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Submarine Warfare in World War I: The History a...
9,95 € *
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Submarine warfare began tentatively during the American Civil War (though the Netherlands and England made small prototypes centuries earlier, and the American sergeant Ezra Lee piloted the one-man "Turtle" vainly against HMS Eagle near New York in 1776). Robert Whitehead's invention of the torpedo introduced the weapon later used most frequently by submarines. Steady improvements to Whitehead's design led to the military torpedoes deployed against shipping during both World Wars. World War I witnessed the First Battle of the Atlantic, when the Kaiserreich unleashed its U-boats against England. During the war, the German submarines sent much of the British merchant marine to the bottom. Indeed, German reliance on U-boats in both World War I and World War II stemmed largely from their nation's geography. The Germans eventually recognized the superiority of the Royal Navy and its capacity to blockade Germany's short coastline in the event of war. While the British could easily interdict surface ships, submarines slipped from their Kiel or Hamburg anchorages unseen, able to prey upon England's merchant shipping. The sleek hunter-killers lurking beneath the waves, using periscopes to close in unnoticed on their prey, added a new, nerve-wracking element to naval warfare. The mere threat of submarine attack immediately altered naval tactics and strategies employed by both the Western Allies and the Central Powers, shifting them towards a more cautious approach, especially at the war’s start when the submarine threat remained untested. During World War I, German U-boats operated solo except on one occasion. Initially, the British and nations supplying England with food and materiel scattered vessels singly across the ocean, making them vulnerable to the lone submarines. However, widespread late war re-adoption of the convoy system tipped the odds in the surface ships' favor, as one U-boat skipper described: "The oceans at once became bare 1. Language: English. Narrator: Bill Hare. Audio sample: http://samples.audible.de/bk/acx0/110814/bk_acx0_110814_sample.mp3. Digital audiobook in aax.

Anbieter: Audible
Stand: 06.07.2020
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Submarine Warfare in the Atlantic: The History ...
9,95 € *
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Danger prowled under both the cold gray waters of the North Sea and the shimmering blue waves of the tropical Atlantic during World War II as Adolf Hitler's Third Reich attempted to strangle Allied shipping lanes with U-boat attacks. German and British submarines combed the vast oceanic battlefield for prey, while scientists developed new technologies and countermeasures. Submarine warfare began tentatively during the American Civil War (though the Netherlands and England made small prototypes centuries earlier, and the American sergeant Ezra Lee piloted the one-man Turtle vainly against HMS Eagle near New York in 1776). Britisher Robert Whitehead's invention of the torpedo introduced the weapon later used most frequently by submarines. Steady improvements to Whitehead's design led to the military torpedoes deployed against shipping during both World Wars. World War I witnessed the First Battle of the Atlantic, when the Kaiserreich unleashed its U-boats against England. During the war's 52.5 months, the German submarines sent much of the British merchant marine to the bottom. Indeed, German reliance on U-boats in both World War I and World War II stemmed largely from their nation's geography. The Germans eventually recognized the primacy of the Royal Navy and its capacity to blockade Germany's short coastline in the event of war. While the British could easily interdict surface ships, submarines slipped from their Kiel or Hamburg anchorages unseen, able to prey upon England's merchant shipping. During World War I, German U-boats operated solo except on one occasion. Initially, the British and nations supplying England with food and materiel scattered vessels singly across the ocean, making them vulnerable to the lone submarines. However, widespread late war re-adoption of the convoy system tipped the odds in the surface ships' favor, as one U-boat skipper described: "The oceans at once became bare and empty; for long periods at a time the U 1. Language: English. Narrator: Dan Gallagher. Audio sample: http://samples.audible.de/bk/acx0/073327/bk_acx0_073327_sample.mp3. Digital audiobook in aax.

