Miracle-dz/UniFAR-FTUs huggingface dataset:
CONSTRUCTING SIMULATION OUTPUT INTERVALS UNDER INPUT UNCERTAINTY VIA DATA SECTIONING

We study the problem of constructing confidence intervals (CIs) for simulation outputs in the presence of input uncertainty, where the constructed CIs capture both the statistical noises from the simulation replications and the input data. We present a simple technique based on sectioning input data that provides exact asymptotic confidence guarantees. Unlike some existing approaches, our technique bypasses the need to consistently estimate variances that could be computationally demanding. It can be flexibly applied to dependent data and to both parametric and nonparametric input models.
A Machine Learning Approach to Detect Self-Care Problems of Children with Physical and Motor Disability

Inability to look after and take care of own self in a healthy way is referred to as self-care problems. Physical disabilities are limitations in physical capacity, mobility of a person. Motor disability is defined as the loss of function of any body part. Self-care problems detection is an important sector of the exceptional children health care system because depending on the type of self-care problems, the treatment approach is chosen. Expensive involvement of professional therapists and a long time is required to detect self-care problems. To predict the self-care problems of children with physical and motor disability in advance, an expert system is proposed using machine learning. To do the task, the only available self-care activities dataset SCADI (Self-Care Activities Dataset based on ICF-CY) is used. Principal Component Analysis (PCA) is used to reduce the feature vector size. Different machine learning classification algorithms are used to find out the prediction accuracy. Prediction performances on different partitions of the dataset are analyzed. Finally, a system with K Nearest Neighbor (KNN) is proposed to predict self-care problems.
Investigating the Polarization of an Omnidirectional Patch Antenna: Polarization Change With Respect to Radiation Angle

In ultradense networks (UDNs), finding an omnidirectional, yet orthogonally polarized, antenna in a reasonable size has been a challenge. We have developed an antenna type that appears to address this need. These antennas, which operate in pairs, offer enhanced polarization characteristics and can be scaled to operate at different frequencies. Moreover, the gain of each antenna, and thus the pair, can be increased by using more than one radiating element as suggested by array theory. To analyze the polarization characteristics of the antenna pair, a complex polarization ratio was used and compared to more traditional methods. The antenna has been prototyped and tested.
Activity Recognition Using One-Versus-All Strategy with Relief-F and Self-Adaptive Algorithm

Many researchers dealing with smartphone sensors to recognize human activities using machine learning algorithms. In this paper, we proposed One-versus-All (OVA) strategy with relief-f and self-adaptive algorithm to recognize these activities. Relief-f used to rank the features and prune insignificant features, self-adaptive algorithm selects the relevant ones, and OVA transform features into a series of two-class classification problems, and later recognized by based classifier. Experiments were carried out to study the performance of our proposed algorithm using publicly activity datasets namely Physical Activity Monitoring for Aging People. It covers eighteen activities comprising various simple and complex activities. The performance of our method is compared to One-versus-One algorithm. The results have significantly promised an improvement of activity recognition level, mainly involving very similar activities.
DIGITAL MANUFACTURING: REQUIREMENTS AND CHALLENGES FOR IMPLEMENTING DIGITAL SURROGATES

A key challenge for manufacturers today is efficiently producing and delivering products on time. Issues include demand for customized products, changes in orders, and equipment status change, complicating the decision-making process. A real-time digital representation of the manufacturing operation would help address these challenges. Recent technology advancements of smart sensors, IoT, and cloud computing make it possible to realize a "digital twin" of a manufacturing system or process. Digital twins or surrogates are data-driven virtual representations that replicate, connect, and synchronize the operation of a manufacturing system or process. They utilize dynamically collected data to track system behaviors, analyze performance, and help make decisions without interrupting production. In this paper, we define digital surrogate, explore their relationships to simulation, digital thread, artificial intelligence, and IoT. We identify the technology and standard requirements and challenges for implementing digital surrogates. A production planning case is used to exemplify the digital surrogate concept.
Outlier Detection Using Diverse Neighborhood Graphs

Owing to its wide applications in both industry and academia, a large number of new approaches are emerging every year in the field of outlier detection. Among which, neighborhood-based approaches are adopted by a great number of researchers and they still represent the mainstream in the field. However, how to determine appropriate local information from the definition of neighbors is an arduous problem which still has no widely accepted solution. In this study, we propose a new outlier detection model utilizing multiple neighborhood graphs, each of which is based on changed neighbors to capture various local information from different perspectives. An outlier score for each object is then deduced by performing random walk on the predefined graphs. Experiments on ten real-world datasets suggested that the proposed model could obtain promising results compared with four state-of-the-art algorithms by the measure of ROC AUC and precision at n.
Handover Management in 5G Software Defined Network Based V2X Communication

