7,139 research outputs found
Real-time-controlled artificial quiet channel for acoustic cloaking under varying detection conditions
We consider the problem of hiding non-stationary objects from acoustic
detection in a two-dimensional environment, where both the object's impedance
and the properties of the detection signal may vary during operation. The
detection signal is assumed to be an acoustic beam created by an array of
emitters, which scans the area at different angles and different frequencies.
We propose an active control-based solution that creates an effective moving
dead zone around the object, and results in an artificial quiet channel for the
object to pass through undetected. The control principle is based on mid-domain
generation of near uni-directional beams using only monopole actuators. Based
on real-time response prediction, these beams open and close the dead zone with
a minimal perturbation backwards, which is crucial due to detector observers
being located on both sides of the object's route. The back action wave
determines the cloak efficiency, and is traded-off with the control effort; the
higher is the effort the quieter is the cloaking channel. We validate our
control algorithm via numerical experiments in a two-dimensional acoustic
waveguide, testing variation in frequency and incidence angle of the detection
source. Our cloak successfully intercepts the source by steering the control
beams and adjusting their wavelength accordingly
2023-2024 Boise State University Undergraduate Catalog
This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State
Emerging Power Electronics Technologies for Sustainable Energy Conversion
This Special Issue summarizes, in a single reference, timely emerging topics related to power electronics for sustainable energy conversion. Furthermore, at the same time, it provides the reader with valuable information related to open research opportunity niches
Human Gait Analysis using Spatiotemporal Data Obtained from Gait Videos
Mit der Entwicklung von Deep-Learning-Techniken sind Deep-acNN-basierte Methoden
zum Standard für Bildverarbeitungsaufgaben geworden, wie z. B. die Verfolgung menschlicher
Bewegungen und Posenschätzung, die Erkennung menschlicher Aktivitäten und
die Erkennung von Gesichtern. Deep-Learning-Techniken haben den Entwurf, die Implementierung
und den Einsatz komplexer und vielfältiger Anwendungen verbessert, die nun
in einer Vielzahl von Bereichen, einschließlich der Biomedizintechnik, eingesetzt werden.
Die Anwendung von Computer-Vision-Techniken auf die medizinische Bild- und Videoanalyse
hat zu bemerkenswerten Ergebnissen bei der Erkennung von Ereignissen geführt. Die
eingebaute Fähigkeit von convolutional neural network (CNN), Merkmale aus komplexen
medizinischen Bildern zu extrahieren, hat in Verbindung mit der Fähigkeit von long short
term memory network (LSTM), die zeitlichen Informationen zwischen Ereignissen zu erhalten,
viele neue Horizonte für die medizinische Forschung geschaffen. Der Gang ist einer der
kritischen physiologischen Bereiche, der viele Störungen im Zusammenhang mit Alterung
und Neurodegeneration widerspiegeln kann. Eine umfassende und genaue Ganganalyse
kann Einblicke in die physiologischen Bedingungen des Menschen geben. Bestehende
Ganganalyseverfahren erfordern eine spezielle Umgebung, komplexe medizinische Geräte
und geschultes Personal für die Erfassung der Gangdaten. Im Falle von tragbaren Systemen
kann ein solches System die kognitiven Fähigkeiten beeinträchtigen und für die Patienten
unangenehm sein.
Außerdem wurde berichtet, dass die Patienten in der Regel versuchen, während des
Labortests bessere Leistungen zu erbringen, was möglicherweise nicht ihrem tatsächlichen
Gang entspricht. Trotz technologischer Fortschritte stoßen wir bei der Messung des menschlichen
Gehens in klinischen und Laborumgebungen nach wie vor an Grenzen. Der Einsatz
aktueller Ganganalyseverfahren ist nach wie vor teuer und zeitaufwändig und erschwert den
Zugang zu Spezialgeräten und Fachwissen.
