Polycyclic aromatic hydrocarbons and toxic trace metals in seawater and sediments from the west coast of India and their bioaccumulation in some selected species of fishes

Polycyclic Aromatic Hydrocarbons and other toxic compounds from industrial effluents are noted for their high potency for skin, lung, bladder and gastrointestinal cancers. Increased industrialization, and population growth led to greater production of wastes, Pesticides and PAHs have received attention due to their carcinogenic effects. The main objectives of the study were; to collect base line data on the concentration of PAHs in seawater and sediment from the west coast of India, the concentration of PAHs in certain species of fishes, the comparative levels of PAHs in fish, the influence of sediment characteristics on the concentration of PAH in sediment, changes in PAH concentration in water, sediment and fish, to provide a base line concentration of trace metals in water, sediment and fish, the seasonal changes in content of selected trace metals in water, sediment and fish from the west coast of India. The present study revealed that a predominance of silt and clay at all stations in the off Cochin area. The study has provided comprehensive information available to date for PAHs in seawater, sediment and fishes from the west cost of India especially from the Quilon to Mangalore region.

Investigations on the Distribution Characteristics of Heavy Metals in Squid (Loligo spp.) in Relation to Levels in Food Fishes from the West Coast of India with a Perspective on Seafood Safety

The present study entitled "Investigations on the Distribution Characteristics of Heavy Metals in Squid (Loligo spp.) in Relation to Levels in Food Fishes from the West Coast of India with a Perspective on Seafood Safety"attempts to establish the base line data on metal levels in squids along the west coast of India. The study is of great relevance in the present context when utmost importance is being given for producing wholesome seafoods especially in the export market with a perspective on seafood safety.The thesis presents a comprehensive account of the base line data on important heavy metals, viz., Hg, Cd, Pb, Cu, Zn, Fe, Mn Cr and Ni in the edible and non-edible body components of the most abundant Loligo species, viz., L. duvauceli caught along the west coast of India.

Design and Development of data mining models for the predictions of manpower placement in the technical Domain

Data mining is one of the hottest research areas nowadays as it has got wide variety of applications in common man’s life to make the world a better place to live. It is all about finding interesting hidden patterns in a huge history data base. As an example, from a sales data base, one can find an interesting pattern like “people who buy magazines tend to buy news papers also” using data mining. Now in the sales point of view the advantage is that one can place these things together in the shop to increase sales. In this research work, data mining is effectively applied to a domain called placement chance prediction, since taking wise career decision is so crucial for anybody for sure. In India technical manpower analysis is carried out by an organization named National Technical Manpower Information System (NTMIS), established in 1983-84 by India's Ministry of Education & Culture. The NTMIS comprises of a lead centre in the IAMR, New Delhi, and 21 nodal centres located at different parts of the country. The Kerala State Nodal Centre is located at Cochin University of Science and Technology. In Nodal Centre, they collect placement information by sending postal questionnaire to passed out students on a regular basis. From this raw data available in the nodal centre, a history data base was prepared. Each record in this data base includes entrance rank ranges, reservation, Sector, Sex, and a particular engineering. From each such combination of attributes from the history data base of student records, corresponding placement chances is computed and stored in the history data base. From this data, various popular data mining models are built and tested. These models can be used to predict the most suitable branch for a particular new student with one of the above combination of criteria. Also a detailed performance comparison of the various data mining models is done.This research work proposes to use a combination of data mining models namely a hybrid stacking ensemble for better predictions. A strategy to predict the overall absorption rate for various branches as well as the time it takes for all the students of a particular branch to get placed etc are also proposed. Finally, this research work puts forward a new data mining algorithm namely C 4.5 * stat for numeric data sets which has been proved to have competent accuracy over standard benchmarking data sets called UCI data sets. It also proposes an optimization strategy called parameter tuning to improve the standard C 4.5 algorithm. As a summary this research work passes through all four dimensions for a typical data mining research work, namely application to a domain, development of classifier models, optimization and ensemble methods.

