Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of the intermediate filament (nanofilament) protein nestin in neural progenitor cell and astrocyte differentiation

Nestin, a class VI intermediate filament (nanofilament) protein, is commonly used as a marker for neural stem/progenitor cells (NSPCs), but its role in neurogenesis remains elusive. Nestin is also expressed in immature and reactive astrocytes. The up-regulation of intermediate filament proteins glial fibrillary acidic protein (GFAP), vimentin and nestin is a characteristic feature of reactive astrocytes, accompanied by alterations in the expression of many other genes. We found that transgenic mice deficient for nestin have increased number of newly born hippocampal neurons 6 weeks after BrdU in vivo labeling, suggesting increased generation and/or survival of newly born neurons. We also showed that in vitro nestin deficient astrocytes provide a more pro-neurogenic environment that results in a 2-fold increase in neuronal differentiation of NSPCs. Astrocytes are highly heterogeneous cells and fulfill a variety of important functions in healthy as well as diseased brain. In addition, astrocytes can exhibit features characteristic of NSPCs and modulate neurogenesis by inhibiting neuronal differentiation of NSPCs through Jagged1-mediated Notch signaling. Given the vast array of astrocyte functions, classification of astrocyte subpopulations on a molecular level is highly desirable. We used single cell quantitative real-time PCR to investigate the heterogeneity of astrocytes with respect to their Notch signaling competence. Our results show that the Notch signal sending but not Notch signal receiving competence of astrocytes depends on GFAP and vimentin. Further, we showed that nestin and heparin binding EGF-like growth factor (HB-EGF) may serve as classifiers of astrocyte subpopulations in vitro. Utilizing our newly developed Bioactive3D and standard 2D cell culture systems, we showed that HB-EGF alters astrocyte morphology towards a more radial glia-like phenotype. HB-EGF affects proliferation, differentiation and expression of Notch signal pathway related genes and leads to the up-regulation of nestin expression. HBEGF in cell culture media results in partial de-differentiation of astrocytes and therefore should be used with caution.
Reactive power based MRAC for robustness and efficiency improvements on a IFOC induction motor drive

Indirect Field Oriented Control (IFOC) provides to the Three-phase Induction Motor (TIM) a great dynamic response, which is a solution widely employed in the industry. However, this technique is sensible to variations in the rotor bandwidth parameter and, due to temperature and saturation effects, this coefficient changes during operation degrading the overall response of the system. In this work, an indirect Model Reference Adaptive Control (MRAC) based on the TIM reactive power model it is used to adapt this parameter value in real-time during motor operation. Moreover, a Loss Model based Control (LMC) is introduced with the purpose to reduce the system power losses. The strategy proposed here increases the efficiency and robustness of the control system, maintaining the good dynamic response even with the variations of the rotor bandwidth parameter.
Bangla Compound Character Recognition by Combining Deep Convolutional Neural Network with Bidirectional Long Short-Term Memory

Recognition of Bangla handwritten compound characters has a significant role in Bangla language in order to develop a complete Bangla OCR. It is a challenging task owing to its high variation in individual writing style and structural resemblance between characters. This paper proposed a novel approach to recognize Bangla handwritten compound characters by combining deep convolutional neural network with Bidirectional long short-term memory (CNN-BiLSTM). The efficacy of the proposed model is evaluated on test set of compound character dataset CMATERdb 3.1. 3.3, which consists of 171 distinct character classes. The model has gained 98.50% recognition accuracy which is significantly better than current state-of-the-art techniques on this dataset.
Spatial and Temporal Variations of Terrestrial Water Storage in Upper Indus Basin Using GRACE and Altimetry Data

