Fakulta elektrotechniky a informatiky / Faculty of Electrical Engineering and Informatics
Stálý URI pro tuto komunituhttps://hdl.handle.net/10195/3847
Práce obhájené před rokem 2008 jsou uloženy pouze v kolekci Vysokoškolské kvalifikační práce
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Konferenční objektpeer-reviewedpostprint Omezený přístup Passage Detection of a Train via a Reference Point(Springer, 2023) Rejfek, Luboš; Pidanič, Jan; Štursa, Dominik; Nguyen, Tan N.; Tran, Phuong T.; Němec, Zdeněk; Zálabský, TomášA reference point detection system for position validation of a mobile object was developed for verification of experiments. The detection is based on a classic image processing algorithm and a processing algorithm using neural networks. Both approaches are compared. High-precision concept of the system is based on a camera sensor and automatic processing of video frames for position evalua-tion. The designed system was tested on a real application proving correct operation.Konferenční objektpeer-reviewedpostprint Omezený přístup Cybersecurity of Sensors on Smart Vehicles: Review of Threats and Solutions(IEEE (Institute of Electrical and Electronics Engineers), 2023) Putro, Prasetyo Adi Wibowo; Amelia, Fetty; Pidanič, Jan; Suhartanto, Heru; Rahardjo, Imam Arif; Imandeka, EjoThe use of sensors in smart vehicles brings benefits and vulnerabilities. Different kinds of sensors in smart vehicles are vulnerable to cyber-attack. Until now, the investigation of challenges and solutions for in-vehicle cybersecurity hasn’t discussed various sensor objects and their correlation. In this study, we studied the cyber security problems of sensors in smart vehicles and how to overcome them. The research was designed as Systematic Literature Review (SLR) using the Kitchenham methodology with modification in the filtering phase using the artificial intelligence application, Elicit, to identify the problems, conclusions, and methodology description. Seventeen publications from 2016 until 2023 were gained from five databases. As a result, we find that the most discussed object related to cybersecurity sensors on smart vehicles are Electronic Control Units. Spoofing and jamming is still the most addressed threat, and machine learning is the most utilized solution to be implemented in detection systems. Advanced detection systems are incorporating updated attack models. We also suggest using updated attack models and machine learning algorithms to ensure the safety and security of smart vehicle technology. All identified sensor technology correlated using mind maps under the Intelligent Transport System theory.Konferenční objektpeer-reviewedpostprint Omezený přístup Analysis of Faculty of Electrical Engineering and Informatics Building Energy Use Intensity in Pardubice(Institute of Physics, 2023) Arif, Rahardjo Imam; Pidanič, Jan; Roleček, Jiří; Garniwa, Iwa; Suhartanto, HeruThe investigation focused on analyzing the energy usage patterns of the Faculty of Electrical Engineering and Informatics in Pardubice. The recorded energy consumption data were examined and discussed. The average heating energy consumption in the Faculty of Electrical Engineering and Informatics building in 2020, 2021, and 2022 is 686.45 MWh or 57% of the total energy equivalent value. Meanwhile, the average electricity consumption in the building during the same period is 518.97 MWh or 43% of the total energy equivalent value. The average fluctuation in heating energy consumption used in the Faculty of Electrical Engineering and Informatics building tends to increase by 2.9% per year, while the average fluctuation in electricity consumption tends to increase by 3.3% per year. According to the findings, the average comprehensive energy consumption per unit area in the building was 146,03kWh/m2. It was the sum of the energy use intensity for heating and the energy use intensity for electricity. The average energy use intensity value for heating in the building was 83.16 kWh/m2, while the average energy use intensity value for electricity was 62.78 kWh/m2.