Vania V Estrela

Productivity metrics provoke mixed responses from the research community. The Open Researcher and Contributor ID (ORCID) facilitates an open and clear data infrastructure to handle publication products. ORCID is a unique identifier that accepts data interoperability for funders, industries, and publishers by exploring indicators for research aspects that they aspire to evaluate instead of using current indicators because they are readily accessible. This manuscript examines the use of ORCID for publication metrics, grant applications, management and reporting platforms. The benefits of ORCID offer a quick way to promote and organize a researcher's work as well as to foster better communication among researchers.

HAICTA is the Greek Branch of the European Federation for Information Technology in Agriculture (EFITA). Until now HAICTA has organized a series of seven successful international conferences. HAICTA 2017 aims to bring together professionals, experts and researchers working on Information and Communication Technologies in Agriculture, Food and Environment. We additionally aim to emphasize on the applicability of ICT solutions to real industry cases and the respective challenges. Topics We accept submissions in all areas of ICT in Agriculture, Food, and the Environment.

Interference detection of arbitrary geometric objects is not a trivial task due to the heavy computational load imposed by implementation issues. The hierarchically structured bounding boxes help us to quickly isolate the contour of segments in interference. In this paper, a new approach is introduced to treat the interference detection problem involving the representation of arbitrary shaped objects. Our proposed method relies upon searching for the best possible way to represent contours by means of hierarchically structured rectangular oriented bounding boxes. This technique handles 2D objects boundaries defined by closed B-spline curves with roughness details. Each oriented box is adapted and fitted to the segments of the contour using second order statistical indicators from some elements of the segments of the object contour in a multiresolution framework. Our method is efficient and robust when it comes to 2D animations in real time. It can deal with smooth curves and polygonal approximations as well results are present to illustrate the performance of the new method.

Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, 'Deep Learning for Image Processing Applications', is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data. The book will be of interest to all those whose work involves the use of deep learning and image processing techniques

The objectives of this chapter are: (i) to introduce a concise overview of regularization; (ii) to define and to explain the role of a particular type of regularization called total variation norm (TV-norm) in computer vision tasks; (iii) to set up a brief discussion on the mathematical background of TV methods; and (iv) to establish a relationship between models and a few existing methods to solve problems cast as TV-norm. For the most part, image-processing algorithms blur the edges of the estimated images, however TV regularization preserves the edges with no prior information on the observed and the original images. The regularization scalar parameter λ controls the amount of regularization allowed and it is an essential to obtain a high-quality regularized output. A wide-ranging review of several ways to put into practice TV regularization as well as its advantages and limitations are discussed.

Progress in image sensors and computation power has fueled studies to improve acquisition, processing, and analysis of 3D streams along with 3D scenes/objects reconstruction. The role of motion compensation/ motion estimation (MCME) in 3D TV from end-to-end user is investigated in this chapter. Motion vectors (MVs) are closely related to the concept of disparities, and they can help improving dynamic scene acquisition, content creation, 2D to 3D conversion, compression coding, decompression/decoding, scene rendering, error concealment, virtual/augmented reality handling, intelligent content retrieval, and displaying. Although there are different 3D shape extraction methods, this chapter focuses mostly on shape-from-motion (SfM) techniques due to their relevance to 3D TV. SfM extraction can restore 3D shape information from a single camera data.

CALL FOR BOOK CHAPTERS

Agriculture 4.0 in Latin America - Fostering Technological
Innovation and Sustainability for the Next Decade

Keywords: Agriculture 4.0, Internet of Things, Sustainability, Remote Sensing, Disaster Mitigation, Production Engineering, Cyber-Physical Systems, Smart Designs, Blockchain, Digital Twins, Farming.

Editors:
Vania V. Estrela [email protected]
Federal Fluminense University, Telecommunications Department, RJ, Brazil

Henrique R. M. da Hora [email protected]
Rogerio de Carvalho Atem [email protected]
Instituto Federal de Educação, Ciência e Tecnologia Fluminense (IFFCampos), RJ, Brazil

Book Content Description:

