Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time Tracking of Guidewire Robot Tips Using Deep Convolutional Neural Networks on Successive Localized Frames

Studies are proceeded to stabilize cardiac surgery using thin micro-guidewires and catheter robots. To control the robot to a desired position and pose, it is necessary to accurately track the robot tip in real time but tracking and accurately delineating the thin and small tip is challenging. To address this problem, a novel image analysis-based tracking method using deep convolutional neural networks (CNN) has been proposed in this paper. The proposed tracker consists of two parts; (1) a detection network for rough detection of the tip position and (2) a segmentation network for accurate tip delineation near the tip position. To learn a robust real-time tracker, we extract small image patches, including the tip in successive frames and then learn the informative spatial and motion features for the segmentation network. During inference, the tip bounding box is first estimated in the initial frame via the detection network, thereafter tip delineation is consecutively performed through the segmentation network in the following frames. The proposed method enables accurate delineation of the tip in real time and automatically restarts tracking via the detection network when tracking fails in challenging frames. Experimental results show that the proposed method achieves better tracking accuracy than existing methods, with a considerable real-time speed of 19ms.
QUALITY OF LIFE OF ELDERLY PEOPLE AND ASSESSMENT OF FACILITIES AVAILABLE IN OLD AGE HOMES OF LUCKNOW, INDIA

Introduction: The old-age home industry is mostly unregulated and there is a need for putting in place certain minimum standards. Many times poor and destitute persons who may need institution-based care cannot afford them.Long-term care has a price, and there is also a need for debate on its policy and best practice. Objective:The objectives were to study the quality of life of elderly people, to assess the facilities available and the factors associated with elderly people for availing the residential services of old age homes. Methodology: It was a descriptive cross-sectional study. Study population was elderly (age ≥60 years) enrolled in public and private old age homes (OAHs) of Lucknow city. All the elderly living in OAHs for ≥ 6 months and had given the consent for interview were included in the study. Results:The most important reason for elderly people living in public OAHs was no care taking person at home (77.1%) and private OAHs it was (36.4%). Services like medical services, recreational facilities, safety, space availability and staff availability were significantly better (p <0.05) in private type of OAHs. In public type OAHs 85.7 percent inmates had quality of life below average whereas in paid OAHs 63.6 percent inmates had average or above average quality of life. Conclusions:No care taking person at home was the important reasons in OAHs. With the exception of food all the variables like Medical service, Recreational facilities, Safety, Space availability, Staff availability were significantly better in private OAHs. Quality of life in private OAHs was significantly better than public OAHs.
Frequency and Determinants of Triennial Pap Smear Screening Rates

Purpose: Before 1993, no data on either 3 year rates of Papanicolaou (Pap) screening or factors determining screening rates were available for large populations except for results of self-reported patient surveys containing known inaccuracies. The purpose of this study was to investigate the factors determining 3 year rates of Pap screening among health plan members throughout Kaiser Permanente in Southern California. Methods: We analyzed computer files of enrollment, clinical encounters, and Pap reports. Results: Overall, 74.5% of the study cohort received ≥1 Pap smear during the 3 year study interval. Screening rates varied by patient’s age, median income, copayment status, and number of visits to primary care departments. Women who had either their own assigned personal physician, a female personal physician, or both were more likely to have received Pap screening. Screening rates were inversely related to age of personal physician. Women who had personal physicians practicing in family practice (FP) departments were more likely to have received Pap screening than women who had personal physicians practicing in internal medicine (IM) departments. Multivariate analysis of women who had a personal physician assigned to them showed that women were more likely to receive Pap screening if they had a female FP personal physician (odds ratio (OR) = 1.13) or a female IM personal physician (OR = 1.23) than if they had a male IM or FP personal physician. We detected no statistically significant effect of provider age or Hispanic ethnicity when controlling for other variables. Conclusions: High rates of triennial Pap screening rates were documented. Major factors determining receipt of Pap screening were patient age and number of visits to primary care departments. Differences in screening rates associated with median income and personal physician’s gender were smaller than those reported in other settings. Unlike patients in other settings, patients in this population who had older providers, greater out-of-pocket medical expenses, or Hispanic ethnicity did not have lower 3 year screening rates after controlling for other factors.
Predicting the results of RNA molecular specific hybridization using machine learning

