Quantum error-correcting codes play the role of suppressing noise and decoherence in quantum systems by introducing redundancy. Some strategies can be used to improve the parameters of these codes. For example, entanglement can provide a way for quantum error-correcting codes to achieve higher rates than the one obtained by means of the traditional stabilizer formalism. Such codes are called entanglement-assisted quantum error-correcting (EAQEC) codes. In this paper, we utilize algebraic geometry codes to construct several families of EAQEC codes derived from the Euclidean and the Hermitian construction. Three families constructed here consist of codes whose quantum Singleton defect is equal to zero, one, or two. We also construct families of EAQEC codes with an encoding rate exceeding the quantum Gilbert-Varshamov bound. Additionally, asymptotically good towers of linear complementary dual codes are used to obtain asymptotically good families of EAQEC codes consuming maximal entanglement. Furthermore, a simple comparison with the quantum Gilbert-Varshamov bound demonstrates that, by utilizing the proposed construction, it is possible to generate an asymptotically family of EAQEC codes that exceeds this bound.

This study presents a widespread analysis of affective vocal expression classification systems. In this study, the Hilbert–Huang–Hurst coefficient (HHHC) vector is proposed as a non‐linear vocal source feature to represent the emotional states according to their effects on the speech production mechanism. Affective states are highlighted by the empirical mode decomposition‐based method, which exploits the non‐stationarity of the acoustic variations. Hurst coefficients are then estimated from the decomposition modes to form the feature vector. Additionally, a vector of the index of non‐stationarity (INS) is introduced as dynamic information to the HHHC. The proposed feature vector is evaluated in speech emotion classification experiments with three databases in German and English languages. Three state‐of‐the‐art acoustic feature vectors are adopted as a baseline. The ‐integrated Gaussian mixture model ( ‐GMM) is also introduced for the emotion representation and classification. Its performance is compared to competing for stochastic and machine learning classifiers. Results demonstrate that the HHHC leads to significant classification improvement when compared to the baseline acoustic feature vectors. Moreover, results also show that the ‐GMM outperforms the competing classification methods. Finally, the complementarity aspects of HHHC and INS are also evaluated for the GeMAPS and eGeMAPS feature sets.

Quantum error correcting codes play the role of suppressing noise and decoherence in quantum systems by introducing redundancy. Some strategies can be used to improve the parameters of these codes. For example, entanglement can provide a way for quantum error correcting codes to achieve higher rates than the one obtained via traditional stabilizer formalism. Such codes are called entanglement-assisted quantum (QUENTA) codes. In this paper, we use algebraic geometry codes to construct two families of QUENTA codes, where one of them has maximal entanglement and is maximal distance separable. In the end, we show that for any asymptotically good tower of algebraic function fields there is an asymptotically good family of maximal entanglement QUENTA codes with nonzero rate, relative minimal distance, and relative amount of entanglement.

In this paper, we construct new families of classical convolutional codes (CCC's) and new families of quantum convolutional codes (QCC's). The CCC's are derived from (block) algebraic geometry (AG) codes. Furthermore, new families of CCC's are constructed by applying the techniques of puncturing, extending, expanding, and by the direct product code construction applied to AG codes. In addition, utilizing the new CCC's constructed here, we obtain new families of QCC's. The parameters of these new codes are good. More precisely, in the classical case, a family of almost near maximum distance separable (MDS) codes is presented; in the quantum case, we construct a family of MDS (optimal) quantum convolutional codes.

Cryptographic keys based on biometrics, besides offering information security and privacy, have the advantage of being strongly linked to the user. Three main techniques use biometric data to obtain cryptographic keys: cryptographic key release, cryptographic key generation and cryptographic key regeneration. The main objectives of these schemes are to ensure revocability and produce keys with high entropy. In this work, we propose a new technique for secret key agreement based on biometrics. Three security factors are used, represented by an iris code, an RFID tag and a password. The use of the protocol for reconciliation between the RFID reader and tag enables the agreement of a symmetric key with high entropy by discarding all the mismatching bits present in genuine samples, while it fails to do so for impostors that are therefore rejected. A new secret key is agreed after every positive authentication, increasing the security of the system. The system was evaluated on the public database ICE2005 and obtained a 270 binary digit cryptographic key with estimated entropy of about 156 bits at 0% False Acceptance Rate (FAR) and 3.68% False Rejection Rate (FRR).

There are advantages in the use of quantum computing in the elaboration of attacks on certain pseudorandom generators when compared with analogous attacks using classical computing. This paper presents a polynomial time quantum attack on the Blum–Micali generator, which is considered secure against threats from classical computers. The proposed attack uses a Grover inspired procedure together with the quantum discrete logarithm, and is able to recover previous and future outputs of the generator under attack, thereby completely compromising its unpredictability. The attack can also be adapted to other generators, such as Blum–Micali generators with multiple hard-core predicates and generators from the Blum–Micali construction, and also to scenarios where the requirements on the bits are relaxed. Such attacks represent a threat to the security of the pseudorandom generators adopted in many real-world cryptosystems.

The materialist dialectical method is a philosophical investigative method to analyze aspects of reality as complex processes composed by integrating units named poles. Dialectics has experienced considerable progress in the 19th century, with Hegel's dialectics and, in the 20th century, with the works of Marx, Engels, and Gramsci, in philosophy and economics. The movement of poles through their contradictions is viewed as a dynamic process with intertwined phases of evolution and revolutionary crisis. Santos et al. introduced the objective dialectical classifier (ODC), a non-supervised self-organized map for classification. As a case study, we used ODC to classify 181 magnetic resonance synthetic multispectral images composed by proton density, T1- and T2-weighted synthetic brain images. Comparing ODC to k-means, fuzzy c-means, and Kohonen's self-organized maps, concerning with image fidelity indexes as estimatives of quantization distortion, we proved that ODC can reach the same quantization performance as optimal non-supervised classifiers like Kohonen's self-organized maps.

