Search results for: Temporal Ontologies

Authors: Farhad Asadi, Hossein Sadati

Abstract:

In fields such as neuroscience and especially in cognition modeling of mental processes, uncertainty processing in temporal zone of signal is vital. In this paper, Bayesian online inferences in estimation of change-points location in signal are constructed. This method separated the observed signal into independent series and studies the change and variation of the regime of data locally with related statistical characteristics. We give conditions on simulations of the method when the data characteristics of signals vary, and provide empirical evidence to show the performance of method. It is verified that correlation between series around the change point location and its characteristics such as Signal to Noise Ratios and mean value of signal has important factor on fluctuating in finding proper location of change point. And one of the main contributions of this study is related to representing of these influences of signal statistical characteristics for finding abrupt variation in signal. There are two different structures for simulations which in first case one abrupt change in temporal section of signal is considered with variable position and secondly multiple variations are considered. Finally, influence of statistical characteristic for changing the location of change point is explained in details in simulation results with different artificial signals.

Keywords: Time series, fluctuation in statistical characteristics, optimal learning.

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Authors: Hung Tuan Nguyen

Abstract:

In this paper, with the purpose of further reducing the complexity of the system, while keeping its temporal and spatial focusing performance, we investigate the possibility of using optimal one bit time reversal (TR) system for impulse radio ultra wideband multi-user wireless communications. The results show that, by optimally selecting the number of used taps in the pre-filter the optimal one bit TR system can outperform the full one bit TR system. In some cases, the temporal and spatial focusing performance of the optimal one bit TR system appears to be compatible with that of the original TR system. This is a significant result as the overhead cost is much lower than it is required in the original TR system.

Keywords: Time reversal, optimal one bit, UWB, multi-user interference, inter symbol interference

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Authors: Dima Stopel, Zvi Boger, Robert Moskovitch, Yuval Shahar, Yuval Elovici

Abstract:

Computer worm detection is commonly performed by antivirus software tools that rely on prior explicit knowledge of the worm-s code (detection based on code signatures). We present an approach for detection of the presence of computer worms based on Artificial Neural Networks (ANN) using the computer's behavioral measures. Identification of significant features, which describe the activity of a worm within a host, is commonly acquired from security experts. We suggest acquiring these features by applying feature selection methods. We compare three different feature selection techniques for the dimensionality reduction and identification of the most prominent features to capture efficiently the computer behavior in the context of worm activity. Additionally, we explore three different temporal representation techniques for the most prominent features. In order to evaluate the different techniques, several computers were infected with five different worms and 323 different features of the infected computers were measured. We evaluated each technique by preprocessing the dataset according to each one and training the ANN model with the preprocessed data. We then evaluated the ability of the model to detect the presence of a new computer worm, in particular, during heavy user activity on the infected computers.

Keywords: Artificial Neural Networks, Feature Selection, Temporal Analysis, Worm Detection.

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Authors: Maryam Vafadar, Alireza Behrad, Saeed Akbari

Abstract:

Nowadays, with the emerging of the new applications like robot control in image processing, artificial vision for visual servoing is a rapidly growing discipline and Human-machine interaction plays a significant role for controlling the robot. This paper presents a new algorithm based on spatio-temporal volumes for visual servoing aims to control robots. In this algorithm, after applying necessary pre-processing on video frames, a spatio-temporal volume is constructed for each gesture and feature vector is extracted. These volumes are then analyzed for matching in two consecutive stages. For hand gesture recognition and classification we tested different classifiers including k-Nearest neighbor, learning vector quantization and back propagation neural networks. We tested the proposed algorithm with the collected data set and results showed the correct gesture recognition rate of 99.58 percent. We also tested the algorithm with noisy images and algorithm showed the correct recognition rate of 97.92 percent in noisy images.

Keywords: Back propagation neural network, Feature vector, Hand gesture recognition, k-Nearest Neighbor, Learning vector quantization neural network, Robot control, Spatio-temporal volume, Visual servoing

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Authors: Suguru Yoshioka, Satoshi Tojo

Abstract:

This paper introduces a temporal epistemic logic CBCTL that updates agent-s belief states through communications in them, based on computational tree logic (CTL). In practical environments, communication channels between agents may not be secure, and in bad cases agents might suffer blackouts. In this study, we provide inform* protocol based on ACL of FIPA, and declare the presence of secure channels between two agents, dependent on time. Thus, the belief state of each agent is updated along with the progress of time. We show a prover, that is a reasoning system for a given formula in a given a situation of an agent ; if it is directly provable or if it could be validated through the chains of communications, the system returns the proof.

