocated. When to allocate and how many resources to allocate is a challenging task. Resources allocated optimally and at the right time not only improve the utilization of resources but also increase energy efficiency, provider's profit and customers' satisfaction. This paper presents ant colony optimization (ACO) based energy aware solution for resource allocation problem. The proposed energy aware resource allocation (EARA) methodology strives to optimize allocation of resources in order to improve energy efficiency of the cloud infrastructure while satisfying quality of service (QoS) requirements of the end users. Resources are allocated to jobs according to their QoS requirements. For energy efficient and QoS aware allocation of resources, EARA uses ACO at two levels. First level ACO allocates Virtual Machines (VMs) resources to jobs whereas second level ACO allocates Physical Machines (PMs) resources to VMs. Server consolidation and dynamic performance scaling of PMs are employed to conserve energy. The proposed methodology is implemented in CloudSim and the results are compared with existing popular resource allocation methods. Simulation results demonstrate that EARA achieves desired QoS and superior energy gains through better utilization of resources. EARA outperforms major existing resource allocation methods and achieves up to 10.56 % saving in energy consumption

TLMs in teaching of science

Under the present scenario, pedagogy demands our consistent effort to evolve scientific teaching methods. It must involve the use of scientific tools or Teaching Learning Material (TLM) without which neither teaching nor learning is possible. In this article, I will talk about the experiences of my life as a student as well as a teacher and also share my understanding that changed in course of time

EQUAL: Energy and QoS Aware Resource Allocation Approach for Clouds

The popularity of cloud computing is increasing by leaps and bounds. To cope with resource demands of increasing number of cloud users, the cloud market players establish large sized data centers. The huge energy consumption by the data centers and liability of fulfilling Quality of Service (QoS) requirements of the end users have made resource allocation a challenging task. In this paper, energy and QoS aware resource allocation approach which employs Antlion optimization for allocation of resources to virtual machines (VMs) is proposed. It can operate in three modes, namely power aware, performance aware, and balanced mode. The proposed approach enhances energy efficiency of the cloud infrastructure by improving the utilization of resources while fulfilling QoS requirements of the end users. The proposed approach is implemented in CloudSim. The simulation results have shown improvement in QoS and energy efficiency of the cloud

Prevalence of female genital tract tuberculosis in suspected cases attending Gynecology OPD at tertiary centre by various diagnostic methods and comparative analysis

Background: The genital tract tuberculosis is one of the most common causes of tubal factor infertility. This study was conducted to compare the results of different diagnostic methods used in screening for female genital tuberculosis in suspected cases attending Gynecology OPD at RMC, Ajmer.Methods: This prospective study was conducted in department of obstetrics and gynecology, J. L. N. Medical College, Ajmer, Rajasthan, for studying incidence of genital tuberculosis by various diagnostic methods (viz. AFB smear examination, AFB Lowenstein Jensen culture method, TB-PCR and CBNAAT).Results: Prevalence of genital TB was 5.5% in study population of 200 selected women meting the inclusion criteria. 72% women were in between 20-30 years age group. Oligomenorrhoea (24%) was found to be significant symptom with P value of <0.05. TBPCR and CBNAAT were found to be statistically significant with P value of <0.001 for diagnosing FGTTB.Conclusions: We concluded that genital tuberculosis is paucibacillary disease, TBPCR and CBNAAT appears to be rapid and sensitive diagnostic modality