Daher ist es zwingend erforderlich, über Methoden zu verfügen, die langfristige Daten
über den Gesundheitszustand des Patienten liefern, ohne doppelte kognitive Aufgaben oder
Unannehmlichkeiten bei der Verwendung tragbarer Sensoren. In dieser Arbeit wird daher eine einfache, leicht zu implementierende und kostengünstige Methode zur Erfassung von
Gangdaten vorgeschlagen. Diese Methode basiert auf der Aufnahme von Gehvideos mit
einer Smartphone-Kamera in einer häuslichen Umgebung unter freien Bedingungen. Deep
neural network (NN) verarbeitet dann diese Videos, um die Gangereignisse zu extrahieren.
Die erkannten Ereignisse werden dann weiter verwendet, um verschiedene räumlich-zeitliche
Parameter des Gangs zu quantifizieren, die für jedes Ganganalysesystem wichtig sind.
In dieser Arbeit wurden Gangvideos verwendet, die mit einer Smartphone-Kamera mit
geringer Auflösung außerhalb der Laborumgebung aufgenommen wurden. Viele Deep-
Learning-basierte NNs wurden implementiert, um die grundlegenden Gangereignisse wie
die Fußposition in Bezug auf den Boden aus diesen Videos zu erkennen. In der ersten
Studie wurde die Architektur von AlexNet verwendet, um das Modell anhand von Gehvideos
und öffentlich verfügbaren Datensätzen von Grund auf zu trainieren. Mit diesem Modell
wurde eine Gesamtgenauigkeit von 74% erreicht. Im nächsten Schritt wurde jedoch die
LSTM-Schicht in dieselbe Architektur integriert. Die eingebaute Fähigkeit von LSTM in
Bezug auf die zeitliche Information führte zu einer verbesserten Vorhersage der Etiketten
für die Fußposition, und es wurde eine Genauigkeit von 91% erreicht. Allerdings gibt es
Schwierigkeiten bei der Vorhersage der richtigen Bezeichnungen in der letzten Phase des
Schwungs und der Standphase jedes Fußes.
Im nächsten Schritt wird das Transfer-Lernen eingesetzt, um die Vorteile von bereits
trainierten tiefen NNs zu nutzen, indem vortrainierte Gewichte verwendet werden. Zwei
bekannte Modelle, inceptionresnetv2 (IRNV-2) und densenet201 (DN-201), wurden mit
ihren gelernten Gewichten für das erneute Training des NN auf neuen Daten verwendet. Das
auf Transfer-Lernen basierende vortrainierte NN verbesserte die Vorhersage von Kennzeichnungen
für verschiedene Fußpositionen. Es reduzierte insbesondere die Schwankungen
in den Vorhersagen in der letzten Phase des Gangschwungs und der Standphase. Bei der
Vorhersage der Klassenbezeichnungen der Testdaten wurde eine Genauigkeit von 94% erreicht.
Da die Abweichung bei der Vorhersage des wahren Labels hauptsächlich ein Bild
betrug, konnte sie bei einer Bildrate von 30 Bildern pro Sekunde ignoriert werden.
Die vorhergesagten Markierungen wurden verwendet, um verschiedene räumlich-zeitliche
Parameter des Gangs zu extrahieren, die für jedes Ganganalysesystem entscheidend sind.
Insgesamt wurden 12 Gangparameter quantifiziert und mit der durch Beobachtungsmethoden
gewonnenen Grundwahrheit verglichen. Die NN-basierten räumlich-zeitlichen Parameter
zeigten eine hohe Korrelation mit der Grundwahrheit, und in einigen Fällen wurde eine sehr
hohe Korrelation erzielt. Die Ergebnisse belegen die Nützlichkeit der vorgeschlagenen Methode.
DerWert des Parameters über die Zeit ergab eine Zeitreihe, eine langfristige Darstellung des Ganges. Diese Zeitreihe konnte mit verschiedenen mathematischen Methoden weiter
analysiert werden.
Als dritter Beitrag in dieser Dissertation wurden Verbesserungen an den bestehenden
mathematischen Methoden der Zeitreihenanalyse von zeitlichen Gangdaten vorgeschlagen.
