Classify the plant species based on lobes, sinuses and margin

This paper proposed a novel approach to cluster the species of plants based on their lobes, sinuses and margin. Firstly, all the boundary points in the clockwise or anticlockwise direction were selected. Then, an estimated centre point for leaf boundary points was used to compute the distance between the leaf boundary points and centre point. Next, the peaks and valleys from the distance found stated above were located where peaks represent lobes and valleys represent the sinuses. The number of peak and valleys is calculated to cluster the plant according to the rule-based method. From the results obtained, the accuracy for the plant clustering is up to 100 percent

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Enhanced Indoor Localization System based on Inertial Navigation

An algorithm for indoor localization of pedestrians using an improved Inertial Navigation system is presented for smartphone based applications. When using standard inertial navigation algorithm, errors in sensors due to random noise and bias result in a large drift from the actual location with time. Novel corrections are introduced for the basic system to increase the accuracy by counteracting the accumulation of this drift error, which are applied using a Kalman filter framework. A generalized velocity model was applied to correct the walking velocity and the accuracy of the algorithm was investigated with three different velocity models which were derived from the actual velocity measured at the hip of walking person. Spatial constraints based on knowledge of indoor environment were applied to correct the walking direction. Analysis of absolute heading corrections from magnetic direction was performed . Results show that the proposed method with Gaussian velocity model achieves competitive accuracy with a 30\% less variance over Step and Heading approach proving the accuracy and robustness of proposed method. We also investigated the frequency of applying corrections and found that a 4\% corrections per step is required for improved accuracy. The proposed method is applicable in indoor localization and tracking applications based on smart phone where traditional approaches such as GNSS suffers from many issues

Contribuitions and developments on nonintrusive load monitoring

Energy efficiency is a key subject in our present world agenda, not only because of greenhouse gas emissions, which contribute to global warming, but also because of possible supply interruptions. In Brazil, energy wastage in the residential market is estimated to be around 15%. Previous studies have indicated that the most savings were achieved with specific appliance, electricity consumption feedback, which caused behavioral changes and encouraged consumers to pursue energy conservation. Nonintrusive Load Monitoring (NILM) is a relatively new term. It aims to disaggregate global consumption at an appliance level, using only a single point of measurement. Various methods have been suggested to infer when appliances are turned on and off, using the analysis of current and voltage aggregated waveforms. Within this context, we aim to provide a methodology for NILM to determine which sets of electrical features and feature extraction rates, obtained from aggregated household data, are essential to preserve equivalent levels of accuracy; thus reducing the amount of data that needs to be transferred to, and stored on, cloud servers. As an addendum to this thesis, a Brazilian appliance dataset, sampled from real appliances, was developed for future NILM developments and research. Beyond that, a low-cost NILM smart meter was developed to encourage consumers to change their habits to more sustainable methods.Eficiência energética é um assunto essencial na agenda mundial. No Brasil, o desperdício de energia no setor residencial é estimado em 15%. Estudos indicaram que maiores ganhos em eficiência são conseguidos quando o usuário recebe as informações de consumo detalhadas por cada aparelho, provocando mudanças comportamentais e incentivando os consumidores na conservação de energia. Monitoramento não intrusivo de cargas (NILM da sigla em inglês) é um termo relativamente novo. A sua finalidade é inferir o consumo de um ambiente até observar os consumos individualizados de cada equipamento utilizando-se de apenas um único ponto de medição. Métodos sofisticados têm sido propostos para inferir quando os aparelhos são ligados e desligados em um ambiente. Dentro deste contexto, este trabalho apresenta uma metodologia para a definição de um conjunto mínimo de características elétricas e sua taxa de extração que reduz a quantidade de dados a serem transmitidos e armazenados em servidores de processamento de dados, preservando níveis equivalentes de acurácia. São utilizadas diferentes técnicas de aprendizado de máquina visando à caracterização e solução do problema. Como adendo ao trabalho, apresenta-se um banco de dados de eletrodomésticos brasileiros, com amostras de equipamentos nacionais para desenvolvimentos futuros em NILM, além de um medidor inteligente de baixo custo para desagregação de cargas, visando tornar o consumo de energia mais sustentável

IoT and Sensor Networks in Industry and Society

The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society

Modular multilevel converter with embedded batteries as a motor controller.

This thesis details the design of the control system and hardware for a prototype of the new inverter topology the modular multilevel converter with embedded batteries for electric vehicle applications. Within this topology, the battery cells incorporated within the battery pack are directly integrated into the motor controller/ power converter by replacing the individual module capacitors with batteries. Since the batteries are directly connected to the module switching circuit, the batteries can be individually balanced using the same technique as an active battery management system, without the need for external energy-shunting hardware. A control algorithm for balancing the embedded batteries without affecting the motor control scheme with significantly unbalanced battery cells is presented and discussed. A multilevel space vector modulation scheme using the abc-reference frame for the selection of space vectors is developed. Initial testing of both the simulation model and prototype was carried out using a static RL load to test the PWM scheme and battery SOC balancing scheme. A Field-oriented control scheme was then designed and implemented for controlling a salient pole surface-mounted PMSM. The performance of the converter as a motor controller was assessed in terms of ability to balance the SOC of the embedded module batteries and total harmonic distortion over the course of the operating torque-speed range. Simulation of the control system on simulated hardware has been carried out in MATLAB; these simulation results verify the theoretical analysis. Then further verified and analysed using the developed laboratory-scale embedded battery MMC prototype

Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome

Advances in Vibration Analysis Research

Vibrations are extremely important in all areas of human activities, for all sciences, technologies and industrial applications. Sometimes these Vibrations are useful but other times they are undesirable. In any case, understanding and analysis of vibrations are crucial. This book reports on the state of the art research and development findings on this very broad matter through 22 original and innovative research studies exhibiting various investigation directions. The present book is a result of contributions of experts from international scientific community working in different aspects of vibration analysis. The text is addressed not only to researchers, but also to professional engineers, students and other experts in a variety of disciplines, both academic and industrial seeking to gain a better understanding of what has been done in the field recently, and what kind of open problems are in this area