Ribonucleic acid ( RNA ) hybridization is widely used in popular RNA simulation software in bioinformatics. However, limited by the exponential computational complexity of combinatorial problems, it is challenging to decide, within an acceptable time, whether a specific RNA hybridization is effective. We hereby introduce a machine learning based technique to address this problem. Sample machine learning ( ML ) models tested in the training phase include algorithms based on the boosted tree ( BT ) , random forest ( RF ) , decision tree ( DT ) and logistic regression ( LR ), and the corresponding models are obtained. Given the RNA molecular coding training and testing sets, the trained machine learning models are applied to predict the classification of RNA hybridization results. The experiment results show that the optimal predictive accuracies are 96.2%, 96.6%, 96.0% and 69.8% for the RF, BT, DT and LR-based approaches, respectively, under the strong constraint condition, compared with traditional representative methods. Furthermore, the average computation efficiency of the RF, BT, DT and LR-based approaches are 208 679, 269 756, 184 333 and 187 458 times higher than that of existing approach, respectively. Given an RNA design, the BT-based approach demonstrates high computational efficiency and better predictive accuracy in determining the biological effectiveness of molecular hybridization.
DDSA: A Defense Against Adversarial Attacks Using Deep Denoising Sparse Autoencoder

Given their outstanding performance, the Deep Neural Networks (DNNs) models have been deployed in many real-world applications. However, recent studies have demonstrated that they are vulnerable to small carefully crafted perturbations, i.e., adversarial examples, which considerably decrease their performance and can lead to devastating consequences, especially for safety-critical applications, such as autonomous vehicles, healthcare and face recognition. Therefore, it is of paramount importance to offer defense solutions that increase the robustness of DNNs against adversarial attacks. In this paper, we propose a novel defense solution based on a Deep Denoising Sparse Autoencoder (DDSA). The proposed method is performed as a pre-processing step, where the adversarial noise of the input samples is removed before feeding the classifier. The pre-processing defense block can be associated with any classifier, without any change to their architecture or training procedure. In addition, the proposed method is a universal defense, since it does not require any knowledge about the attack, making it usable against any type of attack. The experimental results on MNIST and CIFAR-10 datasets have shown that the proposed DDSA defense provides a high robustness against a set of prominent attacks under white-, gray- and black-box settings, and outperforms state-of-the-art defense methods.
Why Do Older Adults Use Wearable Devices: A Case Study Adopting the Senior Technology Acceptance Model (STAM)

Previous studies have focused much attention on the adoption of technology products; however, research on the adoption of wearable devices for older people is sparse. This study applied the Senior Technology Acceptance Model (STAM) and investigated the factors influencing the acceptance of new technology by focusing on smart watch and bracelet adoption among the elderly. A case study method was adopted, and a group of fifteen older adults and fifteen younger participants (e.g. their children) were interviewed in Taiwan regarding their use of wearable devices. Several questions were raised: What factors influence the elderly in adopting wearable devices? What are the differences between the opinions and attitudes of the elderly and younger generations in the adaption of wearable devices? Based on the results, we concluded that the healthcare and alert functions are the main factors that enhance the use of wearable devices by the elderly. Also, besides their fashionable design and style, older people are more concerned about the practical and efficiency aspects of wearable devices. Finally, information is lacking for the elderly to learn about the advantages of using wearable devices. A revised STAM model considering these learning and culture issues is suggested.
Optimising blood pressure control in Maori and Pacific patients with type 2 diabetes mellitus and established diabetic nephropathy in New Zealand

Diabetes mellitus is having a considerable impact on the health and well-being of Māori and Pacific people in New Zealand (NZ). The prevalence of diabetic nephropathy is high in these groups, who also have a heightened risk of progression to end-stage renal disease (ESRD). Good blood pressure (BP) control is paramount to the prevention and delay of diabetic nephropathy. Success rates in achieving BP control through the utilisation of conventional healthcare approaches have been sub-optimal in comparison to the outcomes achieved in randomised controlled studies, therefore innovative and effective models of care for BP control are critically needed. The aim of this thesis is to examine whether a novel, integrated, communitybased model of care using nurse-led Māori and Pacific healthcare assistants (HCA) to manage hypertension in Māori and Pacific patients with type 2 diabetes and chronic kidney disease (CKD) is more effective than conventional care in reducing BP and delaying progression of cardiac and renal end-organ damage. The DElay Future End-stage Nephropathy due to Diabetes (DEFEND) study was a randomised controlled trial of 12 months duration that examined this model of care. Our findings showed that this model of healthcare delivery is significantly more effective than conventional care in lowering systolic BP, and reducing cardiac and renal end-organ damage in these high-risk patients. The thesis includes reviews and summaries which describe the respectively high prevalence of diabetes and diabetic nephropathy in Māori and Pacific people in NZ, and a cross-sectional analysis illustrates the high rates of Māori and Pacific people receiving renal replacement therapy (RRT), and the associated high comorbidity and mortality rates, particularly in Māori patients with ESRD. The low rate of renal transplantation in Māori and Pacific people is also highlighted.
Accurate and Efficient Finite-Difference Time-Domain Simulation Compared With CCPR Model for Complex Dispersive Media