Vehicle-2-Everything (V2X) communication consists of mainly two types of communication, one is Vehicle to Vehicle (V2V) and the other is a vehicle to infrastructure(V2I). V2X has many uses including road safety that can help avoid collisions to infotainment like traffic information, navigation, and multimedia. As the driver assistance systems are rapidly growing, user interaction and acceptance of these systems has to be evaluated. The aim of this work is to improve an already existing communication model for V2X communication by minimizing the handover duration. IEEE 802.11p has been a standard for V2X communication. In IEEE 802.11p, due to high density, the data collision occurs. So we replaced IEEE 802.11p with 5G SDN based approach. Now the current communication simulation implements the X2 interface based Vehicle Unit Inter-system handover (VUIH)” in a partially 5G SDN-based V2X communication. Our proposed approach has achieved much better results than the baseline results. We have optimized the preparation time of handover by 1.24ms and completion time of handover by 0.8308ms.
A Novel Deeper One-Dimensional CNN With Residual Learning for Fault Diagnosis of Wheelset Bearings in High-Speed Trains

The health condition of a wheelset bearing, the key component of a railway bogie, has a considerable impact on the safety of a train. Traditional bearing fault diagnosis techniques generally extract signals manually and then diagnose the bearing health conditions through the classifier. However, high-speed trains (HSTs) are usually faced with variable loads, variable speeds, and strong environmental noise, which pose a huge challenge to the application of the traditional bearing fault diagnosis methods in wheelset bearing fault diagnosis. Therefore, this paper proposes a 1D residual block, and based on the block, a novel deeper 1D convolutional neural network (Der-1DCNN) is proposed. The framework includes the idea of residual learning and can effectively learn high-level and abstract features while effectively alleviating the problem of training difficulty and the performance degradation of a deeper network. Additionally, for the first time, we fully use the wide convolution kernel and dropout technology to improve the model’s ability to learn low-frequency signal features related to the fault components and to enhance the network’s generalization performance. By constructing a deep residual learning network, Der-1DCNN can adaptively learn the deep fault features of the original vibration signal. This method not only achieves very high diagnostic accuracy for the fault diagnosis task of wheelset bearings in HSTs under strong noise environment, but also its performance is quite superior when the train’s working load changes without any domain adaptation algorithm processing. The proposed Der-1DCNN is evaluated on the dataset of the multi-operating conditions of the wheelset bearings of HSTs. Experiments show that this method shows a better diagnostic performance compared with the state-of-the-art deep learning methods of bearing fault diagnosis, which proves the method’s effectiveness and superiority.
A Scalable Model of On-Chip Inductor Including Tunable Dummy Metal Density Factor

Because of the metal density requirement in the advanced CMOS processes, dummy metal fills (DMFs) are unavoidable. The DMFs degrade the performance of the on-chip inductors, especially for the ones in the microwave and millimeter-wave circuits. This paper analyzes the impact of the floating DMFs and proposes a scalable double-<inline-formula> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula> inductor model including a tunable dummy metal density factor. The metal density factor can be integrated into the process design kit and determined by the designers to pass the design rule check. The proposed model predicts the decrease in the quality factor <inline-formula> <tex-math notation="LaTeX">$(Q)$ </tex-math></inline-formula> and self-resonance frequency <inline-formula> <tex-math notation="LaTeX">$(f_{\mathrm {sr}})$ </tex-math></inline-formula> with the density of DMFs increasing accurately. Furthermore, a fully automatic model extraction method based on the genetic algorithm is presented, which increases the accuracy of models. Inductors with DMFs of different densities are fabricated in a standard 180-nm CMOS technology. Then, the parameters of the inductors’ models are extracted to verify the idea. The models’ calculation results are compared with the measurement results up to 67 GHz.
HYPERPARAMETER OPTIMIZATION FOR APPROXIMATE BAYESIAN COMPUTATION

Approximate Bayesian computation is a popular methodology for simulation-based parameter inference in scenarios where the likelihood function is either analytically intractable or computationally infeasible. The likelihood of simulator parameters fitting given data is approximated by iteratively simulating samples that are generated according to a specified prior distribution. The convergence speed and the quality of inference are highly sensitive to the choice of hyperparameters such as the chosen summary statistic, the value of the acceptance threshold and the distance function. The choice is typically left to the domain expert as summary statistics vary across disciplines and threshold values are problem-specific. This work explores automated hyperparameter optimization for approximate Bayesian computation using Bayesian optimization as an alternative to time consuming manual selection. The problem setting assumes availability of a fast low-fidelity simulator, which is used during the optimization process. The optimized hyperparameters are then used to perform inference using the high-fidelity simulator.
MEMS Gyroscope With Concentrated Springs Suspensions Demonstrating Single Digit Frequency Split and Temperature Robustness

