Miracle-dz/UniFAR-FTUs huggingface dataset:
Constraining the GLAO parameter space with turbulence profile models

Nature provides reduced turbulence at intermediate altitudes and as a result the mean image quality of a ground layer adaptive optics (GLAO) corrected field degrades slowly with increasing diameter. If this function has a shallow slope all the way out to the maximum seeing limited field of view of the telescope then surveys at that telescope may significantly benefit from GLAO. Using published optical turbulence profile models for Cerro Pachon we show that the GLAO gains require that the full seeing limited field of view be corrected for two example telescopes, each attempting two example science cases. We also show that using only four sodium laser guide stars the very wide GLAO system with optimal actuator and subaperture pitch will not be affected by servo lag and wavefront sensor noise.
“I cannot do anything”: User’s Behavior and Protection Strategy upon Losing, or Identifying Unauthorized Access to Online Account

A wide-range of personal and sensitive information are stored in user’s online accounts. Losing access, or an unauthorized access to one of those accounts could put them into the risks of privacy breach, cause ﬁnancial loss, and compromise their accessibility to important information and documents. A large body of prior work focused on developing new schemes and strategies to protect user’s online security. However, there is a dearth in existing literature to understand users’ strategies and contingency plans to protect their online accounts once they lose access, or identify an unauthorized access to one of their accounts. We addressed this gap in our work, where we conducted semi-structured interview with 22 participants. Our ﬁndings reveal the unawareness, misconceptions, and privacy and accessibility concerns of users, which refrain them from taking security-preserving steps to protect their online accounts. We also identiﬁed users’ prevention strategies that could put their online security into further risks.
Planning Method of PON Based on Traffic Analysis

The upgrading of PON network is an important part to meet the needs and development of user traffic in the future. In this paper, the network data collection style is specified by telemetry, and the partial least squares method is used to predict the future traffic trend, so as to formulate the PON network cutover strategy. _Finally the gap between the peak traffic of the OLT in different time periods is narrowed, and the load balance is achieved.
Distributed Iterative Localization for Wireless Sensor Networks Subject to DoS Attacks

This paper investigates the issue of localization for wireless sensor networks resistant to denial-of-service (DoS) attacks with the assumption that each attack consists of an active period and a dormant period due to limited power. On the basis of barycentric coordinates involving relative distance measurements, a hold-on strategy based distributed localization (HS-DILOC) algorithm is proposed. Explicitly when the communication channel of a sensor is perpetrated by DoS attacks, HS-DILOC allows the sensor to update its coordinates utilizing the previous packets collected from its neighbors during the last dormant period. In addition, this paper theoretically shows that the proposed algorithm is capable of converging to the accurate locations of sensors disregarding the attack strategy. Finally, the experiments on Raspberry Pis are used to illustrate the validity of the proposed algorithm.
Electroencephalogram and Electrocardiogram in Human-Computer Interaction

Electroencephalogram (EEG) and Electrocardiogram (ECG) have been widely used in clinical diagnosis and have shown their potential in Human-Computer Interaction (HCI). EEG and ECG contain signals that can directly reveal people's activity neurologically and decode and transfer for further physical monitoring and external control. This paper firstly summarizes heavily used methods of EEG signal process in HCI, which also applies to the ECG process. Then, we reviewed typical applications for EEG in HCI, including the TTD system, P300, and Graz for brain-computer interface and emotion recognition. We conclude ECG classification and acquisition methods and ECG application in HCI, including biometric identification, game input, and medical nursing. Finally, integrating EEG and ECG, there are HCI applications like accurate emotion recognition, physiological monitoring, disease diagnosis, and portable wearable device. In addition, we present the HCI application for Electromyogram (EMG) in gesture, handwriting recognition, and Electrooculogram (EOG) in password security, cursor system, and eye-writing.
Optical fibers of arbitrary cross sections

We investigate the influence of cross-sectional shape on propagation in optical fibers. Remarkably simple, analytical expressions are derived for modes on multimoded fibers, including the fraction of power in the cladding, the group velocity, and the modal birefringence. The fibers have arbitrary cross-sectional shape and arbitrary refractive-index difference. New analytical results are given for the rectangular, elliptical, triangular, and wedge-shaped cross sections.
Impacts of high Solar Inverter Integration on Performance of FLIRS Function: Case Study for Danang Distribution Network

