Browsing by Subject "Bayesian networks"

Now showing 1 - 19 of 19

Open Access
Activity recognition invariant to sensor orientation with wearable motion sensors
(MDPI AG, 2017) Yurtman, A.; Barshan, B.
Most activity recognition studies that employ wearable sensors assume that the sensors are attached at pre-determined positions and orientations that do not change over time. Since this is not the case in practice, it is of interest to develop wearable systems that operate invariantly to sensor position and orientation. We focus on invariance to sensor orientation and develop two alternative transformations to remove the effect of absolute sensor orientation from the raw sensor data. We test the proposed methodology in activity recognition with four state-of-the-art classifiers using five publicly available datasets containing various types of human activities acquired by different sensor configurations. While the ordinary activity recognition system cannot handle incorrectly oriented sensors, the proposed transformations allow the sensors to be worn at any orientation at a given position on the body, and achieve nearly the same activity recognition performance as the ordinary system for which the sensor units are not rotatable. The proposed techniques can be applied to existing wearable systems without much effort, by simply transforming the time-domain sensor data at the pre-processing stage. © 2017 by the authors. Licensee MDPI, Basel, Switzerland.
Open Access
A Bayesian approach to respiration rate estimation via pulse-based ultra-wideband signals
(IEEE, 2009) Soǧancı, Hamza; Gezici, Sinan; Arıkan, Orhan
In this paper, theoretical limits on estimation of respiration rates via pulse-based ultra-wideband (UWB) signals are studied in the presence of prior information about respiration related signal parameters. First, a generalized Cramer-Rao lower bound (G-CRLB) expression is derived, and then simplified versions of the bound are obtained for sinusoidal displacement functions. In addition to the derivation of the theoretical limits, a two-step suboptimal estimator based on matched filter (correlation) processing and maximum a posteriori probability (MAP) estimation is proposed. It is shown that the proposed estimator performs very closely to the theoretical limits under certain conditions. Simulation results are presented to investigate the theoretical results.
Open Access
Blind phase noise estimation in OFDM systems by sequential Monte Carlo method
(Springer, 2006) Panayırcı, Erdal; Çırpan, H. A.; Moeneclaey, M.; Noels, N.
In this paper, based on a sequential Monte Carlo method, a computationally efficient algorithm is presented for estimating the residual phase noise, blindly, generated at the output the phase tracking loop employed in OFDM systems. The basic idea is to treat the transmitted symbols as "missing data" and draw samples sequentially of them based on the observed signal samples up to time t. This way, the Bayesian estimates of the phase noise is obtained through these samples, sequentially drawn, together with their importance weights. The proposed receiver structure is seen to be ideally suited for high-speed parallel implementation using VLSI technology.
Open Access
Classification of leg motions by processing gyroscope signals
(IEEE, 2009) Tunçel, Orkun; Altun, Kerem; Barshan, Billur
In this study, eight different leg motions are classified using two single-axis gyroscopes mounted on the right leg of a subject with the help of several pattern recognition techniques. The methods of least squares, Bayesian decision, k-nearest neighbor, dynamic time warping, artificial neural networks and support vector machines are used for classification and their performances are compared. This study comprises the preliminary work for our future studies on motion recognition with a much wider scope.
Open Access
Dynamic signaling games under Nash and Stackelberg equilibria
(IEEE, 2016) Sarıtaş, Serkan; Yüksel, Serdar; Gezici, Sinan
In this study, dynamic and repeated quadratic cheap talk and signaling game problems are investigated. These involve encoder and decoders with mismatched performance objectives, where the encoder has a bias term in the quadratic cost functional. We consider both Nash equilibria and Stackelberg equilibria as our solution concepts, under a perfect Bayesian formulation. These two lead to drastically different characteristics for the equilibria. For the cheap talk problem under Nash equilibria, we show that fully revealing equilibria cannot exist and the final state equilibria have to be quantized for a large class of source models; whereas, for the Stackelberg case, the equilibria must be fully revealing regardless of the source model. In the dynamic signaling game where the transmission of a Gaussian source over a Gaussian channel is considered, the equilibrium policies are always linear for scalar sources under Stackelberg equilibria, and affine policies constitute an invariant subspace under best response maps for Nash equilibria.
