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Dissertations / Theses on the topic 'Signal processing for network security'
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1
Lu, Xiaotao. "Cost-effective signal processing algorithms for physical-layer security in wireless networks." Thesis, University of York, 2016. http://etheses.whiterose.ac.uk/16043/.
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Data privacy in traditional wireless communications is accomplished by cryptography techniques at the upper layers of the protocol stack. This thesis aims at contributing to the critical security issue residing in the physical-layer of wireless networks, namely, secrecy rate in various transmission environments. Physical-layer security opens the gate to the exploitation of channel characteristics to achieve data secure transmission. Precoding techniques, as a critical aspect in pre-processing signals prior to transmission has become an effective approach and recently drawn significant attention in the literature. In our research, novel non-linear precoders are designed focusing on the improvement of the physical-layer secrecy rate with consideration of computational complexity as well as the Bit Error Ratio (BER) performance. In the process of designing the precoder, strategies such as Lattice Reduction (LR) and Artificial Noise (AN) are employed to achieve certain design requirements. The deployment and allocation of resources such as relays to assist the transmission also have gained significant interest. In multiple-antenna relay networks, we examine various relay selection criteria with arbitrary knowledge of the channels to the users and the eavesdroppers. Furthermore, we provide novel effective relay selection criteria that can achieve a high secrecy rate performance. More importantly they do not require knowledge of the channels of the eavesdroppers and the interference. Combining the jamming technique with resource allocation of relay networks, we investigate an opportunistic relaying and jamming scheme for Multiple-Input Multiple-Output (MIMO) buffer-aided downlink relay networks. More specifically, a novel Relaying and Jamming Function Selection (RJFS) algorithm as well as a buffer-aided RJFS algorithm are developed along with their ability to achieve a higher secrecy rate. Relying on the proposed relay network, we detail the characteristics of the system, under various relay selection criteria, develop exhaustive search and greedy search-based algorithms, with or without inter-relay Interference Cancellation (IC).
2
Di, Mauro Mario. "Statistical models for the characterization, identification and mitigation of distributed attacks in data networks." Doctoral thesis, Universita degli studi di Salerno, 2018. http://hdl.handle.net/10556/3088.
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2016 - 2017<br>The thesis focuses on statistical approaches to model, mitigate, and prevent distributed network attacks.
When dealing with distributed network attacks (and, more in general, with cyber-security problems), three
fundamental phases/issues emerge distinctly.
The first issue concerns the threat propagation across the network, which entails an "avalanche" effect,
with the number of infected nodes increasing exponentially as time elapses.
The second issue regards the design of proper mitigation strategies (e.g., threat detection, attacker's
identification) aimed at containing the propagation phenomenon. Finally (and this is the third issue), it is
also desirable to act on the system infrastructure to grant a conservative design by adding some controlled
degree of redundancy, in order to face those cases where the attacker has not been yet defeated.
The contributions of the present thesis address the aforementioned relevant issues, namely, propagation,
mitigation and prevention of distributed network attacks. A brief summary of the main contributions is
reported below.
The first contribution concerns the adoption of Kendall’s birth-and-death process as an analytical model for
threat propagation. Such a model exhibits two main properties: i) it is a stochastic model (a desirable
requirement to embody the complexity of real-world networks) whereas many models are purely
deterministic; ii) it is able to capture the essential features of threat propagation through a few parameters
with a clear physical meaning. By exploiting the remarkable properties of Kendall’s model, the exact
solution for the optimal resource allocation problem (namely, the optimal mitigation policy) has been
provided for both conditions of perfectly known parameters, and unknown parameters (with the latter case
being solved through a Maximum-Likelihood estimator).
The second contribution pertains to the formalization of a novel kind of randomized Distributed Denial of
Service (DDoS) attack. In particular, a botnet (a network of malicious entities) is able to emulate some
normal traffic, by picking messages from a dictionary of admissible requests. Such a model allows to
quantify the botnet “learning ability”, and to ascertain the real nature of users (normal or bot) via an
indicator referred to as MIR (Message Innovation Rate). Exploiting the considered model, an algorithm that
allows to identify a botnet (possibly) hidden in the network has been devised. The results are then
extended to the case of a multi-cluster environment, where different botnets are concurrently present in
the network, and an algorithm to identify the different clusters is conceived.
The third contribution concerns the formalization of the network resilience problem and the consequent
design of a prevention strategy. Two statistical frameworks are proposed to model the high availability
requirements of network infrastructures, namely, the Stochastic Reward Network (SRN), and the Universal
Generating Function (UGF) frameworks. In particular, since in the network environment dealing with multidimensional
quantities is crucial, an extension of the classic UGF framework, called Multi-dimensional UGF
(MUGF), is devised. [edited by author]<br>XVI n.s.
3
Mynampati, Vittal Reddy, Dilip Kandula, Raghuram Garimilla, and Kalyan Srinivas. "Performance and Security of Wireless Mesh Networks." Thesis, Blekinge Tekniska Högskola, Avdelningen för telekommunikationssystem, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2901.
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The thesis aims to find issues that may affect the performance of meshed wireless networks. There is no denying the fact that out of the wireless technologies being used in today’s environment, the wireless meshed technology is one of the most advanced and can be viewed as the technology of the future. This thesis deals closely with aspects like throughput, security and performance as these metrics have a direct influence on the performance of the wireless mesh.The thesis is subdivided into various categories explaining the primary structure of wireless mesh networks. Performance of the network has always been a key issue and reliability is the core metric of evaluating the quality of a network. Routing protocols for these networks and which help in improving the performance are examined and the best routing protocol is suggested. This helps to improve the throughput which is the main aspect for maintaining a good performance. The main problem with wireless networks is making them security. This area is also considered as it improves the performance of the whole network. Also the network should be scalable to properly utilize the frequency and get optimal performance. This is required for the successful delivery of data packets. Thus, this area is also investigated together with some other factors that influence the behaviour of these networks. Last, but not least, we provide a discussion about possible future work as well as specifying a system that will help to increase the performance.
4
Xu, Jingxin. "Unusual event detection in crowded scenes." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/76365/1/Jingxin_Xu_Thesis.pdf.
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Novel computer vision techniques have been developed to automatically detect unusual events in crowded scenes from video feeds of surveillance cameras. The research is useful in the design of the next generation intelligent video surveillance systems. Two major contributions are the construction of a novel machine learning model for multiple instance learning through compressive sensing, and the design of novel feature descriptors in the compressed video domain.
5
Moore, Patrick. "Architectural investigation into network security processing." Thesis, Queen's University Belfast, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.492519.