The world faces a tremendous challenge: producing more food for a growing population while also taking care of the environment. Sustainable agriculture entails the establishment of strategic partnerships, technological innovation, new ways to treat old problems, and assimilation of solutions from other knowledge areas such as healthcare and governance. These actions and
activities can mitigate several issues while reaching out to more farmers and the communities to which they belong.
    Agriculture 4.0 describes significant trends facing agribusiness, including a greater focus on precision agriculture, the internet of things (IoT), and big data to drive greater business
efficiencies in the face of rising populations climate change.
    Given the pandemic and previous problems in Latin America (LA), the amount of undernourished will increase considerably in the next 10 years. Since very little innovation has
taken place in LA to address food scarcity and hunger.
    Meeting these challenges will require a combined effort by governments, investors, and R & D innovations. Agriculture 4.0 will no longer depend on applying water, fertilizers, and pesticides uniformly across entire fields. Instead, farmers will use the minimum quantities
required and target particular areas. Future farms, agricultural arrangements, agribusiness
operations will be very different. These paradigm changes are primarily due to the advancements in technology, e.g., sensors, actuators, devices, software packages, robots, aerial images,  mapping areas, GPS usage, cloud computing, assessing the stage/vigor of crops, probing of
phytosanitary problems, and impacts due to drought/hail, crop planning, fertilizer spraying, and  pesticides application. These advanced frameworks for precision agriculture must integrate  robotic systems and human beings in a harmonious way to allow farms to be more profitable,
efficient, safe, humane, sustainable, and environmentally friendly.

Introduction The variety and abundance of qualitative characteristics of agricultural products have been the main reason for the development of different types of Non-Destructive Methods (NDTs). Quality control of these products is one of the most important tasks in manufacturing processes. The use of control and automation has become more widespread, and new approaches provide opportunities for production competition through new technologies. The need to intensify the quality and quantity of a product leads to the use of advanced automated machines. Machine tools are increasingly becoming more automated and less reliant on human factors. Nowadays, visual machine technology and image processing techniques are widely used in the industry, particularly common in product quality control, robot guidance and self-guided mechanisms.

We would like to invite you to join this exciting new project as a chapter contributor on one of the topics listed below. Since this is a textbook, a great deal of this chapter entails a survey on the topic under the paradigm of cyber-physical systems, what can be done onboard and remotely, the distributed nature of the system and some exercises on futurology (anticipating trends can shed some light on upcoming designs). IET will bring great visibility to your work. Each chapter should be around 20-25 pages each and can be submitted as a Word or Latex File. The IET will send you additional info (formatting, permission form, etc.) with the contributor's agreement once you have decided to contribute to the book. Visit http://www.theiet.org/resources/author-hub/books/index.cfm to get all contributor's information to an IET research-level book. Each book is expected to have a total number of 500 printed pages (with approximately 550 words per page and a 20% allowance for figures and tables). We have included a tentative schedule and list of topics below. If this is something you would consider, please send us the title of your chapter, a short description/abstract of the chapter content, and your full contact details. We will expect original content and new insights for this book. You can, of course, reuse published material but the percentage of material reuse for the chapter should be less than 40%. The IET will run a piracy software on the full manuscript to control that you are including original material and will reject chapters who contain a large amount of already-published material so please do take this into consideration. We would appreciate your feedback by December 31, 2017. Please do not hesitate to contact us if you have any queries. We look forward to working with you towards the successful publication.

We would like to invite you to join this exciting new project as a chapter contributor. Since this is a textbook, a great deal of this chapter entails a survey on the topic under the paradigm of cyber-physical systems, what can be done onboard and remotely, the distributed nature of the system and some exercises on futurology (anticipating trends can shed some light on upcoming designs). IET will bring great visibility to your work. You are welcome to suggest another topic/chapter title if you feel it would be more suitable. Each chapter should be around 20-25 pages each and can be submitted as a Word or Latex File. The IET will send you additional information (formatting, permission form, etc.) with the contributor's agreement once you have agreed to contribute to the book. Visit http:// www.theiet.org/resources/author-hub/books/index.cfm to get all information you need as a contributor to an IET research-level book. Each book is expected to have a total number of 500 printed pages (based on approximately 550 words per page with a 20% allowance for figures and tables). We have included a tentative schedule and list of topics below. If this is something you would consider, please send me the title of your chapter, a short description/abstract of the chapter content, and your full contact details. We will expect original content and new results for this book. You can, of course, reuse published material but the percentage of material reuse for the chapter should be less than 40%. The IET will run a piracy software on the full manuscript to control that you are including original material and will reject chapters who contain a large amount of already-published material so please do take this into consideration.

