Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentiment Analysis of Yelp Review

Digital data has developed very fast in the current era. Digital data has various forms, one of which is text data. There are a lot of text data source from many ways, such as review text data. Yelp is a local business directory and forum to review products, services, or places. We used Yelp’s review data to determine user’s sentiment or opinion about products, services, or places. Sentiment or opinion are classified into positive reviews, or negative reviews. The algorithms that used are Gaussian Naïve Bayes, Gaussian Naïve Bayes with AdaBoost, and K-NN. In this research review text data will be through a preprocessing and feature extraction stage. We used n-gram to extract the feature from the data with unigram, bigram and uni+bi-gram for indexing text. The result of this research that algorithms that has the highest accuracy rate was Gaussian Naïve Bayes with combined unigram and bigram with 86.7% from 5 fold cross-validation.
Energy-Efficient Channel Switching in Cognitive Radio Networks: A Reinforcement Learning Approach

In this paper, we investigate energy-efficient channel switching for secondary users (SUs) in cognitive radio networks. Unlike existing schemes where SUs adopt the same channel switching strategies regardless of which channel they currently stay at, our scheme allows SUs to adapt their channel switching strategies to the primary users’ (PUs’) behaviors on the current channels and apply different channel switching strategies on different channels. Considering the unknown PUs’ behaviors, we formulate a reinforcement learning problem which allows SUs to learn channel switching schemes by interacting with the environment. Through simulations, we demonstrate the effectiveness of the learned channel switching scheme.
Low-Loss Dual-Polarized Frequency-Selective Rasorber With Graphene-Based Planar Resistor

A novel dual-polarized frequency-selective rasorber (FSR) using an omnidirectional planar resistor based on graphene flake is presented in this article. The FSR exhibits a low-loss transmission window above an absorption band. It consists of a lossy layer and a lossless bandpass frequency-selective surface (FSS). In the lossy layer, a central symmetric graphene-metal hybrid structure is formed by connecting four metal strip-based resonant circuits with a square graphene flake. A low insertion loss transmission band is obtained by removing the resistance out of the parallel resonant circuits. Moreover, equivalent circuits are utilized to demonstrate the working mechanism. The proposed FSR exhibits a good absorption performance from 8.15 to 13.8 GHz, and a low insertion loss of 0.15 dB obtained at 16 GHz. Finally, a prototype FSR is fabricated and good agreement between the simulated and the measured results is obtained. The FSR has further advantages of avoiding the process of welding multiple lumped resistors per unit, thereby greatly reducing the number of lumped components and improving the planar integration of the structure.
Kinematic Model of the Human Leg Using DH Parameters

Robotic-assisted surgical procedures have recently increased in popularity in clinical environments. Applications of clinically approved surgical robots range from minimally invasive surgery to open joint replacements. In hip and knee orthopaedic procedures, access to leg joint cavities require constant manipulation of the patient’s leg to a high degree of accuracy to reduce surgical injuries. This study develops a nine degree of freedom serial kinematic model of the human leg, using the well known Denavit Hartenberg Parameters, for robotic-assisted leg manipulation during orthopaedic leg surgery. The proposed model is validated through human cadaver experiments with an optical tracking system used as ground-truth to measure the leg pose. The knee and foot workspace for the model determines the pose of the leg and in comparing it to cadaver leg position. The positional error relative to the cadaver leg was found to be 0.43mm and 0.4mm respectively, with a maximum uncertainty of 3.51mm in the foot position. It demonstrates that the proposed model provides an accurate representation of the human leg motion for automated leg manipulation during orthopaedic surgery.
A High-Quality VR Calibration and Real-Time Stitching Framework Using Preprocessed Features

