Miracle-dz/UniFAR-FTUs huggingface dataset:
5-Star Hotel Website Quality Criteria Analysis

This research is conducted to identify the dimensions and criteria needed in the implementation of 5-star hotels website based on website quality aspect. This study uses a quantitative approach, by distributing questionnaires to respondents who have booked 5-star hotels through the hotel website. The number of respondents obtained in this study is 152 respondents and data processing are done by Entropy method. This study shown six criterias as a determinant of the quality of 5-star hotel website namely contact availability, complete features, uses a registered hotel logo, display map information, provides complete and detailed information regarding the conference room banquet hall and ensures reservation information undertaken by customers is not known to unauthorized parties.
Implementation of FPGA and DSP combined algorithms for modular Single-Phase Matrix Converter with Medium Frequency Transformer for Traction Drive Application

The paper introduces basic characteristics of the traction drive and the way how modern multi-system traction vehicle can be realized. The main aim of the paper is focused on using single phase matrix converter (1ph MC) as an input converter for medium frequency transformer. The control algorithm for switching of 1ph MC is described in detail and its functionality is verified in simulations and on real laboratory prototype composed of single phase matrix converter and a voltage source active rectifier. This paper describes the suitability of using FPGA and DSP combined algorithms for modular single-phase matrix converters with medium-frequency transformer for traction drive applications.
Involuntary Tremor Analysis based on the Accelerometric Measurements

Many neurological disorders are associated with an involuntary, rhythmic oscillation of a certain moving body part called tremor. A measuring system using an accelerometer sensor has been designed and constructed to measure, quantify and analyze these oscillations. The acquired data is displayed real-time and further processed within the developed software application. It allows determining the dominant tremor frequency information, which is one of the main diagnostic parameters.
OpenGL-based Modular Lightweight 3D Framework with Physics Capabilities

Nowadays, several commercial and open source game engines are available for game developers. These are usually using a monolithic approach as the engines are solely meant to be end-to-end tools for game development without considering their benefits in other areas that may need some of the engine’s features but will not be published as a game. This paper presents a novel fully functional game engine with 3D rendering and physics capabilities that can be used as a lightweight toolbox for games and other not necessarily game related projects.
Towards Direct Localization for Visual Teach and Repeat

Vision-based path following allows robots to autonomously repeat manually taught paths. Stereo Visual Teach and Repeat (VT&R) [1] accomplishes robust long-range path following in unstructured outdoor environments. VT&R uses sparse features to match images for visual odometry (VO) and localization. This paper describes our first implementation of direct localization for VT&R. Instead of using sparse visual features for image matching, we minimize a photometric residual cost over the whole image. We compare the performance of feature-based and direct localization using challenging offroad driving datasets. The results show that direct localization consistently achieves more accurate pose estimation under nominal conditions, but further work is required to increase robustness to large lighting change between the teach and repeat images.
An Experimental Analysis of SNR Performance for LoRa Communication

LoRa is a long range, low power, low data rate and energy efficient communication technology, which has been taken into account to have incredible development potential for Internet of Things (IoT) applications. When we deploy the LoRa based communication device in a real application scenario, it is critical to choose the suitable device configuration parameters. In this article, we made the relation analysis between the Signal to Noise Ratio (SNR) and some key device configuration parameters in our experiments. Firstly, the device is configured with multiple sets of configuration parameters to transmit data. Then, in order to select the key configuration parameters we did data preprocessing with some Exploratory Data Analysis (EDA) techniques. Finally, ID3 find the relationship between configuration parameters and SNR to get the LoRa performance model. The results demonstrate that when using the SF with 8, 9 or 10, the best SNR performance can be received in a Line of Sight (L.O.S) distance of 125m.
Higher-Order Sliding Mode Based High-Resistance Fault-Control in PMSM Drives

This paper focuses on the design of an advanced fault-tolerant control (FTC) scheme for accommodating high-resistance (HR) connection faults in three-phase permanent magnet synchronous motor (PMSM) drives. Unlike existing works on HR faults which are limited to only fault diagnosis, this paper proposes FTC scheme to control the fault and provide safe operation of the drive. The proposed FTC scheme employs a higher-order sliding mode (HOSM) current-controller, which cancels the extra dynamics that are generated due to the HR connection fault in the considered PMSM current-model. The cancellation of extra dynamics provides tracking of actual current dynamics (d, q) to the reference currents. Moreover, the detection of HR connection fault in 3-phases of a PMSM will be shown with the help of asymmetry (which occurs due to HR connection) in the currents. Experimental results followed by simulations for various cases of additional phase resistances are presented to demonstrate the effectiveness of proposed FTC scheme.
On the Extension of the TurboMOR-RC Reduction Method to RLC Circuits

