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
Procházet
7 výsledky
Search Results
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.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.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.Článekpeer-reviewedpostprint (accepted) Otevřený přístup Tensor Based Multivariate Polynomial Modulo Multiplier for Cryptographic Applications(2022) Paul, Bikram; Nath, Angana; Krishnaswamy, Srinivasan; Pidanič, Jan; Němec, Zdeněk; Trivedi, GauravModulo polynomial multiplication is an essential mathematical operation in the area of finite field arithmetic. Polynomial functions can be represented as tensors, which can be utilized as basic building blocks for various lattice-based post-quantum cryptography schemes. This paper presents a tensor-based novel modulo multiplication method for multivariate polynomials over GF(2m) and is realized on the hardware platform (FPGA). The proposed method consumes 6.5× less power and achieves more than 6× speedup compared to other contemporary single variable polynomial multiplication implementations. Our method is embarrassingly parallel and easily scalable for multivariate polynomials. Polynomial functions of nine variables, where each variable is of degree 128, are tested with the proposed multiplier, and its corresponding area, power, and power-delay-area product (PDAP) are presented. The computational complexity of single variable and multivariate polynomial multiplications are O(n) and O(np) , respectively, where n is the maximum degree of a polynomial having p variables. Due to its high speed, low latency, and scalability, the proposed modulo multiplier can be used in a wide range of applications.Článekpeer-reviewedpublished version Otevřený přístup Multipatch-GLCM for Texture Feature Extraction on Classification of the Colon Histopathology Images using Deep Neural Network with GPU Acceleration(2020) Haryanto, Toto; Pratama, Adib; Suhartanto, Heru; Murni, Aniati; Kusmardi, Kusmardi; Pidanič, JanCancer is one of the leading causes of death in the world. It is the main reason why research in this field becomes challenging. Not only for the pathologist but also from the view of a computer scientist. Hematoxylin and Eosin (H&E) images are the most common modalities used by the pathologist for cancer detection. The status of cancer with histopathology images can be classified based on the shape, morphology, intensity, and texture of the image. The use of full high-resolution histopathology images will take a longer time for the extraction of all information due to the huge amount of data. This study proposed advance texture extraction by multi-patch images pixel method with sliding windows that minimize loss of information in each pixel patch. We use texture feature Gray Level Co-Occurrence Matrix (GLCM) with a mean-shift filter as the data pre-processing of the images. The mean-shift filter is a low-pass filter technique that considers the surrounding pixels of the images. The proposed GLCM method is then trained using Deep Neural Networks (DNN) and compared to other classification techniques for benchmarking. For training, we use two hardware: NVIDIA GPU GTX-980 and TESLA K40c. According to the study, Deep Neural Network outperforms other classifiers with the highest accuracy and deviation standard 96.72±0.48 for four cross-validations. The additional information is that training using Theano framework is faster than Tensorflow for both in GTX-980 and Tesla K40c.Článekpeer-reviewedpublished version Omezený přístup Statistical Analysis of Ground Clutter from FM Based Passive Radar(Springer, 2019) Bhatta, Abhishek; Pidanič, Jan; Mishra, AmitStatistical characterization of ground clutter information for radar systems is a topic that requires a detailed understanding. In this work, we characterize the ground clutter information from data captured by two Frequency Modulation (FM) based passive radar systems placed at different locations in the Czech Republic. Since the target models are statistically defined by the Swerling models, each of which is based on a particular distribution, we attempted to connect the ground clutter with some of the known distributions. The analysis is performed in three steps. The empirical distribution is fitted with some of the well-known distributions. Two different goodness-of-fit tests (named chi-squared test and Kolmogorov-Smirnov test) are performed on the obtained data by comparing the empirical distribution with the fitted distributions to determine which distribution is the best fit. The observations are analyzed and the results are considered in detail.Článekpeer-reviewedpostprint Omezený přístup Secondary surveillance radar antenna(IEEE (Institute of Electrical and Electronics Engineers), 2013) Schejbal Vladimír; Bezoušek Pavel; Pidanič Jan; Chyba MilanThis paper deals with a secondary surveillance radar (SSR) array antenna, which is intended for a system combining the secondary surveillance radar antenna and the primary surveillance radar antenna. It describes the patch array elements and the synthesis for the secondary surveillance radar array, considering both elevation and azimuth patterns for sum, difference, and sidelobe-suppression beams, and suspended stripline couplers. The utilization of multilayer techniques allows the connection of layers with patch radiating elements and layers with beamforming networks.