Network data rates are growing rapidly. The data rates provided to the customers by their network providers vary from Mbps to Gbps. However, rarely do users get the promised peak throughput. In cellular networks, network conditions change based on obstacles, weather conditions between the client and the base stations, and even the movement of objects and people. As a result of the changes in the radio link, the data transfer rate can change rapidly, hence devices needs to adjust their communications based on the currently available data rate. The Transmission Control Protocol (TCP) is widely used for reliable data transfer over networks. However, TCP was initially designed when link data rates were much lower than the link data rates commonly available today. As a result, TCP does not perform well at high data rates, despite some of the changes that have been made to the protocol to support high data rate links. Moreover, TCP has problems adapting to large changes in link bandwidth (not caused by congestion), resulting in a lower average throughput than the link could potentially deliver. This thesis evaluates two different versions of the TCP protocol (e.g., TCP Reno and Cubic TCP) and proposes a network coding scheme to enhance users’ experience when communicating over unstable radio links. The performance of the two TCP protocols and Random Linear Network Coding (RLNC) scheme were measured in an emulated network environment. The results of these measurements were analyzed and evaluated. The analysis shows that RLNC can provide a higher throughput than TCP over a network with high packet loss. However, RLNC is a UDP based solution and does not implement congestion control algorithms or reliability. A new solution is proposed that increases reliability and implements network adaptation in RLNC solutions. The results obtained in this thesis can be used to develop a new protocol to increases the quality of users’ experience in high loss networks. / Datahastigheter över nätverk ökar drastiskt. Datahastigheterna som ges tillgängliga till användare av deras respektive dataleverantör kan variera från Mbit/s till Gbit/s. Det är dock inte ofta användare får ut vad som har lovats. I mobila nätverk kan nätverkets tillstånd ändras baserat på hinder, väderleksförhållanden mellan en klient och basstationerna, till och med beroende på förflyttning av objekt eller människor. På grund av detta så behöver användares utrustning anpassa dess kommunikation, baserat på den för närvarande tillgängliga datahastigheten. Transmission Control Protocol (TCP) används i stor utsträckning vid behovet av tillförlitlig dataöverföring över nätverk. Däremot så designades TCP när länkdatahastigheterna var mycket lägre än vad som är vanligen tillgängligt idag. På grund av detta så presterar inte TCP över höga datahastigheter, trots ändringar som har gjorts i protokollet för att stödja höghastighets datalänkar. Utöver det så har TCP svårt att anpassa sig efter stora ändringar i länkens bandbredd (som inte är orsakat av stockning), som resulterar i en mindre genomsnitts-dataström än vad länken potentiellt hade kunnat ge. Detta examensarbete utvärderar två olika versioner av TCP (e.g., TCP Reno och Cubic TCP) och föreslår ett sätt att använda network coding för att öka användares upplevelse vid dataöverföring över instabila radio länkar. Prestationerna av de två TCP versionerna och Random Linear Network Coding (RLNC) metoden har blivit mätt i en emulerad nätverksmiljö. Resultaten från dessa mätningar blev analyserade och utvärderade. Analysen visar att RLNC kan ge en högre dataström än TCP över ett nätverk med hög risk för paketförluster. Däremot så är RLNC en User Datagram Protocol (UDP) baserad lösning, och därav inte implementerar trängselkontrolls-algoritmer eller tillförlitlighet. Ett förslag till en ny lösning som ökar tillförlitlighet och implementerar nätverksanpassning till RLNC lösningar har presenterats. Resultaten från detta examensarbete kan användas till att utveckla nya protokoll för att öka kvalitén av användares upplevelse i nätverk med risk för hög paketförlust.

