Miracle-dz/UniFAR-FTUs huggingface dataset:
Water use efficiency of six woody species in relation to micro-environmental factors of different habitats

The long term water use efficiency (WUE),of key species in deciduous broad-leaved forest in Beijing Mountain area,is strongly related to relative humidity,solar radiation,vapor pressure deficit and temperature. The ranges of water use efficiencies of Quercus liaotungensis,Prunus armeniac var. ansu, Fraxinus rhychophylla, Vitex negundo, Syringa pekingensis, and Juglans mandshurica are from 3 76 mmolCO 2·mol -1H 2O to 4 95mmolCO 2·mol -1H 2O in all growing season,with the mean water use efficiency being 4 428±0 386 mmolCO 2·mol -1H 2O. Prunus armeniac var. ansu has the highest water use efficiency, and Juglans mandshurica has the lowest. In the growing season, the species have relatively higher water use efficiency in early spring than in late of fall. To any kind of species,water use efficiency in dry and infertile land is higher than that in damp and fertile habitat.
Catchicken: A Serious Game Based on the Go/NoGo Task to Estimate Inattentiveness and Impulsivity Symptoms

We present a Go/NoGo 3D game equipped with an eye tracker that records subjects' responses and his gaze position on the monitor over time. The proposed system consists of two functions: training that allows an instructor to modify the game's parameters and make a customized test; and evaluation in which the instructor can fix the parameters to create a standardized test. During the experiment, subjects were required to respond only to Go character by pressing a spacebar. The experimental results from 59 participants demonstrated that one's response time and its variability correlated with one's gaze behavior. Subjects with higher gaze modulation tended to respond faster and more stable. We also observed that utilizing the proposed system we could monitor the improvements in an Autism Spectrum Disorder child during his rehabilitation: his gaze modulation increased and his response time became more steady. In brief, utilizing the proposed system, we could effectively measure participants' response time variability of NoGo errors and their gaze trajectory area, which previous studies found to have a strong relationship with symptoms of mental disorders.
Vehicle Longitudinal Speed Estimation Based on Kalman Filter

In order to accurately calculate the slip rate of driving wheel in ASR, it is necessary to obtain a higher precision reference speed. Based on the Kalman filter algorithm, this paper uses the non drive wheel speed signal and vehicle longitudinal acceleration signal to establish the system model, and estimates the vehicle speed of the two drives in real time, and the real vehicle road test and off-line simulation are carried out under different road conditions to verify the estimation effect of the algorithm. The results show that the algorithm has high estimation accuracy, strong robustness and is easy to implement on ECU.
A new measles epidemic model: analysis, identification and prediction

A new measles epidemic model is proposed and identified by using real data relative to the number of confirmed infected patients in Italy in the period 1970–2018. The possibility of predicting the number of new infection is important for an efficient resource scheduling. Only in the last years great attention has been devoted to reliable data collection; therefore, in general, the model parameters identification is not an easy task. Moreover, the available data are “corrupted” by human intervention, such as prevention campaign, or, whenever possible, vaccination. In this paper, the measles model parameters are identified referring to the data of the period in which there wasn't a significant vaccination coverage; successively, the vaccination action has been identified. The results obtained appear encouraging, confirming the importance of available consistent data.
Quadrifilar Helix Antenna for 28 GHz Band Application

A very compact Quadrifilar Helix Antenna (QHA) design is presented in this paper which can be easily deployed in any handheld low power 5G enabled devices. The antenna is designed to operate at millimetre wave region to provide active support for 5G networks. The proposed antenna is providing a broad radiation pattern with a gain of 9 dBi when operated at 28 GHz. An impedance bandwidth of more than 12% is observed at acceptable return loss (-10dB). The antenna is operating in axial mode with less than 1.25 axial ratio for most of the region of operation. Effect on the radiation pattern and gain of the antenna based on the absence and presence of the dielectric substrate is analyzed and results are compared for the same operating frequency of 28 GHz. All the simulation is performed using a software tool Ansys high-frequency structure simulator (HFSS).
3D Convolutional Neural Networks for Diagnosis of Alzheimer's Disease via Structural MRI

