5 research outputs found
RF circuit and antenna optimization using space mapping technique
No full textThe use of EM simulation in circuit optimization in RF domain is very common. There are some problems with this kind of optimization. Firstly, in many cases EM simulations are expensive process. Another problem is the relationship between circuit response and design variable is not an easy function in many cases.
In order to overcome these problems a technique named space mapping (SM) was suggested. This technique has been experimentally proved as effective and efficient technique to do optimization including RF circuit optimization.
From large number of SM algorithm variants there are only a few that are designed specially for constrained optimization. In this report some constrained SM algorithms using projection method are investigated. These algorithms are variants of Aggressive Space Mapping (ASM) and Aggressive Output Space Mapping (AOSM) and designed to handle convex constraints. The methods are used to optimize multilayer LTCC bandpass filter. The numerical results shows good convergence rate.
All of the constrained SM algorithms designed in this report use equivalent circuit and embedded knowledge in coarse model. Parameter extraction is done using circuit tuning based on physical augmentation. In order to test the efficiency of the tuning method, this tuning method is used to tune multilayer LTCC bandpass filter equivalent circuit. The result shows this tuning algorithm is effective and efficient.
This circuit tuning algorithm is a derivative of a modeling algorithm based on physical augmentation. The use of this modeling technique in producing equivalent circuit for antenna is investigated. It is shown experimentally that this modeling algorithm is effective and efficient.
Although some of proposed constrained SM algorithms are designed to provide global convergence, the author can not prove the global convergence mathematically. The author can only provide the proof for local convergence of some constrained SM algorithms. Further developments may be done in the direction of finding the mathematical analysis of these SM algorithms, refining the methods especially the AOSM based methods and combining the ASM and AOSM.Bachelor of Engineerin
Agro-morphological analysis of yield and yield attributing traits of wheat under heat stress condition
No full textWheat is the most important cereal crop worldwide and ranks third in Nepal. Improvements in wheat yield can be done effectively by selection for yield attributing traits. In this experiment, twenty wheat genotypes were evaluated in the terai region of Nepal at Paklihawa, Rupandehi in Alpha lattice design under heat stress conditions. The characters were evaluated to find their correlation and direct and indirect effects on yield. Positive significant correlation of grain yield with No. of spikes m-2 (0.405) and harvest index (0.647) were found whereas Spike weight (-0.322) showed a significant negative correlation with grain yield. Similarly, Path analysis showed that the Harvest index (0.5511) and No. of spikelets per spike (0.3365) had a high direct effect, whereas Thousand kernel weight, Spike m-2, and Plant height showed a lower positive direct effect on grain yield. Ten spikes weight, spike length, and No. of grains per spike showed low negative direct effects. The conclusions drawn from this analysis can be useful for breeding programs under heat stress by providing information on which characteristics significantly affect the yield. However, multi-locations and multi-year trials need to be done for further verifications on the selection of such traits for improving yield
Automatic Detection of Oral Squamous Cell Carcinoma from Histopathological Images of Oral Mucosa Using Deep Convolutional Neural Network
No full textWorldwide, oral cancer is the sixth most common type of cancer. India is in 2nd position, with the highest number of oral cancer patients. To the population of oral cancer patients, India contributes to almost one-third of the total count. Among several types of oral cancer, the most common and dominant one is oral squamous cell carcinoma (OSCC). The major reason for oral cancer is tobacco consumption, excessive alcohol consumption, unhygienic mouth condition, betel quid eating, viral infection (namely human papillomavirus), etc. The early detection of oral cancer type OSCC, in its preliminary stage, gives more chances for better treatment and proper therapy. In this paper, author proposes a convolutional neural network model, for the automatic and early detection of OSCC, and for experimental purposes, histopathological oral cancer images are considered. The proposed model is compared and analyzed with state-of-the-art deep learning models like VGG16, VGG19, Alexnet, ResNet50, ResNet101, Mobile Net and Inception Net. The proposed model achieved a cross-validation accuracy of 97.82%, which indicates the suitability of the proposed approach for the automatic classification of oral cancer data
Coherent control of a strongly driven silicon vacancy optical transition in diamond
No full text© 2017 The Author(s). The ability to prepare, optically read out and coherently control single quantum states is a key requirement for quantum information processing. Optically active solid-state emitters have emerged as promising candidates with their prospects for on-chip integration as quantum nodes and sources of coherent photons connecting these nodes. Under a strongly driving resonant laser field, such quantum emitters can exhibit quantum behaviour such as Autler-Townes splitting and the Mollow triplet spectrum. Here we demonstrate coherent control of a strongly driven optical transition in silicon vacancy centre in diamond. Rapid optical detection of photons enabled the observation of time-resolved coherent Rabi oscillations and the Mollow triplet spectrum. Detection with a probing transition further confirmed Autler-Townes splitting generated by a strong laser field. The coherence time of the emitted photons is comparable to its lifetime and robust under a very strong driving field, which is promising for the generation of indistinguishable photons
Effect of various biochar on selected soil properties and agronomical parameters of okra (Abelmoschus esculentus L.) at Rupandehi, Nepal
No full textBiochar is rich in carbon and obtained by carbonization of biomass heated at 300-1000°C under limited oxygen which improves the soil properties and yield of various crops. This study aimed to determine the changes in soil properties and agronomical characteristics of okra by biochar prepared from different feedstock. The research was conducted in randomized blocks and replicated thrice, with treatments; control, wood ash (WA), rice husk biochar (RHB), bamboo biochar (BB), Ashoka leaves biochar (ALB), coconut husk biochar (CHB), and sawdust biochar (SB), applied at 18 t/ha. Biochar-incorporated soil and the biochar were analyzed for pH, electrical conductivity, nitrogen, P2O5, K2O, and organic matter, and the soil for bulk density, particle density, and porosity. Agronomical parameters like plant height, fruit size, and yield were also recorded. The biochar incorporation modified the soil's chemical properties and significantly decreased bulk and particle density. The highest reduction of 10.9% in bulk density (1.22gm/cm3), and 4.4% in particle density (2.39gm/cm3) were observed in ALB and SB incorporated soil respectively. ALB (50%) followed by BB (49%) showed a significant increase in soil porosity compared to the control (45.18%). BB (15.7cm) significantly increased the fruit size compared to the control (14.06cm) followed by ALB (15.5cm). ALB (8.16t/ha) significantly increased the yield of okra relative to control (7.82t/ha). The findings suggest the use of ALB and BB to improve soil properties and yield in the long run
