Bioimpedance analysis is a noninvasive, low cost and a commonly used approach for body composition measurements and assessment of clinical condition. There are a variety of methods applied for interpretation of measured bioimpedance data and a wide range of utilizations of bioimpedance in body composition estimation and evaluation of clinical status. This paper reviews the main concepts of bioimpedance measurement techniques including the frequency based, the allocation based, bioimpedance vector analysis and the real time bioimpedance analysis systems. Commonly used prediction equations for body composition assessment and influence of anthropometric measurements, gender, ethnic groups, postures, measurements protocols and electrode artifacts in estimated values are also discussed. In addition, this paper also contributes to the deliberations of bioimpedance analysis assessment of abnormal loss in lean body mass and unbalanced shift in body fluids and to the summary of diagnostic usage in different kinds of conditions such as cardiac, pulmonary, renal, and neural and infection diseases.

This paper presents a system for a design and implementation of Optical Arabic Braille Recognition(OBR) with voice and text conversion. The implemented algorithm based on a comparison of Braille dot position extraction in each cell with the database generated for each Braille cell. Many digital image processing have been performed on the Braille scanned document like binary conversion, edge detection, holes filling and finally image filtering before dot extraction. The work in this paper also involved a unique decimal code generation for each Braille cell used as a base for word reconstruction with the corresponding voice and text conversion database. The implemented algorithm achieve expected result through letter and words recognition and transcription accuracy over 99% and average processing time around 32.6 sec per page. using matlab environment.

Quantification of internal body composition such as fat mass and body fluid volume recently relies on expensive and complex modalities. The aim of this study is to validate the ability of a multiple frequency bioimpedance analyzer (MFBIA) to estimate the change of internal fat and fluid content in a phantom model. Biological phantom was structured using fresh cucumbers filled with mixture of different percentages of oil and saline fluid seeds being removed and. Surface impedance was measured in four frequencies using commercial and designed MFBIA. The correlation between internal composition and impedance was evaluated using linear regression model. The regression between impedance obtained in different frequencies and both for fat mass and saline fluid ranges from 0.94 and 0.99 for the commercial and the designed MFBIA respectively. The main achievement of this study is that a MFBIA can detect changes in fat mass and saline fluid contents within a biological phantom.

Real-time monitoring and precise diagnosis of the severity of Dengue infection is needed for better decisions in disease management. The aim of this study is to use the Bioimpedance Vector Analysis (BIVA) method to differentiate between healthy subjects and severe and non-severe Dengue-infected patients. Bioimpedance was measured using a 50 KHz single-frequency bioimpedance analyzer. Data from 299 healthy subjects (124 males and 175 females) and 205 serologically confirmed Dengue patients (123 males and 82 females) were analyzed in this study. The obtained results show that the BIVA method was able to assess and classify the body fluid and cell mass condition between the healthy subjects and the Dengue-infected patients. The bioimpedance mean vectors (95% confidence ellipse) for healthy subjects, severe and non-severe Dengue-infected patients were illustrated. The vector is significantly shortened from healthy subjects to Dengue patients; for both genders the p-value is less than 0.0001. The mean vector of severe Dengue patients is significantly shortened compare to non-severe patients with a p-value of 0.0037 and 0.0023 for males and females, respectively. This study confirms that the BIVA method is a valid method in differentiating the healthy, severe and non-severe Dengue-infected subjects. All tests performed had a significance level with a p-value less than 0.05.