Multifinger Feature Level Fusion Based Fingerprint Identification

Fingerprint based authentication systems are one of the cost-effective biometric authentication techniques employed for personal identification. As the data base population increases, fast identification/recognition algorithms are required with high accuracy. Accuracy can be increased using multimodal evidences collected by multiple biometric traits. In this work, consecutive fingerprint images are taken, global singularities are located using directional field strength and their local orientation vector is formulated with respect to the base line of the finger. Feature level fusion is carried out and a 32 element feature template is obtained. A matching score is formulated for the identification and 100% accuracy was obtained for a database of 300 persons. The polygonal feature vector helps to reduce the size of the feature database from the present 70-100 minutiae features to just 32 features and also a lower matching threshold can be fixed compared to single finger based identification

Some Studies and New Results on Multi Microprocessor Applications

One of the fastest expanding areas of computer exploitation is in embedded systems, whose prime function is not that of computing, but which nevertheless require information processing in order to carry out their prime function. Advances in hardware technology have made multi microprocessor systems a viable alternative to uniprocessor systems in many embedded application areas. This thesis reports the results of investigations carried out on multi microprocessors oriented towards embedded applications, with a view to enhancing throughput and reliability. An ideal controller for multiprocessor operation is developed which would smoothen sharing of routines and enable more powerful and efficient code I data interchange. Results of performance evaluation are appended.A typical application scenario is presented, which calls for classifying tasks based on characteristic features that were identified. The different classes are introduced along with a partitioned storage scheme. Theoretical analysis is also given. A review of schemes available for reducing disc access time is carried out and a new scheme presented. This is found to speed up data base transactions in embedded systems. The significance of software maintenance and adaptation in such applications is highlighted. A novel scheme of prov1d1ng a maintenance folio to system firmware is presented, alongwith experimental results. Processing reliability can be enhanced if facility exists to check if a particular instruction in a stream is appropriate. Likelihood of occurrence of a particular instruction would be more prudent if number of instructions in the set is less. A new organisation is derived to form the basement for further work. Some early results that would help steer the course of the work are presented.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Hydrogeology,Stratigraphy and evolution of the Palaeo-Lagoon (kole land basin ) in the central Kerala coast, India

The present investigation on " Hydrology, stratigraphy, and evolution of the palaeo-lagoon (Koleland basin)in the Central Kerala coast, India" is an integrated approach based on hydrogeological,geophysical,hydrochemical and stratigraphic aspects.A strong scientific data base of the study area is generated using interpretation of well observation and water quality analysis. The salient findings of the present study are given to provide a holistic picture on the hydrogeology (including groundwater resource and its quality),stratigraphy and evolution of the palaeo-lagoon

Studies on Thermal structure in the seas around in India

The thesis entitled Studies on Thermal Structure in the Seas Around India. An attempt is made in this study to document the observed variability of thermal structure, both on seasonal and short-term scales, in the eastern Arabian Sea and southwestern Bay of Bengal, from the spatial and time series data sets from a reasonably strong data base. The present study has certain limitations. The mean temperatures are based on an uneven distribution of data in space and time. Some of the areas, although having a ‘full annual coverage, do not have adequate data for some months. Some portions in the area under study are having data gaps. The consistency and the coherence in the internal wave characteristics could not be examined due to non-availability of adequate data sets. The influence of generating mechanisms; other than winds and tides on the observed internal wave fields could not be ascertained due to lack of data. However, a comprehensive and intensive data collection can overcome these limitations. The deployment of moored buoys with arrays of sensors at different depths at some important locations for about 5 to 10 years can provide intensive and extensive data sets. This strong data base can afford to address the short-term and seasonal variability of thermal field and understand in detail the individual and collective influences of various physical and dynamical mechanisms responsible for such variability.

Bionomics, Resource Characteristics And Distribution Of The Threatened Freshwater Fishes Of Kerala

The previous faunistic studies were concentrated.on the taxonomical and zoogeo— graphical aspects. These studies contributed to many new additions to the fish fauna of Kerala meanwhile many species described earlier are reported missing in recent years. Many fish species were collected only once or twice by scientists. Detailed information on distribution, habitat, feeding habits, reproduction, population size, etc. are available only with regard to a very few fish species. A meaningful assessment on the biodiversity status of the majority of freshwater fishes cannot be done for want of sufficient data base and therefore, no suitable conservation and management programmes are forthcoming for the protection and preservation of the unique fish germplasm resources of Kerala. The present study was conceptualised and undertaken mostly aiming at bridging these gaps by generating an authentic data base on the distribution, resource characteristics and bionomics of the threatened fishes inhabiting the rivers of Kerala. Osteobrama bakeri (Day) is an endemic fish having a very highly restricted and fragmented distribution in Periyar, Chalakudy, Kabini, Kallada and Meenachil rivers of Kerala. This belongs to vulnerable category and is locally known as Mullanpaval which is valued as food fish. Besides, due to its vibrant and attractive colouration and easiness for domestication, it has great potential for being propagated as an ornamental fish. Hitherto, no information is available on the bionomics and resource characteristics of this species. Studies on detailed life history traits are indispensable for fishery management, development of captive breeding technique and implementation of various conservation programmes. In the present study, a pioneer attempt is also made to investigate the life history traits, resource characteristics, proximate composition, etc. of O.baken'.