Článekpeer-reviewedpostprint Omezený přístup An Optimized Low-Power VLSI Architecture for ECG/VCG Data Compression for IoHT Wearable Device Application(IEEE (Institute of Electrical and Electronics Engineers), 2023) Janveja, Meenali; Sharma, Ashwani Kumar; Bhardwaj, Abhyuday; Pidanič, Jan; Trivedi, GauravContinuous monitoring of the electrical activity of heart signals using wearable Internet of Healthcare Things (IoHTs) devices plays a crucial role in decreasing mortality rates. However, this continuous monitoring using an electrocardiogram (ECG) or vectorcardiogram (VCG) generates huge clinical data. Moreover, these devices are constrained in terms of on-chip storage, data transmission capacity, and power. Thus, handling a large amount of data is difficult with these devices, making it necessary to compress these data for storage and transmission. Lossless or near-lossless data compression solves this problem, ensuring that no relevant physiological/clinical information is lost in the compression process. Therefore, low-power, resource-efficient, and lossless VLSI architectures are proposed in this article to compress multichannel ECG/VCG data. The designs are tested using the PTB database for both ECG and VCG data and can achieve compression ratios (CRs) of $3.857$ and $4.45$ with minimal power and area requirements making them suitable for low-power wearable healthcare devices.Konferenční objektpeer-reviewedpostprint Omezený přístup IndiRA: Design and Implementation of a Pipelined RISC-V Processor(IEEE (Institute of Electrical and Electronics Engineers), 2023) Tiwari, Ankita; Guha, Prithwijit; Trivedi, Gaurav; Gupta, Nitesh; Jayaraj, Navneeth; Pidanič, JanThe development of Machine Learning and IoT technology requires fast processing. RISC-V is an open-source reduced instruction set-based instruction set architecture, and the processor based on this architecture can be modified accordingly. The base integer instruction extension supports the operating system environment and is also suitable for embedded systems. It is a 32-bit instruction extension and is defined as RV32I. In this paper, we propose a 32-bit integer instruction-based RISC-V processor core. The proposed core has a five-stage pipeline, including the optimized arithmetic and logic unit. The instruction fetch stage is merged with the pre-fetch stage dynamic branch prediction into a two-stage pipeline. The processor is implemented using Verilog HDL, and the resource utilization is verified for FPGA. The results show that the proposed module performs 30% better than the best-performing processor (considering operating frequency) and showed a 17.6% improvement in the proposed core.Článekpeer-reviewedpostprint Omezený přístup Road to Repair (R2R): An Afrocentric Sensor-Based Solution to Enhanced Road Maintenance(IEEE (Institute of Electrical and Electronics Engineers), 2023) Jordan, Darryn Anton; Paine, Stephen; Mishra, Amit Kumar; Pidanič, JanPotholes are one of the most important issues in African road-networks. They pose a major threat to mobility and, with time, cause accelerated degradation of the underlying road infrastructure as well as extensive vehicle damage. To address the need for improved infrastructure management, an advanced data gathering solution is required. This paper presents one such solution. The pothole detection, classification and logging (PDCL) system is under active development by Sensorit (Pty) Ltd in collaboration with the University of Cape Town (UCT) Radar Remote Sensing Group (RRSG). This system represents the initial step in Sensorit's modular approach to producing fully autonomous vehicles for African markets. In this paper, an overview of the PDCL system is presented and early results are shown. The efficacy of the system's unique combination of active infrared stereo vision and mmWave frequency-modulated continuous-wave (FMCW) radar sensors is explored. Under various experimental conditions, range-Doppler maps (RDMs) produced by the radar were unable to provide meaningful pothole detections. In contrast, processed depth maps produced by the stereo vision system are demonstrated to successfully detect even shallow potholes.Konferenční objektpeer-reviewedpostprint Omezený přístup Employing Quantile and Probability Plots for Comparing and Assessing Goodness of Fit for Stochastic Models of the DCT Coefficients of Lossy Compressed Images(IEEE (Institute of Electrical and Electronics Engineers), 2023) Kotov, Dmytro; Fedorov, Oleksii; Omelchenko, Anatolii; Pidanič, Jan; Doležel, PetrThe paper employs probability plots to performgoodness of fit tests for AC DCT coefficients of compressed images. Attention is paid to various probabilistic models of the DCT coefficients, conventional and rarely used. The Laplacian, generalized Gaussian and doubly Gamma distributions comprise the list. A variation of the method of moments, which involves the 2nd and 4th sample moments as well as Sheppard’s corrections to estimate the shape and scale parameters of the distributions is used. Two types of images are considered, namely, texturelike images and those possessing vast regions of monotonicity. Special effort has been put into adjusting the apparatus of the probability plots to make them suitable for dealing with discrete data, in our case with the quantized DCT coefficients of lossy compressed images. As the source of the DCT coefficients, JPEG images are used. This does not lead us to a significant loss of generality: conclusions drawn in this paper remain applicable to a broad variety of formats of lossy compressed images.