Mahdi Khosravy

A theoretical discussion on the foundation of Stone’s blind source separation

This paper discusses the theoretical foundation of Stone’s BSS (Stone in Neural Comput 13:1559–1574, 2001; Stone in Independent Component Analysis: A Tutorial Introduction, A Bradford Book, London, 2004), and it proposes a novel BSS approach based on second-order statistics of the responses of two different linear filters to source signals. The proposed approach which includes Stone’s BSS as a special case helps us to understand how generalized eigenvalue decomposition (GEVD) concludes separating vectors in Stone’s BSS. It obtains the separating vectors by simultaneous diagonalization of covariance matrices of two different linear filters responses to the mixtures. The two employed linear filters are selected dependent on source signals structures under the assumption that they have different responses to source signals. Here, two FIR filters with coefficients selected in an opposite probabilistic way have been suggested for the proposed BSS. The proposed BSS method has been compared with Stone’s BSS, SOBI and AMUSE over speech and image mixtures in different noise levels.

An optimum ICA based multiuser data separation for short message service

This paper presents a new algorithm for efficient separation of short messages which are mixed in a multi user short message system. Separation of mixed random binary sequences of data is more difficult than mixed sequences of multivalued signals. The proposed algorithm applies Kullback leibler independent component analysis (ICA) over mixed binary sequences of received data. Normally, the length of binary codes of short messages are less than the required length that makes ICA algorithm sufficiently work. To overcome this problem, a random binary tail is inserted after each user short message at the transmitter side. The inserted tails for different users are acquired in a way to conclude the least correlation between them. The optimum choice of random binary tail not only increase the performance of separation by increasing the data length but also by minimizing the correlation between multiuser data.

Perceptual Adaptation of Image Based on Chevreul–Mach Bands Visual Phenomenon

The perceptual adaptation of the image (PAI) is introduced by inspiration from Chevreul–Mach Bands (CMB) visual phenomenon. By boosting the CMB assisting illusory effect on boundaries of the regions, PAI adapts the image to the perception of the human visual system and thereof increases the quality of the image. PAI is proposed for application to standard images or the output of any image processing technique. For the implementation of the PAI on the image, an algorithm of morphological filters (MFs) is presented, which geometrically adds the model of CMB effect. Numerical evaluation by improvement ratios of four no-reference image quality assessment (NR-IQA) indexes approves PAI performance where it can be noticeably observed in visual comparisons. Furthermore, PAI is applied as a postprocessing block for classical morphological filtering, weighted morphological filtering, and median morphological filtering in cancelation of salt and pepper, Gaussian, and speckle noise from MRI images, where the above specified NR-IQA indexes validate it. PAI effect on image enhancement is benchmarked upon morphological image sharpening and high-boost filtering.

Genetic Algorithm Based on Enhanced Selection and Log-Scaled Mutation Technique

In this paper, we introduce the selection and mutation schemes to enhance the computational power of Genetic Algorithm (GA) for global optimization of multi-modal problems. Proposed operators make the GA an efficient optimizer in comparison of other variants of GA with improved precision, consistency and diversity. Due to the presented selection and mutation schemes improved GA, as named Enhanced Selection and Log-scaled Mutation GA (ESALOGA), selects the best chromosomes from a pool of parents and children after crossover. Indeed, the proposed GA algorithm is adaptive due to the log-scaled mutation scheme, which corresponds to the fitness of current population at each stage of its execution. Our proposal is further supported via the simulation and comparative analysis with standard GA (SGA) and other variants of GA for a class of multi-variable objective functions. Additionally, comparative results with other optimizers such as Probabilistic Bee Algorithm (PBA), Invasive Weed Optimizer (IWO), and Shuffled Frog Leap Algorithm (SFLA) are presented on higher number of variables to show the effectiveness of ESALOGA.

Evolutionary Optimization Based on Biological Evolution in Plants

Abstract This paper presents a binary coded evolutionary computational method inspired from the evolution in plant genetics. It introduces the concept of artificial DNA which is an abstract idea inspired from inheritance of characteristics in plant genetics through transmitting dominant and recessive genes and Epimutaiton. It involves a rehabilitation process which similar to plant biology provides further evolving mechanism against environmental mutation for being better and better. Test of the effectiveness, consistency, and efficiency of the proposed optimizer have been demonstrated through a variety of complex benchmark test functions. Simulation results and associated analysis of the proposed optimizer in comparison to Self-learning particle swarm optimization (SLPSO), Shuffled Frog Leap Algorithm (SFLA), Multi-species hybrid Genetic Algorithm (MSGA), Gravitational search algorithm (GSA), Group Search Optimization (GSO), Cuckoo Search (CS), Probabilistic Bee Algorithm (PBA), and Hybrid Differential PSO (HDSO) approve its applicability in solving complex problems. In this paper, we have shown effective results on thirty variables benchmark test problems of different classes.

