In this paper we examine four algorithms for automated ultrasonic boundary detection, and describe the application of these algorithms to the quantification of the intima-media thickness (IMT) in the human carotid art...
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In this paper we examine four algorithms for automated ultrasonic boundary detection, and describe the application of these algorithms to the quantification of the intima-media thickness (IMT) in the human carotid artery. The first algorithm uses a dynamic programming approach to identify the boundary that minimizes a certain cost function. The second algorithm is based on finding points of maximum gradient. The third algorithm employs a mathematical model describing the intensity profile perpendicular to the two boundaries defining the IMT. The last algorithm is based on defining a template representing the intensity profile across boundary and applying a matched filter procedure to find the image region that best matches it. The authors also present a quantitative and qualitative comparison between the four algorithms examined. It is shown that the dynamic programming algorithm provides superior performance in terms of accuracy and robustness. The correlation coefficients between automated measurements and manually obtained reference values were 0.96, 0.94, 0.63, and 0.85 for the dynamic programming, the maximum gradient the model-based, and the matched filter algorithm, respectively (n=30).
In the multi-core era, ensuring deadlock freedom of communication fabrics is an important challenge. Intel proposed xMAS, a microarchitecture description language (MaDL), to support the formal modelling and verificati...
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In the multi-core era, ensuring deadlock freedom of communication fabrics is an important challenge. Intel proposed xMAS, a microarchitecture description language (MaDL), to support the formal modelling and verification of communication fabrics. The xMAS language is restricted to eight basic primitives. Using this restriction, an efficient deadlock detection technique has been defined. This technique is tailored to the eight primitives, which are not sufficient to model many realistic designs. We exhibit two primitives, namely, an adaptive switch and a synchronization barrier, that cannot be expressed or analyzed using the current xMAS language and tools. Our main contribution is to automatically generate an efficient deadlock detection algorithm tailored to a given set of primitives. We define a set of core primitives and extension mechanisms for user-defined primitives. This creates a family of MaDL's together with a family of tailored and efficient deadlock detection algorithms. We prove that the automatically generated algorithms are correct by construction, i.e., they correctly detect deadlocks in all fabrics defined in the language for which they are generated. These algorithms handle message dependencies, counters, virtual channels, parametric buffer sizes, and many other aspects of micro-architectural models. The effectiveness of our approach is demonstrated on models with adaptive switches and synchronization barriers. Our approach automatically provides efficient deadlock detection for a large family of MaDL's.
Now a days social network analysis has gained much attention. A community in social networks indicates that nodes within the group are densely connected and connections between groups are sparse. These social networks...
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Now a days social network analysis has gained much attention. A community in social networks indicates that nodes within the group are densely connected and connections between groups are sparse. These social networks can be modeled as signed social networks that contain both positive and negative links or relations. In the social networks community mining or detection is the task of grouping the nodes together on the basis of their linked pattern. In the literature, various community detection algorithms have been proposed. This paper represents an overview of community detection algorithms in the signed social networks along with their classification. Then after we have a comparative study of the community detection algorithms.
Traditional space time adaptive processors for radar target detection require a training data set which is usually drawn from adjacent range gates. Clutter heterogeneity, however, can severely limit the available trai...
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Traditional space time adaptive processors for radar target detection require a training data set which is usually drawn from adjacent range gates. Clutter heterogeneity, however, can severely limit the available training sample support and consequently degrade the detection performance. The SDS algorithms, on the other hand, overcome this problem by operating solely on the test data without recourse to training data. In this paper we evaluate both of these approaches, in particular the AMF and MLED, using the MCARM data set. We illustrate the performance degradation of the AMF that results from the clutter heterogeneity and the corresponding advantage of the MLED. We also show that a calibration step of the spatial steering vectors results in significant performance improvement of all of the algorithms considered here.
Corners are important image features whose detection is very important in many computer vision tasks. In this paper we have evaluated the performance of five intensity based corner detectors with the help of a dozen t...
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Corners are important image features whose detection is very important in many computer vision tasks. In this paper we have evaluated the performance of five intensity based corner detectors with the help of a dozen test images, artificial and real, based on six performance measures of which three are proposed by us. We then propose a new approach, using bitplane decomposition, in which a grayscale image is first divided into several bit-planes, and then the original corner detectors are applied on all the bit-planes simultaneously and finally using a threshold, all the higher bit-plane corners are recombined up to the thresholded bit-plane to obtain the final set of corners. Using this approach, we have seen that the performance of the algorithms has improved significantly with respect to both detection and time.
It is well-known that the brain is a complex network""brain areas dedicated to different functions. As such,""consisting of""it is natural to shift toward brain network from brain mapping...
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It is well-known that the brain is a complex network""brain areas dedicated to different functions. As such,""consisting of""it is natural to shift toward brain network from brain mapping for deeper understanding of brain functions. Although graph theoretical network metrics measuring global or local properties of network topology have been used to investigate the brain network, they do no provide any information about intermediate scale of the brain network, which is provided by the community structure analysis.""In this paper, we propose a method to compare different community detection algorithms for multiple subjects data in terms of the agreement of a group-based community structure with individual community structures. As it is crucial to find a single group-based community structure for a group of subjects to discuss about brain areas and connections, a number of algorithms based on different approaches have been proposed. To show the feasibility of the method for comparing different algorithms, two community detection algorithms based on different approaches ("virtual-typical-subject" and "group analysis") were examined. The Normalized Mutual Information was computed to measure similarity between the group-based community structure and individual community structures derived from resting-state fMRI functional network, and was used for comparing the two algorithms. Our method demonstrated that the algorithm based on the group-analysis approach detected a group-based community structure with greater agreement with individual community structures.
To explain super resolution focusing achieved in time reversal, matched field processing (TR/MFP), an analytical multipath model based on geometric optics approximation with strong total internal reflections was prese...
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To explain super resolution focusing achieved in time reversal, matched field processing (TR/MFP), an analytical multipath model based on geometric optics approximation with strong total internal reflections was presented in A. Asif and Q. Bai, (2006). This paper uses the multipath model to derive the system response matrix in TRTMFP and exploits the structure of the response matrix to develop detection algorithms for determining the number and locations of passive targets embedded in a random medium. Experimental results prove the accuracy of the algorithms in detecting passive targets
The performance of two major Radio Frequency Interference (RFI) detection algorithms is compared. The peak detection algorithm and the kurtosis detection algorithm are characterized using the receiver operating charac...
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The performance of two major Radio Frequency Interference (RFI) detection algorithms is compared. The peak detection algorithm and the kurtosis detection algorithm are characterized using the receiver operating characteristic (ROC) for pulsed sinusoid RFI. Downlink data bandwidth is one of the major design factors to be considered when comparing detection algorithms. Results are presented in this paper that compare the kurtosis algorithm performance with an ideally matched peak detection algorithm. The RFI parameters are also varied to analyze the overall detection performance of both algorithms. Factors such as implementation details, resource usage, post-processing and practicality are also addressed for both algorithms.
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