Rotation invariant iris feature extraction using Gaussian Markov random fields with non-separable wavelet

Rotation invariance is important for an iris recognition system since changes of head orientation and binocular vergence may cause eye rotation. The conventional methods of iris recognition cannot achieve true rotation invariance. They only achieve approximate rotation invariance by rotating the feature vector before matching or unwrapping the iris ring at different initial angles. In these methods, the complexity of the method is increased, and when the rotation scale is beyond the certain scope, the error rates of these methods may substantially increase. In order to solve this problem, a new rotation invariant approach for iris feature extraction based on the non-separable wavelet is proposed in this paper. Firstly, a bank of non-separable orthogonal wavelet filters is used to capture characteristics of the iris. Secondly, a method of Markov random fields is used to capture rotation invariant iris feature. Finally, two-class kernel Fisher classifiers are adopted for classification. Experimental results on public iris databases show that the proposed approach has a low error rate and achieves true rotation invariance. © 2010.

Cost-effective and eco-friendly synthesis of novel and stable N-doped ZnO/g-C3N4 core-shell nanoplates with excellent visible-light responsive photocatalysis

N-doped ZnO/g-C3N4 hybrid core–shell nanoplates have been successfully prepared via a facile, cost-effective and eco-friendly ultrasonic dispersion method for the first time. HRTEM studies confirm the formation of the N-doped ZnO/g-C3N4 hybrid core–shell nanoplates with an average diameter of 50 nm and the g-C3N4 shell thickness can be tuned by varying the content of loaded g-C3N4. The direct contact of the N-doped ZnO surface and g-C3N4 shell without any adhesive interlayer introduced a new carbon energy level in the N-doped ZnO band gap and thereby effectively lowered the band gap energy. Consequently, the as-prepared hybrid core–shell nanoplates showed a greatly enhanced visible-light photocatalysis for the degradation of Rhodamine B compare to that of pure N-doped ZnO surface and g-C3N4. Based on the experimental results, a proposed mechanism for the N-doped ZnO/g-C3N4 photocatalyst was discussed. Interestingly, the hybrid core–shell nanoplates possess high photostability. The improved photocatalytic performance is due to a synergistic effect at the interface of the N-doped ZnO and g-C3N4 including large surface-exposure area, energy band structure and enhanced charge-separation properties. Significantly, the enhanced performance also demonstrates the importance of evaluating new core–shell composite photocatalysts with g-C3N4 as shell material.

Feature Extraction for Classification in the Data Mining Process

Dimensionality reduction is a very important step in the data mining process. In this paper, we consider feature extraction for classification tasks as a technique to overcome problems occurring because of “the curse of dimensionality”. Three different eigenvector-based feature extraction approaches are discussed and three different kinds of applications with respect to classification tasks are considered. The summary of obtained results concerning the accuracy of classification schemes is presented with the conclusion about the search for the most appropriate feature extraction method. The problem how to discover knowledge needed to integrate the feature extraction and classification processes is stated. A decision support system to aid in the integration of the feature extraction and classification processes is proposed. The goals and requirements set for the decision support system and its basic structure are defined. The means of knowledge acquisition needed to build up the proposed system are considered.

A dynamic feature information model for integrated manufacturing planning and optimization

This paper presents a new, dynamic feature representation method for high value parts consisting of complex and intersecting features. The method first extracts features from the CAD model of a complex part. Then the dynamic status of each feature is established between various operations to be carried out during the whole manufacturing process. Each manufacturing and verification operation can be planned and optimized using the real conditions of a feature, thus enhancing accuracy, traceability and process control. The dynamic feature representation is complementary to the design models used as underlining basis in current CAD/CAM and decision support systems. © 2012 CIRP.

Visualisation of heterogeneous data with simultaneous feature saliency using Generalised Generative Topographic Mapping

Most machine-learning algorithms are designed for datasets with features of a single type whereas very little attention has been given to datasets with mixed-type features. We recently proposed a model to handle mixed types with a probabilistic latent variable formalism. This proposed model describes the data by type-specific distributions that are conditionally independent given the latent space and is called generalised generative topographic mapping (GGTM). It has often been observed that visualisations of high-dimensional datasets can be poor in the presence of noisy features. In this paper we therefore propose to extend the GGTM to estimate feature saliency values (GGTMFS) as an integrated part of the parameter learning process with an expectation-maximisation (EM) algorithm. The efficacy of the proposed GGTMFS model is demonstrated both for synthetic and real datasets.

