Tunnel-Based Artificial Neural Network Technique to Calculate the Resonant Frequency of a Thick-Substrate Microstrip Antenna

The mathematical formulation of empirically developed formulas Jirr the calculation of the resonant frequency of a thick-substrate (h s 0.08151 A,,) microstrip antenna has been analyzed. With the use qt' tunnel-based artificial neural networks (ANNs), the resonant frequency of antennas with h satisfying the thick-substrate condition are calculated and compared with the existing experimental results and also with the simulation results obtained with the use of an IE3D software package. The artificial neural network results are in very good agreement with the experimental results

AN ARTIFICIAL NEURAL NETWORK FOR SONAR TARGET DETECTION

Neural Network has emerged as the topic of the day. The spectrum of its application is as wide as from ECG noise filtering to seismic data analysis and from elementary particle detection to electronic music composition. The focal point of the proposed work is an application of a massively parallel connectionist model network for detection of a sonar target. This task is segmented into: (i) generation of training patterns from sea noise that contains radiated noise of a target, for teaching the network;(ii) selection of suitable network topology and learning algorithm and (iii) training of the network and its subsequent testing where the network detects, in unknown patterns applied to it, the presence of the features it has already learned in. A three-layer perceptron using backpropagation learning is initially subjected to a recursive training with example patterns (derived from sea ambient noise with and without the radiated noise of a target). On every presentation, the error in the output of the network is propagated back and the weights and the bias associated with each neuron in the network are modified in proportion to this error measure. During this iterative process, the network converges and extracts the target features which get encoded into its generalized weights and biases.In every unknown pattern that the converged network subsequently confronts with, it searches for the features already learned and outputs an indication for their presence or absence. This capability for target detection is exhibited by the response of the network to various test patterns presented to it.Three network topologies are tried with two variants of backpropagation learning and a grading of the performance of each combination is subsequently made.

Nutritional responses in Indian white shrimp Fenneropenaeus indicus to varying protein: energy combinations in compounded artificial feeds

Department of Marine Biology, Microbiology and Biochemistry, Cochin University of Science and Technology

Identification of spectral lines of elements using artificial neural networks

Artificial neural networks (ANNs) are relatively new computational tools that have found extensive utilization in solving many complex real-world problems. This paper describes how an ANN can be used to identify the spectral lines of elements. The spectral lines of Cadmium (Cd), Calcium (Ca), Iron (Fe), Lithium (Li), Mercury (Hg), Potassium (K) and Strontium (Sr) in the visible range are chosen for the investigation. One of the unique features of this technique is that it uses the whole spectrum in the visible range instead of individual spectral lines. The spectrum of a sample taken with a spectrometer contains both original peaks and spurious peaks. It is a tedious task to identify these peaks to determine the elements present in the sample. ANNs capability of retrieving original data from noisy spectrum is also explored in this paper. The importance of the need of sufficient data for training ANNs to get accurate results is also emphasized. Two networks are examined: one trained in all spectral lines and other with the persistent lines only. The network trained in all spectral lines is found to be superior in analyzing the spectrum even in a noisy environment.

Carbon/Nitrogen Ratio Optimization and Periphyton Development on the Production and Sustainability of Penaeus Monodon(Fabricius)in Extensive Culture System

Controlling the inorganic nitrogen by manipulating carbon / nitrogen ratio is a method gaining importance in aquaculture systems. Nitrogen control is induced by feeding bacteria with carbohydrates and through the subsequent uptake of nitrogen from the water for the synthesis of microbial proteins. The relationship between addition of carbohydrates, reduction of ammonium and the production of microbial protein depends on the microbial conversion coefficient. The carbon / nitrogen ratio in the microbial biomass is related to the carbon contents of the added material. The addition of carbonaceous substrate was found to reduce inorganic nitrogen in shrimp culture ponds and the resultant microbial proteins are taken up by shrimps. Thus, part of the feed protein is replaced and feeding costs are reduced in culture systems.The use of various locally available substrates for periphyton based aquaculture practices increases production and profitability .However, these techniques for extensive shrimp farming have not so far been evaluated. Moreover, an evaluation of artificial substrates together with carbohydrate source based farming system in reducing inorganic nitrogen production in culture systems has not yet been carried-out. Furthermore, variations in water and soil quality, periphyton production and shrimp production of the whole system have also not been determined so-far.This thesis starts with a general introduction , a brief review of the most relevant literature, results of various experiments and concludes with a summary (Chapter — 9). The chapters are organised conforming to the objectives of the present study. The major objectives of this thesis are, to improve the sustainability of shrimp farming by carbohydrate addition and periphyton substrate based shrimp production and to improve the nutrient utilisation in aquaculture systems.

