Analisa Pengiriman Citra Terkompresi SPIHT dengan Teknik Spread Spectrum Direct Sequence (DS-SS)

In this information technology era, the demand for multimedia communications needs continue to increase. Website on the internet made it interesting by including a visualization of image or video that can be played. Image (image) as one of the multimedia component hold a very important role as a form of visual information. This image file has a capacity large enough, and to access it takes a long time, so it takes a data compression technique, with the aim to reduce the redundancy of the data contained in the image file, so that the image files can be stored or transmitted efficiently. In this case the image files will be compressed using the algorithm SPIHT (Set Partitioning In Hierarchical Trees), SPIHT image compression algorithm that is capable of achieving high compression ratios. By way of coding wavelet transforms coefficients gradually. Due to the compressed data stream is very vulnerable to channel interference. In the process of transmission technique was used Direct Sequence Spread Spectrum (DS-SS). Results obtained after simulation, reconstruction of the images measured by an objective assessment parameters based on the value Peak Signal to Noise Ratio. Objective assessment of efficient reconstruction image quality with the rate 2bpp (bit/pixel) 46,834 dB PSNR are values obtained Keywords: SPIHT, wavelet, DSSS, rate, PSN

Implementasi Algoritma Konsensus untuk Melokalisir Keberadaan Api Kebakaran pada Jaringan Sensor Nirkabel

The development and use of Wireless Sensor Network (WSN) is very fast especially for human life. WSN applications are generally used for monitoring, tracking and controlling in some applications, WSN uses many sensors are deployed randomly in a wide area.But in fact range of WSN is extremely close and have limited energy so necessary to an agreement between neighboring sensors so that information can be up to simultaneously. At this final project conducted the analysis using Distributed Consensus Algorithm called the Average Network Consensus (ANC) on optimizing the data obtained by each sensor. In addition, it also should localize the presence of the source signal by a nearby sensor then sends the data to sink The simulation results show that the power optimization algorithm is computed by the ANC with the input data Castalia higher value than with ideal simulation (with MATLAB) and if the number of sensors more and more the value of the MSE is go down.. Keywords:. Localize, Wireless Sensor Networks, Consensus Algorith

Definitive Consensus for Distributed Data Inference

Inference from data is of key importance in many applications of informatics. The current trend in performing such a task of inference from data is to utilise machine learning algorithms. Moreover, in many applications that it is either required or is preferable to infer from the data in a distributed manner. Many practical difficulties arise from the fact that in many distributed applications we avert from transferring data or parts of it due to costs, privacy and computation considerations. Admittedly, it would be advantageous if the final knowledge, attained through distributed data inference, is common to every participating computing node. The key in achieving the aforementioned task is the distributed average consensus algorithm or simply the consensus algorithm herein. The latter has been used in many applications. Initially the main purpose has been for the estimation of the expectation of scalar valued data distributed over a network of machines without a central node. Notably, the algorithm allows the final outcome to be the same for every participating node. Utilising the consensus algorithm as the centre piece makes the task of distributed data inference feasible. However, there are many difficulties that hinder its direct applicability. Thus, we concentrate on the consensus algorithm with the purpose of addressing these difficulties. There are two main concerns. First, the consensus algorithm has asymptotic convergence. Thus, we may only achieve maximum accuracy if the algorithm is left to run for a large number of iterations. Second, the accuracy attained at any iteration during the consensus algorithm is correlated with the standard deviation of the initial value distribution. The consensus algorithm is inherently imprecise at finite time and this hardens the learning process. We solve this problem by introducing the definitive consensus algorithm. This algorithm attains maximum precision in a finite number of iterations, namely in a number of iterations equal to the diameter of the graph in a distributed and decentralised manner. Additionally, we introduce the nonlinear consensus algorithm and the adaptive consensus algorithm. These are modifications of the original consensus algorithm that allow improved precision with fewer iterations in cases of unknown, partially known and stochastically time-varying network topologies. The definitive consensus algorithm can be incorporated in a distributed data inference framework. We approach the problem of data inference from the perspective of machine learning. Specifically, we tailor this distributed inference framework for machine learning on a communication network with data partitioned on the participating computing nodes. Particularly, the distributed data inference framework is detailed and applied to the case of a multilayer feed forward neural network with error back-propagation. A substantial examination of its performance and its comparison with the non-distributed case, is provided. Theoretical foundation for the definitive consensus algorithm is provided. Moreover, its superior performance is validated by numerical experiments. A brief theoretical examination of the nonlinear and the adaptive consensus algorithms is performed to justify their improved performance with respect to the original consensus algorithm. Moreover, extensive numerical simulations are given to compare the nonlinear and the adaptive algorithm with the original consensus algorithm. The most important contributions of this research are principally the definitive consensus algorithm and the distributed data inference framework. Their combination yields a decentralised distributed process over a communication network capable for inference in agreement over the entire network