Validation of VLSI designs can be substantially hindered by the computational complexity of interconnect parasitics, modeled as RLC circuits. Model order reduction (MOR) addresses this issue, but the existing MOR techniques are challenged by the ever-increasing number of unknowns and ports in the interconnect models. A recently developed MOR method, TurboMOR-RC, can provide significant speedup compared to state of the art techniques, but is currently available only for RC networks. In this work, we show how the method can be generalized to handle inductors.
Combining Subspace Methods and CNN Segmentation for Iris Identification

Biometrics provides a wide range of methods for the reliable identification of individuals. Many biometric features are known, but the most reliable among them is the iris texture. It has several advantages, such as uniqueness, durability, stability, collectability and unforgeability. The iris biometric has undergone significant progress in the last few years. Many state-of-the-art methods and approaches are known. This paper presents an iris segmentation and recognition system. The segmentation part is solved by a retrained version of SegNet CNN. It uses the raw image features or Gabor filter responses as input images and applies subspace methods such as PCA and LDA for dimensionality reduction. The final decision in identification is made by a multi-class one-against-one SVM. The performances measured are compared to the CASIA Internal and UPOL databases. The system foreshadows a fusion identification framework applying several types of biometrics.
Wavelet-based ESS sizing strategy to enable power peak-shaving in PV systems

This work presents an Energy Storage Systems (ESS) sizing strategy, that allows grid connected PV systems to provide peak-shaved maximum-power-Ramp-Rate (RR) compliant power while storing exceeding power. For this purpose power generation and power loss caused by complying several RR restrictions were estimated. The estimation is based on irradiance and temperature real data from a measuring station located in the Atacama desert in Chile. The presented sizing strategy is based on Discrete Wavelet Transform (DWT) and allows to obtain an energy and power ESS sizing matching different criterion. Moreover, the selection of the ad-hoc wavelet and decomposition level, required filter the estimated PV power, is also presented.
Self-Triggered Robust MPC with ISM for Constrained Nonlinear Input-Affine Systems

This paper investigates the self-triggered model predictive control (MPC) with integral sliding mode method (ISM) of networked nonlinear continuous-time system subject to state and input constraints with additive disturbances and uncertainties. In the proposed scheme, the constrained optimization problem is solved aperiodically to generate control signals and the next execution time, leading to possible reductions in both computation and communication. The motivation of using ISM approach is to reject matched uncertainties. First, a self-triggered condition that involves comparing the cost function values with different execution periods is derived. Second, the robust MPC with ISM control strategy is rigorously studied depending on the self-triggered scheme. Finally, a numerical example is used to test the proposed algorithm and to verify our theoretical findings.
Joint Computation Offloading and Wireless Resource Allocation in Mobile Edge Computing

Mobile edge computing (MEC) sinks computing power into distributed base stations, providing cloud computing power at a place that is closer to mobile terminals whose computation capacity and battery life are both constrained. Communication resource allocation is important for improving the system utility in mobile edge computing scenarios. In existing studies, users in the MEC system usually suffer from different levels of interference, which results in the low system utility and unfair user offloading probability. To address this challenge, in this paper we formulate the computation offloading decision and physical resource block (PRB) allocation as optimization problems. We formulate the problem as a pricing Game model, interference management is also considered. Simulation results are presented to show the effectiveness of the proposed scheme with different system parameters.
Research on Parallel Encryption Technology in the Emergency Platform of High Speed Railway in China

Data security in cloud computing is a very challenging task, searchable encryption techniques are used to search on encrypted data without the need for decryption. It's the measure which can satisfy the demanding of security of railway data stored in the cloud. In this paper, the characteristics of high-speed railway data in the emergency platform is analyzed, so a data set of high speed railway emergency platform is built according to parts of scene of accident and classification method. Thus, by searching with parallel searchable encryption schemas, emergence plans for the accident characteristics can be retrieved, which can testify the algorithm's feasibility. CP-ABE is used to realize access control for search result in order to eliminate files without right of users. This method can guarantee security of railway data as well as raise the search efficiency, and it provides a new explication for dealing with high speed railway emergency information.
Inferring Land Use Type in Urban Area with Mobile Big Data

