Circularly Polarized Quasi-Rectangular Patch UWB Antenna for GPR Applications
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Abstract: In this paper, a Circularly Polarized (CP) rectangular patch Ultra-Wideband (UWB) antenna is presented for Ground Penetrating Radar (GPR) applications. The designed geometry is constructed of a quasi-rectangular radiator and a partial ground plane. It is printed on the low-cost FR-4 substrate that has a compact size of 25×30×1.5 mm3. The calculated Impedance Bandwidth (IBW) of the proposed CP quasi-rectangular patch antenna is spanning from 3.16 GHz to 13.7 GHz (125.03%). Moreover, the measured IBW of the fabricated prototype is spanning from 3.2 GHz to greater than 14 GHz (>125.58%) which covers the entire UWB range. Besides, the designed antenna reveals a wide Axial Ratio Band-Width (ARBW) extending from 4.23 GHz to 7.02 GHz (49.6%). Therefore, stable radiation patterns with an agreeable peak gain and high radiation efficiency are simulated over the whole working bandwidth.
Keywords: Rectangular patch antenna, Ultra-wideband antenna, Circularly pola¬rized) antenna, Ground penetrating radar applications.
Tumor Detection using Brain MRI and Low-dimension Co-occurrence Feature Approach
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Abstract: Research in medical imaging focuses on methods useful in computer-aided diagnosis systems. In modern times, these systems often have automatic detection of regions of interest, and imaging technologies offer numerous advantages, like the possibility of developing reliable assisting algorithms. Magnetic Resonance Imaging (MRI) provides compelling features for brain tumor detection due to good soft tissue contrast and has important clinical value. To help clinicians in making diagnoses, current algorithms for processing and medical image classification may depend on intricate deep learning designs that demand large hardware resources and lengthy execution times. This is with no doubt helpful in understanding disease mechanisms and in labeling difficult instances for brain tumor identification. On the other hand, statistical low-dimension feature sets including co-occurrence-based ones could be useful in dealing with tumor detection avoiding possible complexity. In this paper, statistical approaches for feature extraction and reduction are analyzed for MRI brain tumor classification, and the evaluation of the results is presented on one of the publicly available brain tumor detection database commonly used for machine learning tasks. Bayes and kNN classifiers are applied for the analysis, as well as four distance metrics, and two methods for feature reduction. The results seem promising in developing a simple and less hardware-demanding procedure.
Keywords: Brain tumor detection, Region of Interest, Magnetic Resonance Imaging, Statistical moments, Feature extraction and reduction, Machine learning.
Data Augmentation Methods for Semantic Segmentation-based Mobile Robot Perception System
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Abstract: Data augmentation has become a standard technique for increasing deep learning models’ accuracy and robustness. Different pixel intensity modifications, image transformations, and noise additions represent the most utilized data augmentation methods. In this paper, a comprehensive evaluation of data augmentation techniques for mobile robot perception system is performed. The perception system based on a deep learning model for semantic segmentation is augmented by 17 techniques to obtain better generalization characteristics during the training process. The deep learning model is trained and tested on a custom dataset and utilized in real-time scenarios. The experimental results show the increment of 6.2 in mIoU (mean Intersection over Union) for the best combination of data augmentation strategies.
Keywords: Mobile robot perception system, Deep learning, Data augmentation, Semantic segmentation.
Modeling and Practical Implementation of the Optimal Wireless Security Gateway for the Industrial Automation Network
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Abstract: Reducing energy consumption in networks of IoT devices is very important. This paper presents the results of experimental studies of the proposed method of reducing power consumption for the protective gateway of the industrial network. Research was conducted for devices built on Lora modules. The LoRa Modem Calculator software from Semtech was used as a modeling tool. The recommended values of the program registers are given to achieve optimal energy consumption parameters for data reception and transmission modes. Information about the prototype is given and its practical implementation is shown. The developed prototype of the gateway implements the “Protocol Transformation” method, which allows removing possible dangerous inserts from the data packets received by the gateway. This can improve the security of packets from the gateway to IoT devices and back, and thus the network as a whole.
Keywords: IoT, LoRaWAN, Internet of things, Gateway, Industry.