We report on a new design approach for X-Y symmetric resonator, emphasizing the increase in symmetry by localization of the device anchors. The resonator is mechanized as a z-axis rate gyroscope (RG), and the concept is compatible with the rate-integrating gyroscope mechanization. Our approach is based on strategically placing the critical mechanical elements at the center of the resonator structure, to mitigate the effect of fabrication imperfections across the device. We experimentally demonstrated that the design yields a sub-Hz frequency separation between operational modes, which is attributed to the introduced design approach. The high symmetry of the device led to a temperature stability of 50 mHz over 130 °C temperature range. A ring down test demonstrated the Q-factor greater than 330 K. The thermal stability and preliminary characterization results of the gyro in-run stability are presented. [2017-0177]
Study of Double Image Fusion Program on Clothing Defects Image

In this paper, simulating handwork of cut-paste method, a framework of split-merge combination with features of integration of multi-focus image area was built. We also proposed a design of image segmentation on the basis of clarity of images measured, and achieved regional multi-focus vision fusion. According to the standards of image spatial frequency and morphological wavelet transform coefficients, regional image algorithm was design. In the basis of the pixel neighborhood spatial frequency domain as its sharpness measurement, and with the combination of morphological operators, division of clear regional and fuzzy area was directly obtained. And by the multi-focus image that was taken in two different angles and had two printing defects as object image, the actual effect of the double image fusion method was verified.
Error Reduction Scheme in WBSNs Based on Channel Assessment

Wireless Body Sensor Networks (WBSNs) are an exclusive division of sensor networks used for proactive health monitoring. Errors that occur during the transmission of sensed data can corrupt the effectiveness of the network. The tighter resource constraints of WBSNs make the most common method of error control codes (ECC) an inappropriate solution. Hence, error reduction methods at the transceiver level helps in achieving the target of the network within the constraints. Simulation results show that decoding the data based on the threshold which is set depending on the channel assessment reduces the bit error rate (BER) to a considerable extent.
Software Measures for Common Design Patterns Using Visual Studio Code Metrics

During the last decades we have faced an outburst in needs for software and consequently in software engineering, accompanied with numerous problems that needed to be solved and robust, documented solutions that needed to be proposed. Towards this end, software design patterns become practically a necessity, since they offered reusable solutions to commonly occurring problems within specified contexts of software architectures. In this paper we propose one more evaluation dimension for common design patterns, using code metrics and structuring specific experiments of software projects that derive from real life and thus can be considered as quite realistic. Our results indicate that while in many occasions, architectures implementing design patterns are prevailing, there are considerable cases where simplistic, generic approaches seem to outperform them in specific metrics.
Improved Interface Properties and Dielectric Breakdown in Recessed AlGaN/GaN MOS-HEMTs Using HfSiO $_{{x}}$  as Gate Dielectric

In this letter, we report optimized transport properties in gate recessed enhancement-mode GaN MOS-HEMTs by incorporating silicon into atomic layer deposited gate dielectric HfO<sub>2</sub>. Compared with commonly used HfO<sub>2</sub> gate dielectric, the interface trap density can be reduced by nearly an order of magnitude and the fixed oxide traps inside are reduced to almost half using the high-quality passivation of HfSiO<sub><italic>x</italic></sub>. The MOS-HEMTs based on HfSiO<sub><italic>x</italic></sub> exhibit a threshold voltage of 1.5 V, excellent subthreshold swing of 65 mV/dec, and a high on–off ratio of <inline-formula> <tex-math notation="LaTeX">$\textsf {3} \times \textsf {10}^{\textsf {10}}$ </tex-math></inline-formula>. The incorporation of silicon in HfO<sub>2</sub> can also increase the dielectric breakdown property with maximum gate electric field of 2.85 MV/cm for a 10-year time-dependent gate dielectric breakdown lifetime, which is 36% higher than pure HfO<sub>2</sub>. The maximum breakdown voltage of HfSiO<sub><italic>x</italic></sub> MOS-HEMT is 742 V, which is 30% higher than HfO<sub>2</sub> MOS-HEMT.
Online Tools to Support Novice Programming: A Systematic Review

Novice programming is a challenging subject to both the students and the educators. A novice programmer is required to acquire new knowledge to solve a problem and propose a solution systematically. This is followed by constructing the solution in a development environment that they are unfamiliar with. This research looks at the challenges faced by a novice programmer and the online methods that are popular to assist the students. Online block programming is a popular option. One of the software that had been implemented in the various research project is Scratch. From the reviewed research, it shows that the trend is moving towards an intelligent tutoring system, where students can have personalized engagement for their learning experience. This paper presents a systematic review conducted using the keywords “novice programming”, “introductory”, “CS1”, “difficulties”, “challenges”, and “threshold concepts”. From the review conducted, it is observed that most of the work is carried out to ease the implementation of the solution through an integrated development environment, and block programming. On the support for instructors, the discussion on curriculum and challenges in CS1 tops the chart. This is followed by active learning through online tools.
Blockchain Meets Smart Health: Towards Next Generation Healthcare Services

