Institute of Electron Technology
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2189 research outputs found
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A DFT-based Low Complexity LMMSE Channel Estimation Technique for OFDM Systems, Journal of Telecommunications and Information Technology, 2022, nr 1
Full text linkThe linear minimum mean square error (LMMSE) channel estimation technique is often employed in orthogonal frequency division multiplexing (OFDM) systems because of its optimal performance in the mean square error (MSE) performance. However, the LMMSE method requires cubic complexity of order O(N 3 p ), where Np is the number of pilot subcarriers. To reduce the computational complexity, a discrete Fourier transform (DFT) based LMMSE method is proposed in this paper for OFDM systems in the frequency selective channel. To validate the proposed method, the closed form mean square error (MSE) expression is also derived. Finally, a computer simulation is carried out to compare the performance of the proposed LMMSE method with the classical LS and LMMSE methods in terms of bit error rate (BER) and computational complexity. Results of the simulation show that the proposed LMMSE method achieves exactly the same performance as the conventional LMMSE method, with much lower computational complexity
Novel Feature Extraction for Pineapple Ripeness Classification, Journal of Telecommunications and Information Technology, 2022, nr 1
Full text linkA novel feature extraction method has been pro posed to improve the accuracy of the pineapple ripeness clas sification process. The methodology consists of six stages, namely: image acquisition, image pre-processing, color ex traction, feature selection, classification and evaluation of re sults. The red element in the RGB model is selected as the threshold value parameter. The ripeness of pineapples is de termined based on the percentage share of yellowish scales visible in images presenting the front and the back side of the fruit. The prototype system is capable of classifying pineap ples into three main groups: unripe, ripe, and fully ripe. The accuracy of 86.05% has been achieved during experiments
Modeling and Parameter Estimation of Radar Sea-Clutter with Trimodal Gamma Population, Journal of Telecommunications and Information Technology, 2022, nr 2
Full text linkReal radar data often consist of a mixture of Gaussian and non-Gaussian clutter. Such a situation creates one or more inflexion points in the curve of the empirical cumulative distributed function (CDF). In order to obtain an accurate fit with sea reverberation data, we propose, in this paper, a trimodal gamma disturbance model and two parameter estimators. The non-linear least-squares (NLS) fit approach is used to avoid computational issues associated with the maximum likelihood estimator (MLE) and moments-based estimator for parameters of the mixture model. For this purpose, a combination of moment fit and complementary CDF (CCDF) NLS fit methods is proposed. The simplex minimization algorithm is used to simultaneously obtain all parameters of the model. In the case of a single gamma probability density function, a zlog(z) method is derived. Firstly, simulated life tests based on a gamma population with different shape parameter values are worked out. Then, numerical illustrations show that both MLE and zlog(z) methods produce closer results. The proposed trimodal gamma distribution with moments NLS fit and CCDF NLS fit estimators is validated to be in qualitative agreement with different cell resolutions of the available IPIX database
Deep Learning-based SNR Estimation for Multistage Spectrum Sensing in Cognitive Radio Networks, Journal of Telecommunications and Information Technology, 2022, nr 4
Full text linkVacant frequency bands are used in cognitive radio (CR) by incorporating the spectrum sensing (SS) technique. Spectrum sharing plays a central role in ensuring the effectiveness of CR applications. Therefore, a new multi-stage detector for robust signal and spectrum sensing applications is introduced here. Initially, the sampled signal is subjected to SNR estimation by using a convolutional neural network (CNN). Next, the detection strategy is selected in accordance with the predicted SNR levels of the received signal. Energy detector (ED) and singular value-based detector (SVD) are the solutions utilized in the event of high SNR, whilst refined non-negative matrix factorization (MNMF) is employed in the case of low SNR. CNN weights are chosen via the Levy updated sea lion optimization (LU-SLNO) algorithm inspired by the traditional sea lion optimization (SLNO) approach. Finally, the outcomes of the selected detectors are added, offering a precise decision on spectrum tenancy and existence of the signal
Joint Optimization of Sum and Difference Patterns with a Common Weight Vector Using the Genetic Algorithm, Journal of Telecommunications and Information Technology, 2022, nr 3
Full text linkA monopulse searching and tracking radar antenna array with a large number of radiating elements requires a simple and efficient design of the feeding network. In this paper, an effective and versatile method for jointly optimizing the sum and difference patterns using the genetic algorithm is proposed. Moreover, the array feeding network is simplified by attaching a single common weight to each of its elements. The optimal sum pattern with the desired constraints is first generated by independently optimizing amplitude weights of the array elements. The suboptimal difference pattern is then obtained by introducing a phase displacement π to half of the array elements under the condition of sharing some sided elements weights of the sum mode. The sharing percentage is controlled by the designer, such that the best performance can be met. The remaining uncommon weights of the difference mode represent the number of degrees of freedom which create a compromise difference pattern. Simulation results demonstrate the effectiveness of the proposed method in generating the optimal sum and suboptimal difference patterns characterized by independently, partially, and even fully common weight vectors
