The Impact of mobile communication on employee performance

A dissertation submitted to the Department of Computer and Engineering for the MBAABSTRACT In the current competitive environment, the need for better management of all organizational resources, specifically human resource management has become a concern for most of the organizations. Improving employee performance is a prime factor to achieve competitive advantage. It is a common belief that the use of mobile communication improves organizational performance. However, there is little empirical evidence to support this argument in improving employee performance. The main objective of the research is to determine the impact of mobile communication on employee performance./ The research is focused on relationship between use of mobile communication and performance with respect to various aspects of employee performance./ Hence literature provided, discusses the employee performance and mobile communication in Sri Lanka. Information was gathered, using two questionnaires, from a sample of 127 users in thirteen different organizations covering main sectors of the Srilankan economy such as Banks, Tourist industry, Communication, System Integration and Software development. The questionnaire was used to determine the impact of mobile communication on motivation, ability, role perception, situational factors and usage of ,mobile communication. Employee performance was captured and recorded using a separate questionnaire given to respective superiors. The impact of mobile communication on motivation, ability, role perception, situational factors and mobile usage were identified as the independent variables and employee performance as the dependent variable. The data was analyzed through correlation and regression analysis. Data obtained from each of the research instruments was then statistically analyzed through linear regression analysis and simple correlation analysis. It was concluded that there is a significant relationship between employee performances and impart of mobile communication on role perception, skills/ability and situational factors. However, no significant linear relationship was found between employee performance and impact of mobile communication on motivation

Limits of space syntax for urban design: Axiality, scale and sinuosity

Space syntax analysis of the city as a movement economy has made major contributions to our understanding of the spatial structure of cities, particularly the importance of a mapping of network integration in relation to density, functional mix and streetlife vitality. It has focused attention of urban researchers onto the importance of the relations between the sociality and spatiality of the city. The primary methods of syntactic analysis involve a reduction of urban morphology to a set of spatial axes; here, we explore some limits to such analysis for urban design. Topological analysis of axial models has long recognized problems in accounting for distance, scale and sinuous streetscapes. Existing adaptations to axial methods that address such problems are modelled and shown to produce a broad range of results for the same urban morphology. In each case, we also compare different capacities for to-movement and through-movement – the distinction between ‘closeness centrality’ and ‘betweenness centrality’ that shows that network integration is multiple. We argue that axial analyses privilege visibility over accessibility and can produce distorted mapping at walkable scales; only one of the methods tested measures permeability and walkable access. Space syntax analysis is a powerful tool that will be more useful the better such limits are understood

Real-time monitoring of driver drowsiness on mobile platforms using 3D neural networks

Abstract Driver drowsiness increases crash risk, leading to substantial road trauma each year. Drowsiness detection methods have received considerable attention, but few studies have investigated the implementation of a detection approach on a mobile phone. Phone applications reduce the need for specialised hardware and hence, enable a cost-effective roll-out of the technology across the driving population. While it has been shown that three-dimensional (3D) operations are more suitable for spatiotemporal feature learning, current methods for drowsiness detection commonly use frame-based, multi-step approaches. However, computationally expensive techniques that achieve superior results on action recognition benchmarks (e.g. 3D convolutions, optical flow extraction) create bottlenecks for real-time, safety-critical applications on mobile devices. Here, we show how depthwise separable 3D convolutions, combined with an early fusion of spatial and temporal information, can achieve a balance between high prediction accuracy and real-time inference requirements. In particular, increased accuracy is achieved when assessment requires motion information, for example, when sunglasses conceal the eyes. Further, a custom TensorFlow-based smartphone application shows the true impact of various approaches on inference times and demonstrates the effectiveness of real-time monitoring based on out-of-sample data to alert a drowsy driver. Our model is pre-trained on ImageNet and Kinetics and fine-tuned on a publicly available Driver Drowsiness Detection dataset. Fine-tuning on large naturalistic driving datasets could further improve accuracy to obtain robust in-vehicle performance. Overall, our research is a step towards practical deep learning applications, potentially preventing micro-sleeps and reducing road trauma

Sky pixel detection in outdoor imagery using an adaptive algorithm and machine learning

Computer vision techniques enable automated detection of sky pixels in outdoor imagery. In urban climate, sky detection is an important first step in gathering information about urban morphology and sky view factors. However, obtaining accurate results remains challenging and becomes even more complex using imagery captured under a variety of lighting and weather conditions. To address this problem, we present a new sky pixel detection system demonstrated to produce accurate results using a wide range of outdoor imagery types. Images are processed using a selection of mean-shift segmentation, K-means clustering, and Sobel filters to mark sky pixels in the scene. The algorithm for a specific image is chosen by a convolutional neural network, trained with 25,000 images from the Skyfinder data set, reaching 82% accuracy for the top three classes. This selection step allows the sky marking to follow an adaptive process and to use different techniques and parameters to best suit a particular image. An evaluation of fourteen different techniques and parameter sets shows that no single technique can perform with high accuracy across varied Skyfinder and Google Street View data sets. However, by using our adaptive process, large increases in accuracy are observed. The resulting system is shown to perform better than other published techniques