Optimal Siting of Capacitors in Distribution Grids Considering Electric Vehicle Load Growth Using Improved Flower Pollination Algorithm
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Abstract: The optimal VAr compensation using capacitor banks (CBs) in radial distribution networks (RDNs) is solved in this paper while taking the growth of the load from electric vehicles (EVs) into consideration. This is accomplished by adapting an improved variant of the flower pollination algorithm (IFPA) with an enhanced local search capability. The primary objective of determining the locations and sizes of CBs is to minimize the distribution losses in the operation and control of RDNs. Additionally, the effect of CBs is shown by the increased net savings, greater voltage stability, and improved voltage profile. A voltage stability index (VSI) was used in the optimization process to determine the predefined search space for CB locations, and a double-direction learning strategy (DLS) was then considered to optimize the locations and sizes while maintaining a balance between the exploration and exploitation phases. Three IEEE RDNs were used to simulate various EV load increase scenarios as well as typical loading situations. According to a comparison with the literature, the IPFA produced global optimum results, and the proposed CBs allocation approach demonstrated enhanced performance in RDNs under all scenarios of EV load growth.
Keywords: Capacitor banks allocation, Electric vehicle load, Improved flower pollination algorithm, Net-cost optimization, Radial distribution network, VAr compensation, Voltage stability index.
On the Application of Wavelet Transform and Huffman Algorithm to Yorùbá Language Syntax Text Files Compression
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Abstract: Most algorithms of data compression were developed with English language as target text syntax. However, this paper approaches the problem of Yorùbá text files compression via the use of Discrete Wavelet Transform (DWT) and Huffman algorithm. Text files in Yorùbá language syntax are first converted into signal format that are then decomposed using DWT. The decomposed ASCII code representation of the text files are subsequently encoded using Huffman algorithm. Twenty different variants of DWTs taken from four families of wavelet filters (Haar, Daubechies, Symlets and bi-orthogonal) are considered to select the optimal DWT for Yorùbá text files compression. Furthermore, experiments are carried out in the proposed compression scheme with six different Yorùbá text files extracted from the open sources as input data sets. It is found that out of the twenty variants of DWT investigated, sym6 gives the best output for effective Yorùbá text files compression, due to its relatively high compression ratio, high compression factor and lowest compression error. Thus, sym6 as a wavelet transform is suitable for lossy text compression algorithm meant for Yorùbá language syntax text files.
Keywords: Text file, Compression, Wavelet transform, Huffman coding, Yorùbá language syntax.
Identification of a Significant Systematic Error in Pressure Sensor Readings Based on an Autoregressive Model
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Abstract: A new method of identifying the validity of pressure sensor readings has been developed. The method uses duplication of measurements which allows an estimation of the magnitude of an error, although it does not make it possible to establish which sensor is responsible for the error. The method helps to evaluate the systematic error magnitude and to test whether the error exists within a permission range accounting for a correlation structure of measurement series. An autoregressive model with a drift coefficient is applied to investigate a time series of the differences in readings. To test the significance of this coefficient, a modified Student’s test is used. Unlike the standard Student’s test, this new method tests an interval hypothesis. The null hypothesis assumes the systematic error is within the range and the alternative is out of the range. Error probabilities of type I and type II are calculated. An example of 2nd order autoregressive model was considered and the sensitivity of the proposed method was investigated.
Keywords: Pressure monitoring, Systematic error, Validity of statistical data, Time series analysis, Student’s significance test, Interval hypothesis.
New Catalysts for Formic Acid Fuel Cells
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Abstract: Thallium adatoms deposited at under-potentials have shown the catalytic effect during the electrooxidation of formic acid on platinum ruthenium alloys. At Pt/Ru with an optimal coverage degree with adatoms Tl, HCOOH is oxidized at nearly 180 mV more negative potential than at Pt/Ru electrodes. The catalytic effect of modified Pt/Ru electrodes is plausibly caused by interaction of the Tl adatoms, located at Pt atoms with OH species of adjacent Ru atoms. These interactions stabilize Ru-OH species and allows for their formation at more negative potentials than at the Pt/Ru electrodes. The Ru-OH species oxidize firmly bound intermediates COad and thus release the Pt atoms for the oxidation of subsequent HCOOH molecules. The catalytic effect is probably caused by the third-body effect.
Keywords: Formic acid, Thallium, Catalyst, Electrochemistry, Bifunctional mechanism.