Virtual Reality (VR) contents include $360^{\circ }\times 180^{\circ }$ seamless panoramic videos, stitched from multiple overlapping video streams. Many commercial VR devices use a two-camera rig to capture VR content. These devices have increased radial distortion effects along the seams of the stitching boundary. Moreover, a fixed number of cameras in the camera rig makes the VR system non-scalable. Since the VR experience is directly related to the quality of VR content, it is desirable to create a VR framework that is scalable in terms of the number of cameras attached to the camera-rig and has better geometric and photometric quality. In this paper, we propose an end-to-end VR system for stitching full spherical content. The VR system is composed of camera rig calibration and stitching modules. The calibration module performs a geometric alignment of the camera rig. The stitching module transforms texture from the camera or video stream into the VR stream using lookup tables (LUTs) and blend masks (BMs). In this work, our main contribution is the improvement of stitching quality. First, we propose a feature preprocessing method that filters out inconsistent, error-prone features. Secondly, we propose a geometric alignment method that outperforms state-of-the-art VR stitching solutions. We tested our system on diverse image sets and obtained state-of-the-art geometric alignment. Moreover, we achieved real-time stitching of camera and video streams up to 120 fps at 4K resolution. After stitching, we encode VR content for IP multicasting.
Classification of Subject-Independent Motor Imagery EEG Based on Relevance Vector Machine

Brain-computer interface (BCI) is a novel technology that enables human to communicate directly with the outside world through brain consciousness. At present, there are problems such as complicated and time-consuming data collection (calibration process) and lack of sample set in the traditional BCI. Compared with the subject-dependent (SD) BCI, the subject independent (SI) BCI technology can shorten or not go through the calibration process and directly use the general model. It shortens the calibration process and meets the actual user’s requirements for the use of the BCI. In this paper, we design a method for classifying subject-independent motor imagery EEG signals based on relevance vector machine (RVM). By training the existing data of multiple subjects to obtain a general model, new subject can use the BCI system without the calibration process. This method uses the common spatial pattern (CSP) combined with the relevance vector machine classification algorithm, and uses a public data set (BCI competition IV-II-a) to verify its effectiveness. The offline experimental results show that for the classification of SI motor imagery EEG, the average classification accuracy of this method can reach 71.95%, and the highest accuracy can reach 96.14%, which proves that this method can effectively classify SI motor imagery EEG signals, and can achieve satisfactory results.
Wireless Beam Modulation: An Energy- and Spectrum-Efficient Communication Technology for Future Massive IoT Systems

The potential of a system combining millimeter- wave (mmWave) communication and multiple- input multiple-output (MIMO) technology has motivated an extensive effort in both the research community and industry. With the much higher spectrum band, mmWave is considered to be a promising technology to solve the congestion problem in the sub-6 GHz band for future massive Internet of Things (IoT) systems. However, mmWave is still a long way from being a practical implementation for an IoT terminal due to high hardware cost and energy consumption. In this article, we introduce a new mmWave transmission technology called wireless beam modulation (WBM). The distinguishing feature of WBM is that bit information is transmitted through the propagation attenuation differences of signal beams instead of being carried by the original signal beam. While maintaining the high data transmission capability of mmWave frequencies, this change brings the advantage of high energy efficiency and low-cost mmWave hardware implementation to the IoT node. It can be deployed without shifter array and complex signal processing, such as precoding or even channel estimation. The basic idea of WBM is based on the recently proposed over-the-air modulation (OTAM) method but with several significant improvements. By formulating multiple beams at the access point (AP) node, WBM enables multiple- user access with spatial-division multiplexing, which significantly improves the spectrum efficiency compared to OTAM. Moreover, by aligning multiple beams between the IoT nodes and the central AP, WBM provides robust transmission and allows higher-order modulation. Hence, the proposed WBM achieves a good balance between high data transmission and spectrum efficiency with low hardware cost, which is promising for upcoming mmWave massive IoT systems.
Resource Allocation for MC MISO-NOMA SWIPT-Enabled HetNets With Non-Linear Energy Harvesting