Zu diesem Zweck werden zwei Verfeinerungen bestehender entropiebasierter Methoden
zur Analyse von Schrittintervall-Zeitreihen vorgeschlagen. Diese Verfeinerungen wurden
an Schrittintervall-Zeitseriendaten von normalen und neurodegenerativen Erkrankungen
validiert, die aus der öffentlich zugänglichen Datenbank PhysioNet heruntergeladen wurden.
Die Ergebnisse zeigten, dass die von uns vorgeschlagene Methode eine klare Trennung
zwischen gesunden und kranken Gruppen ermöglicht.
In Zukunft könnten fortschrittliche medizinische Unterstützungssysteme, die künstliche
Intelligenz nutzen und von den hier vorgestellten Methoden abgeleitet sind, Ärzte bei der
Diagnose und langfristigen Überwachung des Gangs von Patienten unterstützen und so die
klinische Arbeitsbelastung verringern und die Patientensicherheit verbessern
An Internet of Things (IoT) based wide-area Wireless Sensor Network (WSN) platform with mobility support.
Wide-area remote monitoring applications use cellular networks or satellite links to transfer sensor data to the central storage. Remote monitoring applications uses Wireless Sensor Networks (WSNs) to accommodate more Sensor Nodes (SNs) and for better management. Internet of Things (IoT) network connects the WSN with the data storage and other application specific services using the existing internet infrastructure. Both cellular networks, such as the Narrow-Band IoT (NB-IoT), and satellite links will not be suitable for point-to-point connections of the SNs due to their lack of coverage, high cost, and energy requirement. Low Power Wireless Area Network (LPWAN) is used to interconnect all the SNs and accumulate the data to a single point, called Gateway, before sending it to the IoT network. WSN implements clustering of the SNs to increase the network coverage and utilizes multiple wireless links between the repeater nodes (called hops) to reach the gateway at a longer distance. Clustered WSN can cover up to a few km using the LPWAN technologies such as Zigbee using multiple hops. Each Zigbee link can be from 200 m to 500 m long. Other LPWAN technologies, such as LoRa, can facilitate an extended range from 1km to 15km. However, the LoRa will not be suitable for the clustered WSN due to its long Time on Air (TOA) which will introduce data transmission delay and become severe with the increase of hop count. Besides, a sensor node will need to increase the antenna height to achieve the long-range benefit of Lora using a single link (hop) instead of using multiple hops to cover the same range. With the increased WSN coverage area, remote monitoring applications such as smart farming may require mobile sensor nodes. This research focuses on the challenges to overcome LoRa’s limitations (long TOA and antenna height) and accommodation of mobility in a high-density and wide-area WSN for future remote monitoring applications. Hence, this research proposes lightweight communication protocols and networking algorithms using LoRa to achieve mobility, energy efficiency and wider coverage of up to a few hundred km for the WSN.
This thesis is divided into four parts. It presents two data transmission protocols for LoRa to achieve a higher data rate and wider network coverage, one networking algorithm for wide-area WSN and a channel synchronization algorithm to improve the data rate of LoRa links. Part one presents a lightweight data transmission protocol for LoRa using a mobile data accumulator (called data sink) to increase the monitoring coverage area and data transmission energy efficiency. The proposed Lightweight Dynamic Auto Reconfigurable Protocol (LDAP) utilizes direct or single hop to transmit data from the SNs using one of them as the repeater node. Wide-area remote monitoring applications such as Water Quality Monitoring (WQM) can acquire data from geographically distributed water resources using LDAP, and a mobile Data Sink (DS) mounted on an Unmanned Aerial Vehicle (UAV). The proposed LDAP can acquire data from a minimum of 147 SNs covering 128 km in one direction reducing the DS requirement down to 5% comparing other WSNs using Zigbee for the same coverage area with static DS.
Applications like smart farming and environmental monitoring may require mobile sensor nodes (SN) and data sinks (DS). The WSNs for these applications will require real-time network management algorithms and routing protocols for the dynamic WSN with mobility that is not feasible using static WSN technologies. This part proposes a lightweight clustering algorithm for the dynamic WSN (with mobility) utilizing the proposed LDAP to form clusters in real-time during the data accumulation by the mobile DS. The proposed Lightweight Dynamic Clustering Algorithm (LDCA) can form real-time clusters consisting of mobile or stationary SNs using mobile DS or static GW. WSN using LoRa and LDCA increases network capacity and coverage area reducing the required number of DS. It also reduces clustering energy to 33% and shows clustering efficiency of up to 98% for single-hop clustering covering 100 SNs.
