Real-time Quality of Service Controller for Differentiated Services

IP-verkkojen hyvin tunnettu haitta on, että nämä eivät pysty takaamaan tiettyä palvelunlaatua (Quality of Service) lähetetyille paketeille. Seuraavat kaksi tekniikkaa pidetään lupaavimpina palvelunlaadun tarjoamiselle: Differentiated Services (DiffServ) ja palvelunlaatureititys (QoS Routing). DiffServ on varsin uusi IETF:n määrittelemä Internetille tarkoitettu palvelunlaatumekanismi. DiffServ tarjoaa skaalattavaa palvelujen erilaistamista ilman viestintää joka hypyssä ja per-flow –tilan ohjausta. DiffServ on hyvä esimerkki hajautetusta verkkosuunnittelusta. Tämän palvelutasomekanismin tavoite on viestintäjärjestelmien suunnittelun yksinkertaistaminen. Verkkosolmu voidaan rakentaa pienestä hyvin määritellystä rakennuspalikoiden joukosta. Palvelunlaatureititys on reititysmekanismi, jolla liikennereittejä määritellään verkon käytettävissä olevien resurssien pohjalta. Tässä työssä selvitetään uusi palvelunlaatureititystapa, jota kutsutaan yksinkertaiseksi monitiereititykseksi (Simple Multipath Routing). Tämän työn tarkoitus on suunnitella palvelunlaatuohjain DiffServille. Tässä työssä ehdotettu palvelunlaatuohjain on pyrkimys yhdistää DiffServ ja palvelunlaatureititysmekanismeja. Työn kokeellinen osuus keskittyy erityisesti palvelunlaatureititysalgoritmeihin.

IPSec in 3G packet switched domain

Diplomityö käsittelee IPSec-protokollan (IP Security Protocol) implementointia UMTS:n (Universal Mobile Telecommunications System) pakettikytkentäiseen verkkoon. Runkoverkkoa käytetään mobiilikäyttäjän datan siirtämiseen sekä verkkoelementtien väliseen ohjausinformaation välitykseen. Koska UMTS:n runkoverkot ovat IP-pakettikytkentäisiä verkkoja, IPSec-protokollaa voidaan käyttää lähetettyjen IP-datasähkeiden suojaamiseen. IPSec- ja IKE-protokollien (Internet Key Exchange) käyttö on koettu monimutkaiseksi kiinteissä verkoissa. Tämän saman ongelman edessä tulevat olemaan myös operaattorit, kun he alkavat rakentaa UMTS-verkkojaan. On kuitenkin muistettava se, että tulevaisuudessa lähes kaikki data mukaanlukien ääni ja video on tarkoitus siirtää IP-protokollan avulla. IP-teknologiaan perustuva tiedonsiirron kasvu lisää IPSec-protokollan merkitystä ei ainoastaan runkoverkossa mutta myös radioliityntäverkoissa sekä SS7-merkinantoverkoissa (Signaling System No. 7). Diplomityö on tehty osaksi diplomi-insinöörin tutkintoa Lappeenrannan teknillisessä yliopistossa. Työ on tehty Nokia Networksin palveluksessa Helsingissä, vuosien 2002 ja 2003 välisenä aikana.

Investigation of interface between Multimedia IP Network and Call Processing Server in 3G IP Telephony Core Network

The thesis presents an overview of third generation of IP telephony. The architecture of 3G IP Telephony and its components are described. The main goal of the thesis is to investigate the interface between the Call Processing Server and Multimedia IP Networks. The interface functionality, proposed protocol stack and a general description are presented in the thesis. To provide useful services, 3G IP Telephony requires a set of control protocols for connection establishment, capabilities exchange and conference control. The Session Initiation Protocol (SIP) and the H.323 are two protocols that meet these needs. In the thesis these two protocols are investigated and compared in terms of Complexity, Extensibility, Scalability, Services, Resource Utilization and Management.

