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IEEE International Conference on Intelligent Engineering Systems (INES)

作者： Sándor Szénási Zoltán Vámossy Miklós Kozlovszky Doctoral School of Applied Informatics Óbuda University Budapest Hungary John von Neumann Faculty of Informatics Óbuda University Budapest Hungary Laboratory of Parallel and Distributed Computing MTA SZTAKI Budapest Hungary

The processing of microscopic tissue images and especially the detection of cell nuclei is nowadays done more and more using digital imagery and special immunodiagnostic software products. Since several methods (and applications) were developed for the same purpose, it is important to have a measuring number to determine which one is more efficient than the others. The purpose of the article is to develop a generally usable measurement number that is based on the “gold standard” tests used in the field of medicine and that can be used to perform an evaluation using any of image segmentation algorithms. Since interpreting the results themselves can be a pretty time consuming task, the article also contains a recommendation for the efficient implementation and a simple example to compare three algorithms used for cell nuclei detection.

关键词： Accuracy Algorithm design and analysis Image segmentation Time measurement Gold Standards Classification algorithms

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Biomedical image processing with GPGPU using CUDA

Biomedical image processing with GPGPU using CUDA

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Proceedings of the International Convention MIPRO

作者： A. R. Firstauthor I. B. Secondauthor G. V. Thirdauthor P. B. Fourthauthor M. K. Fifthauthor Óbuda University/John von Neumann Faculty of Informatics Budapest Hungary Semmelweiss University/Znd Department of Internal Medicine Budapest Hungary MTA SZTAKI/Laboratory of Parallel and Distributed Computing Budapest Hungary

The main aim of this work is to show, how the GPGPUs can be used to speed up certain image processing methods. The algorithm explained in this paper is used to detect nuclei on (HE - hematoxilin eosin) stained colon tissue sample images, and includes a Gauss blurring, an RGB-HSV color space conversion, a fixed binarization, an ultimate erode procedure and a local maximum search. Since the images retrieved from the digital slides require significant storage space (up to few hundred megapixels), the usage of GPGPUs to speed up image processing operations is necessary in the interest of achieving reasonable processing time. The CUDA software development kit was used to develop algorithms to GPUs made by NVIDIA. This work focuses on how to achieve coalesced global memory access when working with three-channel RGB images, and how to use the on-die shared memory efficiently. The exact test algorithm also included a linear connected component labeling, which was running on the CPU, and with iterative optimization of the GPU code, we managed to achieve significant speed up in well defined test environment.

关键词： Graphics processing unit Instruction sets Image processing Kernel Optimization Memory management Pixel

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A Connectivity-Enhanced Multi-Task Learning based on Anatomical Priors for 3D Class-Balanced Pulmonary Airway Segmentation

A Connectivity-Enhanced Multi-Task Learning based on Anatomi...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yan Jia Yong Dou Peng Qiao Yuqing Cheng Kele Xu Zhouyu He National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Accurate and efficient airway segmentation is essential for evaluating pulmonary diseases, aiding diagnosis, reducing the preoperative burden of airway identification, and minimizing patient discomfort during prolonged surgeries. However, current pulmonary airway reconstruction techniques are hindered by two major challenges: difficulty in accurately reconstructing fine airway branches due to the tendency to overlook small targets, and insufficient structural connectivity leading to frequent branch discontinuities within the airway tree. These limitations directly affect the clinical applicability of reconstructed airways. To overcome these challenges, a novel 3D pulmonary airway segmentation multi-task framework is proposed, designed to enhance the performance of existing backbone models. This approach integrates Anatomical Prior-Based Multi-Task Learning (AP-MTL) through the use of Gaussian-constructed connectivity-enhanced isosurfaces, significantly improving the network’s ability to maintain airway continuity. Additionally, a Class-Balanced CT Density Distribution Reconstruction mechanism (DDR-CB) is introduced, further refining the model’s capability to detect and segment fine airway branches. As a result of these enhancements, the model demonstrates a 11.5% average improvement in segmentation accuracy and connectivity compared to the baseline. The source code is publicly accessible at https://***/inexhaustible419/APMTLAirwaySegment.

