Miracle-dz/UniFAR-FTUs huggingface dataset:
A New DC-DC Multilevel Breed of XY Converter Family for Renewable Energy Applications: LY Multilevel Structured Boost Converter

This paper presents a new multilevel breed of XY converter family, called LY Multilevel Boost Converter (LY-MBC) topologies for renewable energy applications. Four LY-MBC topologies are designed to obtain a high voltage conversion ratio and which also offer an operative solution for renewable energy systems with minimal number of components. Existing LY converter topologies includes L-L, L-2L, L-2LC and L-2LCm converter topologies and LY converter is one of the breed of the XY converter family. The proposed LY-MBC topologies are derived by attaching the Cockcroft Walton (CW) multiplier to the Y converter of LY converter topologies. The proposed LY-MBC converter topologies are compared with existing LY converter topologies in terms of voltage conversion ratio and number of components. The noticeable features of the proposed LY-MBC topologies are also discussed in details. The working operation, feasibility, and design of the proposed LY-MBC topologies are verified by simulations.
A Trivial Approach for Achieving Smart City: A Way Forward towards a Sustainable Society

Research and development related to smart city is evolving as a response to the issues faced by cities mostly in developing countries. However, prior studies are mostly focused on technical perspective which alone cannot lead to a sustainable society. Accordingly, this study develops a trivial approach that identifies crucial dimensions to be considered in achieving and improving city potential in being a smart city. Moreover, a qualitative research method was adopted by carrying out a single case study to confirm the identified dimensions. The data was collected through archival research technique and semi-structured interviews from practitioners based in Malaysia. Findings from this study indicate that cities should incorporates governance, mobility, living, facilities, services, environmental, social, economic, technologies and information to achieve a sustainable, prosperous and inclusive future for its citizens. Significantly, the developed trivial approach can be adopted by sustainable city managers in planning, designing, deploying and monitoring of sustainable city goals towards a sustainable society for future generations to come.
Building a Mathematical Model To Determine the Relationship between Heat Emission Coefficient and Pressure of the Freeze Drying Environment of Solid Materials*

This article presents research results of a mathematical model in describing the relationship between heat emission coefficient $(\alpha, Wm^{-2}K^{-1})$ and pressure (Pth, mm Hg) of the sublimation environment. This model is applied to solve the heat and mass transfer models in conditions of freeze drying processing. This is the basic to determine technology mode of freeze drying processing as well as calculate, design and make the freeze drying system.
Deep Neural Network-based Feature Descriptor for Retinal Image Registration

Feature description is an important step in image registration work flow. Discriminative power of feature descriptors affects feature matching performance and overall results of image registration. Deep Neural Network-based (DNN) feature descriptors are emerging trend in image registration tasks, often performing equally or better than hand-crafted ones. However, there are no learned local feature descriptors, specifically trained for human retinal image registration. In this paper we propose DNN-based feature descriptor that was trained on retinal image patches and compare it to well-known hand-crafted feature descriptors. Training dataset of image patches was compiled from nine online datasets of eye fundus images. Learned feature descriptor was compared to other descriptors using Fundus Image Registration dataset (FIRE), measuring amount of correctly matched ground truth points (Rank-1 metric) after feature description. We compare the performance of various feature descriptors applied for retinal image feature matching.
All-Fiber Passively Mode-Locked Erbium-Doped Fiber Laser Using a Femtosecond Laser Inscribed 45°-Tilted Fiber Grating

A passively mode-locked fiber laser using femtosecond laser inscribed plane-by-plane 45°-tilted fiber grating is experimentally demonstrated. The generated solitons center at 1561.8 nm with bandwidth reaching 7.14 nm and pulse width of 1.1 ps.
Quartiles based UnderSampling(QUS): A Simple and Novel Method to increase the Classification rate of positives in Imbalanced Datasets

The main challenge in learning from imbalanced datasets is the presence of a large set of training examples available for the negatives(majority class instances), and very few positives(minority class instances). This may result in a good overall performance of the classifier even though there is a huge red uction in the classification rate of positives. Quartiles based UnderSampling(QUS) method proposed in this paper, addresses the above problem in a simple way. That is balancing the dataset by selecting the negatives based on their similarity with respect to 5 quartiles: minimum, quartile1(Q1), median, quartile3(Q3) and maximum. Intention is to reduce the influence of excessive negatives on the classifier, which may bias it towards a better negatives classification otherwise. An advantage of this undersampling method is parameter independence and gives better results compared to the state of the art methods. The proposed method is tested on kNN (k Nearest Neighbour) classifier and empirical results improve the classification rate of positives than the original unprocessed imbalanced dataset.
Trust Region Levenberg-Marquardt Method for Linear SVM

