Search

In this study, a lumped parameter model which includes systemic circulation, cerebral blood vessels, systemic arteriolar resistance control, heart rate control, cerebral autoregulation mechanisms and cerebral CO2 reactivity was developed to simulate healthy and heart failure conditions. In the healthy cardiovascular system model, the results were obtained with all control mechanisms connected to the model. Whilst heart failure cases were simulated, all control mechanisms were removed from the model. Then, cerebral autoregulation and cerebral CO2 reactivity mechanisms were connected to the model. Lastly, systemic arteriolar resistance and heart rate control mechanisms were connected to the model. Also, Monte Carlo Analysis was performed to determine the range of parameters controlled for simulations of healthy and heart failure conditions. The results showed that blood flow rate in cerebral circulation can be simulated more accurately by modeling interaction among autoregulatory mechanisms rather than studying separately.

This paper introduces a simulated grounded inductor (SGI) circuit realization based on two terminal active devices, namely negative impedance converters. The proposed SGI contains a minimum number of passive components when compared to its two terminal active device-based realization counterparts in the literature. It includes a grounded capacitor that is an advantage in integrated circuit process. Nonetheless, a single resistive matching constraint is needed for its realization. SPICE simulations are performed with 0.13 mu m CMOS IBM technology parameters with supply voltages of +/- 0.75 V. Also, an experimental study is included to verify the theory.

Sparse code multiple access (SCMA) is a promising non-orthogonal multiple access (NOMA) scheme for massive machine-type communications for 5G-and-beyond communication systems. Innovative use of orthogonal frequency division multiplexing with index modulation (OFDM-IM) takes advantage of using tone indices in data transmission. In this study, we propose a novel multiplexing and multiple access scheme, namely SCMA based on OFDM-IM (SCMA-IM). SCMA-IM method can be implemented in uplink NOMA systems in an efficient way with similar computational complexity as SCMA. In this method constellation differences and diversity are obtained via SCMA codebooks (CBs) and the employment of CBs is transferred to index modulation (IM). Performance improvement is achieved via CBs so that the number of data bits carried by indices increases more than one bit with different constellations, and also with the number of CBs assigned. In order to determine the indices used in IM, the receiver structure is simplified by the message passing algorithm and log-likelihood ratio detection process used in SCMA receivers without indexing the receiver. Thus, a new NOMA scheme is obtained by coding with IM. Bit error rate performance of the system is studied for 150% and 200% overloading factors.

Introduction: Infra-red (IR) and visible light (VL) based systems developed for transmission of information about physical quantities (e.g. humidity, temperature) out from closed areas, cannot be effectively employed in case of specific conditions in a targeted environment (because of fog or vapor for example). Objectives: In this work, we introduce a concept of wireless short-range transmitter and receiver to sense physical quantities, for instance temperature, with slow variation. The proposed concept is able to transmit analog-based information from isolated environments (e.g. aquariums or environments for plant growing) with high immunity against vapor and fog that limits standard optical (laser, IR band) methods of communication. Methods: In this work, a new concept of short range radiofrequency (RF) communication device consisting of transmitting and receiving parts build from active devices fabricated in 0.35 m I3T25 3.3 V CMOS process and ferrite antennas is selected. RF part uses medium-wave propagation within 10 mm distance at frequency 700 kHz. Such an approach offers minimal path loss of the radiated energy of a signal and low-gain amplification required for restoration of similar levels as available at the transmitting side. Results: The processing of base-band signals of simple (sine wave) and complex (electrocardiogram) character was verified experimentally through the system. Application example of temperature monitoring in a closed environment, based on a temperature sensor (thermistor), verifies operationability in temperature range from 10 °C up to 50 °C. Conclusion: Compared to state-of-the-art solution, the presented concept has several advantages, for instance: less complexity; using of simpler type of modulation and demodulation; lower power consumption and significantly reduced issues caused by an environment with special transmission conditions (e.g. fog and vapor). The obtained results are in good agreement with expectations. Among others, the presented system brings beneficial performances for similar applications targeting on monitoring of low-frequency or slowly varying signals

