Browsing by Subject "Data streams"

Now showing 1 - 6 of 6

Open Access
Connectivity-guided adaptive lifting transform for image like compression of meshes
(IEEE, 2007-05) Köse, Kıvanç; Çetin, A. Enis; Güdükbay, Uğur; Onural, Levent
We propose a new connectivity-guided adaptive wavelet transform based mesh compression framework. The 3D mesh is first transformed to 2D images on a regular grid structure by performing orthogonal projections onto the image plane. Then, this image-like representation is wavelet transformed using a lifting structure employing an adaptive predictor that takes advantage of the connectivity information of mesh vertices. Then the wavelet domain data is encoded using "Set Partitioning In Hierarchical Trees" (SPIHT) method or JPEG2000. The SPIHT approach is progressive because the resolution of the reconstructed mesh can be changed by varying the length of the 1D data stream created by the algorithm. In JPEG2000 based approach, quantization of the coefficients determines the quality of the reconstruction. The results of the SPIHT based algorithm is observed to be superior to JPEG200 based mesh coder and MPEG-3DGC in rate-distortion.
Open Access
İnsan hareketlerinin PIR-sensör tabanlı bir sistemle sınıflandırılması
(IEEE, 2008-04) Urfalıoğlu, Onay; Soyer, Emin B.; Töreyin, B. Uğur; Çetin, A. Enis
Bu bildiride, tek bir pasif kızılberisi sensörü (PIR) kullanarak beş farklı insan hareketi ve bir hareketsiz arkaplan gürültüsünden oluşan toplam 6 çeşit olay için bir sınıflandırma yöntemi önerilmiştir. Otomatik olay sınıflandırma sistemleri, dinamik süreçler barındıran ortamlar için yeni uygulamalara fırsat vermektedir. Olay sınıflandırması, herhangi bir sensör ya da sensör dizisinden gelen işaretlerin analiz edilerek, belirli bir olaya ait dinamik süreçle eşleştirilmesi olarak tanımlanabilir. Genelde, insan etkinliklerinin izlenmesi uygulamalarında kamera ve mikrofonlar kullanılmaktadır. Bir alternatif veya bir tümleyici yaklaşım olarak, bahsi geçen uygulamalarda PIR sensörleri de kullanılabilir. Bu bildiride, olay sınıflandırılması için Bayes yaklaşımına dayalı olan şartlı Gauss karışım modeli (CGMM) kullanımı önerilmektedir. Deneysel çalışmalarda, bu yaklaşımın başarılı olduğu görülmüştür.
Open Access
Nonrectangular wavelets for multiresolution mesh analysis and compression
(IEEE, 2006) Köse, Kıvanç; Çetin, A. Enis; Güdükbay, Uğur; Onural, Levent
We propose a new Set Partitioning In Hierarchical Trees (SPIHT) based mesh compression framework. The 3D mesh is first transformed to 2D images on a regular grid structure. Then, this image-like representation is wavelet transformed and SPIHT is applied on the wavelet domain data. The method is progressive because the resolution of the reconstructed mesh can be changed by varying the length of the one-dimensional data stream created by SPIHT algorithm. Nearly perfect reconstruction is possible if all of the data stream is received. © 2006 IEEE.
Open Access
Processing count queries over event streams at multiple time granularities
(Elsevier Inc., 2006-07-22) Ünal, A.; Saygın, Y.; Ulusoy, Özgür
Management and analysis of streaming data has become crucial with its applications to web, sensor data, network traffic data, and stock market. Data streams consist of mostly numeric data but what is more interesting are the events derived from the numerical data that need to be monitored. The events obtained from streaming data form event streams. Event streams have similar properties to data streams, i.e., they are seen only once in a fixed order as a continuous stream. Events appearing in the event stream have time stamps associated with them at a certain time granularity, such as second, minute, or hour. One type of frequently asked queries over event streams are count queries, i.e., the frequency of an event occurrence over time. Count queries can be answered over event streams easily, however, users may ask queries over different time granularities as well. For example, a broker may ask how many times a stock increased in the same time frame, where the time frames specified could be an hour, day, or both. Such types of queries are challenging especially in the case of event streams where only a window of an event stream is available at a certain time instead of the whole stream. In this paper, we propose a technique for predicting the frequencies of event occurrences in event streams at multiple time granularities. The proposed approximation method efficiently estimates the count of events with a high accuracy in an event stream at any time granularity by examining the distance distributions of event occurrences. The proposed method has been implemented and tested on different real data sets including daily price changes in two different stock exchange markets. The obtained results show its effectiveness. © 2005 Elsevier Inc. All rights reserved.
Open Access
Stance detection: concepts, approaches, resources, and outstanding issues
(Association for Computing Machinery, 2021-07-11) Küçük, Dilek; Can, Fazlı
Stance detection (also known as stance classification and stance prediction) is a problem related to social media analysis, natural language processing, and information retrieval, which aims to de termine the position of a person from a piece of text they produce, towards a target (a concept, idea, event, etc.) either explicitly speci fied in the text, or implied only. The output of the stance detection procedure is usually from this set: {Favor, Against, None}. In this tutorial, we will define the core concepts and research problems re lated to stance detection, present historical and contemporary ap proaches to stance detection, provide pointers to related resources (datasets and tools), and we will cover outstanding issues and ap plication areas of stance detection. As solutions to stance detection can contribute to significant tasks including trend analysis, opin ion surveys, user reviews, personalization, and predictions for ref erendums and elections, it will continue to stand as an important research problem, mostly on textual content currently, and partic ularly on social media. Finally, we believe that image and video content will commonly be the subject of stance detection research soon.
Open Access
A tutorial on stance detection
(Association for Computing Machinery, Inc, 2022) Küçük, Dilek; Can, Fazlı
Stance detection (also known as stance classification, stance prediction, and stance analysis) is a problem related to social media analysis, natural language processing, and information retrieval, which aims to determine the position of a person from a piece of text they produce, towards a target (a concept, idea, event, etc.) either explicitly specified in the text, or implied only. Common stance classes include Favor, Against, and None. In this tutorial, we will define the core concepts and other related research problems, present historical and contemporary approaches to stance detection (including shared tasks and tools employed), provide pointers to related datasets, and cover open research directions and application areas of stance detection. As solutions to stance detection can contribute to diverse applications including trend analysis, opinion surveys, user reviews, personalization, and predictions for referendums and elections, it will continue to stand as an important research problem, mostly on textual content currently, and particularly on Web content including social media.