Organisational innovativeness : the role of strategic orientation and environmental scanning

This study examined the effects of strategic orientation and environmental scanning on a firm’s propensity to innovate (organisational innovativeness). Previous research has mostly proposed descriptive and theoretical relationships between strategic orientation, environmental scanning and organisational innovation adoption (Beal, 2000; Jennings & Lumpkin, 1992; Raymond, Julien, & Ramangalahy, 2001). However, strategic orientation and environmental scanning, as distinct constructs, have not been empirically examined directly before in relation to organisational innovativeness. Furthermore the directionality of the relationship between strategic orientation and environmental scanning on organisational innovation adoption is still unclear (Hagen, Haile, & Maghrabi, 2003). For example, does scanning the environment result in certain types of organisational strategies, and these strategies in turn influence levels of organisational innovativeness? Or do certain types of strategic orientations pre-determine the levels of environmental scanning, and then this environmental scanning influences an organisation’s propensity to innovate? Therefore, this study using a more nuanced measure of strategic orientation; the Venkatraman’s (1989) STROBE framework of analysis, defensiveness, futurity, proactiveness, aggressiveness and riskiness, examined the directional effects of strategy and environmental scanning on organisational innovativeness Specifically, two competing models of directionality between strategic orientation and environmental scanning in relation to organisational innovativeness were proposed. Model 1 (Behavioural View) proposed that certain strategic orientation dimensions affect levels of environmental scanning, which in turn influences organisational innovativeness. In contrast, Model 2 (Open Systems view) proposed that environmental scanning affects the emphasis on certain strategic orientation dimensions, which in turn influences organisational innovativeness. Data was collected from 117 industrial firms and path analyses were used to test the two competing models. The results supported both models, suggesting a bi-directional relationship, as both models had adequate fit indices and significant paths with the data. However, overall Model 2 – the Open Systems Model had the stronger fit indices and stronger indirect effect compared to Model 1 – the Behavioural Model, suggesting that overall environmental scanning does not exert a strong direct effect on innovativeness but has more of a stronger indirect effect through the analysis and proactiveness strategic orientation dimensions. In sum, the thesis results suggest that firms’ that emphasise environmental scanning – that is continually seeking information from the environment about customers, markets, industry and new technology - are more likely to emphasise strategic orientations such as proactiveness – being innovative - and also analysis – being analytical and comprehensive in decision making - and both these strategic orientations in turn greatly influence these firms’ propensity to innovate. Discussion is given to these findings and implications are drawn for organisations and future research.

Image segmentation and pose estimation of humans in video

This thesis introduces improved techniques towards automatically estimating the pose of humans from video. It examines a complete workflow to estimating pose, from the segmentation of the raw video stream to extract silhouettes, to using the silhouettes in order to determine the relative orientation of parts of the human body. The proposed segmentation algorithms have improved performance and reduced complexity, while the pose estimation shows superior accuracy during difficult cases of self occlusion.

Moulding them in the industry's image : journalism education's impact on students' professional views

Long-running debates over the value of university-based journalism education have suffered from a lack of empirical foundation, leading to a wide range of assertions both from those who see journalism education playing a crucial role in moulding future journalists and those who do not. Based on a survey of 320 Australian journalism students from six universities across the country, this study provides an account of the professional views these future journalists hold. Findings show that students hold broadly similar priorities in their role perceptions, albeit to different intensities from working journalists. The results point to a relationship between journalism education and the way in which students' views of journalism's watchdog role and its market orientation change over the course of their degree – to the extent that, once they are near completion of their degree, students have been moulded in the image of industry professionals.

Automatic classification of human epithelial type 2 cell indirect immunofluorescence images using cell pyramid matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.

Pre-processing for approximate Bayesian computation in image analysis

Most of the existing algorithms for approximate Bayesian computation (ABC) assume that it is feasible to simulate pseudo-data from the model at each iteration. However, the computational cost of these simulations can be prohibitive for high dimensional data. An important example is the Potts model, which is commonly used in image analysis. Images encountered in real world applications can have millions of pixels, therefore scalability is a major concern. We apply ABC with a synthetic likelihood to the hidden Potts model with additive Gaussian noise. Using a pre-processing step, we fit a binding function to model the relationship between the model parameters and the synthetic likelihood parameters. Our numerical experiments demonstrate that the precomputed binding function dramatically improves the scalability of ABC, reducing the average runtime required for model fitting from 71 hours to only 7 minutes. We also illustrate the method by estimating the smoothing parameter for remotely sensed satellite imagery. Without precomputation, Bayesian inference is impractical for datasets of that scale.