Blockchain technology is rapidly gaining traction in healthcare industry as one of the most exciting technological developments. In particular, blockchain technology presents numerous opportunities for healthcare industry such as reduced transaction costs, increased transparency for regulatory reporting, efficient healthcare data management and healthcare records universality. In the context of smart health, blockchain may provide distinct benefits, particularly from a context-aware perspective where efficient and personalised solutions may be provided to citizens and the society in general. In this article, we portray the symbiotic relationship between blockchain and smart health. Among others, we identify and analyse three individual streams of possible synergies. In addition, we discuss several challenges for actually implementing blockchain-based applications in the healthcare industry along with several opportunities for future research directions.
Comparison of DBaaS Architectures

Many organizations rely on vast amounts of data to perform their operative and management tasks. At some point, the databases that hold and manage the data cannot grow any further. The data needs to be split and put on several servers to meet the requirements of space, availability and performance. However, this is not a trivial task and also requires professionals to install and maintain such database systems. To follow the spirit and idea of cloud computing, there are organizations like Amazon AWS and Salesforce that provide Database-as-a-Service (DBaaS). This is a new paradigm in which DBaaS providers offer their customers seamless mechanisms to create, store and access the data at the host site, thereby obviating the need for the customers to purchase expensive hardware and software, deal with upgrades, and hire professionals for administrative and maintenance tasks by their own.
DESIGNING CARE PATHWAYS USING SIMULATION MODELING AND MACHINE LEARNING

The development of care pathways is increasingly becoming an instrumental artefact towards improving the quality of care and cutting costs. This paper presents a framework that incorporates Simulation Modeling along with Machine Learning (ML) for the purpose of designing pathways and evaluating the return on investment of implementation. The study goes through a use case in relation to elderly healthcare in Ireland, with a particular focus on the hip-fracture care scheme. Initially, unsupervised ML is utilized to extract knowledge from the Irish Hip Fracture Database. Data clustering is specifically applied to learn potential insights pertaining to patient characteristics, care-related factors, and outcomes. Subsequently, the data-driven knowledge is utilized within the process of simulation model development. Generally, the framework is conceived to provide a systematic approach for developing healthcare policies that help optimize the quality and cost of care.
Adaptive Spatial Modulation Combining BCH Coding and Huffman Coding

Spatial modulation (SM), a modulation scheme that maps transmitted information onto the antenna parameter, appears as an attractive option for the emerging large-scale multiple-input multiple-output (MIMO) systems. Due to the reduced inter-channel interference (ICI) and signal processing complexity, SM has attracted vast attention to motivate further vigorous research, one of which is Huffman-based SM. To further decrease the bit error rate (BER) of SM with acceptable overhead signal processing complexity and power assumption, a new modulation scheme combining BCH coding and Huffman coding is proposed in this paper. Additionally, an adaptive quantization algorithm is derived and applied to the processing procedure of the probability matrix. Numerical results have illustrated that the proposed algorithm achieves considerable performance improvement over the existing schemes.
Research on Digitally Reconstructed Radiograph Algorithm Improvement Based on Computed Tomography Image

A fast ray tracing algorithm from sequence tomographic images for digitally reconstructed radiograph was presented. A ray tracing algorithm based on vector was adopted, and using computed tomography data simulated the absorption of radiation from human tissues,. The algorithm only has the addition and the subtraction operation, and the computation is simple. The algorithm does not need to obtain the corresponding intersection by solving the equation, and it has the characteristics of short imaging time and fast computing speed. At the same time, this paper improves the algorithm of 3D prosthesis storage and retrieval, and the algorithm realized online digitally reconstructed radiograph calculation, which greatly improves the computing speed. Finally, the digitally reconstructed radiograph images obtained in this paper were further processed digitally to improve their practical value.
A Fingerprint Segmentation Scheme Based on Adaptive Threshold Estimation

To achieve quick response in Automatic Fingerprint Identification System (AFIS), segmentation plays an important role in pre-processing step. The particular step filters out the Region of Interest (ROI) from a raw fingerprint image by segregating the unwanted portion that does not contain important image feature. Here we propose a pixel based scheme executes in three phases namely (a) enhancement of scanned image, (b) estimation of threshold to classify each pixel as a foreground or background pixel and (c) a post-processing step employing morphological filters to achieve an proper separation of the entire ROI. We implement and validate the proposed algorithm on various standard databases and the experimental results establish the efficiency of the method. We compare the proposed method with few of the existing approaches to establish the effectiveness and accuracy of the proposed algorithm.