P. Vivekanandan

Bio: P. Vivekanandan is an academic researcher from Park College of Engineering and Technology. The author has contributed to research in topics: Computer science & Population-based incremental learning. The author has an hindex of 11, co-authored 30 publications receiving 471 citations. Previous affiliations of P. Vivekanandan include Anna University & Indian Institute of Management Ahmedabad.

Scalable fabrication of prototype sensor for selective and sub-ppm level ethanol sensing based on TiO2 nanotubes decorated porous silicon

Abstract: A miniaturized prototype sensor based on TiO2 nanotubes/porous silicon (PS) heterojunction is developed for selective ethanol sensing in sub-ppm range. Titanium (Ti) of thickness ∼ 200 nm was deposited on PS using RF sputtering technique. Both silicon and Ti were sequentially anodized to form PS and nanotubes respectively. Electrical contacts for testing of resistive sensors were fabricated using lift off process. The sensor was packaged onto a 12-pin header and tested in presence of different VOCs with concentration ranging from 0.5 to 100 ppm. The selective ethanol sensing at around 150 °C stems from the formation of TiO2 nanotubes/PS heterojunction. The sensitivity of such a sensor, improved manifold in comparison to the response of pure PS and pure TiO2 based sensors. The formation of heterojunction, selective response to ethanol, sub-ppm level sensing at comparatively low operating temperature is discussed. The study unfolds the collective properties of TiO2/PS heterojunction and demonstrates the potential of wafer scale integrated repeatable ethanol sensor tested at sub-ppm level.

An efficient SVM based tumor classification with symmetry Non-negative Matrix Factorization using gene expression data

Abstract: A reliable and accurate identification of the type of tumors is crucial to the proper treatment of cancers. The classification of tumors was and is both a practical and theoretic necessity and requirement. DNA microarrays provide a new technique of measuring gene expression, which has attracted a lot of research interest in recent years. It was suggested that gene expression data from microarrays (biochips) can be employed in many biomedical areas, e.g., in cancer classification. Although several, new and existing, methods of classification were tested, a selection of proper (optimal) set of genes, the expressions of which can serve during classification, is still an open problem. This paper presents a new method for tumor classification using gene expression data. In the proposed method, we first select genes using Nonnegative Matrix Factorization (NMF). In order to improve the performance of classification, Symmetry NMF (SymNMF) is used in this approach. Then, features are extracted from the selected genes by virtue SymNMF. As a last step, an efficient machine learning approach is used to classify the tumor samples using the extracted features. In order for a better classification, Support Vector Machine with Weighted Kernel Width (WSVM) is used in this classification approach. The performance of the proposed approach is tested using colon cancer data set and the acute leukemia data set. It is observed from the experimental results that the proposed approach provides better performance when compared with the traditional approaches.

Theory of open inclusive innovation for reciprocal, responsive and respectful outcomes: coping creatively with climatic and institutional risks

Abstract: Given the economic squeeze world over, search for what we call frugal grassroots innovations in Honey Bee Network, has become even more urgent and relevant in the recent years. And, to shape this search, models and concepts like open innovation, reverse innovation (GE, Market-Relevant Design: Making ECGs Available Across India, 2009); (Govindarajan, Reverse Innovation: a Playbook, 2012); (Govindarajan and Ramamurti. Global Strategy Journal, 1: 191–205, 2011); (Govindarajan and Euchner, Res. Technol. Manage, 55: 13–17, 2012, Govindrajan and Trimble, 40(5), 5–11, 2012), embedded innovation (Simanis and Hart, Innovation from the Inside Out, MIT Sloan Management Review, 2009), extremely affordable, low-cost, frugal innovation (Honey Bee Network, 1989–2016, Gupta, 2000); (Gupta AK, How Local Knowledge can Boost Scientific Studies, 2007); (Gupta AK, Indian Hidden hotebd of invention, 2009a; Gupta AK, http://anilg.sristi.org/harnessing-stimulus-for-promoting-innovations-and-entrepreneurship/ , 2009b) etc., have emerged over time. We wish to trace the evolution of the Open Innovation Theory (Urban and Von Hippel, Manag. Sci. 34(5), 569–582, 1988) in the context of the Honey Bee Network working on such ideas for over 26 years. The idea is to study the different strands of relationships between knowledge providers and seekers which make the system truly reciprocal, responsible and responsive. When systems become open, search cost for inclusive innovation will automatically come down and the knowledge system will also become more symmetrical and inclusive. Inclusive innovation for social development implies that new solutions should help in dealing with one or more of the five factors of exclusion: spatial, seasonal, sectoral, skill and social. These should also be accessible, affordable, available and adaptable to varying and differentiated user endowments and needs, besides being circular. One has to understand the interaction between natural, social, ethical and intellectual capital, situated in the institutional context of innovations: at, from, for and with grassroots level communities for defining inclusivity in the innovation ecosystem. A company or a community, when in need of an innovative solution to a local problem, may seek it from outside, develop it inside, or co-create/contract it out. The nature of reciprocity between knowledge and innovation exchange partners may have different types of asymmetries (Bansemir and Neyer 2009). Different ethical principle enunciated in the Honey Bee Network may or may not be followed. The discourse on open innovation has been biased in the favour of corporates seeking ideas form outside rather than sharing their own innovation/knowledge as a public good or commons, or even at low cost with less-advantaged industry actors. In this paper, we reflect on such biases that companies and scholars have developed and propose a framework to temper it. The need for such a correction becomes even more important when various kind of climatic, institutional and market risks are making socio-economic systems more fragile and vulnerable to various uncertainties and fluctuations. Coping with risks is significantly related to malleability of innovations. The process of evolving and nurturing innovations may have a bearing on their eventual adaptability to user. We argue that when both technology platform and application domains are known well, the incubation model works. Generally, through this process, incremental innovation grows better. But, when both are unknown or are ambiguous, sanctuary model works better. In incubators, the chaos is outside and the order is inside. In sanctuary, it is the opposite. It is not very surprising that sanctuary nurtures innovation which is more suited to fluctuating climate and market-uncertain environments. Innovations don’t have relevance only at artefactual level. One can learn at metaphorical, heuristic and gestalt levels too. Building bridges between formal and informal knowledge systems poses a unique challenge in designing reciprocal and responsible open innovation platforms? This paper pleads for more reciprocal, respectful and responsible exchanges of knowledge between formal and informal sector adding value to the contributions of grassroots green innovators.

