Angebote zu "Signal" (8 Treffer)

Kategorien

Shops

Kiel. Das Signal zur Revolution im Deutschen Ka...
13,99 € *
ggf. zzgl. Versand

Kiel. Das Signal zur Revolution im Deutschen Kaiserreich ab 13.99 € als Taschenbuch: 1. Auflage.. Aus dem Bereich: Bücher, Wissenschaft, Geschichte,

Anbieter: hugendubel
Stand: 28.05.2020
Zum Angebot
KMA Machines Horizont Effektgerät E-Gitarre
289,00 € *
ggf. zzgl. Versand

Phaser auf Betäubung - ein Wurmloch öffnet sich!! KMA Machines hatte mit dem Astrospurt schon die erste Stufe in Sachen Phasing gezündet - jetzt geht es mit dem Horizont INTERDIMENSIONAL MULTISPATIAL STEREO PHASER auf Warp 10!! Die cleveren Jungs von KMA Machines haben sich so richtig ausgetobt und haben einen der umfangreichsten und abgedrehtesten Phaser aller Zeiten auf Kiel gelegt. Basis ist eine analoge JFET Schaltung, die sich bis zur Unkenntlichkeit tweaken und verbiegen läßt. Es stehen 8 Wellenformen ( Ramp Up/Down, Sine/Triangle/Square wave, eine sweeping waveform, Sample & Hold und eine random slope waveform im Bereich 20s down to 40ms) zur Verfügung. Zwei Sweep Modes sind wählbar, mit einem optionalen Expression- bzw. CV Pedal können entweder die Geschwindigkeit oder manuelles Sweeping für Wah ähnliche Filtersounds geregelt werden. Das Horizont Pedal ist eine großartige Spielwiese für kreative Soundschrauber - nicht auszudenken, wäre das Pedal in den glorreichen 70ern einem bekifften Krautrock Keyboarder in die Hände gefallen! · Effekt-Typ: Phaser · Bauart: Analog · Besonderheit(en): zusätzliches digitales LFO mit 8 Wellenformen, · Mono/Stereo: Mono In, Stereo Out · Regler: Signal, Mix, Spurt, Depth, Emphase, Fuel, Decay · Schalter: Emp Stages 1/2/3/4, Expression Mode, Sweep Mode, Decay · Bypass Modus: True Bypass · Stromversorgung: 9 VDC, Center negative · Stromverbrauch: 75 mA · Batteriebetrieb: nicht möglich · Gehäuseformat: Big Size · Auch für Bass geeignet: ja · Produktionsland: Effektgerät E-Gitarre

Anbieter: Musik Produktiv
Stand: 28.05.2020
Zum Angebot
Kiel. Das Signal zur Revolution im Deutschen Ka...
13,99 € *
ggf. zzgl. Versand

Kiel. Das Signal zur Revolution im Deutschen Kaiserreich ab 13.99 EURO 1. Auflage.

Anbieter: ebook.de
Stand: 28.05.2020
Zum Angebot
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: Dodax
Stand: 28.05.2020
Zum Angebot
Die ziellose Repubik
4,19 € *
ggf. zzgl. Versand

Angst vor dem Neuen – Deutschland steckt im Patt der Lager – und findet sich daher unter einer Regierung der Großen Koalition wieder. Die Unschärfe des Neuen im Wechsel irritiert, ängstigt und lähmt die Deutschen, auch ihre politische Führungsschicht. Hierin – und nicht so sehr in den ökonomischen Schwächen – liegen die Wurzeln der viel beklagten German Disease.Kürzlich spendete der 'Economist' reichlich Lob für die Erneuerung der deutschen Wirtschaft. Mehrere internationale Expertenkommissionen haben in den letzten Monaten Deutschland gar zum Musterknaben der ökonomischen Reform deklariert. Doch ist die Stimmung zwischen München und Kiel weiterhin düster. Pessimismus und Depression charakterisieren die kollektive Gemütsverfassung der Nation. Die Wahlbürger hadern mit Parteien und Regierung. Doch zugleich verbirgt sich hinter der Übellaunigkeit keine Alternative. Die Verdrossenheit tritt ziellos auf. Auch der Ausgang der Bundestagswahl im September sandte kein eindeutiges Signal aus, in welche Richtung die Bürger die Politik bewegen wollten. Das Wahlresultat war weder ein Plebiszit für furiose Wettbewerbsreformen noch für robuste Sozialstaatlichkeit. Deutschland steckt im Patt der Lager – und findet sich daher unter einer Regierung der Großen Koalition wieder. Von dieser Ziellosigkeit der Patt-Republik handeln die Essays in diesem Buch. Der Autor diagnostiziert einen schleichenden Wandel der Werte. Er beobachtet die mentalen Veränderungen in den klassischen sozialkulturellen Milieus der alten Bundesrepublik. Er konstatiert einen Gezeitenwechsel in Gesellschaft und Politik.

Anbieter: Dodax
Stand: 28.05.2020
Zum Angebot
Digital Enhancement of EEG/MEG Signals
57,90 CHF *
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: Orell Fuessli CH
Stand: 28.05.2020
Zum Angebot
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: 28.05.2020
Zum Angebot