Open Access
Efficiency of sound energy decay analysis in auditoria
(Institute of Acoustics, 2023-09) Xiang, N.; Gül, Zühre Sü
Recent auditorium acoustics practice has included coupled-volume systems in several performing arts venues. This has stimulated research activities on acoustics in the coupled-volume systems. Based on experimentally measured room impulse responses acquired from existing auditoria, and several historically significant worship spaces, this paper addresses the challenges of analysing single-slope and multiple-slope sound energy decays often encountered in the experimentally measured room impulse responses in these venues. The analysis engages a parametric model of Schroeder decay functions, that decomposes the Schroeder decay data into single or multiple exponential decays along with a noise term. The model has been well validated using many experimental data. Several advanced analysis methods based on the decay model, such as nonlinear regressions, Bayesian probabilistic inference, and artificial neural networks have emerged to cope with analysis challenges raised in auditorium acoustics practice. This paper discusses conditions of implementing Schroeder integration for a higher efficiency of the numerical analysis and clarifies some unreasonable expectations/interpretations of Schroeder decay data. © 2023 Institute of Acoustics. All rights reserved.
Open Access
Human activity recognition using inertial/magnetic sensor units
(Springer, Berlin, Heidelberg, 2010) Altun, Kerem; Barshan, Billur
This paper provides a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). Daily and sports activities are classified using five sensor units worn by eight subjects on the chest, the arms, and the legs. Each sensor unit comprises a triaxial gyroscope, a triaxial accelerometer, and a triaxial magnetometer. Principal component analysis (PCA) and sequential forward feature selection (SFFS) methods are employed for feature reduction. For a small number of features, SFFS demonstrates better performance and should be preferable especially in real-time applications. The classifiers are validated using different cross-validation techniques. Among the different classifiers we have considered, BDM results in the highest correct classification rate with relatively small computational cost. © 2010 Springer-Verlag Berlin Heidelberg.
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
Location recommendations for new businesses using check-in data
(IEEE, 2016-12) Eravci, Bahaeddin; Bulut, Neslihan; Etemoğlu, C.; Ferhatosmanoğlu, Hakan
Location based social networks (LBSN) and mobile applications generate data useful for location oriented business decisions. Companies can get insights about mobility patterns of potential customers and their daily habits on shopping, dining, etc.To enhance customer satisfaction and increase profitability. We introduce a new problem of identifying neighborhoods with a potential of success in a line of business. After partitioning the city into neighborhoods, based on geographical and social distances, we use the similarities of the neighborhoods to identify specific neighborhoods as candidates for investment for a new business opportunity. We present two solutions for this new problem: i) a probabilistic approach based on Bayesian inference for location selection along with a voting based approximation, and ii) an adaptation of collaborative filtering using the similarity of neighborhoods based on co-existence of related venues and check-in patterns. We use Foursquare user check-in and venue location data to evaluate the performance of the proposed approach. Our experiments show promising results for identifying new opportunities and supporting business decisions using increasingly available check-in data sets. © 2016 IEEE.
Open Access
Minyatür eylemsizlik duyucuları ve manyetometre sinyallerinin işlenmesiyle insan aktivitelerinin sınıflandırılması
(IEEE, 2011-04) Yüksek, Murat Cihan; Barshan, Billur
Bu çalışmada insan vücuduna yerleştirilen minyatür eylemsizlik duyucuları ve manyetometreler kullanılarak çeşitli aktiviteler örüntü tanıma yöntemleriyle ayırdedilmiş ve karşılaştırmalı bir çalışmanın sonuçları sunulmuştur. Ayırdetme işlemi için basit Bayeşçi (BB) yöntem, yapay sinir ağları (YSA), benzeşmezlik tabanlı sınıflandırıcı (BTS), ceşitli karar ağacı (KA) yöntemleri, Gauss karışım modeli (GKM) ve destek vektör makinaları (DVM) kullanılmıştır. Aktiviteler gövdeye, kollara ve bacaklara takılan beş duyucu ünitesinden gelen verilerin işlenmesiyle ayırdedilmiştir. Her ünite, her biri üç-eksenli olmak üzere birer ivmeölçer, dönüölçer ve manyetometre içermektedir. Çalışmanın sonuçlarına göre, en iyi ilk üç başarı oranı sırasıyla GKM (%99.12), YSA (%99.09) ve DVM (%98.90) yöntemleri ile elde edilmiştir.
Open Access
Multi-item quick response system with budget constraint
(2012) Serel, D. A.
Quick response mechanisms based on effective use of up-to-date demand information help retailers to reduce their inventory management costs. We formulate a single-period inventory model for multiple products with dependent (multivariate normal) demand distributions and a given overall procurement budget. After placing orders based on an initial demand forecast, new market information is gathered and demand forecast is updated. Using this more accurate second forecast, the retailer decides the total stocking level for the selling season. The second order is based on an improved demand forecast, but it also involves a higher unit supply cost. To determine the optimal ordering policy, we use a computational procedure that entails solving capacitated multi-item newsboy problems embedded within a dynamic programming model. Various numerical examples illustrate the effects of demand variability and financial constraint on the optimal policy. It is found that existence of a budget constraint may lead to an increase in the initial order size. It is also observed that as the budget available decreases, the products with more predictable demand make up a larger share of the procurement expenditure.