Recently, it has received a great deal of attention to analyze the electromagnetic wave problems in dispersive media by using the finite-difference time-domain (FDTD) method. Accordingly, it is of great importance to employ a proper dispersion model which can fit the frequency-dependent permittivity of a medium considered. The reported dispersion models include Debye, Drude, Lorentz, modified Lorentz, quadratic complex rational function, complex-conjugate pole-residue (CCPR) models. The CCPR dispersion model has advantage over other dispersion models in the fact that accurate CCPR dispersion parameters can be simply extracted by using the powerful and robust vector fitting tool which has been widely used in the circuit theory. However, the arithmetic operation of CCPR-based FDTD implementation is involved with complex-valued numbers and thus its numerical computation is not efficient. In this work, we propose an accurate and efficient FDTD simulation for complex dispersive media. In specific, an accurate CCPR dispersion model is simply obtained using the vector fitting tool and then the CCPR dispersion model is converted to the modified Lorentz dispersion model which leads to the arithmetic operation of only real-valued numbers in its FDTD implementation. Numerical examples are used to illustrate the accuracy and efficiency of our dispersive FDTD simulation.
Adaptive Learning Gabor Filter for Finger-Vein Recognition

Presently, finger-vein recognition is a new research direction in the field of biometric recognition. The Gabor filter has been extensively used for finger-vein recognition; however, its parameters are difficult to adjust. To solve this problem, an adaptive-learning Gabor filter is presented herein. We combine convolutional neural networks with a Gabor filter to calculate the gradient of the Gabor-filter parameters, based on the objective function, and to then optimize its parameters via back-propagation. The parameter <inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> of Gabor filter can be trained to the same angle as the vein texture of finger vein image. The parameter <inline-formula> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula> of Gabor filter has a certain relation with <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula>, and the parameter <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> of Gabor filter can converge to the optimal value well. Using this method, we not only select appropriate and effective Gabor filter parameters to design the filter banks, we also consider the relationship between those parameters. Finally, we perform experiments on four public finger-vein datasets. Experimental results demonstrate that our method outperforms state-of-the-art methods in finger-vein classification.
Design of the Input Capacitance and Inductance in PV Converters

This work shows a procedure to build a PV one diode model that use only the datasheet data to obtain the I - V curve. This procedure was presented in the literature, but the equations were not clear to obtain the parameters. After a review the revised equations were presented. The simple model was used to estimate the power reduction when pulsed input current or continuous input current converters are used. The ripple produced by these converters reduces the extracted power even if a maximum power point tracking (MPPT) control technique is used. For pulsed input current converters, the filter capacitance is needed and if the ripple is not correctly reduced there is a significant power reduction or the MPPT control is unfeasible. The contributions of this paper are providing guidelines, equations and graphs in order to design input components or filters to ensure that the connected converter can achieve its best operation point or the best MPPT control for all conditions.
Cardiac Arrhythmia Detection from 2D ECG Images by Using Deep Learning Technique

Arrhythmia is irregular changes of normal heart rhythm and effective manual identifying of them require a lot of time and depends on experience of clinicians. This paper proposes deep learning-based novel 2-D convolutional neural network (CNN) approach for accurate classification of five different arrhythmia types. The performance of the proposed architecture is tested on Electrocardiogram (ECG) signals that are taken from MIT-BIH arrhythmia benchmark database. ECG signals was segmented into heartbeats and each of the heartbeats was converted into 2-D grayscale images as an input data for CNN structure. The accuracy of the proposed architecture was found as 97.42% on the training results revealed that the proposed 2-D CNN architecture with transformed 2-D ECG images can achieve highest accuracy without any preprocessing and feature extraction and feature selection stages for ECG signals.
MSARN: A Deep Neural Network Based on an Adaptive Recalibration Mechanism for Multiscale and Arbitrary-Oriented SAR Ship Detection

Ship detection plays an important role in synthetic aperture radar (SAR) image interpretation. However, there are still some difficulties in SAR ship detection. First, ships often have a large aspect ratio and arbitrary directionality in SAR images. Traditional detection algorithms can cause the detection area to be redundant, which makes it difficult to accurately locate the target in complex scenes. Second, ships in ports are often densely arranged, and the effective identification of densely arranged ships is complicated. Finally, ships in SAR images exist at a variety of scales due to the multiresolution imaging modes used and ship shape variations, which pose a considerable challenge for ship detection. To solve the above problems, we propose a multiscale adaptive recalibration network (MSARN) to detect multiscale and arbitrarily oriented ships in complex scenarios. The recalibration of the extracted multiscale features through global information increases the sensitivity of the network to the target angle, thereby increasing the accuracy of positioning. In particular, we designed a pyramid anchor and a loss function to match the rotated target. In addition, we modified the rotation non-maximum suppression (RNMS) method to solve the problem of the large overlap ratio of the detection box. The proposed model combines the positioning advantage of rotation detection with the speed advantage of a single-stage framework. Experiments show that based on the SAR rotation ship detection (SRSD) data set, the proposed algorithm has a faster detection speed and higher accuracy than some state-of-the-art methods.
A Sliding-Mode Controlled Single-Phase Grid-Connected Quasi-Z-Source NPC Inverter With Double-Line Frequency Ripple Suppression