关键词： Image segmentation Three-dimensional displays Accuracy Lungs Atmospheric modeling Supervised learning Surgery Multitasking Image reconstruction Biomedical imaging

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Deep Isolation Forest for Anomaly Detection

arXiv

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arXiv 2022年

作者： Xu, Hongzuo Pang, Guansong Wang, Yijie Wang, Yongjun The College of Computer National University of Defense Technology Changsha410073 China The Science and Technology on Parallel and Distributed Processing Laboratory China The School of Computing and Information Systems Singapore Management University 178902 Singapore

Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector in recent years due to its general effectiveness across different benchmarks and strong scalability. Nevertheless, its linear axis-parallel isolation method often leads to (i) failure in detecting hard anomalies that are difficult to isolate in high-dimensional/non-linear-separable data space, and (ii) notorious algorithmic bias that assigns unexpectedly lower anomaly scores to artefact regions. These issues contribute to high false negative errors. Several iForest extensions are introduced, but they essentially still employ shallow, linear data partition, restricting their power in isolating true anomalies. Therefore, this paper proposes deep isolation forest. We introduce a new representation scheme that utilises casually initialised neural networks to map original data into random representation ensembles, where random axis-parallel cuts are subsequently applied to perform the data partition. This representation scheme facilitates high freedom of the partition in the original data space (equivalent to non-linear partition on subspaces of varying sizes), encouraging a unique synergy between random representations and random partition-based isolation. Extensive experiments show that our model achieves significant improvement over state-of-the-art isolation-based methods and deep detectors on tabular, graph and time series datasets;our model also inherits desired scalability from iForest. © 2022, CC BY.

关键词： Anomaly detection

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Routing with uncertainty in wireless mesh networks

Routing with uncertainty in wireless mesh networks

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International Workshop on Quality of Service

作者： Fajun Chen Jiangchuan Liu Zongpeng Li Yijie Wang Key Laboratory of Science and Technology for National Defence of Parallel and Distributed Processing National University of Defense Technology China School of Computing Science Simon Fraser University Department of Computer Science University of Caglary

Existing routing protocols for Wireless Mesh Networks (WMNs) are generally optimized with statistical link measures, while not addressing on the intrinsic uncertainty of wireless links. We show evidence that, with the transient link uncertainties at PHY and MAC layers, a pseudo-deterministic routing protocol that relies on average or historic statistics can hardly explore the full potentials of a multi-hop wireless mesh. We study optimal WMN routing using probing-based online anypath forwarding, with explicit consideration of transient link uncertainties. We show the underlying connection between WMN routing and the classic Canadian Traveller Problem (CTP). Inspired by a stochastic recoverable version of CTP (SRCTP), we develop a practical SRCTP-based online routing algorithm under link uncertainties. We study how dynamic next hop selection can be done with low cost, and derive a systematic selection order for minimizing transmission delay. We conduct simulation studies to verify the effectiveness of the SRCTP algorithms under diverse network configurations. In particular, compared to deterministic routing, reduction of end-to-end delay (51.15~73.02%) and improvement on packet delivery ratio (99.76%) are observed.

关键词： Uncertainty Wireless mesh networks Delay Routing protocols Physical layer Statistics Stochastic processes Relays Computer networks Concurrent computing

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Preparing initial population of genetic algorithm for region growing parameter optimization

Preparing initial population of genetic algorithm for region...

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International Symposium on Logistics and Industrial Informatics, LINDI

The processing of microscopic tissue images is nowadays done more and more using special immunodiagnostic-evaluation software products. Often to evaluate the samples, the first step is determining the number and location of cell nuclei. To do this, one of the most promising methods is the region growing, but this algorithm is very sensitive to the appropriate setting of different parameters. Due to the large number of parameters and due to the big set of possible values setting those parameters manually is a quite hard task, so we developed a genetic algorithm to optimize these values. The first step of the development is the statistical analysis of the parameters, and the determination of the important features, to extract valuable information for a to-be-implemented genetic algorithm that will perform the optimization.

关键词： Optimization Genetic algorithms Gold Standards Medical services Shape Databases

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On the performance of Maestro2 high performance network equipment, using new improvement techniques

On the performance of Maestro2 high performance network equi...