Multi-feature based automatic face identification on kernel eigen spaces (KES) under unstable lighting conditions

Abstract: Programmed Face Identification (PFI) of images more reliable even under unstable lighting conditions is one of the most important challenges for practical face recognition systems. We tackle this by combining the strengths of robust illumination normalization, local texture-based face representations, distance transform based matching, multiple feature fusion. Additionally we propose Phase Congruency Features which is an approach for detects points of order in the phase spectrum within images. By combining the results produced from both the above mentioned approaches, we try to increase the accuracy of recognizing the given image from group images even with difficult lighting conditions.

Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling

Abstract: ‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Encyclopedia of Distance Learning

Abstract: In this special issue is to indicate foresight, innovation, and strategy for the future direction of e-learning, more than web-oriented teaching and multipoint videoconferencing, for collaborative, distributed, experiential learning and creation of new knowledge, with youngsters around the world, which hence promote mutual understanding for global peace. Emphasis will be on knowledgeable and inspiring papers (but not limited) on the use of GRID networking technology with distributed computer simulation for experiential (hands-on) learning through broadband Internet, across national, continental and oceanic boundaries. Subjects are in any fields of e-learning and e-healthcare/telemedicine, in research, case studies, project descriptions, implementation, reports from the field or book review. Preparing this special issue with Dr. Salmon, took 6 months, from the beginning to the arrival period to the on your screen. This period was shorter than the first special issue publishing in 2004. Of course this success belongs to this issue's guest editor who I will explain a bit details him belove and what did he do during in his period. During this process, Dr. Salmon served very healthy communication between the authors. In addition, she spared his valuable time, beside his other academic activities and responsibilities in the name of her university, her lectures, and researches, duties for distance education field and for TOJDE as well. Each article in this issue has therefore been read carefully by Gilly; me and Dr. Gokdag. This process has maximized the quality and rigour of the published articles. In addition to the articles, " notes for editor " books review and news, sections are still keeping in this issue too. "Anadolu University's Articles" and "TOJDE's links are getting more" columns are still keeping in this issue too. I would like to express my sincere thanks especially to Dr. Salmon in the name of my University and also for TOJDE she was the leader of the guest editorial team in this special issue. She will give detail info about structure of the articles in her " From Guest Editors " article. I strongly believe that experience gained on this third special issue would encourage us and other interested colleagues in the field in the near future. Below you will find short biodata about guest editors. He interested in heavily with the applications of distance education in Turkey, profile of the distance education students and graduates, additionally relationships of the distance education graduates and market. And last but not …

Making the most of community energies: Three perspectives on grassroots innovation:

Abstract: Grassroots innovations for sustainability are attracting increasing policy attention. Drawing upon a wide range of empirical research into community energy in the UK, and taking recent support from national government as a case study, we apply three distinct analytical perspectives: strategic niche management, niche policy advocacy, and critical niches. Whilst the first and second perspectives appear to explain policy influence in grassroots innovation adequately, each also shuts out more transformational possibilities. We therefore argue that, if grassroots innovation is to realise its full potential, then we need to also pursue a third, critical niches perspective, and open up debate about more socially transformative pathways to sustainability.