Support Vector Machine is an optimal margin based classification technique in Machine Learning. In this paper, we have proposed Trust Region Levenberg-Marquardt (TRLM) method as a novel problem solver for L2 regularized L2 loss (L2RL2) primal SVM classification problem. Levenberg-Marquardt (LM) method is an extension of Gauss-Newton method for solving least squares non-linear optimization problems and Trust-Region (TR) method is used to find the step size. In LM method, LM parameter $A$ is changed by an arbitrary factor but in TRLM instead of changing A, the trust region radius Δ is changed. The proposed solver for L2RL2 primal SVM, performs well with medium sized problems. Experimental results establish TRLM as a solver for linear SVM as it performs at par and better in selective cases than existing state of the art solvers in terms of test accuracy.
Flight Path Planning of Multiple UAVs for Robust Localization near Infrastructure Facilities

To realize automatic inspections for infrastructure facilities by an Unmanned Aerial Vehicle (UAV), improvement of localization performance is needed. Especially, automatic flight does not work in environments near facilities such as under the bridge because GNSS signals does not reach the UAV. We research a localization system using subsidiary UAVs that assist localization of an inspection UAV (main UAV). In this system, the positioning of the subsidiary UAVs is important to correctly complement the position information of the main UAV. In this paper, as the first stage of research, we consider a path of a single subsidiary UAV using preliminary information: the 3D geometrical data of a target facility, the inspection path of the main UAV, and the arrangement of GNSS satellites during the inspection. A simple simulation shows that the localization of the main UAV does not collapse through the whole path under the evaluation of the GNSS positioning precision of each UAV.
Edge Detection for a Children’s Coloring Application

Children need entertainment that at the same time helps develop their mental skills and creative thinking. Coloring applications often attract them as they contain coloring and drawing. However, the majority of them are difficult to use for children and can have a fixed set of available coloring pages, which can make it uninteresting after the child have tried them all. This paper presents a new coloring mobile application that allows children to be creative and make their own coloring pages on-the-go by taking pictures of what they want to color. In this application, a child snaps a photo using the mobile device’s camera, which is then transformed into a coloring page and provided with tools to color and save the new creation in the mobile device. The transformation is done using image processing techniques: image smoothing and edge detection. The application provides three difficulty levels for the children: easy, medium, and advanced. It uses automatic parameter selection for each level that is adaptive to the input image. Evaluation of the parameter selection method proved its suitability for the application. User acceptance test was performed for the application which showed high successful completion rates and user satisfaction.
Boost-Cascaded-by-Buck Power Factor Correction Converter for Universal On-Board Battery Charger in Electric Transportation

A two-stage battery charger in battery operated electric vehicles (BEVs) and plug-in-hybrid electric vehicles (PHEVs) with wide output voltage range of 100–500 V is most suitable for all vehicle architectures. Existing battery chargers have a different limited range of output voltages of 36–48 V, 72–150 V and 200–450 V is achieved by varying the output voltage of DC/DC converters keeping a fixed voltage at DC link. A universal charger, which can address this wide range of battery pack voltages is suitable for all vehicle architectures. The most feasible way to meet this requirement of wide output voltage range is to vary the voltage at DC link with a fixed conversion voltage ratio at DC/DC converter. In this paper, we propose the use of cascaded converter in power factor correction (PFC) converters to achieve the wide DC link voltages for battery chargers. The primary focus of the paper is on the analysis and operation of boost-cascaded by buck (BoCBB) converter. The control implementation presented in the paper achieves a high input power quality, wide DC link voltages with universal input voltage ranges of 85–265 V. It also provides the degree of control freedom to operate even if the VNm (output voltage to the peak of Input) < 0.5. Simulations of the proposed converter with 1 kW power rating are carried out in PSIM 11.0 software and the results with wide DC link voltage of 150–400 V are presented in the paper.
Development of an Autonomous Unmanned Surface Vehicle with Object Detection Using Deep Learning

A large number of research has been accomplished in the field of the Unmanned Surface Vehicle (USV) in recent years. As deep learning has the potential to raise the technology to the next level by teaching the algorithm to learn by itself, we aim at developing an autonomous USV which has the capabilities to acquire various types of data and information in offshore areas, process them, and then execute missions based on the situation with the aid of the deep convolutional neural network. This paper describes the implementation of such USV system outfitted with sensors for localization with autonomous navigation technologies and algorithms being adopted for the potential real-life applications such as identifying approaching vehicles to alert the ground station or exploring the surrounding environment of assigned locations. In this manuscript, Global Positioning System (GPS) and compass are equipped to provide the geolocation and the heading for autonomous navigation. Experimental results are provided to validate the proposed implementation. In the end, a summary of current progress is presented as well as the proposed future works.
Action Recognition Based on Sequential 2D-CNN for Surveillance Systems