The problem of recursively approximating motion resulting from the Optical Flow (OF) in video thru Total Least Squares (TLS) techniques is addressed. TLS method solves an inconsistent system Gu=z , with G and z in error due to temporal/spatial derivatives, and nonlinearity, while the Ordinary Least Squares (OLS) model has noise only in z. Sources of difficulty involve the non-stationarity of the field, the ill-posedness, and the existence of noise in the data. Three ways of applying the TLS with different noise conjectures to the end problem are observed. First, the classical TLS (cTLS) is introduced, where the entries of the error matrices of each row of the augmented matrix [G;z] have zero mean and the same standard deviation. Next, the Generalized Total Least Squares (GTLS) is defined to provide a more stable solution, but it still has some problems. The Generalized Scaled TLS (GSTLS) has G and z tainted by different sources of additive zero-mean Gaussian noise and scaling [G;z] by nonsingular D and E, that is, D[G;z]E makes the errors iid with zero mean and a diagonal covariance matrix. The scaling is computed from some knowledge on the error distribution to improve the GTLS estimate. For moderate levels of additive noise, GSTLS outperforms the OLS, and the GTLS approaches. Although any TLS variant requires more computations than the OLS, it is still applicable with proper scaling of the data matrix.

IET indexes its books and journal in SCOPUS and IEEE Xplore.
Computer Vision (CV) and Sensors play a decisive role in the operation of Unmanned Aerial Vehicle (UAV), but there exists a void when it comes to analysing the extent of their impact on the entire UAV system. In general, the fact that a UAV is a Cyber-Physical System (CPS) is not taken into account. In this proposal, we propose to expand on earlier books covering the use of CV and sensing in UAVs. Among other things, an entirely autonomous UAV can help to (i) obtain information about the environment, (ii) work for an extended period of time without human interference, (iii) move either all or part of itself all over its operating location devoid of human help and (iv) stay away from dangerous situations for people and their possessions. A Vision System (VS) entails the way CV data will be utilized, the appropriate architecture for total avionics integration, the control interfaces, and the UAV operation. Since the VS core is its sensors and cameras, multi-sensor fusion, navigation, hazard detection, and ground correlation in real time are important operational aspects that can benefit from CV knowledge and technology. This book will aim to collect and shed some light on the existing information on CV software and hardware for UAVs as well as pinpoint aspects that need additional thinking. It will list standards and a set of prerequisites (or lack of them thereof) when it comes to CV deployment in UAVs. The issue of data fusion takes a centre place when the book explores ways to deal with sensor data and images as well as their integration and display. The best practices to fuse image and sensor information to enhance UAV performance by means of CV can greatly improve all aspects of the corresponding CPS. The CPS viewpoint can improve the way UAVs interact with the Internet of Things (IoT), use cloud computing, meet communications requirements, implement hardware/software paradigms necessary to handle video streaming, incorporate satellite data, and combine CV with Virtual/Augmented Realities.

VOLUME 2-DEPLOYMENT AND APPLICATIONS: This tome introduces procedures, standards, and prerequisites for the deployment of Computer Vision (CV) in UAVs from their application point of view. It discusses existing/desirable open source software tools, image banks, benchmarks, Quality of Experience (QoE), Quality of Service (QoS) and how CV can benefit from a Robot Operating System (ROS) in surveillance, remote sensing, inspection, maintenance and repair among other usages, while offering an assessment of current bottlenecks and trends. It will pave the road towards better studies on the necessity and viability of implementing collaborative environments for visualization, knowledge management and teleoperation of UAVs. This is planned to be the companion volume of Estrela, Hemanth, Saotome (Eds) / Imaging and Sensing for Unmanned Aerial Vehicles: Volume 1-Control and Performance.

Editor(s):
Dr. Vania V. Estrela, https://www.linkedin.com/in/vania-v-estrela-96b9bb29/
Universidade Federal Fluminense (UFF), RJ, Brazil
[email protected]

Dr. Jude Hemanth,https://www.karunya.edu/ece/drjude.html
Karunya University, Coimbatore, India
[email protected]

Dr. Osamu Saotome, https://www.linkedin.com/in/osamu-saotome-83935818
Instituto Tecnológico de Aeronáutica, CTA-ITA-IEEA, São José dos Campos, SP, Brazil
[email protected]