The increasing integration of distributed energy resources (DERs), especially renewable sources such as photo-voltaic (PV) systems, into distribution networks has presented several challenges to the fault location, isolation, and restoration service (FLIRS) integrated in their distribution automation system (DAS). While majority of studies reported paid attention on the issues related to knowledge- or impedance-based fault location, others only adopted simplified PV models which cannot ensure accurate presentation of actual PV fault behaviors. This paper, therefore, aims at conducting an extensive investigation on impacts of PV increased integration on the performance of the existing FLIRS. Since Danang Power Company has recently put in operation a commercial advanced FLIRS function integrated with its DAS system, considering the extremely rapid and high integration of PV systems into its medium-voltage (MV) distribution network, one of its typical highly PV-penetrated MV feeders is adopted for the study. All the network components relevant to the study are simulated with high details in the software environment PowerFactory/DIgSILENT. A total of 2700 fault scenarios is performed for the investigation purpose.
A Computed Torque Controller for Robotic Manipulators Using Nonlinear Neural Network

In robotic control engineering, the conventional computed-torque control algorithm is a simple method to control robots achieving the desired quality by using model parameters including internal dynamics and external disturbances to establish the control law. Practical applicability of this method is normally low since it is difficult to accurately determine such the parameters. In this paper, we propose an intelligent computed-torque control approach for tracking control problems of robotic systems. A neural network structure is first employed for online estimation of the system dynamics in which the learning process is stimulated by a nonlinear mapping function of the control error. From there, the computed-torque control signal is then synthesized using the estimation result and a proportional-derivative control term to result in expected control performance. Stability of the closed-loop system is maintained by Lyapunov analyses. Effectiveness of the proposed control method is extensively verified through intensive simulation results.
Structure Impact on Phase Transition in Complex Dynamical Networks

The consistency problem of large-scale complex clusters is closely related to the phase transition problem of complex networks. The structure of complex networks mainly affects the phase transition of complex networks. In this paper, a concise model of self-driven motion is adopted. Firstly, through simulation analysis, it is verified that density and noise are two main factors affecting the phase change consistency of complex network. Secondly, the influence of average degree of complex networks on phase transition consistency of complex networks is further analyzed. Finally, based on the research on the average degree of complex networks, the influence of the proportion of pinned nodes on the phase transition consistency of complex networks is explored in depth. At the same time, the effect of the average degree of complex networks and the proportion of pinned nodes on the phase transition consistency of complex networks is analyzed.
LSTM Based Deep Learning Model for Blood Sugar Prediction

Diabetes has become one of the most prominent health problems in the modern era. Neural networks aid in better medical diagnosis considering dynamic nature of learning model. LSTM model is a form of artificial recurrent neural network which is widely used in deep learning, specifically in sequence prediction data elements. Main benefit of opting for LSTMs model in this research is that it provided significant aid in sequence classification using raw time series data for data transformation and classification of blood sugar level. Results showed that blood sugar level can be predicted by using the LSTM model with an error margin of approximately ±39. Accuracy of the model can be improved by inclusion of additional parameters in the model to minimize the variation.
Melanoma Lesion Recognition using Deep Convolutional Neural Network and Global Average Pooling

Melanoma is one of the worst types of skin cancer. It appears on the skin's surface and is caused by melanocytes. The same cells are also responsible for benign lesions known as moles, which resemble melanoma in its early stages. Melanoma is frequently curable if properly treated. Much research is now focused on the automated detection of melanomas. In this paper, we present an automated melanoma identification system based on convolutional neural networks and global average pooling. When compared to other approaches on a large publicly available dataset, the experimental evaluation on the same dataset reveals that our suggested technique has high classification accuracy (98.26%), sensitivity (98.91%), and specificity (97.66%). Thus, the suggested framework may be used to help dermatologists diagnose melanoma.
UAV-based System Energy Efficiency Optimization for Air Pollutant Monitoring

Monitoring air pollution has recently taken on the status of being of utmost importance. Nevertheless, it can be difficult to gather information on air quality close to pollution sources, particularly when the locations are complicated, have physical obstacles, or are in motion. However, small unmanned aerial vehicles provide an innovative and palatable new way of studying air pollution and the atmosphere. In this context, this article suggests using drones with sensors to carry out duties involving air pollution monitoring. In particular, a search approach based on a chemotaxis-metaheuristic and a local particle swarm optimization approach is recommended to build an optimal approach that improves UAV cooperative search by reducing overall search time and optimizing energy consumption. Two phases make up the complete approach. The favored objective in the initial phase is plume detection, where each drone operating in LoPSO mode selects its course based on the planar coordinates produced by this mode. The second stage entails instructing the drones to follow spiral trajectories in order to trace the feather that has been discovered. The experimental findings demonstrate that by concentrating on regions with the largest concentrations of contaminants, an implicit priority directs the creation of pollution maps. According to simulation data, the recommended method performs better and shows promising outcomes in terms of complexity and accuracy. Specifically, it can more precisely locate the locations that are the most contaminated and provide greater coverage within the time constraints imposed by the drone's flying period.
Towards Oscillation Suppression of a Manipulator with Physical Constraints: a Trajectory Planning Approach