Research on the detection of land use has been a challenge for urban sensing and is of great significance to smart city construction. With the popularity of smart phones and the unprecedented growth of mobile phone data, there emerge numerous studies on land use detection with CDR (Call Detail Records), SMS (Short Messaging Service) and mobile traffic data. This is also a combination of urban sensing based on mobile big data and smart city construction. In this paper, we propose a methodology based on existing studies on land use detection with CDR, text data, and mobile traffic data. We propose a new traffic signature that combines the advantages of the above studies and apply it to a real-world dataset collected in northeast of China. We choose the POI (point of interest) data as ground truth data that the obtained land use types are compared with. We find that our method can accurately detect the POI collections of specific types, which proves the effectiveness of our method. After extracting base components of land use types, an NMF (non-negative matrix factorization) based method is applied to explore the mix of land use types in urban areas. Then, we provide reasonable explanations for different areas with mixed land use types. The result of proposed method is evaluated from both components perspective and urban area perspective.
Improving Photoelectric Energy Conversion by structuring Si Surfaces with Ge Quantum Dots

Structuring Si surfaces with Ge quantum dots can have a beneficial effect on the photovoltage (PV), increasing the PV magnitude and decay time. The origin of this behavior is intimately related to mechanical strains developed in the vicinity of the Ge/Si interface. The structuring is a promising means of avoiding the uncontrolled production of recombination centers and offering significant enhancement of the recombination lifetime due to carrier trapping in the dots. This work can open new opportunities for advanced photoelectric devices with recombination center free nanostructures.
Sentiment-Polarized Word Embedding for Multi-label Sentiment Classification

Sentiment analysis of text is an import branch of natural language process. In this paper, we propose a sentiment-polarized word embedding model (SPWE) with emotional dictionary, which is a variant of the C&W. Our model is able to represent and differentiate the emotional semantic of words, which is critical in sentiment classification tasks. This weak supervised model can perform large scale corpus training just with an open source emotional dictionary. In addition, we optimize the objective function from our previous hierarchical model and propose a Relax Margin loss function. The result shows it can reduce overfitting and improve generalization ability by restricting approximation degree to probability distribution of the multi-labels. We release our manually labeled comment dataset with multi tags from our previous work, and our code is available in https://github.com/KillersDeath/SPWE.
Truth Discovery on Categorical Multi-Valued Attributes with Source Relations

In the era of big data, a mass of data fusion applications need to obtain accurate and complete attribute information about various objects. Most existing truth discovery methods are for single-valued attributes, which will be invalid for multi-valued attributes. They are usually based on the assumption that the data sources are independent of each other. It is not in line with the reality that related relationship occurs between them. Since multi-valued attributes are at large discrete, this paper proposes a truth discovery method incorporating the qulity and the correlations of data sources, namely Categorical Multi-Truths Finder algorithm (CMTF), which iteratively derives the accuracy and the recall rate of data source along with the truth set for each attribute. The experimental results show that CMTF outperforms the state-of-the-art.
Comparative Analysis of Online E-Learning and Face To Face Learning: An Experimental Study

Online e-learning (online learning) has developed rapidly in the world. Few said that they were more comfortable to learn via online e-learning. Online e-learning fully support multimedia presentation and give effective interactions. Moreover, online e-learning not only gives students' competencies, but also creates self and collaborative learning processes. In the meantime, some people argued that the face to face learning will be heritage, but others denied it. However, online e-learning should have a wise management, good communication network, and online instructional product. In fact, the face to face learning establishes a meaningful and real interaction among students and lecturers, where it actually could not be substituted by online e-learning. Therefore, there were some people that supported the online e-learning, and another persisted on the face to face learning. This study gives answer that online e-learning can truly replace the face to face learning in the cognitive area. This study result shows that there was no different student skill obtained between the online e-learning and the face to face in algorithm and programming instructional at STMIK Bumigora tertiary institution. In other words, online learning can substitute the face to face learning in cognitive area
Cost-Benefit Analysis of Using Battery Storage for Peak Shaving of Substation Transformer

This paper presents a cost-benefit analysis of using battery storage to provide energy during the peak period of a substation transformer, rated 300-MVA, 230/121-22 kV. The battery sizing is optimized by varying the target power of peak shaving. Although the investment cost of battery is very high, but the benefits from price arbitrage and loss reduction are financially attractive. Thus, using battery storage for peak shaving is viable, but may not be profitable. The breakeven point is then determined to justify the investment.
Enabling Ubiquitous Hardware Security via Energy-Efficient Primitives and Systems : (Invited Paper)