Action recognition plays an important task in surveillance systems, robot-human interaction and autonomous, systems. However, there are many challenging problems due to varieties of shape, illumination conditions, and complex of actions. Consuming time and precision are typically the main challenges for action recognition systems. Deep neural network techniques have dramatically become the state of the art in image processing. The high capacity deep learning on the temporal action from video analysis has been impeded because of varieties of classes, similarity of actions. This paper presents a new approach based on sequential deep neural network and data augmentation for improving accuracy. First, the deep neural network is constructed using several inceptions with different parallel convolutional operations which support reducing consuming time. Second, image augmentation of training dataset generates a larger data enough for deep neural network learning. This proposed task is aimed to address the small data problem. It is utilized for enhancing capabilities of deep learning. The proposed approach was evaluated on some benchmark datasets. Experimental evaluation on public benchmark dataset shows that the proposed approach enhances performance to 89.53% accuracy. Comparison results illustrated that our proposed method reaches higher performance than almost state- of- the- art methods.
Semantic Document Exchange through Mediation of Machine Natural Language

In global e-commerce, semantic document exchange ensures unambiguity and shares same meaning for documents sender and receiver cross different natural languages (e.g., English to Chinese), as the difference makes the translation between cross-language documents complex and inaccurate. This paper proposed a novel collaborative framework of semantic document exchange through a mediation approach which provides a sentence-based machine natural language (MNL). Here, the MNL constructs a grammatical case structure to compatible with all languages. Thus, the mediation framework enables the sentence computer-readable and understandable without ambiguity, simultaneously the MNL can improve accuracy and reduce the complexity of mapping in semantic document exchange.
Active Gate Driver and Management of the Current Switching Speed in GaN Transistors During Tum-On

This paper describes the principle of an active gate driver to control the high current switching speed generated during the tum-on of GaN HEMT transistors. The proposed circuit requires a common-mode parasitic inductance of 1 nH to provide the necessary image of the current slope. The proposed scheme is comparable to a variation of gate resistance of one decade without the inconvenience of the gate resistance. The approach is first validated by circuit simulation then experimentally. The advantages of such an approach are then demonstrated thanks to the comparison with a conventional passive control method.
Link Prediction in Dynamic Social Networks: A Literature Review

Social network link prediction has gained significant attention and become a key research focus over the last two decades. The prediction of missing links in the current network and emerging or broken links in future networks is essential for the understanding of their evolutionary nature. Social networks are changing dynamically over time. Link inference in dynamic social networks is an extremely challenging process and few link prediction methods consider their evolving nature. The aim of this paper is to comprehensively review, analyze, discuss and evaluate state-of-the-art link prediction methods in dynamic social networks. The leading link prediction methods and techniques that network science has produced are categorized and compared. Features and evaluation metrics for each method are presented. Finally, some future directions and recommendations are provided.
Sliding Mode SLAM for Robust Simultaneous Localization and Mapping

Normal SLAMs use the extended Kalman filter to estimate robot localization and the mapping simultaneously. They do not work well under big disturbances and bounded noises. In this paper, the sliding mode method is applied for the SLAM. The proposed sliding model SLAM only requires the noises and the disturbances are bounded. The estimation errors are analyzed, and the stability of the novel SLAM is proposed. A mobile robot is applied in the experiment to show the effectiveness of the sliding mode SLAM in the presence of bounded noises.
Convex Optimization-Based Sensorless Control for IPMSM Drives with Reduced Complexity

This paper proposes a simplified convex optimization-based sensorless scheme for interior permanent magnet synchronous motor (IPMSM)drives. The computational burden of the existing convex optimization-based method is significantly reduced by a single variable cost function, which is based on the machine voltage equations in the stationary reference frame. With less computation, the proposed method provides good performance characteristics similar to the existing one, e.g., dynamic speed response and smooth transition between the low and high-speed ranges. The convergence capability of the cost function is also confirmed by a convexity analysis. The feasibility of the control technique is experimentally validated in a test bench, demonstrating the accuracy of the technique and its reduced computational burden.
Bitcoin Volatility Forecasting with a Glimpse into Buy and Sell Orders

