813 research outputs found
Device Free Localisation Techniques in Indoor Environments
The location estimation of a target for a long period was performed only by device based localisation technique which is difficult in applications where target especially human is non-cooperative. A target was detected by equipping a device using global positioning systems, radio frequency systems, ultrasonic frequency systems, etc. Device free localisation (DFL) is an upcoming technology in automated localisation in which target need not equip any device for identifying its position by the user. For achieving this objective, the wireless sensor network is a better choice due to its growing popularity. This paper describes the possible categorisation of recently developed DFL techniques using wireless sensor network. The scope of each category of techniques is analysed by comparing their potential benefits and drawbacks. Finally, future scope and research directions in this field are also summarised
Device-free localization via an extreme learning machine with parameterized geometrical feature extraction
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. Device-free localization (DFL) is becoming one of the new technologies in wireless localization field, due to its advantage that the target to be localized does not need to be attached to any electronic device. In the radio-frequency (RF) DFL system, radio transmitters (RTs) and radio receivers (RXs) are used to sense the target collaboratively, and the location of the target can be estimated by fusing the changes of the received signal strength (RSS) measurements associated with the wireless links. In this paper, we will propose an extreme learning machine (ELM) approach for DFL, to improve the efficiency and the accuracy of the localization algorithm. Different from the conventional machine learning approaches for wireless localization, in which the above differential RSS measurements are trivially used as the only input features, we introduce the parameterized geometrical representation for an affected link, which consists of its geometrical intercepts and differential RSS measurement. Parameterized geometrical feature extraction (PGFE) is performed for the affected links and the features are used as the inputs of ELM. The proposed PGFE-ELM for DFL is trained in the offline phase and performed for real-time localization in the online phase, where the estimated location of the target is obtained through the created ELM. PGFE-ELM has the advantages that the affected links used by ELM in the online phase can be different from those used for training in the offline phase, and can be more robust to deal with the uncertain combination of the detectable wireless links. Experimental results show that the proposed PGFE-ELM can improve the localization accuracy and learning speed significantly compared with a number of the existing machine learning and DFL approaches, including the weighted K-nearest neighbor (WKNN), support vector machine (SVM), back propagation neural network (BPNN), as well as the well-known radio tomographic imaging (RTI) DFL approach
Compressive Sensing and Its Applications in Automotive Radar Systems
Die Entwicklung in Richtung zu autonomem Fahren verspricht, kĂŒnftig einen sicheren
Verkehr ohne tödliche UnfÀlle zu ermöglichen, indem menschliche Fahrer vollstÀndig
ersetzt werden. Dadurch entfÀllt der Faktor des menschlichen Fehlers, der aus
MĂŒdigkeit, Unachtsamkeit oder Alkoholeinfluss resultiert. Um jedoch eine breite
Akzeptanz fĂŒr autonome Fahrzeuge zu erreichen und es somit eines Tages vollstĂ€ndig
umzusetzen, sind noch eine Vielzahl von Herausforderungen zu lösen. Da in einem
autonomen Fahrzeug kein menschlicher Fahrer mehr in NotfÀllen eingreifen kann,
mĂŒssen sich autonome Fahrzeuge auf leistungsfĂ€hige und robuste Sensorsysteme
verlassen können, um in kritischen Situationen auch unter widrigen Bedingungen
angemessen reagieren zu können. Daher ist die Entwicklung von Sensorsystemen
erforderlich, die fĂŒr FunktionalitĂ€ten jenseits der aktuellen advanced driver assistance
systems eingesetzt werden können. Dies resultiert in neuen Anforderungen, die erfĂŒllt
werden mĂŒssen, um sichere und zuverlĂ€ssige autonome Fahrzeuge zu realisieren, die
weder Fahrzeuginsassen noch Passanten gefÀhrden. Radarsysteme gehören zu den
SchlĂŒsselkomponenten unter der Vielzahl der verfĂŒgbaren Sensorsysteme, da sie im
Gegensatz zu visuellen Sensoren von widrigen Wetter- und Umgebungsbedingungen
kaum beeintrĂ€chtigt werden. DarĂŒber hinaus liefern Radarsysteme zusĂ€tzliche
Umgebungsinformationen wie Abstand, Winkel und relative Geschwindigkeit zwischen
Sensor und reflektierenden Zielen. Die vorliegende Dissertation deckt im Wesentlichen
zwei Hauptaspekte der Forschung und Entwicklung auf dem Gebiet der Radarsysteme
im Automobilbereich ab. Ein Aspekt ist die Steigerung der Effizienz und Robustheit
der Signalerfassung und -verarbeitung fĂŒr die Radarperzeption. Der andere Aspekt ist
die Beschleunigung der Validierung und Verifizierung von automated cyber-physical
systems, die parallel zum Automatisierungsgrad auch eine höhere KomplexitÀt
aufweisen.