Statistical mechanics of multi-edge networks

Statistical properties of binary complex networks are well understood and recently many attempts have been made to extend this knowledge to weighted ones. There are, however, subtle yet important considerations to be made regarding the nature of the weights used in this generalization. Weights can be either continuous or discrete magnitudes, and in the latter case, they can additionally have undistinguishable or distinguishable nature. This fact has not been addressed in the literature insofar and has deep implications on the network statistics. In this work we face this problem introducing multiedge networks as graphs where multiple (distinguishable) connections between nodes are considered. We develop a statistical mechanics framework where it is possible to get information about the most relevant observables given a large spectrum of linear and nonlinear constraints including those depending both on the number of multiedges per link and their binary projection. The latter case is particularly interesting as we show that binary projections can be understood from multiedge processes. The implications of these results are important as many real-agent-based problems mapped onto graphs require this treatment for a proper characterization of their collective behavior.

Towards open access networks – prototyping with the Lappeenranta model

The provision of Internet access to large numbers has traditionally been under the control of operators, who have built closed access networks for connecting customers. As the access network (i.e. the last mile to the customer) is generally the most expensive part of the network because of the vast amount of cable required, many operators have been reluctant to build access networks in rural areas. There are problems also in urban areas, as incumbent operators may use various tactics to make it difficult for competitors to enter the market. Open access networking, where the goal is to connect multiple operators and other types of service providers to a shared network, changes the way in which networks are used. This change in network structure dismantles vertical integration in service provision and enables true competition as no service provider can prevent others fromcompeting in the open access network. This thesis describes the development from traditional closed access networks towards open access networking and analyses different types of open access solution. The thesis introduces a new open access network approach (The Lappeenranta Model) in greater detail. The Lappeenranta Model is compared to other types of open access networks. The thesis shows that end users and service providers see local open access and services as beneficial. In addition, the thesis discusses open access networking in a multidisciplinary fashion, focusing on the real-world challenges of open access networks.

Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.

A cyber security architecture for military networks using a cognitive network approach

Cyber security is one of the main topics that are discussed around the world today. The threat is real, and it is unlikely to diminish. People, business, governments, and even armed forces are networked in a way or another. Thus, the cyber threat is also facing military networking. On the other hand, the concept of Network Centric Warfare sets high requirements for military tactical data communications and security. A challenging networking environment and cyber threats force us to consider new approaches to build security on the military communication systems. The purpose of this thesis is to develop a cyber security architecture for military networks, and to evaluate the designed architecture. The architecture is described as a technical functionality. As a new approach, the thesis introduces Cognitive Networks (CN) which are a theoretical concept to build more intelligent, dynamic and even secure communication networks. The cognitive networks are capable of observe the networking environment, make decisions for optimal performance and adapt its system parameter according to the decisions. As a result, the thesis presents a five-layer cyber security architecture that consists of security elements controlled by a cognitive process. The proposed architecture includes the infrastructure, services and application layers that are managed and controlled by the cognitive and management layers. The architecture defines the tasks of the security elements at a functional level without introducing any new protocols or algorithms. For evaluating two separated method were used. The first method is based on the SABSA framework that uses a layered approach to analyze overall security of an organization. The second method was a scenario based method in which a risk severity level is calculated. The evaluation results show that the proposed architecture fulfills the security requirements at least at a high level. However, the evaluation of the proposed architecture proved to be very challenging. Thus, the evaluation results must be considered very critically. The thesis proves the cognitive networks are a promising approach, and they provide lots of benefits when designing a cyber security architecture for the tactical military networks. However, many implementation problems exist, and several details must be considered and studied during the future work.