LoRa is not suitable for a clustered WSN with multiple hops due to its long TOA, depending on the LoRa link configurations (bandwidth and spreading factor). This research proposes a channel synchronization algorithm to improve the data rate of the LoRa link by combining multiple LoRa radio channels in a single logical channel. This increased data rate will enhance the capacity of the clusters in the WSN supporting faster clustering with mobile sensor nodes and data sink. Along with the LDCA, the proposed Lightweight Synchronization Algorithm for Quasi-orthogonal LoRa channels (LSAQ) facilitating multi-hop data transfer increases WSN capacity and coverage area. This research investigates quasi-orthogonality features of LoRa in terms of radio channel frequency, spreading factor (SF) and bandwidth. It derived mathematical models to obtain the optimal LoRa parameters for parallel data transmission using multiple SFs and developed a synchronization algorithm for LSAQ. The proposed LSAQ achieves up to a 46% improvement in network capacity and 58% in data rate compared with the WSN using the traditional LoRa Medium Access Control (MAC) layer protocols.
Besides the high-density clustered WSN, remote monitoring applications like plant phenotyping may require transferring image or high-volume data using LoRa links. Wireless data transmission protocols used for high-volume data transmission using the link with a low data rate (like LoRa) requiring multiple packets create a significant amount of packet overload. Besides, the reliability of these data transmission protocols is highly dependent on acknowledgement (ACK) messages creating extra load on overall data transmission and hence reducing the application-specific effective data rate (goodput). This research proposes an application layer protocol to improve the goodput while transferring an image or sequential data over the LoRa links in the WSN. It uses dynamic acknowledgement (DACK) protocol for the LoRa physical layer to reduce the ACK message overhead. DACK uses end-of-transmission ACK messaging and transmits multiple packets as a block. It retransmits missing packets after receiving the ACK message at the end of multiple blocks. The goodput depends on the block size and the number of lossy packets that need to be retransmitted. It shows that the DACK LoRa can reduce the total ACK time 10 to 30 times comparing stop-wait protocol and ten times comparing multi-packet ACK protocol.
The focused wide-area WSN and mobility requires different matrices to be evaluated. The performance evaluation matrices used for the static WSN do not consider the mobility and the related parameters, such as clustering efficiency in the network and hence cannot evaluate the performance of the proposed wide-area WSN platform supporting mobility. Therefore, new, and modified performance matrices are proposed to measure dynamic performance. It can measure the real-time clustering performance using the mobile data sink and sensor nodes, the cluster size, the coverage area of the WSN and more. All required hardware and software design, dimensioning, and performance evaluation models are also presented
Application of Conventional Feedforward and Deep Neural Networks to Power Distribution System State Estimation and State Forecasting
Classical neural networks such as feedforward multilayer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. This research investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to mitigate challenges in power distribution system state estimation and forecasting based upon conventional analytic methods. The ability of MLPs to perform regression to perform power system state estimation will be investigated. MLPs are considered based upon their promise to learn complex functional mapping between datasets with many features. CNNs and LSTMs are considered based upon their promise to perform time-series forecasting by learning the autocorrelation of the dataset being predicted. The performance of MLPs will be presented in terms of root-mean-square error (RMSE) between actual and predicted voltage magnitude and voltage phase angles and training execution time for distribution system state estimation (DSSE). The performance of CNNs, and LSTMs will be presented in terms of RMSE between actual and predicted real power demand and execution time when performing distribution system state forecasting (DSSF). Additionally, Bayesian Optimization with Gaussian Processes are used to optimize MLPs for regression. An IEEE standard 34-bus test system is used to illustrate the proposed conventional neural network and deep learning methods and their effectiveness to perform power system state estimation and power system state forecasting respectively
Brain Computations and Connectivity [2nd edition]
This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations.
Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed.
The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes.
Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions.
This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press.
Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics
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