Eco-efficiency measurement and material balance principle:an application in power plants Malmquist Luenberger Index

Incorporating Material Balance Principle (MBP) in industrial and agricultural performance measurement systems with pollutant factors has been on the rise in recent years. Many conventional methods of performance measurement have proven incompatible with the material flow conditions. This study will address the issue of eco-efficiency measurement adjusted for pollution, taking into account materials flow conditions and the MBP requirements, in order to provide ‘real’ measures of performance that can serve as guides when making policies. We develop a new approach by integrating slacks-based measure to enhance the Malmquist Luenberger Index by a material balance condition that reflects the conservation of matter. This model is compared with a similar model, which incorporates MBP using the trade-off approach to measure productivity and eco-efficiency trends of power plants. Results reveal similar findings for both models substantiating robustness and applicability of the proposed model in this paper.

Eco-car : a perfect vehicle for technical design teaching?

Design methods and tools are generally best learned and developed experientially [1]. Finding appropriate vehicles for delivering these to students is becoming increasingly challenging, especially when considering only those that will enthuse, intrigue and inspire. This paper traces the development of different eco-car design and build projects which competed in the Shell Eco-Marathon. The cars provided opportunities for experiential learning through a formal learning cycle of CDIO (Conceive, Design, Implement, Operate) or the more traditional understand, explore, create, validate, with both teams developing a functional finished prototype. Lessons learned were applied through the design of a third and fourth eco-car using experimental techniques with bio-composites, combining the knowledge of fibre reinforced composite materials and adhesives with the plywood construction techniques of the two teams. The paper discusses the importance of applying materials and techniques to a real world problem. It will also explore how eco-car and comparing traditional materials and construction techniques with high tech composite materials is an ideal teaching, learning and assessment vehicle for technical design techniques.

Eco-efficiency considering the issue of heterogeneity among power plants

One of the main objectives in restructuring power industry is enhancing the efficiency of power facilities. However, power generation industry, which plays a key role in the power industry, has a noticeable share in emission amongst all other emission-generating sectors. In this study, we have developed some new Data Envelopment Analysis models to find efficient power plants based on less fuel consumption, combusting less polluting fuel types, and incorporating emission factors in order to measure the ecological efficiency trend. We then applied these models to measuring eco-efficiency during an eight-year period of power industry restructuring in Iran. Results reveal that there has been a significant improvement in eco-efficiency, cost efficiency and allocative efficiency of the power plants during the restructuring period. It is also shown that despite the hydro power plants look eco-efficient; the combined cycle ones have been more allocative efficient than the other power generation technologies used in Iran.

Template-free and eco-friendly synthesis of hierarchical Ag3PO4 microcrystals with sharp corners and edges for enhanced photocatalytic activity under visible light

Herein, we demonstrate a template-free and eco-friendly strategy to synthesize hierarchical Ag3PO4 microcrystals with sharp corners and edges via silver–ammine complex at room temperature. The as-synthesized hierarchical Ag3PO4 microcrystals were characterized by X-ray diffraction, field-emission scanning electron microscope (FESEM), UV–vis diffuse reflectance spectroscopy (UV–vis DRS), BET surface area analyzer, and photoluminescence analysis (PL). Our results clearly indicated that the as-synthesized Ag3PO4 microcrystals possess a hierarchical structure with sharp corners and edges. More attractively, the adsorption ability and visible light photocatalytic activity of the as-synthesized hierarchical Ag3PO4 is much higher than that of conventional Ag3PO4.

Generalized KPCA by adaptive rules in feature space

Principal component analysis (PCA) is well recognized in dimensionality reduction, and kernel PCA (KPCA) has also been proposed in statistical data analysis. However, KPCA fails to detect the nonlinear structure of data well when outliers exist. To reduce this problem, this paper presents a novel algorithm, named iterative robust KPCA (IRKPCA). IRKPCA works well in dealing with outliers, and can be carried out in an iterative manner, which makes it suitable to process incremental input data. As in the traditional robust PCA (RPCA), a binary field is employed for characterizing the outlier process, and the optimization problem is formulated as maximizing marginal distribution of a Gibbs distribution. In this paper, this optimization problem is solved by stochastic gradient descent techniques. In IRKPCA, the outlier process is in a high-dimensional feature space, and therefore kernel trick is used. IRKPCA can be regarded as a kernelized version of RPCA and a robust form of kernel Hebbian algorithm. Experimental results on synthetic data demonstrate the effectiveness of IRKPCA. © 2010 Taylor & Francis.

