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Using Multivariate Statistics

Climate change 2014 - Synthesis Report

The extent to which member states transpose EU directives in a timely fashion is often argued to be strongly associated with the general effectiveness of national bureaucracies. But what kind of institutional solutions ensure better performance? This paper examines the patterns and effects of departmental oversight across 28 ministries in Estonia, Hungary, Poland and Slovenia. In mapping the strength of oversight, it relies on around 90 structured interviews regarding the rules-in-use on transposition planning, legal review and monitoring of deadlines. The analysis of the impact of departmental oversight is based on an original dataset of over 300 directives with transposition deadlines between January 2005 and December 2008.

On the european union

The surge in global population is compelling a shift toward smart agriculture practices. This coupled with the diminishing natural resources, limited availability of arable land, increase in unpredictable weather conditions makes food security a major concern for most countries. As a result, the use of Internet of Things (IoT) and data analytics (DA) are employed to enhance the operational efficiency and productivity in the agriculture sector. There is a paradigm shift from use of wireless sensor network (WSN) as a major driver of smart agriculture to the use of IoT and DA. The IoT integrates several existing technologies, such as WSN, radio frequency identification, cloud computing, middleware systems, and end-user applications. In this paper, several benefits and challenges of IoT have been identified. We present the IoT ecosystem and how the combination of IoT and DA is enabling smart agriculture. Furthermore, we provide future trends and opportunities which are categorized into technological innovations, application scenarios, business, and marketability.

An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges

Background. Rates of depression have been rising, as have rates of work stress. We tested the influence of work stress on diagnosed depression and anxiety in young working adults. Method. Participants were enrolled in the Dunedin study, a 1972-1973 longitudinal birth cohort assessed most recently in 2004-2005, at age 32 (n=972, 96% of 1015 cohort members still alive). Work stress (psychological job demands, work decision latitude, low work social support, physical work demands) was ascertained by interview. Major depressive disorder (MDD) and generalized anxiety disorder (GAD) were ascertained using the Diagnostic Interview Schedule (DIS) and diagnosed according to DSM-IV criteria. Results. Participants exposed to high psychological job demands (excessive workload, extreme time pressures) had a twofold risk of MDD or GAD compared to those with low job demands. Relative risks (RRs) adjusting for all work characteristics were: 1·90 [95% confidence interval (CI) 1·22-2·98] in women, and 2·00 (95 % CI 1·13-3·56) in men. Analyses ruled out the possibility that the association between work stress and disorder resulted from study members' socio-economic position, a personality tendency to report negatively, or a history of psychiatric disorder prior to labour-market entry. Prospective longitudinal analyses showed that high-demand jobs were associated with the onset of new depression and anxiety disorder in individuals without any pre-job history of diagnosis or treatment for either disorder. Conclusions. Work stress appears to precipitate diagnosable depression and anxiety in previously healthy young workers. Helping workers cope with work stress or reducing work stress levels could prevent the occurrence of clinically significant depression and anxiety.

Work stress precipitates depression and anxiety in young, working women and men. Commentary

Deep Learning has pushed the limits of what was possible in the domain of Digital Image Processing. However, that is not to say that the traditional computer vision techniques which had been undergoing progressive development in years prior to the rise of DL have become obsolete. This paper will analyse the benefits and drawbacks of each approach. The aim of this paper is to promote a discussion on whether knowledge of classical computer vision techniques should be maintained. The paper will also explore how the two sides of computer vision can be combined. Several recent hybrid methodologies are reviewed which have demonstrated the ability to improve computer vision performance and to tackle problems not suited to Deep Learning. For example, combining traditional computer vision techniques with Deep Learning has been popular in emerging domains such as Panoramic Vision and 3D vision for which Deep Learning models have not yet been fully optimised.

Deep Learning vs. Traditional Computer Vision

Deep Learning has pushed the limits of what was possible in the domain of Digital Image Processing. However, that is not to say that the traditional computer vision techniques which had been undergoing progressive development in years prior to the rise of DL have become obsolete. This paper will analyse the benefits and drawbacks of each approach. The aim of this paper is to promote a discussion on whether knowledge of classical computer vision techniques should be maintained. The paper will also explore how the two sides of computer vision can be combined. Several recent hybrid methodologies are reviewed which have demonstrated the ability to improve computer vision performance and to tackle problems not suited to Deep Learning. For example, combining traditional computer vision techniques with Deep Learning has been popular in emerging domains such as Panoramic Vision and 3D vision for which Deep Learning models have not yet been fully optimised

With the significant advancement of sensor and communication technology and the reliable application of obstacle detection techniques and algorithms, automated driving is becoming a pivotal technology that can revolutionize the future of transportation and mobility. Sensors are fundamental to the perception of vehicle surroundings in an automated driving system, and the use and performance of multiple integrated sensors can directly determine the safety and feasibility of automated driving vehicles. Sensor calibration is the foundation block of any autonomous system and its constituent sensors and must be performed correctly before sensor fusion and obstacle detection processes may be implemented. This paper evaluates the capabilities and the technical performance of sensors which are commonly employed in autonomous vehicles, primarily focusing on a large selection of vision cameras, LiDAR sensors, and radar sensors and the various conditions in which such sensors may operate in practice. We present an overview of the three primary categories of sensor calibration and review existing open-source calibration packages for multi-sensor calibration and their compatibility with numerous commercial sensors. We also summarize the three main approaches to sensor fusion and review current state-of-the-art multi-sensor fusion techniques and algorithms for object detection in autonomous driving applications. The current paper, therefore, provides an end-to-end review of the hardware and software methods required for sensor fusion object detection. We conclude by highlighting some of the challenges in the sensor fusion field and propose possible future research directions for automated driving systems.

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

This paper will review the main sensor technologies used to create an autonomous vehicle. Sensors are key components for all types of autonomous vehicles because they can provide the data required to perceive the surrounding environment and therefore aid the decision-making process. This paper explains how each of these sensors work, their advantages and disadvantages and how sensor fusion techniques can be utilised to create a more optimum and efficient system for autonomous vehicles.

Sensor Technology in Autonomous Vehicles : A review

Academic and industrial research in Internet of Things (IoT) has increased over the past several years. The Internet of Things aims to connect physical objects (Things) to the Internet using low power network connectivity that is embedded with sensors. IoT is becoming all-encompassing with “Smart” Cities, Homes, Grid, Transportation, Agriculture & Industry 4.0 just some of the application areas of IoT technology. This paper concentrates on the use of IoT technologies in the Food supply chain. The Food supply chain in the context of this paper includes agriculture, food processing, transportation & distribution right through to retail, from “Farm to Fork”. The demand for tracking, tracing and monitoring of fresh has elevated as instances of foodborne illness has increased. In this paper we attempt to contribute a review of state-of-the-art technologies and strategies that constitutes the IoT and how that applies to “Farm to Fork”. We describe the IoT's key enabling technologies including Radio Frequency Identification (RFID), Wireless Sensor Networks (WSN), Cloud Computing and Data Analytics. We present the research trends and challenges in the food supply chain industry, mainly focusing on fresh produce such as meat, fruits & vegetables. We then present applications of IoT technologies in Food Production, Transportation & Distribution and in Food Retail. We conclude the paper with plans for future work.

Joseph Walsh

Papers

Deep Learning vs. Traditional Computer Vision

Deep Learning vs. Traditional Computer Vision

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Sensor Technology in Autonomous Vehicles : A review

Internet of Things: A review from ‘Farm to Fork’