INVESTIGATIONS ON THE RECOVERY OF TITANIUM VANADIUM AND IRON VALUES FROM THE WASTE CHILORIDE LIQUORS OF TITANIA INDUSTRY

The thesis entitled INVESTIDGATIONS ON THE RECOVERY OF TITANIUM VANADIUM AND IRON VALUES FROM THE WASTE CHILORIDE LIQUORS OF TITANIA INDUSTRY embodies the results of the investigations carried out on the solvent extraction separation of iron (III) vanadium(V) and titanium (IV) chlorides from the waste chloride liquors of titanium minerals processing industry by employing tributylphosphate (TBT) as an extractant. The objective of this study is to generate the knowledge base to achieve the recovery of iron, vanadium and titanium cvalues from multi- metal waste chloride liquors originating from ilmenite mineral beneficiation industries through selective separation and value added material development

Analysis and Design of an Adaptive Polynomial Predistorter with the Loop Delay Estimator

Most adaptive linearization circuits for the nonlinear amplifier have a feedback loop that returns the output signal oj'tne eunplifier to the lineurizer. The loop delay of the linearizer most be controlled precisely so that the convergence of the linearizer should be assured lot this Letter a delay control circuit is presented. It is a delay lock loop (ULL) with it modified early-lute gate and can he easily applied to a DSP implementation. The proposed DLL circuit is applied to an adaptive linearizer with the use of a polynomial predistorter, and the simulalion for a 16-QAM signal is performed. The simulation results show that the proposed DLL eliminates the delay between the reference input signal and the delayed feedback signal of the linearizing circuit perfectly, so that the predistorter polynomial coefficients converge into the optimum value and a high degree of linearization is achieved

Production of Alkaline Protease by Free and Immobilised Cells of VIBRIO sp. Under Different Fermentation Systems and Its Application on Deproteinisation of Prawn Shell Waste for Chitin Recovery

This thesis presents a detailed account of a cost - effective approach towards enhanced production of alkaline protease at profitable levels using different fermentation designs employing cheap agro-industrial residues. It involves the optimisation of process parameters for the production of a thermostable alkaline protease by Vibrio sp. V26 under solid state, submerged and biphasic fermentations, production of the enzyme using cell immobilisation technology and the application of the crude enzyme on the deproteinisation of crustacean waste.The present investigation suggests an economic move towards Improved production of alkaline protease at gainful altitudes employing different fermentation designs utilising inexpensive agro-industrial residues. Moreover, the use of agro-industrial and other solid waste substrates for fermentation helps to provide a substitute in conserving the already dwindling global energy resources. Another alternative for accomplishing economically feasible production is by the use of immobilisation technique. This method avoids the wasteful expense of continually growing microorganisms. The high protease producing potential of the organism under study ascertains their exploitation in the utilisation and management of wastes. However, strain improvement studies for the production of high yielding variants using mutagens or by gene transfer are required before recommending them to Industries.Industries, all over the world, have made several attempts to exploit the microbial diversity of this planet. For sustainable development, it is essential to discover, develop and defend this natural prosperity. The Industrial development of any country is critically dependent on the intellectual and financial investment in this area. The need of the hour is to harness the beneficial uses of microbes for maximum utilisation of natural resources and technological yields. Owing to the multitude of applications in a variety of industrial sectors, there has always been an increasing demand for novel producers and resources of alkaline proteases as well as for innovative methods of production at a commercial altitude. This investigation forms a humble endeavour towards this perspective and bequeaths hope and inspiration for inventions to follow.

Dopamine Receptor Subtypes,IP3, cAMP and cGMP Functional Regulation in Parkinsonism Induced by Unilateral Infusion of Rotenone in Rats: Recovery with Bone Marrow Cells,Serotonin and GABA Supplementation

In the present study, the effects of 5-HT, GABA and Bone Marrow Cells infused intranigrally to substantia nigra individually and in combinations on unilateral rotenone infused Parkinsonism induced rats. Scatchard analysis of DA, DA D1 and D2 receptors in the corpus striatum, cerebral cortex, cerebellum, brain stem and hippocampus showed a significant increase in the Brain regions of rotenone infused rat compared to control. Real Time PCR amplification of DA D1, D2, Bax and ubiquitin carboxy-terminal hydrolase were up regulated in the brain regions of rotenone infused rats compared to control. Gene expression studies of -Synuclien, cGMP and Cyclic AMP response element-binding protein showed a significant down regulation in Rotenone infused rats compared to control. Behavioural studies were carried out to confirm the biochemical and molecular studies.Our study demonstrated that BMC administration alone cannot reverse the above said molecular changes occurring in PD rat. 5-HT and GABA acting through their specific receptors in combination with bone marrow cells play a crucial role in the functional recovery of PD rats. 5-HT, GABA and Bone marrow cells treated PD rats showed significant reversal to control in DA receptor binding and gene expression. 5-HT and GABA have co-mitogenic property. Proliferation and differentiation of cells re-establishing the connections in Parkinson's disease facilitates the functional recovery. Thus, it is evident that 5-HT and GABA along with BMC to rotenone infused rats renders protection against oxidative, related motor and cognitive deficits which makes them clinically significant for cellbased therapy. The BMC transformed to neurons when co-transplanted with 5-HT and GABA which was confirmed with PKH2GL and nestin. These newly formed neurons have functional significance in the therapeutic recovery of Parkinson’s disease.