Oscillation problem is one of the most critical factors that intensify the mechanical wear and reduce the accuracy of a manipulator significantly. Oscillation suppression of a manipulator and achieving the high accuracy and high productivity simultaneously in path tracking is a challenging issue due to the existence of physical constraints. In view of this, a trajectory planning based modular framework, which generates an optimized trajectory for a manipulator, is developed to reduce the oscillation of a manipulator on the occasion of fast position control. The original joint space waypoints obtained via inverse kinematics are first interpolated to obtain a path-velocity decomposition form expression. Subsequently, the assigned joint physical constraints are converted to the corresponding bounds in the parametric space via a bounds estimator in real time. Then a nonlinear filter is adopted to optimize the parameter trajectory. Finally, the planned parameter trajectory is substituted into the geometric path function to obtain the planned trajectory for each joint. Practical experimental results are provided to validate the superior motion tracking performance of the proposed approach.
Patient Monitoring and Disease Analysis Based on IoT Wearable Sensors and Cloud Computing

The number of patients to be treated in healthcare facilities is increasing over time due to the growing awareness and importance of formal healthcare. Most healthcare centers lacked modern automation systems, such as continuous patient monitoring, which of schedule the doctor or nurse's visits with the patient. This research is designed to implement a new method of patient monitoring system in a treatment room, using wearable sensors enabled by the Internet of Things (IoT) technology and patient data analysis in cloud computing. The proposed system consists of several sensors to retrieve patient information, such as body temperature, heart rate, blood pressure, Electrocardiogram (ECG), and motion sensor. Those parameters are used to analyze patient disease and healthcare during treatment with real-time monitoring to ensure medical professionals obtain the latest update on patient health. The system is designed in an embedded module that is applicable for mobile phones and connected through a Wireless Fidelity (Wi- Fi) system in healthcare facilities. All the patient data retrieved by IoT sensors is delivered to cloud computing to store the data and then analyzed using Long Short-Term Memory (LSTM) Algorithm to examine data related to the patient health and illness. Results show the performance of the IoT sensing system working well and are able to detect and send the data in real-time to healthcare centers globally through a mobile device. Based on real case scenario testing performance, the system accuracy ability to send data is more than 95% while any abnormality is readily detected. Overall, the system has enormous potential for further development and widespread use in the healthcare industry for efficient operations.
Development of a Visible Light Communication (VLC) System with Noise Suppression and Differentiation between Combined Sequences

The world moves towards innovation; internet and mobile users are rising tremendously, and there is a desire for high-speed and uninterrupted internet access. Because of its high speed, improved bandwidth, and security, everyone is now interested in a new emergent wireless communication technology called Visible Light Communication (VLC). A VLC system with and without noise has been developed and modelled using an optical source of 450 nm LED wavelength and photodiode as a receiver. For noise, white light source is used that has an impact on the performance and quality of the VLC system. At the receiver side, Trapezoidal Optical Filter is employed before the photo detector to reduce ambient light noise, enhance the Q-factor, Bit Error Rate and provides a clear eye diagram. This paper also discusses the effect of Bit Rate with LED Bandwidth and Q-factor. Optisystem-7 software is used to simulate the circuits. In this work, the performance is assessed using Bit Error Rate and Q-factor values, as well as an eye diagram for improved communication and the use of a rectangular optical filter and polarizer to separate the sequences at the receiver side when they are sharing the same channel at the same time.
Effect of Various Surface Treatments on Lap Shear Strength of Aluminum Adhesive Joints

In the research conducted, the impact of various surface treatments on the lap shear strength of single lap joints (SLJ) was investigated experimentally. A parametric methodology was followed to select the best possible treatment in terms of highest joint strength output parameter. Nine surface treatments ranging from mechanical, chemical (etching), and electrochemical (anodizing) nature were performed. Aerospace grade aluminum alloy 6061-T6 of 3 mm thickness was used as adherends. High-strength structural adhesive Huntsman Araldite 2011 was utilized in the fabrication of the lap joints. The joints were manufactured according to the dimensions as per ASTM D1002 standard with a sample size of 4 per configuration. High temperature curing was performed for consistency of curing parameters across the samples. Testing of the samples was performed using Universal Testing Machine (UTM) with a 100 kN load cell. Subsequently, a comparison of results was made.
Sidelobe level and nulls control of defected linear antenna arrays using an optimization algorithm

