Sharif Digital Repository

Semantic video analysis and automatic concept extraction play an important role in several applications; including content-based search engines, video indexing, and video summarization. As the Bayesian network is a powerful tool for learning complex patterns, a novel Bayesian network-based method is proposed for automatic event detection and summarization in soccer videos. The proposed method includes efficient algorithms for shot boundary detection, shot view classification, mid-level visual feature extraction, and construction of the related Bayesian network. The method contains of three main stages. In the first stage, the shot boundaries are detected. Using the hidden Markov model, the... 

Estimation of distribution algorithms have evolved as a technique for estimating population distribution in evolutionary algorithms. They estimate the distribution of the candidate solutions and then sample the next generation from the estimated distribution. Bayesian optimization algorithm is an estimation of distribution algorithm, which uses a Bayesian network to estimate the distribution of candidate solutions and then generates the next generation by sampling from the constructed network. The experimental results show that the Bayesian optimization algorithms are capable of identifying correct linkage between the variables of optimization problems. Since the problem of finding the... 

Noise reduction of speech signals plays an important role in telecommunication systems. Various types of speech additive noise can be introduced such as babble, crowd, large city, and highway which are the main factor of degradation in perceived speech quality. There are some cases on the receiver side of telecommunication systems, where the direct value of interfering noise is not available and there is just access to noisy speech. In these cases the noise cannot be cancelled totally but it may be possible to reduce the noise in a sensible way by utilizing the statistics of the noise and speech signal. In this paper the proposed method for noise reduction is Bayesian recursive state-space... 

Speech enhancement in real-time applications improves the quality and intelligibility of the speech and reduces communication fatigue. Nowadays, due to reactivity of the systems and spread of online real-time applications, including VoIP, state-space models have been used broadly. This paper presents a speech enhancement method based on adaptive Bayesian-Kalman filter and Bayesian-MAP estimation to improve the performance and the quality of the enhancement procedure. The enhancement method includes a combination of Bayesian-Kalman filter for noise reduction and Bayesian-MAP estimation for parameter estimation of the lost speech segments. Performance evaluation and result of the proposed... 

The Markov decision process is one of the most common probabilistic prediction models used in infrastructure management. When existing data are insufficient, expert knowledge is commonly used to derive a Markovian transition probability matrix. Eventually, every pavement management system will progress to a level at which inspection measurements from the network will be organized into a database to be used for performance prediction. The best way to use this body of data to improve the initially developed transition probability matrix is to combine prior expert knowledge with new observations. This paper proposes a method for periodically updating Markovian transition probabilities as new... 

Bayesian optimization algorithm (BOA) utilizes a Bayesian network to estimate the probability distribution of candidate solutions and creates the next generation by sampling the constructed Bayesian network. This paper proposes an improved real-coded BOA (IrBOA) for continuous global optimization. In order to create a set of Bayesian networks, the candidate solutions are partitioned by an adaptive clustering method. Each Bayesian network has its own structure and parameters, and the next generation is produced from this set of networks. The adaptive clustering method automatically determines the correct number of clusters so that the probabilistic building-block crossover (PBBC) is... 

In this paper, a heuristic threshold policy is developed to detect and classify the states of a multivariate quality control system. In this approach, a probability measure called belief is first assigned to the quality characteristics and then the posterior belief of out-of-control characteristics is updated by taking new observations and using a Bayesian rule. If the posterior belief is more than a decision threshold, called minimum acceptable belief determined using a heuristic threshold policy, then the corresponding quality characteristic is classified out-of-control. Besides using a different approach, the main difference between the current research and previous works is that the... 

Herein, we propose a method based on the existing second-order blind identification of underdetermined mixtures technique for identifying the modal characteristics - namely, natural frequencies, damping ratio, and real-valued partial mode shapes of all contributing modes - of structures with a limited number of sensors from recorded free/ambient vibration data. In the second-order blind identification approach, second-order statistics of recorded signals are used to recover modal coordinates and mode shapes. Conventional versions of this approach require the number of sensors to be equal to or greater than the number of active modes. In the present study, we first employ a parallel factor... 

As the oil production from conventional oil reservoirs is decreasing, oil production through Enhanced Oil Recovery (EOR) processes is supposed to compensate for both the oil production reduction in matured oil reservoirs and the worldwide dramatic increase in oil demand. Therefore, developing a strategy to choose an optimized EOR technique is crucial to find a resolution for production decline in oil reservoirs. A screening tool recommending the most appropriate EOR method is proposed in this study. An expert fuzzy logic system is employed to screen four well-known EOR methods including miscible CO2 injection, miscible HC gas injection, polymer flooding and steam injection based on the... 

The noise exists in almost all environments such as cellular mobile telephone systems. Various types of noise can be introduced such as speech additive noise which is the main factor of degradation in perceived speech quality. At some applications for example at the receiver of a telecommunication system, the direct value of interfering noise is not available and there is just access to noisy speech. In these cases the noise cannot be cancelled totally but it may be possible to reduce the noise in a sensible way by utilizing the statistics of the noise and speech signal. In this paper the proposed method for noise reduction is Bayesian recursive state-space Kalman filter, which is a method... 