Nous étudions des modèles mathématiques issus de la théorie des files d’attente pour évaluer et optimiser les performances des mécanismes de partage de ressources entre flots dans les réseaux. Dans une première partie, nous proposons des approximations simples et explicites des principales métriques de performance des flots élastiques dans les réseaux à partage de bande passante opérant sous le mode ”équité équilibré”. Nous étudions ensuite le partage de bande passante entre flux élastiques et flux de streaming en supposant que le nombre de ces derniers est limité par un mécanisme de contrôle d’admission et proposons des approximations de performance basées sur une hypothèse de quasi stationnarité. Les résultats de simulation montrent le bon niveau de précision des approximations proposées.Dans une deuxième partie, nous étudions le compromis entre délai et énergie dans les réseaux à partage de bande passante dont les noeuds peuvent réguler leur vitesse en fonction de la charge du système. En supposant que le réseau est initialement dans un état de congestion, on s’intéresse à la politique optimale d’allocation de débit permettant de le vider à coût minimal. L’analyse de la politique stochastique optimale via la théorie des processus de décision markoviens étant extrêmement difficile, nous proposons de l’approximer en utilisant un modèle fluide déterministe qui peut être résolu grâce à des techniques de contrôle optimal. Pour le cas d’un seul lien partagé par plusieurs classes de trafic, on montre que la politique optimale correspond à la règle cμ et on propose une expression explicite de la vitesse optimale. Enfin, dans une troisième partie, on s’intéresse aux plateformes de Cloud Computing dans le cadre du modèle SaaS. En supposant un partage équitable des ressources physiques entre machines virtuelles s’exécutant de manière concurrente, nous proposons des modèles de file d’attente simples pour prédire les temps de réponse des applications. Les modèles proposés prennent explicitement en compte le comportement des différentes classes d’application (tâches interactives, de calcul ou permanentes). Les expérimentations menées sur une plateforme réelle montrent que les modèles mathématiques obtenus permettent de prédire les temps de réponse avec une bonne précision. / We study queueing-theoretic models for the performance evaluation and optimization of bandwidth-sharing networks. We first propose simple and explicit approximations for the main performance metrics of elastic flows in bandwidth-sharing networks operating under balanced fairness. Assuming that an admission control mechanism is used to limit the number of simultaneous streaming flows, we then study the competition for bandwidth between elastic and streaming flows and propose performance approximations based on a quasi-stationary assumption. Simulation results show the good accuracy of the proposed approximations. We then investigate the energy-delay tradeoff in bandwidth-sharing networks in which nodes can regulate their speed according to the load of the system. Assuming that the network is initially congested, we investigate the rate allocation to the classes that drains out the network with minimum total energy and delay cost. We formulate this optimal resource allocation problem as a Markov decision process which proves tobe both analytically and computationally challenging. We thus propose to solve this stochastic problem using a deterministic fluid approximation. For a single link sharedby an arbitrary number of classes, we show that the optimal-fluid solution follows thewell-known cμ rule and give an explicit expression for the optimal speed. Finally, we consider cloud computing platforms under the SaaS model. Assuming a fair share of the capacity of physical resources between virtual machines executed concurrently, we propose simple queueing models for predicting response times of applications.The proposed models explicitly take into account the different behaviors of the different classes of applications (interactive, CPU-intensive or permanent applications). Experiments on a real virtualized platform show that the mathematical models allow to predict response times accurately

Automatic tracking and video of surveillance on a farm could help to support farm management. In this project, an automated detection system is used to detect sows in surveillance videos. This system is based upon deep learning and computer vision methods. In order to minimize disk storage and to meet the network requirements necessary to achieve the real-performance, tracking in compressed video streams is essential. The proposed system uses a Discriminative Correlation Filter (DCF) as a classifier to detect targets. The tracking model is updated by training the classifier with online learning methods. Compression technology encodes the video data, thus reducing both the bit rates at which video signals are transmitted and helping the video transmission better adapt to the limited network bandwidth. However, compression may reduce the image quality of the videos the precision of our tracking may decrease. Hence, we conducted a performance evaluation of existing visual tracking algorithms on video sequences with quality degradation due to various compression parameters (encoders, target bitrate, rate control model, and Group of Pictures (GOP) size). The ultimate goal of video compression is to realize a tracking system with equal performance, but requiring fewer network resources. The proposed tracking algorithm successfully tracks each sow in consecutive frames in most cases. The performance of our tracker was benchmarked against two state-of-art tracking algorithms: Siamese Fully-Convolutional (FC) and Efficient Convolution Operators (ECO). The performance evaluation result shows our proposed tracker has similar performance to both Siamese FC and ECO. In comparison with the original tracker, the proposed tracker achieved similar tracking performance, while requiring much less storage and generating a lower bitrate when the video was compressed with appropriate parameters. However, the system is far slower than needed for real-time tracking due to high computational complexity; therefore, more optimal methods to update the tracking model will be needed to achieve real-time tracking. / Automatisk spårning av övervakning i gårdens område kan bidra till att stödja jordbruket management. I detta projekt till ett automatiserat system för upptäckt upptäcka suggor från övervaknings filmer kommer att utformas med djupa lärande och datorseende metoder. Av hänsyn till Diskhantering och tid och hastighet Krav över nätverket för att uppnå realtidsscenarier i framtiden är spårning i komprimerade videoströmmar är avgörande. Det föreslagna systemet i detta projekt skulle använda en DCF (diskriminerande korrelationsfilter) som en klassificerare att upptäcka mål. Spårningen modell kommer att uppdateras genom att utbilda klassificeraren med online inlärningsmetoder. Compression teknik kodar videodata och minskar bithastigheter där videosignaler sänds kan hjälpa videoöverföring anpassar bättre i begränsad nätverk. det kan dock reducera bildkvaliteten på videoklipp och leder exakt hastighet av vårt spårningssystem för att minska. Därför undersöker vi utvärderingen av prestanda av befintlig visuella spårningsalgoritmer på videosekvenser Det ultimata målet med videokomprimering är att bidra till att bygga ett spårningssystem med samma prestanda men kräver färre nätverksresurser. Den föreslagna spårning algoritm spår framgångsrikt varje sugga i konsekutiva ramar i de flesta fall prestanda vår tracker var jämföras med två state-of-art spårning algoritmer:. Siamese Fully-Convolutional (FC) och Efficient Convolution Operators (ECO) utvärdering av prestanda Resultatet visar vår föreslagna tracker blir liknande prestanda med Siamese FC och ECO. I jämförelse med den ursprungliga spårningen uppnådde den föreslagna spårningen liknande spårningseffektivitet, samtidigt som det krävde mycket mindre lagring och alstra en lägre bitrate när videon komprimerades med lämpliga parametrar. Systemet är mycket långsammare än det behövs för spårning i realtid på grund av hög beräkningskomplexitet; därför behövs mer optimala metoder för att uppdatera spårningsmodellen för att uppnå realtidsspårning.

