Maurizio Ipsale

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… Visualizza altro

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 Meno dettagli

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… Visualizza altro

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. Meno dettagli

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… Visualizza altro

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. Meno dettagli

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… Visualizza altro

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. Meno dettagli