Energy Management in Wireless Sensor Networks: A Crosslayer,Channel Adaptive Approach Towards Performance Optimization

The proliferation of wireless sensor networks in a large spectrum of applications had been spurered by the rapid advances in MEMS(micro-electro mechanical systems )based sensor technology coupled with low power,Low cost digital signal processors and radio frequency circuits.A sensor network is composed of thousands of low cost and portable devices bearing large sensing computing and wireless communication capabilities. This large collection of tiny sensors can form a robust data computing and communication distributed system for automated information gathering and distributed sensing.The main attractive feature is that such a sensor network can be deployed in remote areas.Since the sensor node is battery powered,all the sensor nodes should collaborate together to form a fault tolerant network so as toprovide an efficient utilization of precious network resources like wireless channel,memory and battery capacity.The most crucial constraint is the energy consumption which has become the prime challenge for the design of long lived sensor nodes.

Speech sample estimation from composite zerocrossings and encoding via adaptive switching of transforms

This thesis investigates the potential use of zerocrossing information for speech sample estimation. It provides 21 new method tn) estimate speech samples using composite zerocrossings. A simple linear interpolation technique is developed for this purpose. By using this method the A/D converter can be avoided in a speech coder. The newly proposed zerocrossing sampling theory is supported with results of computer simulations using real speech data. The thesis also presents two methods for voiced/ unvoiced classification. One of these methods is based on a distance measure which is a function of short time zerocrossing rate and short time energy of the signal. The other one is based on the attractor dimension and entropy of the signal. Among these two methods the first one is simple and reguires only very few computations compared to the other. This method is used imtea later chapter to design an enhanced Adaptive Transform Coder. The later part of the thesis addresses a few problems in Adaptive Transform Coding and presents an improved ATC. Transform coefficient with maximum amplitude is considered as ‘side information’. This. enables more accurate tfiiz assignment enui step—size computation. A new bit reassignment scheme is also introduced in this work. Finally, sum ATC which applies switching between luiscrete Cosine Transform and Discrete Walsh-Hadamard Transform for voiced and unvoiced speech segments respectively is presented. Simulation results are provided to show the improved performance of the coder

Modified Block Adaptive Predictive Coder For Speech Processing

This thesis investigated the potential use of Linear Predictive Coding in speech communication applications. A Modified Block Adaptive Predictive Coder is developed, which reduces the computational burden and complexity without sacrificing the speech quality, as compared to the conventional adaptive predictive coding (APC) system. For this, changes in the evaluation methods have been evolved. This method is as different from the usual APC system in that the difference between the true and the predicted value is not transmitted. This allows the replacement of the high order predictor in the transmitter section of a predictive coding system, by a simple delay unit, which makes the transmitter quite simple. Also, the block length used in the processing of the speech signal is adjusted relative to the pitch period of the signal being processed rather than choosing a constant length as hitherto done by other researchers. The efficiency of the newly proposed coder has been supported with results of computer simulation using real speech data. Three methods for voiced/unvoiced/silent/transition classification have been presented. The first one is based on energy, zerocrossing rate and the periodicity of the waveform. The second method uses normalised correlation coefficient as the main parameter, while the third method utilizes a pitch-dependent correlation factor. The third algorithm which gives the minimum error probability has been chosen in a later chapter to design the modified coder The thesis also presents a comparazive study beh-cm the autocorrelation and the covariance methods used in the evaluaiicn of the predictor parameters. It has been proved that the azztocorrelation method is superior to the covariance method with respect to the filter stabf-it)‘ and also in an SNR sense, though the increase in gain is only small. The Modified Block Adaptive Coder applies a switching from pitch precitzion to spectrum prediction when the speech segment changes from a voiced or transition region to an unvoiced region. The experiments cont;-:ted in coding, transmission and simulation, used speech samples from .\£=_‘ajr2_1a:r1 and English phrases. Proposal for a speaker reecgnifion syste: and a phoneme identification system has also been outlized towards the end of the thesis.