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In this thesis, innovative techniques for accelerating and scaling cryptographic architectures for the provision of network security are presented. The first of these is a novel ASIP based interface, which allows for generic hardware block cipher cores to be used within a 32-bit ASIP platform. An example system consisting of the Altera Nios-II and an AES block cipher is described and how the interface can be extended to support additional encryption modes of operation is discussed. A figure of merit that can be utilised to determine the efficiency of interfaces is proposed. Following on from this a thorough investigation into IKEv2 is presented, With specific focus on hardware/software partitioning of the system. This resulted in the proposal of two systems -- one suitable for use in an embedded context, the other suitable for server deployment -- each capable of processing 250 key exchanges per second. The scalability of the proposed IKEv2 systems is also investigated with respect to the number of processors and the rate at which key exchanges can be sustained. As a result, two scalable architectures are proposed -- pipelined and iterative -- both of which could utilise the embedded or server based implementation. A simulator was developed to determine the optimal configurations within these systems. The iterative architecture was found to be more efficient in terms of throughput and latency. Further to this, the effect of unreliable networks on the developed architectures is investigated with falloffs in performance being observed as the network degrades. The implications of being able to employ the high-speed IKEv2 architecture within a scalable overall IPsec system were investigated. In the course of this exploration, three novel architectures were developed to aid with the scalability: a multi-threaded block cipher' architecture, a system to allow for parallelisation of HMAC structures and finally a system to allow for efficient key distribution within the IPsec architecture.
6
Zhao, Wentao. "Genomic applications of statistical signal processing." [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-2952.
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Liu, Jinshan. "Secure and reliable deep learning in signal processing." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103740.
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In conventional signal processing approaches, researchers need to manually extract features from raw data that can better describe the underlying problem. Such a process requires strong domain knowledge about the given problems. On the contrary, deep learning-based signal processing algorithms can discover features and patterns that would not be apparent to humans by feeding a sufficient amount of training data. In the past decade, deep learning has proved to be efficient and effective at delivering high-quality results.
Deep learning has demonstrated its great advantages in image processing and text mining. One of the most promising applications of deep learning-based signal processing techniques is autonomous driving. Today, many companies are developing and testing autonomous vehicles. High-level autonomous vehicles are expected to be commercialized in the near future. Besides, deep learning has demonstrated great potential in wireless communications applications. Researchers have addressed some of the most challenging problems such as transmitter classification and modulation recognition using deep learning.
Despite these advantages, there exist a wide range of security and reliability issues when applying deep learning models to real-world applications. First, deep learning models could not generate reliable results for testing data if the training data size is insufficient. Since generating training data is time consuming and resource intensive, it is important to understand the relationship between model reliability and the size of training data. Second, deep learning models could generate highly unreliable results if the testing data are significantly different from the training data, which we refer to as ``out-of-distribution (OOD)'' data. Failing to detect OOD testing data may expose serious security risks. Third, deep learning algorithms can be easily fooled when the input data are falsified. Such vulnerabilities may cause severe risks in safety-critical applications such as autonomous driving.
In this dissertation, we focus on the security and reliability issues in deep learning models in the following three aspects. (1) We systematically study how the model performance changes as more training data are provided in wireless communications applications. (2) We discuss how OOD data can impact the performance of deep learning-based classification models in wireless communications applications. We propose FOOD (Feature representation for OOD detection), a unified model that can detect OOD testing data effectively and perform classifications for regular testing data simultaneously. (3) We focus on the security issues of applying deep learning algorithms to autonomous driving. We discuss the impact of Perception Error Attacks (PEAs) on LIDAR and camera and propose a countermeasure called LIFE (LIDAR and Image data Fusion for detecting perception Errors).<br>Doctor of Philosophy<br>Deep learning has provided computers and mobile devices extraordinary powers to solve challenging signal processing problems. For example, current deep learning technologies are able to improve the quality of machine translation significantly, recognize speech as accurately as human beings, and even outperform human beings in face recognition.
Although deep learning has demonstrated great advantages in signal processing, it can be insecure and unreliable if the model is not trained properly or is tested under adversarial scenarios. In this dissertation, we study the following three security and reliability issues in deep learning-based signal processing methods. First, we provide insights on how the deep learning model reliability is changed as the size of training data increases. Since generating training data requires a tremendous amount of labor and financial resources, our research work could help researchers and product developers to gain insights on balancing the tradeoff between model performance and training data size. Second, we propose a novel model to detect the abnormal testing data that are significantly different from the training data. In deep learning, there is no performance guarantee when the testing data are significantly different from the training data. Failing to detect such data may cause severe security risks. Finally, we design a system to detect sensor attacks targeting autonomous vehicles. Deep learning can be easily fooled when the input sensor data are falsified. Security and safety can be enhanced significantly if the autonomous driving systems are able to figure out the falsified sensor data before making driving decisions.
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Farhat, Md Tanzin. "An Artificial Neural Network based Security Approach of Signal Verification in Cognitive Radio Network." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo153511563131623.
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CARDOSO, LUIZ ALBERTO LISBOA DA SILVA. "ANALYSIS OF PLASTIC NEURAL NETWORK MODELLING APPROACH TO SIGNAL PROCESSING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1992. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9512@1.
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MARINHA DO BRASIL<br>Os modelos plásticos de redes neurais são estudados e
avaliados como uma interessante abordagem da
neurocomputação ao processamento de sinais. Dentre estes,
o modelo SONN, recentemente proposto por Tenório e Lee, é
revisado e adotado como base para a implementação de um
ambiente interativo de prototipagem e análise de redes,
dada sua reduzida carga heurística. Como ilustração de seu
emprego, um problema de detecção e classificação de sinais
pulsados é solucionado, com resultados que preliminarmente
indicam a adequação do modelo como ferramenta na filtragem
não-linear de sinais e no reconhecimento de padrões.<br>Plastic neural network models are evaluated as an
attractive neurocomputing approach to signal processing.
Among these, the SONN model, as recently introduced by
Tenorio and Lee, is reviewed and adopted as the basis for
the implementation of an interactive network prototyping
and analysis system, due to its reduced heuristics. Its
use is exemplified in the task of detection and
classification of pulsed signals, showing up results that
preliminarily qualify the model as a tool for non-linear
filtering and pattern recognition applications.
10
Harper, Scott Jeffery. "A Secure Adaptive Network Processor." Diss., Virginia Tech, 2003. http://hdl.handle.net/10919/28023.
Full textAbstract:
Network processors are becoming a predominant feature in the field of network hardware. As new network protocols emerge and data speeds increase, contemporary general-purpose network processors are entering their second generation and academic research is being actively conducted into new techniques for the design and implementation of these systems. At the same time, systems ranging from secured military communications equipment to consumer devices are being updated to provide network connectivity. Many of these devices require, or would benefit from, the inclusion of device security in addition to data security. Whether it is a top-secret encryption scheme that must be concealed or a personal device that needs protection against unauthorized use, security of the device itself is becoming an important factor in system design. Unfortunately, current network processor solutions were not developed with device security in mind. A secure adaptive network processor can provide the means to fill this gap while continuing to provide full support for emerging communication protocols. This dissertation describes the concept and structure of one such device. Analysis of the hardware security provided by the proposed device is provided to highlight strengths and weaknesses, while a prototype system is developed to allow it to be embedded into practical applications for investigation. Two such applications are developed, using the device to provide support for both a secure network edge device and a user-adaptable network gateway. Results of these experiments indicate that the proposed device is useful both as a hardware security measure and as a basis for user adaptation of information-handling systems.<br>Ph. D.
11
Montaño-Gutierrez, Luis Fernando. "Dynamic signal processing by the glucose sensing network of Saccharomyces cerevisiae." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/28973.
