Polar codes have come to the forefront of research in communication channel coding, being a performant scheme used in the fifth $(5 \mathrm{G})$ and sixth generations $(6 \mathrm{G})$ with good results in data transmission reliability. Along with their study, special types of decoders were developed. The original one, proposed by the polar code inventor, was called the Successive Cancellation (SC). Over the years, several polar code decoders have been created and tested. In this paper, we investigate the performance of two types: SC and Belief Propagation (BP) under $\alpha$-stable impulsive noise, a non-Gaussian model that is often present in power line communications or in wireless environments with heavy-tailed interference. Simulation results provide a comparative analysis of the bit error rate (BER) performance as a function of signal-to-noise ratio (SNR), highlighting the degradation caused by varying levels of impulsiveness. There is no significant difference between the decoders considered. SC brings a gain of about 0.1 dB at $S N R=3$, when $\alpha=2$. If the noise is strongly impulsive, $\alpha=1.5$, the BER has a high value for both SC and BP. Additionally, probability density function (PDF) plots and power spectral density (PSD) representations of the noise are included to illustrate its statistical and spectral characteristics for different values of the model parameters. For $\alpha=2$, the distribution approaches a Gaussian one. It is symmetric when $\beta=0$; otherwise, it is skewed to the left (for $\boldsymbol{\beta}=-1$) or to the right (for $\boldsymbol{\beta}=1$). In the presence of SaS noise, even with a flat PSD, the absolute power level is significantly higher than that of AWGN. All the simulations were done in MATLAB and compared with the Gaussian channel case.

Polar codes are known as a great coding scheme used in several of the latest communication technologies, alongside other coding schemes, such as the Alamouti code. In this paper, we propose a Polar-Coded Alamouti MIMO system, and we evaluate its performance achieved over additive white Gaussian noise (AWGN) channels with Rayleigh fading. We analyzed the proposed system under different types of modulation and for various values of the polar code parameters: block-length N and dimension K. The simulation results show that if the rate R of the polar code has a value that is closer to 0, the Alamouti-polar configuration exhibits a lower bit error rate (BER), in comparison to the standalone Alamouti and polar code. The system is more robust in the case of binary phase shift keying (BPSK), and the worst scenario is found with 16 quadrature amplitude modulation (16-QAM).

This paper introduces an innovative Data Collection Platform (DC Platform) developed to enhance driver safety by enabling effective aggregation detection data. Designed to interface with external data detection systems, the DC Platform provide advanced functionalities for efficient management and synchronization of data alongside geospatial information of monitored drivers. Based on the collected data the developed Platform enables the application of analytical tools to assess and predict risk patterns related to impaired parameters driving. Developed CD Platform was implemented through two different approaches: one hosted in a traditional host-based web server and the second one deployed in virtual environment in Docker containers. The platform’s support for real-time data integration facilitates targeted and timely interventions, which may contribute to a reduction in the incidence of -related traffic accidents.

Epilepsy is a brain neurological disease that necessitates automatic systems to be correctly identified and diagnosed. The current paper developed an Empirical Wavelet Transform (EWT) based method to identify epileptic EEG signals type. The data analysis is conducted on two EEG channels, Fpl and Fp2. The research approach focuses on the decomposition of EEG signals into distinct delta, theta, alpha, beta, and gamma rhythms, achieved through the application of the EWT algorithm. For each decomposing sub -band, features such as skewness, kurtosis, median, Stein's Unbiased Risk Estimation (SURE) entropy, threshold entropy, and centered correntropy are computed to extract the signal characteristics. The signals arc classified into focal and generalized using five classifiers. The results show that the classification performance is better for the EEG data from the Fpl channel compared to Fp2, with high accuracies of 81.25%, 87.5%, and 93.75% achieved in the delta, beta, and gamma sub -bands using Fine Tree and Linear Support Vector Machine classifiers on the Fpl channel.