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IEEE International Conference on Performance, computing and Communications (IPCCC)

作者： S. Yamagiwa K. Ferreira L.M. Campos K. Aoki M. Ono K. Wada M. Fukuda L. Sousa PDM&FC Lisboa Portugal Parallel and Distributed Computing Laboratory University of Tsukuba Tsukuba Ibaraki Japan Computing and Software Systems UW1-331 University of Washington Bothell Bothell WA USA IST/INESC Lisboa Portugal

Cluster computers have become the vehicle of choice to build high performance computing environments. To fully exploit the computing power of these environments, one must utilize high performance network and protocol technologies, since the communication patterns of parallel applications running on clusters require low latency and high throughput, not achievable by using off-the-shell network technologies. We have developed a technology to build high performance network equipment, called MaestroS. This paper describes the novel techniques used by Maestro2 to extract maximum performance from the physical medium and studies the impact of software-level parameters. The results obtained clearly show that Maestro2 is a promising technology, presenting very good results both in terms of latency and throughput. The results also show the large impact of software overhead in the overall performance of the system and validate the need for optimized communication libraries for high performance computing.

关键词： High performance computing Delay Throughput Vehicles Computer networks Concurrent computing Protocols Communications technology Application software Communication system software

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distributed Relation Discovery in Internet of Things

Distributed Relation Discovery in Internet of Things

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International Conference on Cloud computing and Big Data (CCBD)

作者： Weiping Zhu Hongliang Lu Xiaohui Cui International School of Software Wuhan University China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology China Department of Computing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781479966226

Internet of things is an emerging technology that aims to connect various smart objects in our daily life. It facilitates the information exchange and control among objects. In the Internet of things, it is important to discover various relations among objects for analyzing and mining useful knowledge. Existing works on relation discovery mainly focus on centralized processing. It is not suitable for Internet of things due to unavailable of server, one-point failure, computation bottleneck, and security and business concerns. In this paper, we propose a distributed approach to discover the relations among objects in Internet of things. We first build the distritbuted system model which may include multiple relation discovery tasks. Based on that, we design an approach utilizing distributed spanning tree to extract the relations. Rather than sending all the information to the server, the objects only need to send the information to a local leader object in our algorithm. We also discuss more about how to improve the performance of the proposed approach and how to relax the system constraints. Extensive simulation have been done and the results show that the proposed approach outperforms existing approaches in terms of the data amount of transmission.

关键词： Internet of things Synchronization Buildings Servers Algorithm design and analysis Delays

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A novel application development environment for large-scale scientific computations 00

A novel application development environment for large-scale ...

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Proceedings of the 14th international conference on Supercomputing

作者： X. Shen W. Liao A. Choudhary G. Memik M. Kandemir S. More G. Thiruvathukal A. Singh Center for Parallel and Distributed Computing Department of Electrical and Computer Engineering Northwestern University Evanston IL Department of Computer Science and Engineering The Pennsylvania State University University Park PA School of Computing Telecommunications and Information Sciences JHPC Laboratory Depaul University

Our results demonstrate that our novel application development environment provides both ease-of-use and high performance for large-scale, I/O-intensive scientific applications.

ISBN: (纸本)9781581132700

Our results demonstrate that our novel application development environment provides both ease-of-use and high performance for large-scale, I/O-intensive scientific applications.

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An enhanced parallel loop self-scheduling scheme for cluster environments

An enhanced parallel loop self-scheduling scheme for cluster...

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International Conference on Advanced Information Networking and Applications (AINA)

作者： Chao-Tung Yang Kuan-Wei Cheng Kuan-Ching Li High-Performance Computing Laboratory Department of Computer Science and Information Engineering Tunghai University Taichung Taiwan Parallel and Distributed Processing Center Department of Computer Science and Information Management Providence University Taichung Taiwan

ISBN: (纸本)0769522491

In this paper, a parallel loop self-scheduling scheme for heterogeneous PC cluster systems is proposed. Though the proposed scheme does allow users to choose parameters before the execution initialization phase, there are still weaknesses that motivate us to go further with new improvements in that scheme. For instance, a decision on a fixed and monotonous parameter can easily lead to invalid schedule by using previous input information. Thus, it is proposed in this paper a new scheme, where the scheduling parameter can be adjusted dynamically and fit into most widely available computer systems, in order to provide higher overall performance.

关键词： Processor scheduling Dynamic scheduling Load management Master-slave Concurrent computing distributed computing Clustering algorithms Runtime Computer science Scheduling algorithm

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