The importance of fields of knowledge like Biology, Psychology, and Social Sciences as sources of inspiration for Computational Intelligence has been increasing in the last years, deeply influencing Evolutionary Computation and its applications, inspiring the development of algorithms and methodologies like evolutionary programming and particle swarm optimization. However, the proliferation of biologically-inspired algorithms and solutions indicates the actual focus of researchers and, consequently, Philosophy is still faced as a sort of obscure and enigmatic knowledge, despite the power of generalization and the systematic nature of philosophical investigative methods like dialectics. This work proposes an evolutionary class of algorithms based on the materialist dialectics, namely the Objective Dialectical Method, to be used in search and optimization problems. To validate our proposal we developed simulations using several benchmarks functions. The generated results were evaluated in minimization problems concerning how near the results are from the minimum value and how many iterations were used until the estimated minimum value reached a specific threshold value set as a determined precision. This work showed that the proposed dialectical algorithm has good performance in global optimization.

Since its advent, wavelet coding has been recognized as a promising technique for wireless transmission systems, especially due to its low decoding complexity and good performance over multipath fading. However, this technique requires unusual modulation schemes in order to be fully exploited. A BER analysis of wavelet-coded transmission systems over Rayleigh fading channels is presented in this work. This analysis is used in the design of novel signal constellations for wavelet-coded systems by means of numerical performance optimization. Numerical results obtained from analysis and simulations show that the wavelet-coded systems derived herein have very good performance characteristics over fading channels.

Multispectral image analysis is a relatively promising field of research with applications in several areas, such as medical imaging and satellite monitoring. However, a considerable number of current methods of analysis are based on parametric statistics. Alternatively, some methods in computational intelligence are inspired by biology and other sciences. Here we claim that philosophy can be also considered as a source of inspiration. This work proposes the objective dialectical method (ODM), which is a computational intelligent method for classification based on the philosophy of praxis. Here, ODM is instrumental in assembling evolvable mathematical tools to analyze multispectral images. In the case study described in this paper, such multispectral images are composed of diffusion weighted (DW) magnetic resonance (MR) images. The results are compared to ground-truth images produced by polynomial networks using a morphological similarity index.

When estimating the bit error rate (BER) of communications systems using Monte Carlo (MC) simulation, importance sampling (IS) may be a powerful method for reducing simulation runtimes. The paper presents an IS technique combined with error event simulation in order to evaluate efficiently the performance of Viterbi decoding used with a binary nonsymmetric channel and with a binary erasure channel. Two biasing approaches are employed, stationary and nonstationary models. Comparative results between the two models and traditional MC methods are presented.

We present a new method to obtain cyclic sub-codes of algebraic geometric codes using their automorphisms. Automorphisms of algebraic geometric codes from F.K. Schmidt curves are proposed. We present an application of this method in designing frequency hopping sequences for spread spectrum systems. Algebraic geometric codes can provide sequences longer (better randomness) than the ones from Reed-Solomon codes.

The main objective of this work is to develop an analytical method for designing translation invariant operators via neural network training. A new neural network architecture, called modular morphological neural network (MMNN), is defined using a fundamental result of minimal representations for translation invariant set mappings via mathematical morphology, proposed by Banon and Barrera (1991). The MMNN general architecture is capable of learning both binary and gray-scale translation invariant operators. For its training, ideas of the backpropagation (BP) algorithm and the methodology proposed by Pessoa and Maragos (see Ph.D. thesis, Georgia Institute of Technology, 1997) for overcoming the problem of non-differentiability of the rank functions are used. An alternative MMNN training method via genetic algorithms (GA) is also developed, and a comparative analysis of BP vs. GA training in problems of image restoration and pattern recognition is provided. The MMNN structure can be viewed as a special case of the morphological/rank/linear neural network (MRL-NN), proposed by Pessoa and Maragos (1997), but with specific architecture and training rules. The effectiveness of the proposed BP and GA training algorithms for MMNNs is encouraging, offering alternative design tools for the important class of translation invariant operators.

Wireless communication systems are disturbed by multipath effects which alter widely the signal amplitude. In such cases coding and diversity schemes are usually employed to lower error rates. It is well known that space-time encoded trellis modulation schemes achieve good performance for transmission through slow fading with perfect CSI. Design criteria used to build space-time codes over slow fading is maximizing rank and determinant from matrices made with codewords pairs. Defining a small size encoder structure and making an exhaustive search over all possible codes with that size, optimum encoders were found. Simulations results are displayed in terms of bit error rate over channels subjected to Doppler and ISI effects.

In this paper we propose new schemes combining space diversity and multidimensional constellations, in order to achieve efficient transmission over slow fading channels. The key idea is mapping information bits to points of an N-dimensional signal constellation and project these points on certain sub-spaces. Coordinates from each sub-space are transmitted by independent flat slow fading sub-channels to get both spectral efficiency and diversity. The new transmission schemes are compared by means simulation with well-known systems.

In this paper, a new method for calculation of Zech's logarithm in GF(2/sup m/) is presented. For a given element, the logarithm is calculated by bit operations performed on its binary representation. No look-up tables are used. The proposed method makes feasible the implementation of an universal-type device for finite field arithmetic.

Frequency hopping sequences generated by either Reed Solomon (RS) or Hermitian codes (AG) are compared. The results show that AG codes do better than RS codes if the ratio of the number of hits by length of sequences is considered.