Gravity Recovery and Climate Experiment (GRACE) and satellite altimetry are suitable for the precise measurement of terrestrial water storage (TWS) and lake water level variations from space. In this study, two GRACE solutions, namely, spherical harmonics (SH) and mascon (MSC), are utilized with the Global Land Data Assimilation System (GLDAS) model to estimate the spatial and temporal variations of TWS in the Upper Indus Basin (UIB) for the study period of January 2003 to December 2016. The TWS estimated by SH, MSC, and the GLDAS model are consistent and generally show negative trends of −4.47 ± 0.38 mm/year, −4.81 ± 0.49 mm/year, and −3.77 ± 0.46 mm/year, respectively. Moreover, we use the GLDAS model data to understand the roles of variations in land surface state variables (snow water equivalent (SWE), soil moisture, and canopy water storage) in enhancing or dissipating the TWS in the region. Results indicate that SWE, which has a significant contribution to GRACE TWS variability, is an important parameter. Spearman’s rank correlations are calculated to demonstrate the relationship of the GLDAS land surface state variables and the GRACE signals. A highly positive correlation between SWE with TWS is estimated by SH and MSC as 0.691 and 0.649, respectively, indicating that the TWS signal is mainly reliant on snow water in the study region. The ground water storages estimated by SH and MSC solutions are nearly stable with slight increasing trends of 0.63 ± 0.48 mm/year and 0.29 ± 0.51 mm/year, respectively. We also take advantage of the potential of satellite altimetry in measuring lake water level variations, and our result indicates that Cryosat-2 SARin mode altimetry data can be used in estimating small water bodies accurately in the high mountainous region of the UIB. Moreover, the climate indices data of El-Niño Southern Oscillation and Pacific Decadal Oscillation are analyzed to determine the influence of pacific climatic variability on TWS.
Tuning of IMC based PID controller for stable and integrating time delayed processes.

The internal model control (IMC) and proportion-integral-derivative (PID) controller is proposed for time delay process with stable and integrating process type model. In IMC PID controller the process model of unstable pole part must be cancelled to the zeros of first order filter for better performance of the controller. In this paper we are introduce the first order Pade approximation to simplify the transportation delay term present in model. Pole–zero cancellation and stability of the process have been done by first order low pass filter, which is considered for stable and integrating system with time delay for better performance. The tuning parameters of the PID controller are calculated by the time constant and time delay of the process model. Simulation of the system gives the better comparisons as the previous proposed method. In this method we comparing the three type of integrating error like integral absolute error(IAE), Integral time weighted absolute error (ITAE) and integral square error (ISE) and observe that the suggest method have been given better result as previous method. The tuning of the process of PID controller has improved by first order Pade approximation.
Fusion of Multi-Size Candidate Regions Enhances Two-Stage Hippocampus Segmentation

The hippocampus plays an important role in the memory and cognition abilities of humans. Precise three-dimensional (3D) segmentation of the hippocampus from magnetic resonance imaging scans is of great importance in the diagnosis of neurological diseases. Conventional automatic segmentation methods poorly achieve satisfactory performance because of the irregular shape and small volume of the hippocampus. We propose a novel two-stage segmentation method, which includes a localization stage and a segmentation stage, to handle the task of the 3D segmentation of the hippocampus. In the localization stage, a novel strategy for localizing multi-size candidate regions was developed to improve the sample balance for the 3D segmentation task. In the segmentation stage, a method which fuses the multi-size candidate regions was proposed to improve the accuracy in predicting the hippocampal boundary, after which we aggregated the segmentation results from three orthogonal views to further improve the performance. Quantitative evaluation was performed on the Alzheimer’s Disease Neuroimaging Initiative dataset. The experimental results achieved Dice similarity coefficients of 92.48 ± 0.61% and 92.90 ± 0.51% for the left and right hippocampus, respectively, outperforming state-of-the-art studies in hippocampus segmentation tasks.
EMSGD: An Improved Learning Algorithm of Neural Networks With Imbalanced Data

