Long Term Evolution (LTE) is the latest step towards the 4th generation (4G) of radio technologies designed to increase the capacity and speed of mobile communications. To support Multimedia Broadcast/Multicast Servic...
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Long Term Evolution (LTE) is the latest step towards the 4th generation (4G) of radio technologies designed to increase the capacity and speed of mobile communications. To support Multimedia Broadcast/Multicast Services (MBMS), LTE offers the possibility to transmit Multimedia Broadcast multicast service over a Single Frequency Network (MBSFN), where a time-synchronized common waveform is transmitted from multiple cells for a given duration. In this paper we analytically present the MBSFN delivery method and evaluate its performance. The critical parameters of primary interest for the evaluation of the scheme are the packet delivery cost and its scalability. To this direction, a telecommunication cost analysis of the MBMS service is presented based on the transmission cost over the air interface, as well as the costs of all interfaces and nodes of the MBSFN architecture. Since the performance of the MBSFN scheme mainly depends on the configuration of the LTE network that is under investigation, we consider different network topologies, MBSFN deployments and user distributions.
This paper is concerned with the robust regional pole placement control problem against time invariant polytopic uncertainties. In this paper we propose an iterative approach to design a fixed-order output feedback co...
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This paper is concerned with the robust regional pole placement control problem against time invariant polytopic uncertainties. In this paper we propose an iterative approach to design a fixed-order output feedback controller achieving the robust regional pole placement. The features of our approach are to be able to use any controller variables as an initial point and to construct a controller sequence approaching a feasible region from the outside. Numerical examples are given to show the efficiency of our approach.
The different modeling approaches in Systems Biology create models with different levels of detail. The transformation techniques in Petri net theory can provide a solid framework for zooming between these different l...
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The different modeling approaches in Systems Biology create models with different levels of detail. The transformation techniques in Petri net theory can provide a solid framework for zooming between these different levels of abstraction and refinement. This work presents a Petri net based approach to Metabolic engineering that implements model reduction methods to reduce the complexity of large-scale metabolic networks. These methods can be complemented with kinetics inference to build dynamic models with a smaller number of parameters. The central carbon metabolism model of E. coli is used as a test-case to illustrate the application of these concepts. Model transformation is a promising mechanism to facilitate pathway analysis and dynamic modeling at the genome-scale level.
In this paper, a statistical learning approach to spatial context exploitation for semantic image analysis is presented. The proposed method constitutes an extension of the key parts of the authors' previous work ...
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In this paper, a statistical learning approach to spatial context exploitation for semantic image analysis is presented. The proposed method constitutes an extension of the key parts of the authors' previous work on spatial context utilization, where a Genetic Algorithm (GA) was introduced for exploiting fuzzy directional relations after performing an initial classification of image regions to semantic concepts using solely visual information. In the extensions reported in this work, a more elaborate approach is followed during the spatial knowledge acquisition and modeling process. Additionally, the impact of every resulting spatial constraint on the final outcome is adaptively adjusted. Experimental results as well as comparative evaluation on three datasets of varying complexity in terms of the total number of supported semantic concepts demonstrate the efficiency of the proposed method.
In this paper, automatic grape leaves processing and preparation of them (fitting of the proper cutting shapes on leaves area) to cover food industry needs, are presented. In the experimental setup, images are capture...
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In this paper, automatic grape leaves processing and preparation of them (fitting of the proper cutting shapes on leaves area) to cover food industry needs, are presented. In the experimental setup, images are captured by means of a single firewire camera and a LED-based lighting system, while grape leaves are moving on a conveyor belt. For image processing, a novel software application (called NtolcutPT) has been developed in visual C++. It is based on known and novel proposed in this paper image processing algorithms and methodologies that ensure automatic leaves processing in real time, with less wastage and more reliability. Adapting the proposed system in a food industry production line the whole process can be optimized. Experimental results prove the effectiveness of this system.
The human brain possesses a highly structured surface. A quantitative examination of the cortical surface is of particular interest for many questions, e.g. the investigation the relation between the spatial frequenci...
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Recently, real-time processing of image recognition is required for embedded applications such as automotive applications, robotics, entertainment, and so on. To realize real-time processing of image recognition on su...
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Intelligent Tutoring Systems (ITS) are computer-based education tools used for adaptive learning. An ITS authoring system allows teachers to create different modules of an intelligent tutoring system. In this paper, w...
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Intelligent Tutoring Systems (ITS) are computer-based education tools used for adaptive learning. An ITS authoring system allows teachers to create different modules of an intelligent tutoring system. In this paper, we present an ITS authoring system that allows teachers with very less computer skill to author most important modules of an ITS with minimum efforts. The salient features of the system include authoring multilingual documents, defining teaching strategies using fuzzy rules, representing student models through fuzzy state transition automata. The authoring system was used by the teachers and evaluated. The results shows fair degree of accuracy, which may further be improved by adding few more features.
This paper presents a new at-speed logic built-in self-test (BIST) architecture using staggered launch-on-shift (LOS) for testing a scan-based BIST design containing asynchronous clock domains. The proposed approach c...
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This paper presents a new at-speed logic built-in self-test (BIST) architecture using staggered launch-on-shift (LOS) for testing a scan-based BIST design containing asynchronous clock domains. The proposed approach can detect inter-clock-domain structural faults and intra-clock-domain delay and structural faults in the BIST design. This solves the long-standing problem of using the conventional one-hot LOS approach that requires testing one clock domain at a time which causes long test time or using the simultaneous LOS approach that requires adding capture-disabled circuitry to normal functional paths across interacting clock domains which causes fault coverage loss. Given a fixed number of BIST patterns, experimental results showed that the proposed staggered clocking scheme can detect more faults than one-hot clocking and simultaneous clocking.
The Internet has been a huge resource for sharing and collecting information including health related information. Some health related information is written by patients (lay persons) discussing their experience about...
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The Internet has been a huge resource for sharing and collecting information including health related information. Some health related information is written by patients (lay persons) discussing their experience about health problems and treatments. This paper introduces our initial work on providing physicians with clinically useful patient health writings. More specifically, the paper presented our experiments, as a part of the whole research work, on filtering health related text from patient health writings. We focused on selecting possible feature for classifying text from breast cancer mailing list into health and non health related text. Using KNN classification method, we experimented with various features, i.e. all terms, all terms except most frequently used terms, UMLS terms, health related UMLS terms, and health related UMLS semantic types. The experiments showed that UMLS terms extracted from the text is a good feature, compared to the other features.
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