CONTENTS:
1. Image Acquisition and Restoration in UAVs
2. Image Fusion in UAVs
3. Super-Resolution Imaging in UAVs
4. 2D/3D/4D Imaging in UAVs
5. Multi-view Image and ToF Sensor Fusion in UAVs
6. Range Imaging in UAVs
7. Multispectral and Hyperspectral Imaging in UAVs
8. Imaging Standards and UAVs
9. Virtual/Augmented Reality in UAVs
10. Collaborative Environments in UAVs
11. Archiving, Storage, and Compression in UAVs
12. Analysis, Indexing, Retrieval in UAVs
13. Multicast/Broadcast/Streaming in UAVs
14. Modelling, Simulation and UAVs
15.  Image-Oriented Estimation and Identification in UAVs
16. Open Source Software in UAVs
17. Image Banks and Benchmarks in UAVs
18. Quality of Experience (QoE) and Quality of Service (QoS) in UAVs
19. Robot Operating System (ROS) in UAVs
20. Cloud Computing in UAVs

Specification and Schedule

July 1st, 2017: Call for Chapter Abstracts
September 1, 2017: One-Page Chapter Abstract (up to 1000 words) Submission Deadline. Free style.
          A proposal must outline one of topics fromthe list above (mention its number, for instance 1. and reference PBCE120B).
November 30, 2017: Last Day for Notification of Acceptance
Jan 30, 2018: Full Chapter Submissions
March 30 2018: Review Chapter Submissions and send comments to authors
May 31, 2018: Receive revised Chapter Submissions
June 30, 2018: Notification of Final Acceptance
July 31, 2018: Gather all material, figure files and copyrights permission forms
Aug 30, 2018: Book editors to finalize introduction and conclusion chapters
Sept 15th, 2018, Delivery of full manuscript to the IET
Scheduled publication: Feb/March 2019

Readership: Graduate students and Researchers in the fields of Electrical and Computer Engineering, Computer Science, Mechanical Engineering, Civil Engineering, Humanitarian Engineering, Control Systems, Geoscience and Remote Sensing, Instrumentation and Measurement, Intelligent Transportation Systems, Oceanic Engineering, Safety Engineering, Reliability, Robotics and Automation, Signal Processing, Technology and Engineering Management, Environmental Engineering, Public Health Management, Non-Invasive Testing/Monitoring and Vehicular Technology.

Additional Information: Dr. Vania V. Estrela,    [email protected]
                                          Dr. Jude Hemanth,      [email protected]

IET indexes its books and journal in SCOPUS and IEEE Xplore.
Computer Vision (CV) and Sensors play a decisive role in the operation of Unmanned Aerial Vehicle (UAV), but there exists a void when it comes to analysing the extent of their impact on the entire UAV system. In general, the fact that a UAV is a Cyber-Physical System (CPS) is not taken into account. In this proposal, we propose to expand on earlier books covering the use of CV and sensing in UAVs. Among other things, an entirely autonomous UAV can help to (i) obtain information about the environment, (ii) work for an extended period of time without human interference, (iii) move either all or part of itself all over its operating location devoid of human help and (iv) stay away from dangerous situations for people and their possessions. A Vision System (VS) entails the way CV data will be utilized, the appropriate architecture for total avionics integration, the control interfaces, and the UAV operation. Since the VS core is its sensors and cameras, multi-sensor fusion, navigation, hazard detection, and ground correlation in real time are important operational aspects that can benefit from CV knowledge and technology. This book will aim to collect and shed some light on the existing information on CV software and hardware for UAVs as well as pinpoint aspects that need additional thinking. It will list standards and a set of prerequisites (or lack of them thereof) when it comes to CV deployment in UAVs. The issue of data fusion takes a centre place when the book explores ways to deal with sensor data and images as well as their integration and display. The best practices to fuse image and sensor information to enhance UAV performance by means of CV can greatly improve all aspects of the corresponding CPS. The CPS viewpoint can improve the way UAVs interact with the Internet of Things (IoT), use cloud computing, meet communications requirements, implement hardware/software paradigms necessary to handle video streaming, incorporate satellite data, and combine CV with Virtual/Augmented Realities.

VOLUME 1-CONTROL AND PERFORMANCE: This tome explores how sensors and computer vision technologies are used in unmanned aerial vehicles for the navigation, control, stability, reliability, guidance, fault detection, self-maintenance, strategic replanning and reconfiguration of the entire system. It helps analyse the manner UAVs interact with the Internet of Things (IoT), use cloud computing, meet communications requirements, implement hardware/software paradigms necessary to handle still imagery, video streaming, incorporate satellite data, and combine computer vision with virtual/augmented realities (VR/AR).NB: This is planned to be the companion volume of Estrela, Hemanth, Saotome (Eds) / Imaging and Sensing for Unmanned Aerial Vehicles: Volume 2-Deployment and Applications

Scope of the book:
This book focusses on the technical concepts of deep learning and its associated branch Neural Networks for the various dimensions of image processing applications. The proposed volume intends to bring together researchers to report the latest results or progress in the development of the above-mentioned areas. Since there is a deficit of books on this specific subject matter, the editors aim to provide a common platform for researchers working in this area to exhibit their novel findings.
Topics of Interest:
This book solicits contributions, which include the fundamentals in the field of Deep Artificial Neural Networks and Image Processing supported by case studies and practical examples. Each chapter is expected to be self-contained and to cover an in-depth analysis of real life applications of neural networks to image analysis.