This article proposes a resource allocation for multi carrier (MC) multiple input single output (MISO) non-orthogonal multiple access (NOMA) enabling simultaneous wireless information and power transfer (SWIPT) where a separated architecture (SA)-based heterogeneous network (HetNet) is considered. More precisely, we focus on designing MC MISO-NOMA scheme in HetNets, where each base stations (BSs) serves multiple information decoding (ID) and energy harvesting (EH) users with a non-linear energy harvesting model which are equipped with multi antennas. For this configuration, we propose a joint subcarrier assignment, beamforming design, and energy beamforming to maximize the total rate of the network. Moreover, we take into account the condition of cross-layer interference constraints to coordinate interference, satisfying a minimum data rate requirement, fulfilling a minimum harvested energy, and respecting the maximum allowed power transfer constraints. We also consider successive interference cancellation (SIC) decoding order to detect the signal of each user due to coupling between SIC order, beamforming design, and subcarrier allocation. As a result of interference in the rate function, our optimization problem is non-convex and thus challenging to solve. Subsequently, first we adopt semi-definite relaxation (SDR) for beamforming design and then an efficient algorithm via the majorization minimization (MM) approach based on the difference of convex (DC) programming is proposed to obtain a locally optimal solution for the original problem. Simulation results reveal the superiority of our proposed scheme as compared to other existing works addressed in the literature.
A High Step-Up Switched-Capacitor 13-Level Inverter With Reduced Number of Switches

This letter presents a new single-phase 13-level switched-capacitor inverter with a reduced number of switches and a voltage boost gain of 6. The proposed topology with a single dc source can achieve 13-level output, sextuple boost, and self-voltage balance of capacitors using only 14 switches. The proposed inverter does not use a back-end H-bridge with four switches that must withstand the peak load voltage, and the voltage stress of all switches does not exceed half of the peak load voltage. The operation principle including the self-voltage balance of capacitors is described in detail. An analysis of the proposed inverter in comparison to recent topologies with a single-input dc source is performed mainly in terms of the number of switches and cost function. Simulation and experimental results verify the feasibility of the proposed topology.
Anomaly detection in ECG using wavelet transformation

The paper considers the wavelet transform in the analysis of electrocardiograms to detect the anomaly. Two types of anomalies have been considered: cardiac, and congestive heart failure. To solutions to this problem have been proposed. The first one applies the discrete wavelet transformation. Based on its results the diagnostic features are defined by applying a special set of statistical parameters of the transformed ECG signals. After the selection process, they are delivered as the input attributes to the set of different classifiers. In the second approach, the continuous wavelet transformation of ECG signals is applied, and the resulting two-dimensional image created in time-frequency space represents the input to the convolutional neural network. This network is responsible for the generation of features, and final classification at the same time. All calculations made in Python were performed on the publically available database Complex Physiologic Signals PhysioNet. The results of experiments have shown better quality measures (accuracy, sensitivity, and precision) when applying the discrete wavelet transform.
LRSD-BGP : A Software-Defined Routing Framework for Inter-Domain Fast Initial Link Setup

In traditional IP networks, border gateway protocol (BGP) serves as the dominant protocol for inter-domain routing information exchange. In a hybrid network with both traditional network domains and software-defined networking (SDN) domains, the simultaneous inter-domain routing information exchange requires collaboration among different autonomous systems (ASes). However, the rigid routing policies of the conventional BGP are sometimes inadequate to meet the demand of applications with low-latency requirements like augmented reality (AR). In this paper, we propose a Latency Reduction Software-Defined BGP (LRSD-BGP) framework to reduce the latency in inter-domain initial link setup. Furthermore, a controller-to-controller (C2C) communication module is designed to accelerate the routing link establishment procedure of inter-domain traffic by pre-allocating and installing routing paths for the AS in the next hop. LRSD-BGP is evaluated in terms of BGP convergence time and first round-trip time (RTT). Experimental results demonstrated that LRSD-BGP can outperform SD-BGP in terms of first RTT even in highly loaded cases.
Ground and Aerial Collaborative Mapping in Urban Environments