Endüstriyel ve akademikçalışmalarda objelerin ağırlıklarının ölçülmesi oldukça önemli bir yeresahiptir. Bu nedenle gerçekleştirilmiş olan bu çalışmada yapay sinir ağları(YSA) kullanılarak görüntü işlemeye dayalı uzaklıktan ve kamera açısındanbağımsız ağırlık tahmini yapılması amaçlanmıştır. Yapay sinir ağı yapısı olarakileri beslemeli çok katmanlı algılayıcı (multi-layer perceptron - MLP) veradyal tabanlı fonksiyon (radial basis function - RBF) ağı kullanılmıştır.Ağırlığı tahmin edilecek obje olarak da portakal ve yumurta örnekleribelirlenmiştir. Bu örnekler ile sistemin eğitilmesi ve test edilmesi için; 4farklı marka ve 4 farklı sınıf (çok büyük - büyük - orta - küçük) olacakşekilde 250 adet yumurta örneği ve farklı boyutlarda 150 adet portakal örneğiseçilmiştir. Bu örnekler kullanılarak; yumurta için dik açı, pozitif açı venegatif açı ile elde edilmiş 750 adet görüntü içeren, portakal için de dik açı,pozitif açı ve negatif açı ile elde edilmiş 450 adet görüntü içeren bir veritabanı oluşturulmuştur. Oluşturulan bu ağırlık tahmin sistemi; bir adet kamera,yapay aydınlatma sistemi, yansıtıcılar ve referans görüntüden oluşmaktadır veayrıca ağırlık tahmin işlemi sırasında MATLAB programı ve araç kutularıkullanılmıştır. Bu çalışmada farklı öznitelik vektörleri, farklı açılardançekilmiş görüntüler ve farklı YSA parametreleri test edilerek başarımı enyüksek olan sistemin kurulması hedeflenmiştir. Her bir değişiklik sonucuoluşturulan sistem beşer kez çalıştırılarak sonuçların aritmetik ortalamasıalınmıştır. Ayrıca başarımı en yüksek olan denemenin, k-katlı çapraz doğrulamayöntemi ile de başarımı hesaplanmıştır. Hassas tartı ile yapılan ölçümlerde,Türk Gıda Kodeksi Yumurta Tebliği’ne göre belirlenmiş ve yumurta kutularınınüzerinde yazan sınıflandırma değerlerine göre doğruluk oranı %47 iken,gerçekleştirilen bu çalışma sonucunda bu oran MLP’de % 90.50’e, RBF’de %86.30’e çıkarılmıştır. Ayrıca ağırlık tahmininin başarımı; MLP için yumurtaörneğinde % 99.42, portakal örneğinde % 98.20 ve RBF için yumurta örneğinde %99.43, portakal örneğinde %98.25 olarak hesaplanmıştır., Measuring the weights of the objects in industrial and academic studiesis highly important. Therefore, it isaimed to perform weight estimation as independent of distance and camera angleusing artificial neural networks (ANN) based on image processing procedure inthis study. Feed-forward multi-layer perceptron (MLP) and radial basis function(RBF) network were used as artificial neural network structure. Orange and eggwere selected as sample objects to be estimated the weights. To train and testthe system with these samples, 250 eggs samples from 4 different brands andclasses (x-large, large, medium, and small) and 150 oranges samples withdifferent sizes were selected. Usingthese samples, a database containing 750 images for eggs and 450 images fororanges taken under the right angle, positive angle and negative angle wascreated. The weight estimation system consists of a camera, artificial lightingsystem, reflector and reference image and also the MATLAB program and toolboxeswere used during the weight estimation process. In this study, to establish a system with highest success value wasaimed by testing the different feature vectors, images taken from differentangles and different ANN parameters. Thesystem created by each change was run five times and arithmetic mean of theresults was taken. In addition, theexperience, which has the highest success value, was also calculated by K-foldcross validation method to obtain the success value. While the accuracy rateobtained from the results weighted by accurate weighting instrument is 47%according to definition of Turkish food codex egg communique and classificationvalues written on the egg cartoon, this rate was increased to 90.5% for MLP,86.3 5 for RBF in this study. Moreover,the success rates of the weight estimation for MLP and RBF were calculated as99.42% for eggs, 98.20% for oranges and 99.43% for eggs, 98.25% for oranges,respectively.

Cerebral blood flow supplies adequate oxygen and energy sources to the brain. Reduction in regional cerebral blood flow rates may lead to some neurological problems. In this study, a direct search optimization method was developed to adjust each resistance of lumped-parameter model of cerebral blood circulation for different physiological conditions. The developed method was tested by using a cardiovascular system model for both healthy and heart failure conditions. The mean blood flow rates of internal carotid and vertebral arteries supplying blood to the cerebral blood circulation were 271 mL/min and 106 mL/min over a cardiac cycle, respectively. In the case of heart failure, the mean blood flow rates of internal carotid arteries and vertebral arteries decreased to 190 mL/min and 74 mL/min over a cardiac cycle, respectively. The results show that regional resistance values of cerebral circulation model were optimized correctly for healthy and heart failure cases.

In this study, a computational model was proposed to assess the interaction among systemic arteriolar resistance control, heart rate control, ventricular elastance control, venous compliance control, respiratory control, cerebral autoregulation mechanisms, and cerebral CO

Background The myelin sheath produced by glial cells insulates the axons, and supports the function of the nervous system. Myelin sheath degeneration causes neurodegenerative disorders, such as multiple sclerosis (MS). There are no therapies for MS that promote remyelination. Drug discovery frequently involves screening thousands of compounds. However, this is not feasible for remyelination drugs, since myelin quantification is a manual labor-intensive endeavor. Therefore, the development of assistive software for expedited myelin detection is instrumental for MS drug discovery by enabling high-content image-based drug screens. New method In this study, we developed a machine learning based expedited myelin detection approach in fluorescence microscopy images. Multi-channel three-dimensional microscopy images of a mouse stem cell-based myelination assay were labeled by experts. A spectro-spatial feature extraction method was introduced to represent local dependencies of voxels both in spatial and spectral domains. Feature extraction yielded two data set of over forty-seven thousand annotated images in total. Results Myelin detection performances of 23 different supervised machine learning techniques including a customized-convolutional neural network (CNN), were assessed using various train/test split ratios of the data sets. The highest accuracy values of 98.84 ± 0.09 % and 98.46 ± 0.11 % were achieved by Boosted Trees and customized-CNN, respectively. Comparison with existing methods Our approach can detect myelin in a common experimental setup. Myelin extending in any orientation in 3 dimensions is segmented from 3 channel z-stack fluorescence images. Conclusions Our results suggest that the proposed expedited myelin detection approach is a feasible and robust method for remyelination drug screening.