Brain Action Inspired Morphological Image Enhancement

The image perception by human brain through the eyes is not exactly what the eyes receive. In order to have an enhanced view of the received image and more clarity in detail, the brain naturally modifies the color tones in adjacent neighborhoods of colors. A very famous example of this human sight natural modification to the view is the famous Chevreul–Mach bands. In this phenomenon, every bar is filled with one solid level of gray, but human brain perceives narrow bands at the edges with increased contrast which does not reflect the physical reality of solid gray bars. This human visual system action in illusion, highlighting the edges, is inspired here in visual illusory image enhancement (VIIE). An algorithm for the newly introduced VIIE by deploying morphological filters is presented as morphological VIIE (MVIIE). It deploys morphological filters for boosting the same effect on the image edges and aiding human sight by increasing the contrast of the sight. MVIIE algorithm is explained in this chapter. Significant image enhancement, by MVIEE, is approved through the experiments in terms of image quality metrics and visual perception.

Morphological Filters: An Inspiration from Natural Geometrical Erosion and Dilation

Morphological filters (MFs) are composed of two basic operators: dilation and erosion, inspired by natural geometrical dilation and erosion. MFs locally modify geometrical features of the signal/image using a probe resembling a segment of a function/image that is called structuring element. This chapter analytically explains MFs and their inspirational features from natural geometry. The basic theory of MFs in the binary domain is illustrated, and at the sequence, it has been shown how it is extended to the domain of multivalued functions. Each morphological operator is clarified by intuitive geometrical interpretations. Creative natural inspired analogies are deployed to give a clear intuition to readers about the process of each of them. In this regard, binary and grayscale morphological operators and their properties are well defined and depicted via many examples.

New crossover operators for real coded genetic algorithm (RCGA)

This paper aims at achieving global optimal solution of complex problems, such as traveling salesman problem (TSP), using extended version of real coded genetic algorithms (RCGA). Since genetic algorithm (GA) consists of several genetic operators, namely selection procedure, crossover, and mutation operators, that offers the choice to be modified in order to improve the performance for particular implementation, we propose three new crossover techniques for Real Coded Genetic Algorithms, which will improve the quality of solution as well as the rate of convergence to the optimum solution. Methods proposed for crossover operators are inspired by asexual reproduction commonly observed in nature. In this regard, new crossover techniques proposed incorporates the concept of Boltzmanns distribution (BD) for escaping local optima by allowing hill-climbing moves and Metropolis Algorithm (MPA), where, survival of offspring is tested before transit to new generation. Finally, these three methods are compared on various aspects like rate of convergence and quality of final solution among each other and against other randomized algorithms.

Blind components processing a novel approach to array signal processing: A research orientation

Blind Components Processing (BCP), a novel approach in processing of data (signal, image, etc.) components, is introduced as well some applications to information communications technology (ICT) are proposed. The newly introduced BCP is with capability of deployment orientation in a wider range of applications. The fundamental of BCP is based on Blind Source Separation (BSS), a methodology which searches for unknown sources of mixtures without a prior knowledge of either the sources or the mixing process. Most of the natural, biomedical as well as industrial observed signals are mixtures of different components while the components and the way they mixed are unknown. If we decompose the signal into its components by BSS, then we can process the components separately without interfering the other components signal/data. Such internal access to signal components leads to extraction of plenty of information as well more efficient processing compared to normal signal processing wherein all the structure of the signal is gone under processing and modification. This manuscript besides the introducing BCP, proposes a practical applications of BCP with technical merit for harmonic noise cancellation as well stock pricing model.

Acoustic OFDM data embedding by reversible Walsh-Hadamard transform

The manuscripts presents a novel acoustic OFDM data embedding by deploying reversible Walsh-Hadamard transform. The classical acoustic OFDM embedding method was replacing the high band of the acoustic signal with power adjusted OFDM data which brings two shortcoming of degrading the spectral efficiency of the acoustic signal for the listener and dependency to the acoustic carrier signal for estimation of high band envelope. Despite the classical method, the proposed approach not only preserves the spectrum of the acoustic signal, but also it is independent from acoustic carrier signal. The proposed approach encodes the OFDM data signal by a weighted reversible Walsh-Hadamard transform via a linear combination of acoustic signal samples and OFDM signal samples. The efficiency of the approach in data transmission as well as listener subjective quality of acoustic carrier signal for different level of background noise has been clarified, and optimum values of the weighting matrix has been obtained by the evaluation of implementation of the system.