Polyphosphate Accumulation by Marine Bacteria

Phosphate (Pi) is one among the most important essential residues in maintenance and inheritance of life, with far diverse physiological role as structural, functional and energy transduction. Phosphate accumulation in wastewaters containing run off of fertilizers and industrial discharges is a global problem that results in algal blooms in bays, lakes and waterways. Currently available methods for removing phosphates from wastewater are based primarily on polyP accumulation by the activated sludge bacteria. PolyP plays a critical role in several environmental and biotechnological problems. Possible relation of interaction between polyP accumulation phenomenon, the low biomass, low Pi uptake, and varying results obtained in response to the impact of sodium chloride, pH, temperature, various inorganic salts and additional carbon sources studied, are all intriguing observations in the present investigation. The results of the present study have evidenced very clearly the scope for potential strains of bacteria from both sea water and marine sediments which could be exploited both for Pi removal in wastewater released by industries and intensive aquaculture practices in to the aquatic environment as well as to harness the potential strains for industrial production of polyP which was wide range of applications.

SUPPLY CHAIN MANAGEMENT IN INDIAN PETROLEUM REFINING INDUSTRY

Model development for selection of location for refinery in India and identification of characteristics to be looked into when configuring it and to develop models for integrated supply chain planning for a refinery. Locating and removing inbound, internal and outbound logistic problems in an existing refinery and overall design of a logistic information system for a refinery are the main objectives of the study. A brief description of supply chain management (SCM), elements of SCM and their significance, logistics cost in petroleum industry and its impacts, and dynamics of petroleum its logistic practices are also to be presented. Scope of application of SCM in petroleum refinery will also be discussed. A review of the investigations carried out by earlier researches in the area of supply chain management in general and with specific reference to petroleum refining.

Application of chitosan and PASS for the removal of turbidity and colour from water

The most common and conventional method for removing turbidity from water is by coagulating with alum or iron salts, and settling the precipitate in suitably designed clarifiers followed by filtration. But the sludge produced is bulky, difficult to dewater and accumulates in the dumping grounds causing environmental problems. Synthetic polymers such as polyacrylamide and polyethyleneoxide have been investigated for their ability to remove turbidity. They overcome many of the disadvantages of conventional methods, but are cost—effective only when rapid flocculation and reduction in sludge volume are demanded. Considering the aforementioned situation, it was felt that more easily available and eco-friendly materials must be developed for removing turbidity from water. The results of our studies in this direction are presented in this thesis. The thesis comprises of nine chapters, with a common bibliography at the end. Chapter 1 gives an introduction to the nature of turbidity and colour usually present in water. Chapter 2 discusses the nature and availability of the principal material used in these studies, namely chitosan. Chapters 3 to 8, which deal with the actual experimental work, are further subdivided into (a) introduction, (b) materials and methods, (c) results and discussion and (d) conclusions. Chapter 9 summarises the entire work so as to put the results and conclusions into proper perspective.

PERFORMANCE OF DIFFERENT CLASSIFIERS IN SPEECH RECOGNITION

Speech is the most natural means of communication among human beings and speech processing and recognition are intensive areas of research for the last five decades. Since speech recognition is a pattern recognition problem, classification is an important part of any speech recognition system. In this work, a speech recognition system is developed for recognizing speaker independent spoken digits in Malayalam. Voice signals are sampled directly from the microphone. The proposed method is implemented for 1000 speakers uttering 10 digits each. Since the speech signals are affected by background noise, the signals are tuned by removing the noise from it using wavelet denoising method based on Soft Thresholding. Here, the features from the signals are extracted using Discrete Wavelet Transforms (DWT) because they are well suitable for processing non-stationary signals like speech. This is due to their multi- resolutional, multi-scale analysis characteristics. Speech recognition is a multiclass classification problem. So, the feature vector set obtained are classified using three classifiers namely, Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Naive Bayes classifiers which are capable of handling multiclasses. During classification stage, the input feature vector data is trained using information relating to known patterns and then they are tested using the test data set. The performances of all these classifiers are evaluated based on recognition accuracy. All the three methods produced good recognition accuracy. DWT and ANN produced a recognition accuracy of 89%, SVM and DWT combination produced an accuracy of 86.6% and Naive Bayes and DWT combination produced an accuracy of 83.5%. ANN is found to be better among the three methods.

Bioremediation of Zinc Using Bacillus sp. Isolated from Metal- Contaminated Industrial Zone

The production of heavy metals has increased quickly since the industrial revolution. Heavy metals frequently form compounds that can be toxic, carcinogenic, or mutagenic, even in very small concentrations. The usual techniques of removing metals from wastewaters are in general expensive and have many restrictions. Alternative methods of metal removal and recovery based on biological materials have been measured. Among various agents, the use of microbes for the removal of metals from industrial and municipal wastewater has been proposed as a promising alternative to conventional heavy metal management strategies in past decades. Thus, the present study aims to isolate and characterize bacteria from soil, sediment, and waters of metal-contaminated industrial area to study the zinc resistance patterns and the zinc bioaccumulation potential of the selected microorganism. Zinc analysis of the samples revealed that concentrations varying from 39.832 m g/L to 310.24 m g/L in water, 12.81 m g/g to 407.53 m g/g in soil, and 81.06 m g/g to 829.54 m g/g in sediment are present. Bacterial zinc resistance study showed that tolerance to Zn was relatively low (<500 m g/ml). Ten bacterial genera were represented in soil and 11 from water, while only 5 bacterial genera were recorded from sediment samples. Bacillus, Pseudomonas , and Enterobacter were found in soil, sediment, and water samples. Highly zincresistant Bacillus sp. was selected for zinc removal experiment. Zinc removal studies revealed that at pH 5 about 40% reduction occurs; at pH 7, 25% occurs; and at pH 9, 50% occurs. Relatively an increased removal of Zinc was observed in the fi rst day of the experiment by Bacillus sp. The metal bioaccumulative potential of the selected isolates may have possible applications in the removal and recovery of zinc from industrial ef fluents.

