Full Research Paper
Faraein Aeini; Alireza Ghonoodi; Rashin Aeini
Volume 12, Issue 3 , November 2018, Pages 173-187
Abstract
Forensic dentistry is the subdivision of forensics that determines the identity of the victim based on dental features. In this paper, we propose a framework for human identification based on dental. The proposed framework consists of two main stages: the first stage is teeth classification and numbering, ...
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Forensic dentistry is the subdivision of forensics that determines the identity of the victim based on dental features. In this paper, we propose a framework for human identification based on dental. The proposed framework consists of two main stages: the first stage is teeth classification and numbering, and the second stage is human recognition. In this study, a new feature has been proposed for each of these two stages: Crown mesiodistal neck and anatomic crown length for the first and a feature vector based on the geodesic distance between important points of teeth contours for the second. The proposed method is capable of solving principally, and automatically problems such as diagnosis of posterior and also anterior teeth, posterior and anterior teeth classification, diagnosis of number and kind of all kind pulled teeth from, which are overlooked or have been left with the simple premises in previous works. To evaluate the proposed method, we experiment on a set of bitewings, periapical and panoramic images. The practical results show an improvement in accuracy of classification with discovering anterior teeth, and also 9% improvement in the first rank of the accuracy of teeth recognition, in comparison with the previous works.
Full Research Paper
Farnaz Saberpour; Mohsen Parto Dezfouli; Vahid Shalchyan; Mohammad Reza Daliri
Volume 12, Issue 3 , November 2018, Pages 189-198
Abstract
Neural adaptation is a brain ability which reduces the neural activities in response to a repeated stimulus. In this study, we examined the effect of adaptation on neural decoding. For this purpose, pure tones with different frequency-amplitude combinations were presented randomly in two sequences (usual ...
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Neural adaptation is a brain ability which reduces the neural activities in response to a repeated stimulus. In this study, we examined the effect of adaptation on neural decoding. For this purpose, pure tones with different frequency-amplitude combinations were presented randomly in two sequences (usual and adaptive). During the task, local field potential (LFP) signals were recorded from the primary auditory cortex of fifteen anesthetized rats. In the usual sequence, the stimuli were presented randomly with 50 ms duration and 300 ms interstimulus interval (ISI). Each combination was presented about 25 times. In the adaptive sequence, same as the usual one, stimuli were presented with this difference that one specific frequency (adapter) with the probability of 80% was presented frequently in this sequence. Comparison between decoding accuracy of two sequences allows us to study the effect of adaptation to a specific frequency on neural decoding. First, considering the power spectrum feature in six frequency bands and using LDA (linear discrimination analysis) classifier, the average decoding accuracy of all frequency-pairs were calculated in the usual sequence. Subsequently, the decoding accuracy of frequency-pairs in the adaptive sequence was calculated and compared with the usual sequence. Results show a significant decoding accuracy between different frequency-pairs in beta, gamma, and high-gamma bands (>12 Hz) of local field potential with an accuracy of about 80%. Moreover, we found that adaptation to one frequency of sound decreases the decoding accuracy of neighbor frequencies. This signature was observed in high-frequency gamma and high-gamma activities (30-120 Hz) of LFPs.
Full Research Paper
Elham Mollaei; Sasan Asiaei; Mahdi Moghimi; Ali Zadekafi
Volume 12, Issue 3 , November 2018, Pages 199-210
Abstract
Microfluidic analytical/diagnostic tools, especially microfluidic paper-based analytical devices ( PADs) have attracted considerable attention due to their numerous advantages including their low operational costs, small analyte consumption, and limited required skills for use, and easy disposal/recycling. ...
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Microfluidic analytical/diagnostic tools, especially microfluidic paper-based analytical devices ( PADs) have attracted considerable attention due to their numerous advantages including their low operational costs, small analyte consumption, and limited required skills for use, and easy disposal/recycling. μPADs have been successful in detection of various diseases with no external deriving units. The aim of this study is to develop a micromixer for colorimetric detection of nitrite in saliva using Griess reaction and widening the limit of detection (LOD) by mixing improvement. Micromixers were fabricated using laser cut after a simple design. Five different geometries were examined and compared including straight, curved, zigzag, square wave and hexagonal, by numerical simulation and experimental tests for mixing part in micromixer. Simulations were performed in ANSYS CFX with homogeneous two-phase flow model in a porous media. As the result inclined hexagonal micromixer showed the best performance (in comparison with the straight one) exhibiting 44.24% of improvement which leads to a detection range and LOD of and , respectively. Considering the significant impact of micromixers in microfluidics, the quality of mixing and therefore the accuracy of the devices was improved by simple geometrical modifications.
Full Research Paper
Biomechatronics
Seyed Hamid Reza Heidary; Mohammad Sajjad Sokout; Borhan Beigzadeh
Volume 12, Issue 3 , November 2018, Pages 211-220
Abstract
Monitoring human body vital signs like heart rate, oxygen saturation and blood pressure, has a profound influence on recognition of cardiovascular diseases which are growing at unprecedented rate all over the world. In recent years, using imaging photoplethysmography (IPPG) signals is one of the most ...