Information-theoretic analysis of brain white matter fiber orientation distribution functions

We propose a new information-theoretic metric, the symmetric Kullback-Leibler divergence (sKL-divergence), to measure the difference between two water diffusivity profiles in high angular resolution diffusion imaging (HARDI). Water diffusivity profiles are modeled as probability density functions on the unit sphere, and the sKL-divergence is computed from a spherical harmonic series, which greatly reduces computational complexity. Adjustment of the orientation of diffusivity functions is essential when the image is being warped, so we propose a fast algorithm to determine the principal direction of diffusivity functions using principal component analysis (PCA). We compare sKL-divergence with other inner-product based cost functions using synthetic samples and real HARDI data, and show that the sKL-divergence is highly sensitive in detecting small differences between two diffusivity profiles and therefore shows promise for applications in the nonlinear registration and multisubject statistical analysis of HARDI data.

Birdcall retrieval from environmental acoustic recordings using image processing

Acoustic recordings of the environment provide an effective means to monitor bird species diversity. To facilitate exploration of acoustic recordings, we describe a content-based birdcall retrieval algorithm. A query birdcall is a region of spectrogram bounded by frequency and time. Retrieval depends on a similarity measure derived from the orientation and distribution of spectral ridges. The spectral ridge detection method caters for a broad range of birdcall structures. In this paper, we extend previous work by incorporating a spectrogram scaling step in order to improve the detection of spectral ridges. Compared to an existing approach based on MFCC features, our feature representation achieves better retrieval performance for multiple bird species in noisy recordings.

The rα structure, chemical shift anisotropy, and the abnormal orientation of methyldichlorophosphine from the NMR spectrum

NMR studies of methyldichlorophosphine have been undertaken in the nematic phase of mixed liquid crystals of opposite diamagnetic anisotropies. The rα structure is derived. The proton chemical-shift anisotropy has been determined from the studies without the use of a reference compound and without a change of experimental conditions. It is shown that the molecule orients in the liquid crystal with positive diamagnetic anisotropy in such a way that the C3 symmetry axis of the CH3P moiety is preferentially aligned perpendicular to the direction of the magnetic field, unlike other similar systems. This is interpreted in terms of the formation of a weak solvent-solute molecular complex. The heteronuclear indirect spin-spin coupling constants are determined. The sign of the two-bond JPH is found to be positive.

Why and when do learning goal orientation and attitude decrease with aging? The role of perceived remaining time and work centrality

We conducted two studies to improve our understanding of why and when older workers are focused on learning. Based on socioemotional selectivity theory, which proposes that goal focus changes with age and the perception of time, we hypothesized and found that older workers perceive their remaining time at work as more limited than younger workers which, in turn, is associated with lower learning goal orientation and a less positive attitude toward learning and development. Furthermore, we hypothesized and found that high work centrality buffers the negative association between age and perceived remaining time, and thus the indirect negative effects of age on learning goal orientation and attitude toward learning and development (through perceived remaining time). These findings suggest that scholars and practitioners should take workers’ perceived remaining time and work centrality into account when examining or stimulating learning activities among aging workers.

FIVDL: Fingerprint Image Verification using Dictionary Learning

Fingerprints are used for identification in forensics and are classified into Manual and Automatic. Automatic fingerprint identification system is classified into Latent and Exemplar. A novel Exemplar technique of Fingerprint Image Verification using Dictionary Learning (FIVDL) is proposed to improve the performance of low quality fingerprints, where Dictionary learning method reduces the time complexity by using block processing instead of pixel processing. The dynamic range of an image is adjusted by using Successive Mean Quantization Transform (SMQT) technique and the frequency domain noise is reduced using spectral frequency Histogram Equalization. Then, an adaptive nonlinear dynamic range adjustment technique is utilized to determine the local spectral features on corresponding fingerprint ridge frequency and orientation. The dictionary is constructed using spatial fundamental frequency that is determined from the spectral features. These dictionaries help in removing the spurious noise present in fingerprints and reduce the time complexity by using block processing instead of pixel processing. Further, dictionaries are used to reconstruct the image for matching. The proposed FIVDL is verified on FVC database sets and Experimental result shows an improvement over the state-of-the-art techniques. (C) 2015 The Authors. Published by Elsevier B.V.