In this paper, the influence of data imbalance on neural networks is discussed, and an improved learning algorithm to solve this problem is proposed. The experimental results show that in the case of imbalanced data, the training error of neural network converges slowly and the generalization ability is poor. Our theoretical analysis shows that in the process of training, the gradient descent direction of the weights is dominated by the major-classes, which accounts for the slow convergence of the training error. Based on these results, we propose the Equilibration Mini-batch Stochastic Gradient Descent (EMSGD) method to ensure the equilibrium of the data in the mini-batch. The advantage of this technique is that it makes full use of the existing random sampling step of MSGD without increasing the computational complexity. In addition, by over-sampling of minor-classes in the mini-batch, duplicated instances would be greatly reduced, thus preventing the model from overfitting. The experimental results show that under the condition of the imbalanced training data, EMSGD can make the neural network training error converge rapidly.
Deep Depthwise Separable Convolutional Network for Change Detection in Optical Aerial Images

In this article, a remote sensing image change detection method based on depthwise separable convolution with U-Net is proposed, which omits the tedious steps of generating and analyzing the difference map in the traditional remote sensing image change detection method. First, two images having c-channel each can be specifically stacked into a 2c-channel image, and the change detection can be converted to an image segmentation problem, an improved full convolution network (FCN) called U-Net is exploited to directly separate the changing regions. Because the capability of the deep convolution network is proportional to the depth of the network and a deeper convolution network means the increase of the training parameters, we then replace the original convolution in FCN by the depthwise separable convolution, making the entire network lighter, while the model performs slightly better than the traditional convolution operation. Besides that, another innovation in our proposed method is to use a preference control loss function to meet the different needs of precision and recall rate. Experimental results validate the effectiveness and robustness of the proposed method.
A Novel Grid Synchronization Method Based on Hybrid Filter Under Distorted Voltage Conditions

The phase-locked accuracy of conventional phase-locked method is reduced when the grid voltage contains fundamental frequency negative sequence(FFNS) component, harmonic component, and DC offset component. Aiming at this problem, a novel adaptive notch filter (NANF) is proposed, and a dual NANF (DNANF) structure is designed to eliminate the FFNS component and extract the fundamental frequency positive sequence(FFPS) component based on NANF. Furthermore, dcDNANF with DC offset rejection capability is proposed by adopting DNANF. Then a novel hybrid filter in dq-frame is designed by combining dcDNANF and the cascaded delay signal cancellation operator filter whose delay parameters are 4 and 24 in dq-frame (dqCDSC4,24). Meanwhile, a new SRF-PLL design method is proposed based on the novel hybrid filter. This proposed method employs dqCDSC4,24 to separate the positive and negative sequences of the voltage and eliminate the high frequency harmonics in the grid voltage, and uses dcDNANF to reject the DC offset, so as to achieve the accurate acquisition of the fundamental voltage information under distortion and unbalanced grid. Simulation and experimental results show that compared with the conventional SRF-PLL methods, the proposed method can obtain faster phase tracking speed, better phase-locked effect, faster dynamic response, and better stability.
Reliability Test of Restructured Power System with Capacity Expansion and Transmission Switching

The time duration required to design, construct and commission a large generating station can be quite extensive depending upon the environmental and regulatory requirements. So it becomes a necessary part to examine the future load demand and solution to serve the demand. Capacity expansion analysis is one of the major solutions for reducing the system risk level and serving the future load demand. This paper discusses about the system reliability performance improvement with capacity expansion of restructured power system. Peak load carrying capability (PLCC) can be observed with different unavailability of generating units and with addition of new generating units in the system. Reliability indices such as LOLE or risk level, EDNS, EENS and DPUI are examine with capacity expansion and transmission switching in different peak load conditions. Effect of maintenance of generating units have been also observed in terms of risk level and reliability performance.
Multielectron Effect for High-Order Harmonic Generation From Molecule Irradiated by Bichromatic Counter-Rotating Circularly Polarized Laser Pulses