The rising occurrence of epilepsy along with the intricate nature of focal epilepsy and its potential to progress to generalized epilepsy requires the advancement of intelligent systems capable of delivering precise diagnoses. This paper developed a novel approach for the classification of patients with focal and generalized epilepsy based on the analysis of EEG signals using the Ensemble Empirical Mode Decomposition (EEMD) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) techniques. These adaptive methods are applied to a comprehensive database consisting of EEG signals captured during sleep from patients with focal and generalized epilepsy. Feature extraction is performed on the resulting Intrinsic Mode Functions (IMFs) obtained from EEMD and CEEMDAN methods, four statistical features being computed for each extracted IMF. Finally, the K -nearest neighbors (KNN) and Na & iuml;ve Bayes (NB) classifiers are employed to accurately classify the EEG signals into focal and generalized categories. The combination of CEEMDAN-based approach and KNN classifier achieved the highest classification rate of 94.54%, exceeding the EEMD-based approach and KNN classifier, which attained a maximum classification rate of 93.32%. By means of the proposed methods, we aim to contribute to the development of effective and efficient diagnostic systems for epilepsy.

This article presents an overview of security considerations in the context of smart cities, highlighting key areas of connectivity of Internet of Things (IoT) devices in a smart city, vulnerabilities, and strategies for risk mitigation. It explores possible vulnerabilities associated with interconnected IoT devices, the potential impact of cyberattacks on essential services, and the importance of implementing robust security measures to mitigate risks. The paper is organized as follows: the devices of a smart intersection and possible attacks on them are presented in Section II and Section III, respectively. Section IV presents security measures which can be incorporated to eliminate or to decrease the risks associated with the attacks from Section III. Finally, Section IV concludes the paper.

In this paper, we address the inverse of a true fourth-degree permutation polynomial (4-PP), modulo a positive integer of the form 32kLΨ, where kL∈{1,3} and Ψ is a product of different prime numbers greater than three. Some constraints are considered for the 4-PPs to avoid some complicated coefficients’ conditions. With the fourth- and third-degree coefficients of the form k4,fΨ and k3,fΨ, respectively, we prove that the inverse PP is (I) a 4-PP when k4,f∈{1,3} and k3,f∈{1,3,5,7} or when k4,f=2 and (II) a 5-PP when k4,f∈{1,3} and k3,f∈{0,2,4,6}.

The diagnosis of epilepsy has increasingly relied on automated algorithms that detect the type of epilepsy with high precision. The objective of this paper is to accurately discriminate between focal and generalized EEG epileptic signals collected in two states: awake and sleep. This study developed and implemented a method based on Empirical Wavelet Transform (EWT) for detecting and classifying EEG signals related with epilepsy. Characteristics such as skewness, kurtosis, median, and the fluctuation index are computed after decomposing each EEG signal into five components by EWT. These derived features are then utilized in classifying EEG signals using the K-Nearest Neighbors (KNN), Naïve Bayes (NB), and Support Vector Machine (SVM) classifiers. The findings of this study demonstrate a maximum classification rate of $\mathbf{9 0. 2 7 \%}$ for data collected in awake state and $83.81 \%$ for data collected during sleep, achieved with KNN classifier in both situations. The results have been clinically validated, emphasizing the efficacy of integrating advanced statistical measures and automatic techniques to enhance the diagnostic processes in epilepsy care.

The increasing incidence of epilepsy has led to the need for automatic systems that can provide accurate diagnoses in order to improve the life quality of people suffering from this neurological disorder. This paper proposes a method to automatically classify epilepsy types using EEG recordings from two databases. This approach uses the spectral power density of intrinsic mode functions (IMFs) that are obtained through the empirical mode decomposition (EMD) of EEG signals. The spectral power density of IMFs has been applied as features for the classification of focal and non-focal, as well as of focal and generalized EEG signals. The data are then classified using K-nearest Neighbor (KNN) and Naïve Bayes (NB) classifiers. The focal and non-focal data were classified with high accuracy, with KNN and NB classifiers achieving a maximum classification rate of 99.90% and 99.80%, respectively. Focal and generalized epilepsy data were classified with high rates of accuracy during wakefulness and sleep stages, with KNN achieving a maximum rate of 99.49% and NB achieving 99.20%. This method shows significant improvements in the classification of EEG signals in epilepsy compared to previous studies. It could potentially aid clinical decisions for epilepsy patients.