Efficient topological localization using orientation adjacency coherence histograms

This paper describes an efficient vision-based global topological localization approach that uses a coarse-to-fine strategy. Orientation Adjacency Coherence Histogram (OACH), a novel image feature, is proposed to improve the coarse localization. The coarse localization results are taken as inputs for the fine localization which is carried out by matching Harris-Laplace interest points characterized by the SIFT descriptor. Computation of OACHs and interest points is efficient due to the fact that these features are computed in an integrated process. We have implemented and tested the localization system in real environments. The experimental results demonstrate that our approach is efficient and reliable in both indoor and outdoor environments. © 2006 IEEE.

Construction site image retrieval based on material cluster recognition

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. This capability is a product of the technological breakthroughs in the area of image processing that has allowed for the development of a large number of digital imaging applications in all industries. In this paper, an automated and content based construction site image retrieval method is presented. This method is based on image retrieval techniques, and specifically those related with material and object identification and matches known material samples with material clusters within the image content. The results demonstrate the suitability of this method for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images.

Identification of Materials from Construction Site Images Using Content Based Image Retrieval Techniques

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. Examples include surface cracks detection, assessment of fire-damaged mortar, fatigue evaluation of asphalt mixes, aggregate shape measurements, velocimentry, vehicles detection, pore size distribution in geotextiles, damage detection and others. This capability is a product of the technological breakthroughs in the area of Image and Video Processing that has allowed for the development of a large number of digital imaging applications in all industries ranging from the well established medical diagnostic tools (magnetic resonance imaging, spectroscopy and nuclear medical imaging) to image searching mechanisms (image matching, content based image retrieval). Content based image retrieval techniques can also assist in the automated recognition of materials in construction site images and thus enable the development of reliable methods for image classification and retrieval. The amount of original imaging information produced yearly in the construction industry during the last decade has experienced a tremendous growth. Digital cameras and image databases are gradually replacing traditional photography while owners demand complete site photograph logs and engineers store thousands of images for each project to use in a number of construction management tasks. However, construction companies tend to store images without following any standardized indexing protocols, thus making the manual searching and retrieval a tedious and time-consuming effort. Alternatively, material and object identification techniques can be used for the development of automated, content based, construction site image retrieval methodology. These methods can utilize automatic material or object based indexing to remove the user from the time-consuming and tedious manual classification process. In this paper, a novel material identification methodology is presented. This method utilizes content based image retrieval concepts to match known material samples with material clusters within the image content. The results demonstrate the suitability of this methodology for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images.

Shape Recognition of Linear Construction Entities from Construction Site Images

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. This capability is a product of the technological breakthroughs in the area of Image Processing that has allowed for the development of a large number of digital imaging applications in all industries. In this paper, an automated and content based shape recognition model is presented. This model was devised to enhance the recognition capabilities of our existing material based image retrieval model. The shape recognition model is based on clustering techniques, and specifically those related with material and object segmentation. The model detects the borders of each previously detected material depicted in the image, examines its linearity (length/width ratio) and detects its orientation (horizontal/vertical). The results emonstrate the suitability of this model for construction site image retrieval purposes and reveal the capability of existing clustering technologies to accurately identify the shape of a wealth of materials from construction site images.

EFFECT OF IMAGE FORCES ON ELECTRONS CONFINED IN LOW-DIMENSIONAL STRUCTURES UNDER A MAGNETIC-FIELD

We consider the effect of image forces, arising due to a difference in dielectric permeabilities of the well layer and barrier layers, on the energy spectrum of an electron confined in a rectangular potential well under a magnetic field. Depending on the value and the sign of the dielectric mismatch, image forces can localize electrons near the interfaces of the well or in well centre and change the direct intersubband gaps into indirect ones. These effects can be controlled by variation of the magnetic field, offering possibilities for exact tuning of electronic devices.