Efficacy of Cluster- Based Approach for the revival of Handloom Co-operative Societies in Kerala- with special reference to Thiruvananthapuram and Kannur Districts

one of the key sectors, identified by the Department of Industries Government of Kerala, for the cluster development initiative is Handloom, which gives employment to over over 50,000 people directly. Despite its age old tradition and fame, the performance of the sector vis-à-vis power looms is not very rosy owing to (i) competition from cheap power loom cloth from other states (ii) scarcity of quality yarn (iii) price escalation of yarn, dyes, chemicals and other raw materials (iv) the shrinking market for handlooms in Kerala (v) non-demand based production and inadequacy of new designs and (vi) inefficiencies in the system, particularly in the co-operative sector. Cluster based approach is adopted in the handloom sector with the objective of providing necessary support mechanism to come out of the crisis that the sector faces now. While four cluster schemes are being implemented in Kerala, it is under IHDS-CDP that the State got a sizeable number of clusters benefiting a large number of societies and weavers- 24 handloom clusters, bringing 152 handloom co-operative societies and over 19,800 handloom workers under the Programme. This research attempts to revisit the underlying rationale and context of the new direction and would attempt to broadly analyze the growth trends under the influence of cluster model adopted by the State IHDS-CDP for the revival of handloom sector through a detailed study of the handloom co-operative societies in Kerala. If handloom sector in Kerala can be revived using cluster based approach, it can be easily concluded that cluster is capable of taking the MSME in Kerala to a ‘high growth path.’ The study is aimed at understanding how best clusters emerge as appropriate industrial organization suitable for the current global structure of manufacture

Grey Level Co-Occurrence Matrices: Generalisation And Some New Features

Grey Level Co-occurrence Matrices (GLCM) are one of the earliest techniques used for image texture analysis. In this paper we defined a new feature called trace extracted from the GLCM and its implications in texture analysis are discussed in the context of Content Based Image Retrieval (CBIR). The theoretical extension of GLCM to n-dimensional gray scale images are also discussed. The results indicate that trace features outperform Haralick features when applied to CBIR.

Vibrational spectroscopic studies of Guanidinium metal (MII) sulphate hexahydrates [MII = Co, Fe, Ni]

The Raman and FTIR spectra of [C(NH2)3]2M(SO4)2 ·6H2O (withM= Co, Fe, Ni) were recorded and analysed. The observed spectral bands are assigned in terms of vibrations of guanidinium ions, sulphate groups and water molecules. The analysis shows that the sulphate tetrahedra are distorted from their free state symmetry Td to C1. This is attributed to the presence of hydrogen bonds from water molecules. The order of distortion of the metal oxygen octahedra influenced the distortion of the sulphate tetrahedra. The appearance of 1– 3 modes of water molecules above 3300 cm−1 indicates the presence of weak hydrogen bonds

Tailoring Magnetic Properties of Co-Fe Thin Films by Topographical Modifications Using Thermal Annealing and Swift Heavy Ion Irradiation and Fabrication of Magnetoelectric Multilayers for Device Applications