We investigated high-order harmonic generation in molecules N<sub>2</sub> and C<sub>6</sub>H<sub>6</sub> irradiated by bichromatic counter-rotating circularly polarized laser pulses by using time-dependent density-functional theory methods. It is found that <inline-formula> <tex-math notation="LaTeX">$3N$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> is an integer) order harmonics in C<sub>6</sub>H<sub>6</sub> disappear. Whereas, <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> order harmonics are obtained in N<sub>2</sub>. Analysis of the high harmonic generated from individual electronic orbital of the molecule, shows that, the characteristics of harmonic spectra are determined by the spatial distribution of electronic orbits. Moreover, effects of molecular orientation on harmonic emission are also presented.
Vehicle Object Detection Based on RGB-Camera and Radar Sensor Fusion

Object detection has always played a major role in the field of computer vision. With the development of faster region-based convolutional neural network (faster-RCNN), single shot multi-box detector (SSD), You Only Look Once (Yolo) and other deep learning networks, 2D object detection has been developed well developed. However, in some key areas, such as driverless vehicles and artificial intelligence robots, cannot meet the requirements of tasks just by detecting two dimensional (2D) objects. So we need to get the three dimensional (3D) position of the object. In 3D object detection, it is difficult to obtain object position information only by red-green-blue (RGB) image. There are some methods to get the 3D position of the target through the point cloud data set. Therefore, this paper proposes a deep learning network, which can combine both color image information and point cloud geometry information, while using MobileNet V3 as the backbone network greatly enhances the operation speed. The region proposal networks (RPN) layer in faster-RCNN is migrated to the network. After testing, the method proposed by us improves obviously in speed and recognition accuracy compared with the previous methods.
Design and Analysis of a Co-Planar Waveguide Fed Ultra-Wideband Modified Patch Antenna

This paper presents a co-planar waveguide(CPW) fed modified patch antenna(MPA) which is operating in the frequency range of 1.5-11.6 GHz, Which covers the whole ultra wideband(UWB),i.e. 3.1-10.6 GHz range, is designed and simulated by Computer Simulation Technology(CST). The antenna utilizes a low-cost FR-4 dielectric substrate. The MPA is excited by a microstrip line. The desired antenna is printed upon the top surface of the substrate. Some of the area on the ground plane is slotted to provide wide fractional bandwidth (FBW) and the corners of the patch are truncated to obtain impedance matching and UWB characterization. In this work mainly the parametric analysis of the antenna in terms of return loss(S11) and voltage standing wave ratio (VSWR)) is explained and the radiation patterns at various frequencies are analyzed and the corresponding gain is observed.
Environmental Insulation of 3D Printable Origamic Soft Actuators

Soft robotics are receiving more and more attentions by utilizing soft actuators to gain inherent compliance and natural adaptions to environment. As the application realm of soft robotics are spreading to various scenarios, the mechanical and functional demands for soft actuators are also increasing, such as mechanical requirements for large contraction ratio in linear actuation, and functional features like water-proof, heat resistance, or UV protection, etc. Researchers have been addressing these problems by designing new types of actuators with better mechanical properties and achieving combined features by seeking composite materials and new fabrication methods. However, it greatly challenges the design and complicates the manufacturing process. In this work, we designed an origamic soft actuator with promising mechanical properties and propose a versatile approach to acquire various combined properties on this kind of actuator without complex manufacturing and redesign process. The origamic actuator has large output force and large contraction ratio (70%), making it promising for elongation-related applications. Additional properties could be achieved by attaching different planar materials outside. The origamic planar structure makes it easy for separated realization of functionalities by layer stacking, greatly lowering the difficulty of developing soft actuators with various properties demands. We experimentally demonstrated the mechanical advantages of the origamic actuators and the effectiveness of the layer stacking strategy by attaching heat resistance sheet onto a 3D printed origamic actuator to slow down the heat dissipation process.
Low-Frequency Instability Induced by Hopf Bifurcation in a Single-Phase Converter Connected to Non-Ideal Power Grid