Článekpeer-reviewedpostprint Omezený přístup Design of DNN-Based Low-Power VLSI Architecture to Classify Atrial Fibrillation for Wearable Devices(IEEE (Institute of Electrical and Electronics Engineers), 2023) Parmar, Rushik; Janveja, Meenali; Pidanič, Jan; Trivedi, GauravAtrial fibrillation (AF) is a recurrent and life-threatening disease leading to rapid growth in the mortality rate due to cardiac abnormalities. It is challenging to manually diagnose AF using electrocardiogram (ECG) signals due to complex and varied changes in its characteristics. In this article, for the first time, an end-to-end edge-enabled machine learning based VLSI architecture is proposed to classify ECG excerpts having AF from normal beats. Researchers have found that abnormal atrial activity is confined to the low-frequency range through the decades. Therefore, in the proposed work, this frequency band is directly analyzed for AF detection, which has not previously been discussed. The proposed architecture is implemented using 180-nm bulk CMOS technology consuming 11.098 mu W at 25 kHz and exhibits an accuracy of 92.37% for class-oriented classification and 81.60% for subject-oriented classification. The low-power realization of the proposed design, as compared to the state-of-the-art methods, makes it suitable to be used for wearable devices.Konferenční objektpeer-reviewedpostprint (accepted version) Otevřený přístup Design and Implementation of Probabilistic Methods for Spectrum Sensing in Cognitive Radios(IEEE (Institute of Electrical and Electronics Engineers), 2022) Ponomarov, Andrii; Ivanenko, Stanislav; Fedorov, Oleksii; Bezruk, Valeriy; Pidanič, Jan; Doležel, PetrThe paper deals with new unconventional methods of detecting unoccupied frequency channels in cognitive radios. The main feature of these methods consists in their ability of detecting unknown signals in the presence of noise under the condition of a priori uncertainty. It makes it possible to increase the efficiency of detecting unoccupied frequency channels in cognitive radios due to the fact that these methods track changes in the probabilistic properties of observations. During the course of spectrum sensing of the frequency range, the detected signals are divided into known (classified training samples of which are available in the system) and unknown ones. Application of methods for recognizing specified signals in the presence of unknown signals makes it possible to simultaneously avoid the erroneous occupation of a frequency channel by a secondary user, in the case when previously unregistered signal occurs, and also refresh the cognitive radio database. To detect unknown signals, only information about probabilistic characteristics of the channel noise is used.Konferenční objektpeer-reviewedpostprint (accepted version) Otevřený přístup Comparison of Floating-point Representations for the Efficient Implementation of Machine Learning Algorithms(IEEE, 2022) Mishra, Saras Mani; Tiwari, Ankita; Shekhawat, Hanumant Singh; Guha, Prithwijit; Trivedi, Gaurav; Pidanič, Jan; Němec, ZdeněkSmart systems are enabled by artificial intelligence (AI), which is realized using machine learning (ML) techniques. ML algorithms are implemented in the hardware using fixedpoint, integer, and floating-point representations. The performance of hardware implementation gets impacted due to very small or large values because of their limited word size. To overcome this limitation, various floating-point representations are employed, such as IEEE754, posit, bfloat16 etc. Moreover, for the efficient implementation of ML algorithms, one of the most intuitive solutions is to use a suitable number system. As we know, multiply and add (MAC), divider and square root units are the most common building blocks of various ML algorithms. Therefore, in this paper, we present a comparative study of hardware implementations of these units based on bfloat16 and posit number representations. It is observed that posit based implementations perform 1.50x better in terms of accuracy, but consume 1.51x more hardware resources as compared to bfloat16 based realizations. Thus, as per the trade-off between accuracy and resource utilization, it can be stated that the bfloat16 number representation may be preferred over other existing number representations in the hardware implementations of ML algorithms.