Boosting Economic Growth Through Advanced Machine Vision

International audienceIn this chapter, we overview the potential of machine vision and related technologies in various application domains of critical importance for economic growth and prospect. Considered domains include healthcare, energy and environment, finance, and industrial innovation. Visibility technologies considered encompass augmented and virtual reality, 3D technologies, and media content authoring tools and technologies. We overview the main challenges facing the application domains and discuss the potential of machine vision technologies to address these challenges. In healthcare, rising cases for chronic diseases among patients and the urgent need for preventive healthcare is accelerating the deployment of telemedicine. Telemedicine as defined in the EU commission staff working paper on “Telemedicine for the benefit of patients, healthcare systems and society” (COM-SEC, 2009) is the delivery of healthcare services at a distance using information and communication technologies. There are two main groups of telemedicine applications: (1) applications linking a patient with a health professional; and (2) applications linking two health professionals (such as tele-second opinion, teleradiology). Machine vision technologies, coupled with reliable networking infrastructure, are key for accelerating the penetration of telemedicine applications. Several examples will be drawn illustrating the use of machine vision technologies in telemedicine. Sustainable energy and environment are key pillars for a sustainable economy. Technology is playing an increasing vital role in energy and environment including water resources management. This would foster greater control of the demand and supply side of energy and water. On the demand side, technologies including machine vision, could help indeveloping advanced visual metering technologies. On the supply side, machine vision technologies could help in exploring alternative sources for the generation of energy and water supply. In the finance domain, financial crises and the failure of banking systems are major challenges facing the coming decade. Recovery is still far from reach entailing a major economic slowdown. Machine vision technologies offer the potential for greater risk visibility, prediction of downturns and stress test of the soundness of the financial system. Examples are drawn from 3D/AR/VR applications in finance. Innovation could be seen as the process of deploying breakthrough outcome of research in industry. The innovation process could be conceived as a feedback loop starting from channelling the outcome of basic research into industrial production. Marketing strategies and novel approaches for customer relationship management draw a feedback loop that continuously update the feed of breakthrough research in industrial production. In this respect, machine vision technologies are key along this feedback process, particularly in the visualisation of the potential market and the potential route to market. CYBER II technology (Hasenfratz et al, 2003 and 2004) is described in section 6 as a machine vision technology that has a potential use in the various application domains considered in this chapter. CYBER II technology is based on multi-camera image acquisition, from different view points, of real moving bodies. Section 6 describes CYBER II technology and its potential application in the considered domains. The chapter concludes with a comparative analysis of the penetration of machine vision in various application domains and reflects on the horizon of machine vision in boosting economic growth

A LightGBM-Based EEG Analysis Method for Driver Mental States Classification

Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is based on gradient boosting framework for EEG mental states identification. ,e comparable results with traditional classifiers, such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI)

The strategic impact of META-NET on the regional, national and international level

This article provides an overview of the dissemination work carried out in META-NET from 2010 until early 2014; we describe its impact on the regional, national and international level, mainly with regard to politics and the situation of funding for LT topics. This paper documents the initiative’s work throughout Europe in order to boost progress and innovation in our field.Postprint (published version

Business Sphere, Vol. 17, no.3

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Comparative Analysis of Fruit Disease Identification Methods: A Comprehensive Study

The need for accurate and efficient technologies for recognising and controlling fruit diseases has increased due to the rising global demand for high-quality agricultural products. This study focuses on the advantages, disadvantages, and potential practical applications of a range of methods for identifying fecundities. Thanks to developments like improved imaging, machine learning, and data analysis tools, old methods of disease diagnosis have altered as technology has developed. The study compares older methods like visual observation, manual symptom correlation, spectroscopy, and chemical procedures with more contemporary methods like computer vision, autonomous learning algorithms, and sensor-based technologies. Precision, efficiency, cost, scalability, and ease of use are used to describe each method's effectiveness. The article reviews the research examples and practical applications of fruit endocrine disease detection in different cultivars and areas to provide a thorough comparison. This comparison focuses on the variations in disease prevalence and the ways that alternative treatments can be customised to certain situations.It is for this reason that this study offers useful information on how the methods for detecting fruit rot have evolved through time. It emphasises the significance of utilising technological advances to enhance the accuracy, effectiveness, and long-term sustainability of the management of agricultural diseases. Based on the unique requirements of their various agricultural systems, this analysis can assist researchers, practitioners, and policymakers in selecting the most effective methods for identifying fruit diseases

Capital Accumulation, External Restriction, Technology Gap and Structural Change: Theory and the Brazilian Experience

Brazilian economy was the most dynamic in terms of growth among developed and developing economies from post-War until 1980, when a severe external constraint interrupted this trend. We propose in this paper a model, based on Kaldor, where capital accumulation, technological gap and long run external constraint are connected. Our hypothesis is that capital accumulation, under certain circumstances, can overcome external constraint if the accumulation effort promotes structural change increasing the importance of sectors more technological-intensive. It is expected that the structural change in this direction will contribute to an increase in the income-elasticity of exports and to a decrease in income-elasticity of imports, resulting in the increase in the growth rate of real product compatible with the balance of payments equilibrium in the long period. The last part of the paper shows that the high investment rate observed in the Brazilian economy from the post-War until the end of the 1970s resulted in the deepening of the import substitution process, what, in our interpretation, contributed to partially increase the long run growth rate of the Brazilian economy compatible with the balance of payment equilibrium.structural change, technological progress, industrialization, external restriction

IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support

In response to the burgeoning global demand for seafood and the challenges of managing fish farms, we introduce an innovative IoT-based environmental control system that integrates sensor technology and advanced machine learning decision support. Deploying a network of wireless sensors within the fish farm, we continuously collect real-time data on crucial environmental parameters, including water temperature, pH levels, humidity, and fish behavior. This data undergoes meticulous preprocessing to ensure its reliability, including imputation, outlier detection, feature engineering, and synchronization. At the heart of our system are four distinct machine learning algorithms: Random Forests predict and optimize water temperature and pH levels for the fish, fostering their health and growth; Support Vector Machines (SVMs) function as an early warning system, promptly detecting diseases and parasites in fish; Gradient Boosting Machines (GBMs) dynamically fine-tune the feeding schedule based on real-time environmental conditions, promoting resource efficiency and fish productivity; Neural Networks manage the operation of critical equipment like water pumps and heaters to maintain the desired environmental conditions within the farm. These machine learning algorithms collaboratively make real-time decisions to ensure that the fish farm's environmental conditions align with predefined specifications, leading to improved fish health and productivity while simultaneously reducing resource wastage, thereby contributing to increased profitability and sustainability. This research article showcases the power of data-driven decision support in fish farming, promising to meet the growing demand for seafood while emphasizing environmental responsibility and economic viability, thus revolutionizing the future of fish farming