Open Access
Nash and Stackelberg equilibria for dynamic cheap talk and signaling games
(IEEE, 2017) Sarıtaş, Serkan; Yüksel, S.; Gezici, Sinan
Simultaneous (Nash) and sequential (Stackelberg) equilibria of two-player dynamic quadratic cheap talk and signaling game problems are investigated under a perfect Bayesian formulation. For the dynamic scalar and multi-dimensional cheap talk, the Nash equilibrium cannot be fully revealing whereas the Stackelberg equilibrium is always fully revealing. Further, the final state Nash equilibria have to be essentially quantized when the source is scalar and has a density, and non-revealing for the multi-dimensional case. In the dynamic signaling game where the transmission of a Gauss-Markov source over a memoryless Gaussian channel is considered, affine policies constitute an invariant subspace under best response maps for both scalar and multi-dimensional sources under Nash equilibria; however, the Stackelberg equilibrium policies are always linear for scalar sources but may be non-linear for multi-dimensional sources. Further, under the Stackelberg setup, the conditions under which the equilibrium is non-informative are derived for scalar sources.
Open Access
Recognizing daily and sports activities in two open source machine learning environments using body-worn sensor units
(Oxford University Press, 2014-11) Barshan, B.; Yüksek, M. C.
This study provides a comparative assessment on the different techniques of classifying human activities performed while wearing inertial and magnetic sensor units on the chest, arms and legs. The gyroscope, accelerometer and the magnetometer in each unit are tri-axial. Naive Bayesian classifier, artificial neural networks (ANNs), dissimilarity-based classifier, three types of decision trees, Gaussian mixture models (GMMs) and support vector machines (SVMs) are considered. A feature set extracted from the raw sensor data using principal component analysis is used for classification. Three different cross-validation techniques are employed to validate the classifiers. A performance comparison of the classifiers is provided in terms of their correct differentiation rates, confusion matrices and computational cost. The highest correct differentiation rates are achieved with ANNs (99.2%), SVMs (99.2%) and a GMM (99.1%). GMMs may be preferable because of their lower computational requirements. Regarding the position of sensor units on the body, those worn on the legs are the most informative. Comparing the different sensor modalities indicates that if only a single sensor type is used, the highest classification rates are achieved with magnetometers, followed by accelerometers and gyroscopes. The study also provides a comparison between two commonly used open source machine learning environments (WEKA and PRTools) in terms of their functionality, manageability, classifier performance and execution times. © 2013 © The British Computer Society 2013. All rights reserved.
Open Access
Sabit genişbantlı telsiz uygulamalarında ÇGÇÇ-DFBÇ için kanal kestirimi
(IEEE, 2006-04) Karakaya, B.; Çırpan, H. A.; Panayırcı, Erdal
Systems employing multiple transmit and receive antennas, known as multiple input multiple output (MIMO) systems can be used with OFDM to improve the resistance to channel impairments. Thus the technologies of OFDM and MIMO are equipped in fixed wireless applications with attractive features, including high data rates and robust performance. However, since different signals are transmitted from different antennas simultaneously, the received signal is the superposition of these signals, which implies new challenges for channel estimation. In this paper we propose a time domain MMSE based channel estimation approach for MIMO-OFDM systems. The proposed approach employs a convenient representation of the discrete multipath fading channel based on the Karhunen-Loeve (KL) orthogonal expansion and finds MMSE estimates of the uncorrelated KL series expansion coefficients. Based on such an expansion, no matrix inversion is required in the proposed MMSE estimator. Also the performance of the proposed approach is studied through the evaluation of minimum Bayesian MSE. © 2006 IEEE.
Open Access
Scene classification using bag-of-regions representations
(IEEE, 2007-06) Gökalp, Demir; Aksoy, Selim
This paper describes our work on classification of outdoor scenes. First, images are partitioned into regions using one-class classification and patch-based clustering algorithms where one-class classifiers model the regions with relatively uniform color and texture properties, and clustering of patches aims to detect structures in the remaining regions. Next, the resulting regions are clustered to obtain a codebook of region types, and two models are constructed for scene representation: a "bag of individual regions" representation where each region is regarded separately, and a "bag of region pairs" representation where regions with particular spatial relationships are considered, together. Given these representations, scene classification is done using Bayesian classifiers. We also propose a novel region selection algorithm that identifies region types that are frequently found in a particular class of scenes but rarely exist in other classes, and also consistently occur together in the same class of scenes. Experiments on the LabelMe data set showed that the proposed models significantly out-perform a baseline global feature-based approach. © 2007 IEEE.
