Medical image retrieval with query-dependent feature fusion based on one-class SVM

Due to the huge growth of the World Wide Web, medical images are now available in large numbers in online repositories, and there exists the need to retrieval the images through automatically extracting visual information of the medical images, which is commonly known as content-based image retrieval (CBIR). Since each feature extracted from images just characterizes certain aspect of image content, multiple features are necessarily employed to improve the retrieval performance. Meanwhile, experiments demonstrate that a special feature is not equally important for different image queries. Most of existed feature fusion methods for image retrieval only utilize query independent feature fusion or rely on explicit user weighting. In this paper, we present a novel query dependent feature fusion method for medical image retrieval based on one class support vector machine. Having considered that a special feature is not equally important for different image queries, the proposed query dependent feature fusion method can learn different feature fusion models for different image queries only based on multiply image samples provided by the user, and the learned feature fusion models can reflect the different importances of a special feature for different image queries. The experimental results on the IRMA medical image collection demonstrate that the proposed method can improve the retrieval performance effectively and can outperform existed feature fusion methods for image retrieval.<br /

A new query dependent feature fusion approach for medical image retrieval based on one-class SVM

With the development of the internet, medical images are now available in large numbers in online repositories, and there exists the need to retrieval the medical images in the content-based ways through automatically extracting visual information of the medical images. Since a single feature extracted from images just characterizes certain aspect of image content, multiple features are necessarily employed to improve the retrieval performance. Furthermore, a special feature is not equally important for different image queries since a special feature has different importance in reflecting the content of different images. However, most existed feature fusion methods for image retrieval only utilize query independent feature fusion or rely on explicit user weighting. In this paper, based on multiply query samples provided by the user, we present a novel query dependent feature fusion method for medical image retrieval based on one class support vector machine. The proposed query dependent feature fusion method for medical image retrieval can learn different feature fusion models for different image queries, and the learned feature fusion models can reflect the different importance of a special feature for different image queries. The experimental results on the IRMA medical image collection demonstrate that the proposed method can improve the retrieval performance effectively and can outperform existed feature fusion methods for image retrieval.<br /

A multi-resource load balancing algorithm for cloud cache systems

With the advent of cloud computing model, distributed caches have become the cornerstone for building scalable applications. Popular systems like Facebook [1] or Twitter use Memcached [5], a highly scalable distributed object cache, to speed up applications by avoiding database accesses. Distributed object caches assign objects to cache instances based on a hashing function, and objects are not moved from a cache instance to another unless more instances are added to the cache and objects are redistributed. This may lead to situations where some cache instances are overloaded when some of the objects they store are frequently accessed, while other cache instances are less frequently used. In this paper we propose a multi-resource load balancing algorithm for distributed cache systems. The algorithm aims at balancing both CPU and Memory resources among cache instances by redistributing stored data. Considering the possible conflict of balancing multiple resources at the same time, we give CPU and Memory resources weighted priorities based on the runtime load distributions. A scarcer resource is given a higher weight than a less scarce resource when load balancing. The system imbalance degree is evaluated based on monitoring information, and the utility load of a node, a unit for resource consumption. Besides, since continuous rebalance of the system may affect the QoS of applications utilizing the cache system, our data selection policy ensures that each data migration minimizes the system imbalance degree and hence, the total reconfiguration cost can be minimized. An extensive simulation is conducted to compare our policy with other policies. Our policy shows a significant improvement in time efficiency and decrease in reconfiguration cost

From AADL to Timed Abstract State Machines: A Verified Model Transformation

International audienceArchitecture Analysis and Design Language (AADL) is an architecture description language standard for embedded real-time systems widely used in the avionics and aerospace industry to model safety-critical applications. To verify and analyze the AADL models, model transformation technologies are often used to automatically extract a formal specification suitable for analysis and verification. In this process, it remains a challenge to prove that the model transformation preserves the semantics of the initial AADL model or, at least, some of the specific properties or requirements it needs to satisfy. This paper presents a machine checked semantics-preserving transformation of a subset of AADL (including periodic threads, data port communications, mode changes, and the AADL behavior annex) into Timed Abstract State Machines (TASM). The AADL standard itself lacks at present a formal semantics to make this translation validation possible. Our contribution is to bridge this gap by providing two formal semantics for the subset of AADL. The execution semantics provided by the AADL standard is formalized as Timed Transition Systems (TTS). This formalization gives a reference expression of AADL semantics which can be compared with the TASM-based translation (for verification purpose). Finally, the verified transformation is mechanized in the theorem prover Coq