Magnetism and magnetic materials have been playing a lead role in improving the quality of life. They are increasingly being used in a wide variety of applications ranging from compasses to modern technological devices. Metallic glasses occupy an important position among magnetic materials. They assume importance both from a scientific and an application point of view since they represent an amorphous form of condensed matter with significant deviation from thermodynamic equilibrium. Metallic glasses having good soft magnetic properties are widely used in tape recorder heads, cores of high-power transformers and metallic shields. Superconducting metallic glasses are being used to produce high magnetic fields and magnetic levitation effect. Upon heat treatment, they undergo structural relaxation leading to subtle rearrangements of constituent atoms. This leads to densification of amorphous phase and subsequent nanocrystallisation. The short-range structural relaxation phenomenon gives rise to significant variations in physical, mechanical and magnetic properties. Magnetic amorphous alloys of Co-Fe exhibit excellent soft magnetic properties which make them promising candidates for applications as transformer cores, sensors, and actuators. With the advent of microminiaturization and nanotechnology, thin film forms of these alloys are sought after for soft under layers for perpendicular recording media. The thin film forms of these alloys can also be used for fabrication of magnetic micro electro mechanical systems (magnetic MEMS). In bulk, they are drawn in the form of ribbons, often by melt spinning. The main constituents of these alloys are Co, Fe, Ni, Si, Mo and B. Mo acts as the grain growth inhibitor and Si and B facilitate the amorphous nature in the alloy structure. The ferromagnetic phases such as Co-Fe and Fe-Ni in the alloy composition determine the soft magnetic properties. The grain correlation length, a measure of the grain size, often determines the soft magnetic properties of these alloys. Amorphous alloys could be restructured in to their nanocrystalline counterparts by different techniques. The structure of nanocrystalline material consists of nanosized ferromagnetic crystallites embedded in an amorphous matrix. When the amorphous phase is ferromagnetic, they facilitate exchange coupling between nanocrystallites. This exchange coupling results in the vanishing of magnetocrystalline anisotropy which improves the soft magnetic properties. From a fundamental perspective, exchange correlation length and grain size are the deciding factors that determine the magnetic properties of these nanocrystalline materials. In thin films, surfaces and interfaces predominantly decides the bulk property and hence tailoring the surface roughness and morphology of the film could result in modified magnetic properties. Surface modifications can be achieved by thermal annealing at various temperatures. Ion irradiation is an alternative tool to modify the surface/structural properties. The surface evolution of a thin film under swift heavy ion (SHI) irradiation is an outcome of different competing mechanism. It could be sputtering induced by SHI followed by surface roughening process and the material transport induced smoothening process. The impingement of ions with different fluence on the alloy is bound to produce systematic microstructural changes and this could effectively be used for tailoring magnetic parameters namely coercivity, saturation magnetization, magnetic permeability and remanence of these materials. Swift heavy ion irradiation is a novel and an ingenious tool for surface modification which eventually will lead to changes in the bulk as well as surface magnetic property. SHI has been widely used as a method for the creation of latent tracks in thin films. The bombardment of SHI modifies the surfaces or interfaces or creates defects, which induces strain in the film. These changes will have profound influence on the magnetic anisotropy and the magnetisation of the specimen. Thus inducing structural and morphological changes by thermal annealing and swift heavy ion irradiation, which in turn induce changes in the magnetic properties of these alloys, is one of the motivation of this study. Multiferroic and magneto-electrics is a class of functional materials with wide application potential and are of great interest to material scientists and engineers. Magnetoelectric materials combine both magnetic as well as ferroelectric properties in a single specimen. The dielectric properties of such materials can be controlled by the application of an external magnetic field and the magnetic properties by an electric field. Composites with magnetic and piezo/ferroelectric individual phases are found to have strong magnetoelectric (ME) response at room temperature and hence are preferred to single phasic multiferroic materials. Currently research in this class of materials is towards optimization of the ME coupling by tailoring the piezoelectric and magnetostrictive properties of the two individual components of ME composites. The magnetoelectric coupling constant (MECC) (_ ME) is the parameter that decides the extent of interdependence of magnetic and electric response of the composite structure. Extensive investigates have been carried out in bulk composites possessing on giant ME coupling. These materials are fabricated by either gluing the individual components to each other or mixing the magnetic material to a piezoelectric matrix. The most extensively investigated material combinations are Lead Zirconate Titanate (PZT) or Lead Magnesium Niobate-Lead Titanate (PMNPT) as the piezoelectric, and Terfenol-D as the magnetostrictive phase and the coupling is measured in different configurations like transverse, longitudinal and inplane longitudinal. Fabrication of a lead free multiferroic composite with a strong ME response is the need of the hour from a device application point of view. The multilayer structure is expected to be far superior to bulk composites in terms of ME coupling since the piezoelectric (PE) layer can easily be poled electrically to enhance the piezoelectricity and hence the ME effect. The giant magnetostriction reported in the Co-Fe thin films makes it an ideal candidate for the ferromagnetic component and BaTiO3 which is a well known ferroelectric material with improved piezoelectric properties as the ferroelectric component. The multilayer structure of BaTiO3- CoFe- BaTiO3 is an ideal system to understand the underlying fundamental physics behind the ME coupling mechanism. Giant magnetoelectric coupling coefficient is anticipated for these multilayer structures of BaTiO3-CoFe-BaTiO3. This makes it an ideal candidate for cantilever applications in magnetic MEMS/NEMS devices. SrTiO3 is an incipient ferroelectric material which is paraelectric up to 0K in its pure unstressed form. Recently few studies showed that ferroelectricity can be induced by application of stress or by chemical / isotopic substitution. The search for room temperature magnetoelectric coupling in SrTiO3-CoFe-SrTiO3 multilayer structures is of fundamental interest. Yet another motivation of the present work is to fabricate multilayer structures consisting of CoFe/ BaTiO3 and CoFe/ SrTiO3 for possible giant ME coupling coefficient (MECC) values. These are lead free and hence promising candidates for MEMS applications. The elucidation of mechanism for the giant MECC also will be the part of the objective of this investigation.

Supporting co-operative work in apron control and resource management offices on airports

A high demand exists to increase the efficiency of present airport ground facilities and the co-ordination of traffic and services. The Traffic Office plays a crucial role in managing the airport. The main tasks of the Traffic Office is management of equipment, services, and ressources based on the flight schedule and resolving conflicts arising from deviations from the schedule. A new tool will support information exchange between Traffic Office and other facilities on the airport.