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Monitoring human body vital signs like heart rate, oxygen saturation and blood pressure, has a profound influence on recognition of cardiovascular diseases which are growing at unprecedented rate all over the world. In recent years, using imaging photoplethysmography (IPPG) signals is one of the most interesting issues among researchers to measure the vital signs of the human body. Decreasing the values of hemoglobin in blood, which is called Anemia, and it's more common among women, can be detected through the processing of the IPPG signals. In this article, the magnitude of hemogolobin level is measured by a suggested approach applied on the IPPG signals taken by means of a physical setup. To make the signals, after capturing video from the fingertip pulse of index right finger with various light sources in wavelengths consisting of white, 520nm and 980nm; the IPPG signals will be accessible as a result of applying the proposed algorithm on the videos. In the next step, providing appropriate signals to the implementation of the regarded method, the signals are preprocessed. Considering physics-based models, the time domain features are extracted. In the final step, utilizing the support vector regression, accuracy of the prediction is 82%, which is shown reliability, repeatability, and reproducibility of the designed configuration.
Full Research Paper
Medical Instrumentation
Zahra-Sadat Fatemi; Mohammad Mahdi Ahmadi
Volume 12, Issue 3 , November 2018, Pages 221-234
Abstract
The use of smart medical implants to study the human brain and the interaction of neurons with each other has recently gained much attention. These implants contain microelectrode arrays in which the size of an electrode is in the order of the size of a neuron; therefore they allow recording signals ...
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The use of smart medical implants to study the human brain and the interaction of neurons with each other has recently gained much attention. These implants contain microelectrode arrays in which the size of an electrode is in the order of the size of a neuron; therefore they allow recording signals from single neuron or stimulating a single neuron with considerable precision. Design of such implants entails many challenges, one of which is the design of power and data recovery blocks. In this paper, we describe the design of a new power and data recovery unit for an implantable neural stimulating microsystem. The power recovery unit generates two supply voltages: a 1.8-V supply for the core circuits and a higher supply voltage for the stimulation front-end. An active rectifier is used to generate the 1.8-V supply. The active rectifier achives a 89% power conversion efficiency and 150mV voltage drop with a 3-V sinusoidal input voltage. In order to maximize the efficiency of the stimulation front-end, the supply voltage of that circuit should be adaptively adjusted according to the amplitude of the stimulation current. As a result, a phase-controlled active rectifier is utilized to generate the supply voltage for the neural stimulation front-end. The phase-controlled active rectifier can generate out voltages ranging from 1.8V to 2.5V. Using phase-controlled active rectifier can increase the power conversion efficiency up to 50%. In addition to power recovery, neuroelectrical stimulation microsystems should receive stimulation data from outside of the body. Hence, this paper also circuits required for clock and daterecovery. The data recovery block is able to demodulate the ASK-modulated signal with 3-V to 5-V amplitude and 5% to 25% modulation index.
Full Research Paper
Medical Ultrasound / Diagnostic Sonography / Ultrasonography
Hannaneh Keyhanian; Sayed Mahmoud Sakhaei
Volume 12, Issue 3 , November 2018, Pages 235-248
Abstract
The method of multi-beam beamforming is a low-computational adaptive beamforming method in which, instead of calculating the covariance matrix and inverting it for each point of the image, only one matrix is calculated for all points on the same radial distance. Then, to reduce the complexity of the ...
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The method of multi-beam beamforming is a low-computational adaptive beamforming method in which, instead of calculating the covariance matrix and inverting it for each point of the image, only one matrix is calculated for all points on the same radial distance. Then, to reduce the complexity of the inverse matrix calculation, the problem is solved in the beamspace domain. We introduce a new two-stage method to reduce the complexity of the minimum variance (MV) beamforming method, which outperforms the beamspace method in computational burden aspect in multi-beam method. In the first step, instead of using the signals of all array elements in calculating the covariance matrix, the signals of a decimated one are chosen such that the resulting covariance matrix contains all the correlation information of the signals. In the second stage, the weights of all elements of the array are determined by a proper interpolation method from the weights of the decimated array. According to the simulation results of point targets and cyst phantom, the new method has a performance similar to that of the beamspace multi-beam method in terms of resolution, contrast, and robustness against the errors with at least 3 times lower computational burden.
Full Research Paper
Biological Computer Modeling / Biological Computer Simulation
Somayeh Raiesdana
Volume 12, Issue 3 , November 2018, Pages 249-263
Abstract
Sleep is an essential process to maintain and improve human activities, while many details related to sleep are still not well understood. Decreased or fragmented sleep is a health risk that might result in heart disease or diabetes on one hand and degradation of consciousness and cognition on the other ...
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Sleep is an essential process to maintain and improve human activities, while many details related to sleep are still not well understood. Decreased or fragmented sleep is a health risk that might result in heart disease or diabetes on one hand and degradation of consciousness and cognition on the other hand. Sleep fragmentation is a phenomenon in which an individual's sleep is intermittently disrupted by arousal caused by external factors (noise) or internal factors (apnea) although sleep deprivation does not completely occur. Computational modeling is a suitable framework for understanding complex biological mechanisms. In this paper, the fundamental phenomena underlying the sleep-wake transition was reviewed and simulated. The dynamical behavior of model was then investigated and afterwards the factors that might cause obstructive sleep apnea were implemented and evaluated. The model includes two main neuronal populations: the ascending arousal system in the brain stem that is responsible for awakening and a neuronal population in the hypothalamus, called VLPO, which mediates sleep. These populations have mutual inhibition on each other causing a flip-flop or switching behavior between sleep and wake. The results of modeling in this paper showed hysteresis in the sleep-wake cycle, the size of which is affected by factors causing arousal. In OSA, intermittent and unstable transitions as well as the shrinking of bistable zone is expected. The model could reproduce some experimental results related to obstructive apneas.