Security down to hardware (HW) has become a fundamental requirement in highly-connected and ubiquitously deployed systems, as a result of the recent discovery of a wide range of vulnerabilities in commercial devices, as well as the affordability of several attacks that were traditionally considered unlikely. HW security is now a fundamental requirement in view of the massive attack surface that they expose, and the substantial power penalty entailed by solutions at higher levels of abstraction.In large-scale networks of connected devices, attacks need to be counteracted at low cost down to individual nodes, which need to be identified or authenticated securely, and protect confidentiality and integrity of the data that is sensed, stored, processed and wirelessly exchanged. In many security-sensitive applications, physical attacks against individual chips need to be counteracted to truly enable an end-to-end chain of trust from nodes to cloud and actuation (i.e., always-on security). These requirements have motivated the on-going global research and development effort to assure hardware security at low cost and power penalty down to low-end devices (i.e., ubiquitous security). This paper provides a fresh overview of the fundamentals, the design requirements and the state of the art in primitives for HW security. Challenges and future directions are discussed using recent silicon demonstrations as case studies.
Distributed adaptive anti-windup consensus tracking of networked systems with switching topologies

In reality, the input saturation is quite common when implementing a controller, which may cause failure of the control tasks. This paper investigates the distributed adaptive anti-windup protocol design for consensus tracking of second-order networked systems. It is assumed that only relative output information can be used, and meanwhile the communication topologies are switching. By modifying the edge-based distributed adaptive output protocol applicable to the case without input saturation, a novel distributed adaptive anti-windup protocol is proposed. The main feature of the protocol is that an extra internal state is introduced as the compensator, playing a fundamental role in tackling input saturation. Simulation results are also given to show the effectiveness of the proposed protocol.
A CNN-Based Broad Learning System

Deep learning is a very active research field in machine learning. The most popular used convolutional neural networks (CNN) is one of deep learning models. However. , deep learning suffers from a time consuming training process because of a large number of parameters in filters and layers to be learned. Broad Learning System (BLS) offers an alternative way of machine learning in deep structure. BLS is established based on the idea of the random vector function-link neural network (RVFLNN) which eliminates the drawback of long training process and also provides the generalization capability in function approximation. In this paper, a CNN-based broad learning system (CNNBLS) which is more useful than BLS for computer vision is proposed. In the proposed method, convolution and max pooling are used for feature extraction and then the principal component analysis (PCA) is used for feature dimensionality reduction, following a ridge regression learning for pattern classification. Parameters in convolution filters are randomly initialized which is similar with BLS and RVFLNN. Therefore the proposed method needs less parameters to be optimized at the same time it adopts the very useful feature extraction method for computer vision. Experimental results on the MNIST handwritten digits recognition data set and the NYU NORB object recognition data set demonstrate the effectiveness of the proposed method.
Autonomous Tracking and Landing of QUAV Based on Air-Ground Cooperation

Quadrotor Unmanned Aircraft Vehicle (QUAV) and ground mobile platform work together to accomplish tasks, which have significant application value in military and civilian fields. In this paper, in the process of autonomous tracking and landing of the QUAV on the ground mobile platform, the method of "air-ground cooperation" is proposed aiming at the loss of target caused by sudden changes of the ground moving target position and the delay caused by information processing of the QUAV. The movement of the platform is estimated through the inertial sensors on the ground platform, and it is fused with the visual servo information of the QUAV to obtain an advance compensational control quantity. According to the motion state of the QUAV and the platform, the control strategies of horizontal position tracking, vertical landing and the adaptive dynamic adjustment of the control parameters are designed. The simulation results show that the proposed method improves the real-time performance and stability compared with the traditional visual servo-based method, and ensures the effective tracking and stable landing of the QUAV.
A Sensitivity Analysis Method of Human Factors Measurement Tools in Pilot Distraction

In the process of the pilot executing the flight task, the distraction will cause a decrease in the pilot's situation awareness. Seriously, it may cause an aviation accident. In the paper, based on experiments and statistics, the sensitivity of human factors measurement tools (human performance, questionnaires, and eye movement data) in the pilot distraction is analyzed. It can provide a basis for pilot distraction modeling. Specifically, in the experimental phase, participants are required to complete the flight task with three interference intensities (low, medium, high). In the data collection phase, the data of the human performance, questionnaires, and eye movement has been collected. In the statistical analysis phase, based on statistical methods such as ANOVA (one -way analysis of variance) and post hoc analysis, the sensitivity of the human performance, questionnaires and eye movement data at different interference intensities are explored. Finally, the experimental results shows that human performance, questionnaires, pupil area, PERCLOS (the percentage of eye closure), and fixation duration were sensitive, which are available for the analysis and modeling of pilot distraction.