Keywords: communication channel, computational tree logic, reasoning system, temporal epistemic logic.

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Authors: Salam Khalifa, Naveed Ahmed

Abstract:

We present a new method to reconstruct a temporally coherent 3D animation from single or multi-view RGB-D video data using unbiased feature point sampling. Given RGB-D video data, in form of a 3D point cloud sequence, our method first extracts feature points using both color and depth information. In the subsequent steps, these feature points are used to match two 3D point clouds in consecutive frames independent of their resolution. Our new motion vectors based dynamic alignement method then fully reconstruct a spatio-temporally coherent 3D animation. We perform extensive quantitative validation using novel error functions to analyze the results. We show that despite the limiting factors of temporal and spatial noise associated to RGB-D data, it is possible to extract temporal coherence to faithfully reconstruct a temporally coherent 3D animation from RGB-D video data.

Keywords: 3D video, 3D animation, RGB-D video, Temporally Coherent 3D Animation.

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Authors: Rajdeep Niyogi

Abstract:

The objective of the paper is twofold. First, to develop a formal framework for planning for mobile agents. A logical language based on a temporal logic is proposed that can express a type of tasks which often arise in network management. Second, to design a planning algorithm for such tasks. The aim of this paper is to study the importance of finding plans for mobile agents. Although there has been a lot of research in mobile agents, not much work has been done to incorporate planning ideas for such agents. This paper makes an attempt in this direction. A theoretical study of finding plans for mobile agents is undertaken. A planning algorithm (based on the paradigm of mobile computing) is proposed and its space, time, and communication complexity is analyzed. The algorithm is illustrated by working out an example in detail.

Keywords: Acting, computer network, mobile agent, mobile computing, planning, temporal logic.

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Authors: Luka Pavlič, Marjan Heričko

Abstract:

E-services have significantly changed the way of doing business in recent years. We can, however, observe poor use of these services. There is a large gap between supply and actual eservices usage. This is why we started a project to provide an environment that will encourage the use of e-services. We believe that only providing e-service does not automatically mean consumers would use them. This paper shows the origins of our project and its current position. We discuss the decision of using semantic web technologies and their potential to improve e-services usage. We also present current knowledge base and its real-world classification. In the paper, we discuss further work to be done in the project. Current state of the project is promising.

Keywords: E-Services, E-Services Repository, Ontologies, Semantic Web

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Authors: Ibrahim Ozkan

Abstract:

In this study, a cross-layer design which combines adaptive modulation and coding (AMC) and hybrid automatic repeat request (HARQ) techniques for a cooperative wireless network is investigated analytically. Previous analyses of such systems in the literature are confined to the case where the fading channel is independent at each retransmission, which can be unrealistic unless the channel is varying very fast. On the other hand, temporal channel correlation can have a significant impact on the performance of HARQ systems. In this study, utilizing a Markov channel model which accounts for the temporal correlation, the performance of non-cooperative and cooperative networks are investigated in terms of packet loss rate and throughput metrics for Chase combining HARQ strategy.

Keywords: Cooperative network, adaptive modulation and coding, hybrid ARQ, correlated fading.

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Authors: Deng Zengming, Wang Mingjiang

Abstract:

As 3D video is explored as a hot research topic in the last few decades, free-viewpoint TV (FTV) is no doubt a promising field for its better visual experience and incomparable interactivity. View synthesis is obviously a crucial technology for FTV; it enables to render images in unlimited numbers of virtual viewpoints with the information from limited numbers of reference view. In this paper, a novel hybrid synthesis framework is proposed and blending priority is explored. In contrast to the commonly used View Synthesis Reference Software (VSRS), the presented synthesis process is driven in consideration of the temporal correlation of image sequences. The temporal correlations will be exploited to produce fine synthesis results even near the foreground boundaries. As for the blending priority, this scheme proposed that one of the two reference views is selected to be the main reference view based on the distance between the reference views and virtual view, another view is chosen as the auxiliary viewpoint, just assist to fill the hole pixel with the help of background information. Significant improvement of the proposed approach over the state-of –the-art pixel-based virtual view synthesis method is presented, the results of the experiments show that subjective gains can be observed, and objective PSNR average gains range from 0.5 to 1.3 dB, while SSIM average gains range from 0.01 to 0.05.