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Organisms must constantly face and adapt to environmental change. Although unpredictable events may inevitably impose threats, temporally correlated changes may also provide opportunities from which an organism can profit. An evolutionarily successful microbe must collect enough information to distinguish threats from opportunities. Indeed, for nutrient transport, it is not clear how organisms distinguish one from the other. Fluctuations in nutrient levels can quickly render any transporter's capabilities obsolete. Identifying the environment's dynamic identity is therefore a highly valuable asset for a cell to elicit an accurate physiological response. Recent evidence suggests that the baker's yeast Saccharomyces cerevisiae can exert anticipatory responses to environmental shifts. Nevertheless, the mechanisms by which cells are able to incorporate information from the environment's dynamic features is not understood. A potential source of complex information processing is a highly intricate biochemical network that controls glucose transport. The understanding of this network, however, has revolved around its ability to adjust expression of 17 hexose transporter genes (HXT) to glucose levels. In this thesis, I postulate that instead the glucose sensing network is dynamically controlling the 7 major hexose transporters. By studying transporter dynamics in several scenarios, I provide substantial evidence for this hypothesis. I find that hexose transporters with similar reported affinities (Hxt2 and Hxt4) are robustly allocated to separate stages of growth for multiple initial glucose concentrations. Using single-cell studies, I show that Hxt4 expresses exclusively during glucose downshifts, in contrast with Hxt2. From multiple approaches, I demonstrate that Mig1 is mostly responsible for reporting on the time derivative of glucose, and harnessing it to differentially regulate both transporters. I also provide evidence for the roles of Rgt2 and Std1 in modulating long-term glucose repression of Hxt4. This work extends our ideas on the functionality of transport and gene regulation beyond the established steady-state models. The ability to decode environmental dynamics is likely to be present in other signaling systems and may impact a cell's decision to use fermentation - a decision which is of fundamental interest both for cancer research and for biotechnology.
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Hirotsu, Kenichi. "Neural network hardware with random weight change learning algorithm." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/15765.
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Hloupis, Georgios. "Seismological data acquisition and signal processing using wavelets." Thesis, Brunel University, 2009. http://bura.brunel.ac.uk/handle/2438/3470.
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This work deals with two main fields: a) The design, built, installation, test, evaluation, deployment and maintenance of Seismological Network of Crete (SNC) of the Laboratory of Geophysics and Seismology (LGS) at Technological Educational Institute (TEI) at Chania. b) The use of Wavelet Transform (WT) in several applications during the operation of the aforementioned network. SNC began its operation in 2003. It is designed and built in order to provide denser network coverage, real time data transmission to CRC, real time telemetry, use of wired ADSL lines and dedicated private satellite links, real time data processing and estimation of source parameters as well as rapid dissemination of results. All the above are implemented using commercial hardware and software which is modified and where is necessary, author designs and deploy additional software modules. Up to now (July 2008) SNC has recorded 5500 identified events (around 970 more than those reported by national bulletin the same period) and its seismic catalogue is complete for magnitudes over 3.2, instead national catalogue which was complete for magnitudes over 3.7 before the operation of SNC. During its operation, several applications at SNC used WT as a signal processing tool. These applications benefited from the adaptation of WT to non-stationary signals such as the seismic signals. These applications are: HVSR method. WT used to reveal undetectable non-stationarities in order to eliminate errors in site’s fundamental frequency estimation. Denoising. Several wavelet denoising schemes compared with the widely used in seismology band-pass filtering in order to prove the superiority of wavelet denoising and to choose the most appropriate scheme for different signal to noise ratios of seismograms. EEWS. WT used for producing magnitude prediction equations and epicentral estimations from the first 5 secs of P wave arrival. As an alternative analysis tool for detection of significant indicators in temporal patterns of seismicity. Multiresolution wavelet analysis of seismicity used to estimate (in a several years time period) the time where the maximum emitted earthquake energy was observed.
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Fießler, Andreas Christoph Kurt. "Hybrid Hardware/Software Architectures for Network Packet Processing in Security Applications." Doctoral thesis, Humboldt-Universität zu Berlin, 2019. http://dx.doi.org/10.18452/20023.
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Die Menge an in Computernetzwerken verarbeiteten Daten steigt stetig, was Netzwerkgeräte wie Switches, Bridges, Router und Firewalls vor Herausfordungen stellt.
Die Performance der verbreiteten, CPU/softwarebasierten Ansätze für die Implementierung dieser Aufgaben ist durch den inhärenten Overhead in der sequentiellen Datenverarbeitung limitiert, weshalb solche Funktionalitäten vermehrt auf dedizierten Hardwarebausteinen realisiert werden.
Diese bieten eine schnelle, parallele Verarbeitung mit niedriger Latenz, sind allerdings aufwendiger in der Entwicklung und weniger flexibel.
Nicht jede Anwendung kann zudem für parallele Verarbeitung optimiert werden.
Diese Arbeit befasst sich mit hybriden Ansätzen, um eine bessere Ausnutzung der jeweiligen Stärken von Soft- und Hardwaresystemen zu ermöglichen, mit Schwerpunkt auf der Paketklassifikation.
Es wird eine Firewall realisiert, die sowohl Flexibilität und Analysetiefe einer Software-Firewall als auch Durchsatz und Latenz einer Hardware-Firewall erreicht.
Der Ansatz wird auf einem Standard-Rechnersystem, welches für die Hardware-Klassifikation mit einem rekonfigurierbaren Logikbaustein (FPGA) ergänzt wird, evaluiert.
Eine wesentliche Herausforderung einer hybriden Firewall ist die Identifikation von Abhängigkeiten im Regelsatz.
Es werden Ansätze vorgestellt, welche den redundanten Klassifikationsaufwand auf ein Minimum reduzieren, wie etwa die Wiederverwendung von Teilergebnissen der hybriden Klassifikatoren oder eine exakte Abhängigkeitsanalyse mittels Header Space Analysis.
Für weitere Problemstellungen im Bereich der hardwarebasierten Paketklassifikation, wie dynamisch konfigurierbare Filterungsschaltkreise und schnelle, sichere Hashfunktionen für Lookups, werden Machbarkeit und Optimierungen evaluiert.
Der hybride Ansatz wird im Weiteren auf ein System mit einer SDN-Komponente statt einer FPGA-Erweiterung übertragen. Auch hiermit können signifikante Performancegewinne erreicht werden.<br>Network devices like switches, bridges, routers, and firewalls are subject to a continuous development to keep up with ever-rising requirements.
As the overhead of software network processing already became the performance-limiting factor for a variety of applications, also former software functions are shifted towards dedicated network processing hardware.
Although such application-specific circuits allow fast, parallel, and low latency processing, they require expensive and time-consuming development with minimal possibilities for adaptions.
Security can also be a major concern, as these circuits are virtually a black box for the user.
Moreover, the highly parallel processing capabilities of specialized hardware are not necessarily an advantage for all kinds of tasks in network processing, where sometimes a classical CPU is better suited.
This work introduces and evaluates concepts for building hybrid hardware-software-systems that exploit the advantages of both hardware and software approaches in order to achieve performant, flexible, and versatile network processing and packet classification systems.
The approaches are evaluated on standard software systems, extended by a programmable hardware circuit (FPGA) to provide full control and flexibility.
One key achievement of this work is the identification and mitigation of challenges inherent when a hybrid combination of multiple packet classification circuits with different characteristics is used.