Multi-modal video analysis for early fire detection

In dit proefschrift worden verschillende aspecten van een intelligent videogebaseerd branddetectiesysteem onderzocht. In een eerste luik ligt de nadruk op de multimodale verwerking van visuele, infrarood en time-of-flight videobeelden, die de louter visuele detectie verbetert. Om de verwerkingskost zo minimaal mogelijk te houden, met het oog op real-time detectie, is er voor elk van het type sensoren een set ’low-cost’ brandkarakteristieken geselecteerd die vuur en vlammen uniek beschrijven. Door het samenvoegen van de verschillende typen informatie kunnen het aantal gemiste detecties en valse alarmen worden gereduceerd, wat resulteert in een significante verbetering van videogebaseerde branddetectie. Om de multimodale detectieresultaten te kunnen combineren, dienen de multimodale beelden wel geregistreerd (~gealigneerd) te zijn. Het tweede luik van dit proefschrift focust zich hoofdzakelijk op dit samenvoegen van multimodale data en behandelt een nieuwe silhouet gebaseerde registratiemethode. In het derde en tevens laatste luik van dit proefschrift worden methodes voorgesteld om videogebaseerde brandanalyse, en in een latere fase ook brandmodellering, uit te voeren. Elk van de voorgestelde technieken voor multimodale detectie en multi-view lokalisatie zijn uitvoerig getest in de praktijk. Zo werden onder andere succesvolle testen uitgevoerd voor de vroegtijdige detectie van wagenbranden in ondergrondse parkeergarages

Advances in Deep Learning Towards Fire Emergency Application : Novel Architectures, Techniques and Applications of Neural Networks

Paper IV is not published yet.With respect to copyright paper IV and paper VI was excluded from the dissertation.Deep Learning has been successfully used in various applications, and recently, there has been an increasing interest in applying deep learning in emergency management. However, there are still many significant challenges that limit the use of deep learning in the latter application domain. In this thesis, we address some of these challenges and propose novel deep learning methods and architectures. The challenges we address fall in these three areas of emergency management: Detection of the emergency (fire), Analysis of the situation without human intervention and finally Evacuation Planning. In this thesis, we have used computer vision tasks of image classification and semantic segmentation, as well as sound recognition, for detection and analysis. For evacuation planning, we have used deep reinforcement learning.publishedVersio

Fire Detection and Localization Using Wireless Sensor Networks

A Wireless Sensor Network (WSN) is a network of usually a large number of small sensor nodes that are wirelessly connected to each other in order to remotely monitor an environment or phenomena. Sensor nodes use the data aggregation method as an effective tool for estimating the desired parameters accurately and trustfully. In this paper, we have applied a cellular-automata-like algorithm and an averaging consensus algorithm for fire detection and localization with sensor networks. Indeed, when fire is detected somewhere in the network, our algorithm makes aware all the nodes in the network with a very short delay. Afterwards, the algorithm estimates the parameters of the circle surrounding the fire. To simulate the fire outbreak and the reaction of the sensor network equipped with our algorithm, we enabled the data exchange between the fire simulation software FARSITE and the communication software Castalia. The results show that our method detects the fire rapidly and monitors the extension of the fire in real time. The information about the outbreak and the extension of the fire is available from every live sensor in the network, even when part of the sensors are destroyed by the fire