Keywords: View synthesis, Gaussian mixture model, hybrid framework, fusion method.

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Authors: Xiangbin Zhu

Abstract:

Advances in computing applications in recent years have prompted the demand for more flexible scheduling models for QoS demand. Moreover, in practical applications, partly violated temporal constraints can be tolerated if the violation meets certain distribution. So we need extend the traditional Liu and Lanland model to adapt to these circumstances. There are two extensions, which are the (m, k)-firm model and Window-Constrained model. This paper researches on weakly hard real-time constraints and their combination to support QoS. The fact that a practical application can tolerate some violations of temporal constraint under certain distribution is employed to support adaptive QoS on the open real-time system. The experiment results show these approaches are effective compared to traditional scheduling algorithms.

Keywords: Weakly Hard Real-Time, Real-Time, Scheduling, Quality of Service.

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Authors: Sumiko Anno, Keiji Imaoka, Takeo Tadono, Tamotsu Igarashi, Subramaniam Sivaganesh, Selvam Kannathasan, Vaithehi Kumaran, Sinnathamby Noble Surendran

Abstract:

Dengue outbreaks are affected by biological, ecological, socio-economic and demographic factors that vary over time and space. These factors have been examined separately and still require systematic clarification. The present study aimed to investigate the spatial-temporal clustering relationships between these factors and dengue outbreaks in the northern region of Sri Lanka. Remote sensing (RS) data gathered from a plurality of satellites were used to develop an index comprising rainfall, humidity and temperature data. RS data gathered by ALOS/AVNIR-2 were used to detect urbanization, and a digital land cover map was used to extract land cover information. Other data on relevant factors and dengue outbreaks were collected through institutions and extant databases. The analyzed RS data and databases were integrated into geographic information systems, enabling temporal analysis, spatial statistical analysis and space-time clustering analysis. Our present results showed that increases in the number of the combination of ecological factor and socio-economic and demographic factors with above the average or the presence contribute to significantly high rates of space-time dengue clusters.

Keywords: ALOS/AVNIR-2, Dengue, Space-time clustering analysis, Sri Lanka.

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Authors: Eric Jacob Bacon, Chaoyang Jin, Dianning He, Shuaishuai Hu, Lanbo Wang, Han Li, Shouliang Qi

Abstract:

Refractory epilepsy is a complicated type of epilepsy that can be difficult to diagnose. Recent technological advancements have made resting-state functional magnetic resonance (rsfMRI) a vital technique for studying brain activity. However, there is still much to learn about rsfMRI. Investigating rsfMRI connectivity may aid in the detection of abnormal activities. In this paper, we propose studying the functional connectivity of rsfMRI candidates to diagnose epilepsy. 45 rsfMRI candidates, comprising 26 with refractory epilepsy and 19 healthy controls, were enrolled in this study. A data-driven approach known as Independent Component Analysis (ICA) was used to achieve our goal. First, rsfMRI data from both patients and healthy controls were analyzed using group ICA. The components that were obtained were then spatially sorted to find and select meaningful ones. A two-sample t-test was also used to identify abnormal networks in patients and healthy controls. Finally, based on the fractional amplitude of low-frequency fluctuations (fALFF), a chi-square statistic test was used to distinguish the network properties of the patient and healthy control groups. The two-sample t-test analysis yielded abnormal in the default mode network, including the left superior temporal lobe and the left supramarginal. The right precuneus was found to be abnormal in the dorsal attention network. In addition, the frontal cortex showed an abnormal cluster in the medial temporal gyrus. In contrast, the temporal cortex showed an abnormal cluster in the right middle temporal gyrus and the right fronto-operculum gyrus. Finally, the chi-square statistic test was significant, producing a p-value of 0.001 for the analysis. This study offers evidence that investigating rsfMRI connectivity provides an excellent diagnosis option for refractory epilepsy.