From eco-efficiency to eco-effectiveness: The policy-performance paradox

The internalisation level of sustainability issues varies among topics and among countries. Companies give up less internalised issues for more internalised ones. Discrepancies between legal, market and cultural internalisation lead to different escape strategies: firms develop a high level environmental management system and they have nice sustainability policy and reports. These achievements cover the fact that their total emission keeps increasing and they do not proceed in solving the most crucial global community or corporate governance problems. ‘Escaper’ firms are often qualified as ‘leading’ ones, as a current stream of research is also ‘escapist’: it puts too much emphasis on sustainability efforts as compared to sustainability performance. Genuine strategies focus on hardcore sustainability issues and absolute effects rather than on issues easily solved and having high PR effects. They allow for growth in innovative firms, if they crowd out less efficient or more polluting ones. They produce positive environmental value added when sector average eco-efficiency is used as benchmark and do not accelerate market expansion and consumerism.

A framework for automatic feature extraction from airborne light detection and ranging data

Recent advances in airborne Light Detection and Ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. Airborne LIDAR systems usually return a 3-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. This technology is becoming a primary method for extracting information of different kinds of geometrical objects, such as high-resolution digital terrain models (DTMs), buildings and trees, etc. In the past decade, LIDAR gets more and more interest from researchers in the field of remote sensing and GIS. Compared to the traditional data sources, such as aerial photography and satellite images, LIDAR measurements are not influenced by sun shadow and relief displacement. However, voluminous data pose a new challenge for automated extraction the geometrical information from LIDAR measurements because many raster image processing techniques cannot be directly applied to irregularly spaced LIDAR points. ^ In this dissertation, a framework is proposed to filter out information about different kinds of geometrical objects, such as terrain and buildings from LIDAR automatically. They are essential to numerous applications such as flood modeling, landslide prediction and hurricane animation. The framework consists of several intuitive algorithms. Firstly, a progressive morphological filter was developed to detect non-ground LIDAR measurements. By gradually increasing the window size and elevation difference threshold of the filter, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Then, building measurements are identified from no-ground measurements using a region growing algorithm based on the plane-fitting technique. Raw footprints for segmented building measurements are derived by connecting boundary points and are further simplified and adjusted by several proposed operations to remove noise, which is caused by irregularly spaced LIDAR measurements. To reconstruct 3D building models, the raw 2D topology of each building is first extracted and then further adjusted. Since the adjusting operations for simple building models do not work well on 2D topology, 2D snake algorithm is proposed to adjust 2D topology. The 2D snake algorithm consists of newly defined energy functions for topology adjusting and a linear algorithm to find the minimal energy value of 2D snake problems. Data sets from urbanized areas including large institutional, commercial, and small residential buildings were employed to test the proposed framework. The results demonstrated that the proposed framework achieves a very good performance. ^

Toward An Eco-Egalitarian University

This article argues for a political transformation and reorganization of the university so that it is capable of challenging the "hierarchy of power in a neoliberal society." Faculty democracy, administrative accountability to faculty, and the education of students to become critical, thinking citizens would be a major part of this reorganization. This article first appeared in The Contemporary Condition: http://contemporarycondition.blogspot.com/2014/07/toward-eco-egalitarian-university.html

400 Parts Per Million: An Eco-Political Music Video

400 ppm is an eco-political music video which encapsulates climate crisis and climate justice in three minutes flat. It is an intervention in popular political ecology/economy, aimed at those who are uneasy with the increasingly obvious deterioration of the living systems of which we are an inextricable part.

Economy of the island of eco-tourism: the role of shark-diving and shark harvesting

Beyond its importance in maintaining ecosystems, sharks provide services that play important socioeconomic roles. The rise in their exploitation as a tourism resource in recent years has highlighted economic potential of non-destructive uses of sharks and the extent of economic losses associated to declines in their population. In this work, we present estimates for use value of sharks in Fernando de Noronha Island - the only ecotouristic site offering shark diving experience in the Atlantic coast of South America. Through the Travel Cost Method we estimate the total touristic use value aggregated to Noronha Island by the travel cost was up to USD 312 million annually, of which USD 91.1 million are transferred to the local economy. Interviewing people from five different economic sectors, we show shark-diving contribute with USD 2.5 million per year to Noronha’s economy, representing 19% of the island’s GDP. Shark-diving provides USD 128.5 thousand of income to employed islanders, USD 72.6 thousand to government in taxes and USD 5.3 thousand to fishers due to the increase in fish consumption demanded by shark divers. We discover, though, that fishers who actually are still involved in shark fishing earn more by catching sharks than selling other fish for consumption by shark divers. We conclude, however, that the non-consumptive use of sharks is most likely to benefit large number of people by generating and money flow if compared to the shark fishing, providing economic arguments to promote the conservation of these species.