Defining a no-reference image quality assessment by means of the self-affine analysis.

In this paper we propose a novel Blind Image Quality Assessment via Self-Affine Analysis (BIQSAA) method by considering the wavelet transform as a linear operation that decomposes a complex signal into elementary blocks at different scales or resolutions. BIQSAA decomposes a distorted image into a set of wavelet planes ω λ, ϕ of different spatial frequencies λ and spatial orientations ϕ, and it transforms these wavelet planes into one-dimension vector Ω using a Hilbert scanning. From the vector Ω there were obtained their wavelet coefficient fluctuations estimated by the inverse of the Hurst exponent in decibels, whose scaling-law or fractal behavior was obtained by applying Fractal Geometry or Self-Affine Analysis. The scaling exponents calculated for the coefficient fluctuation behavior of Image Lena at 24bpp, at 1.375bpp, and at 0.50bpp were H 24b p p = 0.0395, H 1.375b p p = 0.0551, and H 0.50b p p = 0.0612, respectively. Our experiments show that BIQSAA algorithm improves in 14.36% the Human Visual System correlation, respect to the four state-of-the-art No-Reference Image Quality Assessments.

Neurodynamics of Patients during a Dolphin-Assisted Therapy by Means of a Fractal Intraneural Analysis.

The recent proliferation of sensor technology applications in therapies for children's disabilities to promote positive behavior among such children has produced optimistic results in developing a variety of skills and abilities in them. Dolphin-Assisted Therapy (DAT) has also become a topic of public and research interest for these disorders' intervention and treatment. This work exposes the development of a system that controls brain-computer interaction when a patient with different abilities undergoes a DAT. To develop the proposed system, TGAM1, i.e., ThinkGear-AM1 series of NeuroSky company, was used, connecting it to an isolated Bluetooth 4.0 communication protocol from a brackish and humid environment, and a Notch Filter was applied to reduce the input noise. In this way, at Definiti Ixtapa-Mexico facilities, we explored the behavior of three children with Infantile Spastic Cerebral Palsy (Experiment 1), as well as the behavior of Obsessive Compulsive Disorder and neurotypic children (Experiment 2). This was done applying the Power Spectrum Density (PSD) and the Self-Affine Analysis (SSA) from Electroencephalogram (EEG) biosignals. The EEG Raw data were time series showing the cerebral brain activity (voltage versus time) before and during DAT for the Experiment 1, and before, during DAT and after for the Experiment 2. Likewise, the EEW RAW data were recorded by the first frontopolar electrode (FP1) by means of an EEG biosensor TGAM1 Module. From the PSD we found that in all child patients a huge increment of brain activity during DAT regarding the before and after therapy periods around 376.28%. Moreover, from the SSA we found that the structure function of the all five child patients displayed an antipersistent behavior, characterized by σ ∝ δ t H , for before, during DAT and after. Nonetheless, we propose that one way to assess whether a DAT is being efficient to the child patients is to increase the during DAT time when the samples are collected, supposing the data fitting by a power law will raise the time, displaying a persistent behavior or positive correlations, until a crossover appears and the curve tends to be horizontal, pointing out that our system has reached a stationary state.

Fractal Characterization of Stochastic Series Fluctuations of Children with Reading Disorders.

Reading is an emerging process from human brain activity. This process sometimes is subject to disorders which has been studied from the performance of studies that provide data that are treated with qualitative and quantitative linear tools to obtain the average behavior determined and the causality of it. This research focuses on the nonlinear quantitative study of reading disorder and in this way fractal geometry and roughness interface growth theory approach were selected to be used in the processing of brain wave quantification (EEG). From the EEG of children with and without reading disorders in the State of Mexico (experimental and control group) were built time series of standard deviation for each of the 19 channels distributed in cerebral cortex. The self-affinity of these time series (treated as interfaces in motion) is studied by the scaling behavior of their structure functions. It was found that the behavior of the time series of children with reading problems (experimental group) and without them (control group) is similar to the Family-Vicsek scaling dynamic for a kinetic roughening of moving interface.