Implementation of basic QoS mechanisms on videoconferencing network model

Ovo istraživanje je posljedica višegodišnjeg korištenja videokonferencijske veze te pojave raznih problema koji prate istu. Kašnjenje slike i zvuka, pucanje veze, prekid slike ili zvuka samo su neki od razloga zbog kojih je nastao ovaj rad. U ovom radu pokušava se primjenom mehanizama kvalitete usluge na modelu CARNet-ove mreže poboljšati kvaliteta videokonferencijske veze. Na osnovu dobivenih rezultata simulacije videokonferencijske veze prikazani su grafovi ispuštanja paketa, kašnjenja te ostalih parametara bitnih za videokonferencijsku vezu.This research is the outcome of multiannual use of videoconferencing services and the emersion of various problems that come with videoconferencing applications. Video and audio delay, dropped connection, missing audio or video, are just some of the reasons for creating this paper. In this article quality of videoconferencing link in CARNet network is improved by implementing various QoS mechanisms. The obtained results of the videoconferencing simulation are represented in graphs which display dropped packets, delay and other videoconferencing parameters

Kompresija slike korištenjem B-tree algoritma kodiranja poboljšan modeliranjem podataka Burrows-Wheeler transformacijom

The paper shows that the partial differential based compression framework, Edge Enhancing Diffusion Compression (EEDC) on high compression ratios can come close to or even be better than present compression standard - JPEG2000 thus presenting a novel method for image compression. In this paper EEDC will be enhanced by changing its data coding, i.e. Huffman coding will be changed with an entropy coder accompanied with Burrows-Wheeler transformation and context mixing. Images, graphs and tables show image compression results. The purpose of this article is to examine the effectiveness of the PDEs in image compression and to evaluate it by comparing to cosine and wavelet transform based compression methods.Ovaj rad pokazuje kako je kompresija temeljena na parcijalnim diferencijalnim jednadžbama, tj. EEDC na velikom stupnju kompresije dovoljno dobra ili čak bolja od trenutačnog kompresijskog standarda - JPEG2000 time predstavljajući novu metodu kompresije slika. U ovom radu EEDC je poboljšan tako da je promijenjeno kodiranje podataka svjetline slike, tj. Huffmanovo kodiranje je zamijenjeno entropijskim koderom i Burrows-Wheeler transformacijom s miješanjem konteksta. Slike, grafovi i tablice prikazuju rezultate kompresije slika. Svrha ovog rada je ispitati efikasnost parcijalnih diferencijalnih jednadžbi u kompresiji slike te ju usporediti metodama kompresije koje su bazirane na kosinusnoj i wavelet transformaciji

Coded images sensitivity on the errors in the communication channel transmission

Ovo istraživanje pokušava analizirati posljedice gubitka paketa u prijenosu komunikacijskim kanalom u vidu pogrešaka na slici. Greške se simuliraju korištenjem tri najčešća tipa grešaka u komunikacijskom kanalu uz korištenje dva tipa entropijskog kodiranja. Na dobivenim, oštećenim, slikama provodi se objektivna analiza slike te se rezultati prikazuju u tabličnom obliku.This research attempts to analyze the effects of packet loss in the transmission through communication channel in the form of picture quality degradation. Errors are simulated using the three most common types of errors in the communication channel with the use of two types of entropy coders. On obtained, damaged pictures an objective image analysis is performed and the results are presented in tabular form

Single and Multi-Person Face Recognition Using the Enhanced Eigenfaces Method

This research studies and analyzes the possibility of single-person and multi-person face detection and recognition. Face detection is performed by the Viola-Jones face detection method and recognition is performed by the Eigenfaces method. Unchanged face detection and recognition methods are explained and tested to their limits. Improvement in face recognition is achieved by observing the flaws of the Eigenfaces method and their enhancement

Q-learning by the nth step state and multi-agent negotiation in unknown environment

U ovom radu je predstavljen novi postupak Q-učenja kod kojega agent odluku o sljedećoj akciji donosi na osnovu korisnosti nekog budućeg stanja, a ne na osnovu trenutno optimalne akcije. Implementirana je komunikacija agenata u okolini koji si međusobno javljaju svoje buduće akcije što doprinosi kvalitetnijem odabiru akcija pojedinog agenta. Nova metoda nazvana je Q-učenje prema stanju n-tog koraka i dogovaranjem više agenata. Uspoređeni su rezultati testiranja ovdje predstavljenog algoritma s osnovnim QL algoritmom što je i grafički prikazano te su navedene prednosti novog algoritma. Postignuto je prosječno smanjenje od 40 % sudara tijekom postupka učenja.This work will show a new procedure of Q-learning in which the agent’s decision, regarding the next step, is not based on the optimal action at that moment but on the usefulness of a future state. A near agent communication has been implemented so that the agents signal each other their future actions which contribute to a better choice of actions for each of the agents. The new method is named Q-learning by the nth step and multi-agent negotiation. The results of the testing of this algorithm are compared with the basic QL algorithm which is also graphically demonstrated and the advantages of the new algorithm are listed too. An average of 40 % collision decrease is obtained during learning procedure

Towards fixed facial features face recognition

In this paper we propose a framework for recognition of faces in controlled conditions. The framework consists of two parts: face detection and face recognition. For face detection we are using the Viola-Jones face detector. The proposal for face recognition part is based on the calculation of certain ratios on the face, where the features on the face are located by the use of Hough transform for circles. Experiments show that this framework presents a possible solution for the problem of face recognition

Cardiac CT image enhancement for 3D heart registration and visualization

Cardiac CT imaging has been established as one of the most valuable tools in diagnosis and treatment of cardio vascular diseases. Modern CT imaging devices provide a significant amount of data with high precision. However, resulting CT images can often suffer from significant quality degradations, especially in the preferable case of low level of radiation. Such degradations can complicate the registration and visualization processes, thus making it harder for medical doctors to analyze the acquired data. The goal of this paper is to propose a method for the enhancement of input CT data based on Multiscale Wavelet Decomposition and Spatial Pixel Profiling in 2D slices. Proposed methods are used as preprocessing steps on input CT data, preparing the data for segmentation and registration using Active Contour Models segmentation method. This paper displays the benefits of such approach for the purpose of 3D reconstruction

Image segmentation using active contour models and partial differential equations

This article displays benefits of image segmentation for purpose of 3D reconstruction. Medical images and in this case CT slices are being reconstructed in 3D space for better medical investigation. It is relatively hard to use raw images for 3D reconstruction, so certain areas are being singled out using active contour models. Partial Differential Equations (PDEs), as non-linear diffusion, are used for image denoising because CT images have large gradiental areas that need to be evened out