We introduce approaches to reduce redundant classification effort to a minimum, like re-usage of intermediate classification results and determination of dependencies by header space analysis.
In addition, for some further challenges in hardware based packet classification like filtering circuits with dynamic updates and fast hash functions for lookups, we describe feasibility and optimizations.
At last, the hybrid approach is evaluated using a standard SDN switch instead of the FPGA accelerator to prove portability.
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Wang, Zhenzhong. "System Design and Implementation of a Fast and Accurate Bio-Inspired Spiking Neural Network." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/2227.
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Neuron models are the elementary units which determine the performance of an artificial spiking neural network (ASNN). This study introduces a new Generalized Leaky Integrate-and-Fire (GLIF) neuron model with variable leaking resistor and bias current in order to reproduce accurately the membrane voltage dynamics of a biological neuron. The accuracy of this model is ensured by adjusting its parameters to the statistical properties of the Hodgkin-Huxley model outputs; while the speed is enhanced by introducing a Generalized Exponential Moving Average method that converts the parameterized kernel functions into pre-calculated lookup tables based on an analytic solution of the dynamic equations of the GLIF model.
Spike encoding is the initial yet crucial step for any application domain of ASNN. However, current encoding methods are not suitable to process complex temporal signal. Motivated by the modulation relationship found between afferent synaptic currents in biological neurons, this study proposes a biologically plausible spike phase encoding method based on a novel spiking neuron model which could perform wavelet decomposition of the input signal, and encode the wavelet spectrum into synchronized output spike trains. The spike delays in each synchronizing period represent the spectrum amplitudes. The encoding method was tested in encoding of human voice records for speech recognition purposes. Empirical evaluations confirm that encoded spike trains constitute a good representation of the continuous wavelet transform of the original signal.
Interictal spike (IS) is a type of transient discharge commonly found in the electroencephalography (EEG) records from epilepsy patients. The detection of IS remains an essential task for 3D source localization as well as in developing algorithms for essential in seizure prediction and guided therapy. We present in this work a new IS detection technology method using the phase encoding method with customized wavelet sensor neuron and a specially designed ASNN structure. The detection results confirm the ability of such ASNN to capture IS automatically from multichannel EEG records.
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Fukuzono, Hayato. "Spatial Signal Processing on Distributed MIMO Systems." 京都大学 (Kyoto University), 2016. http://hdl.handle.net/2433/217206.
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Ratiu, Alin. "Continuous time signal processing for wake-up radios." Thesis, Lyon, INSA, 2015. http://www.theses.fr/2015ISAL0078/document.
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La consommation des systèmes de communication pour l'IoT peut être réduite grâce à un nouveau paradigme de réception radio. La technique consiste à ajouter un récepteur supplémentaire à chaque noeud IoT, appelé Wake Up Radio (WU-RX). Le rôle du WU-RX est de surveiller le canal de communication et de réveiller le récepteur principal (aussi appelé récepteur de données) lors de la réception d'une demande de communication. Une analyse des implémentations des WU-RX existants montre que les systèmes de l'état de l'art sont suffisamment sensibles par rapport aux récepteurs de données classiques mais manquent de robustesse face aux brouilleurs. Pour améliorer cette caractéristique nous proposons un étage de filtrage accordable `a fréquence intermédiaire qui nous permet de scanner toute la bande FI en cherchant le canal utilisé pour la demande de réveil. Ce filtre a été implémenté en utilisant les principes du traitement numérique de données à temps continu et consiste en un CAN suivi par un processeur numérique à temps continu. Le principe de fonctionnement du CAN est basé sur les modulateurs delta, avec une boucle de retour améliorée qui lui permet la quantification des signaux de fréquence plus élevé pour une consommation énergétique plus faible. Par conséquent, il a une plage de fonctionnement entre 10MHz et 50MHz ; pour un SNDR entre 32dB et 42dB et une consommation de 24uW. Cela se traduit par une figure de mérite entre 3fJ/conv-step et 10fJ/conv-step, une des meilleures pour la gamme de fréquences sélectionnée. Le processeur numérique est constitué d'un filtre IIR suivi par un filtre FIR. L'atténuation hors bande apportée par le filtre IIR permet de réduire le taux d'activité vu par le filtre FIR qui, par conséquent, consomme moins d'énergie. Nous avons montré, en simulation, une réduction de la puissance consommée par le filtre FIR d'un facteur entre 2 et 3. Au total, les deux filtres atteignent plus que 40dB de réjection hors bande, avec une bande passante de 2MHz qui peut être délacée sur toute la bande passante du CAN. Dans un pire cas, le système proposé (CAN et processeur numérique) consomme moins de 100uW, cependant la configuration des signaux à l'entrée peut rendre cette consommation plus faible<br>Wake-Up Receivers (WU-RX) have been recently proposed as candidates to reduce the communication power budget of wireless networks. Their role is to sense the environment and wake up the main receivers which then handle the bulk data transfer. Existing WU-RXs achieve very high sensitivities for power consumptions below 50uW but severely degrade their performance in the presence of out-of-band blockers. We attempt to tackle this problem by implementing an ultra low power, tunable, intermediate frequency filtering stage. Its specifications are derived from standard WU-RX architectures; it is shown that classic filtering techniques are either not tunable enough or demand a power consumption beyond the total WU-RX budget of 100uW. We thus turn to the use of Continuous Time Digital Signal Processing (CT-DSP) which offers the same level of programmability as standard DSP solutions while providing an excellent scalability of the power consumption with respect to the characteristics of the input signal. A CT-DSP chain can be divided into two parts: the CT-ADC and the CT-DSP itself; the specifications of these two blocks, given the context of this work, are also discussed. The CT-ADC is based on a novel, delta modulator-based architecture which achieves a very low power consumption; its maximum operation frequency was extended by the implementation of a very fast feedback loop. Moreover, the CT nature of the ADC means that it does not do any sampling in time, hence no anti-aliasing filter is required. The proposed ADC requires only 24uW to quantize signals in the [10MHz 50MHz] bandwidth for an SNR between 32dB and 42dB, resulting in a figure of merit of 3-10fJ/conv-step, among the best reported for the selected frequency range. Finally, we present the architecture of the CT-DSP which is divided into two parts: a CT-IIR and a CT-FIR. The CT-IIR is implemented by placing a standard CT-FIR in a feedback loop around the CT-ADC. If designed correctly, the feedback loop can now cancel out certain frequencies from the CT-ADC input (corresponding to those of out-of-band interferers) while boosting the power of the useful signal. The effective amplitude of the CT-ADC input is thus reduced, making it generate a smaller number of tokens, thereby reducing the power consumption of the subsequent CT-FIR by a proportional amount. The CT-DSP consumes around 100uW while achieving more than 40dB of out-of-band rejection; for a bandpass implementation, a 2MHz passband can be shifted over the entire ADC bandwidth
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Larson, P. T., and D. A. Sheaffer. "TRANSIENT REDUCTION ANALYSIS using NEURAL NETWORKS (TRANN)." International Foundation for Telemetering, 1992. http://hdl.handle.net/10150/608892.