Nach der Analyse zahlreicher möglicher Compressive Sensing Methoden, die im
Bereich Fahrzeugradarsysteme angewendet werden können, wird ein rauschmoduliertes
gepulstes Radarsystem vorgestellt, das kommerzielle Fahrzeugradarsysteme in
seiner Robustheit gegenĂŒber Rauschen ĂŒbertrifft. Die Nachteile anderer gepulster
Radarsysteme hinsichtlich des Signalerfassungsaufwands und der Laufzeit werden
durch die Verwendung eines Compressive Sensing-Signalerfassungs- und Rekonstruktionsverfahrens
in Kombination mit einer Rauschmodulation deutlich verringert.
Mit Compressive Sensing konnte der Aufwand fĂŒr die Signalerfassung um 70% reduziert
werden, wĂ€hrend gleichzeitig die Robustheit der Radarwahrnehmung auch fĂŒr signal-to-noise-ratio-Pegel nahe oder unter Null erreicht wird. Mit einem validierten
Radarsensormodell wurde das Rauschradarsystem emuliert und mit einem
kommerziellen Fahrzeugradarsystem verglichen. Datengetriebene Wettermodelle
wurden entwickelt und wÀhrend der Simulation angewendet, um die Radarleistung
unter widrigen Bedingungen zu bewerten. WĂ€hrend eine BesprĂŒhung mit Wasser die
RadomdÀmpfung um 10 dB erhöht und Spritzwasser sogar um 20 dB, ergibt sich die
eigentliche Begrenzung aus der Rauschzahl und Empfindlichkeit des EmpfÀngers. Es
konnte bewiesen werden, dass das vorgeschlagene Compressive Sensing Rauschradarsystem
mit einer zusÀtzlichen SignaldÀmpfung von bis zu 60 dB umgehen kann
und damit eine hohe Robustheit in ungĂŒnstigen Umwelt- und Wetterbedingungen
aufweist.
Neben der Robustheit wird auch die Interferenz berĂŒcksichtigt. Zum einen wird
die erhöhte Störfestigkeit des Störradarsystems nachgewiesen. Auf der anderen
Seite werden die Auswirkungen auf bestehende Fahrzeugradarsysteme bewertet und
Strategien zur Minderung der Auswirkungen vorgestellt.
Die Struktur der Arbeit ist folgende. Nach der EinfĂŒhrung der Grundlagen
und Methoden fĂŒr Fahrzeugradarsysteme werden die Theorie und Metriken hinter
Compressive Sensing gezeigt. DarĂŒber hinaus werden weitere Aspekte wie Umgebungsbedingungen,
unterschiedliche Radararchitekturen und Interferenz erlÀutert.
Der Stand der Technik gibt einen Ăberblick ĂŒber Compressive Sensing-AnsĂ€tze und
Implementierungen mit einem Fokus auf Radar. DarĂŒber hinaus werden Aspekte
von Fahrzeug- und Rauschradarsystemen behandelt. Der Hauptteil beginnt mit
der Vorstellung verschiedener AnsĂ€tze zur Nutzung von Compressive Sensing fĂŒr
Fahrzeugradarsysteme, die in der Lage sind, die Erfassung und Wahrnehmung von
Radarsignalen zu verbessern oder zu erweitern. AnschlieĂend wird der Fokus auf
ein Rauschradarsystem gelegt, das mit Compressive Sensing eine effiziente Signalerfassung
und -rekonstruktion ermöglicht. Es wurde mit verschiedenen Compressive
Sensing-Metriken analysiert und in einer Proof-of-Concept-Simulation bewertet. Mit
einer Emulation des Rauschradarsystems wurde das Potential der Compressive Sensing
Signalerfassung und -verarbeitung in einem realistischeren Szenario demonstriert.
