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- ItemDiagnostic Performance of an Automated Robot for MALDI Target Preparation in Microbial Identification(American Society for Microbiology, 2024-09-19) Pranada, Arthur B.; Čičatka, Michal; Heß, Clara; Karásek, JanThe MBT Pathfinder® is an automated colony-picking robot designed for efficient sample preparation in matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry. This article presents results from three key experiments evaluating the instrument's performance in conjunction with MALDI Biotyper® instrument. The method comparison experiment assessed its clinical performance, demonstrating comparable results with Gram-positive, Gram-negative, and anaerobic bacteria (scores larger than 2.00) and superior performance over simple direct yeast transfer (score: 1.80) when compared to samples prepared manually. The repeatability experiment confirmed consistent performance over multiple days and labs (average log score: 2.12, std. deviation: 0.59). The challenge panel experiment showcased its consistent and accurate performance across various samples and settings, yielding average scores between 1.76 and 2.19. These findings underline the MBT Pathfinder® as a reliable and efficient tool for MALDI-TOF mass spectrometry sample preparation in clinical and research applications.
- ItemReinterpreting Usability of Semantic Segmentation Approach for Darknet Traffic Analysis(ELSEVIER, 2024-05-20) Mezina, Anzhelika; Burget, Radim; Ometov, AleksandrWith a growing number of smart interconnected devices and services, managing and controlling network traffic is getting more complicated. Among the network traffic, the Darknet-related one is particularly interesting, as it is often used for anonymous and illicit activities that pose cyber security threats. Therefore, designing and developing methods for detecting and categorizing Darknet traffic is essential. Applying Deep Learning (DL) is one of the most suitable options in this case. The main reasons are the ability to process a large amount of data and detect the hidden patterns and relationships in these data. This work proposes a DL architecture based on UNet++, which can detect and categorize anonymous traffic. The core idea of this model is semantic segmentation, which can identify meaningful segments that share some common patterns in given data. Hereby, semantic segmentation is postulated as a possible way to investigate Darknet traffic to find some common and related features instead of widely used Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). According to the results on comparison with other Machine Learning (ML) and DL models, the UNet++ model outperforms the methods with a higher accuracy of 98.19% and 87.27% for Darknet detection and traffic categorization. Our work shows the potential of using UNet++ for network traffic analysis and Darknet traffic detection. We have also demonstrated that more advanced architecture with skip connections and trainable blocks provides more accurate results than pure U -Net, CNN, and other evaluated models.
- ItemComparative Analysis of Classification Methods and Suitable Datasets for Protocol Recognition in Operational Technologies(MDPI, 2024-05-11) Holasová, Eva; Fujdiak, Radek; Mišurec, JiříThe interconnection of Operational Technology (OT) and Information Technology (IT) has created new opportunities for remote management, data storage in the cloud, real-time data transfer over long distances, or integration between different OT and IT networks. OT networks require increased attention due to the convergence of IT and OT, mainly due to the increased risk of cyber-attacks targeting these networks. This paper focuses on the analysis of different methods and data processing for protocol recognition and traffic classification in the context of OT specifics. Therefore, this paper summarizes the methods used to classify network traffic, analyzes the methods used to recognize and identify the protocol used in the industrial network, and describes machine learning methods to recognize industrial protocols. The output of this work is a comparative analysis of approaches specifically for protocol recognition and traffic classification in OT networks. In addition, publicly available datasets are compared in relation to their applicability for industrial protocol recognition. Research challenges are also identified, highlighting the lack of relevant datasets and defining directions for further research in the area of protocol recognition and classification in OT environments.
- ItemSpecial Transfer Section for Selective Rejecting and Amplification of Bands in Equalization(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2024-06-10) Šotner, Roman; Svoboda, Marek; Semenov, Dmitrii; Polák, Ladislav; Jeřábek, Jan; Theumer, Radek; Jaikla, WinaiThis paper introduces a novel and simple filtering topology for band-reject (notch) and inverse band-reject applications, utilizing two voltage-adjustable operational transconductance amplifiers. The utilization of these active devices enables the implementation of identical topologies for both band-reject and inverse band-reject transfer functions. The resulting responses are harnessed for the cascade synthesis of a specialized comb filter, capable of amplification or attenuating/rejecting specific bands shown on three sub-bands. Both the design of individual sections and the entire cascade have been experimentally verified using active devices manufactured in the TSMC 180 nm CMOS process. Measurements conducted over a range from 10 Hz to 100 kHz demonstrate the importance of the selective filtering, which is significant for various applications, particularly in acoustic, vibration, and magnetic sensing readouts. The example of peaking suppression in magnitude response of emulated environment (acoustic coupling of piezo and microphone) is shown.
- ItemSingle-Channel Speech Quality Enhancement in Mobile Networks Based on Generative Adversarial Networks(SPRINGER, 2024-04-02) Wu, Guifen; Herencsár, NorbertA large amount of randomly generated noise in mobile networks leads to a lack of targeting and gaming processes in the speech enhancement process, and the enhancement process from the perspective of acoustic features alone suffers from major drawbacks. Propose a single-channel speech quality enhancement method based on generative adversarial networks in mobile networks. Explain the principle of generative adversarial network to realize single-channel speech quality enhancement in mobile networks and clarify its shortcomings. Design an improved Mel frequency cepstral coefficient extraction method to extract 12 base features as the enhancement basis. Use the relative average least squares loss instead of the traditional loss function to enhance the training efficiency, use the hybrid penalty term to enhance the generator's ability to generate single-channel speech, and optimize the discriminator through the multi-layer convolution and the addition of fully connected layers to enhance the speech quality enhancement ability of adversarial generative networks in various aspects, forming a relative average generative adversarial network (RaGAN) based on hybrid penalty term to realize single-channel speech quality enhancement processing. Through the experiment, when the discriminator is applied with the size of a 3*3 convolutional kernel, the best effect of speech quality enhancement is achieved in the mobile network. This method can complete the enhancement of single-channel speech quality in the mobile network, and the effect is significant, which can effectively reduce the noise in the original single-channel speech.