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International Telemetering Conference Proceedings / October 26-29, 1992 / Town and Country Hotel and Convention Center, San Diego, California<br>Our telemetry department has an application for a data categorization/compression of a
high speed transient signal in a short period of time. Categorization of the signal reveals
important system performance and compression is required because of the terminal nature
of our telemetry testing. Until recently, the hardware for the system of this type did not
exist. A new exploratory device from Intel has the capability to meet these extreme
requirements. This integrated circuit is an analog neural network capable of performing 2
billion connections per second. The two main advantages of this chip over traditional
hardware are the obvious computation speed of the device and the ability to compute a
three layer feed-forward neural network classifier. The initial investigative development
work using the Intel chip has been completed. The results from this proof of concept will
show data categorization/compression performed on the neural network integrated circuit
in real time. We will propose a preliminary design for a transient measurement system
employing the Intel integrated circuit.
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Hussain, A. "Novel artificial neural network architectures and algorithms for non-linear dynamical system modelling and digital communications applications." Thesis, University of Strathclyde, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263481.
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Al-Breiki, Mohamed Ahmed Mohamed Naser. "Digital signal processing extra-tropical cyclones warning system using WiMAX." Thesis, University of Hertfordshire, 2013. http://hdl.handle.net/2299/10628.
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This research project proposed a unique solution to make use of these base stations to keep all subscribers alerted with warning of possible disaster should that be required. As the current, network does not provide a provision for such a noble approach, a new network model has been developed and simulated to interface a sensor (weather station, WeS), with WiMAX weather station. The weather station is based on DSP processor to receive a digitised sensor values, process these values, analyse them and if they fall within the alert zones, packet them according to WiMAX protocol and send them to subscribers. The developed standard bypasses any commercial network to offer free transmission to subscribers. This setup is also able to extract information on weather condition or react on uncertainty, i.e. disaster scenarios. Natural disasters, such as torrent, tornado/ hurricane, volcano eruption, earthquake, Tsunamis or landslide are increasing. Unfortunately they bring with them human tragedies, environment catastrophes, villages, cities and counties are subject to endless devastation during and after the destructive forces. Water, electricity and gas supply are most disrupted and difficult to restore in short time. However, communication is another item that can be affected adversely but WLAN with specific considerations, should be excluded from the effect. This project presents a solution, albeit minor relative to the maximum effect of the disaster, but will keep the telecommunication/communication in operation. Our novel technique, a “Clone Wireless Wide Area Network (CloneWAN)” is a clone wireless network to the wired Network. In the event of natural calamities, it gives continuity of network operation. It is based on WiMAX. The realization of CloneWAN has been formed and simulated to set the national network of the UAE at its correct form. CloneWAN model has been simulated with Opnet platform. All results revealed that the model is complete. The interface to Alerting System is discussed. Results show that the dynamic behavior of the parameters delay and Throughput of CloneWAN model is stable over various and different load scenarios. WiMAX is a de-facto standard in the current and future network requirement standards. Its main component is the Base Station which is normally stationed in the air, high enough to couple signals from other base stations. It is purpose is merely focused on networking signals for commercial purposes. The suggested hardware interface for the Weather Station is based on DSP SHARC processor. The model has been written in C and simulated under Opnet package. A number of scenarios have been set to represent different disasters worldwide. All results are listed and discussed later in the thesis.
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Wang, Lu. "Task Load Modelling for LTE Baseband Signal Processing with Artificial Neural Network Approach." Thesis, KTH, Signalbehandling, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-160947.
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This thesis gives a research on developing an automatic or guided-automatic tool to predict the hardware (HW) resource occupation, namely task load, with respect to the software (SW) application algorithm parameters in an LTE base station. For the signal processing in an LTE base station it is important to get knowledge of how many HW resources will be used when applying a SW algorithm on a specic platform. The information is valuable for one to know the system and platform better, which can facilitate a reasonable use of the available resources. The process of developing the tool is considered to be the process of building a mathematical model between HW task load and SW parameters, where the process is dened as function approximation. According to the universal approximation theorem, the problem can be solved by an intelligent method called articial neural networks (ANNs). The theorem indicates that any function can be approximated with a two-layered neural network as long as the activation function and number of hidden neurons are proper. The thesis documents a work ow on building the model with the ANN method, as well as some research on data subset selection with mathematical methods, such as Partial Correlation and Sequential Searching as a data pre-processing step for the ANN approach. In order to make the data selection method suitable for ANNs, a modication has been made on Sequential Searching method, which gives a better result. The results show that it is possible to develop such a guided-automatic tool for prediction purposes in LTE baseband signal processing under specic precision constraints. Compared to other approaches, this model tool with intelligent approach has a higher precision level and a better adaptivity, meaning that it can be used in any part of the platform even though the transmission channels are dierent.<br>Denna avhandling utvecklar ett automatiskt eller ett guidat automatiskt verktyg for att forutsaga behov av hardvaruresurser, ocksa kallat uppgiftsbelastning, med avseende pa programvarans algoritmparametrar i en LTE basstation. I signalbehandling i en LTE basstation, ar det viktigt att fa kunskap om hur mycket av hardvarans resurser som kommer att tas i bruk nar en programvara ska koras pa en viss plattform. Informationen ar vardefull for nagon att forsta systemet och plattformen battre, vilket kan mojliggora en rimlig anvandning av tillgangliga resurser. Processen att utveckla verktyget anses vara processen att bygga en matematisk modell mellan hardvarans belastning och programvaruparametrarna, dar processen denieras som approximation av en funktion. Enligt den universella approximationssatsen, kan problemet losas genom en intelligent metod som kallas articiella neuronnat (ANN). Satsen visar att en godtycklig funktion kan approximeras med ett tva-skiktS neuralt natverk sa lange aktiveringsfunktionen och antalet dolda neuroner ar korrekt. Avhandlingen dokumenterar ett arbets- ode for att bygga modellen med ANN-metoden, samt studerar matematiska metoder for val av delmangder av data, sasom Partiell korrelation och sekventiell sokning som dataforbehandlingssteg for ANN. For att gora valet av uppgifter som lampar sig for ANN har en andring gjorts i den sekventiella sokmetoden, som ger battre resultat. Resultaten visar att det ar mojligt att utveckla ett sadant guidat automatiskt verktyg for prediktionsandamal i LTE basbandssignalbehandling under specika precisions begransningar. Jamfort med andra metoder, har dessa modellverktyg med intelligent tillvagagangssatt en hogre precisionsniva och battre adaptivitet, vilket innebar att den kan anvandas i godtycklig del av plattformen aven om overforingskanalerna ar olika.
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Peng, Liangjian. "Applications of artificial neural networks to power systems network reduction and static security assessment." Thesis, University of Strathclyde, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366090.
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Hu, Xi. "Network and sensor management for mulitiple sensor emitter location system." Diss., Online access via UMI:, 2008.
Find full textAbstract:
Thesis (Ph. D.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Electrical and Computer Engineering, 2008.<br>Includes bibliographical references.
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Ali, Rozniza. "Ensemble classification and signal image processing for genus Gyrodactylus (Monogenea)." Thesis, University of Stirling, 2014. http://hdl.handle.net/1893/21734.