Alzheimer's Disease (AD) is a widespread neurodegenerative disease caused by structural changes in the brain and leads to deterioration of cognitive functions. Patients usually experience diagnostic symptoms at later stages after irreversible neural damage occurs. Therefore, early detection of AD is crucial to start treatments to decelerate the progress of the disease and to maximize patients' quality of life. With the rapid advances in machine learning and scanning, early detection of AD may be possible via computer-assisted systems using neuroimaging data. Among all, deep learning utilizing magnetic resonance imaging (MRI) has become a prominent tool due to its capability to extract high-level features through local connectivity, weight sharing, and spatial invariance. This paper describes our investigation of the classification accuracy based on two publicly available data sets, namely, ADNI and OASIS, by building a 3D VGG variant convolutional network (CNN). We used 3D models to avoid information loss, which occurs during the process of slicing 3D MRI into 2D images and analyzing them by 2D convolutional filters. We also conducted a pre-processing of the data to enhance the effectiveness and classification performance of the model. The proposed model achieved 73.4% classification accuracy on ADNI and 69.9% on OASIS dataset with 5-fold cross-validation (CV), outperforming 2D network models.
System on Chip Implementation of Low Complex Orthogonal Matching Pursuit Algorithm on FPGA

This paper presents a System on Chip (SoC) implementation of the standard Orthogonal Matching Pursuit (OMP) algorithm using matrix partition and Modified Cholesky factorization techniques. A fixed-point optimized hardware Intellectual Property (IP) of the OMP algorithm is designed using a high-level synthesis (HLS) tool. The execution time of the optimized fixed-point hardware IP for different sparsity is compared with the equivalent fixed-point and floating-point realization on the Zynq-7000 Field Programmable Gate Array (FPGA). Intel senor data is used for verifying the functionality of the SoC design. The experiment is carried out for signal length (N), compressed signal length (M) as 256 and 84 respectively with different sparsity factor (K) as 5, 10 and 15. The acceleration factor of 70 and 73 is achieved for the fixed-point and floating-point software realization of the OMP algorithm, respectively.
Performance Analysis of Cryptographic Algorithm in FPGA

Elliptic Curve Digital Signature Algorithm (ECDSA) is an asymmetric algorithm used for expanded security with data integrity and authentication in the On-Board Units (OBU’s) of Intelligent Transport Systems (ITS). Field programmable gate array is an attractive platform for hardware implementation of cryptographic algorithms, providing better security and performance in response to need. Hence, the various performance parameters of ECDSA such as key size, Look up Table (LUTs) slices and key pair points on the elliptic curve are analyzed in FPGA. In this work, we have designed a 16-bit Elliptic curve cryptography digital signature algorithm. The Elliptic curve cryptography covers all relevant asymmetric cryptographic natives like digital signature and key exchange. ECC key generation, signature generation, and verification generation algorithms were executed for the 16-bit ECDSA in Verilog HDL and then simulated and synthesized using Xilinx-virtex7 and Vivado 2018.3-zed board. It was seen that the number of slices involved by the ECDSA 16-bit algorithm in Xilinx (virtex7) is lesser than the available number of slices.
Improvement of Model Simplification Algorithm Based on LOD and Implementation of WebGL

With the continuous progress of computer graphics, virtual reality and other technologies, 3D models in the field of industrial production have become more and more sophisticated, and the triangular surfaces need to be rendered are more than one million, which poses a great challenge to the storage, transmission and computing power of computers. Therefore, in order to adapt to the current performance of the computer while taking into account the rendering effect of the model, the Level-of-Detail(LOD) technology has been spawned. Industrial models tend to have complex structures and need to be displayed accurately in the rendering process, and in the case of a large number of holes in the model, the common algorithm is difficult to maintain the topology of the model well. Therefore, the article uses the edge collapse algorithm. To improve it, an algorithm that uses the mean deviation to guide the simplification process of edge collapse is proposed. While ensuring sufficient triangular mesh simplification, the topology of complex industrial models is maintained.
Potential Simulation Frameworks and Challenges for Internet of Vehicles Networks

The mixed population of semi-/full-autonomous and conventional vehicles on the roads originates a heterogeneous network with moving nodes. Therefore, it is worth to explore the potential frameworks and challenges for the implementation of next generation Internet-of-Vehicles (IoVs) networks. In this paper, we have presented the potential frameworks of IoVs network for heterogeneous environment. Further, various simulation tools for mimicking and analyzing the IoVs network are presented and compared on the bases of their performance. Moreover, the key challenges for the implementation of IoVs networks except the challenges faced by conventional wireless ad-hoc networks are illustrated.
Classification of Breast Cancer Histopathological Images Using Discriminative Patches Screened by Generative Adversarial Networks