Die Entwicklung und Validierung des zugrunde liegenden Sensormodells wird ebenso
dokumentiert wie die Entwicklung der datengetriebenen Wettermodelle. Nach der
Betrachtung von Interferenz und der Koexistenz des Rauschradars mit kommerziellen
Radarsystemen schlieĂt ein letztes Kapitel mit Schlussfolgerungen und einem
Ausblick die Arbeit ab.Developments towards autonomous driving promise to lead to safer traffic, where fatal
accidents can be avoided after making human drivers obsolete and hence removing
the factor of human error. However, to ensure the acceptance of automated driving
and make it a reality one day, still a huge amount of challenges need to be solved.
With having no human supervisors, automated vehicles have to rely on capable and
robust sensor systems to ensure adequate reactions in critical situations, even during
adverse conditions. Therefore, the development of sensor systems is required that
can be applied for functionalities beyond current advanced driver assistance systems.
New requirements need to be met in order to realize safe and reliable automated
vehicles that do not harm passersby.
Radar systems belong to the key components among the variety of sensor systems.
Other than visual sensors, radar is less vulnerable towards adverse weather and
environment conditions. In addition, radar provides complementary environment
information such as target distance, angular position or relative velocity, too. The
thesis ad hand covers basically two main aspects of research and development in the
field of automotive radar systems. One aspect is to increase efficiency and robustness
in signal acquisition and processing for radar perception. The other aspect is to
accelerate validation and verification of automated cyber-physical systems that
feature more complexity along with the level of automation.
After analyzing a variety of possible Compressive Sensing methods for automotive
radar systems, a noise modulated pulsed radar system is suggested in the thesis at
hand, which outperforms commercial automotive radar systems in its robustness
towards noise. Compared to other pulsed radar systems, their drawbacks regarding
signal acquisition effort and computation run time are resolved by using noise modulation
for implementing a Compressive Sensing signal acquisition and reconstruction
method. Using Compressive Sensing, the effort in signal acquisition was reduced by
70%, while obtaining a radar perception robustness even for signal-to-noise-ratio
levels close to or below zero. With a validated radar sensor model the noise radar
was emulated and compared to a commercial automotive radar system. Data-driven
weather models were developed and applied during simulation to evaluate radar performance
in adverse conditions. While water sprinkles increase radome attenuation
by 10 dB and splash water even by 20 dB, the actual limitation comes from noise
figure and sensitivity of the receiver. The additional signal attenuation that can be
handled by the proposed compressive sensing noise radar system proved to be even up to 60 dB, which ensures a high robustness of the receiver during adverse weather
and environment conditions.
Besides robustness, interference is also considered. On the one hand the increased
robustness towards interference of the noise radar system is demonstrated. On
the other hand, the impact on existing automotive radar systems is evaluated and
strategies to mitigate the impact are presented.