Reliability of Communication Systems Used in Offshore Wind Farms, Journal of Telecommunications and Information Technology, 2022, nr 4
Full text linkIn the era of renewable energy, offshore wind farms play a very important role. The number of such installations in Europe is increasing rapidly. With the growing capacity of wind turbines installed in these farms (3, 5, 10 MW), the profitability of this type of energy systems plays an increasing role. The number of wind energy turbines installed at offshore wind farms is growing constantly as well. Once installed, the power plants must be under constant technical supervision, with reliability of electronic communication systems being a particularly important aspect in the operation of offshore wind farms. Considerations focusing on this subject form the very core of this paper. After an introduction to offshore wind farms, the following aspects will be discussed: redundant topologies, e.g. multiple HiPERRings, redundant switches and routers within the backbone networks, redundancy of the transmission media used, alternative transmission technologies, e.g. WLANs (IEEE 802.11h, IEEE 802.11g). Finally, requirements applicable to reliable electronic communication systems used in offshore wind farms will be formulated
Detection of Monocrystalline Silicon Wafer Defects Using Deep Transfer Learning, Journal of Telecommunications and Information Technology, 2022, nr 1
Full text linkDefect detection is an important step in industrial production of monocrystalline silicon. Through the study of deep learning, this work proposes a framework for classifying monocrystalline silicon wafer defects using deep transfer learning (DTL). An existing pre-trained deep learning model was used as the starting point for building a new model. We studied the use of DTL and the potential adaptation of MobileNetV2 that was pre-trained using ImageNet for extracting monocrystalline silicon wafer defect features. This has led to speeding up the training process and to improving performance of the DTL-MobileNetV2 model in detecting and classifying six types of monocrystalline silicon wafer defects (crack, double contrast, hole, microcrack, saw-mark and stain). The process of training the DTL-MobileNetV2 model was optimized by relying on the dense block layer and global average pooling (GAP) method which had accelerated the convergence rate and improved generalization of the classification network. The monocrystalline silicon wafer defect classification technique relying on the DTL-MobileNetV2 model achieved the accuracy rate of 98.99% when evaluated against the testing set. This shows that DTL is an effective way of detecting different types of defects in monocrystalline silicon wafers, thus being suitable for minimizing misclassification and maximizing the overall production capacities
A Comparative Study of Various Edge Detection Techniques for Underwater Images, Journal of Telecommunications and Information Technology, 2022, nr 1
Full text linkNowadays, underwater image identification is a challenging task for many researchers focusing on various ap plications, such as tracking fish species, monitoring coral reef species, and counting marine species. Because underwater im ages frequently suffer from distortion and light attenuation, pre-processing steps are required in order to enhance their quality. In this paper, we used multiple edge detection tech niques to determine the edges of the underwater images. The pictures were pre-processed with the use of specific techniques, such as enhancement processing, Wiener filtering, median fil tering and thresholding. Coral reef pictures were used as a dataset of underwater images to test the efficiency of each edge detection method used in the experiment. All coral reef image datasets were captured using an underwater GoPro camera. The performance of each edge detection technique was evalu ated using mean square error (MSE) and peak signal to noise ratio (PSNR). The lowest MSE value and the highest PSNR value represent the best quality of underwater images. The re sults of the experiment showed that the Canny edge detection technique outperformed other approaches used in the course of the project
An Attribute-Based Encryption Method Using Outsourced Decryption and Hierarchical Access Structure, Journal of Telecommunications and Information Technology, 2022, nr 2
Full text linkCloud computing is being rapidly adopted by many organizations from different domains and large amounts of data is stored in the cloud. In order to ensure data security, the attribute-based access control mechanism has been emerging recently as a fine-grained access control model that grants access based on the data user’s attributes. In this model, the data owner builds the access policy using the attributes of the data users and access to the data is granted only if the requirements of such an access policy are satisfied. Ciphertext policy-based attribute-based encryption (CPABE) is one of the most widely used methods for providing encrypted access control. Complex, time consuming and costly paring operations are the major issue with the CPABE method. Hence, another efficient method is needed to reduce the data user’s overhead while decrypting data. This paper presents an efficient method consisting in outsourcing decryption operations to a third-party server, so that complex operations may be performed by that machine with only some simple calculations left on the data user’s side. The concept of a hierarchical access structure is also integrated with the traditional CPABE technique. The hierarchical approach enables the data owner to encrypt multiple data using a single common hierarchical access structure. This allows the user to decrypt only the relevant part of ciphertext, depending on which fragment of the hierarchical access structure is satisfied. The paper evaluates also the performance of the proposed model in terms of time and storage cost