With the high-density operation of China Railways High-speed (CRH) series vehicles in railways, a large number of power electronic converters are connected to the traction network. Due to the nonlinear characteristics of power electronic converter, it is easy to cause the low-frequency oscillation (LFO) in the single-phase vehicle-grid system of high-speed railways when the converter is connected to a non-ideal power grid. In order to solve this problem, the time domain nonlinear dynamic average model is proposed in this paper. Given Jacobian method to probe into the low-frequency stability of single-phase vehicle-grid system based on Hopf bifurcation, the characteristics and applicability of this method is summarized. The effects of electrical and control parameters can be analyzed with this model. In addition, with the emergence of LFO, the stability boundary leading to catastrophic bifurcation is discussed in detail. In the end, theoretical analysis results are verified by simulations and hardware-in-the-loop (HIL) platform.
Automated Helmet Detection for Multiple Motorcycle Riders using CNN

Automated detection of traffic rule violators is an essential component of any smart traffic system. In a country like India with high density of population in all big cities, motorcycle is one of the main modes of transport. It is observed that most of the motorcyclists avoid the use of helmet within the city or even in highways. Use of helmet can reduce the risk of head and severe brain injury of the motorcyclists in most of the motorcycle accident cases. Today violation of most of the traffic and safety rules are detected by analysing the traffic videos captured by surveillance camera. This paper proposes a framework for detection of single or multiple riders travel on a motorcycle without wearing helmets. In the proposed approach, at first stage, motorcycle riders are detected using YOLOv3 model which is an incremental version of YOLO model, the state-of-the-art method for object detection. In the second stage, a Convolutional Neural Network (CNN) based architecture has been proposed for helmet detection of motorcycle riders. The proposed model is evaluated on traffic videos and the obtained results are promising in comparison with other CNN based approaches.
Analytical Model Approximation for Defect Classification in Fiberglass Composites Inspected by Long-Pulse Thermography

This paper presents a complete pipeline for automatic detection and classification of defects within composite laminates inspected by active IR thermography. Specifically, long-pulse thermography is proposed for nondestructive evaluation of samples made of Glass Fiber Reinforced Polymer (GFRP). A model approximation based on exponential functions is used to achieve an efficient representation of temperature decays at the surface of the samples. At the end of the pipeline, several decision forests are implemented to process input features and label corresponding areas among three classes of interest: sound regions, surface defects, and in-depth discontinuities. Results prove that the proposed methodology performs with good accuracy also in case of inspection of GFRP samples tested by long-pulse thermography.
A Hybrid Solution To Abstractive Multi-Document Summarization Using Supervised and Unsupervised Learning

In this work, we aim to develop an abstractive summarization system in the multi-document setup. The main challenge in this kind of a system is the identification of redundant information. Our approach hybridizes three components, viz. Clustering, Word Graphs, Neural Networks. In clustering, all the information from multiple documents is divided amongst clusters based on context and importance analysis, such that each cluster possesses sentences of a similar context - Redundancy Identification. Further, Shortest Path Detection in Word Graphs reduces the text. Along with that, we use a sequence to sequence sentence compression and perform paraphrasing using Supervised Recurrent Neural Network to generate an almost completely abstractive summary. The dataset DUC 2004 that was used indicates that the proposed system outperforms other systems in terms of metrics like ROUGE[1] and BLEU[2].
Integrated Zeta Inverter Applied in a Single-Phase Grid-Connected Photovoltaic System

This paper presents a novel integrated topology for step-up the photovoltaic array voltage, as well as to inject all the extracted PV array energy into the single-phase AC utility grid. The topology is based on the Zeta converter, named as Zeta Inverter, and its output current is controlled using hysteresis current control. The maximum power point tracking is performed employing the perturb and observe algorithm. Besides the obtaining low total harmonic distortion in the grid-injected current, the proposed system is proposed to replace the traditional double stage photovoltaic system. This paper presents the converter operation, the control technique, and the main simulation results in order to demonstrate the feasibility of the proposed system.