Removal of Cadmium, Lead and Arsenic from Water by Lactic Acid Bacteria

A number of contaminants such as arsenic, cadmium and lead are released into the environment from natural and anthropogenic sources contaminating food and water. Chronic oral ingestion of arsenic, cadmium and lead is associated with adverse effects in the skin, internal organs and nervous system. In addition to conventional methods, biosorption using inactivated biomasses of algae, fungi and bacteria has been introduced as a novel method for decontamination of toxic metals from water. The aim of this work was to evaluate the applicability of lactic acid bacteria as tools for heavy metal removal from water and characterize their properties for further development of a biofilter. The results established that in addition to removal of mycotoxins, cyanotoxins and heterocyclic amines, lactic acid bacteria have a capacity to bind cationic heavy metals, cadmium and lead. The binding was found to be dependent on the bacterial strain and pH, and occurred rapidly on the bacterial surface, but was reduced in the presence of other cationic metals. The data demonstrates that the metals were bound by electrostatic interactions to cell wall components. Transmission electron micrographs showed the presence of lead deposits on the surface of biomass used in the lead binding studies, indicating involvement of another uptake/binding mechanism. The most efficient strains bound up to 55 mg Cd and 176 mg Pb / g dry biomass. A low removal of anionic As(V) was also observed after chemical modification of the cell wall. Full desorption of bound cadmium and lead using either dilute HNO₃ or EDTA established the reversibility of binding. Removal of both metals was significantly reduced when biomass regenerated with EDTA was used. Biomass regenerated with dilute HNO3 retained its cadmium binding capacity well, but lead binding was reduced. The results established that the cadmium and lead binding capacity of lactic acid bacteria, and factors affecting it, are similar to what has been previously observed for other biomasses used for the same purpose. However, lactic acid bacteria have a capacity to remove other aqueous contaminants such as cyanotoxins, which may give them an additional advantage over the other alternatives. Further studies focusing on immobilization of biomass and the removal of several contaminants simultaneously using immobilized bacteria are required.

Classification-Based compression of Multispectral Images

The purpose of this thesis is to present a new approach to the lossy compression of multispectral images. Proposed algorithm is based on combination of quantization and clustering. Clustering was investigated for compression of the spatial dimension and the vector quantization was applied for spectral dimension compression. Presenting algo¬rithms proposes to compress multispectral images in two stages. During the first stage we define the classes' etalons, another words to each uniform areas are located inside the image the number of class is given. And if there are the pixels are not yet assigned to some of the clusters then it doing during the second; pass and assign to the closest eta¬lons. Finally a compressed image is represented with a flat index image pointing to a codebook with etalons. The decompression stage is instant too. The proposed method described in this paper has been tested on different satellite multispectral images from different resources. The numerical results and illustrative examples of the method are represented too.

On the Analysis and Control of a Linear Synchronous Servomotor with a Flexible Load

Industry's growing need for higher productivity is placing new demands on mechanisms connected with electrical motors, because these can easily lead to vibration problems due to fast dynamics. Furthermore, the nonlinear effects caused by a motor frequently reduce servo stability, which diminishes the controller's ability to predict and maintain speed. Hence, the flexibility of a mechanism and its control has become an important area of research. The basic approach in control system engineering is to assume that the mechanism connected to a motor is rigid, so that vibrations in the tool mechanism, reel, gripper or any apparatus connected to the motor are not taken into account. This might reduce the ability of the machine system to carry out its assignment and shorten the lifetime of the equipment. Nonetheless, it is usually more important to know how the mechanism, or in other words the load on the motor, behaves. A nonlinear load control method for a permanent magnet linear synchronous motor is developed and implemented in the thesis. The purpose of the controller is to track a flexible load to the desired velocity reference as fast as possible and without awkward oscillations. The control method is based on an adaptive backstepping algorithm with its stability ensured by the Lyapunov stability theorem. As a reference controller for the backstepping method, a hybrid neural controller is introduced in which the linear motor itself is controlled by a conventional PI velocity controller and the vibration of the associated flexible mechanism is suppressed from an outer control loop using a compensation signal from a multilayer perceptron network. To avoid the local minimum problem entailed in neural networks, the initial weights are searched for offline by means of a differential evolution algorithm. The states of a mechanical system for controllers are estimated using the Kalman filter. The theoretical results obtained from the control design are validated with the lumped mass model for a mechanism. Generalization of the mechanism allows the methods derived here to be widely implemented in machine automation. The control algorithms are first designed in a specially introduced nonlinear simulation model and then implemented in the physical linear motor using a DSP (Digital Signal Processor) application. The measurements prove that both controllers are capable of suppressing vibration, but that the backstepping method is superior to others due to its accuracy of response and stability properties.

Investigation and data adaptive analysis of CARS spectra

In this diploma work advantages of coherent anti-Stokes Raman scattering spectrometry (CARS) and various methods of the quantitative analysis of substance structure with its help are considered. The basic methods and concepts of the adaptive analysis are adduced. On the basis of these methods the algorithm of automatic measurement of a scattering strip size of a target component in CARS spectrum is developed. The algorithm uses known full spectrum of target substance and compares it with a CARS spectrum. The form of a differential spectrum is used as a feedback to control the accuracy of matching. To exclude the influence of a background in CARS spectra the differential spectrum is analysed by means of its second derivative. The algorithm is checked up on the simulated simple spectra and on the spectra of organic compounds received experimentally.