English to Malayalam Translation: A Statistical Approach

This paper underlines a methodology for translating text from English into the Dravidian language, Malayalam using statistical models. By using a monolingual Malayalam corpus and a bilingual English/Malayalam corpus in the training phase, the machine automatically generates Malayalam translations of English sentences. This paper also discusses a technique to improve the alignment model by incorporating the parts of speech information into the bilingual corpus. Removing the insignificant alignments from the sentence pairs by this approach has ensured better training results. Pre-processing techniques like suffix separation from the Malayalam corpus and stop word elimination from the bilingual corpus also proved to be effective in training. Various handcrafted rules designed for the suffix separation process which can be used as a guideline in implementing suffix separation in Malayalam language are also presented in this paper. The structural difference between the English Malayalam pair is resolved in the decoder by applying the order conversion rules. Experiments conducted on a sample corpus have generated reasonably good Malayalam translations and the results are verified with F measure, BLEU and WER evaluation metrics

A framework for translating English text into Malayalam using statistical models

A methodology for translating text from English into the Dravidian language, Malayalam using statistical models is discussed in this paper. The translator utilizes a monolingual Malayalam corpus and a bilingual English/Malayalam corpus in the training phase and generates automatically the Malayalam translation of an unseen English sentence. Various techniques to improve the alignment model by incorporating the morphological inputs into the bilingual corpus are discussed. Removing the insignificant alignments from the sentence pairs by this approach has ensured better training results. Pre-processing techniques like suffix separation from the Malayalam corpus and stop word elimination from the bilingual corpus also proved to be effective in producing better alignments. Difficulties in translation process that arise due to the structural difference between the English Malayalam pair is resolved in the decoding phase by applying the order conversion rules. The handcrafted rules designed for the suffix separation process which can be used as a guideline in implementing suffix separation in Malayalam language are also presented in this paper. Experiments conducted on a sample corpus have generated reasonably good Malayalam translations and the results are verified with F measure, BLEU and WER evaluation metrics

ECG Noise Removal using GA tuned Sign-Data Least Mean Square Algorithm

Adaptive filter is a primary method to filter Electrocardiogram (ECG), because it does not need the signal statistical characteristics. In this paper, an adaptive filtering technique for denoising the ECG based on Genetic Algorithm (GA) tuned Sign-Data Least Mean Square (SD-LMS) algorithm is proposed. This technique minimizes the mean-squared error between the primary input, which is a noisy ECG, and a reference input which can be either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Noise is used as the reference signal in this work. The algorithm was applied to the records from the MIT -BIH Arrhythmia database for removing the baseline wander and 60Hz power line interference. The proposed algorithm gave an average signal to noise ratio improvement of 10.75 dB for baseline wander and 24.26 dB for power line interference which is better than the previous reported works

Development of Directionally Adaptive Techniques for Single Image Super-Resolution

Super Resolution problem is an inverse problem and refers to the process of producing a High resolution (HR) image, making use of one or more Low Resolution (LR) observations. It includes up sampling the image, thereby, increasing the maximum spatial frequency and removing degradations that arise during the image capture namely aliasing and blurring. The work presented in this thesis is based on learning based single image super-resolution. In learning based super-resolution algorithms, a training set or database of available HR images are used to construct the HR image of an image captured using a LR camera. In the training set, images are stored as patches or coefficients of feature representations like wavelet transform, DCT, etc. Single frame image super-resolution can be used in applications where database of HR images are available. The advantage of this method is that by skilfully creating a database of suitable training images, one can improve the quality of the super-resolved image. A new super resolution method based on wavelet transform is developed and it is better than conventional wavelet transform based methods and standard interpolation methods. Super-resolution techniques based on skewed anisotropic transform called directionlet transform are developed to convert a low resolution image which is of small size into a high resolution image of large size. Super-resolution algorithm not only increases the size, but also reduces the degradations occurred during the process of capturing image. This method outperforms the standard interpolation methods and the wavelet methods, both visually and in terms of SNR values. Artifacts like aliasing and ringing effects are also eliminated in this method. The super-resolution methods are implemented using, both critically sampled and over sampled directionlets. The conventional directionlet transform is computationally complex. Hence lifting scheme is used for implementation of directionlets. The new single image super-resolution method based on lifting scheme reduces computational complexity and thereby reduces computation time. The quality of the super resolved image depends on the type of wavelet basis used. A study is conducted to find the effect of different wavelets on the single image super-resolution method. Finally this new method implemented on grey images is extended to colour images and noisy images