Aims. We present 11 high-precision photometric transitobservations of the transiting super-Earth planet GJ≠1214≠b. Combining these data with observations from other authors, we investigate the ephemeris for possible signs of transit timing variations (TTVs) using a Bayesian approach. Methods. The observations were obtained using telescope-defocusing techniques, and achieve a high precision with random errors in the photometry as low as 1 mmag per point. To investigate the possibility of TTVs in the light curve, we calculate the overall probability of a TTV signal using Bayesian methods. Results. The observations are used to determine the photometric parameters and the physical properties... 

In this paper, Bayesian Network (BN) is used for reliability assessment of composite power systems with emphasis on the importance of system components. A simple approach is presented to construct the BN associated with a given power system. The approach is based on the capability of the BN to learn from data which makes it possible to be applied to large power systems. The required training data is provided by state sampling using the Monte Carlo simulation. The constructed BN is then used to perform different probabilistic assessments such as ranking the criticality and importance of system components from reliability perspective. The BN is also used to compute the frequency and... 

In this paper, we address the problem of data description using a Bayesian framework. The goal of data description is to draw a boundary around objects of a certain class of interest to discriminate that class from the rest of the feature space. Data description is also known as one-class learning and has a wide range of applications. The proposed approach uses a Bayesian framework to precisely compute the class boundary and therefore can utilize domain information in form of prior knowledge in the framework. It can also operate in the kernel space and therefore recognize arbitrary boundary shapes. Moreover, the proposed method can utilize unlabeled data in order to improve accuracy of... 

In this letter, a new adaptive Gaussian process (GP) frame work for signal extrapolation is proposed. Signal extrapolation is an essential task in many applications such as concealment of corrupted data in image and video communications. While possessing many interesting properties, Gaussian process priors with inappropriate stationary kernels may create extremely blurred edges in concealed areas of the image. To address this problem, we propose adaptive non-stationary kernels in a Gaussian process framework. The proposed adaptive kernel functions are defined based on the hypothesized edges of the missing areas. Experimental results verify the effectiveness of the proposed method compared to... 

Clustering Web data is one important technique for extracting knowledge from the Web. In this paper, a novel method is presented to facilitate the clustering. The method determines the appropriate number of clusters and provides suitable representatives for each cluster by inference from a Bayesian network. Furthermore, by means of the Bayesian network, the contents of the Web pages are converted into vectors of lower dimensions. The method is also extended for hierarchical clustering, and a useful heuristic is developed to select a good hierarchy. The experimental results show that the clusters produced benefit from high quality  

In this paper, we propose a Gaussian Process Regression (GPR) framework for concealment of corrupted motion vectors in predictive video coding of packet video systems. The problem of estimating the lost motion vectors is modelled as a kernel construction problem in a Bayesian framework. First, to describe the similarity between the neighboring motion vectors, a kernel function is defined. Then the parameters of the kernel function is estimated as the coefficients of a linear Bayesian estimator. The experimental results verify the superiority of the proposed algorithm over the conventional and state of the art motion vector concealment methods. Moreover, noticeable improvements on both... 

If at least one out of two serial machines that produce a specific product in manufacturing environments malfunctions, there will be non conforming items produced. Determining the optimal time of the machines' maintenance is the one of major concerns. While a convenient common practice for this kind of problem is to fit a single probability distribution to the combined defect data, it does not adequately capture the fact that there are two different underlying causes of failures. A better approach is to view the defects as arising from a mixture population: one due to the first machine failures and the other due to the second one. In this article, a mixture model along with both Bayesian... 

A TI-ADC is a circuitry to achieve high sampling rates by passing the signal and its shifted versions through a number of parallel ADCs with lower sampling rates. When the time shifts between the C channels of a TI-ADC are properly tuned, the aggregate of the obtained samples is equivalent to that of a single ADC with C-times the sampling rate. However, the performance of a TI-ADC can be seriously degraded under interchannel timing mismatch. As this non-ideality cannot be avoided in practice, we need to first estimate the mismatch value, and then, compensate it. In this paper, by adopting a stochastic bandlimited signal model we study the signal recovery problem from the samples of a TI-ADC... 

The QoS attributes of Grid services play important roles in several tasks in Grid computing such as QoS-aware service composition, service negotiation, resource management, service discovery and scheduling. By considering the dynamic aspects of the Grid environments and also the uncertainty related to Grid services, in this paper, we present BNQM, a Bayesian network based probabilistic QoS Model for Grid service composition. Application of Bayesian network in QoS management makes it possible to indicate the conditional independence relationships among QoS attributes and to provide an effective probabilistic approach to predict new values for some QoS attributes while others are changed.... 

Ambient Intelligence is the future of computing where devices predict what users need and help them carry out their everyday life activities easier. To make this prediction possible these environments should be aware of the context. Activity recognition is one of the most complex problems in context-aware environments. In this paper we propose a layered Dynamic Bayesian Network (DBN) to recognize activities in an oral presentation. The layered architecture gives us the opportunity to recognize complex activities using the classification results of sensory data in the first layer regardless of the physical environment. Our model is event-driven meaning the classification takes place only when...