Keywords: Independent Component Analysis, Resting State Network, refractory epilepsy, rsfMRI.

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Authors: Rafat Alshorman, Walter Hussak

Abstract:

Existing work in temporal logic on representing the execution of infinitely many transactions, uses linear-time temporal logic (LTL) and only models two-step transactions. In this paper, we use the comparatively efficient branching-time computational tree logic CTL and extend the transaction model to a class of multistep transactions, by introducing distinguished propositional variables to represent the read and write steps of n multi-step transactions accessing m data items infinitely many times. We prove that the well known correspondence between acyclicity of conflict graphs and serializability for finite schedules, extends to infinite schedules. Furthermore, in the case of transactions accessing the same set of data items in (possibly) different orders, serializability corresponds to the absence of cycles of length two. This result is used to give an efficient encoding of the serializability condition into CTL.

Keywords: computational tree logic, serializability, multi-step transactions.

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Authors: Sungju Yun, Anjin Park, Keechul Jung

Abstract:

Motion capture devices have been utilized in producing several contents, such as movies and video games. However, since motion capture devices are expensive and inconvenient to use, motions segmented from captured data was recycled and synthesized to utilize it in another contents, but the motions were generally segmented by contents producers in manual. Therefore, automatic motion segmentation is recently getting a lot of attentions. Previous approaches are divided into on-line and off-line, where on-line approaches segment motions based on similarities between neighboring frames and off-line approaches segment motions by capturing the global characteristics in feature space. In this paper, we propose a graph-based high-level motion segmentation method. Since high-level motions consist of several repeated frames within temporal distances, we consider all similarities among all frames within the temporal distance. This is achieved by constructing a graph, where each vertex represents a frame and the edges between the frames are weighted by their similarity. Then, normalized cuts algorithm is used to partition the constructed graph into several sub-graphs by globally finding minimum cuts. In the experiments, the results using the proposed method showed better performance than PCA-based method in on-line and GMM-based method in off-line, as the proposed method globally segment motions from the graph constructed based similarities between neighboring frames as well as similarities among all frames within temporal distances.

Keywords: Capture Devices, High-Level Motion, Motion Segmentation, Normalized Cuts

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Authors: Yaser Mohammad Taheri, Alireza Zolghadr–asli, Mehran Yazdi

Abstract:

Video watermarking is usually considered as watermarking of a set of still images. In frame-by-frame watermarking approach, each video frame is seen as a single watermarked image, so collusion attack is more critical in video watermarking. If the same or redundant watermark is used for embedding in every frame of video, the watermark can be estimated and then removed by watermark estimate remodolulation (WER) attack. Also if uncorrelated watermarks are used for every frame, these watermarks can be washed out with frame temporal filtering (FTF). Switching watermark system or so-called SS-N system has better performance against WER and FTF attacks. In this system, for each frame, the watermark is randomly picked up from a finite pool of watermark patterns. At first SS-N system will be surveyed and then a new collusion attack for SS-N system will be proposed using a new algorithm for separating video frame based on watermark pattern. So N sets will be built in which every set contains frames carrying the same watermark. After that, using WER attack in every set, N different watermark patterns will be estimated and removed later.

Keywords: Watermark estimation remodulation (WER), Frame Temporal Averaging (FTF), switching watermark system.

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Authors: A. Mirrashid, M. Khoshbin, A. Atghaei, H. Shahbazi

Abstract:

In various industries, smoke and fire are two of the most important threats in the workplace. One of the common methods for detecting smoke and fire is the use of infrared thermal and smoke sensors, which cannot be used in outdoor applications. Therefore, the use of vision-based methods seems necessary. The problem of smoke and fire detection is spatiotemporal and requires spatiotemporal solutions. This paper presents a method that uses spatial features along with temporal-based features to detect smoke and fire in the scene. It consists of three main parts; the task of each part is to reduce the error of the previous part so that the final model has a robust performance. This method also uses transformer modules to increase the accuracy of the model. The results of our model show the proper performance of the proposed approach in solving the problem of smoke and fire detection and can be used to increase workplace safety.

Keywords: Attention, fire detection, smoke detection, spatiotemporal.