Integer filling facfor phases and isospin in vertical diatomic artificial molecules

Integer filling factor phases of many-electron vertically coupled diatomic artificial quantum dot molecules are investigated for different values of the interdot coupling. The experimental results are analyzed within local-spin density functional theory for which we have determined a simple lateral confining potential law that can be scaled for the different coupling regimes, and Hartree-Fock theory. Maximum density droplets composed of electrons in both bonding and antibonding or just bonding states are revealed, and interesting isospin-flip physics appears for weak interdot coupling when the systematic depopulation of antibonding states leads to changes in isospin.

Magnetoresistance at artificial interfaces in the itinerant SrRuO3 ferromagnet

The magnetoresistance across interfaces in the itinerant ferromagnetic oxide SrRuO3 have been studied. To define appropriately the interfaces, epitaxial thin films have been grown on bicrystalline and laser-patterned SrTiO3 substrates. Comparison is made with results obtained on similar experiments using the double-exchange ferromagnetic oxide La2/3Sr1/3MnO3. It is found that in SrRuO3, interfaces induce a substantial negative magnetoresistance, although no traces of the low-field spin tunneling magnetoresistance are found. We discuss these results on the basis of the distinct degree of spin polarization in ruthenates and manganites and the different nature of the surface magnetic layer formed at interfaces.

Document Image Segmentation and Compression using Artificial Neural Networks and Evolutionary Methods

The work is intended to study the following important aspects of document image processing and develop new methods. (1) Segmentation ofdocument images using adaptive interval valued neuro-fuzzy method. (2) Improving the segmentation procedure using Simulated Annealing technique. (3) Development of optimized compression algorithms using Genetic Algorithm and parallel Genetic Algorithm (4) Feature extraction of document images (5) Development of IV fuzzy rules. This work also helps for feature extraction and foreground and background identification. The proposed work incorporates Evolutionary and hybrid methods for segmentation and compression of document images. A study of different neural networks used in image processing, the study of developments in the area of fuzzy logic etc is carried out in this work

Artificial neural network modeling for surface roughness prediction in cylindrical grinding of Al‐SiCp metal matrix composites and ANOVA analysis

Metal matrix composites (MMC) having aluminium (Al) in the matrix phase and silicon carbide particles (SiCp) in reinforcement phase, ie Al‐SiCp type MMC, have gained popularity in the re‐cent past. In this competitive age, manufacturing industries strive to produce superior quality products at reasonable price. This is possible by achieving higher productivity while performing machining at optimum combinations of process variables. The low weight and high strength MMC are found suitable for variety of components

Artificial boundary conditions for the Stokes and Navier-Stokes equations in domains that are layer-like at infinity

Artificial boundary conditions are presented to approximate solutions to Stokes- and Navier-Stokes problems in domains that are layer-like at infinity. Based on results about existence and asymptotics of the solutions v^infinity, p^infinity to the problems in the unbounded domain Omega the error v^infinity - v^R, p^infinity - p^R is estimated in H^1(Omega_R) and L^2(Omega_R), respectively. Here v^R, p^R are the approximating solutions on the truncated domain Omega_R, the parameter R controls the exhausting of Omega. The artificial boundary conditions involve the Steklov-Poincare operator on a circle together with its inverse and thus turn out to be a combination of local and nonlocal boundary operators. Depending on the asymptotic decay of the data of the problems, in the linear case the error vanishes of order O(R^{-N}), where N can be arbitrarily large.

