Showing papers by "Mansour Youseffi published in 2018"

Fourth-Order Butterworth Active Bandpass Filter Design for Single-Sided Magnetic Particle Imaging Scanner

Abstract: This paper describes the design and simulation of a fourth-order Butterworth active bandpass filter designed for single-sided magnetic particle imaging (MPI) scanner. Bandpass filters (BPF) are used in MPI scanner set-up to attenuate the harmonic distortions generated by the power amplifier before passing the purely sinusoidal signal to the excitation (Drive) coils. The BPF is designed based on the excitation frequency of 22.8 kHz, having Butterworth response, and realized using Sallen-Key topology by cascading one second-order highpass filter and one second-order lowpass filter, with values of the passive components calculated using the coefficient matching method. MATLAB and NI Multisim software are used to simulate the filter, and the results are compared. The magnitude response obtained using MATLAB have monotonic amplitude response in the pass and stopband, and maximally flat with small ripples in the passband than the NI Multisim implementation, while NI Multisimimplementation has better roll-off than MATLAB implementation.

Comparison of biophysical properties characterized for microtissues cultured using microencapsulation and liquid crystal based 3D cell culture techniques

Abstract: Growing three dimensional (3D) cells is an emerging research in tissue engineering. Biophysical properties of the 3D cells regulate the cells growth, drug diffusion dynamics and gene expressions. Scaffold based or scaffoldless techniques for 3D cell cultures are rarely being compared in terms of the physical features of the microtissues produced. The biophysical properties of the microtissues cultured using scaffold based microencapsulation by flicking and scaffoldless liquid crystal (LC) based techniques were characterized. Flicking technique produced high yield and highly reproducible microtissues of keratinocyte cell lines in alginate microcapsules at approximately 350 ± 12 pieces per culture. However, microtissues grown on the LC substrates yielded at lower quantity of 58 ± 21 pieces per culture. The sizes of the microtissues produced using alginate microcapsules and LC substrates were 250 ± 25 μm and 141 ± 70 μm, respectively. In both techniques, cells remodeled into microtissues via different growth phases and showed good integrity of cells in field-emission scanning microscopy (FE-SEM). Microencapsulation packed the cells in alginate scaffolds of polysaccharides with limited spaces for motility. Whereas, LC substrates allowed the cells to migrate and self-stacking into multilayered structures as revealed by the nuclei stainings. The cells cultured using both techniques were found viable based on the live and dead cell stainings. Stained histological sections showed that both techniques produced cell models that closely replicate the intrinsic physiological conditions. Alginate microcapsulation and LC based techniques produced microtissues containing similar bio-macromolecules but they did not alter the main absorption bands of microtissues as revealed by the Fourier transform infrared spectroscopy. Cell growth, structural organization, morphology and surface structures for 3D microtissues cultured using both techniques appeared to be different and might be suitable for different applications.

A Novel Algorithm for Human Fall Detection using Height, Velocity and Position of the Subject from Depth Maps

Abstract: Human fall detection systems play an important role in our daily life, because falls are the main obstacle for elderly people to live independently and it is also a major health concern due to aging population. Different approaches are used to develop human fall detection systems for elderly and people with special needs. The three basic approaches include some sort of wearable devices, ambient based devices or non-invasive vision-based devices using live cameras. Most of such systems are either based on wearable or ambient sensor which is very often rejected by users due to the high false alarm and difficulties in carrying them during their daily life activities. This paper proposes a fall detection system based on the height, velocity and position of the subject using depth information from Microsoft Kinect sensor. Classification of human fall from other activities of daily life is accomplished using height and velocity of the subject extracted from the depth information. Finally position of the subject is identified for fall confirmation. From the experimental results, the proposed system was able to achieve an average accuracy of 94.81% with sensitivity of 100% and specificity of 93.33%.