A priority step in the diagnosis of epilepsy consists of the accurately identification of the epilepsy type. One significant benchmark for assessing patients with epilepsy and identifying the onset zones is the use of electroencephalographic (EEG) signals. The main objective of this study was to differentiate patients with focal epilepsy from patients with generalized epilepsy. To achieve this, we applied an approach based on the empirical mode decomposition (EMD) method and four features. Based on the extracted features, the signals were classified using the k-nearest neighbor (KNN) classifier with 10-fold cross-validation. The handled database consists of EEG signals with interictal EEG epileptic recordings collected during two states: wakefulness and sleep. The performance metrics of the KNN classifier verified the appropriateness of the proposed approach. The attained results demonstrate that the method is suitable for recognizing both focal and generalized EEG signals, making it potentially valuable in the diagnosis and treatment of epilepsy.

The aim of the research consists in finding an efficient features vector in order to classify the type of seizures of epileptic patients. The handled database consists of focal and nonfocal EEG recordings from epileptic patients. It is performed higher-order spectral analysis applying bispectrum and bicoherence methods to accurately identify the type of seizure so that epileptic patients could undergo surgical rejection of epileptic area. This approach is based on a new way of forming the features vectors, from 10% bispectrum and 90% bicoherence. In the classification stage, the k-Nearest neighbors (kNN) classifier was used, because it performs the best, leading to a maximum value of the classification rate of 99.55%, to a sensitivity of 100%, and a specificity of 99.09%.

Abstract Epilepsy is a neurological disorder characterized by recurrent seizures and has a high incidence rate. The aim of this research is to classify EEG signals as either focal and non-focal in order to identify the epileptogenic area of the brain, which can be surgically treated to manage epilepsy. In this paper, was proposed a classification method based on higher order spectra (HOS) parameters and four different classifiers: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-Nearest neighbors (KNN), and Mahalanobis distance (MD). The method was evaluated using a public dataset that consists in EEG recordings from epileptic patients. The classifiers performances were evaluated and it was shown that KNN classifier achieves a maximum classification rate of 99.55%, sensitivity of 100%, and specificity of 99.09%. The data classification was performed with maximum values of 0.96 for F1-score, and 0.91 for both Kappa and Matthews Coefficient. The results demonstrate the efficiency of the proposed method to identify the type of EEG signals.

In this paper, we obtain upper bounds on the minimum distance for turbo codes using fourth degree permutation polynomial (4-PP) interleavers of a specific interleaver length and classical turbo codes of nominal 1/3 coding rate, with two recursive systematic convolutional component codes with generator matrix G = [ 1 , 15 / 13 ] . The interleaver lengths are of the form 16 Ψ or 48 Ψ , where Ψ is a product of different prime numbers greater than three. Some coefficient restrictions are applied when for a prime p i ∣ Ψ , condition 3 ∤ ( p i − 1 ) is fulfilled. Two upper bounds are obtained for different classes of 4-PP coefficients. For a 4-PP f 4 x 4 + f 3 x 3 + f 2 x 2 + f 1 x ( mod 16 k L Ψ ) , k L ∈ { 1 , 3 } , the upper bound of 28 is obtained when the coefficient f 3 of the equivalent 4-permutation polynomials (PPs) fulfills f 3 ∈ { 0 , 4 Ψ } or when f 3 ∈ { 2 Ψ , 6 Ψ } and f 2 ∈ { ( 4 k L − 1 ) · Ψ , ( 8 k L − 1 ) · Ψ } , k L ∈ { 1 , 3 } , for any values of the other coefficients. The upper bound of 36 is obtained when the coefficient f 3 of the equivalent 4-PPs fulfills f 3 ∈ { 2 Ψ , 6 Ψ } and f 2 ∈ { ( 2 k L − 1 ) · Ψ , ( 6 k L − 1 ) · Ψ } , k L ∈ { 1 , 3 } , for any values of the other coefficients. Thus, the task of finding out good 4-PP interleavers of the previous mentioned lengths is highly facilitated by this result because of the small range required for coefficients f 4 , f 3 and f 2 . It was also proven, by means of nonlinearity degree, that for the considered inteleaver lengths, cubic PPs and quadratic PPs with optimum minimum distances lead to better error rate performances compared to 4-PPs with optimum minimum distances.