Bitcoin is one of the most prominent decentralized digital cryptocurrencies. Ability to understand which factors drive the fluctuations of the Bitcoin price and to what extent they are predictable is interesting both from the theoretical and practical perspective. In this paper, we study the problem of the Bitcoin short-term volatility forecasting based on volatility history and order book data. Order book, consisting of buy and sell orders over time, reflects the intention of the market and is closely related to the evolution of volatility. We propose temporal mixture models capable of adaptively exploiting both volatility history and order book features. By leveraging rolling and incremental learning and evaluation procedures, we demonstrate the prediction performance of our model as well as studying the robustness, in comparison to a variety of statistical and machine learning baselines. Meanwhile, our temporal mixture model enables to decipher the time-varying effect of order book features on volatility. It demonstrates the prospect of our temporal mixture model as an interpretable forecasting framework over heterogeneous Bitcoin data.
Susceptibility of the LLC Resonance Generator for Induction Heating on Changes in Load Parameters Caused by Heating the Charge

In the induction heating processes, there are usually large changes of the impedance of the inductor-charge system. They may be caused by a change in the using inductor-charge system, but also result from the charge heating process itself, which is related to the change of its electrical material parameters. Particularly the latter cause, related to the dynamics of the heating process and the type of charge materials, is difficult to correct and may lead to large restrictions on the use of the potentially available power of the power source, which is usually a resonance inverter. Using a simulation package taking into account the interactive cooperation of the source and calculated by the finite-element method inductor-charge system, the sensitivity of inverters with series, parallel and LLC resonance on changes in load parameters caused by process of the charge heating have been compared. For the LLC generator, it was analyzed whether through the selection of its parameters, one can influence the limitation of sensitivity to load change, and thus improve the possibility of better use of the potentially available power of source.
Low Power Design of a Wireless Sensor Node to Monitor Electric Car Batteries

Electric vehicles require monitoring of car batteries. Wireless sensing is desirable to eliminate connecting wires, resulting in low installation and maintenance cost. This paper presents a low-power wireless sensor node and an interface circuit to monitor the battery pack of an electric vehicle. To save power, the proposed sensor node adopts the wake-up/sleep mode and reduces the transmission time through compaction of the data to transmit. The wireless sensor node is prototyped with a Texas Instruments low power microcontroller CC1310, where CC1310 embeds a variant of Zigbee radio and a 12-bit analog-to-digital converter. The prototype monitors the voltage and temperature of nine battery cells and the battery pack current. The measured power dissipation of the prototype is, on average, 1.2 mW. Considering the power dissipation of the microcontroller is 22 mw during transmission, the proposed method reduces the power substantially.
Unsupervised Feature Extraction from RGB-D Data for Object Classification: a Case Study on the YCB Object and Model Set

Object recognition has attracted increasing attention of researchers due to its numerous applications. For instance, it enables robots to carry out tasks like searching for an object in an unstructured environment or retrieving a tool for a human co-worker. In this study, we present a new technique for unsupervised feature extraction from red, green, blue, plus depth (RGB-D) data, which is then combined with several classifiers to perform object recognition. Specifically, our architecture segments all objects in a table top scene through an unsupervised clustering technique. It focuses separately on each object to extract both shape and visual features. We conduct experiments on a subset of 20 objects selected from the YCB object and model set and evaluate the performance of several classifiers. The most effective one achieves an accuracy of 99.7% when trained and tested on samples acquired with the same conditions (equipment and environment). Results degrade when the system is trained with YCB data and tested with data acquired from a Kinect sensor in online laboratorial implementation.
Failure Analysis and Characterization of Scheduling Jobs in Google Cluster Trace

Most public and private cloud providers have experienced failure in one of their services that may affect numerous applications and websites. Thus, in order to understand the causes of different types of failures and remediate the issue, failure analysis is one of the most critical steps. Failure analysis has been developed based on monitoring the most significant metrics of the system in order to study the behavior and frequency changes in the systems. Then, the monitored data will be stored in log files to be utilized for analysis and prediction tasks. In this paper, we primarily focus on analyzing and interpreting the characteristic behavior of finished/failed jobs in association with physically available resources using a publicly available dataset, Google cluster trace. The primary objective of our work is to enhance the understanding of job failure in cloud computing environments. Our results show a clear correlation between failed jobs and requested resources including memory, CPU, and disk space. Based on our results, we find that many techniques can be applied to increase the reliability and availability of cloud applications, such as developing scheduling algorithms, predicting job failure, limiting task resubmission or changing the priority policies.