Maurizio Ipsale

Maurizio Ipsale

Modena, Emilia Romagna, Italia
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AI/ML & Cloud Innovation Leader | Technology Educator & Consultant

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Attività

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Esperienza

  • Grafico Datatonic

    Datatonic

    London, England, United Kingdom

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    Italy

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    Modena, Emilia-Romagna, Italy

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    Modena

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Formazione

  • Grafico University of Messina

    University of Messina

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    Attività e associazioni:Advanced Technologies for Optoelectronics and Photonics and Electromagnetic Modelling

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Licenze e certificazioni

  • Grafico CCSI: Certified Cisco Systems Instructor

    CCSI: Certified Cisco Systems Instructor

    Cisco

  • HCDI - Huawei Certified Datacom Instructor

    Huawei Technologies

  • HCDP - Huawei Certified Datacom Professional

    Huawei Technologies

  • Grafico JNCI-ENT: Juniper Networks Certified Instructor - Enterprise Routing and Switching

    JNCI-ENT: Juniper Networks Certified Instructor - Enterprise Routing and Switching

    Juniper Networks

  • Grafico JNCI-SEC: Juniper Networks Certified Instructor - Security

    JNCI-SEC: Juniper Networks Certified Instructor - Security

    Juniper Networks

  • Grafico JNCI-SP: Juniper Networks Certified Instructor - Service Provider

    JNCI-SP: Juniper Networks Certified Instructor - Service Provider

    Juniper Networks

  • Grafico JNCIP-ENT: Juniper Networks Certified Internet Professional - Enterprise

    JNCIP-ENT: Juniper Networks Certified Internet Professional - Enterprise

    Juniper Networks

  • Grafico JNCIP-SEC: Juniper Networks Certified Internet Professional - Security

    JNCIP-SEC: Juniper Networks Certified Internet Professional - Security

    Juniper Networks

  • Grafico JNCIP-SP: Juniper Networks Certified Internet Professional - Service Provider

    JNCIP-SP: Juniper Networks Certified Internet Professional - Service Provider

    Juniper Networks

  • Grafico VMware Certified Professional 5 Data Center Virtualization

    VMware Certified Professional 5 Data Center Virtualization

    VMware

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Pubblicazioni

  • Google Cloud Certified Professional Cloud Network Engineer Guide: Design, implement, manage, and secure a network architecture in Google Cloud

    Packt Publishing

    Google Cloud, the public cloud platform from Google, has a variety of networking options, which are instrumental in managing a networking architecture. This book will give you hands-on experience of implementing and securing networks in Google Cloud Platform (GCP).

    You will understand the basics of Google Cloud infrastructure and learn to design, plan, and prototype a network on GCP. After implementing a Virtual Private Cloud (VPC), you will configure network services and implement…

    Google Cloud, the public cloud platform from Google, has a variety of networking options, which are instrumental in managing a networking architecture. This book will give you hands-on experience of implementing and securing networks in Google Cloud Platform (GCP).

    You will understand the basics of Google Cloud infrastructure and learn to design, plan, and prototype a network on GCP. After implementing a Virtual Private Cloud (VPC), you will configure network services and implement hybrid connectivity. Later, the book focuses on security, which forms an important aspect of a network. You will also get to grips with network security and learn to manage and monitor network operations in GCP. Finally, you will learn to optimize network resources and delve into advanced networking. The book also helps you to reinforce your knowledge with the help of mock tests featuring exam-like questions.

    By the end of this book, you will have gained a complete understanding of networking in Google Cloud and learned everything you need to pass the certification exam.

    What you will learn

    Understand the fundamentals of Google Cloud architecture
    Implement and manage network architectures in Google Cloud Platform
    Get up to speed with VPCs and configure VPC networks, subnets, and routers
    Understand the command line interface and GCP console for networking
    Get to grips with logging and monitoring to troubleshoot network and security
    Use the knowledge you gain to implement advanced networks on GCP

    Table of Contents

    Google Cloud Platform Infrastructure
    Designing, Planning, and Prototyping a GCP Network
    Implementing a GCP Virtual Private Cloud (VPC)
    Configuring Network Services in GCP
    Implementing Hybrid Connectivity in GCP
    Implementing Network Security
    Managing and Monitoring Network Operations
    Advanced Networking in Google Cloud Platform
    Professional Cloud Network Engineer Certification Preparation

    Altri autori
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  • Revealing Bioelectric Muscle Activity corrupted by superimposed Magnetic Resonance Field

    "Ieee transactions on magnetics", n. 43 (4), 2007, pp. 1705-1708, ISSN: 0018-9464