A heterogeneous multi-robot system consisting of Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs) have advantages over a single-robot system in efficiency and flexibility, enabling them to perform a larger range of tasks. To allow heterogeneous platforms to work together in GPS-denied scenarios, it is crucial to build a complete 3D map of the environment. In this letter, a novel method is presented to perform ground and aerial collaborative mapping leveraging visual and range data collected by cameras and 3D LiDAR sensors. In the proposed system, a visual-LiDAR ego-motion estimation module that considers point, line and planar constraints can provide robust odometry information. Thumbnail images representing obstacle outlines are generated and descriptors are extracted using a neural network to help perform data association between separate runs. Map segments and the robot poses are organized together and are updated during a pose graph optimization procedure. The proposed ground-aerial collaborative mapping approach is evaluated on both synthetic and real-world datasets comparing with other methods. Experiment results demonstrate that our method can achieve outstanding mapping results.
Passenger Exposure of Electromagnetic Field in the Train

The modelling of passenger exposure of electromagnetic (EM) field by mobile phone in the train is presented in the paper with comparison of electric field strength values obtained by measurements in real situation inside moving train. Simulations were performed in cases where passengers were exposed to long-term radiofrequency field stemming from the use of mobile phones during railway transport. Dual band (900 and 1800MHz) PIFA antennas were designed and employed as radio frequency sources. Measurements of electric field strength were performed within a railway vehicle during ongoing mobile voice communication. The obtained levels of electric field strength did not exceed the limits for EM fields for human safety purposes.
Dynamic Sampling RRT for Improved Performance in Large Environments

Basic RRT path planning algorithm uses a uniform random sampling over the entire free space of the environment in the process of producing new points for the RRT tree, which attain good results in cases of environments with simple and far apart obstacles. But it becomes much more challenging in large and complex environments with narrow passages between obstacles.This paper presents an improvement on the sampling method used in the rapidly-exploring random trees (RRT) search and path planning algorithm to improve its ability to explore in large, complex, and dense environments. The main idea is to make the sampling over a dynamic space that grows with the RRT tree, which increase the ability of exploring and finding narrow passages in large and complicated environments. A theoretical analysis of the fundamental principle is also presented with results from a simulated experiment to illustrate the benefits of the dynamic sampling.
Design and Simulation of A Novel Three-Dimensional Multi-Degree-Of-Freedom Electrothermal Microgripper

This paper presents a novel design of a three-dimensional (3D) multi- degree-of-freedom (MDOF), electrothermal microgripper. The 3D microgripper is made up of three identical fingers, each of which has three degrees-of- freedoms (DOFs) and stands on a rotational platform actuated by one pair of V-shaped electrothermal actuators. Each finger is composed of a V-shaped and a novel 3D U-shaped electrothermal actuator, independently providing one longitudinal and two transverse DOFs, respectively. The 3D U-shaped electrothermal actuator proposed in this paper can generate displacement and force on the end tip based on elongation and bending of a pair of slender beams. Functionality and performance analyses of the proposed 3D finger are performed based on finite-element simulations.
O-GlcNAc modification of nuclear pore complexes accelerates bi-directional transport

Macromolecular transport across the nuclear envelope depends on facilitated diffusion through nuclear pore complexes (NPCs). The interior of NPCs contains a permeability barrier made of phenylalanine-glycine (FG) repeat domains that selectively facilitates the permeation of cargoes bound to nuclear transport receptors (NTRs). FG repeats in NPC are a major site of O-linked N-acetylglucosamine (O-GlcNAc) modification, but the functional role of this modification in nucleocytoplasmic transport is unclear. We developed high-throughput assays based on optogenetic probes to quantify the kinetics of nuclear import and export in living human cells. We found that increasing O-GlcNAc modification of the NPC accelerated NTR-facilitated nucleocytoplasmic transport of proteins in both directions, and decreasing modification slowed transport. Super-resolution imaging revealed strong enrichment of O-GlcNAc at the FG-repeat barrier. O-GlcNAc modification also accelerated passive permeation of a small, inert protein through NPCs. We conclude that O-GlcNAc modification accelerates nucleocytoplasmic transport by enhancing the non-specific permeability the FG-repeat barrier, perhaps by steric inhibition of interactions between FG repeats. Summary Nuclear pore complexes mediate nuclear transport and are highly modified with O-linked N-acetylglucosamine (O-GlcNAc) on FG repeat domains. Using a new quantitative live-cell imaging assay, Yoo and Mitchison demonstrate acceleration of nuclear import and export by O-GlcNAc modification.
Sentiment Analysis about Product and Service Evaluation of PT Telekomunikasi Indonesia Tbk from Tweets Using TextBlob, Naive Bayes & K-NN Method