From evolution to co-evolution – An empirical study of the role of innovation, policy and public research in industrial dynamics

In the past decades since Schumpeter’s influential writings economists have pursued research to examine the role of innovation in certain industries on firm as well as on industry level. Researchers describe innovations as the main trigger of industry dynamics, while policy makers argue that research and education are directly linked to economic growth and welfare. Thus, research and education are an important objective of public policy. Firms and public research are regarded as the main actors which are relevant for the creation of new knowledge. This knowledge is finally brought to the market through innovations. What is more, policy makers support innovations. Both actors, i.e. policy makers and researchers, agree that innovation plays a central role but researchers still neglect the role that public policy plays in the field of industrial dynamics. Therefore, the main objective of this work is to learn more about the interdependencies of innovation, policy and public research in industrial dynamics. The overarching research question of this dissertation asks whether it is possible to analyze patterns of industry evolution – from evolution to co-evolution – based on empirical studies of the role of innovation, policy and public research in industrial dynamics. This work starts with a hypothesis-based investigation of traditional approaches of industrial dynamics. Namely, the testing of a basic assumption of the core models of industrial dynamics and the analysis of the evolutionary patterns – though with an industry which is driven by public policy as example. Subsequently it moves to a more explorative approach, investigating co-evolutionary processes. The underlying questions of the research include the following: Do large firms have an advantage because of their size which is attributable to cost spreading? Do firms that plan to grow have more innovations? What role does public policy play for the evolutionary patterns of an industry? Are the same evolutionary patterns observable as those described in the ILC theories? And is it possible to observe regional co-evolutionary processes of science, innovation and industry evolution? Based on two different empirical contexts – namely the laser and the photovoltaic industry – this dissertation tries to answer these questions and combines an evolutionary approach with a co-evolutionary approach. The first chapter starts with an introduction of the topic and the fields this dissertation is based on. The second chapter provides a new test of the Cohen and Klepper (1996) model of cost spreading, which explains the relationship between innovation, firm size and R&D, at the example of the photovoltaic industry in Germany. First, it is analyzed whether the cost spreading mechanism serves as an explanation for size advantages in this industry. This is related to the assumption that the incentives to invest in R&D increase with the ex-ante output. Furthermore, it is investigated whether firms that plan to grow will have more innovative activities. The results indicate that cost spreading serves as an explanation for size advantages in this industry and, furthermore, growth plans lead to higher amount of innovative activities. What is more, the role public policy plays for industry evolution is not finally analyzed in the field of industrial dynamics. In the case of Germany, the introduction of demand inducing policy instruments stimulated market and industry growth. While this policy immediately accelerated market volume, the effect on industry evolution is more ambiguous. Thus, chapter three analyzes this relationship by considering a model of industry evolution, where demand-inducing policies will be discussed as a possible trigger of development. The findings suggest that these instruments can take the same effect as a technical advance to foster the growth of an industry and its shakeout. The fourth chapter explores the regional co-evolution of firm population size, private-sector patenting and public research in the empirical context of German laser research and manufacturing over more than 40 years from the emergence of the industry to the mid-2000s. The qualitative as well as quantitative evidence is suggestive of a co-evolutionary process of mutual interdependence rather than a unidirectional effect of public research on private-sector activities. Chapter five concludes with a summary, the contribution of this work as well as the implications and an outlook of further possible research.

Statistical Models for Co-occurrence Data

Modeling and predicting co-occurrences of events is a fundamental problem of unsupervised learning. In this contribution we develop a statistical framework for analyzing co-occurrence data in a general setting where elementary observations are joint occurrences of pairs of abstract objects from two finite sets. The main challenge for statistical models in this context is to overcome the inherent data sparseness and to estimate the probabilities for pairs which were rarely observed or even unobserved in a given sample set. Moreover, it is often of considerable interest to extract grouping structure or to find a hierarchical data organization. A novel family of mixture models is proposed which explain the observed data by a finite number of shared aspects or clusters. This provides a common framework for statistical inference and structure discovery and also includes several recently proposed models as special cases. Adopting the maximum likelihood principle, EM algorithms are derived to fit the model parameters. We develop improved versions of EM which largely avoid overfitting problems and overcome the inherent locality of EM--based optimization. Among the broad variety of possible applications, e.g., in information retrieval, natural language processing, data mining, and computer vision, we have chosen document retrieval, the statistical analysis of noun/adjective co-occurrence and the unsupervised segmentation of textured images to test and evaluate the proposed algorithms.

Memory Hierarchy Hardware-Software Co-design in Embedded Systems

The memory hierarchy is the main bottleneck in modern computer systems as the gap between the speed of the processor and the memory continues to grow larger. The situation in embedded systems is even worse. The memory hierarchy consumes a large amount of chip area and energy, which are precious resources in embedded systems. Moreover, embedded systems have multiple design objectives such as performance, energy consumption, and area, etc. Customizing the memory hierarchy for specific applications is a very important way to take full advantage of limited resources to maximize the performance. However, the traditional custom memory hierarchy design methodologies are phase-ordered. They separate the application optimization from the memory hierarchy architecture design, which tend to result in local-optimal solutions. In traditional Hardware-Software co-design methodologies, much of the work has focused on utilizing reconfigurable logic to partition the computation. However, utilizing reconfigurable logic to perform the memory hierarchy design is seldom addressed. In this paper, we propose a new framework for designing memory hierarchy for embedded systems. The framework will take advantage of the flexible reconfigurable logic to customize the memory hierarchy for specific applications. It combines the application optimization and memory hierarchy design together to obtain a global-optimal solution. Using the framework, we performed a case study to design a new software-controlled instruction memory that showed promising potential.