Forecasting of wind speeds and directions with artificial neural networks

In this master’s thesis, wind speeds and directions were modeled with the aim of developing suitable models for hourly, daily, weekly and monthly forecasting. Artificial Neural Networks implemented in MATLAB software were used to perform the forecasts. Three main types of artificial neural network were built, namely: Feed forward neural networks, Jordan Elman neural networks and Cascade forward neural networks. Four sub models of each of these neural networks were also built, corresponding to the four forecast horizons, for both wind speeds and directions. A single neural network topology was used for each of the forecast horizons, regardless of the model type. All the models were then trained with real data of wind speeds and directions collected over a period of two years in the municipal region of Puumala in Finland. Only 70% of the data was used for training, validation and testing of the models, while the second last 15% of the data was presented to the trained models for verification. The model outputs were then compared to the last 15% of the original data, by measuring the mean square errors and sum square errors between them. Based on the results, the feed forward networks returned the lowest generalization errors for hourly, weekly and monthly forecasts of wind speeds; Jordan Elman networks returned the lowest errors when used for forecasting of daily wind speeds. Cascade forward networks gave the lowest errors when used for forecasting daily, weekly and monthly wind directions; Jordan Elman networks returned the lowest errors when used for hourly forecasting. The errors were relatively low during training of the models, but shot up upon simulation with new inputs. In addition, a combination of hyperbolic tangent transfer functions for both hidden and output layers returned better results compared to other combinations of transfer functions. In general, wind speeds were more predictable as compared to wind directions, opening up opportunities for further research into building better models for wind direction forecasting.

Aligning Regional Repository Networks

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

An in-depth analysis of real and perceived barriers to speech and language program participation for children with speech and language delays : a question of barriers to service?

The current study investigated the effects that barriers (both real and perceived) had on participation and completion of speech and language programs for preschool children with communication delays. I compared 36 families of preschool children with an identified communication delay that have completed services (completers) to 13 families that have not completed services (non-completers) prescribed by Speech and Language professionals. Data findings reported were drawn from an interview with the mother, a speech and language assessment of the child, and an extensive package of measures completed by the mother. Children ranged in age from 32 to 71 mos. These data were collected as part of a project funded by the Canadian Language and Literacy Research Networks of Centres of Excellence. Findings suggest that completers and non-completers shared commonalities in a number of parenting characteristics but differed significantly in two areas. Mothers in the noncompleting group were more permissive and had lower maternal education than mothers in the completing families. From a systemic standpoint, families also differed in the number of perceived barriers to treatment experienced during their time with Speech Services Niagara. Mothers in the non-completing group experienced more perceived barriers to treatment than completing mothers. Specifically, these mothers perceived more stressors and obstacles that competed with treatment, perceived more treatment demands and they perceived the relevance of treatment as less important than the completing group. Despite this, the findings suggest that non-completing families were 100% satisfied with services. Contrary to predictions, there were no significant differences in child characterisfics and economic characteristics between completers and non-completers. The findings in this study are considered exploratory and tentative due to the small sample size.

Modeling High-Dimensional Audio Sequences with Recurrent Neural Networks

Cette thèse étudie des modèles de séquences de haute dimension basés sur des réseaux de neurones récurrents (RNN) et leur application à la musique et à la parole. Bien qu'en principe les RNN puissent représenter les dépendances à long terme et la dynamique temporelle complexe propres aux séquences d'intérêt comme la vidéo, l'audio et la langue naturelle, ceux-ci n'ont pas été utilisés à leur plein potentiel depuis leur introduction par Rumelhart et al. (1986a) en raison de la difficulté de les entraîner efficacement par descente de gradient. Récemment, l'application fructueuse de l'optimisation Hessian-free et d'autres techniques d'entraînement avancées ont entraîné la recrudescence de leur utilisation dans plusieurs systèmes de l'état de l'art. Le travail de cette thèse prend part à ce développement. L'idée centrale consiste à exploiter la flexibilité des RNN pour apprendre une description probabiliste de séquences de symboles, c'est-à-dire une information de haut niveau associée aux signaux observés, qui en retour pourra servir d'à priori pour améliorer la précision de la recherche d'information. Par exemple, en modélisant l'évolution de groupes de notes dans la musique polyphonique, d'accords dans une progression harmonique, de phonèmes dans un énoncé oral ou encore de sources individuelles dans un mélange audio, nous pouvons améliorer significativement les méthodes de transcription polyphonique, de reconnaissance d'accords, de reconnaissance de la parole et de séparation de sources audio respectivement. L'application pratique de nos modèles à ces tâches est détaillée dans les quatre derniers articles présentés dans cette thèse. Dans le premier article, nous remplaçons la couche de sortie d'un RNN par des machines de Boltzmann restreintes conditionnelles pour décrire des distributions de sortie multimodales beaucoup plus riches. Dans le deuxième article, nous évaluons et proposons des méthodes avancées pour entraîner les RNN. Dans les quatre derniers articles, nous examinons différentes façons de combiner nos modèles symboliques à des réseaux profonds et à la factorisation matricielle non-négative, notamment par des produits d'experts, des architectures entrée/sortie et des cadres génératifs généralisant les modèles de Markov cachés. Nous proposons et analysons également des méthodes d'inférence efficaces pour ces modèles, telles la recherche vorace chronologique, la recherche en faisceau à haute dimension, la recherche en faisceau élagué et la descente de gradient. Finalement, nous abordons les questions de l'étiquette biaisée, du maître imposant, du lissage temporel, de la régularisation et du pré-entraînement.

