In light of this unmistakable exponential data expansion, visual media archiving must be rethought. Human generated meta-data might not be sufficient for efficient data retrieval. Object detection and object recogniti...
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Safety/mission-critical applications require high dependability of the control systems. Their state-of-the-art protection approach is a system-level lockstep. This paper compares the system-level dual and triple locks...
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The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific f...
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The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific flows by matching them against a set of dynamic rules. This basic process accelerates the processing of data, so that instead of processing singular packets repeatedly, corresponding actions are performed on corresponding flows of packets. In this paper, first, we address limitations on a typical packet classification algorithm like Tuple Space Search (TSS). Then, we present a set of different scenarios to parallelize it on different parallel processing platforms, including Graphics Processing Units (GPUs), clusters of Central Processing Units (CPUs), and hybrid clusters. Experimental results show that the hybrid cluster provides the best platform for parallelizing packet classification algorithms, which promises the average throughput rate of 4.2 Million packets per second (Mpps). That is, the hybrid cluster produced by the integration of Compute Unified Device Architecture (CUDA), Message Passing Interface (MPI), and OpenMP programming model could classify 0.24 million packets per second more than the GPU cluster scheme. Such a packet classifier satisfies the required processing speed in the programmable network systems that would be used to communicate big medical data.
Image deraining aims to improve the visibility of images damaged by rainy conditions, targeting the removal of degradation elements such as rain streaks, raindrops, and rain accumulation. While numerous single image d...
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Information security incidents are most commonly caused by user behaviour, placing the user in focus. In order to mitigate information security threats and thereby protect the organisation, more and more are adopting ...
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In an era of growing digitization, technology is essential for communication and daily life. However, inaccessible websites, including in Sweden, create barriers for individuals with disabilities, often due to insuffi...
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Group testing is a well-studied approach for identifying defective items among a large amount of items by conducting a relatively small number of tests on pools of items. In this paper, we propose a novel method for g...
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Optimizing therapy and rehabilitation for Parkinson's disease (PD) requires early identification and precise evaluation of the illness's course. However, there is disagreement about the best way to use gait an...
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Intermittent photic stimulation (IPS) is a commonly used activation method in clinical applications, e.g. epilepsy, but also used in research for investigating excitability states in the brain. Effects in the brain fo...
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In this paper, we present a new version of our bioimaging tool PartSeg. It allows integration of deep learning models from the Bioimage Model Zoo, which is a community-driven AI model repository. We also show how Part...
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