    Most clinical measurements rely on bioelectromagnetic phenomena. These events allow us to record electric or magnetic signals during the activity of living tissues. In this paper, we put our attention on the bioelectric fields that occur in the muscle activity. In fact during the body movements, the muscle contractions produce a bioelectric potential distribution that can be measured by putting the electrodes on the skin. In clinical applications, the monitoring of muscle activity can be…

    Most clinical measurements rely on bioelectromagnetic phenomena. These events allow us to record electric or magnetic signals during the activity of living tissues. In this paper, we put our attention on the bioelectric fields that occur in the muscle activity. In fact during the body movements, the muscle contractions produce a bioelectric potential distribution that can be measured by putting the electrodes on the skin. In clinical applications, the monitoring of muscle activity can be performed in a noninvasive way, by placing a fixed number of electrodes on the skin surface; this technique is called surface electromyography (sEMG), and it is able to reveal the electric field generated by each muscle activity. Unfortunately, the sEMG suffers from the external electromagnetic fields. In recent years, many authors investigated the correlation between muscle and brain activity by performing the sEMG during the functional magnetic resonance imaging (fMRI). The fMRI is the most reliable technique to evaluate the brain activity because it allows us to obtain some images of the human body with very high resolutions. Unfortunately, joint measurement is a very difficult task because of the high electromagnetic interference between the resonance coils (very high magnetic fields) and the sEMG electrodes. In this paper, we present a method based on wavelet analysis to reveal sEMG in voluntary contractions when the measurement is made in a fMRI environment.

  • Neural Networks and Time–Frequency Analysis of Surface Electromyographic Signals for Muscle Cerebral Control

    Handbook of Neural Engineering, John Wiley & Sons, ISBN 9780470056691, Chapter 8, pages 131–155

    Control of motor units (MUs) is one of the most complex tasks of the brain. The involved
    signal, which starts from some neurons of the brain and arrives at the muscle, is managed
    by a very complicated control system. Usually, the path of this signal is crossed through
    the body, that is, a signal starting from the left brain lobe controls muscles located on the
    right side of the body, whereas a signal starting from the right brain lobe controls muscles
    located on the left. Because…

    Control of motor units (MUs) is one of the most complex tasks of the brain. The involved
    signal, which starts from some neurons of the brain and arrives at the muscle, is managed
    by a very complicated control system. Usually, the path of this signal is crossed through
    the body, that is, a signal starting from the left brain lobe controls muscles located on the
    right side of the body, whereas a signal starting from the right brain lobe controls muscles
    located on the left. Because of this process, if a stroke appears in one brain lobe, the pathological
    patient cannot control the opposite side of the body [1]. However, this behavior
    does not work in some type of muscle, such as the postural ones, since a stroke in one
    lobe side does not imply the inhibition of these muscles and the pathological patient
    still assumes a right posture [1]. This experimental observation suggests that a common
    drive could start at both brain lobes and affect muscles located in both body sides. The
    main concept proposed here, aimed at validating the last sentence, exploits the correlation
    techniques to show that they can be used to investigate dependencies between neurophysiological
    signals and to determine signal pathways in the central nervous system.

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  • A New Approach to Detection of Muscle Activation by Independent Component Analysis and Wavelet Transform

    In Neural Nets, Lecture Notes in Computer Science, 2002, Vol. 2486, pp. 109-116. doi:10.1007/3-540-45808-5_11

    Recent works have demonstrated that the Independent Components (ICs) of simultaneously-recorded surface Electromyography (sEMG) recordings are more reliable in monitoring repetitive movements and better correspond with ongoing brain-wave activity than raw sEMG recordings. In this paper we propose to detect single muscle activation, when the arms reach a target, by means of ICs time-scale decomposition. Our analysis starts with acquisition of sEMG (surface EMG) signals; source separation is…

    Recent works have demonstrated that the Independent Components (ICs) of simultaneously-recorded surface Electromyography (sEMG) recordings are more reliable in monitoring repetitive movements and better correspond with ongoing brain-wave activity than raw sEMG recordings. In this paper we propose to detect single muscle activation, when the arms reach a target, by means of ICs time-scale decomposition. Our analysis starts with acquisition of sEMG (surface EMG) signals; source separation is performed by a neural net-work that implements on Independent Component Analysis algorithm. In this way we obtain a signal set each representing single muscle activity. The wave-let transform, lastly, is utilised to detect muscle activation intervals.

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Progetti

Lingue

  • English

    Conoscenza professionale

  • French

    Conoscenza lavorativa limitata

  • Italian

    Conoscenza madrelingua o bilingue

Organizzazioni

  • ROI Training

    Google Cloud Authorized Instructor

    - Presente

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