In this paper, double-line frequency (<inline-formula> <tex-math notation="LaTeX">$2\omega$ </tex-math></inline-formula>) ripple suppression and SMC with time-invariant (fixed) switching frequency methods are proposed for single-phase grid-connected three-level neutral-point-clamped quasi-Z-source inverters. The <inline-formula> <tex-math notation="LaTeX">$2\omega $ </tex-math></inline-formula> ripple suppression method is based on the 180° phase difference existing between the <inline-formula> <tex-math notation="LaTeX">$2\omega $ </tex-math></inline-formula> ripple components of the capacitor and inductor voltages in the dc-side. Hence, when these components are added in the closed-loop, a phase cancellation occurs so that the inductor current reference can be generated without <inline-formula> <tex-math notation="LaTeX">$2\omega $ </tex-math></inline-formula> ripple component. In this case, the actual inductor current, which is forced to track its reference, has no <inline-formula> <tex-math notation="LaTeX">$2\omega $ </tex-math></inline-formula> ripple component. In addition, the grid current control is achieved via sliding mode control (SMC). Unlike the existing SMC methods, the proposed SMC achieves fixed switching frequency which is made possible by eliminating the discontinuities in the sliding surface function using a boundary layer. The proposed ripple suppression method together with the SMC method offers many advantages such as fast dynamic response, zero grid current error, simple implementation, robustness to parameter variations and fixed switching frequency. The effectiveness of the proposed control method is verified experimentally under steady-state and transient conditions.
Synchronization Cost of Coupled Oscillators With a Spatial Embedding

Synchronous behavior brings advantages for complex systems. Yet, this advantage comes with a cost: Emergence and sustainability of synchronization requires a continuous exchange among the interacting units. Thus, networks with optimal synchronization dynamics is a subject of active research. In this study, we define a cost function for synchronization which takes into account both the network structure which is embedded in space and the dynamic coupling within the system. Beginning from a fully connected network and using an edge pruning strategy based on simulated annealing, we searched for optimal configurations for synchronization with minimum cost. We observed that the same levels of synchronization with a fully connected network can be reached by different networks with sparse connections. We conclude that the chain structure, clustering behavior and degree-frequency relations are the main determinants of optimally synchronizable network structures.
A Quad Motifs Index for Directed Link Prediction

Network motif is a sub-graph pattern may reflect structure functions or reveal underneath mechanics. Some scholars have used triadic motifs for link prediction and achieve convincing results. But most of them ignore the effectiveness of quad motifs in calculating the similarity of two endpoints. In this article, a new method based on quad motifs is proposed. Two winner motifs are chosen among 199 sub-graphs. Local information of motifs is also taken into consideration. Experiments on nine real networks show that the index we proposed can improve prediction accuracy, compared with seven well-known measures.
Placement and Sizing of Distributed Generation in Distribution System

Advances in power systems motivated by the increasing demand of generation sources connected to the power distribution system have brought several benefits, such as losses reduction and improved voltage profile. However, further research must be conducted to avoid the disadvantages and to improve the benefits. This paper applies differential evolution meta-heuristic to perform sizing and placement of Distributed Generation units in radial distribution networks in order to reduce active power losses. Besides, the proposed algorithm allows an evaluation of the active power losses behavior when compared to the number of generating units placed and dimensioned by the algorithm. Simulations have been performed using the IEEE 33-bus and IEEE 69-bus for evaluating the proposed methodology. The results obtained demonstrate the ability of the algorithm for placing and sizing Distributed Generation units in addition to evaluate the optimal number of generating units to minimize the active power losses in the networks.
Distributed Consensus for Smart Grid Economic Dispatch with Prohibited Operating Zones

The present paper exhibits a fully distributed cooperative algorithm based on consensus protocols to solve the economic dispatch problem (EDP) in the concept of the smart grids. In particular, the proposed protocol eliminates the need for a leader or a coordinated center and considers the vulnerability of the communication networks such as the link or/and node failure. In the power layer, for the formulation of the EDP, we consider the power generation limit, the ramp-rate limits, the power balance supply and the prohibited operating zones constraints, which makes the solution space of the (EDP) discontinuous. The evaluation of the proposed algorithm is effected on the IEEE 30 bus test system. Compared to other existing algorithms, numerical results prove the efficiency of the proposed approach to solve the discontinuous decentralized EDP under a practical communication network