Studies on arbuscular mycorrhiza (AM) in the Alentejo (Portugal) using pea mutants resistant to AM fungi as a control tool for field conditions

The utilization and management of arbuscular mycorrhiza (AM) symbiosis may improve production and sustainability of the cropping system. For this purpose, native AM fungi (AMF) were sought and tested for their efficiency to increase plant growth by enhanced P uptake and by alleviation of drought stress. Pot experiments with safflower (Carthamus tinctorius) and pea (Pisum sativum) in five soils (mostly sandy loamy Luvisols) and field experiments with peas were carried out during three years at four different sites. Host plants were grown in heated soils inoculated with AMF or the respective heat sterilized inoculum. In the case of peas, mutants resistant to AMF colonization were used as non-mycorrhizal controls. The mycorrhizal impact on yields and its components, transpiration, and P and N uptake was studied in several experiments, partly under varying P and N levels and water supply. Screening of native AMF by most probable number bioassays was not very meaningful. Soil monoliths were placed in the open to simulate field conditions. Inoculation with a native AMF mix improved grain yield, shoot and leaf growth variables as compared to control. Exposed to drought, higher soil water depletion of mycorrhizal plants resulted in a haying-off effect. The growth response to this inoculum could not be significantly reproduced in a subsequent open air pot experiment at two levels of irrigation and P fertilization, however, safflower grew better at higher P and water supply by multiples. The water use efficiency concerning biomass was improved by the AMF inoculum in the two experiments. Transpiration rates were not significantly affected by AM but as a tendency were higher in non-mycorrhizal safflower. A fundamental methodological problem in mycorrhiza field research is providing an appropriate (negative) control for the experimental factor arbuscular mycorrhiza. Soil sterilization or fungicide treatment have undesirable side effects in field and greenhouse settings. Furthermore, artificial rooting, temperature and light conditions in pot experiments may interfere with the interpretation of mycorrhiza effects. Therefore, the myc- pea mutant P2 was tested as a non-mycorrhizal control in a bioassay to evaluate AMF under field conditions in comparison to the symbiotic isogenetic wild type of var. FRISSON as a new integrative approach. However, mutant P2 is also of nod- phenotype and therefore unable to fix N2. A 3-factorial experiment was carried out in a climate chamber at high NPK fertilization to examine the two isolines under non-symbiotic and symbiotic conditions. P2 achieved the same (or higher) biomass as wild type both under good and poor water supply. However, inoculation with the AMF Glomus manihot did not improve plant growth. Differences of grain and straw yields in field trials were large (up to 80 per cent) between those isogenetic pea lines mainly due to higher P uptake under P and water limited conditions. The lacking N2 fixation in mutants was compensated for by high mineral N supply as indicated by the high N status of the pea mutant plants. This finding was corroborated by the results of a major field experiment at three sites with two levels of N fertilization. The higher N rate did not affect grain or straw yields of the non-fixing mutants. Very efficient AMF were detected in a Ferric Luvisol on pasture land as revealed by yield levels of the evaluation crop and by functional vital staining of highly colonized roots. Generally, levels of grain yield were low, at between 40 and 980 kg ha-1. An additional pot trial was carried out to elucidate the strong mycorrhizal effect in the Ferric Luvisol. A triplication of the plant equivalent field P fertilization was necessary to compensate for the mycorrhizal benefit which was with five times higher grain yield very similar to that found in the field experiment. However, the yield differences between the two isolines were not always plausible as the evaluation variable because they were also found in (small) field test trials with apparently sufficient P and N supply and in a soil of almost no AMF potential. This similarly occurred for pea lines of var. SPARKLE and its non-fixing mycorrhizal (E135) and non-symbiotic (R25) isomutants, which were tested in order to exclude experimentally undesirable benefits by N2 fixation. In contrast to var. FRISSON, SPARKLE was not a suitable variety for Mediterranean field conditions. This raises suspicion putative genetic defects other than symbiotic ones may be effective under field conditions, which would conflict with the concept of an appropriate control. It was concluded that AMF resistant plants may help to overcome fundamental problems of present research on arbuscular mycorrhiza, but may create new ones.

Low-altitude aerial photography for optimum N fertilization of winter wheat on the North China Plain

Previous research has shown that site-specific nitrogen (N) fertilizer recommendations based on an assessment of a soil’s N supply (mineral N testing) and the crop’s N status (sap nitrate analysis) can help to decrease excessive N inputs for winter wheat on the North China Plain. However, the costs to derive such recommendations based on multiple sampling of a single field hamper the use of this approach at the on-farm level. In this study low-altitude aerial true-color photographs were used to examine the relationship between image-derived reflectance values and soil–plant data in an on-station experiment. Treatments comprised a conventional N treatment (typical farmers’ practice), an optimum N treatment (N application based on soil–plant testing) and six treatments without N (one to six cropping seasons without any N fertilizer input). Normalized intensities of the red, green and blue color bands on the photographs were highly correlated with total N concentrations, SPAD readings and stem sap nitrate of winter wheat. The results indicate the potential of aerial photography to determine in combination with on site soil–plant testing the optimum N fertilizer rate for larger fields and to thereby decrease the costs for N need assessments.