The structure of the thesis is the following. After introducing basic principles
and methods for automotive radar systems, the theory and metrics of Compressive
Sensing is presented. Furthermore some particular aspects are highlighted such as
environmental conditions, different radar architectures and interference. The state of
the art provides an overview on Compressive Sensing approaches and implementations
with focus on radar. In addition, it covers automotive radar and noise radar related
aspects. The main part starts with presenting different approaches on making use
of Compressive Sensing for automotive radar systems, that are capable of either
improving or extending radar signal acquisition and perception. Afterwards the focus
is put on a noise radar system that uses Compressive Sensing for an efficient signal
acquisition and reconstruction. It was analyzed using different Compressive Sensing
metrics and evaluated in a proof-of-concept simulation. With an emulation of the
noise radar system the feasibility of the Compressive Sensing signal acquisition and
processing was demonstrated in a more realistic scenario. The development and
validation of the underlying sensor model is documented as well as the development
of the data-driven weather models. After considering interference and co-existence
with commercial radar systems, a final chapter with conclusions and an outlook
completes the work
Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research
Emerging applications such as Internet of Everything, Holographic Telepresence, collaborative robots, and space and deep-sea tourism are already highlighting the limitations of existing fifth-generation (5G) mobile networks. These limitations are in terms of data-rate, latency, reliability, availability, processing, connection density and global coverage, spanning over ground, underwater and space. The sixth-generation (6G) of mobile networks are expected to burgeon in the coming decade to address these limitations. The development of 6G vision, applications, technologies and standards has already become a popular research theme in academia and the industry. In this paper, we provide a comprehensive survey of the current developments towards 6G. We highlight the societal and technological trends that initiate the drive towards 6G. Emerging applications to realize the demands raised by 6G driving trends are discussed subsequently. We also elaborate the requirements that are necessary to realize the 6G applications. Then we present the key enabling technologies in detail. We also outline current research projects and activities including standardization efforts towards the development of 6G. Finally, we summarize lessons learned from state-of-the-art research and discuss technical challenges that would shed a new light on future research directions towards 6G
Studies on Spinal Fusion from Computational Modelling to âSmartâ Implants
Low back pain, the worldwide leading cause of disability, is commonly treated with lumbar interbody fusion surgery to address degeneration, instability, deformity, and trauma of the spine. Following fusion surgery, nearly 20% experience complications requiring reoperation while 1 in 3 do not experience a meaningful improvement in pain. Implant subsidence and pseudarthrosis in particular present a multifaceted challenge in the management of a patientâs painful symptoms. Given the diversity of fusion approaches, materials, and instrumentation, further inputs are required across the treatment spectrum to prevent and manage complications.
This thesis comprises biomechanical studies on lumbar spinal fusion that provide new insights into spinal fusion surgery from preoperative planning to postoperative monitoring. A computational model, using the finite element method, is developed to quantify the biomechanical impact of temporal ossification on the spine, examining how the fusion mass stiffness affects loads on the implant and subsequent subsidence risk, while bony growth into the endplates affects load-distribution among the surrounding spinal structures. The computational modelling approach is extended to provide biomechanical inputs to surgical decisions regarding posterior fixation. Where a patient is not clinically pre-disposed to subsidence or pseudarthrosis, the results suggest unilateral fixation is a more economical choice than bilateral fixation to stabilise the joint.
While finite element modelling can inform pre-surgical planning, effective postoperative monitoring currently remains a clinical challenge. Periodic radiological follow-up to assess bony fusion is subjective and unreliable. This thesis describes the development of a âsmartâ interbody cage capable of taking direct measurements from the implant for monitoring fusion progression and complication risk. Biomechanical testing of the âsmartâ implant demonstrated its ability to distinguish between graft and endplate stiffness states. The device is prepared for wireless actualisation by investigating sensor optimisation and telemetry. The results show that near-field communication is a feasible approach for wireless power and data transfer in this setting, notwithstanding further architectural optimisation required, while a combination of strain and pressure sensors will be more mechanically and clinically informative. Further work in computational modelling of the spine and âsmartâ implants will enable personalised healthcare for low back pain, and the results presented in this thesis are a step in this direction
Exploiting wireless received signal strength indicators to detect evil-twin attacks in smart homes
Evil-twin is becoming a common attack in Smart Home environments where an attacker can set up a fake AP to compromise the security of the connected devices. To identify the fake APs, The current approaches of detecting Evil-twin attacks all rely on information such as SSIDs, the MAC address of the genuine AP or network traffic patterns. However, such information can be faked by the attacker, often leading to low detection rates and weak protection. This paper presents a novel evil-twin attack detection method based on the received signal strength indicator (RSSI). Our key insight is that the location of the genuine AP rarely moves in a home environment and as a result the RSSI of the genuine AP is relatively stable. Our approach considers the RSSI as a fingerprint of APs and uses the fingerprint of the genuine AP to identify fake ones. We provide two schemes to detect a fake AP in two different scenarios where the genuine AP can be located at either a single or multiple locations in the property, by exploiting the multipath effect of the WIFI signal. As a departure from prior work, our approach does not rely on any professional measurement devices. Experimental results show that our approach can successfully detect 90% of the fake APs, at the cost of an one-off, modest connection delay
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