Web-based Multi-dimensional Medical Image Collaborative Annotation System

Medical image annotation is playing an increasingly important role in clinical diagnosis and medical research. Existing medical image annotation is faced with many demands and challenges. 1) The emergence and sharp increasing speed of multi-dimensional medical images. 2) Image annotation includes not only text annotation, but also graphical annotation, clinical diagnostic information and image content features information. 3) Uneven distribution of medical resources, which makes difficult to aggregate group intelligence from a much larger scale of distributed experts. Most of the present study is texted based within hospitals on single images annotation. It is difficult to organize and manage unstructured medi-cal image annotation and collaborative sharing information. This paper dedicated to the research on collaborative web-based multi-dimensional medical image an-notation and retrieval in order to address these problems, overcome the shortcom-ing of traditional thin client and facilitate medical experts in different locations to exchange views and comments,. It proposed 1) a system architecture that provides authoring, storing, querying, and exchanging of annotations, and supports web-based collaboration. 2) 2D multi-frame and 3D medical image collaborative anno-tation data model. 3) Collaborative annotation mechanisms

Detection of hepatitis A virus in shellfish and berries by digital polymerase chain reaction

Objective To establish a digital polymerase chain reaction (PCR) method for hepatitis A virus (HAV) in shellfish and berry foods. Methods After sample enrichment by proteinase K digestion polyethylene glycol method, RNA was extracted by high purity virus nucleic acid kit, and then digital PCR was used to detect HAV. Results This method had typical amplification, good repeatability and stability for HAV. The sensitivity of HAV in strawberry, raspberry and shellfish samples was 25.30 CCID50/20 g, 6.32 CCID50/20 g and 12.54 CCID50/2 g respectively, which means that the detection sensitivity of HAV was high. Conclusion This method is rapid, accurate, sensitive, and is suitable for the determination of HAV in shellfish and berry foods

A Framework for Model and Verification of Safety-Critical Operating System Based on ARINC653

As the scale and complexity of safety-critical software continue to grow, it is necessary to ensure safety and reliability to avoid minor errors leading to catastrophic disasters. Meantime, the traditional method, such as testing and simulation alone is insufficient to ensure the correctness of systems. This leads to using formal methods to provide sufficient evidence for systems. However, design a high assurance safety-critical system by formal methods is challenging due to the complexity of operating systems. In addition, the traditional interactive theorem prover used in system verification requires hand-written proofs, which are more expensive. Therefore, the efforts of providing a standardized formal framework as well as safety proofs, are notable for the develop a safety-critical system. The purpose of this paper is to provide a safety framework to establish a highly reliable and safety-critical operating system based on the ARINC653 standard, a multilevel and standardized formal model. To verify the functional correctness of this model, we propose a context-based formal proof method for programs. To achieve this goal, we first model 57 core services of ARINC653 and define the high-level requirements as pre-and post-conditions. Then, we construct a set of specification statements a formal axiom system transformed into logical sentences, and the core service model is transformed into a logical sentence sequence to be proved. Finally, a context-based formal proof system for specification correctness is developed. We have verified the correctness of safety-critical operating system core services with this system. Experience shows that the verification system we developed can be achieved the functional correctness of a complete OS with a low implement burden, and that can simplify the difficulty of automated verification and increase the degree of automation of proof

H∞-Based Pinning Synchronization of General Complex Dynamical Networks with Coupling Delays

This paper investigates the synchronization of complex dynamical networks with coupling delays and external disturbances by applying local feedback injections to a small fraction of nodes in the whole network. Based on H∞ control theory, some delay-independent and -dependent synchronization criteria with a prescribed H∞ disturbances attenuation index are derived for such controlled networks in terms of linear matrix inequalities (LMIs), which guarantee that by placing a small number of feedback controllers on some nodes, the whole network can be pinned to reach network synchronization. A simulation example is included to validate the theoretical results

Combining POS tagging, lucene search and similarity metrics for entity linking

Entity linking is to detect proper nouns or concrete concepts (a.k.a mentions) from documents, and to map them to the corresponding entries in a given knowledge base. In this paper, we propose an entity linking framework POSLS consisting of three components: mention detection, candidate selection and entity disambiguation. First, we use part of speech tagging and English syntactic rules to detect mentions. We then choose candidates with Lucene search. Finally, we identify the best matchings with a similarity based disambiguation method. Experimental results show that our approach has an acceptable accuracy