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This thesis presents an investigation into Gyrodactylus species recognition, making use of machine learning classification and feature selection techniques, and explores image feature extraction to demonstrate proof of concept for an envisaged rapid, consistent and secure initial identification of pathogens by field workers and non-expert users. The design of the proposed cognitively inspired framework is able to provide confident discrimination recognition from its non-pathogenic congeners, which is sought in order to assist diagnostics during periods of a suspected outbreak. Accurate identification of pathogens is a key to their control in an aquaculture context and the monogenean worm genus Gyrodactylus provides an ideal test-bed for the selected techniques. In the proposed algorithm, the concept of classification using a single model is extended to include more than one model. In classifying multiple species of Gyrodactylus, experiments using 557 specimens of nine different species, two classifiers and three feature sets were performed. To combine these models, an ensemble based majority voting approach has been adopted. Experimental results with a database of Gyrodactylus species show the superior performance of the ensemble system. Comparison with single classification approaches indicates that the proposed framework produces a marked improvement in classification performance. The second contribution of this thesis is the exploration of image processing techniques. Active Shape Model (ASM) and Complex Network methods are applied to images of the attachment hooks of several species of Gyrodactylus to classify each species according to their true species type. ASM is used to provide landmark points to segment the contour of the image, while the Complex Network model is used to extract the information from the contour of an image. The current system aims to confidently classify species, which is notifiable pathogen of Atlantic salmon, to their true class with high degree of accuracy. Finally, some concluding remarks are made along with proposal for future work.
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FERRETTI, DANIELE. "Signal Processing algorithms and Learning Systems for Infant Cry Detection." Doctoral thesis, Università Politecnica delle Marche, 2019. http://hdl.handle.net/11566/263671.
Full textAbstract:
I segnali associati al pianto dei neonati contengono preziose informazioni relative allo stato del bambino. L’estrazione di queste informazioni richiede un algoritmo di rilevazione del pianto in grado di operare in ambienti con condizioni acustiche difficili caratterizzati dalla presenza di fonti di rumore come pianti interferenti, apparecchiature mediche e persone. Il rilevamento del pianto infantile è una funzione importante sia negli ambienti residenziali che in quelli pubblici, in grado di rispondere alle differenti esigenze dei professionisti e degli utenti privati. Nella presente dissertazione viene presentata una indagine riguardo alla problematica questione della rilevazione del pianto infantile in ambienti professionali ed acusticamente rumorosi come le unità di terapia intensiva neonatale (UTIN). La ricerca descritta in questa tesi è volta allo sviluppo di approcci per la rilevazione del pianto adatti alle UTIN, nonché alla definizione di una efficace metodologia di allenamento degli algoritmi che non necessiti di dati raccolti negli specifici domini di utilizzo. Negli approcci descritti, la riduzione del rumore acustico viene eseguita su canali audio multipli con tecniche di elaborazione del segnale digitale e strategie neurali. Questi approcci utilizzano delle reti neurali profonde addestrate su un set di dati sintetico creato mediante un’adeguata procedura di simulazione di scene acustiche, senza la necessità di accedere ad una UTIN. I risultati ottenuti confermano la bontà degli approcci sviluppati superando le prestazioni ottenute dagli algoritmi dello stato dell’arte presi come riferimento, dimostrando che un set di dati sintetico può essere un utile rimpiazzo rispetto ad un set di dati della vita reale. La metodologia proposta per l’allenamento delle reti neurali consente di ridurre al minimo l’interazione con ambienti sensibili come le UTIN e permette di elaborare modifiche dei domini di utilizzo senza richiedere sessioni di acquisizione aggiuntive.<br>Newborns’ cry signals contain valuable information related to the state of the infant. Extracting this information requires a cry detection algorithm able to operate in environments with challenging acoustic conditions, since multiple noise sources, such as interferent cries, medical equipments, and persons may be present. Cry detection is an important facility in both residential and public environments, which can answer to different needs of both private and professional users. In the current dissertation the issue of cry detection in professional and acoustic noisy environments such as Neonatal Intensive care units (NICUs) will be investigate. The research, presented in this thesis, describes the developed approaches for the infant cry detection suitable for NICUs as well as an effective training methodology that does not require labeled data collected in the specific domains of use. In the described approaches the acoustic noise reduction is performed processing multiple audio channels using digital signal processing techniques as well as neural strategies. These approaches use Deep Neural Networks, whose training is conducted on a synthetic dataset created by means of a suitable Acoustic Scene Simulation procedure. The Acoustic Scene Simulation allows the creation of a synthetic dataset that, differently from a real-life dataset, can be acquired without access a NICU. The obtained detection results confirm the goodness of the developed approaches overcoming the performance achieved by the algorithms of the state of art taken as reference and proving that a synthetic dataset can be a useful replacement with respect to a real-life dataset, at least in the early design process. The proposed training methodology permits to lower the interaction with a sensitive environment such as a NICU, to the bare minimum and can be exploited to include changes to the environment as needed, without requiring additional acquisition sessions.
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Legge, Bruce A. "Code division multiple access local area network communications employing fiber optic signal processing techniques." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA274897.
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Thesis (M.S. in Electrical Engineering) Naval Postgraduate School, September 1993.<br>Thesis advisor(s): Powers, John Patrick. "September 1993." Includes bibliographical references. Also available online.
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Balupari, Ravindra. "Real-time network-based anomaly intrusion detection." Ohio : Ohio University, 2002. http://www.ohiolink.edu/etd/view.cgi?ohiou1174579398.
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Mei, Jonathan B. "Principal Network Analysis." Research Showcase @ CMU, 2018. http://repository.cmu.edu/dissertations/1175.
Full textAbstract:
Many applications collect a large number of time series, for example, temperature continuously monitored by weather stations across the US or neural activity recorded by an array of electrical probes. These data are often referred to as unstructured. A first task in their analytics is often to derive a low dimensional representation { a graph or discrete manifold { that describes the inter relations among the time series and their intrarelations across time. In general, the underlying graphs can be directed and weighted, possibly capturing the strengths of causal relations, not just the binary existence of reciprocal correlations. Furthermore, the processes generating the data may be non-linear and observed in the presence of unmodeled phenomena or unmeasured agents in a complex networked system. Finally, the networks describing the processes may themselves vary through time. In many scenarios, there may be good reasons to believe that the graphs are only able to vary as linear combinations of a set of \principal graphs" that are fundamental to the system. We would then be able to characterize each principal network individually to make sense of the ensemble and analyze the behaviors of the interacting entities. This thesis acts as a roadmap of computationally tractable approaches for learning graphs that provide structure to data. It culminates in a framework that addresses these challenges when estimating time-varying graphs from collections of time series. Analyses are carried out to justify the various models proposed along the way and to characterize their performance. Experiments are performed on synthetic and real datasets to highlight their effectiveness and to illustrate their limitations.
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Brown, Trevor Junior. "Time division multiple access/code division multiple access for the optical local access network." Thesis, Manchester Metropolitan University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.243716.
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El-Menhawy, A. El-H. "Computer Aided Design of VLSI algorithms for digital signal processing based on the Residue Number System." Thesis, University of Kent, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.376344.
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Kohout, James. "Design and performance analysis of MPI-SHARC a high-speed network service for distributed digital signal processor systems /." [Gainesville, Fla.] : University of Florida, 2001. http://etd.fcla.edu/etd/UF/anp4297/MASTER.pdf.