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Authors: Pei-Jeng Kuo, Terumasa Aoki, Hiroshi Yasuda

Abstract:

PARIS (Personal Archiving and Retrieving Image System) is an experiment personal photograph library, which includes more than 80,000 of consumer photographs accumulated within a duration of approximately five years, metadata based on our proposed MPEG-7 annotation architecture, Dozen Dimensional Digital Content (DDDC), and a relational database structure. The DDDC architecture is specially designed for facilitating the managing, browsing and retrieving of personal digital photograph collections. In annotating process, we also utilize a proposed Spatial and Temporal Ontology (STO) designed based on the general characteristic of personal photograph collections. This paper explains PRAIS system.

Keywords: Ontology, Databases and Information Retrieval, MPEG-7, Spatial-Temporal, Digital Library Designs l, metadata, Semantic Web, semi-automatic annotation

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Authors: Yuki. Akiyama, Hiroaki. Sengoku, Ryosuke. Shibasaki

Abstract:

In this study, the locations and areas of commercial accumulations were detected by using digital yellow page data. An original buffering method that can accurately create polygons of commercial accumulations is proposed in this paper.; by using this method, distribution of commercial accumulations can be easily created and monitored over a wide area. The locations, areas, and time-series changes of commercial accumulations in the South Kanto region can be monitored by integrating polygons of commercial accumulations with the time-series data of digital yellow page data. The circumstances of commercial accumulations were shown to vary according to areas, that is, highly- urbanized regions such as the city center of Tokyo and prefectural capitals, suburban areas near large cities, and suburban and rural areas.

Keywords: Commercial accumulations, Spatio-temporal analysis, Urban monitoring, Yellow page data

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Authors: Meshkati M. E., Dehghani A. A., Naser G., E mamgholizadeh S., Mosaedi A.

Abstract:

Sedimentation in reservoirs and the corresponding loss of storage capacity is one of the most serious problems in dam engineering. Pressurized flushing, a way to remove sediments from the reservoir, is flushing under a pressurized flow condition and nearly constant water level. Pressurized flushing has only local effects around the outlet. Sediment in the vicinity of the outlet openings is scoured and a funnel shaped crater is created. In this study, the temporal development of flushing cone under various hydraulic conditions was studied experimentally. Time variations of parameters such as maximum length and width of flushing and also depth of scouring cone was measured. Results indicated that an increase in flow velocity (and consequently in Froude number) established new hydraulically conditions for flushing mechanism and so a sudden growth was observed in the amount of sediment released and also scouring dimenssions. In addition, a set of nondimensional relationships were identified for temporal variations of flushing scour dimenssions, which can eventuallt be used to estimate the development of flushing cone.

Keywords: Pressure Flushing, Dam, Sediment, Scouring.

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Authors: Manfred Hartmann, Andreas Graef, Hannes Perko, Christoph Baumgartner, Tilmann Kluge

Abstract:

In this paper we introduce a novel method for the characterization of synchronziation and coupling effects in multivariate time series that can be used for the analysis of EEG or ECoG signals recorded during epileptic seizures. The method allows to visualize the spatio-temporal evolution of synchronization and coupling effects that are characteristic for epileptic seizures. Similar to other methods proposed for this purpose our method is based on a regression analysis. However, a more general definition of the regression together with an effective channel selection procedure allows to use the method even for time series that are highly correlated, which is commonly the case in EEG/ECoG recordings with large numbers of electrodes. The method was experimentally tested on ECoG recordings of epileptic seizures from patients with temporal lobe epilepsies. A comparision with the results from a independent visual inspection by clinical experts showed an excellent agreement with the patterns obtained with the proposed method.

Keywords: EEG, epilepsy, regression analysis, seizurepropagation.

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Authors: Toan Vu Duc, Son Ha Viet

Abstract:

An evaluation of the PCBs residues in the surface soils from Bacninh, Vietnam was carried out. Sixty representative soil samples were collected from the centre of Bacninh and three surrounding districts. The analyzed results indicated the wide extent of contamination of total PCBs in Bacninh. In industrial and urban zones, total PCBs concentrations ranged from ranged from <0.02 to 32.68ng g-1 (mean 19.89 ±15.64ng g-1) dry weight, while those in agricultural zones ranged from <0.02 to 13.26ng g-1 (mean 8.14 ± 4.89ng g-1) dry weight. The mean percentages of PCB28, PCB52, PCB101, PCB138, PCB153 and PCB180 compared with Σ6PCBs in the analyzed soil samples are 3.1%, 13.9%, 21.7%, 30.7%, 25.8% and 4.8%, respectively. These values can be explained by the chemical properties as well as the compositions of PCBs mixture which probably escaped from dielectric oil. An increasing trend and the long-time release of PCBs are observed.