International Conference on Signal Processing and Communication - ICSPC'17
July 28-29 2017 Coimbatore, Tamil Nadu, India

The conference aims to bring together the Scientists, Scholars, Engineers, Industrialists and Students from in and around the world to present and share their knowledge and experience on the on-going research activities and hence to foster research relations between universities and industries. This conference provides opportunities for the delegates to exchange new ideas, insights, applications and experiences, to establish research relations and to find global partners for collaborations and to solve future problems in signal processing and communication.

=== Important Dates ===

March 15, 2017........................Tutorial  Special Session proposals
March 15, 2017.........................Paper submission deadline
April 15, 2017........................... Notification of acceptance
April 30, 2017............................Camera-ready paper submission
July 28-29, 2017........................ICSPC17 Conference

The authors are invited to submit technical papers of their original, unpublished work under the below-mentioned sub-themes. The works within the broad areas (but not limited to) are acceptable for the presentation in the conference.

Track 1
Bio-Signal Processing
Audio/Speech Processing and Coding
Statistical Signal Processing
Signal Processing for Communications
Medical Image Processing
Array Signal Processing
Soft Computing for Image Processing
Remote Sensing
Signal Processing for Security
Video Processing
Biometrics
Cryptography/Steganography/Watermarking
VLSI Signal Processing

Track 2

Mobile and Cellular Communications
Vehicular Communication Networks
Wireless Sensor Networks
Antennas, RF, and Microwave Communications
Optical Communication
MIMO and Space-Time Communications
Cognitive Radio and Cognitive Networks
Satellite Communication
Virtual Reality and Gaming
OFDM and CDMA Communication Receivers

ISSN : 2200 - 0011
Computational science is interdisciplinary fields in which mathematical models are combined with scientific computing, methods to study of a wide range of problems in science and engineering. Modeling and simulation tools find increasing applications not only in fundamental research but also in real-world design and industry applications.Topics of interest include, but are not limited to, t he following
• Modeling, Algorithms, and Simulations
• Problem Solving
• Scientific Computing
• Computational Sciences (Biology, Chemistry, mathematics, physics, forensics, etc.)
• High Performance Computing
• Machine Learning
• Network Analysis
• Computer Architecture
• Visualization and Virtual Reality as Applied to Computational Science
• Architectures and computation models, compiler, hardware and OS issues
• Large Scale Scientific Instruments
• Memory system, I/O, tools, programming environment and language supports

Scope & Topics Computational science is interdisciplinary fields in which mathematical models are combined with scientific computing methods to study of a wide range of problems in science and engineering. Modeling and simulation tools find increasing applications not only in fundamental research, but also in real­world design and industry applications.

Trata-se de uma coletânea de  pequenas estórias que ajudam a edificar o espírito e crescer interiormente.
Nota1: Apólogos são como fábulas, mas os personagens são virtudes, sentimentos, etc.
Nota2: "Irmão X" é um pseudônimo do escritor Humberto de Campos. A família dele não só reconheceu o estilo literário psicografado, como exigiu direitos autorais, dai a mudança. Houve um processo bizarro na época!

Geralmente, ao comentar sobre música, dificilmente pensamos na possibilidade da interatividade das ciências exatas como a física e a  matemática, necessárias para entender as propriedades e os detalhes que ocorrem na música. Essa monografia despertará o interesse de muitos leitores para a obtenção de um melhor conhecimento e enriquecimento na aprendizagem, quando raramente pensamos na análise científica com a arte musical. Conhecer essas influências matemáticas é, antes de tudo, conhecer a essência da própria música.Basicamente, os objetivos específicos relacionados a matemática, música e educação são:
• Aplicar as relações entre a matemática e a música na construção de significados educacionais;
• Reconhecer o enriquecimento, a importância da matemática na música;
• Associar a ciência exata com a arte musical; e
• Saber da existência da música que se tornou uma extensão da matemática.