Teak plantations in kerala an analysis of productivity and profitability

Teak plantations were initiated in Kerala in 1842, and extended almost continuously. Among plantations raised by the Forest Department, teak occupies the largest area and a substantial asset base has been created. Of late, several teak growing private companies have come up offering investors high returns from their plantations. However, no study has been carried out in Kerala on the economic status of teak plantations in the government forests and prospects of investing in teak plantation ventures in the private sector. The present study is relevant in presenting the productivity status of teak plantations in government forests in Kerala and its commercial profitability. This will be useful to the government for planning management strategies and investment priorities. The study will also serve as a base—line information for comparative studies.

The Systematics, Distribution and Bionomics of Deep sea fishes beyond depth 200m along the South west coast of India

Reducing fishing pressure in coastal waters is the need of the day in the Indian marine fisheries sector of the country which is fast changing from a mere vocational activity to a capital intensive industry. It requires continuous monitoring of the resource exploitation through a scientifically acceptable methodology, data on production of each species stock, the number and characteristics of the fishing gears of the fleet, various biological characteristics of each stock, the impact of fishing on the environment and the role of fishery—independent on availability and abundance. Besides this, there are issues relating to capabilities in stock assessment, taxonomy research, biodiversity, conservation and fisheries management. Generation of reliable data base over a fixed time frame, their analysis and interpretation are necessary before drawing conclusions on the stock size, maximum sustainable yield, maximum economic yield and to further implement various fishing regulatory measures. India being a signatory to several treaties and conventions, is obliged to carry out assessments of the exploited stocks and manage them at sustainable levels. Besides, the nation is bound by its obligation of protein food security to people and livelihood security to those engaged in marine fishing related activities. Also, there are regional variabilities in fishing technology and fishery resources. All these make it mandatory for India to continue and strengthen its marine capture fisheries research in general and deep sea fisheries in particular. Against this background, an attempt is made to strengthen the deep sea fish biodiversity and also to generate data on the distribution, abundance, catch per unit effort of fishery resources available beyond 200 m in the EEZ of southwest coast ofIndia and also unravel some of the aspects of life history traits of potentially important non conventional fish species inhabiting in the depth beyond 200 m. This study was carried out as part of the Project on Stock Assessment and Biology of Deep Sea Fishes of Indian EEZ (MoES, Govt. of India).

Mineralogical And Sedimentological Studies Of Innershelf And Beach Sediments Along The Coast Between Paravur And Kovalam, South Kerala, India

Continental shelf is of particular significance in marine geology , because it links the two basically different structural zones in the earth's crust; the continents and ocean basins. The shelf area has much wider importance in many fields of activity such as scientific, economic, social, political and strategic. The pace of development has ultimately put pressure on mankind to look for exploitable resources and accessibility to the continental shelf area and beyond. Added to the above, the developmental activities in the coastal area would readily and directly influence the innershelf sediments. This situation demands a thorough geological knowledge of the continental shelf area. Moreover, a successful management of the continental shelf zone requires an optimum data base on the physico-chemical nature of the shelf sediments. Although sedimentological studies were carried out along the western continental shelf of India, a well documented systematic study of the inner shelf off Trivandrum coast is still found to be lacking. Considering the physiographic settings and the vicinity of two renowned placer deposits at Chavara and Manavalakurichi, such a sedimetological inventory has become all the more vital. In view of the above, a research programme has been drawn up to account the salient sedimentological and mineralogical aspects of the innershelf and beach sediments between Paravur and Kovalam, Trivandrum district, Kerala (latitudes 8° 7'00" to 8° 47'45" and longitudes 76°43'00" to 77° 40'45"). The findings are presented in six chapters formatted to address the aim of this research.

Connected digit speech recognition system for Malayalam language

A connected digit speech recognition is important in many applications such as automated banking system, catalogue-dialing, automatic data entry, automated banking system, etc. This paper presents an optimum speaker-independent connected digit recognizer forMalayalam language. The system employs Perceptual Linear Predictive (PLP) cepstral coefficient for speech parameterization and continuous density Hidden Markov Model (HMM) in the recognition process. Viterbi algorithm is used for decoding. The training data base has the utterance of 21 speakers from the age group of 20 to 40 years and the sound is recorded in the normal office environment where each speaker is asked to read 20 set of continuous digits. The system obtained an accuracy of 99.5 % with the unseen data.