Keywords: Contamination, PCBs, soil, temporal trend.

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Authors: Dominykas Broga

Abstract:

Until recently it would have been unusual to consider classifying population movements and refugees as security problem. However, efforts at shaping our world to make ourselves secure have paradoxically led to ever greater insecurity. The feeling of uncertainty, pertinent throughout all discourses of security, has led to the creation of security production into seemingly benign routines of everyday life. Yet, the paper argues, neither of security discourses accounted for, disclosed and challenged the fundamental aporias embedded in Western security narratives. In turn, the paper aims to unpick the conventional security wisdom, which is haunted with strong ontologies, embedded in the politics of Orientalism, and (in)security nexus. The paper concludes that current security affair conceals the integral impossibility of fulfilling its very own promise of assured security. The paper also provides suggestions about alternative security discourse based on mutual dialogue.

Keywords: Identity, (in)security, migration, ontology

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Authors: Yunyoung Nam, Junghun Ryu, Yoo-Joo Choi, We-Duke Cho

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This paper presents a novel approach for representing the spatio-temporal topology of the camera network with overlapping and non-overlapping fields of view (FOVs). The topology is determined by tracking moving objects and establishing object correspondence across multiple cameras. To track people successfully in multiple camera views, we used the Merge-Split (MS) approach for object occlusion in a single camera and the grid-based approach for extracting the accurate object feature. In addition, we considered the appearance of people and the transition time between entry and exit zones for tracking objects across blind regions of multiple cameras with non-overlapping FOVs. The main contribution of this paper is to estimate transition times between various entry and exit zones, and to graphically represent the camera topology as an undirected weighted graph using the transition probabilities.

Keywords: Surveillance, multiple camera, people tracking, topology.

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Authors: Ahmad Aljaafreh

Abstract:

This paper proposes a hierarchical hidden Markov model (HHMM) to model the detection of M vehicles in a wireless sensor network (WSN). The HHMM model contains an extra level of hidden Markov model to model the temporal transitions of each state of the first HMM. By modeling the temporal transitions, only those hypothesis with nonzero transition probabilities needs to be tested. Thus, this method efficiently reduces the computation load, which is preferable in WSN applications.This paper integrates several techniques to optimize the detection performance. The output of the states of the first HMM is modeled as Gaussian Mixture Model (GMM), where the number of states and the number of Gaussians are experimentally determined, while the other parameters are estimated using Expectation Maximization (EM). HHMM is used to model the sequence of the local decisions which are based on multiple hypothesis testing with maximum likelihood approach. The states in the HHMM represent various combinations of vehicles of different types. Due to the statistical advantages of multisensor data fusion, we propose a heuristic based on fuzzy weighted majority voting to enhance cooperative classification of moving vehicles within a region that is monitored by a wireless sensor network. A fuzzy inference system weighs each local decision based on the signal to noise ratio of the acoustic signal for target detection and the signal to noise ratio of the radio signal for sensor communication. The spatial correlation among the observations of neighboring sensor nodes is efficiently utilized as well as the temporal correlation. Simulation results demonstrate the efficiency of this scheme.

Keywords: Classification, decision fusion, fuzzy logic, hidden Markov model

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Authors: Sallama Athab, Hala Bahjat, Zhang Yinghui

Abstract:

Background detection is essential in video analyses; optimization is often needed in order to achieve real time calculation. Information gathered by dual cameras placed in the front and rear part of an Autonomous Vehicle (AV) is integrated for background detection. In this paper, real time calculation is achieved on the proposed technique by using Priority Regions (PR) and Parallel Processing together where each frame is divided into regions then and each region process is processed in parallel. PR division depends upon driver view limitations. A background detection system is built on the Temporal Difference (TD) and Gaussian Filtering (GF). Temporal Difference and Gaussian Filtering with multi threshold and sigma (weight) value are be based on PR characteristics. The experiment result is prepared on real scene. Comparison of the speed and accuracy with traditional background detection techniques, the effectiveness of PR and parallel processing are also discussed in this paper.