Open Access
Sequential sensor installation for wiener disorder detection
(Institute for Operations Research and the Management Sciences (I N F O R M S), 2016) Dayanik, S.; Sezer, S. O.
We consider a centralized multisensor online quickest disorder detection problem where the observation from each sensor is a Wiener process gaining a constant drift at a common unobservable disorder time. The objective is to detect the disorder time as quickly as possible with small probability of false alarms. Unlike the earlier work on multisensor change detection problems, we assume that the observer can apply a sequential sensor installation policy. At any time before a disorder alarm is raised, the observer can install new sensors to collect additional signals. The sensors are statistically identical, and there is a fixed installation cost per sensor. We propose a Bayesian formulation of the problem. We identify an optimal policy consisting of a sequential sensor installation strategy and an alarm time, which minimize a linear Bayes risk of detection delay, false alarm, and new sensor installations. We also provide a numerical algorithm and illustrate it on examples. Our numerical examples show that significant reduction in the Bayes risk can be attained compared to the case where we apply a static sensor policy only. In some examples, the optimal sequential sensor installation policy starts with 30% less number of sensors than the optimal static sensor installation policy and the total percentage savings reach to 12%.
Open Access
Software design, implementation, application, and refinement of a Bayesian approach for the assessment of content and user qualities
(2011) Türk, Melihcan
The internet provides unlimited access to vast amounts of information. Technical innovations and internet coverage allow more and more people to supply contents for the web. As a result, there is a great deal of material which is either inaccurate or out-of-date, making it increasingly difficult to find relevant and up-to-date content. In order to solve this problem, recommender systems based on collaborative filtering have been introduced. These systems cluster users based on their past preferences, and suggest relevant contents according to user similarities. Trustbased recommender systems consider the trust level of users in addition to their past preferences, since some users may not be trustworthy in certain categories even though they are trustworthy in others. Content quality levels are important in order to present the most current and relevant contents to users. The study presented here is based on a model which combines the concepts of content quality and user trust. According to this model, the quality level of contents cannot be properly determined without considering the quality levels of evaluators. The model uses a Bayesian approach, which allows the simultaneous co-evaluation of evaluators and contents. The Bayesian approach also allows the calculation of the updated quality values over time. In this thesis, the model is further refined and configurable software is implemented in order to assess the qualities of users and contents on the web. Experiments were performed on a movie data set and the results showed that the Bayesian co-evaluation approach performed more effectively than a classical approach which does not consider user qualities. The approach also succeeded in classifying users according to their expertise level.
Open Access
Test-cost sensitive classification based on conditioned loss functions
(Springer, 2007-09) Cebe, Mümin; Gündüz-Demir, Çiğdem
We report a novel approach for designing test-cost sensitive classifiers that consider the misclassification cost together with the cost of feature extraction utilizing the consistency behavior for the first time. In this approach, we propose to use a new Bayesian decision theoretical framework in which the loss is conditioned with the current decision and the expected decisions after additional features are extracted as well as the consistency among the current and expected decisions. This approach allows us to force the feature extraction for samples for which the current and expected decisions are inconsistent. On the other hand, it forces not to extract any features in the case of consistency, leading to less costly but equally accurate decisions. In this work, we apply this approach to a medical diagnosis problem and demonstrate that it reduces the overall feature extraction cost up to 47.61 percent without decreasing the accuracy. © Springer-Verlag Berlin Heidelberg 2007.
Open Access
Understanding and managing complexity through Bayesian network approach: the case of bribery in business transactions
(Elsevier, 2019) Ekici, Ahmet; Ekici, Ş. Ö.
Managing complex business problems requires decision makers to take a systemic perspective and utilize tools that can generate knowledge from the interdependencies of the system’s complex properties. As such, the current research focuses on an important yet ambiguous business problem–bribery. Using the Global Competitiveness Index data provided by the World Economic Forum, the authors constructed and analysed a Bayesian network to delineate a ‘system’ of bribery in business transactions. In this context, they first determined the factors related to bribery activities and then developed a structural model (the Bayesian network). Through scenario and sensitivity analyses performed over the constructed model, the authors identified the factors that have the greatest impact on bribery activities. They further analysed the resulting model based on the countries’ stage of economic development in order to provide the manager and policy maker with a more informative diagnostic tool to understand and deal with bribery activities locally and globally.