Anbieter: Audible
Stand: 06.07.2020
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Digital Enhancement of EEG/MEG Signals
48,80 € *
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Electroencephalography (EEG) and Magnetoencephalography (MEG) recordings are commonly used for analyzing the brain. However, in most cases, the recordings not only contain brain waves, but also artifacts of physiological (ocular, muscle, ECG artifacts) or technical (electrode popping, power-line) origins, and noise from different sources. The main aim of the work described in this thesis is the noise reduction and artifact suppression from EEG and MEG signals.Different techniques for artifact suppression have been used: A Low-Pass Filter (LPF), an instantaneous Independent Component Analysis (ICA) algorithm, a combination of ICA and LPF, a combination of ICA and State-Space Modeling (SSM), a combination of ICA and Wiener filters, and a hybrid filter (i.e., a filter that works in the time- and frequency-domains). These techniques have been tested only offline in the present work.Additionally, two artifact suppression methods that could work either offline or in real-time have been tested in real-time. The first one is a recent approach used for signal enhancement, called Empirical Mode Decomposition (EMD). This method is employed in this work for denoising, for detrending, and for suppressing the muscle artifacts from EEG signals. The second method is an algorithm here called Classification-based Signal Enhancement (CBSE). It was also used to suppress muscle artifacts in EEG signals, in real-time, using Wiener filters for signal enhancement.In order to use any artifact suppression technique, the artifacts to be removed have to be previously identified. If the artifact suppression is done offline, the detection can be carried out by visual inspection of the data by an expert, or in an automatic way. On the other hand, if the suppression of artifacts has to be done in real-time, the artifacts have to be detected automatically. A detection technique is proposed in the present work. First, different features are extracted from the independent components, and then a threshold-based classification is performed to determine which components are contaminated, what kind of artifacts they contain, and how the suppression of the artifacts is realized. This method was tested in an offline manner in this thesis.The effectiveness of the proposed artifact suppression techniques was demonstrated by application to either “semi-simulated” EEG signals artificially contaminated with artifacts, or to real EEG/MEG data from a healthy subject or a patient suffering from epilepsy (inherently contaminated with different kinds of artifacts). It is shown by visual inspection and in a quantitative manner that, after applying the different techniques, the EEG/MEG signals are enhanced.To reduce the noise, an equalizer and a Wiener filter have been used. The signals employed for this purpose correspond to those from the newly developed magnetoelectric (ME) sensors at Kiel University.

Anbieter: Dodax
Stand: 06.07.2020
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Plasma Physics
83,86 € *
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The enlarged new edition of this textbook provides a comprehensive introduction to the basic processes in plasmas and demonstrates that the same fundamental concepts describe cold gas-discharge plasmas, space plasmas, and hot fusion plasmas. Starting from particle drifts in magnetic fields, the principles of magnetic confinement fusion are explained and compared with laser fusion. Collective processes are discussed in terms of plasma waves and instabilities. The concepts of plasma description by magnetohydrodynamics, kinetic theory, and particle simulation are stepwise introduced. Space charge effects in sheath regions, double layers and plasma diodes are given the necessary attention. The novel fundamental mechanisms of dusty plasmas are explored and integrated into the framework of conventional plasmas. The book concludes with a concise description of modern plasma discharges.Written by an internationally renowned researcher in experimental plasma physics, the text keeps the mathematical apparatus simple and emphasizes the underlying concepts. The guidelines of plasma physics are illustrated by a host of practical examples, preferentially from plasma diagnostics. There, Langmuir probe methods, laser interferometry, ionospheric sounding, Faraday rotation, and diagnostics of dusty plasmas are discussed. Though primarily addressing students in plasma physics, the book is easily accessible for researchers in neighboring disciplines, such as space science, astrophysics, material science, applied physics, and electrical engineering.This second edition has been thoroughly revised and contains substantially enlarged chapters on plasma diagnostics, dusty plasmas and plasma discharges. Probe techniques have been rearranged into basic theory and a host of practical examples for probe techniques in dc, rf, and space plasmas. New topics in dusty plasmas, such as plasma crystals, Yukawa balls, phase transitions and attractive forces have been adopted. The chapter on plasma discharges now contains a new section on conventional and high-power impulse magnetron sputtering. The recently discovered electrical asymmetry effect in capacitive rf-discharges is described.The text is based on an introductory course to plasma physics and advanced courses in plasma diagnostics, dusty plasmas, and plasma waves, which the author has taught at Kiel University for three decades. The pedagogical approach combines detailed explanations, a large number of illustrative figures, short summaries of the basics at the end of each chapter, and a selection of problems with detailed solutions.