The data traffic has been growing drastic over the past few years due to digitization and new technologies that are introduced to the market, such as autonomous cars. In order to meet this demand, the MIMO-OFDM system is used in the fifth generation wireless network, 5G. Designing the optimal wireless network is currently the main research within the area of telecommunication. In order to achieve such a system, multiple factors has to be taken into account, such as the suppression of interference from other users. A traditional method called linear minimum mean square error filter is currently used to suppress the interferences. To derive such a filter, a selection of parameters has to be estimated. One of these parameters is the ideal interference plus noise covariance matrix. By gathering prior knowledge of the underlying structure of the interference channel matrices in terms of the number of interferers and their corresponding bandwidths, the estimation of the ideal covariance matrix could be facilitated. As for this thesis, machine learning algorithms were used to extract these prior knowledge. More specifically, a two or three hidden layer feedforward neural network and a support vector machine with a linear kernel was used. The empirical findings implies promising results with accuracies above 95% for each model. / Under de senaste åren har dataanvändningen ökat drastiskt på grund av digitaliseringen och allteftersom nya teknologier introduceras på marknaden, exempelvis självkörande bilar. För att bemöta denna efterfrågan används ett s.k. MIMO-OFDM system i den femte generationens trådlösa nätverk, 5G. Att designa det optimala trådlösa nätverket är för närvarande huvudforskningen inom telekommunikation och för att uppnå ett sådant system måste flera faktorer beaktas, bland annat störningar från andra användare. En traditionell metod som används för att dämpa störningarna kallas för linjära minsta medelkvadratfelsfilter. För att hitta ett sådant filter måste flera olika parametrar estimeras, en av dessa är den ideala störning samt bruskovariansmatrisen. Genom att ta reda på den underliggande strukturen i störningsmatriserna i termer av antal störningar samt deras motsvarande bandbredd, är något som underlättar uppskattningen av den ideala kovariansmatrisen. I följande avhandling har olika maskininlärningsalgoritmer applicerats för att extrahera dessa informationer. Mer specifikt, ett neuralt nätverk med två eller tre gömda lager samt stödvektormaskin med en linjär kärna har använts. De slutliga resultaten är lovande med en noggrannhet på minst 95% för respektive modell.

Fiber based ultrashort pulse laser sources are desirable for many applications; however generating high peak powers in fiber lasers is primarily limited by the onset of nonlinear effects such as self-phase modulation, stimulated Raman scattering, and self-focusing. Increasing the fiber core diameter mitigates the onset of these nonlinear effects, but also allows unwanted higher-order transverse spatial modes to propagate. Both large core diameters and single-mode propagation can be simultaneously attained using photonic crystal fibers. Thulium-doped fiber lasers are attractive for high peak power ultrashort pulse systems. They offer a broad gain bandwidth, capable of amplifying sub-100 femtosecond pulses. The longer center wavelength at 2 ?m theoretically enables higher peak powers relative to 1 [micro]m systems since nonlinear effects inversely scale with wavelength. Also, the 2 [micro]m emission is desirable to support applications reaching further into the mid-IR. This work evaluates the performance of a novel all-fiber pump combiner that incorporates a thulium-doped photonic crystal fiber. This fully integrated amplifier is characterized and possesses a large gain bandwidth, essentially single-mode propagation, and high degree of polarization. This innovative all-fiber, thulium-doped photonic crystal fiber amplifier has great potential for enabling high peak powers in 2 [micro]m fiber systems; however the current optical-to-optical efficiency is low relative to similar free-space amplifiers. Further development and device optimization will lead to higher efficiencies and improved performance.