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Thesis (M.S.)--University of Florida, 2001.<br>Title from first page of PDF file. Document formatted into pages; contains ix, 69 p.; also contains graphics. Vita. Includes bibliographical references (p. 66-68).
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Perry, Stuart William. "Adaptive image restoration perception based neural network models and algorithms /." Connect to full text, 1998. http://hdl.handle.net/2123/389.
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Thesis (Ph. D.)--University of Sydney, 1999.<br>Title from title screen (viewed Apr. 16, 2008). Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy to the School of Electrical and Information Engineering, Faculty of Engineering. Degree awarded 1999; thesis submitted 1998. Includes bibliography. Also available in print form.
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Kalibjian, Jeff. "AN UPDATE ON NETWORK-BASED SECURITY TECHNOLOGIES APPLICABLE TO TELEMETRY POST-PROCESSING AND ANALYSIS ACTIVITIES." International Foundation for Telemetering, 2007. http://hdl.handle.net/10150/604578.
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ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada<br>Networked based technologies (i.e. TCP/IP) have come to play an important role in the evolution
of telemetry post processing services. A paramount issue when using networking to access/move
telemetry data is security. In past years papers have focused on individual security technologies
and how they could be used to secure telemetry data. This paper will review currently available
network based security technologies, update readers on enhancements, and discuss their
appropriate uses in the various phases of telemetry post-processing and analysis activities.
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Choi, Hyunjong. "Medical Image Registration Using Artificial Neural Network." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1523.
Full textAbstract:
Image registration is the transformation of different sets of images into one coordinate system in order to align and overlay multiple images. Image registration is used in many fields such as medical imaging, remote sensing, and computer vision. It is very important in medical research, where multiple images are acquired from different sensors at various points in time. This allows doctors to monitor the effects of treatments on patients in a certain region of interest over time. In this thesis, artificial neural networks with curvelet keypoints are used to estimate the parameters of registration. Simulations show that the curvelet keypoints provide more accurate results than using the Discrete Cosine Transform (DCT) coefficients and Scale Invariant Feature Transform (SIFT) keypoints on rotation and scale parameter estimation.
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Tepvorachai, Gorn. "An Evolutionary Platform for Retargetable Image and Signal Processing Applications." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1209504058.
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Kalibjian, J. R. "Telemetry Post-Processing in the Clouds: A Data Security Challenge." International Foundation for Telemetering, 2011. http://hdl.handle.net/10150/595799.
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ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada<br>As organizations move toward cloud [1] computing environments, data security challenges will begin to take precedence over network security issues. This will potentially impact telemetry post processing in a myriad of ways. After reviewing how data security tools like Enterprise Rights Management (ERM), Enterprise Key Management (EKM), Data Loss Prevention (DLP), Database Activity Monitoring (DAM), and tokenization are impacting cloud security, their effect on telemetry post-processing will also be examined. An architecture will be described detailing how these data security tools can be utilized to make telemetry post-processing environments in the cloud more robust.
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Kalibjian, Jeff. "Storage Systems and Security Challenges in Telemetry Post Processing Environments." International Foundation for Telemetering, 2008. http://hdl.handle.net/10150/606206.
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ITC/USA 2008 Conference Proceedings / The Forty-Fourth Annual International Telemetering Conference and Technical Exhibition / October 27-30, 2008 / Town and Country Resort & Convention Center, San Diego, California<br>A common concern in telemetry post-processing environments is adequate disk storage capacity to house captured and post-processed telemetry data. In today's network environments there are many storage solutions that can be deployed to address storage needs. Recent trends in storage systems reveal movement to implement security services in storage systems. After reviewing storage options appropriate for telemetry post-processing environments; the security services such systems typically offer will also be discussed and contrasted with other third party security services that might be implemented directly on top of a networked storage system.
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Flowers, Bryse Austin. "Adversarial RFML: Evading Deep Learning Enabled Signal Classification." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/91987.
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Deep learning has become an ubiquitous part of research in all fields, including wireless communications. Researchers have shown the ability to leverage deep neural networks (DNNs) that operate on raw in-phase and quadrature samples, termed Radio Frequency Machine Learning (RFML), to synthesize new waveforms, control radio resources, as well as detect and classify signals. While there are numerous advantages to RFML, this thesis answers the question "is it secure?" DNNs have been shown, in other applications such as Computer Vision (CV), to be vulnerable to what are known as adversarial evasion attacks, which consist of corrupting an underlying example with a small, intelligently crafted, perturbation that causes a DNN to misclassify the example. This thesis develops the first threat model that encompasses the unique adversarial goals and capabilities that are present in RFML. Attacks that occur with direct digital access to the RFML classifier are differentiated from physical attacks that must propagate over-the-air (OTA) and are thus subject to impairments due to the wireless channel or inaccuracies in the signal detection stage. This thesis first finds that RFML systems are vulnerable to current adversarial evasion attacks using the well known Fast Gradient Sign Method originally developed for CV applications. However, these current adversarial evasion attacks do not account for the underlying communications and therefore the adversarial advantage is limited because the signal quickly becomes unintelligible. In order to envision new threats, this thesis goes on to develop a new adversarial evasion attack that takes into account the underlying communications and wireless channel models in order to create adversarial evasion attacks with more intelligible underlying communications that generalize to OTA attacks.<br>Master of Science<br>Deep learning is beginning to permeate many commercial products and is being included in prototypes for next generation wireless communications devices. This technology can provide huge breakthroughs in autonomy; however, it is not sufficient to study the effectiveness of deep learning in an idealized laboratory environment, the real world is often harsh and/or adversarial. Therefore, it is important to know how, and when, these deep learning enabled devices will fail in the presence of bad actors before they are deployed in high risk environments, such as battlefields or connected autonomous vehicle communications. This thesis studies a small subset of the security vulnerabilities of deep learning enabled wireless communications devices by attempting to evade deep learning enabled signal classification by an eavesdropper while maintaining effective wireless communications with a cooperative receiver. The primary goal of this thesis is to define the threats to, and identify the current vulnerabilities of, deep learning enabled signal classification systems, because a system can only be secured once its vulnerabilities are known.
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Rochford, Matthew. "Visual Speech Recognition Using a 3D Convolutional Neural Network." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/2109.
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Main stream automatic speech recognition (ASR) makes use of audio data to identify spoken words, however visual speech recognition (VSR) has recently been of increased interest to researchers. VSR is used when audio data is corrupted or missing entirely and also to further enhance the accuracy of audio-based ASR systems. In this research, we present both a framework for building 3D feature cubes of lip data from videos and a 3D convolutional neural network (CNN) architecture for performing classification on a dataset of 100 spoken words, recorded in an uncontrolled envi- ronment. Our 3D-CNN architecture achieves a testing accuracy of 64%, comparable with recent works, but using an input data size that is up to 75% smaller. Overall, our research shows that 3D-CNNs can be successful in finding spatial-temporal features using unsupervised feature extraction and are a suitable choice for VSR-based systems.
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Han, Seon Yeong. "Shadowing effect on ad hoc network." Diss., Online access via UMI:, 2004. http://wwwlib.umi.com/dissertations/fullcit/1422359.
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Ruprecht, Nathan Alexander. "Implementation of Compressive Sampling for Wireless Sensor Network Applications." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1157614/.