Anbieter: Dodax
Stand: 06.07.2020
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Digital Enhancement of EEG/MEG Signals
57,90 CHF *
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Electroencephalography (EEG) and Magnetoencephalography (MEG) recordings are commonly used for analyzing the brain. However, in most cases, the recordings not only contain brain waves, but also artifacts of physiological (ocular, muscle, ECG artifacts) or technical (electrode popping, power-line) origins, and noise from different sources. The main aim of the work described in this thesis is the noise reduction and artifact suppression from EEG and MEG signals. Different techniques for artifact suppression have been used: A Low-Pass Filter (LPF), an instantaneous Independent Component Analysis (ICA) algorithm, a combination of ICA and LPF, a combination of ICA and State-Space Modeling (SSM), a combination of ICA and Wiener filters, and a hybrid filter (i.e., a filter that works in the time- and frequency-domains). These techniques have been tested only offline in the present work. Additionally, two artifact suppression methods that could work either offline or in real-time have been tested in real-time. The first one is a recent approach used for signal enhancement, called Empirical Mode Decomposition (EMD). This method is employed in this work for denoising, for detrending, and for suppressing the muscle artifacts from EEG signals. The second method is an algorithm here called Classification-based Signal Enhancement (CBSE). It was also used to suppress muscle artifacts in EEG signals, in real-time, using Wiener filters for signal enhancement. In order to use any artifact suppression technique, the artifacts to be removed have to be previously identified. If the artifact suppression is done offline, the detection can be carried out by visual inspection of the data by an expert, or in an automatic way. On the other hand, if the suppression of artifacts has to be done in real-time, the artifacts have to be detected automatically. A detection technique is proposed in the present work. First, different features are extracted from the independent components, and then a threshold-based classification is performed to determine which components are contaminated, what kind of artifacts they contain, and how the suppression of the artifacts is realized. This method was tested in an offline manner in this thesis. The effectiveness of the proposed artifact suppression techniques was demonstrated by application to either “semi-simulated” EEG signals artificially contaminated with artifacts, or to real EEG/MEG data from a healthy subject or a patient suffering from epilepsy (inherently contaminated with different kinds of artifacts). It is shown by visual inspection and in a quantitative manner that, after applying the different techniques, the EEG/MEG signals are enhanced. To reduce the noise, an equalizer and a Wiener filter have been used. The signals employed for this purpose correspond to those from the newly developed magnetoelectric (ME) sensors at Kiel University.

Anbieter: Orell Fuessli CH
Stand: 06.07.2020
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Digital Enhancement of EEG/MEG Signals
48,80 € *
ggf. zzgl. Versand

Electroencephalography (EEG) and Magnetoencephalography (MEG) recordings are commonly used for analyzing the brain. However, in most cases, the recordings not only contain brain waves, but also artifacts of physiological (ocular, muscle, ECG artifacts) or technical (electrode popping, power-line) origins, and noise from different sources. The main aim of the work described in this thesis is the noise reduction and artifact suppression from EEG and MEG signals. Different techniques for artifact suppression have been used: A Low-Pass Filter (LPF), an instantaneous Independent Component Analysis (ICA) algorithm, a combination of ICA and LPF, a combination of ICA and State-Space Modeling (SSM), a combination of ICA and Wiener filters, and a hybrid filter (i.e., a filter that works in the time- and frequency-domains). These techniques have been tested only offline in the present work. Additionally, two artifact suppression methods that could work either offline or in real-time have been tested in real-time. The first one is a recent approach used for signal enhancement, called Empirical Mode Decomposition (EMD). This method is employed in this work for denoising, for detrending, and for suppressing the muscle artifacts from EEG signals. The second method is an algorithm here called Classification-based Signal Enhancement (CBSE). It was also used to suppress muscle artifacts in EEG signals, in real-time, using Wiener filters for signal enhancement. In order to use any artifact suppression technique, the artifacts to be removed have to be previously identified. If the artifact suppression is done offline, the detection can be carried out by visual inspection of the data by an expert, or in an automatic way. On the other hand, if the suppression of artifacts has to be done in real-time, the artifacts have to be detected automatically. A detection technique is proposed in the present work. First, different features are extracted from the independent components, and then a threshold-based classification is performed to determine which components are contaminated, what kind of artifacts they contain, and how the suppression of the artifacts is realized. This method was tested in an offline manner in this thesis. The effectiveness of the proposed artifact suppression techniques was demonstrated by application to either “semi-simulated” EEG signals artificially contaminated with artifacts, or to real EEG/MEG data from a healthy subject or a patient suffering from epilepsy (inherently contaminated with different kinds of artifacts). It is shown by visual inspection and in a quantitative manner that, after applying the different techniques, the EEG/MEG signals are enhanced. To reduce the noise, an equalizer and a Wiener filter have been used. The signals employed for this purpose correspond to those from the newly developed magnetoelectric (ME) sensors at Kiel University.

Anbieter: Thalia AT
Stand: 06.07.2020
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