Identification of spectral lines of elements using artificial neural networks

Artificial neural networks (ANNs) are relatively new computational tools that have found extensive utilization in solving many complex real-world problems. This paper describes how an ANN can be used to identify the spectral lines of elements. The spectral lines of Cadmium (Cd), Calcium (Ca), Iron (Fe), Lithium (Li), Mercury (Hg), Potassium (K) and Strontium (Sr) in the visible range are chosen for the investigation. One of the unique features of this technique is that it uses the whole spectrum in the visible range instead of individual spectral lines. The spectrum of a sample taken with a spectrometer contains both original peaks and spurious peaks. It is a tedious task to identify these peaks to determine the elements present in the sample. ANNs capability of retrieving original data from noisy spectrum is also explored in this paper. The importance of the need of sufficient data for training ANNs to get accurate results is also emphasized. Two networks are examined: one trained in all spectral lines and other with the persistent lines only. The network trained in all spectral lines is found to be superior in analyzing the spectrum even in a noisy environment.

Wireless Sensor Networks for Paddy Field Crop Monitoring Application in Kuttanad

The evolution of wireless sensor network technology has enabled us to develop advanced systems for real time monitoring. In the present scenario wireless sensor networks are increasingly being used for precision agriculture. The advantages of using wireless sensor networks in agriculture are distributed data collection and monitoring, monitor and control of climate, irrigation and nutrient supply. Hence decreasing the cost of production and increasing the efficiency of production.This paper describes the application of wireless sensor network for crop monitoring in the paddy fields of kuttand, a region of Kerala, the southern state of India.

Energy Optimized Secure Routing Protocol for Wireless Sensor Networks

The evolution of wireless sensor network technology has enabled us to develop advanced systems for real time monitoring. In the present scenario wireless sensor networks are increasingly being used for precision agriculture. The advantages of using wireless sensor networks in agriculture are distributed data collection and monitoring, monitor and control of climate, irrigation and nutrient supply. Hence decreasing the cost of production and increasing the efficiency of production. This paper describes the security issues related to wireless sensor networks and suggests some techniques for achieving system security. This paper also discusses a protocol that can be adopted for increasing the security of the transmitted data

The World of Sensor Networks

Page 1. The World of Sensor Networks G Santhosh Kumar, CUSAT Kumar, CUSAT Page 2. Are you as quick as Messi or Bale? WSN adidas innovation (source: http://www.wsnblog. com/) Page 3. Fukushima nuclear disaster • Fukushima Rescue Workers Facing Depression and Death • How to measure the levels of radiation of the affected zones without compromising the life of the workers? • Radiation measurements in real-time Page 4. Fukushima nuclear disaster Page 5. Fukushima nuclear disaster Page 6. Goals .