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One of the challenges of utilizing higher frequencies in the RF spectrum, for any number of applications, is the hardware constraints of analog-to-digital converters (ADCs). Since mid-20th century, we have accepted the Nyquist-Shannon Sampling Theorem in that we need to sample a signal at twice the max frequency component in order to reconstruct it. Compressive Sampling (CS) offers a possible solution of sampling sub-Nyquist and reconstructing using convex programming techniques. There has been significant advancements in CS research and development (more notably since 2004), but still nothing to the advantage of everyday use. Not for lack of theoretical use and mathematical proof, but because of no implementation work. There has been little work on hardware in finding the realistic constraints of a working CS system used for digital signal process (DSP). Any parameters used in a system is usually assumed based on stochastic models, but not optimized towards a specific application. This thesis aims to address a minimal viable platform to implement compressive sensing if applied to a wireless sensor network (WSN), as well as address certain parameters of CS theory to be modified depending on the application.
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Chance, Christopher P. "Designing and implementing a network authentication service for providing a secure communication channel." Thesis, Kansas State University, 1986. http://hdl.handle.net/2097/9903.
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Bajzík, Jakub. "Rozpoznání zvukových událostí pomocí hlubokého učení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-401993.
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This paper deals with processing and recognition of events in audio signal. The work explores the possibility of using audio signal visualization and subsequent use of convolutional neural networks as a classifier for recognition in real use. Recognized audio events are gunshots placed in a sound background such as street noise, human voice, animal sounds, and other forms of random noise. Before the implementation, a large database with various parameters, especially reverberation and time positioning within the processed section, is created. In this work are used freely available platforms Keras and TensorFlow for work with neural networks.
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Costa, Pascale. "Contribution à l'utilisation des réseaux de neurones à couches en traitement du signal." Cachan, Ecole normale supérieure, 1996. http://www.theses.fr/1996DENS0030.
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Le travail rapporte dans ce document concerne une étude sur les réseaux de neurones (rn) en vue de leur introduction dans la résolution de certains problèmes classiquement rencontres en traitement du signal. Ce travail est restreint à l'étude des rn à couches qui ont atteint une maturité scientifique qui les rend séduisants. Nous avons pris soins de séparer les idées et principes de base de leur réalisation. Ainsi, nous avons propose une méthodologie de mise en œuvre qui peut intéresser des utilisateurs potentiels des rn. Diverses techniques d'initialisation originales ont été proposées. Cette méthodologie est appliquée a quatre problèmes génériques fréquemment rencontres en traitement du signal: la résolution d'un problème inverse, la sélection de l'ordre d'un modèle, la détermination adaptative d'un sous espace signal, et finalement un problème de localisation sous-marine. De nombreuses comparaisons sont effectuées par rapport aux traitements dits conventionnels. Nous avons ainsi montre l'intérêt des rn a couches en terme de charge de calcul, de vitesse de convergence, d'absence de modèle et d'hypothèses restrictives
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Liu, Cheng. "Advanced system design and signal processing techniques for converged high-speed optical and wireless applications." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49058.
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The ever-increasing data traffic demand drives the evolution of telecommunication networks, including the last-mile access networks as well as the long-haul backbone networks. This Ph.D. dissertation focuses on system design and signal processing techniques for next-generation converged optical-wireless access systems and the high-speed long-haul coherent optical communication systems. The convergence of high-speed millimeter-wave wireless communications and high-capacity fiber-optic backhaul networks provides tremendous potential to meet the capacity requirements of future access networks. In this work, a cloud-radio-over-fiber access architecture is proposed. The proposed architecture enables a large-scale small-cell system to be deployed in a cost-effective, power-efficient, and flexible way. Based on the proposed architecture, a multi-service reconfigurable small-cell backhaul network is developed and demonstrated experimentally. Additionally, the combination of high-speed millimeter-wave radio and fiber-optic backhaul is investigated. Several novel methods that enable high-spectral-efficient vector signal transmission in millimeter-wave radio-over-fiber systems are proposed and demonstrated through both theoretical analysis and experimental verification. For long-haul core networks, ultra-high-speed optical communication systems which can support 1Terabit/s per channel transmission will soon be required to meet the increasing capacity demand in the core networks. Grouping a number of tightly spaced optical subcarriers to form a terabit superchannel has been considered as a promising solution to increases channel capacity while minimizing the need for high-level modulation formats and high baud rate. Conventionally, precise spectral control at transmitter side is required to avoid strong inter-channel interference (ICI) at tight channel spacing. In this work, a novel receiver-side approach based on “super receiver” architecture is proposed and demonstrated. By jointly detecting and demodulating multiple channels simultaneously, the penalties associated with the limitations of generating ideal spectra can be mitigated. Several joint DSP algorithms are developed for linear ICI cancellation and joint carrier-phase recovery. Performance analysis under different system configurations is conducted to demonstrate the feasibility and robustness of the proposed joint DSP algorithms, and improved system performance is observed with both experimental and simulation data.
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Karunanidhi, Karthikeyan. "ARROS; distributed adaptive real-time network intrusion response." Ohio : Ohio University, 2006. http://www.ohiolink.edu/etd/view.cgi?ohiou1141074467.
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Björk, Tim. "Exploring Change Point Detection in Network Equipment Logs." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-85626.
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Change point detection (CPD) is the method of detecting sudden changes in timeseries, and its importance is great concerning network traffic. With increased knowledge of occurring changes in data logs due to updates in networking equipment,a deeper understanding is allowed for interactions between the updates and theoperational resource usage. In a data log that reflects the amount of network traffic, there are large variations in the time series because of reasons such as connectioncount or external changes to the system. To circumvent these unwanted variationchanges and assort the deliberate variation changes is a challenge. In this thesis, we utilize data logs retrieved from a network equipment vendor to detect changes, then compare the detected changes to when firmware/signature updates were applied, configuration changes were made, etc. with the goal to achieve a deeper understanding of any interaction between firmware/signature/configuration changes and operational resource usage. Challenges in the data quality and data processing are addressed through data manipulation to counteract anomalies and unwanted variation, as well as experimentation with parameters to achieve the most ideal settings. Results are produced through experiments to test the accuracy of the various change pointdetection methods, and for investigation of various parameter settings. Through trial and error, a satisfactory configuration is achieved and used in large scale log detection experiments. The results from the experiments conclude that additional information about how changes in variation arises is required to derive the desired understanding.
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Mackenzie, Mark. "Correlation with the hermite series using artificial neural network technology." Access electronically, 2004. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20050202.122218/index.html.
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Hümmer, Christian Verfasser], and Walter [Akademischer Betreuer] [Gutachter] [Kellermann. "A Bayesian Network Approach to Selected Problems in Speech Signal Processing / Christian Hümmer ; Gutachter: Walter Kellermann ; Betreuer: Walter Kellermann." Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2019. http://d-nb.info/1180028368/34.
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Hümmer, Christian [Verfasser], and Walter [Akademischer Betreuer] [Gutachter] Kellermann. "A Bayesian Network Approach to Selected Problems in Speech Signal Processing / Christian Hümmer ; Gutachter: Walter Kellermann ; Betreuer: Walter Kellermann." Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2019. http://d-nb.info/1180028368/34.
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