Keywords: Autonomous Vehicle, Background Detection, Dual Camera, Gaussian Filtering, Parallel Processing.

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Authors: Arnab Bandyopadhyay, Anubhab Pal, Subhajit Debnath

Abstract:

Climate change is a cumulative change in weather patterns over a period of time. Trend analysis using non-parametric Mann-Kendall test may help to determine the existence and magnitude of any statistically significant trend in the climatic data. Another index called Sen slope may be used to quantify the magnitude of such trends. A toolbar extension to ESRI ArcGIS named Arc Trends has been developed in this study for performing the above mentioned tasks. To study the temporal trend of meteorological parameters, 32 years (1971-2002) monthly meteorological data were collected for 133 selected stations over different agro-ecological regions of India. Both the maximum and minimum temperatures were found to be rising. A significant increasing trend in the relative humidity and a consistent significant decreasing trend in the wind speed all over the country were found. However, a general increase in rainfall was not found in recent years.

Keywords: Temporal trend, climate change, ArcGIS, Mann- Kendall test, Sen slope

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Authors: Heejung Lee, Hyo-Won Suh

Abstract:

Hybrid knowledge model is suggested as an underlying framework for product development management. It can support such hybrid features as ontologies and rules. Effective collaboration in product development environment depends on sharing and reasoning product information as well as engineering knowledge. Many studies have considered product information and engineering knowledge. However, most previous research has focused either on building the ontology of product information or rule-based systems of engineering knowledge. This paper shows that F-logic based knowledge model can support such desirable features in a hybrid way.

Keywords: Ontology, rule, F-logic, product development.

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Authors: Tao Dai, Andy Adler, Behnam Shahrrava

Abstract:

This paper presents a forgetting factor scheme for variable step-size affine projection algorithms (APA). The proposed scheme uses a forgetting processed input matrix as the projection matrix of pseudo-inverse to estimate system deviation. This method introduces temporal weights into the projection matrix, which is typically a better model of the real error's behavior than homogeneous temporal weights. The regularization overcomes the ill-conditioning introduced by both the forgetting process and the increasing size of the input matrix. This algorithm is tested by independent trials with coloured input signals and various parameter combinations. Results show that the proposed algorithm is superior in terms of convergence rate and misadjustment compared to existing algorithms. As a special case, a variable step size NLMS with forgetting factor is also presented in this paper.

Keywords: Adaptive signal processing, affine projection algorithms, variable step-size adaptive algorithms, regularization.

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Authors: Muhammad Arslan Usman, Muhammad Rehan Usman, Soo Young Shin

Abstract:

Live video streaming is one of the most widely used service among end users, yet it is a big challenge for the network operators in terms of quality. The only way to provide excellent Quality of Experience (QoE) to the end users is continuous monitoring of live video streaming. For this purpose, there are several objective algorithms available that monitor the quality of the video in a live stream. Subjective tests play a very important role in fine tuning the results of objective algorithms. As human perception is considered to be the most reliable source for assessing the quality of a video stream subjective tests are conducted in order to develop more reliable objective algorithms. Temporal impairments in a live video stream can have a negative impact on the end users. In this paper we have conducted subjective evaluation tests on a set of video sequences containing temporal impairment known as frame freezing. Frame Freezing is considered as a transmission error as well as a hardware error which can result in loss of video frames on the reception side of a transmission system. In our subjective tests, we have performed tests on videos that contain a single freezing event and also for videos that contain multiple freezing events. We have recorded our subjective test results for all the videos in order to give a comparison on the available No Reference (NR) objective algorithms. Finally, we have shown the performance of no reference algorithms used for objective evaluation of videos and suggested the algorithm that works better. The outcome of this study shows the importance of QoE and its effect on human perception. The results for the subjective evaluation can serve the purpose for validating objective algorithms.

Keywords: Objective evaluation, subjective evaluation, quality of experience (QoE), video quality assessment (VQA).

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