In the presented work, patterns are recovered (in terms of SLL, nulls positions and nulls depth) using PSO (particle swam optimization) algorithm on different size of linear arrays with random elements failure. 8 and 32 elements linear arrays are used with equidistance inter-element spacing of λ/2 at 10 GHz center frequency in X-band. The investigation is done only for broadside radiation patterns, and amplitude-only excitations are used for symmetrical nulls, while in asymmetric nulls, complex weights are required. The computed amplitudes/complex weights for sidelobe level (SLL) and nulls control with random elements failure are obtained using PSO through MATLAB coding. These PSO computed amplitudes/phases are then applied on designed microstrip patch antenna array at 10 GHz in CST Studio 2019. It is considered that the defected elements are not participating in the far field radiation, hence their excitation amplitudes/phases are set to zero in the CST setup. This work has three investigation cases, case 1: recovering SLL=-25 dB of defected arrays, case 2: recovering SLL=-25 dB with double symmetrical nulls at ±30° having null depth of -60 dB of defected arrays, and case 3: recovering SLL=-25 dB with double asymmetrical nulls at –30°, +60° dB having null depth of of -60 dB of defected arrays. Each case is performed on two different sizes of array: 1×8 and 1×32 linear arrays. In these three cases, 25% random elements are made defected and then the patterns are recovered using PSO. The original patterns (all elements are excited and contributing to radiation pattern), defected patterns (patterns with random 25% elements failure) and corrected patterns (patterns recovered using PSO) are compared and analyzed in MATLAB and CST simulators.
Model of automated processing of bank digital statements

General globalization is a global trend caused primarily by global technological advances with the goal of connecting as many world populations as possible. The national economy is increasingly becoming part of a single global space thus information models are gaining increasing influence on economic globalization. The banking sector has a special interest in globalization, and the positive consequences are raising the level of quality of financial services, modernization, and global access to the financial market. The bank statement is a report by which the bank informs in realized financial transactions and changes in the account balance (BS). This paper describes an automated model for downloading, recording and processing the digitized content of electronic statements that a commercial bank creates for a client. The paper presents the improvement of execution, and also provides guidelines for further development
FCMD-Net: A Full-Connection Multi-Decoder Network for Polyp Segmentation

Colorectal cancer is ranked 4th in the list of leading causes of death after lung, stomach, and liver cancers. Most colorectal cancers arise from growths in the mucosa of the colon called polyps. Therefore, early detection of polyps can help to faster monitor and treat for both patients and doctors. This requires an automatic, accurate, efficient, and reliable polyp segmentation system. Currently, U-shape architectures have shown promising performance with developments and advancements in encoder and decoder designs as well as skip connection solutions. However, existing skip connection approaches face some problems such as plenty of redundant information, or lack of generality causing to weakening of feature maps. To handle this challenge, we propose a new architecture namely Full-Connection Multi-Decoder Network (FCMD-Net) for the polyp segmentation task. First, we design a new model, using an encoder and three decoder branches to take advantage of multi-path information to increase inference speed and produce a better prediction mask. Besides, we create a skip connection method called full-connection to reduce effectively the semantic gap between two components encoder and decoder. In addition, we inherit and flexibly apply Atrous Spatial Pyramidal Pooling blocks to increase the performance of the model. The results on the Kvaisir-Seg dataset and CVC-ClinicDB dataset demonstrate that FCMD-Net has advantages over state of the art methods in the benchmark.
Pressure matching with optimized target phase for personal sound zone systems

Pressure matching (PM) is an advanced digital signal processing technique aimed at designing loudspeaker filters to achieve a target acoustic field in a desired sound region. This enables to establish personal sound zones (PSZs) in a desired environment. The target sound field is chosen during the design stage and influences the performance of the system in terms of acoustic contrast (AC) between bright and dark zones, as well as fidelity of the reproduced audio. The sound regions are represented by groups of control points (microphones) properly placed in the considered environment. With more than one control point in the bright region, the same target sound field is usually considered for all the microphones. This paper investigates the optimization of the phase of the target acoustic field required by the PM algorithm in terms of AC. The achievable performance is analyzed in a realistic automotive environment with two sound zones and two control points per zone. The numerical results show a potential improvement of the achievable AC at the cost of some spatial effects and possibly non constant group delay in the reproduced sound, which should nonetheless be tolerable in voice applications.