Computer-aided diagnosis (CAD) systems of breast cancer histopathological images automated classification can help reduce the manual observation workload of pathologists. In the classification of breast cancer histopathology images, due to the small number and high-resolution of the training samples, the patch-based image classification methods have become very necessary. However, adopting a patches-based classification method is very challenging, since the patch-level datasets extracted from whole slide images (WSIs) contain many mislabeled patches. Existing patch-based classification methods have paid little attention to addressing the mislabeled patches for improving the performance of classification. To solve this problem, we propose a novel approach, named DenseNet121-AnoGAN, for classifying breast histopathological images into benign and malignant classes. The proposed approach consists of two major parts: using an unsupervised anomaly detection with generative adversarial networks (AnoGAN) to screen mislabeled patches and using densely connected convolutional network (DenseNet) to extract multi-layered features of the discriminative patches. The performance of the proposed approach is evaluated on the publicly available BreaKHis dataset using 5-fold cross validation. The proposed DenseNet121-AnoGAN can be better suited to coarse-grained high-resolution images and achieved satisfactory classification performance in 40X and 100X images. The best accuracy of 99.13% and the best $F1_{score}$ of 99.38% have been obtained at the image level for the 40X magnification factor. We have also investigated the performance of AnoGAN on the other classification networks, including AlexNet, VGG16, VGG19, and ResNet50. Our experiments show that whether it is at the patient-level accuracy or at the image-level accuracy, the classification networks with AnoGAN have provided better performance than the classification networks without AnoGAN.
Exploring Hippocampal Asymmetrical Features from Magnetic Resonance Images for the Classification of Alzheimer's Disease

Alzheimer's disease (AD) is the most common cause of dementia, accounting for 60 to 80% of all cases. Because of population aging, this disease has become one of the most relevant global public health problems. Several studies have shown the hippocampal structures present significant asymmetry in AD, and that difference, measured from the volumes between left and right hippocampus, varies with the disease progression. Although imaging biomarkers have been proposed to investigate whether the asymmetry of hippocampus subfields changes through the disease progression, little attention has been paid to explore asymmetrical hippocampal image features to aid for AD early diagnosis. In this study, we propose a new method for the classification of Magnetic Resonance (MR) images in both cognitively normal (CN) versus mild cognitive impairment (MCI) and CN versus mild-AD patient groups using only asymmetrical features extracted from the hippocampal MR image hemispheres. The features, devised from the magnitude response images resulting from applying 3-D log-Gabor filters to an MR input image, are used to train Support Vector Machine classifiers for the MR image classification. Quantitative evaluation of our proposed method applied to MR image classification resulted in accuracy, F1-score, and AUC average values of 71.23%, 0.67, and 0.77 for the CNxMCI case, and 80.43%, 0.75, and 0.88 for the CNxAD case. These results are very promising, considering we used only asymmetrical features from the hippocampal regions in this study.
Non-Linear Measures of Dependence in the MIMO Systems Identification with Applications to NPP Efficiency Evaluation

The paper deals with the problem of input/output system identification in the light of selecting representative outputs of the system under study, accompanied with its application to the analysis of the nuclear power plant efficiency. Features of the approach are based on an extension of the Rényi axioms for measures of dependence to the case of the multivariate dependence and applying an information-theoretic measure of dependence originated from a symmetric Tsallis divergence of probabilistic distributions.
Improvement of Load Regulation in 33- Bus Closed loop Distribution System using Sliding Mode Controlled Unified Power Flow Controller

Recent developments in FACTS-devices have produced conditioner equipments to mitigate sag and attenuate Total Harmonic Distortion (THD). U.P.Q.C has been urbanized as a FACTS controller between feeding end & far end of distribution system. The UPQC is capable of improving the voltage profile and reducing THD of distribution system by regulating voltage using FOPID (Fractional-order-proportional-integral-controller) and utilizing SMC (sliding-mode-controller) controller. This work proposes UPQC for Thirty Three Bus System (TTBS). The objective of this work is to enhance-voltage-profile of TTBS. The TTBS in open loop & closed loop-TTBS UPQC using FOPID and SMC-controllers are-modeled pretend and their consequences are represented. The outcomes are estimated in terms of settling-time, steady-state-error. The outcomes indicate that SM Controlled TTBS system has better response than FOPID controlled TTBS system.
InTracker: An Integrated Detector-Tracker Framework for Cell Detection and Tracking

Automatic tracking of moving cells in time-lapse image sequences plays an important role in studying many biological processes in development and diseases. Large variations in cell appearances, limited image resolution, and various cell behaviors (e.g., division, apoptosis, deformation, clustering, and migration in or out of the imaging window) make cell tracking a challenging task. However, known cell tracking methods were designed for and tailored to specific cell image sequences and behaviors, thus having limited applicability to various cell image sequences. Aiming toward more robust cell tracking, we propose a new detector-tracker approach for detection and association based cell tracking. First, we propose a new deep learning based detector to detect cells in each image frame and assign division/non-division labels to them. Second, we carefully design an Earth Mover's Distance (EMD) based hierarchical tracker to associate detected cells through the image sequence and form moving cell trajectories. The tracker is able to correct possible detection errors made by the detector. Evaluated on several open challenge datasets, our approach outperforms state-of-the-art cell tracking methods for determining cell trajectories.
$H\infty$ Delay-independent stabilization for Takagi Sugeno fuzzy system based on Saturated Output Control