Online reviews are very important for any business that wants to control its online reputation. This allows businesses to have active and positive participation from consumers. As an information and communication company in Indonesia PT Telekomunikasi Indonesia Tbk commonly called Telkom require a customer’s perspective or review to maintain the relevance of their digital products on the market. One method often used to analyze online reviews is sentiment analysis. Sentiment Analysis is used to gain an understanding of the opinions, attitudes, and emotions expressed in the mention of online by determining the emotional tone behind a series of words.This research tries to compare classifications in sentiment analysis of Telkom’s product from consumer reviews written in the form of tweets on Twitter. Each tweet about Telkom digital products such as Indihome, UseeTV, and Wifi.id will be collected as data. The use of classification types will be compared to help with the accuracy of sentiment analysis based on three types of methods TextBlob, Naïve Bayes & K-NN (K-Nearest Neighbor). The best result of this research is the K-NN algorithm with an accuracy score of 75% followed by Naïve Bayes 69.44% and the last is TextBolb with 54.67%.
Grid Frequency Regulation Support From Back-to-Back Motor Drive System With Virtual-Synchronous-Generator-Based Coordinated Control

The virtual synchronous generator (VSG) control, which enables inverter-interfaced distributed generators to possess inertia like synchronous generators, provides a promising solution to the lack of inertia of a future power grid. The energy buffer emulating the kinetic energy variation is essential to this technique. In this article, we propose a new control scheme for a smart motor, i.e., a back-to-back motor drive system which temporarily utilizes the kinetic energy stored in rotating loads. With this system, significant frequency regulation support can be achieved through merely a control mechanism update, and without any dedicated energy storage system. To achieve it, the VSG control is applied to the grid-side converter of the back-to-back motor drive system, and a coordinated control between the VSG control and the motor speed control is proposed. The proposed coordinated control has a faster frequency support response and a lower sensitivity to grid voltage unbalance and distortion than previous control schemes based on frequency measurement. The parameter tuning of the proposed control is discussed based on stability analyses and verified by simulation studies. The effectiveness of suppression of grid frequency fluctuation by the proposed system is verified through experimental results obtained with a commercial blower.
Low-Loss Light Guiding at the Subwavelength Scale in a Nanowire-Loaded Hybrid Bloch-Surface-Polariton Waveguide

We report the realization of strongly confined low-loss optical waveguiding at telecommunication wavelength by exploiting the hybridization of photonic modes guided by coupled dielectric mode and Bloch surface polaritons on the interface between one-dimensional photonic crystal and dielectric nanowire. Owing to the combined effects induced by the coupling between two types of guided modes, tight field localization in conjunction with significantly lower propagation loss can be achieved simultaneously. The characteristics of the hybrid modes are comprehensively numerically investigated through tuning key structural parameters of the coupled dielectric nanowire and its distance to the photonic crystal surface. The hybrid mode could effectively balance the compromise between the mode confinement and propagation loss for a wide geometrical parameter range. It reveals the hybrid mode is capable of enabling larger propagation distance (centimeter level) with subwavelength mode confinement, leading to a high figure of merit nearly one order of magnitude larger as compared to the conventional hybrid waves in dielectric-metal-based surface plasmon waveguides over the range of dimensions considered. The outstanding properties of the proposed guiding scheme could open up possibilities for a variety of high-performance intriguing nanophotonic components.