Punctual identification of protein-coding regions in Deoxyribonucleic Acid (DNA) sequences because of their 3-base periodicity has been a challenging issue in bioinformatics. Many DSP (Digital Signal Processing) techniques have been applied for identification task and concentrated on assigning numerical values to the symbolic DNA sequence and then applying spectral analysis tools such as the short-time discrete Fourier transform (ST-DFT) to locate periodicity components. In this paper, first, the symbolic DNA sequences are converted to digital signal using the Z-curve method, which is a unique 3-D plot to illustrate DNA sequence and presents the biological behavior of DNA sequence. Then a novel fast algorithm is proposed to investigate the location of exons in DNA strand based on the combination of Linear Predictive Coding Model (LPCM) and Goertzel algorithm. The proposed algorithm leads to increase the speed of process and therefor reduce the computational complexity. Detection of small size exons in DNA sequences, exactly, is another advantage of our algorithm. The proposed algorithm ability in exon prediction is compared with several existing methods at the nucleotide level using: (i) specificitysensitivity values; (ii) Receiver Operating Curves (ROC); and (iii) area under ROC curve. Simulation results show that our algorithm increases the accuracy of exon detection relative to other methods for exon prediction. In this paper, we have also developed a useful user friendly package to analyze DNA sequences.

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Background: Foot pressure assessment systems are widely used to diagnose foot pathologies. Human foot plays an important role in maintaining the biomechanical function of the lower extremities which includes provision of balance and stabilization of the body during gait.Objective: There are different types of assessment tools with different capabilities which are discussed in detail in this paper. In this project, we introduce a new camera-based pressure distribution estimation system which can give a numerical estimation in addition to giving a visual illustration of pressure distribution of the sole.Material and Methods: In the first step, an image is captured from the traditional Podoscope devices. Then, HMRFEM image segmentation scheme is implemented to extract the contacting part of the sole to the ground. Finally, based on a simple calibration method, per mm2 pressure is estimated to give an accurate pressure distribution measure.Results: A significant and usable estimation of foot pressure has been introduced in this article. The main drawback of introduced systems is low resolution of sensors which is solved using a high resolution camera as a sensor. Another problem is patchy edge extracted by the systems which is automatically solved in the proposed device using an accurate image segmentation algorithm. Also the LCE, GCE and BCE measures demonstrate that lowest error rates are obtained with HMRF segmentation method.Conclusion: we introduced a camera-based plantar pressure assessment tool which uses we introduced a camera-based plantar pressure assessment tool which uses HMRF-EM-based method has been explained in more detail which gives a brilliant sole segmentation from the captured images. Most of the marketable measurement systems use electronic sensors to estimate the pressure distribution, but here we used the captured image and grayscale levels to compute a per pixel pressure which can be converted to N/mm2 scale.

Introduction Body vision is a novel method which examines postural indices through photogrammetric essentials. Nevertheless, its reliability and validity has not been appraised till now. We aimed to evaluate the reliability and validity of body vision system for posture assessment Methods This was a cross sectional study in which two examiners evaluated photographs of 71 subject using body vision system twice with two-week interval. The Body Vision system involves a Grid wall and a camera fixed in front of the grid wall at about 390 cm distances. Three standing photographs (anterior, right lateral, and posterior view) were captured for participants. Results The results for inter-rater reliability analysis showed most of the parameters (74%) had excellent 95% Confidence Interval (CI), 10 % had good to excellent 95% CI, 13% had moderate to good 95% CI, and 1% had poor to moderate 95% CI (Table 2). The results for intra-rater reliability analysis showed 70-72% of the parameters had excellent 95% Confidence Interval (CI), 6-9% had good to excellent 95% CI, 12-13% had moderate to good 95% CI, and 9% had poor to moderate 95% CI. The comparison between known distances and angles on grid wall and those obtained from photogrammetric measurements showed there is no statistical significant difference (p > 0.05). Also the regression analysis showed there is a significant and positive relationship between them (R2 = 1, p < 0.05). Conclusion The results of this study showed that body vision system is a valid and reliable tool for measuring postural parameters.

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