В работе представлены результатыисследования метода, который позволяет увеличить полосу частот электрически малой антенны с помощью “нефостеровской цепи”на основе операционного усилителя. Были получены графики, которые позволяют оценить входное реактивное сопротивление и полосу частот электрически малой антенны с представленным методом расширения полосы частот. / The paper presents the results of a study of the method, which makes it possible to increase the frequency band of an electrically small antenna by means of a "Non-foster circuit"with operational amplifier. The graphs were obtain, which allow estimating the input reactance and the bandwidth of an electrically small antenna with the method of bandwidth extension represented.

В работе представлены результатыисследования метода, который позволяет увеличить полосу частот электрически малой антенны с помощью “нефостеровской цепи”.Также были представлены классические методы расширения полосы частот антенны с помощью индуктивности и колебательного контура, чтобы сравнить их эффективность с исследуемым методом. Были получены графики, которые позволяют оценить входное реактивное сопротивление и полосу частот электрически малой антенны со всеми представленными методами расширения полосы частот. / The paper presents the results of a study of the method, which makes it possible to increase the frequency band of an electrically small antenna by means of a "Non-foster circuit". Also, classical methods for extending the frequency band of the antenna with the help of inductance and a vibrational circuit were presented to compare their effectiveness with the method being studied.

The main objectives of this research are to investigate and design low profile antennas for mobile handsets applications using the balanced concept. These antennas are considered to cover a wide range of wireless standards such as: DCS (1710¿1880 MHz), PCS (1850¿1990 MHz), UMTS (1920¿2170 MHz), WLAN (2400¿2500 MHz and 5000 ¿ 5800 MHz) and UWB frequency bands. Various antennas are implemented based on built-in planar dipole with a folded arm structure. The performance of several designed antennas in terms of input return loss, radiation patterns, radiation efficiency and power gain are presented and several remarkable results are obtained. The measurements confirm the theoretical design concept and show reasonable agreement with computations. The stability performance of the proposed antenna is also evaluated by analysing the current distribution on the mobile phone ground plane. The specific absorption rate (SAR) performance of the antenna is also studied experimentally by measuring antenna near field exposure. The measurement results are correlated with the calculated ones. A new dual-band balanced antenna using coplanar waveguide structure is also proposed, discussed and tested; this is intended to eliminate the balanced feed network. The predicted and measured results show good agreement, confirming good impedance bandwidth characteristics and excellent dual-band performance. In addition, a hybrid method to model the human body interaction with a dual band balanced antenna structure covering the 2.4 GHz and 5.2 GHz bands is presented. Results for several test cases of antenna locations on the body are presented and discussed. The near and far fields were incorporated to provide a full understanding of the impact on human tissue. The cumulative distribution function of the radiation efficiency and absorbed power are also evaluated. / UK Engineering and Physical Sciences Research Council (EPSRC)

Multi-Protocol Label Switching (MPLS) makes use of traffic engineering (TE) techniques and a variety of protocols to establish pre-determined highly efficient routes in Wide Area Network (WAN). Unlike IP networks in which routing decision has to be made through header analysis on a hop-by-hop basis, MPLS makes use of a short bit sequence that indicates the forwarding equivalence class (FEC) of a packet and utilises a predefined routing table to handle packets of a specific FEC type. Thus header analysis of packets is not required, resulting in lower latency. In addition, packets of similar characteristics can be routed in a consistent manner. For example, packets carrying real-time information can be routed to low latency paths across the networks. Thus the key success to MPLS is to efficiently control and distribute the bandwidth available between applications across the networks. A lot of research effort on bandwidth management in MPLS networks has already been devoted in the past. However, with the imminent roll out of 5G, MPLS is seen as a key technology for mobile backhaul. To cope with the 5G demands of rich, context aware and multimedia-based user applications, more efficient bandwidth management solutions need to be derived. This thesis focuses on the design of bandwidth management algorithms, more specifically QoS scheduling, in MPLS network for 5G mobile backhaul. The aim is to ensure the reliability and the speed of packet transfer across the network. As 5G is expected to greatly improve the user experience with innovative and high quality services, users’ perceived quality of service (QoS) needs to be taken into account when deriving such bandwidth management solutions. QoS expectation from users are often subjective and vague. Thus this thesis proposes the use of fuzzy logic based solution to provide service aware and user-centric bandwidth management in order to satisfy requirements imposed by the network and users. Unfortunately, the disadvantage of fuzzy logic is scalability since dependable fuzzy rules and membership functions increase when the complexity of being modelled increases. To resolve this issue, this thesis proposes the use of neuro-fuzzy to solicit interpretable IF-THEN rules.The algorithms are implemented and tested through NS2 and Matlab simulations. The performance of the algorithms are evaluated and compared with other conventional algorithms in terms of average throughput, delay, reliability, cost, packet loss ratio, and utilization rate. Simulation results show that the neuro-fuzzy based algorithm perform better than fuzzy and other conventional packet scheduling algorithms using IP and IP over MPLS technologies. / Tertiary Education Trust Fund (TETFUND)

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