Abstract
Shack-Hartmann wavefront sensing relies on accurate spot centre measurement. Several algorithms were developed with this aim, mostly focused on precision, i.e. minimizing random errors. In the solar and extended scene community, the importance of the accuracy (bias error due to peak-locking, quantization, or sampling) of the centroid determination was identified and solutions proposed. But these solutions only allow partial bias corrections. To date, no systematic study of the bias error was conducted. This article bridges the gap by quantifying the bias error for different correlation peak-finding algorithms and types of sub-aperture images and by proposing a practical solution to minimize its effects. Four classes of sub-aperture images (point source, elongated laser guide star, crowded field, and solar extended scene) together with five types of peak-finding algorithms (1D parabola, the centre of gravity, Gaussian, 2D quadratic polynomial, and pyramid) are considered, in a variety of signal-to-noise conditions. The best performing peak-finding algorithm depends on the sub-aperture image type, but none is satisfactory to both bias and random errors. A practical solution is proposed that relies on the antisymmetric response of the bias to the sub-pixel position of the true centre. The solution decreases the bias by a factor of similar to 7 to values of less than or similar to 0.02 pix. The computational cost is typically twice of current cross-correlation algorithms.

Abstract
Knowledge of the optical properties of tissues is necessary, since they change from tissue to tissue and can differ between normal and pathological conditions. These properties are used in light transport models with various areas of application. In general, tissues have significantly high scattering coefficient when compared to the absorption coefficient and such difference usually increases with decreasing wavelength. The study of the wavelength dependence of the optical properties has been already made for several animal and human tissues, but extensive research is still needed in this field. Considering that most of the Biophotonics techniques used in research and clinical practice use visible to NIR light, we have estimated the optical properties of colorectal muscle (muscularis propria) between 400 and 1000 nm. The samples used were collected from patients undergoing resection surgery for colorectal carcinoma. The estimated scattering coefficient for colorectal muscle decreases exponentially with wavelength from 122 cm(-1) at 400 nm to 95 cm(-1) at 650 nm and to 91 cm(-1) at 1000 nm. The absorption coefficient shows a wavelength dependence according to the behavior seen for other tissues, since it decreases from 8 cm(-1) at 400 nm to 2.6 cm(-1) at 650 nm and to 1.3 cm(-1) at 1000 nm. The estimated optical properties differ from the ones that we have previously obtained for normal and pathological colorectal mucosa. The data obtained in this study covers an extended spectral range and it can be used for planning optical clearing treatments for some wavelengths of interest.

Abstract
To characterize the optical clearing treatments in human colorectal tissues and possibly to differentiate between treatments of normal and pathological tissues, we have used a simple indirect method derived from Mie scattering theory to estimate the kinetics of the reduced scattering coefficient. A complementary method to estimate the kinetics of the scattering coefficient is also used so that the kinetics of the anisotropy factor and of the refractive index are also calculated. Both methods rely only on the thickness and collimated transmittance measurements made during treatment. The results indicate the expected time dependencies for the optical properties of both tissues: an increase in the refractive index and anisotropy factor and a decrease in the scattering coefficients. The similarity in the kinetics obtained for normal and pathological tissues indicates that optical clearing treatments can be applied also in pathological tissues to produce similar effects. The estimated time dependencies using experimental spectral data in the range from 400 to 1000 nm allowed us to compare the kinetics of the optical properties between different wavelengths. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)

Abstract
With the objective of developing a diagnostic tool, we have used the immersion optical clearing method and studied normal and pathological tissues (cancer, diabetes) under treatment by optical clearing agents (OCAs). In order to quantify pathology status OCA diffusion properties in different tissues were measured. We have demonstrated that free water content in cancerous tissues is higher than in normal.

Abstract
The increasing demand for intelligent and sustainable healthcare services has prompted the development of smart health systems. Rapid advances in wireless access technologies and in-network data reduction techniques can significantly assist in implementing such smart systems through providing seamless integration of heterogeneous wireless networks, medical devices, and ubiquitous access to data. Utilization of the spectrum across diverse radio technologies is expected to significantly enhance network capacity and quality of service (QoS) for emerging applications such as remote monitoring over mobile-health (m-health) systems. However, this imposes an essential need to develop innovative networks selection mechanisms that account for energy efficiency while meeting application quality requirements. In this context, this paper proposes an efficient networks selection mechanism with adaptive compression for improving medical data delivery over heterogeneous m-health systems. We consider different performance aspects, as well as networks characteristics and application requirements, so as to obtain an efficient solution that grasps the conflicting nature of the various users' objectives and addresses their inherent tradeoffs. The proposed methodology advocates a user-centric approach towards leveraging heterogeneous wireless networks to enhance the performance of m-health systems. Simulation results show that our solution significantly outperforms state-of-the-art techniques.

Abstract
The emergence of Internet of Things (IoT) applications and rapid advances in wireless communication technologies have motivated a paradigm shift in the development of viable applications such as mobile-health (m-health). These applications boost the opportunity for ubiquitous real-Time monitoring using different data types such as electroencephalography (EEG), electrocardiography (ECG), etc. However, many remote monitoring applications require continuous sensing for different signals and vital signs, which result in generating large volumes of real time data that requires to be processed, recorded, and transmitted. Thus, designing efficient transceivers is crucial to reduce transmission delay and energy through leveraging data reduction techniques. In this context, we propose an efficient data-specific transceiver design that leverages the inherent characteristics of the generated data at the physical layer to reduce transmitted data size without significant overheads. The goal is to adaptively reduce the amount of data that needs to be transmitted in order to efficiently communicate and possibly store information, while maintaining the required application quality-of-service (QoS) requirements. Our results show the excellent performance of the proposed design in terms of data reduction gain, signal distortion, low complexity, and the advantages that it exhibits with respect to state-of-The-Art techniques since we could obtain about 50% compression ratio at 0% distortion and sample error rate.