Artificial micro RNA system in Dictyostelium discoideum

Dictyostelium discoideum is a social amoeba that serves as a model system for RNA interference and related mechanisms. Its position between plants and animals enables evolutionary snapshot of mechanisms and protein machinery involved in investigated subjects. MiRNAs are small regulatory RNAs that are evolutionary conserved and present in animals, plants, viruses and some prokaryotes. They have roles in development, cell growth and differentiation, apoptosis and their miss-regulation is associated with many diseases such as cancer, neurodegenerative disorders and diabetes. Recently, through sequencing of DNA libraries miRNAs have been discovered in D. discoideum. In this work, it has been shown that heterologues miRNA let-7 can be expressed and processed in D. discoideum. Expression of let-7 miRNA in social amoeba resulted in a strong developmental phenotype suggesting an overload of the processing/silencing system or/and endogenous targets. The various effects on prel-7 strain have been observed and characterized, serving as a background for postulation of miRNA roles. An artificial miRNA system has been established and imposed to D. discoideum, showing that miRNAs in Dictyostelium could mediate gene expression on the level of mRNA stability and on the posttranscriptional level. Furthermore, presence of translational inhibition as a type of gene control was shown for the first time in this organism. Due to it new structures representing co-localities of miRNA and target mRNA have been detected. Taken together, this work shows functional artificial miRNA system and postulates roles of endogenous small RNA in social amoeba.

Development of thermo-analytical prediction and classification models for food in thermal devices using a multi sensor system and artificial neural networks

Die thermische Verarbeitung von Lebensmitteln beeinflusst deren Qualität und ernährungsphysiologischen Eigenschaften. Im Haushalt ist die Überwachung der Temperatur innerhalb des Lebensmittels sehr schwierig. Zudem ist das Wissen über optimale Temperatur- und Zeitparameter für die verschiedenen Speisen oft unzureichend. Die optimale Steuerung der thermischen Zubereitung ist maßgeblich abhängig von der Art des Lebensmittels und der äußeren und inneren Temperatureinwirkung während des Garvorgangs. Das Ziel der Arbeiten war die Entwicklung eines automatischen Backofens, der in der Lage ist, die Art des Lebensmittels zu erkennen und die Temperatur im Inneren des Lebensmittels während des Backens zu errechnen. Die für die Temperaturberechnung benötigten Daten wurden mit mehreren Sensoren erfasst. Hierzu kam ein Infrarotthermometer, ein Infrarotabstandssensor, eine Kamera, ein Temperatursensor und ein Lambdasonde innerhalb des Ofens zum Einsatz. Ferner wurden eine Wägezelle, ein Strom- sowie Spannungs-Sensor und ein Temperatursensor außerhalb des Ofens genutzt. Die während der Aufheizphase aufgenommen Datensätze ermöglichten das Training mehrerer künstlicher neuronaler Netze, die die verschiedenen Lebensmittel in die entsprechenden Kategorien einordnen konnten, um so das optimale Backprogram auszuwählen. Zur Abschätzung der thermische Diffusivität der Nahrung, die von der Zusammensetzung (Kohlenhydrate, Fett, Protein, Wasser) abhängt, wurden mehrere künstliche neuronale Netze trainiert. Mit Ausnahme des Fettanteils der Lebensmittel konnten alle Komponenten durch verschiedene KNNs mit einem Maximum von 8 versteckten Neuronen ausreichend genau abgeschätzt werden um auf deren Grundlage die Temperatur im inneren des Lebensmittels zu berechnen. Die durchgeführte Arbeit zeigt, dass mit Hilfe verschiedenster Sensoren zur direkten beziehungsweise indirekten Messung der äußeren Eigenschaften der Lebensmittel sowie KNNs für die Kategorisierung und Abschätzung der Lebensmittelzusammensetzung die automatische Erkennung und Berechnung der inneren Temperatur von verschiedensten Lebensmitteln möglich ist.