An adaptive cluster based routing scheme for mobile wireless sensor networks

Clustering schemes improve energy efficiency of wireless sensor networks. The inclusion of mobility as a new criterion for the cluster creation and maintenance adds new challenges for these clustering schemes. Cluster formation and cluster head selection is done on a stochastic basis for most of the algorithms. In this paper we introduce a cluster formation and routing algorithm based on a mobility factor. The proposed algorithm is compared with LEACH-M protocol based on metrics viz. number of cluster head transitions, average residual energy, number of alive nodes and number of messages lost

Channel Adaptive MAC Protocol with Traffic-Aware Distributed Power Management in Wireless Sensor Networks-Some Performance Issues

In Wireless Sensor Networks (WSN), neglecting the effects of varying channel quality can lead to an unnecessary wastage of precious battery resources and in turn can result in the rapid depletion of sensor energy and the partitioning of the network. Fairness is a critical issue when accessing a shared wireless channel and fair scheduling must be employed to provide the proper flow of information in a WSN. In this paper, we develop a channel adaptive MAC protocol with a traffic-aware dynamic power management algorithm for efficient packet scheduling and queuing in a sensor network, with time varying characteristics of the wireless channel also taken into consideration. The proposed protocol calculates a combined weight value based on the channel state and link quality. Then transmission is allowed only for those nodes with weights greater than a minimum quality threshold and nodes attempting to access the wireless medium with a low weight will be allowed to transmit only when their weight becomes high. This results in many poor quality nodes being deprived of transmission for a considerable amount of time. To avoid the buffer overflow and to achieve fairness for the poor quality nodes, we design a Load prediction algorithm. We also design a traffic aware dynamic power management scheme to minimize the energy consumption by continuously turning off the radio interface of all the unnecessary nodes that are not included in the routing path. By Simulation results, we show that our proposed protocol achieves a higher throughput and fairness besides reducing the delay

Lignin recovery, Biochar Production and Decolourisation of Coir pith Black Liquor

Coir pith black liquor obtained as a dark brown filtrate from oxidative delignification needs to be decolourised before releasing to open environment. From this liquor industrially valuable lignin was recovered using acid precipitation method. ‘Biochar’ was produced by slow pyrolysis of coir pith at 500oC and 600oC. Water holding capacity and pH of the biochar were estimated. CHNS analysis was carried out to identify the nutrient profile. Structural characterization was done using FTIR and SEM Studies. Biochar produced at 600oC was found to be more suitable for decolourisation of the coir pith black liquor. FTIR analysis indicated peak changes while SEM analysis indicated surface area and porosity changes. Biochar decolourisation experiments were carried out on crude coir pith black liquor and also on lignin recovered coir pith black liquor.

Short-Term Load Forecast Of A Low Loadfactor Power System For Optimization Of Merit Order Dispatch Using Adaptive Learning Algorithm

Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. Cost of power depends on source of power. Hence any optimization strategy involves optimization in scheduling power from various sources. As the scheduling involves many technical and commercial considerations and constraints, the efficiency in scheduling depends on the accuracy of load forecast. Load forecasting is a topic much visited in research world and a number of papers using different techniques are already presented. The accuracy of forecast for the purpose of merit order dispatch decisions depends on the extent of the permissible variation in generation limits. For a system with low load factor, the peak and the off peak trough are prominent and the forecast should be able to identify these points to more accuracy rather than minimizing the error in the energy content. In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor. Such power systems are usual in tropical areas with concentrated rainy season for a considerable period of the year

Image Denoising Using Sure-Based Adaptive Thresholding In Directionlet Domain

The standard separable two dimensional wavelet transform has achieved a great success in image denoising applications due to its sparse representation of images. However it fails to capture efficiently the anisotropic geometric structures like edges and contours in images as they intersect too many wavelet basis functions and lead to a non-sparse representation. In this paper a novel de-noising scheme based on multi directional and anisotropic wavelet transform called directionlet is presented. The image denoising in wavelet domain has been extended to the directionlet domain to make the image features to concentrate on fewer coefficients so that more effective thresholding is possible. The image is first segmented and the dominant direction of each segment is identified to make a directional map. Then according to the directional map, the directionlet transform is taken along the dominant direction of the selected segment. The decomposed images with directional energy are used for scale dependent subband adaptive optimal threshold computation based on SURE risk. This threshold is then applied to the sub-bands except the LLL subband. The threshold corrected sub-bands with the unprocessed first sub-band (LLL) are given as input to the inverse directionlet algorithm for getting the de-noised image. Experimental results show that the proposed method outperforms the standard wavelet-based denoising methods in terms of numeric and visual quality

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

Spatially Adaptive Image Denoising Techniques Using Directionlets

The aim of the thesis was to design and develop spatially adaptive denoising techniques with edge and feature preservation, for images corrupted with additive white Gaussian noise and SAR images affected with speckle noise. Image denoising is a well researched topic. It has found multifaceted applications in our day to day life. Image denoising based on multi resolution analysis using wavelet transform has received considerable attention in recent years. The directionlet based denoising schemes presented in this thesis are effective in preserving the image specific features like edges and contours in denoising. Scope of this research is still open in areas like further optimization in terms of speed and extension of the techniques to other related areas like colour and video image denoising. Such studies would further augment the practical use of these techniques.