This paper is focused on non-quadratic SOF control for delayed Takagi Sugeno (T-S) models with input saturation and external disturbances. A polytopic representation is first used to describe the input nonlinearity. By using a descriptor redundancy approach, an augmented form of the closed-loop system is established. Then, in order to reduce the conservatism of the quadratic approach, a poly-quadratic function and a Lyapunov-Krasovskii function (LKF) are used to derive $H\infty$ stabilization conditions. The design conditions are formulated and resolved in LMIs terms. A simulation example is made to show the successful of the proposed method.
Measuring the Impact of Scope Changes on Project Plan Using EVM

Earned Value Management (EVM) measures project performance against a baseline plan. It identifies deviations in budget and schedule, aids project managers in taking earlier corrective actions against cost and schedule overruns. Although the literature highlights the significance of scope by adopting it as a leading indicator to measure project success or failure. However, EVM does not include scope when evaluating the performance of any software project. While considering the importance of scope and its ever-changing nature, it is imperative to measure the effect of changes in scope on the project plan. To analyse such effects, this study aims to enhance the traditional EVM by incorporating scope into it. The main objectives of this paper are: i) to extract the effects of project scope changes, ii) to map extracted effects of project scope changes with Software Project Scope Rating Index (SPSRI) elements, and iii) to quantify the extracted effects and integrate them with EVM. An extensive literature review is conducted to achieve the first objective, which results in the seventeen unique effects; that were used to map with SPSRI elements. To forecast the variations in scope for a given project budget, Monte Carlo simulations were run on the top eight scope elements, whereas, the results were incorporated with EVM to identify the deviations between actual and projected values of scope’s score and cost. Finally, the multivariate regression model was used to evaluate the influence of individual element on the overall estimated cost of the project. The correlation between the independent variables (SPSRI elements) and the dependent variable (overall cost) was calculated along with the valuation of each independent variable on the dependent variable. Moreover, the effects are statistically shown that independent variables have influenced the dependent variable. This technique could assist the project managers to forecast deviations in project scope earlier.
Relationship between Mycoplasma Pneumoniae Infection and Asthma

There are a close relationship between Mycoplasma pneumoniae(MP)infection and Asthma.An acute or chronic infection with MP may precede the onset of asthma or exacerbate the symptoms of asthma.The organism can infect airway epithelia and stimulate the production of multi-factor related airway inflammation and the hyper-responsiveness mediated by IgE; besides this,asthmatic patients may have deficient cellular and humoral responses to MP infection,which makes the organism resistant.Macrolides has both anti-bacterial and anti-inflammatory properties.Macrolides improve the symptoms and pulmonary function in MP-infected patients,one of whose properties is more useful in the treatment in an unclear manner.
Deep Learning based Attack for AI oriented Authentication Module

Neural Network Physical Unclonable Function (NN-PUF) has been proposed for the secure implementation of Edge AI. This study evaluates the tamper resistance of NN-PUF against machine learning attacks. The machine learning attack in this study learns CPRs using deep learning. As a result of the evaluation experiment, the machine learning attack predicted about 82% for CRPs. Therefore, this study revealed that NN-PUF is vulnerable to machine learning attacks.
Pixel-Based Tool Segmentation in Cataract Surgery Videos with Mask R-CNN

Automatically detecting surgical tools in recorded surgery videos is an important building block of further content-based video analysis. In ophthalmology, the results of such methods can support training and teaching of operation techniques and enable investigation of medical research questions on a dataset of recorded surgery videos. While previous methods used frame-based classification techniques to predict the presence of surgical tools - but did not localize them, we apply a recent deep-learning segmentation method (Mask R-CNN) to localize and segment surgical tools used in ophthalmic cataract surgery. We add ground-truth annotations for multi-class instance segmentation to two existing datasets of cataract surgery videos and make resulting datasets publicly available for research purposes. In the absence of comparable results from literature, we tune and evaluate the Mask R-CNN approach on these datasets for instrument segmentation/localization and achieve promising results (61% mean average precision on 50% intersection over union for instance segmentation, working even better for bounding box detection or binary segmentation), establishing a reasonable baseline for further research. Moreover, we experiment with common data augmentation techniques and analyze the achieved segmentation performance with respect to each class (instrument), providing evidence for future improvements of this approach.