The objective of this paper is to find an upper bound on the minimum distance for turbo codes with true cubic permutation polynomial (CPP) interleavers for some particular interleaver lengths. The method we have used consists in proofing that, for the considered interleaver lengths, every true CPP has an inverse true CPP. Then we have proved that some interleaver patterns appear for every CPP. We address interleavers of lengths of the form 8p or 24p, with p a prime number so that 3∣(p−1), used in classical 1/3 rate turbo codes with recursive systematic convolutional component codes having generator matrix G=[1,15/13], in octal form. We prove that 27 is an upper bound on the minimum distance for these types of lengths. We also derive the coefficients of the inverse true CPP for a true CPP of the considered lengths. It is known that for these interleaver lengths an upper bound on the minimum distance for quadratic PP (QPP) interleavers is equal to 36, a much higher value compared to 27. Thus, the importance of the result in the paper consists in that, for interleaver lengths mentioned above, CPP interleavers perform weaker compared to QPP interleavers. Consequently, to find better PP interleavers their degree has to be at least five.

This paper addresses turbo codes with component convolutional codes as those in the Long Term Evolution (LTE) standard. We consider the interleaver lengths of the form L=32kLΨ, with kL∈{1,3} and Ψ is a product of prime numbers greater than three. For these interleaver lengths, we have shown that a true cubic permutation polynomial (CPP) f1x+f2x2+f3x3(modL), under the constraint f3=0(modpi) when a prime pi>3 and 3 does not divide (pi−1), always has an inverse true CPP, except when f3=L∕(4kL) and f2∈{L∕16,3L∕16,5L∕16,7L∕16}. For the previously mentioned exception, a true CPP has an inverse true quadratic PP (QPP). For CPP interleavers with true inverse CPPs, we have shown that the minimum distance is upper bounded by the values of 44, 36, and 27, for three different classes of coefficients. Previously, it was shown that for the same interleaver lengths and for QPP interleavers, the upper bound on the minimum distance is equal to 50. Due to the symmetry of the classical turbo codes, the minimum distance is also upper bounded by the value of 50 for CPP interleavers with a QPP inverse. The nonlinearity degrees for all considered CPP interleavers are computed. Several examples of dmin-optimal CPP interleavers and their inverse QPPs are given.

In this paper, the development of an eye-tracking-based human-computer interface for real-time applications is presented. To identify the most appropriate pupil detection algorithm for the proposed interface, we analyzed the performance of eight algorithms, six of which we developed based on the most representative pupil center detection techniques. The accuracy of each algorithm was evaluated for different eye images from four representative databases and for video eye images using a new testing protocol for a scene image. For all video recordings, we determined the detection rate within a circular target 50-pixel area placed in different positions in the scene image, cursor controllability and stability on the user screen, and running time. The experimental results for a set of 30 subjects show a detection rate over 84% at 50 pixels for all proposed algorithms, and the best result (91.39%) was obtained with the circular Hough transform approach. Finally, this algorithm was implemented in the proposed interface to develop an eye typing application based on a virtual keyboard. The mean typing speed of the subjects who tested the system was higher than 20 characters per minute.

In the original version of this article, unfortunately, there are mistakes in some formulas for determining the number of true different cubic, fourth degree, and fifth degree permutation polynomial based interleavers under Zhao and Fan sufficient conditions.

In this paper, we have obtained the prime factorization form of positive integers N for which the number of true different fourth- and fifth-degree permutation polynomials (PPs) modulo N is equal to zero. We have also obtained the prime factorization form of N so that the number of any degree PPs nonreducible at lower degree PPs, fulfilling Zhao and Fan (ZF) sufficient conditions, is equal to zero. Some conclusions are drawn comparing all fourth- and fifth-degree permutation polynomials with those fulfilling ZF sufficient conditions.

Using more processors for parallel turbo decoding is an important issue to speed up the processing at the receiver of a communication system. Butterfly networks used to map the addresses of extrinsic values represent an elegant and simple solution in parallel turbo decoding. Recently, it has been shown that quadratic permutation polynomial (QPP) interleavers allow an easy way to compute the control bits for a butterfly network. In this paper we show that not only QPP interleavers, but any degree permutation polynomial (PP) interleavers and almost regular permutation (ARP) interleavers also allow the same easy way to compute the control bits required in butterfly networks. As a consequence, it is useful to apply the butterfly networks in parallel turbo decoding when using these performant algebraic interleavers.